From 61c02a67d81c3346e93807d878a4a598c5fe811d Mon Sep 17 00:00:00 2001 From: emse-p4a-gwu Date: Fri, 3 Jan 2025 08:11:46 -0500 Subject: [PATCH] revise all hw --- figs/grades-1.png | Bin 68902 -> 71127 bytes fragments/hw.qmd | 2 +- hw/1-getting-started.qmd | 108 ++++++++++++++++- hw/10-data-visualization-temp.qmd | 109 +++++++++++++++++ hw/11-programming-with-data-temp.qmd | 122 +++++++++++++++++++ hw/12-webscraping-temp.qmd | 118 ++++++++++++++++++ hw/13-monte-carlo-methods-temp.qmd | 79 ++++++++++++ hw/2-functions-packages-temp.qmd | 154 +++++++++++++++++++++++ hw/3-creating-functions-temp.qmd | 128 ++++++++++++++++++++ hw/4-conditionals-testing-temp.qmd | 73 +++++++++++ hw/5-iteration-temp.qmd | 171 ++++++++++++++++++++++++++ hw/6-vectors-temp.qmd | 86 +++++++++++++ hw/7-strings-temp.qmd | 102 ++++++++++++++++ hw/8-data-frames-temp.qmd | 82 +++++++++++++ hw/9-data-wrangling-temp.qmd | 175 +++++++++++++++++++++++++++ hw/images/turtle_cross_box.png | Bin 7467 -> 10623 bytes hw/templates/final.zip | Bin 0 -> 172405 bytes hw/templates/hw10.zip | Bin 0 -> 1446 bytes hw/templates/hw11.zip | Bin 0 -> 1338 bytes hw/templates/hw12.zip | Bin 0 -> 1338 bytes hw/templates/hw13.zip | Bin 0 -> 1329 bytes hw/templates/hw3.zip | Bin 0 -> 1647 bytes hw/templates/hw4.zip | Bin 0 -> 1717 bytes hw/templates/hw5.zip | Bin 0 -> 1700 bytes hw/templates/hw6.zip | Bin 0 -> 1659 bytes hw/templates/hw7.zip | Bin 0 -> 1660 bytes hw/templates/hw8.zip | Bin 0 -> 3915 bytes hw/templates/hw9.zip | Bin 0 -> 1436 bytes schedule.csv | 26 ++-- styles.css | 7 +- syllabus.qmd | 27 +++-- 31 files changed, 1543 insertions(+), 26 deletions(-) create mode 100644 hw/10-data-visualization-temp.qmd create mode 100644 hw/11-programming-with-data-temp.qmd create mode 100644 hw/12-webscraping-temp.qmd create mode 100644 hw/13-monte-carlo-methods-temp.qmd create mode 100644 hw/2-functions-packages-temp.qmd create mode 100644 hw/3-creating-functions-temp.qmd create mode 100644 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z(;+j@98p&|Kww@6$jZ9$R3B`bo-1jWxX?1}e+_Ersa$dB!{1p(_}{fa!by=FRI9MQ z03d6`ijenJ7gDzV_1o8`lpl#SW6!07KY;YG4;Qq11=vLNBMB>Qq)B$jC2271r_0eG ziBJ9YnaHPa2}F>d8C-?UPnAPbCNS5onUc6pSv4Q>GPod^%lBuYX#n%LuT@OBY<;=@ zeoNcV8!pa{i8>6a5CF0sO3-D>PwS}kq&NH8U)B6X-q%Yc`QcyBdn=0 zzWV;G!{1-sWd*U(2)(G{|M8vw_>mt1ax@LHHmZMp{-5dJ`06V_)o9lDd)Dgu|5{)F z@libeiQt6tGvnxgFN)t&_In9df)T%=49``DjYTKUgssiw|4!J(TKoTZh4p*Tj15hcM3N~K Q8u)WT@sdK;c|))N0c84OGXMYp diff --git a/fragments/hw.qmd b/fragments/hw.qmd index 1af793d..4eef296 100644 --- a/fragments/hw.qmd +++ b/fragments/hw.qmd @@ -30,7 +30,7 @@ url_template <- paste0( > **Due**: `r due` > -> **Weight**: This assignment is worth 4% of your final grade. +> **Weight**: This assignment is worth 3.75% of your final grade. > > **Purpose**: `r params$purpose` > diff --git a/hw/1-getting-started.qmd b/hw/1-getting-started.qmd index efa24bd..dc5df35 100644 --- a/hw/1-getting-started.qmd +++ b/hw/1-getting-started.qmd @@ -19,4 +19,110 @@ params: > - Know the distinctions between how R handles different types of data types (numbers, strings, & logicals). --- -Coming soon! \ No newline at end of file +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +

+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 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](../software.html) page to get set up. + +Once you have joined the [class slack]({{< var 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](https://www.youtube.com/watch?v=Tnlaokj1opA) and read the entire [Course Syllabus](../syllabus.html) (actually read the whole thing). Then review the [schedule](../schedule.html) 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](https://p4a.jhelvy.com/getting-started.html#rstudio-projects){target="_blank"} 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"`): + +```{r eval=FALSE} +# 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](https://p4a.jhelvy.com/getting-started.html#use-meaningful-variable-names){target="_blank"} 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. + +a) The value of `time_in_seconds` in minutes +b) The value of `time_in_seconds` in hours +c) The value of `time_in_seconds` in days +d) The value of `time_in_seconds` in years + +### 5) Logical and relational operators [SOLO, 20%] + +Consider the following objects: + +```{r} +w <- FALSE +x <- TRUE +y <- FALSE +z <- TRUE +``` + +Write code to answer the following questions: + +a) Write a statement with _logical_ operators that compares the objects `x`, `y`, and `z` and returns `TRUE` +b) Write a statement with _logical_ operators that compares the objects `x`, `y`, and `z` and returns `FALSE` +c) Fill in _relational_ operators to make the following statement return `TRUE`: + +`! (x __ y) & ! (z __ y)` + +d) Fill in _relational_ operators to make this statement return `FALSE`: + +`! (w __ y) | (z __ y)` + +### 6) Data types [COLLABORATIVE, 20%] + +Consider the following objects: + +```{r} +number <- typeof('3') +character <- typeof(7) +false <- typeof("FALSE") +true <- typeof(TRUE) +``` + +Write code to answer the following questions: + +a) Write a statement with both _relational_ & _logical_ operators that compares the four objects `number`, `character`, `false`, and `true` and returns `TRUE`. +b) Write a statement with both _relational_ & _logical_ operators that compares the four objects `number`, `character`, `false`, and `true` and returns `FALSE`. + +### 7) Read and reflect [SOLO, 10%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +[Create a zip file](https://p4a.seas.gwu.edu/2024-Spring/faq.html#how-do-i-make-a-zip-file-for-my-homework) of all the files in your R project folder for this assignment, then submit your zip file on the corresponding assignment submission on Blackboard. diff --git a/hw/10-data-visualization-temp.qmd b/hw/10-data-visualization-temp.qmd new file mode 100644 index 0000000..d367687 --- /dev/null +++ b/hw/10-data-visualization-temp.qmd @@ -0,0 +1,109 @@ +--- +title: "Homework 10 - Data Visualization" +params: + number: 10 + 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 +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### **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](templates/hw10.zip) 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. A bar chart involving at least one variable. + +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. Describe the primary relationship / trend / information you hope the reader will gain from your visualization. + +### 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: + +> - Sections 26.3 (Data Frame Functions) & 26.4 (Plot Functions) in Hadley Wickham's book R4DS: [https://r4ds.hadley.nz/functions.html#data-frame-functions](https://r4ds.hadley.nz/functions.html#data-frame-functions) +> - This blog post on iteration with the {purrr} package: [https://www.rebeccabarter.com/blog/2019-08-19_purrr](https://www.rebeccabarter.com/blog/2019-08-19_purrr) + +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 + +{{< var hw_submit >}} diff --git a/hw/11-programming-with-data-temp.qmd b/hw/11-programming-with-data-temp.qmd new file mode 100644 index 0000000..b6944a3 --- /dev/null +++ b/hw/11-programming-with-data-temp.qmd @@ -0,0 +1,122 @@ +--- +title: "Homework 11 - Programming with Data" +params: + number: 11 + 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. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +> - Sections 26.3 (Data Frame Functions) & 26.4 (Plot Functions) in Hadley Wickham's book R4DS: [https://r4ds.hadley.nz/functions.html#data-frame-functions](https://r4ds.hadley.nz/functions.html#data-frame-functions) +> - This blog post on iteration with the {purrr} package: [https://www.rebeccabarter.com/blog/2019-08-19_purrr](https://www.rebeccabarter.com/blog/2019-08-19_purrr) + +### **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](templates/hw11.zip) 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): + +```{r} +library(tidyverse) + +head(stringr::sentences) +``` + +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: + +```{r} +word_list <- str_split(stringr::sentences, " ") + +word_list[1:3] +``` + +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: + +```{r} +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: + +> - Chapter 25 (Web scraping) in Hadley Wickham's book R4DS: [https://r4ds.hadley.nz/webscraping.html](https://r4ds.hadley.nz/webscraping.html) +> - This post on accessing and collecting data with APIs in R: [https://statisticsglobe.com/api-in-r](https://statisticsglobe.com/api-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 + +{{< var hw_submit >}} diff --git a/hw/12-webscraping-temp.qmd b/hw/12-webscraping-temp.qmd new file mode 100644 index 0000000..8dabbc3 --- /dev/null +++ b/hw/12-webscraping-temp.qmd @@ -0,0 +1,118 @@ +--- +title: "Homework 12 - Webscraping" +params: + number: 12 + 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. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +> - Chapter 25 (Web scraping) in Hadley Wickham's book R4DS: [https://r4ds.hadley.nz/webscraping.html](https://r4ds.hadley.nz/webscraping.html) +> - This post on accessing and collecting data with APIs in R: [https://statisticsglobe.com/api-in-r](https://statisticsglobe.com/api-in-r) + +### **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](templates/hw12.zip) 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](https://www.weather.gov/). 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): + +```{r} +#| eval: false + +# A tibble: 1 × 4 + location temp humidity wind_speed + +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](http://bulletin.gwu.edu/courses/emse/). 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](https://tidyr.tidyverse.org/reference/separate.html) 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](https://www.boxofficemojo.com/chart/top_lifetime_gross/?ref_=bo_cso_ac). 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](https://openweathermap.org/price) site and register for a free account to get access to an API key: + +
+ +
+ +2. Once registered, go get your API key [here](https://home.openweathermap.org/api_keys). +3. Open your .Renviron with the command `usethis::edit_r_environ()` +4. Copy-paste your key into your .Renviron: `OPEN_WEATHER_MAP_KEY=your_key_here` +5. Restart RStudio +6. Go read the [current weather](https://openweathermap.org/current) API documentation. +7. Write R code to get the current weather in your location of choice. + +**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: + +```{r} +#| eval: false + +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: + +```{r} +#| eval: false + +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: + +> - Chapter 14: [14 Monte Carlo Methods](https://p4a.jhelvy.com/monte-carlo-methods.html) in the P4A book. + +**Optional Reading**: Read about how I used Monte Carlo simulation to simulate the [Squid Game bridge scene in R](https://www.jhelvy.com/blog/2021-10-19-monte-carlo-bridge-game/). + +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 + +{{< var hw_submit >}} diff --git a/hw/13-monte-carlo-methods-temp.qmd b/hw/13-monte-carlo-methods-temp.qmd new file mode 100644 index 0000000..08f2881 --- /dev/null +++ b/hw/13-monte-carlo-methods-temp.qmd @@ -0,0 +1,79 @@ +--- +title: "Homework 13 - Monte Carlo Methods" +params: + number: 13 + 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. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +### **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](templates/hw13.zip) 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"](https://en.wikipedia.org/wiki/List_of_poker_hands#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](https://www.wikihow.com/Determine-if-Three-Side-Lengths-Are-a-Triangle) 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](https://gwu.smartevals.com/): The official survey GW uses to evaluate how good a job I'm doing. +- [Unofficial survey](https://forms.gle/iabhiMqVQk9iPpHa8): This is a survey I put together each year where you can provide me direct, anonymous feedback about things specific to this course. + +### Submit + +{{< var hw_submit >}} diff --git a/hw/2-functions-packages-temp.qmd b/hw/2-functions-packages-temp.qmd new file mode 100644 index 0000000..068ce48 --- /dev/null +++ b/hw/2-functions-packages-temp.qmd @@ -0,0 +1,154 @@ +--- +title: "Homework 2 - Functions & Packages" +params: + number: 2 + 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. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 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: + +```{r eval=FALSE} +# 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: + +
+ +Point | `x` | `y` +------|-----|----- +A | 1 | 1 +B | 5 | 1 +C | 5 | 4 + +
+ +Your task is to find out if the triangle formed by the points A, B, and C is a [right triangle](https://en.wikipedia.org/wiki/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. **Devise a plan**: In code comments, describe the steps you will take in your plan to solve the problem. +3. **Carry out the plan**: Write code below your comments to carry out your plan. +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? + +### 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: + +a. 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? +b. 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? +c. 