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+} +@media print { +pre > code.sourceCode { white-space: pre-wrap; } +pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; } +} +pre.numberSource code + { counter-reset: source-line 0; } +pre.numberSource code > span + { position: relative; left: -4em; counter-increment: source-line; } +pre.numberSource code > span > a:first-child::before + { content: counter(source-line); + position: relative; left: -1em; text-align: right; vertical-align: baseline; + border: none; display: inline-block; + -webkit-touch-callout: none; -webkit-user-select: none; + -khtml-user-select: none; -moz-user-select: none; + -ms-user-select: none; user-select: none; + padding: 0 4px; width: 4em; + } +pre.numberSource { margin-left: 3em; padding-left: 4px; } +div.sourceCode + { } +@media screen { +pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } +} @@ -264,9 +298,145 @@

Homework 1 - Getting Started

-

Coming soon!

+
+

Due: Jan 21 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are to:

+
    +
  • Make sure you understand what you need to do to succeed in this class
    • +
  • Make sure you have properly set up the software and tools we will use this semester
    • +
  • Get started practicing basic concepts with R.
    • +
+

Skills & Knowledge: After completing these exercises, you should:

+
    +
  • Be familiar with the main course tools: Slack, R, and RStudio.
    • +
  • Be familiar how to use R projects to stay organized.
    • +
  • Be able to use R to create and store values as objects with meaningful names.
    • +
  • Know how to compare values in R.
    • +
  • Know how to use arithmetic, relational, and logical operators in R.
    • +
  • Know the distinctions between how R handles different types of data types (numbers, strings, & logicals).
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

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    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Getting Started
Operations & Data Types +
+
+
+
+

1) Class setup [SOLO, 10%]

+

For this class, you’ll need to install some software and register for some tools. You should have already done this, but in case you haven’t,go to the course software page to get set up.

+

Once you have joined the class slack, make a post to the #welcome channel introducing yourself - provide your name, year / program, and something interesting about yourself.

+
+
+

2) Getting familiar with the course [SOLO, 10%]

+

Follow Snoop’s advice and read the entire Course Syllabus (actually read the whole thing). Then review the schedule and make sure to note important upcoming deadlines.

+
+
+

3) Staying organized [SOLO, 10%]

+

Open RStudio and create a new R project called “hw1” (see the reading for details on how to do this). Within your project, create a new R script (a “.R” file) and save it as “hw1.R”. When you save it, it should show up in the R project folder you just created. Finally, copy the following code to the top of this script and fill out your name, netID, and the names of anyone you worked with on this assignment (your netID is the part of your email address before "@gwu.edu"):

+
+
# Name:  Last, First
+# netID: Insert your netID here
+
+# I worked with the following classmates on this assignment:
+# 1) Name: Last, First
+# 2) Name: Last, First
+
+

Write your responses to all other questions in this assignment in your R file.

+
+
+

4) Objects & Operators: Converting Time [COLLABORATIVE, 20%]

+

Create objects to store each of the following two values - be sure to use meaningful variable names when creating your objects:

+
    +
  • The number of seconds in a minute
    • +
  • The number of minutes in an hour
    • +
  • The number of hours in a day
    • +
  • The number of days in a typical year (not a leap year)
    • +
+

Now, say you have another object called time_in_seconds that contains an integer number of seconds (for example, time_in_seconds <- 8675309). Write code to convert the value stored in time_in_seconds into the units described below. Your solution may only use arithmetic operators and the objects you created (i.e. you may not use any numbers). You may also use the new objects you create in sequential order. For example, you may use the object created in part a) to create the object in part b), and so on.

+
    +
  1. The value of time_in_seconds in minutes
    2. +
  2. The value of time_in_seconds in hours
    3. +
  3. The value of time_in_seconds in days
    4. +
  4. The value of time_in_seconds in years
    5. +
+
+
+

5) Logical and relational operators [SOLO, 20%]

+

Consider the following objects:

+
+
w <- FALSE
+x <- TRUE
+y <- FALSE
+z <- TRUE
+
+

Write code to answer the following questions:

+
    +
  1. Write a statement with logical operators that compares the objects x, y, and z and returns TRUE
    2. +
  2. Write a statement with logical operators that compares the objects x, y, and z and returns FALSE
    3. +
  3. Fill in relational operators to make the following statement return TRUE:
    4. +
+

! (x __ y) & ! (z __ y)

+
    +
  1. Fill in relational operators to make this statement return FALSE:
    2. +
+

! (w __ y) | (z __ y)

+
+
+

6) Data types [COLLABORATIVE, 20%]

+

Consider the following objects:

+
+
number    <- typeof('3')
+character <- typeof(7)
+false     <- typeof("FALSE")
+true      <- typeof(TRUE)
+
+

Write code to answer the following questions:

+
    +
  1. Write a statement with both relational & logical operators that compares the four objects number, character, false, and true and returns TRUE.
    2. +
  2. Write a statement with both relational & logical operators that compares the four objects number, character, false, and true and returns FALSE.
    3. +
+
+
+

7) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Functions & Packages +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 10 - Data Visualization

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Apr 08 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice exploring and data frames in R using the dplyr package
    • +
  • To practice generating charts using the ggplot2 package
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Data Visualization +
+
+
+
+

Using AI tools

+
+

On this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

+
+
+
+

1) Staying organized [5%]

+

Download and use this template for your assignment. Inside the “hw10” folder, open and edit the R script called hw10.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.

+
+
+

2) Choose and load some data [5%]

+

For this assignment, you will need to find a dataset of your choosing and create three summary visualizations. To keep things manageable, choose one of the following datasets from the following libraries. Note that to load any of these data frames, all you need to do is install and load the package.

+

dplyr:

+
    +
  • storms
    • +
  • starwars
    • +
+

ggplot2:

+
    +
  • diamonds
    • +
  • economics
    • +
  • midwest
    • +
  • mpg
    • +
  • msleep
    • +
  • txhousing
    • +
+

dslabs:

+
    +
  • gapminder
    • +
  • movielens
    • +
  • murders
    • +
  • stars
    • +
+
+
+

3) Inspect your data [10%]

+

Once you’ve chosen a data set, open your hw10.R file and begin exploring the data (be sure to load the package that contains the dataset at the top of your file). Write some code in code chunks to preview and summarize the data frame using some of the methods we’ve used in class. You should be able to quickly get an understanding of what variables are included and their nature. Consider the following questions in your exploration (you don’t have to write out answers to these questions - just write code to help you answer them by previewing the data in different ways):

+
    +
  • What is the total size of the data frame?
    • +
  • What type of data is each variable (numeric, character, logical, date)?
    • +
  • Do any variables have missing values? Why might that be?
    • +
  • What are the “boundaries” of each period of observation: +
      +
    • For numeric variables, what are the min and max values?
      • +
    • For character variables, what are the unique values in the variable?
      • +
    • For date variables, what time period do the observations in these data frames span?
      • +
    • +
+

Do not brush this step off - the more thoroughly you inspect your dataset, the easier (and better) you data exploration will be. This will be absolutely critical for making your charts. Make sure you take the time to develop an understanding of the variables in your dataset as it is nearly impossible to imagine what different charts might be worth creating otherwise.

+
+
+

4) Make charts [40%]

+

Now that you have a basic understanding of the dataset, make some charts to explore the variables in the data and their potential relationships. You may use base R plotting functions or the ggplot2 package to make your figures, but you must make at least two different types of figures, including:

+
    +
  1. A scatterplot of involving at least two variables.
    2. +
  2. A bar chart involving at least one variable.
    3. +
+

You can choose to plot whichever variables you wish, but you must be able to interpret the results of your chart.

+
+
+

5) Interpret your charts [15%]

+

Below the code for each of your charts, write a description and interpretation of your chart in a comment. Make sure you address at least the following questions:

+
    +
  1. Describe what variables you are plotting and why.
    2. +
  2. Describe the primary relationship / trend / information you hope the reader will gain from your visualization.
    3. +
+
+
+

6) Save your charts [15%]

+

At the bottom of your hw10.R file, write code to save each of your charts in the plots folder. Save them as .png files.

+
+
+

7) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+ +
+

Afterwards, in a comment (#) in your .R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/11-programming-with-data-temp.html b/hw/11-programming-with-data-temp.html new file mode 100644 index 0000000..30eac60 --- /dev/null +++ b/hw/11-programming-with-data-temp.html @@ -0,0 +1,906 @@ + + + + + + + + + +Homework 11 - Programming with Data – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 11 - Programming with Data

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Apr 15 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • Be able to compose functions using the ‘tidy evaluation’ syntax to work with data.
    • +
  • Be able to parse through lists using purrr::map() functions.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+ +
+
+
+

Using AI tools

+
+

On this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw11” folder, open and edit the R script called hw11.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.

+
+
+

Data Frame Functions

+
+

For questions 2 - 5, after writing your function, demonstrate it using a data frame of your choice from the dslabs package. For example, for question 2, you could use var_summary(dslabs::movielens, rating) (so, obviously, you should use a different example).

+
+
+

2) var_summary(df, var) [SOLO, 10%]

+

Write the function var_summary(df, var) that takes a data frame (df) and a variable (var) as inputs, and returns the minimum, maximum, mean, and median value of that variable. The function should remove any NA values in var when computing these summary statistics. The object returned should be a single data frame / tibble (not a vector).

+
+
+

3) group_summary(df, var, group_var) [SOLO, 10%]

+

Write the function group_summary(df, var, group_var) that takes a data frame (df), a variable (var), and a grouping variable (group_var) as inputs, and returns a summary table showing the count, mean, and standard deviation of the variable var grouped by group_var. The function should remove any NA values in var when computing these summary statistics. The object returned should be a single data frame / tibble (not a vector).

+
+
+

4) var_hist(df, var, bins) [SOLO, 10%]

+

Write the function var_hist(df, var, bins) that takes a data frame (df), a variable (var), and the number of bins (bins) as inputs, and returns a histogram of that variable with a user-specified number of bins as a ggplot object. The default number of bins should be 30.

+
+
+

5) scatterplot(df, x, y) [SOLO, 10%]

+

Write the function scatterplot(df, x, y) that takes a data frame (df) and two variables (x and y) as inputs, and returns a scatter plot of those two variables as a ggplot object.

+
+
+
+

Iteration across lists with purrr

+
+

Problems using word_list

+

For these questions, we will work with the sentences vector that comes loaded with the stringr package (which is loaded when you load the tidyverse package):

+
+
library(tidyverse)
+
+head(stringr::sentences)
+
+
#> [1] "The birch canoe slid on the smooth planks." 
+#> [2] "Glue the sheet to the dark blue background."
+#> [3] "It's easy to tell the depth of a well."     
+#> [4] "These days a chicken leg is a rare dish."   
+#> [5] "Rice is often served in round bowls."       
+#> [6] "The juice of lemons makes fine punch."
+
+
+

This vector contains lots of random sentences. When we break those sentences into individual words using str_split(), we will get a list back where each item in the list is a vector of words:

+
+
word_list <- str_split(stringr::sentences, " ")
+
+word_list[1:3]
+
+
#> [[1]]
+#> [1] "The"     "birch"   "canoe"   "slid"    "on"      "the"     "smooth" 
+#> [8] "planks."
+#> 
+#> [[2]]
+#> [1] "Glue"        "the"         "sheet"       "to"          "the"        
+#> [6] "dark"        "blue"        "background."
+#> 
+#> [[3]]
+#> [1] "It's"  "easy"  "to"    "tell"  "the"   "depth" "of"    "a"     "well."
+
+
+

We will use this word_list for questions 6 - 8.

+
+
+

6) [COLLABORATIVE, 5%]

+

Using map(), write R code to obtain a vector of how many words are in each item in word_list.

+
+
+

7) [COLLABORATIVE, 5%]

+

Using map(), write R code to obtain a vector of the total number of characters in each item in word_list.

+
+
+

8) [COLLABORATIVE, 5%]

+

Using map(), write R code to obtain a vector of the number of times the word "the" appears in each item in word_list. Your result should ignore casing, so both "the" and "The" should count.

+
+
+

Problems using sw_people

+

As we saw in class, the sw_people list contains a list of information about each character in Star Wars. You can load the list from the repurrrsive package:

+
+
library(repurrrsive)
+
+

We will use the sw_people and sw_films lists for questions 9 & 10.

+
+
+

9) [COLLABORATIVE, 10%]

+

Using map() and the sw_films list, write R code to obtain a vector of integers that contains the number of characters in each Star Wars film.

+
+
+

10) [COLLABORATIVE, 10%]

+

Using map_df(), create a data frame where each row represents a character from sw_people. The columns should contain the following:

+
    +
  • name: The character’s name, as a character.
    • +
  • height: The character’s height, as a number
    • +
  • is_male: Whether the character’s gender is "male" (TRUE or FALSE)
    • +
  • film_count: The number of films they have appeared in, as an integer.
    • +
+
+
+

11) [SOLO, 10%]

+

For the last problem, write your own homework question that requires the student (you) to use map() in the solution. You can use any lists of data you want for your question (e.g. sw_people, sw_films, got_chars, etc.). Then provide the answer to your question. As with all the other questions, if you use an AI tool to help you create and / or solve your question, include the prompt you used and comment on any changes you had to make to improve your outcome.

+
+
+

12) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+ +
+

Afterwards, in a comment (#) in your .R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+ + +
+
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/12-webscraping-temp.html b/hw/12-webscraping-temp.html new file mode 100644 index 0000000..b7abe81 --- /dev/null +++ b/hw/12-webscraping-temp.html @@ -0,0 +1,874 @@ + + + + + + + + + +Homework 12 - Webscraping – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 12 - Webscraping

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Apr 22 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • Be able to manage scrape data from static web pages in R.
    • +
  • Be able to collect and format data from a website using an API in R.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+ +
+
+
+

Using AI tools

+
+

On this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw12” folder, open and edit the R script called hw12.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.

+
+
+

2) Scrape the weather!

+

Write R code to scrape the current weather conditions for a city of your choice from the National Weather Service. Create a data frame with the location, temperature (in Fahrenheit), humidity (%), and wind speed (mph). Your final data frame should look like this (with values reflecting those from your location of choice):

+
+
# A tibble: 1 × 4
+  location        temp humidity wind_speed
+  <chr>          <dbl>    <dbl>      <dbl>
+1 Kennett Square    64       32         18
+
+

Note: You will have to navigate and inspect the website to come up with a strategy to scrape it.

+
+
+

3) Scrape the EMSE course bulletin!

+

Write R code to scrape all of the EMSE courses listed on the GW Course Bulletin. You should create a data frame that contains the following fields:

+
    +
  • number: The course number, e.g. "EMSE 4571".
    • +
  • title: The course title, e.g. "Intro. to Programming for Analytics"
    • +
  • credits: The number of credits, e.g. 1, 3, etc.
    • +
  • desc: The course description.
    • +
+

Hint: To get to this final format, you may want to use either str_split() or the separate() function to break up the scraped data into these variables. This will be helpful after you’ve scraped the data from the page.

+

Note: You will have to navigate and inspect the website to come up with a strategy to scrape it.

+
+
+

4) Scrape the top movies!

+

Write R code to scrape data on the top 1000 grossing movies of all time from Box Office Mojo. Create a data frame with the movie titles, lifetime box office gross, and year of release. In your solution, you should use some form of iteration (e.g. map(), map_df(), a for loop) to loop through the tables on multiple pages.

+

Note: You will have to navigate and inspect the website to come up with a strategy to scrape it.

+
+
+

5) Get the weather from an API!

+

Follow these steps:

+
    +
  1. Go to the openweathermap.org site and register for a free account to get access to an API key:
    2. +
+
+ +
+
    +
  1. Once registered, go get your API key here.
    2. +
  2. Open your .Renviron with the command usethis::edit_r_environ()
    3. +
  3. Copy-paste your key into your .Renviron: OPEN_WEATHER_MAP_KEY=your_key_here
    4. +
  4. Restart RStudio
    5. +
  5. Go read the current weather API documentation.
    6. +
  6. Write R code to get the current weather in your location of choice.
    7. +
+

Tips:

+
    +
  • You’ll need to install and load the httr and jsonlite packages.
    • +
  • If you followed the instructions above, you can get your API key as an object in R with this code:
    • +
+
+
api_key <- Sys.getenv("OPEN_WEATHER_MAP_KEY")
+
+
    +
  • Once you have constructed the url request you want to make, use the following code to get the request as a data frame:
    • +
+
+
library(httr)
+library(jsonlite)
+
+response <- content(GET(url), as = "text")
+weather <- fromJSON(response)
+
+

The GET() function is from the httr package, and it retrieves whatever is returned from the website. This site returns data in JSON format, so the fromJSON() from the jsonlite package helps us convert that into a nice data frame.

