1 ggplot in 3 easy steps (maybe 2 easy steps)

2 Background

R has a number of graphing libraries, including base graphics that are installed whenever you install R.

ggplot2, is a graphing library in R that makes beautiful graphs. ggplot2 graph syntax can be formidably complex, with a somewhat steep learning curve.

That being said, learning ggplot2 is worth the effort for a couple of reasons. First, the graphs are beautiful. Second, ggplot2’s syntax, though seemingly arcane at times, forces you to think about the nature of your data, and the ideas that you are graphing. Lastly, a little bit of knowledge about ggplot2 can go a long way, and can build a powerful foundation for future learning.

3 A Simple Quick Example

The intent of this tutorial is to build the foundation of this idea that:

A little bit of ggplot can go a long way

and to give you a simple introduction to the idea that any ggplot graph is composed of:

an aesthetic + a geom or two + other optional elements like titles and themes.

So, as a quick and simple example…

library(ggplot2)

ggplot(my_demo_data, # the data that I am using
       aes(x = my_outcome)) + # aesthetic: what I am graphing
  geom_histogram(fill = "red", # geom: how I am graphing it
                 color = "black") 

And now, with titles…

ggplot(my_demo_data, # the data that I am using
       aes(x = my_outcome)) + # aesthetic: what I am graphing
  geom_histogram(fill = "red", # geom: how I am graphing it
                 color = "black") +
  labs(title = "Your Title Here",
       subtitle = "Your Subtitle Here",
       caption = "A Caption, If You Want One",
       x = "my outcome",
       y = "count")

This document is a very brief introduction to the basic ideas of ggplot2. More information about ggplot can be found here. More ggplot2 examples can be found here.

4 Call The Relevant Libraries

You will need a few R libraries to work in ggplot. You may only need library(ggplot2), but some of these other libraries may also be helpful.

library(ggplot2) # beautiful graphs

library(ggthemes) # nice themes for ggplot2

library(ggbeeswarm) # "beeswarm" plots

library(cowplot) # arrrange graphs

library(pander) # nice tables

library(psych) # nice table of descriptive statistics

5 Now We Simulate Some Data

In this example, we simulate some data. But your own learning of ggplot will progress more quickly if you use data that you have access to, on an issue that you care about.

Here are the first few rows of simulated data:

# simulated data

N <- 500 # set sample size 

predictor <- rnorm(n = N, mean = 100, sd = 25) # n, mean, sd

group <- rbinom(n = N, 1, .5) # n, number of trials, probability

outcome <- predictor + 
  10 * group +
  rnorm(n=N, 
        mean = 0, 
        sd = 15) # outcome is a function of predictor + group + error

group <- factor(group)

mydata <- data.frame(predictor, outcome, group) # make data frame 

pander(head(mydata, 10)) # nice looking table of first few rows of data

6 The Essential Idea Of ggplot2 Is Simple.

There are 3 essential elements to any ggplot call:

1. An aesthetic that tells ggplot which variables are being mapped to the x axis, y axis, (and often other attributes of the graph, such as the color fill). Intuitively, the aesthetic can be thought of as what you are graphing.
2. A geom or geometry that tells ggplot about the basic structure of the graph. Intuitively, the geom can be thought of as how you are graphing it.
3. Other options, such as a graph title, axis labels and overall theme for the graph.

7 Examples

# call ggplot2 where aesthetic is: x uses our predictor variable

p1 <- ggplot(mydata, 
             aes(x = predictor))  

p1 + 
  geom_dotplot(dotsize = .15) # add dotplot geom

p1 + 
  geom_dotplot(dotsize = .15, fill="red") + # add dotplot geom in red
  labs(title ="Dotplot of predictor") # Add title

p1 + geom_histogram(fill = "blue") + # add histogram geom in blue
  labs(title ="Histogram of predictor") # Add title

p1 + geom_density(fill = "gold") + # add density geom in gold
  labs(title ="Density of predictor") # Add title

p2 <- ggplot(mydata, 
             aes(x = 1, # we need an aesthetic with _x_
                 y = predictor)) # & _y_

p2 + geom_violin(fill = "blue") +
  labs(title ="Violin Plot of predictor") # Add title

p3 <- ggplot(mydata, 
             aes(x = predictor, # we need an aesthetic with _x_
                 y = 1)) # & _y_