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? + +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. **Devise a plan**: In code comments, describe the steps you will take in your plan to solve the problem. +3. **Carry out the plan**: Write code below your comments to carry out your plan. +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? + +### 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](https://www.britannica.com/topic/list-of-superheroes-2024795) (mine is [Professor Xavier](https://en.wikipedia.org/wiki/Professor_X), 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](https://p4a.jhelvy.com/getting-started.html#use-meaningful-variable-names){target="_blank"} 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](https://cran.r-project.org/) (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]({{< var repo >}})). + +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: + +```{r} +#| eval: false + +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](https://github.com/brooke-watson/BRRR). 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](https://p4a.jhelvy.com/functions-packages.html#turtle-graphics){target="_blank"} and the [class slides](../class/2-functions-packages.html). Then complete these tasks: + +a) Load the **TurtleGraphics** package, then initiate your turtle with the `turtle_init()` function. +b) 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. +```{r eval=FALSE} +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() +``` +c) 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. +d) 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): + +
+
+ +
+
+ +### 7) Read and reflect [SOLO, 10%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} diff --git a/hw/3-creating-functions-temp.qmd b/hw/3-creating-functions-temp.qmd new file mode 100644 index 0000000..581ae14 --- /dev/null +++ b/hw/3-creating-functions-temp.qmd @@ -0,0 +1,128 @@ +--- +title: "Homework 3 - Creating Functions" +params: + number: 3 + 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). +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 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](templates/hw3.zip) 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](../autograder.html) +> +> - You can check your solutions to problems 2 - 8 by logging into the [autograder](../autograder.html) 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](https://en.wikipedia.org/wiki/Heron%27s_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: + +```{r, eval=FALSE} +library(TurtleGraphics) +turtle_init() +turtleSquare(50) +``` + +
+
+ +
+
+ +### 10) Read and reflect [SOLO, 10%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} + +--- + +### Bonus 1) `numberOfPoolBalls(rows)` [SOLO, 2%] + +![](images/poolballs.jpg){ width=150 } + +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](https://en.wikipedia.org/wiki/Triangular_number). + +### 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](https://en.wikipedia.org/wiki/Equilateral_triangle) and [apothems](https://en.wikipedia.org/wiki/Apothem). The code below produces a triangle with side length of 50: + +```{r, eval=FALSE} +library(TurtleGraphics) +turtle_init() +turtleTriangle(50) +``` + +
+
+ +
+
diff --git a/hw/4-conditionals-testing-temp.qmd b/hw/4-conditionals-testing-temp.qmd new file mode 100644 index 0000000..8aba0ef --- /dev/null +++ b/hw/4-conditionals-testing-temp.qmd @@ -0,0 +1,73 @@ +--- +title: "Homework 4 - Conditionals & Testing" +params: + number: 4 + 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. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 1) Staying organized [SOLO, 5%] + +Download and use [this template](templates/hw4.zip) 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](../autograder.html) +> +> - You can check your solutions to problems 2 - 6 by logging into the [autograder](../autograder.html) 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](https://www.mathsisfun.com/leap-years.html) 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%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} diff --git a/hw/5-iteration-temp.qmd b/hw/5-iteration-temp.qmd new file mode 100644 index 0000000..2660c07 --- /dev/null +++ b/hw/5-iteration-temp.qmd @@ -0,0 +1,171 @@ +--- +title: "Homework 5 - Iteration" +params: + number: 5 + purpose: | + The purposes of this assignment are: + > + > - To practice using for and while loops in R. + > - To practice computational problem solving with loops. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 1) Staying organized [SOLO, 5%] + +Download and use [this template](templates/hw5.zip) 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](http://adv-r.had.co.nz/Style.html). + +> ### Using the [autograder](../autograder.html) +> +> - You can check your solutions to problems 2 - 7 by logging into the [autograder](../autograder.html) 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](https://en.wikipedia.org/wiki/Happy_number) 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](https://p4a.seas.gwu.edu/2023-Spring/hw/3-creating-functions.html#turtlesquares-collaborative-10), 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: + +```{r, eval=FALSE} +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: + +```{r, eval=FALSE} +library(TurtleGraphics) + +turtle_init() +turtle_do({ + concentricTurtleSquares(5) +}) +``` + +