+
+
+

6) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+ +
+

Optional Reading: Read about how I used Monte Carlo simulation to simulate the Squid Game bridge scene in R.

+

Afterwards, in a comment (#) in your .R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/13-monte-carlo-methods-temp.html b/hw/13-monte-carlo-methods-temp.html new file mode 100644 index 0000000..cedfa5d --- /dev/null +++ b/hw/13-monte-carlo-methods-temp.html @@ -0,0 +1,828 @@ + + + + + + + + + +Homework 13 - Monte Carlo Methods – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 13 - Monte Carlo Methods

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Apr 29 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • Be able to implement a simple Monte Carlo simulation in R to estimate the probability an even occurring.
    • +
  • Be able to conduct a simple Monte Carlo simulation to integrate a function.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Monte Carlo Methods +
+
+
+

Using AI tools

+
+

On this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw13” folder, open and edit the R script called hw13.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment. Coming soon!

+
+
+

2) Monte Carlo Dice [SOLO, 15%]

+

Using the sample() function, conduct a monte carlo simulation to estimate the probability of rolling a sum of 7 using two 6-sided dice.

+
+
+

3) Monte Carlo Full House [COLLABORATIVE, 15%]

+

Using the sample() function, conduct a monte carlo simulation to estimate the probability of drawing a “Full House” poker hand from a standard 52-card deck of cards.

+
+
+

4) Monte Carlo Triangles [COLLABORATIVE, 15%]

+

Using the sample() function, conduct a Monte Carlo simulation to estimate the probability of randomly picking three integer values between 1 and 100 (inclusive, and without replacement) that can be used to form a triangle. As a hint, it would be helpful to first write a simple function called isTriangle(a, b, c) that takes the lengths of three line segments and returns TRUE if the lines can make a triangle and FALSE otherwise. The Triangle Inequality Theorem may come in handy here.

+
+
+

5) Monte Carlo Integration [COLLABORATIVE, 20%]

+

Use a Monte Carlo simulation to estimate the integral of the function \(y = log(x)\) between \(5 \le x \le 20\) by estimating the area under the curve of the function.

+
+
+

6) Your Own Monte Carlo Simulation [SOLO, 20%]

+

For the last problem, write your own homework question that requires the student (you) to use a Monte Carlo simulation to compute the probability of an event occurring. Provide both the question and the solution to your question. As with all the other questions, if you use an AI tool like ChatGPT to help you create and / or solve your question, include the prompt you used and comment on any changes you had to make to improve your outcome.

+
+
+

7) Final reflection [SOLO, 10%]

+

This is your last homework - congrats! 🎉

+

For this reflection, please reflect on what we have covered in this class, then write about your reflection. I would appreciate it if you could answer the following questions:

+
    +
  • What was the most impactful thing you gained from this course?
    • +
  • What was the most challenging thing you experience in this course?
    • +
  • What do you think I can do better in the future?
    • +
+

Write your reflection in a comment (#) in your .R file.

+
+
+

8) Surveys 🙏

+

This is not for credit, but please, please, please fill out the course surveys. It’s SUPER important that I hear from you so I can learn what is and is not working in this class. Both are anonymous, so be as honest as possible:

+
    +
  • Official GW Course Evaluation: The official survey GW uses to evaluate how good a job I’m doing.
    • +
  • Unofficial survey: This is a survey I put together each year where you can provide me direct, anonymous feedback about things specific to this course.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/2-functions-packages-temp.html b/hw/2-functions-packages-temp.html new file mode 100644 index 0000000..3b2568a --- /dev/null +++ b/hw/2-functions-packages-temp.html @@ -0,0 +1,931 @@ + + + + + + + + + +Homework 2 - Functions & Packages – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 2 - Functions & Packages

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Jan 28 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice using functions in R.
    • +
  • To practice converting data types in R.
    • +
  • To practice installing, loading, and using external packages.
    • +
+

Skills & Knowledge: After completing these exercises, you should be able to:

+
    +
  • Be familiar with how to specify arguments in functions.
    • +
  • Be comfortable converting data between logical, character, and numeric types.
    • +
  • Be comfortable installing external libraries and accessing functions in them.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Functions & Packages +
+
+
+
+

1) Staying organized [SOLO, 10%]

+

Just like we did in HW1, we’re going to use an R project to keep everything in this assignment tidy. Create a new R project called “hw2” and save a new R script in it called “hw2.R”. Copy the following code to the top of this script and fill out your name, GW netID, and the names of anyone you worked with on this assignment:

+
+
# Name:  Last, First
+# netID: Insert your netID here
+
+# I worked with the following classmates on this assignment:
+# 1) Name: Last, First
+# 2) Name: Last, First
+
+

Write your responses to all other questions in this assignment in your hw2.R file.

+
+
+

2) Polya problem solving 1 [COLLABORATIVE, 15%]

+

Consider the points A, B, and C defined by the following cartesian coordinates:

+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
Pointxy
A11
B51
C54
+
+

Your task is to find out if the triangle formed by the points A, B, and C is a right triangle. Apply Polya’s problem solving technique:

+
    +
  1. Understand the problem: In a code comment, write the question you are trying to solve in your own words, then create objects for each relevant piece of information provided in the problem statement.
    2. +
  2. Devise a plan: In code comments, describe the steps you will take in your plan to solve the problem.
    3. +
  3. Carry out the plan: Write code below your comments to carry out your plan.
    4. +
  4. Check your work: Examine your solution and any intermediate values you computed along the way. Do the values make sense (e.g. do you get negative numbers when you expected positive numbers)? Is there another way you could have solved it to check that you get the same result?
    5. +
+
+
+

3) Polya problem solving 2 [COLLABORATIVE, 20%]

+

Kevin is deciding between purchasing a Chevy Bolt, which sells for $29,390 (after subsidies), and a Toyota Camry, which sells for $24,000. The Bolt efficiency is 28 kWh of electricity per 100 miles while the Camry efficiency is 28 miles per gallon (mpg) on average. The average price of electricity (in DC, where Kevin lives) is $0.13 / kWh, and the average price of gasoline is $3.00 / gallon. Kevin drives 12,000 miles each year on average.

+

To help him make a decision about which car to buy, answer the following questions:

+
    +
  1. How long (in years) would he have to drive the Bolt for the money he saves in fuel savings to be greater than the price premium compared to the Camry?
    2. +
  2. What would the price of gasoline need to be such that the fuel savings from driving the Bolt for 3 years would be greater than the price premium compared to the Camry?
    3. +
  3. How many miles would Kevin need to drive each year such that the fuel savings from driving the Bolt for 3 years would be greater than the price premium compared to the Camry?
    4. +
+

To solve each problem, apply Polya’s problem solving technique:

+
    +
  1. Understand the problem: In a code comment, write the question you are trying to solve in your own words, then create objects for each relevant piece of information provided in the problem statement.
    2. +
  2. Devise a plan: In code comments, describe the steps you will take in your plan to solve the problem.
    3. +
  3. Carry out the plan: Write code below your comments to carry out your plan.
    4. +
  4. Check your work: Examine your solution and any intermediate values you computed along the way. Do the values make sense (e.g. do you get negative numbers when you expected positive numbers)? Is there another way you could have solved it to check that you get the same result?
    5. +
+
+
+

4) Working with strings: the stringr package [SOLO, 15%]

+

Who is your favorite super hero character? If you don’t already have one, pick one (mine is Professor Xavier, and no it’s not just because he’s a professor :P).

+

In your hw2.R file, create two objects that store the following character values:

+
    +
  • The name of your favorite super hero.
    • +
  • The super power(s) your hero has.
    • +
+

Use meaningful variable names to store these values.

+

Next, install the stringr package. Try using the str_to_lower(), str_to_upper(), and str_to_title() functions from the stringr package on the objects you created. In a code comment, describe what these functions do.

+

Finally, use the paste() function to merge your super hero’s name and power in a coherent sentence. For example, if the name is "Professor Xavier" and the the power is "reads peoples' minds", then a final sentence might be: "Professor Xavier reads peoples' minds!". (Note that I intentionally did not tell you how to use the paste() function…you’ll have to figure that one out)

+
+
+

5) Installing packages from Github: the BRRR package [SOLO, 10%]

+

The vast majority of the time, you will install external packages using the install.packages() function. This installs packages from the Comprehensive R Archive Network (CRAN), where most packages are published. But you can also install packages that are under development or haven’t been published to CRAN yet. Most of the time, these packages are hosted on GitHub - an online platform for sharing code (it’s also where all of the files that make up this website are stored).

+

To install a package from GitHub, you first need to install the remotes package. Then you can use the remotes::install_github() function to install packages directly from GitHub. To try this out, install the remotes package, then trying installing the BRRR package:

+
+
remotes::install_github("brooke-watson/BRRR")
+
+
+

Note: Packages on GitHub are in development and often require other packages to work. So if you get an installation error about some other package dependency, try restarting your R session and try again.

+
+

Not sure what this package does? Well, one of the other nice things about packages listed on GitHub is the authors tend to write detailed descriptions - check out the GitHub page for the BRRR package. Then try using the BRRR::skrrrahh() function with different number arguments (turn your volume up). In the #hw channel on slack, post your favorite argument to skrrrahh() (mine is 24).

+
+
+

6) Turtle practice [COLLABORATIVE, 20%]

+

In case you missed the intro to the TurtleGraphics package in class, go ahead and install and load the package. You might also want to review the related lesson page and the class slides. Then complete these tasks:

+
    +
  1. Load the TurtleGraphics package, then initiate your turtle with the turtle_init() function.
    2. +
  2. Read through the following commands (don’t run them). In a code comment, type a description of what you think the turtle is going to do.
    3. +
+
+
turtle_init()
+turtle_move(distance = 25, direction = "forward")
+turtle_move(distance = 50, direction = "backward")
+turtle_reset()
+turtle_turn(angle = 90, direction = "right")
+turtle_move(distance = 25, direction = "forward")
+turtle_move(distance = 50, direction = "backward")
+turtle_reset()
+
+
    +
  1. Now run the above commands to check if the turtle did what you expected. Congratulations, you’ve just practiced “code tracing”! Reading through code before running it then comparing the result with your expectation is an excellent method for improving your your computational thinking skills and your fluency with a particular language.
    2. +
  2. Using what you’ve learned about how to manipulate your turtle, write two different solutions for producing the following image (hint: the length of one side of the square is 50 units):
    3. +
+
+
+

+
+
+
+
+

7) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Creating Functions +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/3-creating-functions-temp.html b/hw/3-creating-functions-temp.html new file mode 100644 index 0000000..495aa66 --- /dev/null +++ b/hw/3-creating-functions-temp.html @@ -0,0 +1,890 @@ + + + + + + + + + +Homework 3 - Creating Functions – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 3 - Creating Functions

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Feb 04 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice creating your own functions in R from scratch.
    • +
  • To practice writing code using good style.
    • +
  • To practice problem solving (e.g. using Polya’s technique).
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Creating Functions +
+
+
+
+

1) Staying organized [SOLO, 5%]

+

As always, we’re going to use an R project to keep everything in this assignment tidy. Download and use this template for your assignment. Inside the “hw3” folder, open and edit the R script called “hw3.R” and fill out your name, Net ID, and the names of anyone you worked with on this assignment.

+
+

Using the autograder

+
    +
  • You can check your solutions to problems 2 - 8 by logging into the autograder and uploading your hw3.R file.
    • +
  • The file must be named hw3.R or it won’t work.
    • +
  • Your user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.
    • +
+
+
+
+

2) integerSquareRoot(n) [SOLO, 10%]

+

Given a non-negative integer n, return the integer value that is closest to its square root. For example, integerSquareRoot(10) returns 3.

+
+
+

3) fabricYards(inches) [SOLO, 10%]

+

Fabric must be purchased in whole yards. Write a function that takes a non-negative number of inches of fabric desired, and returns the smallest number of whole yards of fabric that must be purchased. Thus, fabricYards(1) is 1 (you need a full yard if you buy one inch) and fabricYards(36) is also 1, but fabricYards(37) is 2. Hint: There are 36 inches in a yard!

+
+
+

4) fabricExcess(inches) [SOLO, 10%]

+

Write a function that takes a non-negative number of inches of fabric desired and returns the number of inches of excess fabric that must be purchased (as purchases must be in whole yards). Thus, since you need a whole yard when you buy 1 inch, fabricExcess(1) is 35. Similarly, fabricExcess(36) is 0, and fabricExcess(37) is 35.

+

Hint: there are (at least) two good ways to write this. One way involves a simple expression using one of the math operators we have learned. The other way uses fabricYards(inches) (which you just wrote!).

+
+
+

5) isPerfectCube(x) [SOLO, 15%]

+

Given an integer value x, returns TRUE if it is a perfect cube and FALSE otherwise. That is, return TRUE if there is another integer y such that x = y^3. Thus, isPerfectCube(27) returns TRUE, but isPerfectCube(16) returns FALSE.

+
+
+

6) distance(x1, y1, x2, y2) [SOLO, 5%]

+

Given four numeric values representing the points (x1, y1) and (x2, y2), write the function distance(x1, y1, x2, y2) returns the Euclidean distance between those points.

+
+
+

7) triangleArea(x1, y1, x2, y2, x3, y3) [COLLABORATIVE, 10%]

+

Given six numeric values representing the points (x1, y1), (x2, y2), and (x3, y3), return the area of the triangle formed by connecting the 3 points. You may wish to use the distance(x1, y1, x2, y2) function as a helper function. You may ignore the case where the 3 points are collinear (and so do not form a triangle). Hint: Heron’s Formula.

+
+
+

8) kthDigit(x, k) [COLLABORATIVE, 15%]

+

Given two integers, x and k, return the kth digit of x, counting from the right. So:

+
    +
  • kthDigit(789, 1) returns 9
    • +
  • kthDigit(789, 2) returns 8
    • +
  • kthDigit(789, 3) returns 7
    • +
  • kthDigit(789, 4) returns 0
    • +
+

Negative numbers should work, too, so kthDigit(-789, 1) returns 9.

+
+
+

9) turtleSquare(s) [COLLABORATIVE, 10%]

+
+

Note: Make sure your solutions for the remaining problem(s) are below the ignore_rest comment in your .R file (otherwise the autograder will run this code and it will be slow.

+
+

Write the function turtleSquare(s) which uses the TurtleGraphics package to draw a square with side length s < 100 (because the turtle will “escape” the terrarium when s >= 100). The square should be centered in the turtle’s terrarium, and your turtle should be placed in the center when done. Important: Do not put the turtle_init() command inside your function - the turtle should already be “initialized” inside the terrarium before calling your turtleSquare(s) function. For example, the code below produces a square with side length of 50:

+
+
library(TurtleGraphics)
+turtle_init()
+turtleSquare(50)
+
+
+
+

+
+
+
+
+

10) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Conditionals
Testing & Debugging +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+
+
+
+

Bonus 1) numberOfPoolBalls(rows) [SOLO, 2%]

+

+

Pool balls are arranged in rows where the first row contains 1 pool ball and each row contains 1 more pool ball than the previous row. Thus, for example, 3 rows contain 6 total pool balls (1+2+3). With this in mind, write the function numberOfPoolBalls(rows) that takes a non-negative integer value (the number of rows) and returns another integer value (the number of pool balls in that number of full rows). For example, numberOfPoolBalls(3) returns 6. We will not limit our analysis to a “rack” of 15 balls; rather, our pool table can contain an unlimited number of rows. Hint: For this problem you should research Triangular Numbers.

+
+
+

Bonus 2): turtleTriangle(s) [SOLO, 3%]

+

Write the function turtleTriangle(s) which uses the TurtleGraphics package to draw an equilateral triangle with side length s < 100. The triangle should be centered in the turtle’s terrarium, and your turtle should be placed in the center when done. Just like with turtleSquare(s), do not put the turtle_init() command inside your function - the turtle should already be “initialized” inside the terrarium before calling turtleTriangle(s). Hint: Getting the triangle centered is the tricky part - you may want to read more about equilateral triangles and apothems. The code below produces a triangle with side length of 50:

+
+
library(TurtleGraphics)
+turtle_init()
+turtleTriangle(50)
+
+
+
+

+
+
+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/4-conditionals-testing-temp.html b/hw/4-conditionals-testing-temp.html new file mode 100644 index 0000000..b822102 --- /dev/null +++ b/hw/4-conditionals-testing-temp.html @@ -0,0 +1,805 @@ + + + + + + + + + +Homework 4 - Conditionals & Testing – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 4 - Conditionals & Testing

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Feb 11 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice using conditional statements while writing functions in R.
    • +
  • To practice the skill of writing and using test functions to understand the problem.
    • +
  • To practice computational problem solving.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Conditionals
Testing & Debugging +
+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw4” folder, open and edit the R script called “hw4.R” and fill out your name, Net ID, and the names of anyone you worked with on this assignment.

