ggplot is a super useful graphics tool. See its website at dplyr tutorial
This tutorial also supports Datacamp’s Introduction to Data Visualization with ggplot2.
Datacamp makes a distinction between two types of graphs:
There are number of key layers in a ggplot.
We also have several other types of things:
There are different types of geoms:
A bar plot will show the count of items at every point in an axis (either x or y).
Note that we use +
to join it with ggplot.
We need to add a mapping argument to geom_bar, and will use an aesthetic (aes). This allows us to define which parts of the chart will map to our dataset.
Map a field to either x or y. The axis other will be the
count
(i.e., number of rows).
library(tidyverse)
# Minimal example
ggplot(data = mpg) +
geom_bar(mapping = aes(y = hwy)) # or x
Any properties that don’t map to the aes
(data set) need
to be placed outside of the mapping argument.
library(tidyverse)
# Example with color/fill
ggplot(data = mpg) +
geom_bar(mapping = aes(x = hwy),
color = 'blue',
fill = 'green')
A bar plot can also show specific values (instead of
count
). A geom_col
is the same as
geom_bar
, except for a default stat
of
“identity”. Geom_bar uses count
(i.e., the number of rows
matching the dimension).
library(tidyverse)
t_identity <- tibble(
state = c('tx', 'ca', 'wv'),
sales = c(100, 120, 70)
)
# Minimal example
#
#
# Alternatively, we can use a bar with stat set to identity
# ggplot(data = t_identity) +
# geom_bar(mapping = aes(x = state, y = sales), stat = 'identity')
#
ggplot(data = t_identity) +
geom_col(mapping = aes(x = state, y = sales))
The smooth geom shows a smoothed line. It shows the average value at each point. Error bars are shown in gray.
We need x and y values.
library(tidyverse)
# Show with error bars
ggplot(data = mpg) +
geom_smooth(mapping = aes(y = hwy, x = cty))
library(tidyverse)
# Full example
# method: loess is default for small items. Can also use lm (linear reg)
# color: line color (as a string value)
# linewidth: width for the bar (number)
# se: FALSE to remove error bars (confidence interval around smooth)
# na.rm: TRUE to remove na values with no error
# stat: default is 'smooth' (shows a smoothed line). To show all items, use 'identity'.
ggplot(data = mpg) +
geom_smooth(mapping = aes(y = hwy, x = cty),
method = 'lm',
na.rm = TRUE,
stat = 'identity',
se = FALSE,
color = 'green',
linewidth = 3)
This is identical to geom_smooth
, but defaults to
showing the stat as identity, instead of smooth. This is similar to the
difference between geom_bar
and geom_col
.
# Show with error bars
ggplot(data = mpg) +
geom_line(mapping = aes(y = hwy, x = cty))
This shows a separate dot for each row in the dataset (identity)
They are great for showing a large amount of data, but you typically will need to account for overlapping / overplotting data by changing the alpha or adding jitter.
library(tidyverse)
# Basic example
# x and y: can be either categorical or numerical data
ggplot(data = mpg) +
geom_point(mapping = aes(y = hwy, x = class))
We can map a lot of different point properties to our tibble.
library(tidyverse)
# We have a lot of properties that can be mapped
# shape: best discrete, (suggested max of 6 different items)
# size: continuous value
# alpha: continuous values
# color: either continuous or discrete
ggplot(data = mpg) +
geom_point(mapping = aes(y = hwy,
x = manufacturer,
shape = class,
size = cyl,
color = cty,
alpha = displ))
## Warning: The shape palette can deal with a maximum of 6 discrete values because more than 6 becomes difficult to discriminate
## ℹ you have requested 7 values. Consider specifying shapes manually if you need that many have them.
## Warning: Removed 62 rows containing missing values (`geom_point()`).
We can also set individual values for each property (instead of mapping them to fields in our tibble).
library(tidyverse)
# Note that this can also be set manually
# shape: Number, 1-20
# size: Number
# alpha: Number 0-1
# color: Text, such as "black" or a hex code "#2211aa"
ggplot(data = mpg) +
geom_point(mapping = aes(y = hwy,
x = manufacturer),
shape = 2,
size = 3,
color = '#ffbbff',
alpha = 0.9)
Another useful function combined with point is jitter.
This will slightly adjust the values given, making it easier to see over
plotted data (where we have a lot of different items with the same x and
y)
Use in combination with a low alpha value to see the density of points on your plot.
library(tidyverse)
# jitter takes in a variable, and adjusts it by the factor
# Note that it only works with continuous data
ggplot(data = mpg) +
geom_point(mapping = aes(y = jitter(hwy, factor = 1.5),
x = manufacturer),
alpha = 0.1)
Show the count of items using bins
library(tidyverse)
ggplot(data = mpg) +
geom_histogram(mapping = aes(x = hwy),
binwidth = 5)
Similar to a histogram, but you can layer different lines on top of each other.
library(tidyverse)
ggplot(data = mpg) +
geom_density(mapping = aes(x =hwy, color = class, fill = class),
alpha = 0.2)
Show a boxplot for a series of discrete values (usually text)
library(tidyverse)
ggplot(data = mpg) +
geom_boxplot(mapping = aes(x = class, y = hwy))
We can create multiple plots using facets
Small multiples (facets) are a great technique for exploring categorical data # and its relationship with continuous data.
Note the ~ symbol. ~ (tilde) is commonly use in lm (regression) to show [dependent variable] ~ [independent variable] For now, just remember to keep the left blank and put your category variable on the right.
Show a separate plot for each categorical data
library(tidyverse)
# nrow says how many plots to put per row
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap( ~ class, nrow = 3)
Show a grid of charts, using two continuous variables.
library(tidyverse)
# Small multiples (facets) can be used with two separate variables.
# Use facet_grid instead of facet_wrap.
# Use categorical variables.
# Note the ~ uses drv (type of drive-train) and cyl (cylinders)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(cyl ~ drv)
Plotly can be used to make a plot more dynamic. See its website for more information.
There is also a very nice cheat cheat by Afshine Amidi & Shervine Amidi
Here is a great systematic introduction: Elegant Graphics for Data Analysis
Here is a list of example charts: R Graphics Cookbook
Here is a great step-by-step tutorial: Show the right numbers