+
+ +
+
+ +### 10) Read and reflect [SOLO, 10%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} + +--- + +### 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: + +```{r, eval=FALSE} +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: + +```{r, eval=FALSE} +library(TurtleGraphics) +turtle_init() +turtle_do({ + turtleSquareStar(50, 20) +}) +``` + +
+
+ +
+
diff --git a/hw/6-vectors-temp.qmd b/hw/6-vectors-temp.qmd new file mode 100644 index 0000000..6ac4580 --- /dev/null +++ b/hw/6-vectors-temp.qmd @@ -0,0 +1,86 @@ +--- +title: "Homework 6 - Vectors" +params: + number: 6 + purpose: | + The purposes of this assignment are: + > + > - To practice using vectors in R. + > - To practice computational problem solving with vectors. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 1) Staying organized [SOLO, 5%] + +Download and use [this template](templates/hw6.zip) 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](http://adv-r.had.co.nz/Style.html). + +> ### Using the [autograder](../autograder.html) +> +> - You can check your solutions to problems 2 - 7 by logging into the [autograder](../autograder.html) 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](https://p4a.jhelvy.com/vectors.html#numeric-vectors) 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](https://en.wikipedia.org/wiki/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%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} + +--- + +### 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)`. diff --git a/hw/7-strings-temp.qmd b/hw/7-strings-temp.qmd new file mode 100644 index 0000000..7a3e752 --- /dev/null +++ b/hw/7-strings-temp.qmd @@ -0,0 +1,102 @@ +--- +title: "Homework 7 - Strings" +params: + number: 7 + purpose: | + The purposes of this assignment are: + > + > - To practice manipulating strings in R with the **stringr** package. + > - To practice computational problem solving with strings. +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 1) Staying organized [SOLO, 5%] + +Download and use [this template](templates/hw7.zip) 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](http://adv-r.had.co.nz/Style.html). + +> ### Using the [autograder](../autograder.html) +> +> - You can check your solutions to problems 2 - 8 by logging into the [autograder](../autograder.html) 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: + +```{r, eval=FALSE} +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: + +```{r, eval=FALSE} +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. + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} + +--- + +### 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. diff --git a/hw/8-data-frames-temp.qmd b/hw/8-data-frames-temp.qmd new file mode 100644 index 0000000..9d3737e --- /dev/null +++ b/hw/8-data-frames-temp.qmd @@ -0,0 +1,82 @@ +--- +title: "Homework 8 - Data Frames" +params: + number: 8 + purpose: | + The purposes of this assignment are: + > + > - To practice creating data frames in R. + > - To practice merging and slicing data frames in R. + +--- + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ +### 1) Staying organized [SOLO, 5%] + +Download and use [this template](templates/hw8.zip) 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. + +> ### **Using good style** +> +> For this assignment, you must use good style to receive full credit. Follow the best practices described in [this style guide](http://adv-r.had.co.nz/Style.html). + +### 2) Inspect package data [SOLO, 15%] + +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: + +- What is this dataset about? +- How many observations are in the data frame? +- What is the original source of the data? +- What type of data is each variable? +- What are the years of the earliest and most recent observations in the data set? + +### 3) Answer questions about the data [COLLABORATIVE, 