+
+

Writing test functions

+

For each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.

+
+
+

Using the autograder

+
    +
  • You can check your solutions to problems 2 - 6 by logging into the autograder and uploading your hw4.R file.
    • +
  • The file must be named hw4.R or it won’t work.
    • +
  • Your user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.
    • +
+
+
+
+

2) isPositiveMultipleOf4Or7(n) [SOLO, 10%]

+

Write the function isPositiveMultipleOf4Or7(n) that returns TRUE if n is a positive multiple of 4 or 7 and FALSE otherwise. Note than n could be any data type.

+
+
+

3) isEvenPositiveInt(x) [SOLO, 15%]

+

Given an arbitrary value x, return TRUE if it is an integer, and it is positive, and it is even (all 3 must be true), or FALSE otherwise. If the value x is not an integer, the function should return FALSE rather than error. So, isEvenPositiveInt("yikes!") returns FALSE, and isEvenPositiveInt(123456) returns TRUE.

+
+
+

4) isLeapYear(year) [COLLABORATIVE, 20%]

+

Write the function isLeapYear(year) that returns TRUE if year is a leap year and FALSE otherwise. Assume that year is a positive integer.

+
+
+

5) getTheCents(n) [COLLABORATIVE, 20%]

+

Write the function getTheCents(n) which takes a value n that represents a payment in US dollars and returns the number of cents in the payment. For example, if n is 2.45, the function should return 45. If n is an integer, the function should return 0, as it has 0 cents; if it isn’t a number, it should return NULL, because a non-number payment make no cents (ha!). If the payment has partial cents (for example, 3.953), it should be rounded to the nearest cent (in this case, 95 cents).

+
+
+

6) isNumericLooking(n) [COLLABORATIVE, 20%]

+

Write a function that returns TRUE if the value n is “numeric looking”, meaning that it is either of a numeric type or a character type of a number (e.g. “2”). All other data types and special values (e.g. NA, NaN, Inf, NULL, TRUE, FALSE, etc.) should return FALSE.

+
+
+

7) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Iteration +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/5-iteration-temp.html b/hw/5-iteration-temp.html new file mode 100644 index 0000000..91d5542 --- /dev/null +++ b/hw/5-iteration-temp.html @@ -0,0 +1,958 @@ + + + + + + + + + +Homework 5 - Iteration – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 5 - Iteration

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Feb 18 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice using for and while loops in R.
    • +
  • To practice computational problem solving with loops.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Iteration +
+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw5” folder, open and edit the R script called “hw5.R” and fill out your name, Net ID, and the names of anyone you worked with on this assignment.

+
+

Writing test functions

+

For each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.

+
+
+

Using good style

+

For this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.

+
+
+

Using the autograder

+
    +
  • You can check your solutions to problems 2 - 7 by logging into the autograder and uploading your hw5.R file.
    • +
  • The file must be named hw5.R or it won’t work.
    • +
  • Your user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.
    • +
+
+
+
+

2) loopFactorial(n) [SOLO, 10%]

+

Use a for loop to write the function loopFactorial(n) that should return n!, i.e. “n factorial”, which is defined for all non-negative integers. For example, 3! = 3*2*1 = 6, 4! = 4*3*2*1 = 24, and 5! = 5*4*3*2*1 = 120. Note that 0 is a special case, and 0! = 1. Assume n >= 0.

+
+
+

3) largestDigit(n) [SOLO, 10%]

+

Write the function largestDigit(n) that takes a possibly-negative integer and returns the largest digit in it.

+
+
+

4) removeRepeats(x) [COLLABORATIVE, 10%]

+

Write the function removeRepeats(x) that takes a vector, x, and removes any repeated elements. The function should work just like the unique() function, except you can’t use unique() in your solution. For example, the following should return TRUE:

+
    +
  • identical(removeRepeats(c(1, 3, 5, 3, 3, 2, 1, 7, 5)), c(1, 3, 5, 2, 7))
    • +
  • identical(removeRepeats(c('foo', 'foo', 'bar', 'test')), c('foo', 'bar', 'test'))
    • +
+

Note: Be careful to use identical() to compare if two vectors are the same in your test function.

+
+
+

Happy Numbers [COLLABORATIVE]

+

Background: Read the first paragraph from the Wikipedia page on happy numbers. After some thought, we see that no matter what number we start with, when we keep replacing the number by the sum of the squares of its digits, we’ll always either arrive at 4 (unhappy) or at 1 (happy). With that in mind, we want to write the function nthHappyNumber(n). However, to write that function, we’ll first need to write isHappyNumber(n), which determines whether a number is “happy” or not. And to right that function, we’ll first need to write sumOfSquaresOfDigits(n). And that’s top-down design! Here we go…

+
+
+

5) sumOfSquaresOfDigits(n) [10%]

+

Write the function sumOfSquaresOfDigits(n) which takes a non-negative integer, n, and returns the sum of the squares of its digits (assume that n will always be a positive integer, so no need to check for bad inputs).

+
+
+

6) isHappyNumber(n) [10%]

+

Write the function isHappyNumber(n) which takes a possibly-negative integer and returns TRUE if it is happy and FALSE otherwise. Note that all numbers less than 1 are not happy.

+
+
+

7) nthHappyNumber(n) [15%]

+

Write the function nthHappyNumber(n) which takes a non-negative integer, n, and returns the nth happy number, where nthHappyNumber(1) returns the first happy number (1).

+
+
+

Turtle loops! [COLLABORATIVE]

+

(Note: Please put your solutions to these below the # ignore_rest comment so that the autograder doesn’t test these functions)

+
+
+

8) turtleSquare(s) redux [10%]

+

Re-write the turtleSquare(s) function from HW3, but this time you must use a for loop to draw the sides of the square. The following code should produce a square with a side length of 50:

+
+
library(TurtleGraphics)
+
+turtle_init()
+turtle_do({
+    turtleSquare(50)
+})
+
+
+
+

+
+
+
+
+

9) concentricTurtleSquares(spacing = 5) [10%]

+

Write the function concentricTurtleSquares(spacing) that uses the TurtleGraphics package to draw concentric squares from the center of the terrarium and outward. The spacing argument determines the spacing between each square, and the default value should be spacing = 5. Also, spacing >= 1, and your function must not allow the turtle to escape the terrarium. Hint: you may want to use turtleSquare(s) as a helper function. The following code should produce concentric squares with a spacing of 5:

+
+
library(TurtleGraphics)
+
+turtle_init()
+turtle_do({
+    concentricTurtleSquares(5)
+})
+
+
+
+

+
+
+
+

10) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Vectors +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+
+
+
+

Bonus 1) turtleSquareRotated(s, degrees) [SOLO, 3%]

+

Write the function turtleSquareRotated(s, degrees) that uses the TurtleGraphics package to draw a square with side length s < 100 and rotated by degrees <= 180 counterclockwise from the horizontal plane. The rotated square should be centered in the turtle’s terrarium. Hint: There are multiple ways to solve this, one of which involves using the cosine (cos()) and sine (sin()) functions. In R, these functions take angles in radians (not degrees), so remember to convert your angles (180 degrees = \(\pi\)).

+

The following code should produce a square with a side length of 30 and rotated by 30 degrees:

+
+
library(TurtleGraphics)
+turtle_init()
+turtle_do({
+    turtleSquareRotated(30, 30)
+})
+
+
+
+

+
+
+
+
+

Bonus 2) turtleSquareStar(s, degreeSpacing = 20) [SOLO, 2%]

+

Write the function turtleSquareStar(s, degreeSpacing) that uses the turtleSquareRotated(s, degrees) as a helper function to draw a sequence of overlapping rotated squares with side length s < 70 to form a star. The degreeSpacing argument determines the spacing in degrees between each rotated square, and the default value should be degreeSpacing = 20. Also, 1 <= degreeSpacing <= 60, and your function must not allow the turtle to escape the terrarium. The following code should produce the star of rotated squares with a side length of 50 and 20 degree spacings between each square:

+
+
library(TurtleGraphics)
+turtle_init()
+turtle_do({
+    turtleSquareStar(50, 20)
+})
+
+
+
+

+
+
+ + +
+
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/6-vectors-temp.html b/hw/6-vectors-temp.html new file mode 100644 index 0000000..f85c81e --- /dev/null +++ b/hw/6-vectors-temp.html @@ -0,0 +1,817 @@ + + + + + + + + + +Homework 6 - Vectors – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 6 - Vectors

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Feb 25 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice using vectors in R.
    • +
  • To practice computational problem solving with vectors.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Vectors +
+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw6” folder, open and edit the R script called “hw6.R” and fill out your name, GW Net ID, and the names of anyone you worked with on this assignment.

+
+

Writing test functions

+

For each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.

+
+
+

Using good style

+

For this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.

+
+
+

Using the autograder

+
    +
  • You can check your solutions to problems 2 - 7 by logging into the autograder and uploading your hw6.R file.
    • +
  • The file must be named hw6.R or it won’t work.
    • +
  • Your user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.
    • +
+
+
+
+

2) vectorFactorial(n) [SOLO, 10%]

+

Write the function vectorFactorial(n) which computes the factorial of n using vectors to avoid using a loop. Hint: there are some useful functions listed in the vectors chapter for performing operators on a numeric vector.

+
+
+

3) nthHighestValue(n, x) [SOLO, 10%]

+

Write a function to find the nth highest value in a given vector. For example, if x equals c(5, 1, 3), then nthHighestValue(1, x) should return 5, because 5 is the 1st highest value in x, and nthHighestValue(2, x) should return 3 because it’s the 2nd highest value in x. Assume only numeric inputs, and assume that n <= length(x). You may not use loops.

+
+
+

4) dotProduct(a, b) [COLLABORATIVE, 15%]

+

Background: the “dot product” of two vectors is the sum of the products of the corresponding terms. So the dot product of the vectors c(1, 2, 3) and c(4, 5, 6) is (1*4) + (2*5) + (3*6), or 4 + 10 + 18 = 32. With this in mind, write the function dotProduct(a, b). This function takes two vectors and returns the dot product of those vectors. If the vectors are not equal length, ignore the extra elements in the longer vector. You may not use loops.

+
+
+

5) middleValue(a) [COLLABORATIVE, 15%]

+

Write the function middleValue(a) that takes a vector of numbers a and returns the value of the middle element (or the average of the two middle elements).

+
+
+

6) isRotation(a1, a2) [COLLABORATIVE, 15%]

+

Write the function isRotation(a1, a2) that takes two vectors, a1 and a2, and returns TRUE if a2 is a rotation of a1 and FALSE otherwise. For example, c(2,3,4,5) is a rotation of c(4,5,2,3). Obviously, two vectors of different lengths cannot be rotations. Also, any vector is a rotation of itself, so isRotation(c(2,3,4,5), c(2,3,4,5)) should return TRUE.

+
+
+

7) fibonacciSequence(n) [COLLABORATIVE, 20%]

+

Write the function fibonacciSequence(n) that returns a vector containing the first n numbers in the Fibonacci Sequence. Assume that n is a whole number greater or equal to 1, and also that the first number in the sequence is 1, so fibonacciSequence(1) == 1 should be TRUE. For all numbers where n > 1, the function should return a vector, e.g. identical(idenfibonacciSequence(2), c(1, 1)) should be TRUE.

+
+
+

8) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Strings +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+
+
+
+

Bonus 1) rotateVector(a, n) [SOLO, 3%]

+

Write the function rotateVector(a, n) which takes a vector a and an integer n and returns a new vector where each element in a is shifted to the right by n indices. For example, if a is c(1, 2, 3, 4) and n is 1, the result should be c(4, 1, 2, 3), but if n is -1, the result should be c(2, 3, 4, 1). If n is larger than the length of a, the function should continue to rotate the vector beyond its starting point. So, if a = c(1, 2, 3, 4) and n = 5, then the result should be a = c(4, 1, 2, 3).

+ + +
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/hw/7-strings-temp.html b/hw/7-strings-temp.html new file mode 100644 index 0000000..7331a6a --- /dev/null +++ b/hw/7-strings-temp.html @@ -0,0 +1,862 @@ + + + + + + + + + +Homework 7 - Strings – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ +
+
+

Homework 7 - Strings

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Due: Mar 18 by 11:59pm

+

Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice manipulating strings in R with the stringr package.
    • +
  • To practice computational problem solving with strings.
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Strings +
+
+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw7” folder, open and edit the R script called “hw7.R” and fill out your name, GW Net ID, and the names of anyone you worked with on this assignment.

+
+

Writing test functions

+

For each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.

+
+
+

Using good style

+

For this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.

+
+
+

Using the autograder

+
    +
  • You can check your solutions to problems 2 - 8 by logging into the autograder and uploading your hw7.R file.
    • +
  • The file must be named hw7.R or it won’t work.
    • +
  • Your user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.
    • +
+
+
+
+

2) strToLower(s) [SOLO, 10%]

+

Write a function that does exactly what str_to_lower() does (i.e. returns the same string but with all letters in lower case) without using str_to_lower() or the Base R tolower() function. Hint: check out the str_replace_all() function!

+
+
+

3) rotateStringLeft(s, k) [COLLABORATIVE, 15%]

+

Write the function rotateStringLeft(s, k) that takes a string s and a non-negative integer k, and returns the string s rotated k places to the left. So, if s = "iknowkungfu" and k = 2, then the result should be "nowkungfuik". If k is larger than the length of s, the function should continue to rotate the string beyond its starting point. So, if s = "iknowkungfu" and k = 11, then the result should be "iknowkungfu", but if k = 12, the result should be "knowkungfui".

+
+
+

4) isDigit(s) [COLLABORATIVE, 15%]

+

Write the function isDigit(s) that takes a string s and returns TRUE if all characters in the string are numeric integers and FALSE otherwise. So isDigit("123") should return TRUE, isDigit("123N") should return FALSE, and isDigit("") should return FALSE. Hint: the value '\\d' can be used to search for whether a string has as digit, so str_detect("r2d2", '\\d') would return TRUE but str_detect("rd", '\\d') would return FALSE.

+
+
+

5) getTheGerunds(sentence) [COLLABORATIVE, 15%]

+

Write a function that takes a single string, sentence, and returns a vector of all the gerunds in it (i.e. all the words that end in "ing"). So getTheGerunds("I like hiking and swimming") should return c("hiking", "swimming"). If there are no gerunds in sentence, the function should return NULL. Hint: first solve how you might separate a single-string sentence into a vector of words.

+
+
+

6) letterCount(s) [COLLABORATIVE, 15%]

+

Write the function letterCount(s) that takes a string s and returns a named vector with the count of each letter in s, spanning the alphabet. If a letter does not appear in s, it should have a value of 0. You may assume that s only contains upper and/or lower case letters. You should ignore cases, so "a" and "A" should be both treated as "a".

+

For example, letterCount("aaaaabbbc") should return:

+
+
a b c d e f g h i j k l m n o p q r s t u v w x y z
+5 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+
+

And letterCount("someString") should return:

+
+
a b c d e f g h i j k l m n o p q r s t u v w x y z
+0 0 0 0 1 0 1 0 1 0 0 0 1 1 2 0 0 1 2 1 0 0 0 0 0 0
+
+
+
+

7) sameChars(s1, s2) [COLLABORATIVE, 15%]

+

Write the function sameChars(s1, s2) that takes two strings and returns TRUE if the two strings are composed of the same characters (though perhaps in different orders); that is, if every character that is in the first string is in the second (and vice versa), return TRUE, otherwise return FALSE. Assume that s1 and s2 are both strings. This test is case-sensitive, so "ABC" and "abc" do not contain the same characters. The function returns FALSE if either parameter is not a string, but returns TRUE if both strings are empty.

+
+
+

8) Read and reflect [SOLO, 10%]

+

When we come back from spring break, we’ll be shifting the focus of the class to working with data.