p3 + geom_beeswarm(color = "red", 
                   groupOnX = FALSE) +
  labs(title ="Beeswarm Plot of predictor") + # Add title
  theme(axis.title.y = element_blank(), 
        axis.text.y = element_blank()) # tweak y axis

p_barchart <- ggplot(mydata,
                     aes(x = group)) +
  geom_bar(fill = "red") 

p_barchart

p_stacked_barchart <- ggplot(mydata, 
                             aes(x = 1,
                                 fill = group)) +
  geom_bar() +
  scale_fill_manual(values = c("red", "blue"))

p_stacked_barchart

p_barchart_of_mean <- ggplot(mydata, 
                             aes(x = group, # slightly different aesthetic
                                 y = outcome)) + 
  stat_summary(fun.y = mean, # take the mean of the data
               fill = "blue", # fill color
               geom = "bar") # we want to summarize data with bars

p_barchart_of_mean

# call ggplot2 where aesthetic uses both predictor and outcome

p4 <- ggplot(mydata, 
             aes(x = predictor, 
                 y = outcome)) # set up aesthetic

p4 + geom_point() # add point geom (scatterplot)

p4 + # start with basic plot that has only an aesthetic
  geom_point(color = "blue") + # add point geom in blue
  labs(title ="Scatterplot of Outcome by Predictor") # add title

p4 + geom_smooth() + # add smooth geom
  labs(title ="Smoother of Outcome by Predictor") # add title

p4 + geom_density2d(color = "blue") + # add density geom 
  labs(title ="Density Plot of Outcome by Predictor") # add title

p4 + 
  stat_density_2d(aes(fill = ..level..), 
                  geom = "polygon") + # add filled density geom 
  scale_fill_gradient(low = "blue",
                      high = "red") +
  labs(title ="Density Plot of Outcome by Predictor") # add title

p4 + 
  stat_density_2d(aes(fill = ..level..), 
                  geom = "polygon") + # add filled density geom
  geom_point(color = "orange") +
    scale_fill_gradient(low = "blue",
                      high = "red") +
  labs(title ="Density Plot of Outcome by Predictor") # add title

p4 + 
  stat_density_2d(geom = "raster", 
                  aes(fill = ..density..),
                  contour = FALSE) +
  scale_fill_gradient(low = "blue",
                      high = "red") +
  labs(title ="Density Plot (Raster) of Outcome by Predictor") # add title

p4 + 
  geom_hex() +
   scale_fill_gradient(low = "blue",
                      high = "red") +
  labs(title ="Hexagon Plot of Outcome by Predictor") # add title

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_grey() # default theme

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_minimal() # default theme

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_fivethirtyeight() + # "538"-like theme
  scale_color_fivethirtyeight() # "538"-like colors

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_solarized() + # Google Docs theme
  scale_colour_solarized() # Google Docs colors

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_solarized(light = FALSE) + # solarized dark theme
  scale_colour_solarized("blue") # solarized dark color palette

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_economist() + # Economist magazine theme
  scale_colour_economist() # Economist magazine colors

# same plot with theme and geom based on the work of Edward Tufte

p4 + geom_point() + 
  geom_smooth(color = "red") + 
  geom_rangeframe() +
  theme_tufte() +
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor")

p4 + geom_point() + # point geom
  geom_smooth() + # add smooth geom
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor") + # add title
  theme_gdocs() + # Google Docs theme
  scale_colour_gdocs() # Google Docs colors

p5 <- ggplot(mydata, 
             aes(x = predictor, y = outcome, 
                 color = group)) # aesthetic includes color by group

p5 + geom_point() + 
  geom_smooth() + 
  theme_economist() +
  scale_color_economist() +
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor")

p5 + geom_point() + 
  geom_smooth() + 
  facet_wrap(~group) + # facets or "small multiples" by group
  theme_economist() +
  scale_color_economist() +
  labs(title ="Scatterplot And Smoother of Outcome \nby Predictor")

8 There Is A Lot More That Can Be Done With ggplot2

More information can be found at ggplot2.

More ggplot2 examples can be found here.

Graphics made with the ggplot2 graphing library created by Hadley Wickham.

Available online at https://agroganweb.wordpress.com/data-visualization-dataviz/

Quick Introduction to ggplot2 by Andrew Grogan-Kaylor is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Last updated: February 05 2019 at 10:55