Box/Barplot HW

Homework :

Part 1: Boxplots

install.packages("ggplot2") #install ggplot

## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)

library(ggplot2) #load in ggplot to create the plot
data(mpg) #load in the mpg data
ggplot(mpg,aes(x=class, y=hwy, fill=drv))+ #set x/y axes and fill by drivetrain (drv)
  geom_boxplot()+ #create boxplot
  theme(axis.text.x= element_text(angle=45))+ #rotate x-axis text 45 degrees
  labs( #specify captions, titles, etc.
    title = "Highway mpg per Vehicle Class", #set title
    subtitle = "Boxplot shows distribution across vehicle classes", #set subtitle
    x = "Vehicle Class", #set x axis title
    y = "Highway mpg", #set y axis title
    caption = "Source: ggplot2 mpg dataset" #set caption
  )+
  theme_bw() #change theme for visual clarity

Questions:

1.Which vehicle class has the highest median highway mpg? Compact vehicles have the highest median highway mpg.

2. Which has the largest variability? Subcompact vehicles have the largest variability.

ggplot(mpg,aes(x=class, y=hwy, fill=drv))+ #set x/y axes and fill by drivetrain (drv)
  geom_boxplot()+ #create boxplot
  geom_jitter(alpha= 0.5, width = 0.5, aes(color=drv))+ #jitter so outliers don't overlap + lowered opacity + color by drv
  theme(axis.text.x= element_text(angle=45))+ #rotate x-axis text 45 degrees
  labs( #specify captions, titles, etc.
    title = "Highway mpg per Vehicle Class", #set title
    subtitle = "Boxplot shows distribution across vehicle classes", #set subtitle
    x = "Vehicle Class", #set x axis title
    y = "Highway mpg", #set y axis title
    caption = "Source: ggplot2 mpg dataset" #set caption
  )+
  theme_bw() #change theme for visual clarity

Part 2: Violin Plots

ggplot(mpg, aes(x=class, y=cty))+ #specify dataset and x/y axes
  geom_violin(fill= "purple1", alpha= 0.5, trim= FALSE)+ #add violin, fill purple, lower opacity, don't trim
  geom_boxplot(width= 0.1, alpha = 0.5)+ #add boxplot overlay, half opacity
  labs( #create labels
    x = "Vehicle Class", #name x axis
    y = "City mpg", #name y axis
    title = "City mpg per Vehicle Class", #set title
    subtitle= "Violin plot with overlayed boxplot showing average city mpg per vehicle class", #set subtitle
    caption = "Source = ggplot2 mpg dataset" #set caption
  )

Questions:

1. How does the violin plot help you understand the distribution compared to the boxplot alone? It gives us a better idea of the distribution, showing density across the boxplot.

2. Are there any classes with unusual distributions or outliers? Subcompact cars have a wider range/further-away outliers than any other vehicle class.

Part 3: Barplots

Simple Barplot:

data(diamonds) #load in diamonds data
ggplot(diamonds, aes(x=cut))+ #create plot, use diamonds dataset and organize by cut
  geom_bar() #specify the data will be in a barplot

Stacked Barplot:

ggplot(diamonds, aes(x=cut, fill=color))+ #create plot, use diamonds dataset and organize by cut, fill by color
  geom_bar() #specify the data will be in a barplot

Grouped Barplot:

ggplot(diamonds, aes(x=cut, y=carat, fill=color))+ #create plot, use diamonds dataset and organize by cut
  stat_summary(fun = mean, #create grouped plot
               geom = "col",
               position = "dodge")

Questions:

1. What does the stacked barplot show that the simple barplot does not? It shows the frequency of each color per cut category.

2. When is the grouped barplot more useful than the stacked barplot? When you need a clearer side-by-side comparison of the data.

Part 4: Barplots Cont.

install.packages("dplyr") #installing dplyr

## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)

library(dplyr) #load in 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, union

avg_ppc <- diamonds %>% #create new variable (avg price of diamonds per cut)
  group_by(cut, color, price) %>% #group by cut
  summarise(mean_pr = mean(price)) #summarize avg price per cut

## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by cut, color, and price.
## ℹ Output is grouped by cut and color.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(cut, color, price))` for per-operation grouping
##   (`?dplyr::dplyr_by`) instead.

order_pr <- diamonds %>% # create new variable (avg price)
  group_by(cut) %>% #group by cut
  summarise(mean_pr = mean(price)) %>% #summarise prices
  arrange(mean_pr) #arrange by price


ggplot(avg_ppc, aes(x= cut, y=mean_pr, fill= color))+ #plot using new variables, specify x/y axes and fill by color
  geom_col()+ #create barplot, show avg price per cut
  theme(axis.text.x= element_text(angle=45, hjust= 1))+ #rotate x-axis text 45 degrees
  labs(
    x = "Cut", #name x axis
    y = "Average Price", #name y axis
    title = "Average Price per Diamond Cut", #add title
    subtitle = "Barplot showing the relationship between diamond cut and price", #add subtitle
    caption = "Source:ggplot 2 diamonds dataset" #add caption
  )

Using Stat_Summary:

ggplot(diamonds, aes(x= cut, y=price, fill= color))+ #plot using new variables, specify x/y axes and fill by color
  geom_col()+ #create barplot, show avg price per cut
  stat_summary(fun = mean, #calculate mean
               geom = "bar", #create bars
               position = "dodge")+
  theme(axis.text.x= element_text(angle=45, hjust= 1))+ #rotate x-axis text 45 degrees
  labs(
    x = "Cut", #name x axis
    y = "Average Price", #name y axis
    title = "Average Price per Diamond Cut", #add title
    subtitle = "Barplot showing the relationship between diamond cut and price", #add subtitle
    caption = "Source:ggplot 2 diamonds dataset") #add caption