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Data Analysis
Data Frames +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+
+
+
+

Bonus 1) hasBalancedParentheses(s) [SOLO, 3%]

+

Write the function hasBalancedParentheses(s), which takes a string s and returns TRUE if the parentheses in s are balanced and FALSE otherwise (ignoring all non-parentheses in the string). We say that parentheses are “balanced” if each right parenthesis closes (matches) an open (unmatched) left parenthesis, and no left parentheses are left unclosed (unmatched) at the end of the text. So, for example, "( ( ( ) ( ) ) ( ) )" is balanced, but "( ) )" is not balanced, and "( ) ) (" is also not balanced. Hint: keep track of how many right parentheses remain unmatched as you iterate over the string.

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+ + + + + + + \ No newline at end of file diff --git a/hw/8-data-frames-temp.html b/hw/8-data-frames-temp.html new file mode 100644 index 0000000..ae5906c --- /dev/null +++ b/hw/8-data-frames-temp.html @@ -0,0 +1,817 @@ + + + + + + + + + +Homework 8 - Data Frames – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Homework 8 - Data Frames

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Due: Mar 25 by 11:59pm

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Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

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    +
  • To practice creating data frames in R.
    • +
  • To practice merging and slicing data frames in R.
    • +
+
+
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

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    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

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    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
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+
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+

Readings

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The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

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+
+Data Analysis
Data Frames +
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1) Staying organized [SOLO, 5%]

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Download and use this template for your assignment. Inside the “hw8” folder, open and edit the R script called hw8.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.

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+

Using good style

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For this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.

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+
+
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2) Inspect package data [SOLO, 15%]

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Write R code to install the dslabs package from CRAN, then write code to load the package. Write some code to preview and inspect the movielens data frame that gets loaded when you load the package using some of the techniques we saw in class. For each of the following questions, write code to find your answer and leave a detailed response in a comment:

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  • What is this dataset about?
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  • How many observations are in the data frame?
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  • What is the original source of the data?
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  • What type of data is each variable?
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  • What are the years of the earliest and most recent observations in the data set?
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3) Answer questions about the data [COLLABORATIVE, 25%]

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For each of the following questions, write code to find your answer and leave a detailed response in a comment:

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    +
  • What is the min, mean, and max rating in the data set?
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  • How many observations received the maximum rating?
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  • What percentage of total observations received the maximum rating?
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  • What is the title of the observation with the longest title (in terms of numbers of letters in the title)?
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4) Loading and inspecting external data [SOLO, 20%]

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Write R code to read in the prisoners2019.csv file located in the data folder. Store the object as df. Write some code to preview and inspect the df data frame using some of the techniques we saw in class. For each of the following questions, write code to find your answer and leave a detailed response in a comment:

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    +
  • What do you think this dataset is about?
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  • How many observations are in the data frame?
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  • What type of data is each variable?
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+
+
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5) Answer questions about the data [COLLABORATIVE, 25%]

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For each of the following questions, write code to find your answer and leave a detailed response in a comment:

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    +
  • Which states have the highest and lowest total prison population?
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  • Which states have the highest and lowest total prison population as a percentage of the total state population?
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  • According to the 2020 U.S. Census, only 12.4% of the U.S. population is black, but some states have imprisoned more black people than any other race. Which states fit this description?
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+
+

6) Read and reflect [SOLO, 10%]

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Read and reflect on the following readings to preview what we will be covering next:

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+
+Data Wrangling +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

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Some thoughts you may want to try in your reflection:

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    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
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+
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Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

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+ + + + + + + \ No newline at end of file diff --git a/hw/9-data-wrangling-temp.html b/hw/9-data-wrangling-temp.html new file mode 100644 index 0000000..06ac443 --- /dev/null +++ b/hw/9-data-wrangling-temp.html @@ -0,0 +1,930 @@ + + + + + + + + + +Homework 9 - Data Wrangling – EMSE 4571 / 6571 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Homework 9 - Data Wrangling

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+

Due: Apr 01 by 11:59pm

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Weight: This assignment is worth 3.75% of your final grade.

+

Purpose: The purposes of this assignment are:

+
    +
  • To practice exploring and wrangling data frames in R using the dplyr package
    • +
+

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

+
    +
  • 0% for not attempting a response.
    • +
  • 50% for attempting the question but with major errors.
    • +
  • 75% for attempting the question but with minor errors.
    • +
  • 100% for correctly answering the question.
    • +
+

The reflection portion is always worth 10% and graded for completion.

+

Rules:

+
    +
  • Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
    • +
  • For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.
    • +
+
+
+

Readings

+

The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:

+
+
+Data Wrangling +
+
+
+
+

Using AI tools

+
+

On this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

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+
+
+

1) Staying organized [SOLO, 5%]

+

Download and use this template for your assignment. Inside the “hw9” folder, open and edit the R script called hw9.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.

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+
+

2) Load the data [SOLO, 5%]

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For this assignment, we will work with data on flights from New York City airports during 2013. The data are accessible from the nycflights13 package. Write R code to install and then load the package.

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3) Inspect the data [SOLO, 5%]

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Look at the datasets that are included in this package:

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data(package = "nycflights13")
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Data sets in package 'nycflights13':
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+airlines                Airline names.
+airports                Airport metadata
+flights                 Flights data
+planes                  Plane metadata.
+weather                 Hourly weather data
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Write some code to preview and summarize each of these data frames using some of the methods we’ve used in class. You should be able to quickly get an understanding of what variables are included in each data frame and their nature. For each dataset, consider the following questions in your exploration (you don’t have to write out answers to these questions - just write code to help you answer them by previewing the data in different ways):

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  • What is the total size of each data frame?
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  • What type of data is each variable (numeric, character, logical, date)?
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  • Do any variables have missing values? Why might that be?
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  • What are the “boundaries” of each period of observation: +
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    • For numeric variables, what are the min and max values?
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    • For character variables, what are the unique values in the variable?
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    • For date variables, what time period do the observations in these data frames span?
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Answer questions about the data

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Use the data frames in the nycflights13 package to answer the following questions. For each question, write R code to find the solution. Leave comments where appropriate to explain what you are doing, and then write your final answer as a comment at the end.

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For example, if the question was “how many observations are in the flights data frame?”, here is an acceptable answer:

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# Find the number of rows in the flights data frame
+nrow(flights)
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#> [1] 336776
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# Answer: There are 336,776 observations in the flights data frame
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+

You do not have to use the dplyr package functions (i.e. filter(), arrange(), mutate(), etc.) to answer these questions, but they generally make it easier, and I suggest you use them.

+
+

Using AI tools

+

On this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, include the prompt(s) you used (using a comment with #). Did you have to change anything to your prompt to get better results? If so, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.

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+
+

4) [SOLO, 5%]

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How many flights out of NYC airports in 2013 had an arrival delay of two or more hours? Hint: use filter()

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5) [SOLO, 5%]

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How many flights out of NYC airports in 2013 departed in fall semester (i.e. the months August - December, inclusive)? Hint: use filter()

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6) [SOLO, 5%]

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How many flights out of NYC airports in 2013 were operated by United, American, or Delta airlines? Hint: use filter()

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7) [SOLO, 5%]

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List the top 3 airlines (by name, not carrier code) that had the highest delay time of any one flight leaving a NYC airport in 2013. Hint: use arrange()

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8) [SOLO, 5%]

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How many flights out of NYC airports in 2013 flew to the 3 major DC-area airports: Reagan National, Dulles, or BWI? Hint: use filter()

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9) [COLLABORATIVE, 10%]

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What is the year manufactured and tail number of the oldest airplane that any one airline used in 2013 to fly out of NYC airports, and which airline operated that plane? Hint: use arrange() and filter()

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10) [COLLABORATIVE, 10%]

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Using the flights data frame, compute a new variable speed (in miles per hour) using the air_time and distance variables. For the fastest flight in the dataset, what was its speed and what were the origin and destination airport codes? Hint: use mutate() and arrange()

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11) [COLLABORATIVE, 10%]

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Of all the flights in 2013 departing from NYC airports, list the top 3 destinations (airport names, not airport codes) with the highest mean arrival delay. Hint: Use a “pipeline” of group_by(), summarise(), and arrange(). Don’t forget to filter out any NA values before summarizing!

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12) [COLLABORATIVE, 10%]

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Use the flights data frame to create a new summary data frame called dailyDelaySummary that contains the following variables for each day in 2013:

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  • meanDepDelay: the mean departure delay (in minutes)
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  • numDelayedFlights: the total number of delayed flights
    • +
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Save this file in your “data” folder as “dailyDelaySummary.csv” Hint: Use a “pipeline” of group_by() and summarise(), and don’t forget to filter out any NA values before summarizing!

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13) [COLLABORATIVE, 10%]

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Using the dailyDelaySummary data frame that you created in part i), answer the following two questions:

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    +
  • Find the day in 2013 with the highest number of delayed flights. On that day, how many flights were delayed, and what was the mean delay time (in minutes)?
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  • Find the day in 2013 with the highest mean departure delay (in minutes). On that day, how many flights were delayed and what was the mean delay time (in minutes)?
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+
+
+

14) Read and reflect [SOLO, 10%]

+

Read and reflect on the following readings to preview what we will be covering next:

+
+
+Data Visualization +
+
+

Afterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?

+

In a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

+

Some thoughts you may want to try in your reflection:

+
    +
  • “I used to think ______, now I think ______ 🤔”
    • +
  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
    • +
  • Connect the course content to your own work or projects you’re working on.
    • +
+
+
+

Submit

+

Create a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.

+
+
+
+

Bonus 1) [SOLO, 1%]

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How many flights have a missing departure time? What might these rows represent?

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+
+

Bonus 2) [SOLO, 1%]

+

Which flights (i.e. carrier + flight) departed every day of the year, and which airports did they fly to?

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+
+

Bonus 3) [SOLO, 2%]

+

Use the flights data frame to create a season variable. The seasons are defined as the following:

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    +
  • Spring: March, April, May
    • +
  • Summer: June, July, August
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  • Fall: September, October, November
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  • Winter: December, January, February
    • +
+

What season experiences the largest mean delay, and why might that be? Hint: Use a “pipeline” of mutate, group_by() and summarise(). Don’t forget to filter out any NA values before summarizing!

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+ + + + + + + \ No newline at end of file diff --git a/hw/images/openweathermap.png b/hw/images/openweathermap.png new file mode 100644 index 0000000000000000000000000000000000000000..0baf7d0611c8861b4481595e6ca73619e3cbd033 GIT binary patch literal 59392 zcmaHT1zeTO^EcgaKyYl??pGyL(B$bPse7k|w3- z=&S+J*PvB;A@US^nygVGN$+bJzLX1MU#L^b$C^*L#VP#Uu?O_2#6UGGDMfq&U6PQ> zNi$H~Y^rGHDION^hABu1o9`n2 z${8yXB{C8ZD722M_;d%yMTj@Rg>F>B3k1rhfdV)&j22FSz-u}qgEg;Ebmdw2b9+`M zLTOq_I_V%@|1$y^$Fv%Zps|h&5Xfcm-?`2Yl9hd3m|H2LECJ%cygp+hL6W z0&`Y(pB$HtA1qTNQw=Gz_wONS!1o9cP>`=6puu;L;2(%kO9+@h?jay3!B_B9#D_q@ zgRif^{}r;KewRX@W<&qJha~=0QCL}2N(y{cHvV90YUgBO?@anSlmP+)O2AS@!&&3K zERV6hErX$ny^$${yRE~oCJ=n?Jm6bfQ)fd`cUv1fCmwfxvOj9@fbW03W+Ws1ql&XN zKbgjR1yWJ_52mCX3``76WCBQ}q@;WwOw4$c#KixvIrxd6%);5(frpXN&CQL$jg`Uv zgE=EJH#avU6AL2?3q807y_1KXv!Oe^ozt5?JNey@n5mQT2TKQMOM5%gU;P>y*}FLN zlac)*^w+;X`80L6{1+uVr~exkcz}$*N*I|Lm>B=AnX{$YKQ#MQ@@KO@#`PyTzF(d3 zC|J6i+GvVd+JdDDW=(*Fi-YeEGXGQZFGl}tsp@3hm%FlJLJt{Lkt9<1KhT 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Schedule

Getting Started
Class orientation, intro to R, workflow, operators, data types HW 1
-Due: Jan 24 +Due: Jan 21 Getting Started
Operations & Data Types @@ -288,7 +288,7 @@

Schedule

Functions & Packages
Functions, arguments, installing external packages, problem solving HW 2
-Due: Jan 31 +Due: Jan 28 Functions & Packages @@ -299,7 +299,7 @@

Schedule

Creating Functions
Defining your own functions, top-down structure HW 3
-Due: Feb 07 +Due: Feb 04 Creating Functions @@ -310,7 +310,7 @@

Schedule

Conditionals & Testing
If / else statements, writing unit tests HW 4
-Due: Feb 14 +Due: Feb 11 Conditionals
Testing & Debugging @@ -322,7 +322,7 @@

Schedule

Iteration
Ins and outs of loops HW 5
-Due: Feb 21 +Due: Feb 18 Iteration @@ -333,7 +333,7 @@

Schedule

Vectors
Creating and slicing collections of values HW 6
-Due: Feb 28 +Due: Feb 25 Vectors @@ -344,7 +344,7 @@

Schedule

Strings
All things between quotes HW 7
-Due: Mar 13 +Due: Mar 18 Strings @@ -372,7 +372,7 @@

Schedule

Data Frames
Rows and columns of data HW 8
-Due: Mar 26 +Due: Mar 25 Data Analysis
Data Frames @@ -384,7 +384,7 @@

Schedule

Data Wrangling
Modifying data frames HW 9
-Due: Apr 02 +Due: Apr 01 Data Wrangling @@ -395,7 +395,7 @@

Schedule

Data Visualization
Visualizing your data HW 10
-Due: Apr 09 +Due: Apr 08 Data Visualization @@ -406,7 +406,7 @@

Schedule

Programming with Data
Iterating through data frames and writing functions to summarize data HW 11
-Due: Apr 16 +Due: Apr 15 Sections 26.3 & 26.4 in R4DS
Post on iteration with {purrr} @@ -418,7 +418,7 @@

Schedule

Webscraping
Collecting data embedded in web pages HW 12
-Due: Apr 23 +Due: Apr 22 Chapter 25 in R4DS
Post on APIs in R @@ -429,7 +429,7 @@

