Comics
In this chapter, you will learn how to create graphical and numerical summaries of two categorical variables.
url <- "https://assets.datacamp.com/production/course_1796/datasets/comics.csv"
filename <- basename(url)
if (!file.exists(filename)) download(url,destfile=filename)
comics <- read.csv(filename)
# comics %>%
# rename(
# sepal_length = Sepal.Length,
# sepal_width = Sepal.Width
# )Video: Exploring categorical data
str(comics)## 'data.frame': 23272 obs. of 11 variables:
## $ name : Factor w/ 23272 levels "'Spinner (Earth-616)",..: 19830 3335 22769 9647 20956 2220 17576 9347 18794 10957 ...
## $ id : Factor w/ 4 levels "No Dual","Public",..: 3 2 2 2 1 2 2 2 2 2 ...
## $ align : Factor w/ 4 levels "Bad","Good","Neutral",..: 2 2 3 2 2 2 2 2 3 2 ...
## $ eye : Factor w/ 26 levels "Amber Eyes","Auburn Hair",..: 11 5 5 5 5 5 6 6 6 5 ...
## $ hair : Factor w/ 28 levels "Auburn Hair",..: 7 27 3 3 4 14 7 7 7 4 ...
## $ gender : Factor w/ 3 levels "Female","Male",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ gsm : Factor w/ 6 levels "Bisexual Characters",..: NA NA NA NA NA NA NA NA NA NA ...
## $ alive : Factor w/ 2 levels "Deceased Characters",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ appearances : int 4043 3360 3061 2961 2258 2255 2072 2017 1955 1934 ...
## $ first_appear: Factor w/ 1606 levels "1935, October",..: 874 1278 1513 1296 1424 1432 1432 1364 1570 1432 ...
## $ publisher : Factor w/ 2 levels "dc","marvel": 2 2 2 2 2 2 2 2 2 2 ...levels(comics$align)## [1] "Bad" "Good" "Neutral"
## [4] "Reformed Criminals"levels(comics$id)## [1] "No Dual" "Public" "Secret" "Unknown"table(comics$id, comics$align)##
## Bad Good Neutral Reformed Criminals
## No Dual 474 647 390 0
## Public 2172 2930 965 1
## Secret 4493 2475 959 1
## Unknown 7 0 2 0ggplot(comics, aes(x=align, fill=id)) +
geom_bar()
ggplot(comics, aes(x=id, fill=align)) +
geom_bar()
Question: Bar chart expectations
Which one of the barcharts shows no relationship between age and flavor? In other words, which shows that pie preference is the same for both young and old? Source: DataCamp
It’s the first one.
Contingency table review
In this chapter you’ll continue working with the comics dataset introduced in the video. This is a collection of characteristics on all of the superheroes created by Marvel and DC comics in the last 80 years.
Let’s start by creating a contingency table, which is a useful way to represent the total counts of observations that fall into each combination of the levels of categorical variables.
# Print the first rows of the data
head(comics)## name id align eye hair
## 1 Spider-Man (Peter Parker) Secret Good Hazel Eyes Brown Hair
## 2 Captain America (Steven Rogers) Public Good Blue Eyes White Hair
## 3 Wolverine (James \\"Logan\\" Howlett) Public Neutral Blue Eyes Black Hair
## 4 Iron Man (Anthony \\"Tony\\" Stark) Public Good Blue Eyes Black Hair
## 5 Thor (Thor Odinson) No Dual Good Blue Eyes Blond Hair
## 6 Benjamin Grimm (Earth-616) Public Good Blue Eyes No Hair
## gender gsm alive appearances first_appear publisher
## 1 Male <NA> Living Characters 4043 Aug-62 marvel
## 2 Male <NA> Living Characters 3360 Mar-41 marvel
## 3 Male <NA> Living Characters 3061 Oct-74 marvel
## 4 Male <NA> Living Characters 2961 Mar-63 marvel
## 5 Male <NA> Living Characters 2258 Nov-50 marvel
## 6 Male <NA> Living Characters 2255 Nov-61 marvel# Check levels of align
levels(comics$align)## [1] "Bad" "Good" "Neutral"
## [4] "Reformed Criminals"# Check the levels of gender
levels(comics$gender)## [1] "Female" "Male" "Other"# Create a 2-way contingency table
table(comics$align, comics$gender)##
## Female Male Other
## Bad 1573 7561 32
## Good 2490 4809 17
## Neutral 836 1799 17
## Reformed Criminals 1 2 0Dropping levels
The contingency table from the last exercise revealed that there are some levels that have very low counts. To simplify the analysis, it often helps to drop such levels.
In R, this requires two steps: first filtering out any rows with the levels that have very low counts, then removing these levels from the factor variable with droplevels(). This is because the droplevels() function would keep levels that have just 1 or 2 counts; it only drops levels that don’t exist in a dataset.
