Boxplot, Histogram, Barplots Homework
Andrew Hand
2026-03-10
Homework:
Part 1: Boxplots (20 pts)
- Dataset: mpg
- Create a boxplot of hwy (highway mpg) for each vehicle class (class).
- Rotate the x-axis text 45 degrees for readability.
- Add a title, subtitle, and caption to your plot.
- Color the boxes by drivetrain (drv).
# library
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionhead(mpg, n = 10)## # A tibble: 10 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…mpg %>%
ggplot(aes(class, hwy, fill = drv)) +
geom_boxplot() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45)) +
labs(title = "Highway Gas Mileage per Car Class and Wheel Drive",
x = "Motor Vehicle Class",
y = "Gas Mileage on Highways (mpg)",
fill = "Wheel Drive Type") 
Questions:
1.Which vehicle class has the highest median highway mpg?
compact followed closely by subcompact
- Which has the largest variability?
subcompact
Create an alteration of this plot where the outliers are shown in a way in which they do not overlap (consider jittering and coloring).
mpg %>%
ggplot(aes(class, hwy, fill = drv)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.5, height = 0.5, alpha = 0.4) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45)) +
labs(title = "Highway Gas Mileage per Car Class and Wheel Drive",
x = "Motor Vehicle Class",
y = "Gas Mileage on Highways (mpg)",
fill = "Wheel Drive Type") 
Part 2: Violin Plots
- Dataset: mpg
- Create a violin plot of cty (city mpg) for each vehicle class.
- Overlay boxplots on top of the violins. Change as necessary for aesthetics.
- Use a transparent fill so that both distributions and boxplots are visible.
- Add a descriptive title, subtitle, and labels.
mpg %>%
ggplot(aes(class, cty)) +
geom_violin(fill = "pink") +
geom_boxplot(alpha = 0.4, fill = "lightblue") +
theme_minimal() +
labs(title = "City Gas Mileage per Car Type",
x = "Motor Vehicle Class",
y = "In City Gas Mileage (mpg)")
Questions:
How does the violin plot help you understand the distribution compared to the boxplot alone?
violin plots show density distributions of data, unlike boxplots which don't show were most data tends to lean lean towards, violin shows you where the most and least amount of data points lie.Are there any classes with unusual distributions or outliers?
compact and subcompact have large outliers skewing up. The 2-seater looks to have a very compact distribution of data.
Part 3: Barplots
- Dataset: diamonds
- Create a simple barplot showing the number of diamonds for each cut.
- Create a stacked barplot showing the number of diamonds by cut and colored by color.
- Create a grouped (side-by-side) barplot showing the same data.
diamonds %>%
ggplot(aes(cut)) +
geom_bar() +
theme_minimal()
diamonds %>%
ggplot(aes(cut, fill = color)) +
geom_bar(position = "fill") +
theme_minimal()
diamonds %>%
ggplot(aes(cut, fill = color)) +
geom_bar(position = "dodge") +
theme_minimal()
Questions:
What does the stacked barplot show that the simple barplot does not?
it shows another dimension of the data by combining a categorical variable into the bar plot.When is the grouped barplot more useful than the stacked barplot?
grouped barplots allow for better comparisons across multiple cartegories.
Part 4: Barplots Cont.
Dataset: diamonds
- Compute the average price of diamonds for each cut using dplyr.
- Create a barplot showing the average price for each cut (geom_col()).
- Add meaningful axis labels, a title, subtitle, and caption.
- Color the bars by color or clarity to show subgroup differences.
- Rotate x-axis labels if needed for readability.
- Reorder by average price to make comparisons clearer.
library(dplyr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.4 ✔ tibble 3.3.0
## ✔ purrr 1.2.0 ✔ tidyr 1.3.1
## ✔ readr 2.1.5
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#head(diamonds, n = 5)
d1 <- diamonds %>%
group_by(cut) %>%
summarise(mean_price = mean(price))
d1 %>%
ggplot(aes(cut, mean_price, fill = cut)) +
geom_col() +
theme_minimal() +
theme() +
labs(title = "Average Diamond Price by Cut",
x = "Cut",
y = "Average Diamond price",
caption = "Figure 1. Average diamond price by cut")
Create the same plot using the stat_summary approach. Add error bars to the plot.
diamonds %>%
ggplot(aes(cut, price)) +
stat_summary(fun = mean,
geom = "col") +
stat_summary(fun.data = mean_se,
geom = "errorbar",
width = 0.3) +
theme_minimal() 