25%] + +For each of the following questions, write code to find your answer and leave a detailed response in a comment: + +- What is the min, mean, and max rating in the data set? +- How many observations received the maximum rating? +- What percentage of total observations received the maximum rating? +- What is the title of the observation with the longest `title` (in terms of numbers of letters in the title)? + +### 4) Loading and inspecting external data [SOLO, 20%] + +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: + +- What do you think this dataset is about? +- How many observations are in the data frame? +- What type of data is each variable? + +### 5) Answer questions about the data [COLLABORATIVE, 25%] + +For each of the following questions, write code to find your answer and leave a detailed response in a comment: + +- Which states have the highest and lowest total prison population? +- Which states have the highest and lowest total prison population _as a percentage of the total state population_? +- According to the 2020 U.S. Census, only [12.4%](https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-much-more-multiracial.html) of the U.S. population is black, but some states have imprisoned more black people than any other race. Which states fit this description? + +### 6) Read and reflect [SOLO, 10%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} diff --git a/hw/9-data-wrangling-temp.qmd b/hw/9-data-wrangling-temp.qmd new file mode 100644 index 0000000..299b6f7 --- /dev/null +++ b/hw/9-data-wrangling-temp.qmd @@ -0,0 +1,175 @@ +--- +title: "Homework 9 - Data Wrangling" +params: + number: 9 + purpose: | + The purposes of this assignment are: + > + > - To practice exploring and wrangling data frames in R using the **dplyr** package +--- + +```{r, echo=FALSE} +library(nycflights13) +``` + +```{r child = here::here("fragments", "hw.qmd")} +``` + +### Readings + +The readings from the last week will serve as a helpful reference as you complete this assignment. You can review them here: + +
+ +```{r} +#| echo: false +#| message: false +#| results: asis + +htmltools::HTML(readings_current) +``` + +
+ + +### **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](templates/hw9.zip) 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. + +### 2) Load the data [SOLO, 5%] + +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. + +### 3) Inspect the data [SOLO, 5%] + +Look at the datasets that are included in this package: + +```{r} +#| eval: false + +data(package = "nycflights13") +``` + +``` +Data sets in package 'nycflights13': + +airlines Airline names. +airports Airport metadata +flights Flights data +planes Plane metadata. +weather Hourly weather data +``` + +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): + +- What is the total size of each 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? + +## Answer questions about the data + +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. + +For example, if the question was "how many observations are in the `flights` data frame?", here is an acceptable answer: + +```{r} +# Find the number of rows in the flights data frame +nrow(flights) +``` + +```{r} +# Answer: There are 336,776 observations in the flights data frame +``` + +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. + +### 4) [SOLO, 5%] + +How many flights out of NYC airports in 2013 had an arrival delay of two or more hours? Hint: use `filter()` + +### 5) [SOLO, 5%] + +How many flights out of NYC airports in 2013 departed in fall semester (i.e. the months August - December, inclusive)? Hint: use `filter()` + +### 6) [SOLO, 5%] + +How many flights out of NYC airports in 2013 were operated by United, American, or Delta airlines? Hint: use `filter()` + +### 7) [SOLO, 5%] + +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()` + +### 8) [SOLO, 5%] + +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()` + +### 9) [COLLABORATIVE, 10%] + +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()` + +### 10) [COLLABORATIVE, 10%] + +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()` + +### 11) [COLLABORATIVE, 10%] + +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! + +### 12) [COLLABORATIVE, 10%] + +Use the `flights` data frame to create a new summary data frame called `dailyDelaySummary` that contains the following variables for each day in 2013: + +- `meanDepDelay`: the mean departure delay (in minutes) +- `numDelayedFlights`: the total number of delayed flights + +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! + +### 13) [COLLABORATIVE, 10%] + +Using the `dailyDelaySummary` data frame that you created in part i), answer the following two questions: + +- 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)? +- 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)? + +### 14) Read and reflect [SOLO, 10%] + +```{r child = here::here("fragments", "reflection.qmd")} +``` + +### Submit + +{{< var hw_submit >}} + +--- + +### Bonus 1) [SOLO, 1%] + +How many flights have a missing departure time? What might these rows represent? + +### Bonus 2) [SOLO, 1%] + +Which flights (i.e. carrier + flight) departed every day of the year, and which airports did they fly to? + +### Bonus 3) [SOLO, 2%] + +Use the `flights` data frame to create a `season` variable. The seasons are defined as the following: + +- Spring: March, April, May +- Summer: June, July, August +- Fall: September, October, November +- 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()`. 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z0>>4jWqy1O(79NmJGbvVFs^vofpN93VbKG}s?YalD9FV;|8isPOp$EsS!Z`{-#(XJ z&_=ape~st47oOIqZ`N2bTPSWlm;CV0T&YOK^yoFI=}{-1e3p-!8OJAbIK;j5huenD zn@n~Ta(xeb=zUu{JzV7k=jI))LeE=u>_6PFt$V2SV`}i?sog1?c<$facc7qa^O}B9 zzMu!Ko87J^vu}8}B`_)DkVm8HORTt(w&_rEgcU27yJ29 zfi>URN=LCxYr|%l%1xJk-g&fj&T;cCXRj)r+xr6KEp`r#tDY{cKrezk6X4CrB*Kh4 zD+85*!COZVMP$Z?7zxYT$k_mtwP9dMqcf0+nclHvbeJn)Mk1$AQ2v2|C5?taKcPns zN-=^ZVvtP-rE%nB07~O9u%z)d&~&haG2$CbibppVIoLsI2nLokK4QXjG=>dW9nQ)I QGKCcg_X3@Ek{QGU01WW082|tP literal 0 HcmV?d00001 diff --git a/schedule.csv b/schedule.csv index c17858d..0ffc829 100644 --- a/schedule.csv +++ b/schedule.csv @@ -1,24 +1,24 @@ week,date,quiz,quiz_coverage,n_class,stub_class,name_class,description_class,n_assign,stub_assign,name_assign,due_assign,name_reading,stub_reading,reading -1,2024-01-16,,,1,getting-started,Getting Started,"Class orientation, intro to R, workflow, operators, data types",1,getting-started,Getting Started,2024-01-24,"Getting Started +1,2024-01-16,,,1,getting-started,Getting Started,"Class orientation, intro to R, workflow, operators, data types",1,getting-started,Getting Started,2024-01-21,"Getting Started Operations & Data Types","getting-started operators-data-types", -2,2024-01-23,1,Week 1,2,functions-packages,Functions & Packages,"Functions, arguments, installing external packages, problem solving",2,functions-packages,Functions & Packages,2024-01-31,Functions & Packages,functions-packages, -3,2024-01-30,2,Weeks 1 - 2,3,creating-functions,Creating Functions,"Defining your own functions, top-down structure",3,creating-functions,Creating Functions,2024-02-07,Creating Functions,creating-functions, -4,2024-02-06,3,Weeks 2 - 3,4,conditionals-testing,Conditionals & Testing,"If / else statements, writing unit tests",4,conditionals-testing,Conditionals & Testing,2024-02-14,"Conditionals +2,2024-01-23,1,Week 1,2,functions-packages,Functions & Packages,"Functions, arguments, installing external packages, problem solving",2,functions-packages,Functions & Packages,2024-01-28,Functions & Packages,functions-packages, +3,2024-01-30,2,Weeks 1 - 2,3,creating-functions,Creating Functions,"Defining your own functions, top-down structure",3,creating-functions,Creating Functions,2024-02-04,Creating Functions,creating-functions, +4,2024-02-06,3,Weeks 2 - 3,4,conditionals-testing,Conditionals & Testing,"If / else statements, writing unit tests",4,conditionals-testing,Conditionals & Testing,2024-02-11,"Conditionals Testing & Debugging","conditionals testing-debugging", -5,2024-02-13,4,Weeks 3 - 4,5,iteration,Iteration,Ins and outs of loops,5,iteration,Iteration,2024-02-21,Iteration,iteration, -6,2024-02-20,5,Weeks 4 - 5,6,vectors,Vectors,Creating and slicing collections of values,6,vectors,Vectors,2024-02-28,Vectors,vectors, -7,2024-02-27,6,Weeks 5 - 6,7,strings,Strings,All things between quotes,7,strings,Strings,2024-03-13,Strings,strings, +5,2024-02-13,4,Weeks 3 - 4,5,iteration,Iteration,Ins and outs of loops,5,iteration,Iteration,2024-02-18,Iteration,iteration, +6,2024-02-20,5,Weeks 4 - 5,6,vectors,Vectors,Creating and slicing collections of values,6,vectors,Vectors,2024-02-25,Vectors,vectors, +7,2024-02-27,6,Weeks 5 - 6,7,strings,Strings,All things between quotes,7,strings,Strings,2024-03-18,Strings,strings, 8,2024-03-06,,,,,Midterm,Don't panic!,,,,,,, 9,2024-03-13,,,,,Spring Break,No class,,,,,,, -10,2024-03-20,,,8,data-frames,Data Frames,Rows and columns of data,8,data-frames,Data Frames,2024-03-26,"Data Analysis +10,2024-03-20,,,8,data-frames,Data Frames,Rows and columns of data,8,data-frames,Data Frames,2024-03-25,"Data Analysis Data Frames","data-analysis data-frames", -11,2024-03-27,7,Week 10,9,data-wrangling,Data Wrangling,Modifying data frames,9,data-wrangling,Data Wrangling,2024-04-02,Data Wrangling,data-wrangling, -12,2024-04-03,8,Week 11,10,data-visualization,Data Visualization,Visualizing your data,10,data-visualization,Data Visualization,2024-04-09,Data Visualization,data-visualization, -13,2024-04-10,9,Week 12,11,programming-with-data,Programming with Data,Iterating through data frames and writing functions to summarize data,11,programming-with-data,Programming with Data,2024-04-16,Programming with Data,,"Sections 26.3 & 26.4 in R4DS
Post on iteration with {purrr}" -14,2024-04-17,10,Week 13,12,webscraping,Webscraping,Collecting data embedded in web pages,12,webscraping,Webscraping,2024-04-23,Webscraping,,"Chapter 25 in R4DS
Post on APIs in R" -15,2024-04-24,,,13,monte-carlo-methods,Monte Carlo Methods,Using simulation to solve problems,13,monte-carlo-methods,Monte Carlo Methods,2024-04-30,Monte Carlo Methods,monte-carlo-methods, +11,2024-03-27,7,Week 10,9,data-wrangling,Data Wrangling,Modifying data frames,9,data-wrangling,Data Wrangling,2024-04-01,Data Wrangling,data-wrangling, +12,2024-04-03,8,Week 11,10,data-visualization,Data Visualization,Visualizing your data,10,data-visualization,Data Visualization,2024-04-08,Data Visualization,data-visualization, +13,2024-04-10,9,Week 12,11,programming-with-data,Programming with Data,Iterating through data frames and writing functions to summarize data,11,programming-with-data,Programming with Data,2024-04-15,Programming with Data,,"Sections 26.3 & 26.4 in R4DS
Post on iteration with {purrr}" +14,2024-04-17,10,Week 13,12,webscraping,Webscraping,Collecting data embedded in web pages,12,webscraping,Webscraping,2024-04-22,Webscraping,,"Chapter 25 in R4DS
Post on APIs in R" +15,2024-04-24,,,13,monte-carlo-methods,Monte Carlo Methods,Using simulation to solve problems,13,monte-carlo-methods,Monte Carlo Methods,2024-04-29,Monte Carlo Methods,monte-carlo-methods, 16,2024-05-01,,,14,final-review,Final Exam Review,Overview of entire course,,,,,,, 17,2024-05-08,,,,,Final Exam,Time and location TBD,,,,,,, \ No newline at end of file diff --git a/styles.css b/styles.css index 4001009..5b19e22 100644 --- a/styles.css +++ b/styles.css @@ -73,4 +73,9 @@ tr:last-child { .sidebar-title { color: #cbcccc; } - \ No newline at end of file + +.shadow-wrapper { + box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); + display: inline-block; + border-radius: 4px; +} diff --git a/syllabus.qmd b/syllabus.qmd index 0b22f9f..47e96fe 100644 --- a/syllabus.qmd +++ b/syllabus.qmd @@ -134,8 +134,8 @@ Final grades will be calculated as follows:
Component | Weight | Notes ---------------------------|--------|---------------- -Participation / Attendance | 6% | (Yes, I take attendance) -Homeworks & Readings (13x) | 46% | Lowest 1 dropped +Participation / Attendance | 7% | (Yes, I take attendance) +Homeworks & Readings (13x) | 45% | Lowest 1 dropped Quizzes (10x) | 18% | Lowest 2 dropped Midterm Exam | 10% | Final Exam | 20% | @@ -147,6 +147,7 @@ Here's a visual breakdown by category: #| echo: false #| label: grades #| fig.height: 3 +#| fig.width: 6 #| fig.align: 'left' library(tidyverse) @@ -158,28 +159,34 @@ df <- data.frame( category = c( 'Participation', 'Homeworks', 'Quizzes', 'Midterm', 'Final' ), - percent = c(6, 46, 18, 10, 20)) %>% + percent = c(7, 45, 18, 10, 20) / 100) %>% arrange(desc(percent)) %>% mutate( cumpercent = cumsum(percent), - label_y = cumpercent - (percent / 2), + label = cumpercent - (percent / 2), category = fct_reorder(category, percent)) ggplot(df) + geom_col( - aes(x = "", y = percent, fill = category), + aes(y = "", x = percent, fill = category), width = 1.1, alpha = 0.8 ) + geom_label_repel( - aes(x = "", y = label_y, label = category), + aes(y = "", x = label, label = category), direction = 'y', label.padding = 0.25, segment.size = 0 ) + - coord_flip() + - scale_y_continuous(expand = expansion(mult = c(0, 0.05))) + - scale_fill_viridis(discrete = TRUE) + + scale_x_continuous( + labels = scales::percent, + expand = expansion(mult = c(0, 0.05)) + ) + + scale_fill_viridis( + option = 'inferno', + discrete = TRUE, + direction = -1 + ) + theme_minimal_vgrid() + theme(legend.position = 'none') + - labs(y = 'Percent of final grade', x = NULL) + labs(x = 'Percent of final grade', y = NULL) ``` ## AMG Grading