SunKyoung Moon Solution for Kickbones1 project

Projects/kickbones1/Solution for kickbones1 (first trial).Rmd

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+---
+title: "kickbones1_from SunKyoung Moon(fist trial)"
+author: "Luis"
+output: md_document
+date: "2024-12-03"
+---
+
+library(ggplot2)
+library(dplyr)
+library(tidyr)
+
+#Loading Data
+url <- "https://raw.githubusercontent.com/Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub/refs/heads/main/Projects/SunKyoung%20Moon/kaggle_dataset.csv"
+
+data <- read.csv(url)
+head(data)
+
+# Convert app usage time from minutes/day to hours/day
+data <- data %>%
+  mutate(AppUsageHours = `App.Usage.Time..min.day.` / 60)
+
+# Compute median app usage time and sum users for each device model
+device_summary <- data %>%
+  group_by(`Device.Model`, Gender) %>%
+  summarize(
+    MedianAppUsage = median(AppUsageHours, na.rm = TRUE),
+    UserCount = n()
+  ) %>%
+  ungroup()
+
+# Summarize age groups for scatter plot
+data <- data %>%
+  mutate(AgeGroup = case_when(
+    Age >= 20 & Age <= 29 ~ "20-29",
+    Age >= 30 & Age <= 39 ~ "30-39",
+    Age >= 40 & Age <= 49 ~ "40-49",
+    Age >= 50 & Age <= 59 ~ "50-59",
+    TRUE ~ "Others"
+  ))
+
+#Visualization part!
+ggplot(data, aes(x = Device.Model, y = AppUsageHours, fill = Gender)) +
+  geom_violin(alpha = 0.7, scale = "width") +
+  geom_point(aes(color = AgeGroup), position = position_jitter(width = 0.2, height = 0), alpha = 0.5) +
+  scale_fill_manual(values = c("Male" = "blue", "Female" = "red")) +
+  scale_color_manual(values = c(
+    "20-29" = "gray",
+    "30-39" = "green",
+    "40-49" = "pink",
+    "50-59" = "purple"
+  )) +
+  labs(
+    title = "Mobile Device Usage for Different Models",
+    subtitle = "Median app usage time differentiated by gender and age groups",
+    x = "Device Model",
+    y = "Median App Usage Time (hours/day)",
+    fill = "Gender",
+    color = "Age Group"
+  ) +
+  theme_minimal()
+
+device_user_counts <- data %>%
+  group_by(Device.Model) %>%
+  summarize(UserCount = n())
+
+ggplot(device_user_counts, aes(x = Device.Model, y = UserCount)) +
+  geom_col(fill = "lightblue") +
+  geom_text(aes(label = UserCount), vjust = -0.5) +
+  labs(
+    title = "User Count per Device Model",
+    x = "Device Model",
+    y = "User Count"
+  ) +
+  theme_minimal()
+
+#Result
+
+![visualization_Result](https://imgur.com/jni0RwO)
+![Visualization_Result2](https://imgur.com/DOEtSmH)
+

Projects/kickbones1/Solution-for-kickbones1--first-trial-.md

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+library(ggplot2) library(dplyr) library(tidyr)
+
+\#Loading Data url <-
+“<https://raw.githubusercontent.com/Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub/refs/heads/main/Projects/SunKyoung%20Moon/kaggle_dataset.csv>”
+
+data &lt;- read.csv(url) head(data)
+
+# Convert app usage time from minutes/day to hours/day
+
+data &lt;- data %&gt;% mutate(AppUsageHours = `App.Usage.Time..min.day.`
+/ 60)
+
+# Compute median app usage time and sum users for each device model
+
+device\_summary &lt;- data %&gt;% group\_by(`Device.Model`, Gender)
+%&gt;% summarize( MedianAppUsage = median(AppUsageHours, na.rm = TRUE),
+UserCount = n() ) %&gt;% ungroup()
+
+# Summarize age groups for scatter plot
+
+data &lt;- data %&gt;% mutate(AgeGroup = case\_when( Age &gt;= 20 & Age
+&lt;= 29 ~ “20-29”, Age &gt;= 30 & Age &lt;= 39 ~ “30-39”, Age &gt;= 40
+& Age &lt;= 49 ~ “40-49”, Age &gt;= 50 & Age &lt;= 59 ~ “50-59”, TRUE ~
+“Others” ))
+
+\#Visualization part! ggplot(data, aes(x = Device.Model, y =
+AppUsageHours, fill = Gender)) + geom\_violin(alpha = 0.7, scale =
+“width”) + geom\_point(aes(color = AgeGroup), position =
+position\_jitter(width = 0.2, height = 0), alpha = 0.5) +
+scale\_fill\_manual(values = c(“Male” = “blue”, “Female” = “red”)) +
+scale\_color\_manual(values = c( “20-29” = “gray”, “30-39” = “green”,
+“40-49” = “pink”, “50-59” = “purple” )) + labs( title = “Mobile Device
+Usage for Different Models”, subtitle = “Median app usage time
+differentiated by gender and age groups”, x = “Device Model”, y =
+“Median App Usage Time (hours/day)”, fill = “Gender”, color = “Age
+Group” ) + theme\_minimal()
+
+device\_user\_counts &lt;- data %&gt;% group\_by(Device.Model) %&gt;%
+summarize(UserCount = n())
+
+ggplot(device\_user\_counts, aes(x = Device.Model, y = UserCount)) +
+geom\_col(fill = “lightblue”) + geom\_text(aes(label = UserCount), vjust
+= -0.5) + labs( title = “User Count per Device Model”, x = “Device
+Model”, y = “User Count” ) + theme\_minimal()
+
+\#Result ![visualization\_Result](https://imgur.com/jni0RwO)
+![Visualization\_Result2](https://imgur.com/DOEtSmH)