Schedule

Monte Carlo Methods
Using simulation to solve problems HW 13
-Due: Apr 30 +Due: Apr 29 Monte Carlo Methods diff --git a/search.json b/search.json index 60a823f..9472da6 100644 --- a/search.json +++ b/search.json @@ -114,7 +114,7 @@ "href": "syllabus.html#standard-grading", "title": "Syllabus", "section": "Standard Grading", - "text": "Standard Grading\nFinal grades will be calculated as follows:\n\n\n\n\nComponent\nWeight\nNotes\n\n\n\n\nParticipation / Attendance\n6%\n(Yes, I take attendance)\n\n\nHomeworks & Readings (13x)\n46%\nLowest 1 dropped\n\n\nQuizzes (10x)\n18%\nLowest 2 dropped\n\n\nMidterm Exam\n10%\n\n\n\nFinal Exam\n20%\n\n\n\n\n\nHere’s a visual breakdown by category:", + "text": "Standard Grading\nFinal grades will be calculated as follows:\n\n\n\n\nComponent\nWeight\nNotes\n\n\n\n\nParticipation / Attendance\n7%\n(Yes, I take attendance)\n\n\nHomeworks & Readings (13x)\n45%\nLowest 1 dropped\n\n\nQuizzes (10x)\n18%\nLowest 2 dropped\n\n\nMidterm Exam\n10%\n\n\n\nFinal Exam\n20%\n\n\n\n\n\nHere’s a visual breakdown by category:", "crumbs": [ "Syllabus" ] @@ -314,7 +314,7 @@ "href": "schedule.html", "title": "Schedule", "section": "", - "text": "Schedule\nAll assignments are due by 11:59pm on the due date.\n\n\n\n\n\nWeek\nDate\nQuiz\nClass\nAssignments\nReadings\n\n\n\n\n1\nJan 16\n\nGetting Started\nClass orientation, intro to R, workflow, operators, data types\nHW 1\nDue: Jan 24\nGetting Started\nOperations & Data Types\n\n\n2\nJan 23\nQuiz 1:\nWeek 1\nFunctions & Packages\nFunctions, arguments, installing external packages, problem solving\nHW 2\nDue: Jan 31\nFunctions & Packages\n\n\n3\nJan 30\nQuiz 2:\nWeeks 1 - 2\nCreating Functions\nDefining your own functions, top-down structure\nHW 3\nDue: Feb 07\nCreating Functions\n\n\n4\nFeb 06\nQuiz 3:\nWeeks 2 - 3\nConditionals & Testing\nIf / else statements, writing unit tests\nHW 4\nDue: Feb 14\nConditionals\nTesting & Debugging\n\n\n5\nFeb 13\nQuiz 4:\nWeeks 3 - 4\nIteration\nIns and outs of loops\nHW 5\nDue: Feb 21\nIteration\n\n\n6\nFeb 20\nQuiz 5:\nWeeks 4 - 5\nVectors\nCreating and slicing collections of values\nHW 6\nDue: Feb 28\nVectors\n\n\n7\nFeb 27\nQuiz 6:\nWeeks 5 - 6\nStrings\nAll things between quotes\nHW 7\nDue: Mar 13\nStrings\n\n\n8\nMar 06\n\nMidterm\nDon't panic!\n\n\n\n\n9\nMar 13\n\nSpring Break\nNo class\n\n\n\n\n10\nMar 20\n\nData Frames\nRows and columns of data\nHW 8\nDue: Mar 26\nData Analysis\nData Frames\n\n\n11\nMar 27\nQuiz 7:\nWeek 10\nData Wrangling\nModifying data frames\nHW 9\nDue: Apr 02\nData Wrangling\n\n\n12\nApr 03\nQuiz 8:\nWeek 11\nData Visualization\nVisualizing your data\nHW 10\nDue: Apr 09\nData Visualization\n\n\n13\nApr 10\nQuiz 9:\nWeek 12\nProgramming with Data\nIterating through data frames and writing functions to summarize data\nHW 11\nDue: Apr 16\nSections 26.3 & 26.4 in R4DS\nPost on iteration with {purrr}\n\n\n14\nApr 17\nQuiz 10:\nWeek 13\nWebscraping\nCollecting data embedded in web pages\nHW 12\nDue: Apr 23\nChapter 25 in R4DS\nPost on APIs in R\n\n\n15\nApr 24\n\nMonte Carlo Methods\nUsing simulation to solve problems\nHW 13\nDue: Apr 30\nMonte Carlo Methods\n\n\n16\nMay 01\n\nFinal Exam Review\nOverview of entire course\n\n\n\n\n17\nMay 08\n\nFinal Exam\nTime and location TBD", + "text": "Schedule\nAll assignments are due by 11:59pm on the due date.\n\n\n\n\n\nWeek\nDate\nQuiz\nClass\nAssignments\nReadings\n\n\n\n\n1\nJan 16\n\nGetting Started\nClass orientation, intro to R, workflow, operators, data types\nHW 1\nDue: Jan 21\nGetting Started\nOperations & Data Types\n\n\n2\nJan 23\nQuiz 1:\nWeek 1\nFunctions & Packages\nFunctions, arguments, installing external packages, problem solving\nHW 2\nDue: Jan 28\nFunctions & Packages\n\n\n3\nJan 30\nQuiz 2:\nWeeks 1 - 2\nCreating Functions\nDefining your own functions, top-down structure\nHW 3\nDue: Feb 04\nCreating Functions\n\n\n4\nFeb 06\nQuiz 3:\nWeeks 2 - 3\nConditionals & Testing\nIf / else statements, writing unit tests\nHW 4\nDue: Feb 11\nConditionals\nTesting & Debugging\n\n\n5\nFeb 13\nQuiz 4:\nWeeks 3 - 4\nIteration\nIns and outs of loops\nHW 5\nDue: Feb 18\nIteration\n\n\n6\nFeb 20\nQuiz 5:\nWeeks 4 - 5\nVectors\nCreating and slicing collections of values\nHW 6\nDue: Feb 25\nVectors\n\n\n7\nFeb 27\nQuiz 6:\nWeeks 5 - 6\nStrings\nAll things between quotes\nHW 7\nDue: Mar 18\nStrings\n\n\n8\nMar 06\n\nMidterm\nDon't panic!\n\n\n\n\n9\nMar 13\n\nSpring Break\nNo class\n\n\n\n\n10\nMar 20\n\nData Frames\nRows and columns of data\nHW 8\nDue: Mar 25\nData Analysis\nData Frames\n\n\n11\nMar 27\nQuiz 7:\nWeek 10\nData Wrangling\nModifying data frames\nHW 9\nDue: Apr 01\nData Wrangling\n\n\n12\nApr 03\nQuiz 8:\nWeek 11\nData Visualization\nVisualizing your data\nHW 10\nDue: Apr 08\nData Visualization\n\n\n13\nApr 10\nQuiz 9:\nWeek 12\nProgramming with Data\nIterating through data frames and writing functions to summarize data\nHW 11\nDue: Apr 15\nSections 26.3 & 26.4 in R4DS\nPost on iteration with {purrr}\n\n\n14\nApr 17\nQuiz 10:\nWeek 13\nWebscraping\nCollecting data embedded in web pages\nHW 12\nDue: Apr 22\nChapter 25 in R4DS\nPost on APIs in R\n\n\n15\nApr 24\n\nMonte Carlo Methods\nUsing simulation to solve problems\nHW 13\nDue: Apr 29\nMonte Carlo Methods\n\n\n16\nMay 01\n\nFinal Exam Review\nOverview of entire course\n\n\n\n\n17\nMay 08\n\nFinal Exam\nTime and location TBD", "crumbs": [ "Schedule" ] @@ -330,46 +330,158 @@ ] }, { - "objectID": "hw/8-data-frames.html", - "href": "hw/8-data-frames.html", + "objectID": "hw/9-data-wrangling-temp.html", + "href": "hw/9-data-wrangling-temp.html", + "title": "Homework 9 - Data Wrangling", + "section": "", + "text": "Due: Apr 01 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice exploring and wrangling data frames in R using the dplyr package\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions." + }, + { + "objectID": "hw/9-data-wrangling-temp.html#answer-questions-about-the-data", + "href": "hw/9-data-wrangling-temp.html#answer-questions-about-the-data", + "title": "Homework 9 - Data Wrangling", + "section": "Answer questions about the data", + "text": "Answer questions about the data\nUse the data frames in the nycflights13 package to answer the following questions. For each question, write R code to find the solution. Leave comments where appropriate to explain what you are doing, and then write your final answer as a comment at the end.\nFor example, if the question was “how many observations are in the flights data frame?”, here is an acceptable answer:\n\n# Find the number of rows in the flights data frame\nnrow(flights)\n\n#> [1] 336776\n\n\n\n# Answer: There are 336,776 observations in the flights data frame\n\nYou do not have to use the dplyr package functions (i.e. filter(), arrange(), mutate(), etc.) to answer these questions, but they generally make it easier, and I suggest you use them.\n\nUsing AI tools\nOn this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, include the prompt(s) you used (using a comment with #). Did you have to change anything to your prompt to get better results? If so, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.\n\n\n4) [SOLO, 5%]\nHow many flights out of NYC airports in 2013 had an arrival delay of two or more hours? Hint: use filter()\n\n\n5) [SOLO, 5%]\nHow many flights out of NYC airports in 2013 departed in fall semester (i.e. the months August - December, inclusive)? Hint: use filter()\n\n\n6) [SOLO, 5%]\nHow many flights out of NYC airports in 2013 were operated by United, American, or Delta airlines? Hint: use filter()\n\n\n7) [SOLO, 5%]\nList the top 3 airlines (by name, not carrier code) that had the highest delay time of any one flight leaving a NYC airport in 2013. Hint: use arrange()\n\n\n8) [SOLO, 5%]\nHow many flights out of NYC airports in 2013 flew to the 3 major DC-area airports: Reagan National, Dulles, or BWI? Hint: use filter()\n\n\n9) [COLLABORATIVE, 10%]\nWhat is the year manufactured and tail number of the oldest airplane that any one airline used in 2013 to fly out of NYC airports, and which airline operated that plane? Hint: use arrange() and filter()\n\n\n10) [COLLABORATIVE, 10%]\nUsing the flights data frame, compute a new variable speed (in miles per hour) using the air_time and distance variables. For the fastest flight in the dataset, what was its speed and what were the origin and destination airport codes? Hint: use mutate() and arrange()\n\n\n11) [COLLABORATIVE, 10%]\nOf all the flights in 2013 departing from NYC airports, list the top 3 destinations (airport names, not airport codes) with the highest mean arrival delay. Hint: Use a “pipeline” of group_by(), summarise(), and arrange(). Don’t forget to filter out any NA values before summarizing!\n\n\n12) [COLLABORATIVE, 10%]\nUse the flights data frame to create a new summary data frame called dailyDelaySummary that contains the following variables for each day in 2013:\n\nmeanDepDelay: the mean departure delay (in minutes)\nnumDelayedFlights: the total number of delayed flights\n\nSave this file in your “data” folder as “dailyDelaySummary.csv” Hint: Use a “pipeline” of group_by() and summarise(), and don’t forget to filter out any NA values before summarizing!\n\n\n13) [COLLABORATIVE, 10%]\nUsing the dailyDelaySummary data frame that you created in part i), answer the following two questions:\n\nFind the day in 2013 with the highest number of delayed flights. On that day, how many flights were delayed, and what was the mean delay time (in minutes)?\nFind the day in 2013 with the highest mean departure delay (in minutes). On that day, how many flights were delayed and what was the mean delay time (in minutes)?\n\n\n\n14) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nData Visualization\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.\n\n\n\nBonus 1) [SOLO, 1%]\nHow many flights have a missing departure time? What might these rows represent?\n\n\nBonus 2) [SOLO, 1%]\nWhich flights (i.e. carrier + flight) departed every day of the year, and which airports did they fly to?\n\n\nBonus 3) [SOLO, 2%]\nUse the flights data frame to create a season variable. The seasons are defined as the following:\n\nSpring: March, April, May\nSummer: June, July, August\nFall: September, October, November\nWinter: December, January, February\n\nWhat season experiences the largest mean delay, and why might that be? Hint: Use a “pipeline” of mutate, group_by() and summarise(). Don’t forget to filter out any NA values before summarizing!" + }, + { + "objectID": "hw/8-data-frames-temp.html", + "href": "hw/8-data-frames-temp.html", "title": "Homework 8 - Data Frames", "section": "", - "text": "Coming soon!" + "text": "Due: Mar 25 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice creating data frames in R.\nTo practice merging and slicing data frames in R.\n\n\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nData AnalysisData Frames\n\n\n\n\n1) Staying organized [SOLO, 5%]\nDownload and use this template for your assignment. Inside the “hw8” folder, open and edit the R script called hw8.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.\n\nUsing good style\nFor this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.\n\n\n\n2) Inspect package data [SOLO, 15%]\nWrite R code to install the dslabs package from CRAN, then write code to load the package. Write some code to preview and inspect the movielens data frame that gets loaded when you load the package using some of the techniques we saw in class. For each of the following questions, write code to find your answer and leave a detailed response in a comment:\n\nWhat is this dataset about?\nHow many observations are in the data frame?\nWhat is the original source of the data?\nWhat type of data is each variable?\nWhat are the years of the earliest and most recent observations in the data set?\n\n\n\n3) Answer questions about the data [COLLABORATIVE, 25%]\nFor each of the following questions, write code to find your answer and leave a detailed response in a comment:\n\nWhat is the min, mean, and max rating in the data set?\nHow many observations received the maximum rating?\nWhat percentage of total observations received the maximum rating?\nWhat is the title of the observation with the longest title (in terms of numbers of letters in the title)?\n\n\n\n4) Loading and inspecting external data [SOLO, 20%]\nWrite R code to read in the prisoners2019.csv file located in the data folder. Store the object as df. Write some code to preview and inspect the df data frame using some of the techniques we saw in class. For each of the following questions, write code to find your answer and leave a detailed response in a comment:\n\nWhat do you think this dataset is about?\nHow many observations are in the data frame?\nWhat type of data is each variable?\n\n\n\n5) Answer questions about the data [COLLABORATIVE, 25%]\nFor each of the following questions, write code to find your answer and leave a detailed response in a comment:\n\nWhich states have the highest and lowest total prison population?\nWhich states have the highest and lowest total prison population as a percentage of the total state population?\nAccording to the 2020 U.S. Census, only 12.4% of the U.S. population is black, but some states have imprisoned more black people than any other race. Which states fit this description?\n\n\n\n6) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nData Wrangling\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." }, { - "objectID": "hw/6-vectors.html", - "href": "hw/6-vectors.html", + "objectID": "hw/7-strings-temp.html", + "href": "hw/7-strings-temp.html", + "title": "Homework 7 - Strings", + "section": "", + "text": "Due: Mar 18 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice manipulating strings in R with the stringr package.\nTo practice computational problem solving with strings.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nStrings\n\n\n\n\n1) Staying organized [SOLO, 5%]\nDownload and use this template for your assignment. Inside the “hw7” folder, open and edit the R script called “hw7.R” and fill out your name, GW Net ID, and the names of anyone you worked with on this assignment.\n\nWriting test functions\nFor each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.\n\n\nUsing good style\nFor this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.\n\n\nUsing the autograder\n\nYou can check your solutions to problems 2 - 8 by logging into the autograder and uploading your hw7.R file.\nThe file must be named hw7.R or it won’t work.\nYour user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.\n\n\n\n\n2) strToLower(s) [SOLO, 10%]\nWrite a function that does exactly what str_to_lower() does (i.e. returns the same string but with all letters in lower case) without using str_to_lower() or the Base R tolower() function. Hint: check out the str_replace_all() function!\n\n\n3) rotateStringLeft(s, k) [COLLABORATIVE, 15%]\nWrite the function rotateStringLeft(s, k) that takes a string s and a non-negative integer k, and returns the string s rotated k places to the left. So, if s = \"iknowkungfu\" and k = 2, then the result should be \"nowkungfuik\". If k is larger than the length of s, the function should continue to rotate the string beyond its starting point. So, if s = \"iknowkungfu\" and k = 11, then the result should be \"iknowkungfu\", but if k = 12, the result should be \"knowkungfui\".\n\n\n4) isDigit(s) [COLLABORATIVE, 15%]\nWrite the function isDigit(s) that takes a string s and returns TRUE if all characters in the string are numeric integers and FALSE otherwise. So isDigit(\"123\") should return TRUE, isDigit(\"123N\") should return FALSE, and isDigit(\"\") should return FALSE. Hint: the value '\\\\d' can be used to search for whether a string has as digit, so str_detect(\"r2d2\", '\\\\d') would return TRUE but str_detect(\"rd\", '\\\\d') would return FALSE.\n\n\n5) getTheGerunds(sentence) [COLLABORATIVE, 15%]\nWrite a function that takes a single string, sentence, and returns a vector of all the gerunds in it (i.e. all the words that end in \"ing\"). So getTheGerunds(\"I like hiking and swimming\") should return c(\"hiking\", \"swimming\"). If there are no gerunds in sentence, the function should return NULL. Hint: first solve how you might separate a single-string sentence into a vector of words.\n\n\n6) letterCount(s) [COLLABORATIVE, 15%]\nWrite the function letterCount(s) that takes a string s and returns a named vector with the count of each letter in s, spanning the alphabet. If a letter does not appear in s, it should have a value of 0. You may assume that s only contains upper and/or lower case letters. You should ignore cases, so \"a\" and \"A\" should be both treated as \"a\".\nFor example, letterCount(\"aaaaabbbc\") should return:\n\na b c d e f g h i j k l m n o p q r s t u v w x y z\n5 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n\nAnd letterCount(\"someString\") should return:\n\na b c d e f g h i j k l m n o p q r s t u v w x y z\n0 0 0 0 1 0 1 0 1 0 0 0 1 1 2 0 0 1 2 1 0 0 0 0 0 0\n\n\n\n7) sameChars(s1, s2) [COLLABORATIVE, 15%]\nWrite the function sameChars(s1, s2) that takes two strings and returns TRUE if the two strings are composed of the same characters (though perhaps in different orders); that is, if every character that is in the first string is in the second (and vice versa), return TRUE, otherwise return FALSE. Assume that s1 and s2 are both strings. This test is case-sensitive, so \"ABC\" and \"abc\" do not contain the same characters. The function returns FALSE if either parameter is not a string, but returns TRUE if both strings are empty.\n\n\n8) Read and reflect [SOLO, 10%]\nWhen we come back from spring break, we’ll be shifting the focus of the class to working with data.\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nData AnalysisData Frames\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.