# Assign contingency table to tab
tab <- table(comics$align, comics$gender)
# Load dplyr
library(dplyr)
# Print tab
tab##
## Female Male Other
## Bad 1573 7561 32
## Good 2490 4809 17
## Neutral 836 1799 17
## Reformed Criminals 1 2 0# Remove align level
comics_filtered <- comics %>%
filter(align != "Reformed Criminals") %>%
droplevels()
# See the result
head(comics_filtered)## name id align eye hair
## 1 Spider-Man (Peter Parker) Secret Good Hazel Eyes Brown Hair
## 2 Captain America (Steven Rogers) Public Good Blue Eyes White Hair
## 3 Wolverine (James \\"Logan\\" Howlett) Public Neutral Blue Eyes Black Hair
## 4 Iron Man (Anthony \\"Tony\\" Stark) Public Good Blue Eyes Black Hair
## 5 Thor (Thor Odinson) No Dual Good Blue Eyes Blond Hair
## 6 Benjamin Grimm (Earth-616) Public Good Blue Eyes No Hair
## gender gsm alive appearances first_appear publisher
## 1 Male <NA> Living Characters 4043 Aug-62 marvel
## 2 Male <NA> Living Characters 3360 Mar-41 marvel
## 3 Male <NA> Living Characters 3061 Oct-74 marvel
## 4 Male <NA> Living Characters 2961 Mar-63 marvel
## 5 Male <NA> Living Characters 2258 Nov-50 marvel
## 6 Male <NA> Living Characters 2255 Nov-61 marvel# Check contingency table
table(comics_filtered$align, comics_filtered$gender)##
## Female Male Other
## Bad 1573 7561 32
## Good 2490 4809 17
## Neutral 836 1799 17Side-by-side barcharts
While a contingency table represents the counts numerically, it’s often more useful to represent them graphically.
Here you’ll construct two side-by-side barcharts of the comics data. This shows that there can often be two or more options for presenting the same data. Passing the argument position = "dodge" to geom_bar() says that you want a side-by-side (i.e. not stacked) barchart.
# Load ggplot2
library(ggplot2)
# Create side-by-side barchart of gender by alignment
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar(position = "dodge")
# Create side-by-side barchart of alignment by gender
ggplot(comics, aes(x = gender, fill = align)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 90))
Take a moment to toggle between the resulting plots in the plotting window.
Question: Bar chart interpretation
Which of the following interpretations of the bar charts to your right is not valid? Source: DataCamp
It’s “Across all genders, Bad is the most common alignment.”
Video: Counts vs. proportions
Question: Conditional proportions
tab <- table(comics$align, comics$gender)
options(scipen = 999, digits = 3) # Print fewer digits
prop.table(tab) # Joint proportions##
## Female Male Other
## Bad 0.0821968 0.3950985 0.0016722
## Good 0.1301144 0.2512933 0.0008883
## Neutral 0.0436850 0.0940064 0.0008883
## Reformed Criminals 0.0000523 0.0001045 0.0000000prop.table(tab, 2) # Conditional on columns##
## Female Male Other
## Bad 0.321020 0.533554 0.484848
## Good 0.508163 0.339355 0.257576
## Neutral 0.170612 0.126949 0.257576
## Reformed Criminals 0.000204 0.000141 0.000000Approximately what proportion of all female characters are good?
It’s 51%. To answer this question, you needed to look at how align was distributed within each gender. That is, you wanted to condition on the gender variable.
Counts vs. proportions (2)
Bar charts can tell dramatically different stories depending on whether they represent counts or proportions and, if proportions, what the proportions are conditioned on. To demonstrate this difference, you’ll construct two barcharts in this exercise: one of counts and one of proportions.
# Plot of gender by align
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar()
# Plot proportion of gender, conditional on align
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar(position = "fill") +
ylab("proportion")
By adding position = "fill" to geom_bar(), you are saying you want the bars to fill the entire height of the plotting window, thus displaying proportions and not raw counts.
Video: Distribution of one variable
Take your original two-way table, then sum the cells across each level of one of the variables. Since we’ve summed over the margins of the other variable, this is sometimes called a marginal distribution.
Faceting vs. stacking. Source: DataCamp
Areas are easier to compare in bar charts. Source: DataCamp
Marginal barchart
If you are interested in the distribution of alignment of all superheroes, it makes sense to construct a barchart for just that single variable.
You can improve the interpretability of the plot, though, by implementing some sensible ordering. Superheroes that are "Neutral" show an alignment between "Good" and "Bad", so it makes sense to put that bar in the middle.
# Change the order of the levels in align
comics$align <- factor(comics$align,
levels = c("Bad", "Neutral", "Good"))
# Create plot of align
comics %>%
filter(!is.na(align)) %>%
ggplot(aes(x = align)) +
geom_bar()
Conditional barchart
Now, if you want to break down the distribution of alignment based on gender, you’re looking for conditional distributions.
You could make these by creating multiple filtered datasets (one for each gender) or by faceting the plot of alignment based on gender. As a point of comparison, we’ve provided your plot of the marginal distribution of alignment from the last exercise.
# Plot of alignment broken down by gender
ggplot(comics, aes(x = align)) +
geom_bar() +
facet_wrap(~ gender)
Improve piechart
The piechart is a very common way to represent the distribution of a single categorical variable, but they can be more difficult to interpret than barcharts.
This is a piechart of a dataset called ‘pies’ that contains the favorite pie flavors of 98 people. Improve the representation of these data by constructing a barchart that is ordered in descending order of count.
pies <- data.frame(c(rep("apple", times = 17), rep("blueberry", times = 14), rep("boston creme", times =15), rep("cherry", times =13), rep("key lime", times =16), rep("pumpkin", times =12), rep("strawberry", times =11)))
names(pies) <- "flavor"
# Create pie chart of flavor
pie(table(pies$flavor))
ggplot(pies, aes(x = flavor)) +
geom_bar()
# Put levels of flavor in descending order
lev <- c("apple", "key lime", "boston creme", "blueberry", "cherry", "pumpkin", "strawberry")
pies$flavor <- factor(pies$flavor, levels = lev)
# Create barchart of flavor
ggplot(pies, aes(x = flavor)) +
geom_bar(fill = "chartreuse") +
theme(axis.text.x = element_text(angle = 90))
# Alternative solution to finding levels
# lev <- unlist(select(arrange(cnt, desc(n)), flavor))