\n\n\n\nBonus 1) hasBalancedParentheses(s) [SOLO, 3%]\nWrite the function hasBalancedParentheses(s), which takes a string s and returns TRUE if the parentheses in s are balanced and FALSE otherwise (ignoring all non-parentheses in the string). We say that parentheses are “balanced” if each right parenthesis closes (matches) an open (unmatched) left parenthesis, and no left parentheses are left unclosed (unmatched) at the end of the text. So, for example, \"( ( ( ) ( ) ) ( ) )\" is balanced, but \"( ) )\" is not balanced, and \"( ) ) (\" is also not balanced. Hint: keep track of how many right parentheses remain unmatched as you iterate over the string." + }, + { + "objectID": "hw/6-vectors-temp.html", + "href": "hw/6-vectors-temp.html", "title": "Homework 6 - Vectors", "section": "", - "text": "Coming soon!" + "text": "Due: Feb 25 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice using vectors in R.\nTo practice computational problem solving with vectors.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nVectors\n\n\n\n\n1) Staying organized [SOLO, 5%]\nDownload and use this template for your assignment. Inside the “hw6” folder, open and edit the R script called “hw6.R” and fill out your name, GW Net ID, and the names of anyone you worked with on this assignment.\n\nWriting test functions\nFor each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.\n\n\nUsing good style\nFor this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.\n\n\nUsing the autograder\n\nYou can check your solutions to problems 2 - 7 by logging into the autograder and uploading your hw6.R file.\nThe file must be named hw6.R or it won’t work.\nYour user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.\n\n\n\n\n2) vectorFactorial(n) [SOLO, 10%]\nWrite the function vectorFactorial(n) which computes the factorial of n using vectors to avoid using a loop. Hint: there are some useful functions listed in the vectors chapter for performing operators on a numeric vector.\n\n\n3) nthHighestValue(n, x) [SOLO, 10%]\nWrite a function to find the nth highest value in a given vector. For example, if x equals c(5, 1, 3), then nthHighestValue(1, x) should return 5, because 5 is the 1st highest value in x, and nthHighestValue(2, x) should return 3 because it’s the 2nd highest value in x. Assume only numeric inputs, and assume that n <= length(x). You may not use loops.\n\n\n4) dotProduct(a, b) [COLLABORATIVE, 15%]\nBackground: the “dot product” of two vectors is the sum of the products of the corresponding terms. So the dot product of the vectors c(1, 2, 3) and c(4, 5, 6) is (1*4) + (2*5) + (3*6), or 4 + 10 + 18 = 32. With this in mind, write the function dotProduct(a, b). This function takes two vectors and returns the dot product of those vectors. If the vectors are not equal length, ignore the extra elements in the longer vector. You may not use loops.\n\n\n5) middleValue(a) [COLLABORATIVE, 15%]\nWrite the function middleValue(a) that takes a vector of numbers a and returns the value of the middle element (or the average of the two middle elements).\n\n\n6) isRotation(a1, a2) [COLLABORATIVE, 15%]\nWrite the function isRotation(a1, a2) that takes two vectors, a1 and a2, and returns TRUE if a2 is a rotation of a1 and FALSE otherwise. For example, c(2,3,4,5) is a rotation of c(4,5,2,3). Obviously, two vectors of different lengths cannot be rotations. Also, any vector is a rotation of itself, so isRotation(c(2,3,4,5), c(2,3,4,5)) should return TRUE.\n\n\n7) fibonacciSequence(n) [COLLABORATIVE, 20%]\nWrite the function fibonacciSequence(n) that returns a vector containing the first n numbers in the Fibonacci Sequence. Assume that n is a whole number greater or equal to 1, and also that the first number in the sequence is 1, so fibonacciSequence(1) == 1 should be TRUE. For all numbers where n > 1, the function should return a vector, e.g. identical(idenfibonacciSequence(2), c(1, 1)) should be TRUE.\n\n\n8) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nStrings\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.\n\n\n\nBonus 1) rotateVector(a, n) [SOLO, 3%]\nWrite the function rotateVector(a, n) which takes a vector a and an integer n and returns a new vector where each element in a is shifted to the right by n indices. For example, if a is c(1, 2, 3, 4) and n is 1, the result should be c(4, 1, 2, 3), but if n is -1, the result should be c(2, 3, 4, 1). If n is larger than the length of a, the function should continue to rotate the vector beyond its starting point. So, if a = c(1, 2, 3, 4) and n = 5, then the result should be a = c(4, 1, 2, 3)." }, { - "objectID": "hw/4-conditionals-testing.html", - "href": "hw/4-conditionals-testing.html", + "objectID": "hw/5-iteration-temp.html", + "href": "hw/5-iteration-temp.html", + "title": "Homework 5 - Iteration", + "section": "", + "text": "Due: Feb 18 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice using for and while loops in R.\nTo practice computational problem solving with loops.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions." + }, + { + "objectID": "hw/5-iteration-temp.html#happy-numbers-collaborative", + "href": "hw/5-iteration-temp.html#happy-numbers-collaborative", + "title": "Homework 5 - Iteration", + "section": "Happy Numbers [COLLABORATIVE]", + "text": "Happy Numbers [COLLABORATIVE]\nBackground: Read the first paragraph from the Wikipedia page on happy numbers. After some thought, we see that no matter what number we start with, when we keep replacing the number by the sum of the squares of its digits, we’ll always either arrive at 4 (unhappy) or at 1 (happy). With that in mind, we want to write the function nthHappyNumber(n). However, to write that function, we’ll first need to write isHappyNumber(n), which determines whether a number is “happy” or not. And to right that function, we’ll first need to write sumOfSquaresOfDigits(n). And that’s top-down design! Here we go…" + }, + { + "objectID": "hw/5-iteration-temp.html#sumofsquaresofdigitsn-10", + "href": "hw/5-iteration-temp.html#sumofsquaresofdigitsn-10", + "title": "Homework 5 - Iteration", + "section": "5) sumOfSquaresOfDigits(n) [10%]", + "text": "5) sumOfSquaresOfDigits(n) [10%]\nWrite the function sumOfSquaresOfDigits(n) which takes a non-negative integer, n, and returns the sum of the squares of its digits (assume that n will always be a positive integer, so no need to check for bad inputs)." + }, + { + "objectID": "hw/5-iteration-temp.html#ishappynumbern-10", + "href": "hw/5-iteration-temp.html#ishappynumbern-10", + "title": "Homework 5 - Iteration", + "section": "6) isHappyNumber(n) [10%]", + "text": "6) isHappyNumber(n) [10%]\nWrite the function isHappyNumber(n) which takes a possibly-negative integer and returns TRUE if it is happy and FALSE otherwise. Note that all numbers less than 1 are not happy." + }, + { + "objectID": "hw/5-iteration-temp.html#nthhappynumbern-15", + "href": "hw/5-iteration-temp.html#nthhappynumbern-15", + "title": "Homework 5 - Iteration", + "section": "7) nthHappyNumber(n) [15%]", + "text": "7) nthHappyNumber(n) [15%]\nWrite the function nthHappyNumber(n) which takes a non-negative integer, n, and returns the nth happy number, where nthHappyNumber(1) returns the first happy number (1)." + }, + { + "objectID": "hw/5-iteration-temp.html#turtle-loops-collaborative", + "href": "hw/5-iteration-temp.html#turtle-loops-collaborative", + "title": "Homework 5 - Iteration", + "section": "Turtle loops! [COLLABORATIVE]", + "text": "Turtle loops! [COLLABORATIVE]\n(Note: Please put your solutions to these below the # ignore_rest comment so that the autograder doesn’t test these functions)" + }, + { + "objectID": "hw/5-iteration-temp.html#turtlesquares-redux-10", + "href": "hw/5-iteration-temp.html#turtlesquares-redux-10", + "title": "Homework 5 - Iteration", + "section": "8) turtleSquare(s) redux [10%]", + "text": "8) turtleSquare(s) redux [10%]\nRe-write the turtleSquare(s) function from HW3, but this time you must use a for loop to draw the sides of the square. The following code should produce a square with a side length of 50:\n\nlibrary(TurtleGraphics)\n\nturtle_init()\nturtle_do({\n turtleSquare(50)\n})" + }, + { + "objectID": "hw/5-iteration-temp.html#concentricturtlesquaresspacing-5-10", + "href": "hw/5-iteration-temp.html#concentricturtlesquaresspacing-5-10", + "title": "Homework 5 - Iteration", + "section": "9) concentricTurtleSquares(spacing = 5) [10%]", + "text": "9) concentricTurtleSquares(spacing = 5) [10%]\nWrite the function concentricTurtleSquares(spacing) that uses the TurtleGraphics package to draw concentric squares from the center of the terrarium and outward. The spacing argument determines the spacing between each square, and the default value should be spacing = 5. Also, spacing >= 1, and your function must not allow the turtle to escape the terrarium. Hint: you may want to use turtleSquare(s) as a helper function. The following code should produce concentric squares with a spacing of 5:\n\nlibrary(TurtleGraphics)\n\nturtle_init()\nturtle_do({\n concentricTurtleSquares(5)\n})\n\n\n\n\n\n\n\n10) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nVectors\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.\n\n\n\nBonus 1) turtleSquareRotated(s, degrees) [SOLO, 3%]\nWrite the function turtleSquareRotated(s, degrees) that uses the TurtleGraphics package to draw a square with side length s < 100 and rotated by degrees <= 180 counterclockwise from the horizontal plane. The rotated square should be centered in the turtle’s terrarium. Hint: There are multiple ways to solve this, one of which involves using the cosine (cos()) and sine (sin()) functions. In R, these functions take angles in radians (not degrees), so remember to convert your angles (180 degrees = \\(\\pi\\)).\nThe following code should produce a square with a side length of 30 and rotated by 30 degrees:\n\nlibrary(TurtleGraphics)\nturtle_init()\nturtle_do({\n turtleSquareRotated(30, 30)\n})\n\n\n\n\n\n\n\n\nBonus 2) turtleSquareStar(s, degreeSpacing = 20) [SOLO, 2%]\nWrite the function turtleSquareStar(s, degreeSpacing) that uses the turtleSquareRotated(s, degrees) as a helper function to draw a sequence of overlapping rotated squares with side length s < 70 to form a star. The degreeSpacing argument determines the spacing in degrees between each rotated square, and the default value should be degreeSpacing = 20. Also, 1 <= degreeSpacing <= 60, and your function must not allow the turtle to escape the terrarium. The following code should produce the star of rotated squares with a side length of 50 and 20 degree spacings between each square:\n\nlibrary(TurtleGraphics)\nturtle_init()\nturtle_do({\n turtleSquareStar(50, 20)\n})" + }, + { + "objectID": "hw/4-conditionals-testing-temp.html", + "href": "hw/4-conditionals-testing-temp.html", "title": "Homework 4 - Conditionals & Testing", "section": "", - "text": "Coming soon!" + "text": "Due: Feb 11 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice using conditional statements while writing functions in R.\nTo practice the skill of writing and using test functions to understand the problem.\nTo practice computational problem solving.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nConditionalsTesting & Debugging\n\n\n\n\n1) Staying organized [SOLO, 5%]\nDownload and use this template for your assignment. Inside the “hw4” folder, open and edit the R script called “hw4.R” and fill out your name, Net ID, and the names of anyone you worked with on this assignment.\n\nWriting test functions\nFor each of the following functions, write a test function first, and then write the function. Your test functions will count for half of the available credit for each problem. Think carefully about the test cases to include in your test functions.\n\n\nUsing the autograder\n\nYou can check your solutions to problems 2 - 6 by logging into the autograder and uploading your hw4.R file.\nThe file must be named hw4.R or it won’t work.\nYour user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.\n\n\n\n\n2) isPositiveMultipleOf4Or7(n) [SOLO, 10%]\nWrite the function isPositiveMultipleOf4Or7(n) that returns TRUE if n is a positive multiple of 4 or 7 and FALSE otherwise. Note than n could be any data type.\n\n\n3) isEvenPositiveInt(x) [SOLO, 15%]\nGiven an arbitrary value x, return TRUE if it is an integer, and it is positive, and it is even (all 3 must be true), or FALSE otherwise. If the value x is not an integer, the function should return FALSE rather than error. So, isEvenPositiveInt(\"yikes!\") returns FALSE, and isEvenPositiveInt(123456) returns TRUE.\n\n\n4) isLeapYear(year) [COLLABORATIVE, 20%]\nWrite the function isLeapYear(year) that returns TRUE if year is a leap year and FALSE otherwise. Assume that year is a positive integer.\n\n\n5) getTheCents(n) [COLLABORATIVE, 20%]\nWrite the function getTheCents(n) which takes a value n that represents a payment in US dollars and returns the number of cents in the payment. For example, if n is 2.45, the function should return 45. If n is an integer, the function should return 0, as it has 0 cents; if it isn’t a number, it should return NULL, because a non-number payment make no cents (ha!). If the payment has partial cents (for example, 3.953), it should be rounded to the nearest cent (in this case, 95 cents).\n\n\n6) isNumericLooking(n) [COLLABORATIVE, 20%]\nWrite a function that returns TRUE if the value n is “numeric looking”, meaning that it is either of a numeric type or a character type of a number (e.g. “2”). All other data types and special values (e.g. NA, NaN, Inf, NULL, TRUE, FALSE, etc.) should return FALSE.\n\n\n7) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nIteration\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." }, { - "objectID": "hw/2-functions-packages.html", - "href": "hw/2-functions-packages.html", + "objectID": "hw/3-creating-functions-temp.html", + "href": "hw/3-creating-functions-temp.html", + "title": "Homework 3 - Creating Functions", + "section": "", + "text": "Due: Feb 04 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice creating your own functions in R from scratch.\nTo practice writing code using good style.\nTo practice problem solving (e.g. using Polya’s technique).\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nCreating Functions\n\n\n\n\n1) Staying organized [SOLO, 5%]\nAs always, we’re going to use an R project to keep everything in this assignment tidy. Download and use this template for your assignment. Inside the “hw3” folder, open and edit the R script called “hw3.R” and fill out your name, Net ID, and the names of anyone you worked with on this assignment.\n\nUsing the autograder\n\nYou can check your solutions to problems 2 - 8 by logging into the autograder and uploading your hw3.R file.\nThe file must be named hw3.R or it won’t work.\nYour user name is your netID, and your password is inside the readme.txt file in the Box folder I shared with you.\n\n\n\n\n2) integerSquareRoot(n) [SOLO, 10%]\nGiven a non-negative integer n, return the integer value that is closest to its square root. For example, integerSquareRoot(10) returns 3.\n\n\n3) fabricYards(inches) [SOLO, 10%]\nFabric must be purchased in whole yards. Write a function that takes a non-negative number of inches of fabric desired, and returns the smallest number of whole yards of fabric that must be purchased. Thus, fabricYards(1) is 1 (you need a full yard if you buy one inch) and fabricYards(36) is also 1, but fabricYards(37) is 2. Hint: There are 36 inches in a yard!\n\n\n4) fabricExcess(inches) [SOLO, 10%]\nWrite a function that takes a non-negative number of inches of fabric desired and returns the number of inches of excess fabric that must be purchased (as purchases must be in whole yards). Thus, since you need a whole yard when you buy 1 inch, fabricExcess(1) is 35. Similarly, fabricExcess(36) is 0, and fabricExcess(37) is 35.\nHint: there are (at least) two good ways to write this. One way involves a simple expression using one of the math operators we have learned. The other way uses fabricYards(inches) (which you just wrote!).\n\n\n5) isPerfectCube(x) [SOLO, 15%]\nGiven an integer value x, returns TRUE if it is a perfect cube and FALSE otherwise. That is, return TRUE if there is another integer y such that x = y^3. Thus, isPerfectCube(27) returns TRUE, but isPerfectCube(16) returns FALSE.\n\n\n6) distance(x1, y1, x2, y2) [SOLO, 5%]\nGiven four numeric values representing the points (x1, y1) and (x2, y2), write the function distance(x1, y1, x2, y2) returns the Euclidean distance between those points.\n\n\n7) triangleArea(x1, y1, x2, y2, x3, y3) [COLLABORATIVE, 10%]\nGiven six numeric values representing the points (x1, y1), (x2, y2), and (x3, y3), return the area of the triangle formed by connecting the 3 points. You may wish to use the distance(x1, y1, x2, y2) function as a helper function. You may ignore the case where the 3 points are collinear (and so do not form a triangle). Hint: Heron’s Formula.\n\n\n8) kthDigit(x, k) [COLLABORATIVE, 15%]\nGiven two integers, x and k, return the kth digit of x, counting from the right. So:\n\nkthDigit(789, 1) returns 9\nkthDigit(789, 2) returns 8\nkthDigit(789, 3) returns 7\nkthDigit(789, 4) returns 0\n\nNegative numbers should work, too, so kthDigit(-789, 1) returns 9.\n\n\n9) turtleSquare(s) [COLLABORATIVE, 10%]\n\nNote: Make sure your solutions for the remaining problem(s) are below the ignore_rest comment in your .R file (otherwise the autograder will run this code and it will be slow.\n\nWrite the function turtleSquare(s) which uses the TurtleGraphics package to draw a square with side length s < 100 (because the turtle will “escape” the terrarium when s >= 100). The square should be centered in the turtle’s terrarium, and your turtle should be placed in the center when done. Important: Do not put the turtle_init() command inside your function - the turtle should already be “initialized” inside the terrarium before calling your turtleSquare(s) function. For example, the code below produces a square with side length of 50:\n\nlibrary(TurtleGraphics)\nturtle_init()\nturtleSquare(50)\n\n\n\n\n\n\n\n\n10) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nConditionalsTesting & Debugging\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard.\n\n\n\nBonus 1) numberOfPoolBalls(rows) [SOLO, 2%]\n\nPool balls are arranged in rows where the first row contains 1 pool ball and each row contains 1 more pool ball than the previous row. Thus, for example, 3 rows contain 6 total pool balls (1+2+3). With this in mind, write the function numberOfPoolBalls(rows) that takes a non-negative integer value (the number of rows) and returns another integer value (the number of pool balls in that number of full rows). For example, numberOfPoolBalls(3) returns 6. We will not limit our analysis to a “rack” of 15 balls; rather, our pool table can contain an unlimited number of rows. Hint: For this problem you should research Triangular Numbers.\n\n\nBonus 2): turtleTriangle(s) [SOLO, 3%]\nWrite the function turtleTriangle(s) which uses the TurtleGraphics package to draw an equilateral triangle with side length s < 100. The triangle should be centered in the turtle’s terrarium, and your turtle should be placed in the center when done. Just like with turtleSquare(s), do not put the turtle_init() command inside your function - the turtle should already be “initialized” inside the terrarium before calling turtleTriangle(s). Hint: Getting the triangle centered is the tricky part - you may want to read more about equilateral triangles and apothems. The code below produces a triangle with side length of 50:\n\nlibrary(TurtleGraphics)\nturtle_init()\nturtleTriangle(50)" + }, + { + "objectID": "hw/2-functions-packages-temp.html", + "href": "hw/2-functions-packages-temp.html", "title": "Homework 2 - Functions & Packages", "section": "", - "text": "Coming soon!" + "text": "Due: Jan 28 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice using functions in R.\nTo practice converting data types in R.\nTo practice installing, loading, and using external packages.\n\nSkills & Knowledge: After completing these exercises, you should be able to:\n\nBe familiar with how to specify arguments in functions.\nBe comfortable converting data between logical, character, and numeric types.\nBe comfortable installing external libraries and accessing functions in them.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nFunctions & Packages\n\n\n\n\n1) Staying organized [SOLO, 10%]\nJust like we did in HW1, we’re going to use an R project to keep everything in this assignment tidy. Create a new R project called “hw2” and save a new R script in it called “hw2.R”. Copy the following code to the top of this script and fill out your name, GW netID, and the names of anyone you worked with on this assignment:\n\n# Name: Last, First\n# netID: Insert your netID here\n\n# I worked with the following classmates on this assignment:\n# 1) Name: Last, First\n# 2) Name: Last, First\n\nWrite your responses to all other questions in this assignment in your hw2.R file.\n\n\n2) Polya problem solving 1 [COLLABORATIVE, 15%]\nConsider the points A, B, and C defined by the following cartesian coordinates:\n\n\n\n\nPoint\nx\ny\n\n\n\n\nA\n1\n1\n\n\nB\n5\n1\n\n\nC\n5\n4\n\n\n\n\nYour task is to find out if the triangle formed by the points A, B, and C is a right triangle. Apply Polya’s problem solving technique:\n\nUnderstand the problem: In a code comment, write the question you are trying to solve in your own words, then create objects for each relevant piece of information provided in the problem statement.\nDevise a plan: In code comments, describe the steps you will take in your plan to solve the problem.\nCarry out the plan: Write code below your comments to carry out your plan.\nCheck your work: Examine your solution and any intermediate values you computed along the way. Do the values make sense (e.g. do you get negative numbers when you expected positive numbers)? Is there another way you could have solved it to check that you get the same result?\n\n\n\n3) Polya problem solving 2 [COLLABORATIVE, 20%]\nKevin is deciding between purchasing a Chevy Bolt, which sells for $29,390 (after subsidies), and a Toyota Camry, which sells for $24,000. The Bolt efficiency is 28 kWh of electricity per 100 miles while the Camry efficiency is 28 miles per gallon (mpg) on average. The average price of electricity (in DC, where Kevin lives) is $0.13 / kWh, and the average price of gasoline is $3.00 / gallon. Kevin drives 12,000 miles each year on average.\nTo help him make a decision about which car to buy, answer the following questions:\n\nHow long (in years) would he have to drive the Bolt for the money he saves in fuel savings to be greater than the price premium compared to the Camry?\nWhat would the price of gasoline need to be such that the fuel savings from driving the Bolt for 3 years would be greater than the price premium compared to the Camry?\nHow many miles would Kevin need to drive each year such that the fuel savings from driving the Bolt for 3 years would be greater than the price premium compared to the Camry?\n\nTo solve each problem, apply Polya’s problem solving technique:\n\nUnderstand the problem: In a code comment, write the question you are trying to solve in your own words, then create objects for each relevant piece of information provided in the problem statement.\nDevise a plan: In code comments, describe the steps you will take in your plan to solve the problem.\nCarry out the plan: Write code below your comments to carry out your plan.\nCheck your work: Examine your solution and any intermediate values you computed along the way. Do the values make sense (e.g. do you get negative numbers when you expected positive numbers)? Is there another way you could have solved it to check that you get the same result?\n\n\n\n4) Working with strings: the stringr package [SOLO, 15%]\nWho is your favorite super hero character? If you don’t already have one, pick one (mine is Professor Xavier, and no it’s not just because he’s a professor :P).\nIn your hw2.R file, create two objects that store the following character values:\n\nThe name of your favorite super hero.\nThe super power(s) your hero has.\n\nUse meaningful variable names to store these values.\nNext, install the stringr package. Try using the str_to_lower(), str_to_upper(), and str_to_title() functions from the stringr package on the objects you created. In a code comment, describe what these functions do.\nFinally, use the paste() function to merge your super hero’s name and power in a coherent sentence. For example, if the name is \"Professor Xavier\" and the the power is \"reads peoples' minds\", then a final sentence might be: \"Professor Xavier reads peoples' minds!\". (Note that I intentionally did not tell you how to use the paste() function…you’ll have to figure that one out)\n\n\n5) Installing packages from Github: the BRRR package [SOLO, 10%]\nThe vast majority of the time, you will install external packages using the install.packages() function. This installs packages from the Comprehensive R Archive Network (CRAN), where most packages are published. But you can also install packages that are under development or haven’t been published to CRAN yet. Most of the time, these packages are hosted on GitHub - an online platform for sharing code (it’s also where all of the files that make up this website are stored).\nTo install a package from GitHub, you first need to install the remotes package. Then you can use the remotes::install_github() function to install packages directly from GitHub. To try this out, install the remotes package, then trying installing the BRRR package:\n\nremotes::install_github(\"brooke-watson/BRRR\")\n\n\nNote: Packages on GitHub are in development and often require other packages to work. So if you get an installation error about some other package dependency, try restarting your R session and try again.\n\nNot sure what this package does? Well, one of the other nice things about packages listed on GitHub is the authors tend to write detailed descriptions - check out the GitHub page for the BRRR package. Then try using the BRRR::skrrrahh() function with different number arguments (turn your volume up). In the #hw channel on slack, post your favorite argument to skrrrahh() (mine is 24).\n\n\n6) Turtle practice [COLLABORATIVE, 20%]\nIn case you missed the intro to the TurtleGraphics package in class, go ahead and install and load the package. You might also want to review the related lesson page and the class slides. Then complete these tasks:\n\nLoad the TurtleGraphics package, then initiate your turtle with the turtle_init() function.\nRead through the following commands (don’t run them). In a code comment, type a description of what you think the turtle is going to do.\n\n\nturtle_init()\nturtle_move(distance = 25, direction = \"forward\")\nturtle_move(distance = 50, direction = \"backward\")\nturtle_reset()\nturtle_turn(angle = 90, direction = \"right\")\nturtle_move(distance = 25, direction = \"forward\")\nturtle_move(distance = 50, direction = \"backward\")\nturtle_reset()\n\n\nNow run the above commands to check if the turtle did what you expected. Congratulations, you’ve just practiced “code tracing”! Reading through code before running it then comparing the result with your expectation is an excellent method for improving your your computational thinking skills and your fluency with a particular language.\nUsing what you’ve learned about how to manipulate your turtle, write two different solutions for producing the following image (hint: the length of one side of the square is 50 units):\n\n\n\n\n\n\n\n\n7) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nCreating Functions\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." }, { - "objectID": "hw/12-webscraping.html", - "href": "hw/12-webscraping.html", + "objectID": "hw/13-monte-carlo-methods-temp.html", + "href": "hw/13-monte-carlo-methods-temp.html", + "title": "Homework 13 - Monte Carlo Methods", + "section": "", + "text": "Due: Apr 29 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nBe able to implement a simple Monte Carlo simulation in R to estimate the probability an even occurring.\nBe able to conduct a simple Monte Carlo simulation to integrate a function.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nMonte Carlo Methods\n\n\n\nUsing AI tools\n\nOn this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.\n\n\n\n1) Staying organized [SOLO, 5%]\nDownload and use this template for your assignment. Inside the “hw13” folder, open and edit the R script called hw13.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment. Coming soon!\n\n\n2) Monte Carlo Dice [SOLO, 15%]\nUsing the sample() function, conduct a monte carlo simulation to estimate the probability of rolling a sum of 7 using two 6-sided dice.\n\n\n3) Monte Carlo Full House [COLLABORATIVE, 15%]\nUsing the sample() function, conduct a monte carlo simulation to estimate the probability of drawing a “Full House” poker hand from a standard 52-card deck of cards.\n\n\n4) Monte Carlo Triangles [COLLABORATIVE, 15%]\nUsing the sample() function, conduct a Monte Carlo simulation to estimate the probability of randomly picking three integer values between 1 and 100 (inclusive, and without replacement) that can be used to form a triangle. As a hint, it would be helpful to first write a simple function called isTriangle(a, b, c) that takes the lengths of three line segments and returns TRUE if the lines can make a triangle and FALSE otherwise. The Triangle Inequality Theorem may come in handy here.\n\n\n5) Monte Carlo Integration [COLLABORATIVE, 20%]\nUse a Monte Carlo simulation to estimate the integral of the function \\(y = log(x)\\) between \\(5 \\le x \\le 20\\) by estimating the area under the curve of the function.\n\n\n6) Your Own Monte Carlo Simulation [SOLO, 20%]\nFor the last problem, write your own homework question that requires the student (you) to use a Monte Carlo simulation to compute the probability of an event occurring. Provide both the question and the solution to your question. As with all the other questions, if you use an AI tool like ChatGPT to help you create and / or solve your question, include the prompt you used and comment on any changes you had to make to improve your outcome.\n\n\n7) Final reflection [SOLO, 10%]\nThis is your last homework - congrats! 🎉\nFor this reflection, please reflect on what we have covered in this class, then write about your reflection. I would appreciate it if you could answer the following questions:\n\nWhat was the most impactful thing you gained from this course?\nWhat was the most challenging thing you experience in this course?\nWhat do you think I can do better in the future?\n\nWrite your reflection in a comment (#) in your .R file.\n\n\n8) Surveys 🙏\nThis is not for credit, but please, please, please fill out the course surveys. It’s SUPER important that I hear from you so I can learn what is and is not working in this class. Both are anonymous, so be as honest as possible:\n\nOfficial GW Course Evaluation: The official survey GW uses to evaluate how good a job I’m doing.\nUnofficial survey: This is a survey I put together each year where you can provide me direct, anonymous feedback about things specific to this course.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." + }, + { + "objectID": "hw/12-webscraping-temp.html", + "href": "hw/12-webscraping-temp.html", "title": "Homework 12 - Webscraping", "section": "", - "text": "Coming soon!" + "text": "Due: Apr 22 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nBe able to manage scrape data from static web pages in R.\nBe able to collect and format data from a website using an API in R.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nChapter 25 (Web scraping) in Hadley Wickham’s book R4DS: https://r4ds.hadley.nz/webscraping.html\nThis post on accessing and collecting data with APIs in R: https://statisticsglobe.com/api-in-r\n\n\n\n\nUsing AI tools\n\nOn this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.\n\n\n\n1) Staying organized [SOLO, 5%]\nDownload and use this template for your assignment. Inside the “hw12” folder, open and edit the R script called hw12.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.\n\n\n2) Scrape the weather!\nWrite R code to scrape the current weather conditions for a city of your choice from the National Weather Service. Create a data frame with the location, temperature (in Fahrenheit), humidity (%), and wind speed (mph). Your final data frame should look like this (with values reflecting those from your location of choice):\n\n# A tibble: 1 × 4\n location temp humidity wind_speed\n <chr> <dbl> <dbl> <dbl>\n1 Kennett Square 64 32 18\n\nNote: You will have to navigate and inspect the website to come up with a strategy to scrape it.\n\n\n3) Scrape the EMSE course bulletin!\nWrite R code to scrape all of the EMSE courses listed on the GW Course Bulletin. You should create a data frame that contains the following fields:\n\nnumber: The course number, e.g. \"EMSE 4571\".\ntitle: The course title, e.g. \"Intro. to Programming for Analytics\"\ncredits: The number of credits, e.g. 1, 3, etc.\ndesc: The course description.\n\nHint: To get to this final format, you may want to use either str_split() or the separate() function to break up the scraped data into these variables. This will be helpful after you’ve scraped the data from the page.\nNote: You will have to navigate and inspect the website to come up with a strategy to scrape it.\n\n\n4) Scrape the top movies!\nWrite R code to scrape data on the top 1000 grossing movies of all time from Box Office Mojo. Create a data frame with the movie titles, lifetime box office gross, and year of release. In your solution, you should use some form of iteration (e.g. map(), map_df(), a for loop) to loop through the tables on multiple pages.\nNote: You will have to navigate and inspect the website to come up with a strategy to scrape it.\n\n\n5) Get the weather from an API!\nFollow these steps:\n\nGo to the openweathermap.org site and register for a free account to get access to an API key:\n\n\n\n\n\nOnce registered, go get your API key here.\nOpen your .Renviron with the command usethis::edit_r_environ()\nCopy-paste your key into your .Renviron: OPEN_WEATHER_MAP_KEY=your_key_here\nRestart RStudio\nGo read the current weather API documentation.\nWrite R code to get the current weather in your location of choice.\n\nTips:\n\nYou’ll need to install and load the httr and jsonlite packages.\nIf you followed the instructions above, you can get your API key as an object in R with this code:\n\n\napi_key <- Sys.getenv(\"OPEN_WEATHER_MAP_KEY\")\n\n\nOnce you have constructed the url request you want to make, use the following code to get the request as a data frame:\n\n\nlibrary(httr)\nlibrary(jsonlite)\n\nresponse <- content(GET(url), as = \"text\")\nweather <- fromJSON(response)\n\nThe GET() function is from the httr package, and it retrieves whatever is returned from the website. This site returns data in JSON format, so the fromJSON() from the jsonlite package helps us convert that into a nice data frame.\n\n\n6) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nChapter 14: 14 Monte Carlo Methods in the P4A book.\n\n\nOptional Reading: Read about how I used Monte Carlo simulation to simulate the Squid Game bridge scene in R.\nAfterwards, in a comment (#) in your .R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." }, { - "objectID": "hw/10-data-visualization.html", - "href": "hw/10-data-visualization.html", + "objectID": "hw/11-programming-with-data-temp.html", + "href": "hw/11-programming-with-data-temp.html", + "title": "Homework 11 - Programming with Data", + "section": "", + "text": "Due: Apr 15 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nBe able to compose functions using the ‘tidy evaluation’ syntax to work with data.\nBe able to parse through lists using purrr::map() functions.\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions." + }, + { + "objectID": "hw/11-programming-with-data-temp.html#data-frame-functions", + "href": "hw/11-programming-with-data-temp.html#data-frame-functions", + "title": "Homework 11 - Programming with Data", + "section": "Data Frame Functions", + "text": "Data Frame Functions\n\nFor questions 2 - 5, after writing your function, demonstrate it using a data frame of your choice from the dslabs package. For example, for question 2, you could use var_summary(dslabs::movielens, rating) (so, obviously, you should use a different example).\n\n\n2) var_summary(df, var) [SOLO, 10%]\nWrite the function var_summary(df, var) that takes a data frame (df) and a variable (var) as inputs, and returns the minimum, maximum, mean, and median value of that variable. The function should remove any NA values in var when computing these summary statistics. The object returned should be a single data frame / tibble (not a vector).\n\n\n3) group_summary(df, var, group_var) [SOLO, 10%]\nWrite the function group_summary(df, var, group_var) that takes a data frame (df), a variable (var), and a grouping variable (group_var) as inputs, and returns a summary table showing the count, mean, and standard deviation of the variable var grouped by group_var. The function should remove any NA values in var when computing these summary statistics. The object returned should be a single data frame / tibble (not a vector).\n\n\n4) var_hist(df, var, bins) [SOLO, 10%]\nWrite the function var_hist(df, var, bins) that takes a data frame (df), a variable (var), and the number of bins (bins) as inputs, and returns a histogram of that variable with a user-specified number of bins as a ggplot object. The default number of bins should be 30.\n\n\n5) scatterplot(df, x, y) [SOLO, 10%]\nWrite the function scatterplot(df, x, y) that takes a data frame (df) and two variables (x and y) as inputs, and returns a scatter plot of those two variables as a ggplot object." + }, + { + "objectID": "hw/11-programming-with-data-temp.html#iteration-across-lists-with-purrr", + "href": "hw/11-programming-with-data-temp.html#iteration-across-lists-with-purrr", + "title": "Homework 11 - Programming with Data", + "section": "Iteration across lists with purrr", + "text": "Iteration across lists with purrr\n\nProblems using word_list\nFor these questions, we will work with the sentences vector that comes loaded with the stringr package (which is loaded when you load the tidyverse package):\n\nlibrary(tidyverse)\n\nhead(stringr::sentences)\n\n#> [1] \"The birch canoe slid on the smooth planks.\" \n#> [2] \"Glue the sheet to the dark blue background.\"\n#> [3] \"It's easy to tell the depth of a well.\" \n#> [4] \"These days a chicken leg is a rare dish.\" \n#> [5] \"Rice is often served in round bowls.\" \n#> [6] \"The juice of lemons makes fine punch.\"\n\n\nThis vector contains lots of random sentences. When we break those sentences into individual words using str_split(), we will get a list back where each item in the list is a vector of words:\n\nword_list <- str_split(stringr::sentences, \" \")\n\nword_list[1:3]\n\n#> [[1]]\n#> [1] \"The\" \"birch\" \"canoe\" \"slid\" \"on\" \"the\" \"smooth\" \n#> [8] \"planks.\"\n#> \n#> [[2]]\n#> [1] \"Glue\" \"the\" \"sheet\" \"to\" \"the\" \n#> [6] \"dark\" \"blue\" \"background.\"\n#> \n#> [[3]]\n#> [1] \"It's\" \"easy\" \"to\" \"tell\" \"the\" \"depth\" \"of\" \"a\" \"well.\"\n\n\nWe will use this word_list for questions 6 - 8.\n\n\n6) [COLLABORATIVE, 5%]\nUsing map(), write R code to obtain a vector of how many words are in each item in word_list.\n\n\n7) [COLLABORATIVE, 5%]\nUsing map(), write R code to obtain a vector of the total number of characters in each item in word_list.\n\n\n8) [COLLABORATIVE, 5%]\nUsing map(), write R code to obtain a vector of the number of times the word \"the\" appears in each item in word_list. Your result should ignore casing, so both \"the\" and \"The\" should count.\n\n\nProblems using sw_people\nAs we saw in class, the sw_people list contains a list of information about each character in Star Wars. You can load the list from the repurrrsive package:\n\nlibrary(repurrrsive)\n\nWe will use the sw_people and sw_films lists for questions 9 & 10.\n\n\n9) [COLLABORATIVE, 10%]\nUsing map() and the sw_films list, write R code to obtain a vector of integers that contains the number of characters in each Star Wars film.\n\n\n10) [COLLABORATIVE, 10%]\nUsing map_df(), create a data frame where each row represents a character from sw_people. The columns should contain the following:\n\nname: The character’s name, as a character.\nheight: The character’s height, as a number\nis_male: Whether the character’s gender is \"male\" (TRUE or FALSE)\nfilm_count: The number of films they have appeared in, as an integer.\n\n\n\n11) [SOLO, 10%]\nFor the last problem, write your own homework question that requires the student (you) to use map() in the solution. You can use any lists of data you want for your question (e.g. sw_people, sw_films, got_chars, etc.). Then provide the answer to your question. As with all the other questions, if you use an AI tool to help you create and / or solve your question, include the prompt you used and comment on any changes you had to make to improve your outcome.\n\n\n12) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nChapter 25 (Web scraping) in Hadley Wickham’s book R4DS: https://r4ds.hadley.nz/webscraping.html\nThis post on accessing and collecting data with APIs in R: https://statisticsglobe.com/api-in-r\n\n\nAfterwards, in a comment (#) in your .R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." + }, + { + "objectID": "hw/10-data-visualization-temp.html", + "href": "hw/10-data-visualization-temp.html", "title": "Homework 10 - Data Visualization", "section": "", - "text": "Coming soon!" + "text": "Due: Apr 08 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are:\n\nTo practice exploring and data frames in R using the dplyr package\nTo practice generating charts using the ggplot2 package\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nData Visualization\n\n\n\n\nUsing AI tools\n\nOn this assignment, you are encouraged to use ChatGPT and other AI tools (e.g. Github Copilot). But don’t just blindly copy-paste code. The code provided by these tools is not perfect, and you will likely need to modify it to get the correct solution. If you do use an AI tools, you must include the prompt(s) you used (using a comment with #) to recieve full credit. If you had to change anything to your prompt to get better results, write that down too in your code with a comment. Learn to use tools like ChatGPT as a learning assistant - a tool to help you accomplish the task - rather than just a solutions manual. One version of using it makes you a better and more efficient coder, the other robs you of that.\n\n\n\n1) Staying organized [5%]\nDownload and use this template for your assignment. Inside the “hw10” folder, open and edit the R script called hw10.R and fill out your name, GW netID, and the names of anyone you worked with on this assignment.\n\n\n2) Choose and load some data [5%]\nFor this assignment, you will need to find a dataset of your choosing and create three summary visualizations. To keep things manageable, choose one of the following datasets from the following libraries. Note that to load any of these data frames, all you need to do is install and load the package.\ndplyr:\n\nstorms\nstarwars\n\nggplot2:\n\ndiamonds\neconomics\nmidwest\nmpg\nmsleep\ntxhousing\n\ndslabs:\n\ngapminder\nmovielens\nmurders\nstars\n\n\n\n3) Inspect your data [10%]\nOnce you’ve chosen a data set, open your hw10.R file and begin exploring the data (be sure to load the package that contains the dataset at the top of your file). Write some code in code chunks to preview and summarize the data frame using some of the methods we’ve used in class. You should be able to quickly get an understanding of what variables are included and their nature. Consider the following questions in your exploration (you don’t have to write out answers to these questions - just write code to help you answer them by previewing the data in different ways):\n\nWhat is the total size of the data frame?\nWhat type of data is each variable (numeric, character, logical, date)?\nDo any variables have missing values? Why might that be?\nWhat are the “boundaries” of each period of observation:\n\nFor numeric variables, what are the min and max values?\nFor character variables, what are the unique values in the variable?\nFor date variables, what time period do the observations in these data frames span?\n\n\nDo not brush this step off - the more thoroughly you inspect your dataset, the easier (and better) you data exploration will be. This will be absolutely critical for making your charts. Make sure you take the time to develop an understanding of the variables in your dataset as it is nearly impossible to imagine what different charts might be worth creating otherwise.\n\n\n4) Make charts [40%]\nNow that you have a basic understanding of the dataset, make some charts to explore the variables in the data and their potential relationships. You may use base R plotting functions or the ggplot2 package to make your figures, but you must make at least two different types of figures, including:\n\nA scatterplot of involving at least two variables.\nA bar chart involving at least one variable.\n\nYou can choose to plot whichever variables you wish, but you must be able to interpret the results of your chart.\n\n\n5) Interpret your charts [15%]\nBelow the code for each of your charts, write a description and interpretation of your chart in a comment. Make sure you address at least the following questions:\n\nDescribe what variables you are plotting and why.\nDescribe the primary relationship / trend / information you hope the reader will gain from your visualization.\n\n\n\n6) Save your charts [15%]\nAt the bottom of your hw10.R file, write code to save each of your charts in the plots folder. Save them as .png files.\n\n\n7) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nSections 26.3 (Data Frame Functions) & 26.4 (Plot Functions) in Hadley Wickham’s book R4DS: https://r4ds.hadley.nz/functions.html#data-frame-functions\nThis blog post on iteration with the {purrr} package: https://www.rebeccabarter.com/blog/2019-08-19_purrr\n\n\nAfterwards, in a comment (#) in your .R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." }, { "objectID": "faq.html", @@ -606,6 +718,13 @@ "href": "hw/1-getting-started.html", "title": "Homework 1 - Getting Started", "section": "", + "text": "Due: Jan 21 by 11:59pm\nWeight: This assignment is worth 3.75% of your final grade.\nPurpose: The purposes of this assignment are to:\n\nMake sure you understand what you need to do to succeed in this class\nMake sure you have properly set up the software and tools we will use this semester\nGet started practicing basic concepts with R.\n\nSkills & Knowledge: After completing these exercises, you should:\n\nBe familiar with the main course tools: Slack, R, and RStudio.\nBe familiar how to use R projects to stay organized.\nBe able to use R to create and store values as objects with meaningful names.\nKnow how to compare values in R.\nKnow how to use arithmetic, relational, and logical operators in R.\nKnow the distinctions between how R handles different types of data types (numbers, strings, & logicals).\n\nAssessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:\n\n0% for not attempting a response.\n50% for attempting the question but with major errors.\n75% for attempting the question but with minor errors.\n100% for correctly answering the question.\n\nThe reflection portion is always worth 10% and graded for completion.\nRules:\n\nProblems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.\nFor problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together and submit your own solutions.\n\n\n\nReadings\nThe readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here:\n\n\nGetting StartedOperations & Data Types\n\n\n\n\n1) Class setup [SOLO, 10%]\nFor this class, you’ll need to install some software and register for some tools. You should have already done this, but in case you haven’t,go to the course software page to get set up.\nOnce you have joined the class slack, make a post to the #welcome channel introducing yourself - provide your name, year / program, and something interesting about yourself.\n\n\n2) Getting familiar with the course [SOLO, 10%]\nFollow Snoop’s advice and read the entire Course Syllabus (actually read the whole thing). Then review the schedule and make sure to note important upcoming deadlines.\n\n\n3) Staying organized [SOLO, 10%]\nOpen RStudio and create a new R project called “hw1” (see the reading for details on how to do this). Within your project, create a new R script (a “.R” file) and save it as “hw1.R”. When you save it, it should show up in the R project folder you just created. Finally, copy the following code to the top of this script and fill out your name, netID, and the names of anyone you worked with on this assignment (your netID is the part of your email address before \"@gwu.edu\"):\n\n# Name: Last, First\n# netID: Insert your netID here\n\n# I worked with the following classmates on this assignment:\n# 1) Name: Last, First\n# 2) Name: Last, First\n\nWrite your responses to all other questions in this assignment in your R file.\n\n\n4) Objects & Operators: Converting Time [COLLABORATIVE, 20%]\nCreate objects to store each of the following two values - be sure to use meaningful variable names when creating your objects:\n\nThe number of seconds in a minute\nThe number of minutes in an hour\nThe number of hours in a day\nThe number of days in a typical year (not a leap year)\n\nNow, say you have another object called time_in_seconds that contains an integer number of seconds (for example, time_in_seconds <- 8675309). Write code to convert the value stored in time_in_seconds into the units described below. Your solution may only use arithmetic operators and the objects you created (i.e. you may not use any numbers). You may also use the new objects you create in sequential order. For example, you may use the object created in part a) to create the object in part b), and so on.\n\nThe value of time_in_seconds in minutes\nThe value of time_in_seconds in hours\nThe value of time_in_seconds in days\nThe value of time_in_seconds in years\n\n\n\n5) Logical and relational operators [SOLO, 20%]\nConsider the following objects:\n\nw <- FALSE\nx <- TRUE\ny <- FALSE\nz <- TRUE\n\nWrite code to answer the following questions:\n\nWrite a statement with logical operators that compares the objects x, y, and z and returns TRUE\nWrite a statement with logical operators that compares the objects x, y, and z and returns FALSE\nFill in relational operators to make the following statement return TRUE:\n\n! (x __ y) & ! (z __ y)\n\nFill in relational operators to make this statement return FALSE:\n\n! (w __ y) | (z __ y)\n\n\n6) Data types [COLLABORATIVE, 20%]\nConsider the following objects:\n\nnumber <- typeof('3')\ncharacter <- typeof(7)\nfalse <- typeof(\"FALSE\")\ntrue <- typeof(TRUE)\n\nWrite code to answer the following questions:\n\nWrite a statement with both relational & logical operators that compares the four objects number, character, false, and true and returns TRUE.\nWrite a statement with both relational & logical operators that compares the four objects number, character, false, and true and returns FALSE.\n\n\n\n7) Read and reflect [SOLO, 10%]\nRead and reflect on the following readings to preview what we will be covering next:\n\n\nFunctions & Packages\n\n\nAfterwards, reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing?\nIn a comment (#) in your .R file, write at least a paragraph about your thoughts, and include at least one question. This can be on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can be related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.\nSome thoughts you may want to try in your reflection:\n\n“I used to think ______, now I think ______ 🤔”\nDiscuss some of the key insights or things you found interesting in the readings or recent class periods.\nConnect the course content to your own work or projects you’re working on.\n\n\n\nSubmit\nCreate a zip file of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard." + }, + { + "objectID": "hw/10-data-visualization.html", + "href": "hw/10-data-visualization.html", + "title": "Homework 10 - Data Visualization", + "section": "", "text": "Coming soon!" }, { @@ -615,6 +734,13 @@ "section": "", "text": "Coming soon!" }, + { + "objectID": "hw/12-webscraping.html", + "href": "hw/12-webscraping.html", + "title": "Homework 12 - Webscraping", + "section": "", + "text": "Coming soon!" + }, { "objectID": "hw/13-monte-carlo-methods.html", "href": "hw/13-monte-carlo-methods.html", @@ -622,6 +748,13 @@ "section": "", "text": "Coming soon!" }, + { + "objectID": "hw/2-functions-packages.html", + "href": "hw/2-functions-packages.html", + "title": "Homework 2 - Functions & Packages", + "section": "", + "text": "Coming soon!" + }, { "objectID": "hw/3-creating-functions.html", "href": "hw/3-creating-functions.html", @@ -629,6 +762,13 @@ "section": "", "text": "Coming soon!" }, + { + "objectID": "hw/4-conditionals-testing.html", + "href": "hw/4-conditionals-testing.html", + "title": "Homework 4 - Conditionals & Testing", + "section": "", + "text": "Coming soon!" + }, { "objectID": "hw/5-iteration.html", "href": "hw/5-iteration.html", @@ -636,6 +776,13 @@ "section": "", "text": "Coming soon!" }, + { + "objectID": "hw/6-vectors.html", + "href": "hw/6-vectors.html", + "title": "Homework 6 - Vectors", + "section": "", + "text": "Coming soon!" + }, { "objectID": "hw/7-strings.html", "href": "hw/7-strings.html", @@ -643,6 +790,13 @@ "section": "", "text": "Coming soon!" }, + { + "objectID": "hw/8-data-frames.html", + "href": "hw/8-data-frames.html", + "title": "Homework 8 - 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Standard Grading

Participation / Attendance -6% +7% (Yes, I take attendance) Homeworks & Readings (13x) -46% +45% Lowest 1 dropped