ggplot2.md

ggplot2

author: Malie Lessard-Therrien and Étienne Low-Décarie date: May 8, 2017 width: 1920 height: 1080

What is a graph?

• Explore and explain
• Statistics and design combined
• Communicating results

ggplot2

Beautiful and flexible

Outline (ggplot2)

Part I

Your first ggplot plot

• Basic scatter plot
• Exercise 1

Grammar of graphics

• Layer system
• Aesthetics, Geometrics
• Exercise 2

---

Part II

Pretty graphs for presentation

• More advanced plots
• Fine tunning
• Exercise 3

Part III

Adapting your graph for publishing

• B&W
• package cowplot
• Saving a plot
• Exercise 4
• Maps

Install/load ggplot2

if(!require(ggplot2)){install.packages("ggplot2")}
require(ggplot2)

Grammar of graphics (gg)

2 principles:

• distinc layers of graphical elements
• meaningful plots using aesthetic mapping

---

Graph elements

3 essentials:

• Data (what you collected, in the right format)
• Aesthetics (aes)
• Geometries (geom)

4 optionals:

• Facets
• Coordinates
• Themes
• Statistics

---

Available elements:

http://docs.ggplot2.org

Iris dataset

• Edgar Anderson
• RA Fischer

head (iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

What to use when

Explore your data using function "qplot"

• For you and your colleagues
• Quick
• Default settings can't be changed

Explain a pattern (or lack of) in your data using function "ggplot"

• For presentation or publication
• All settings must be specified
• Flexible (you have the control over all settings)

Your first graph with ggplot2

A basic scatter plot

basic_graph <- qplot(data=iris,
                    x=Sepal.Length,
                    y=Sepal.Width)

print (basic_graph)

Change axis names

basic_graph_axis <- qplot(data=iris,
      x=Sepal.Length,
      y=Sepal.Width,
      xlab="Sepal length (mm)",
      ylab="Sepal width (mm)")

print (basic_graph_axis)

Exercice 1

produce a basic plot with built in data

data()
CO2
?CO2

WARNING: THERE ARE MULTIPLE CO2/co2 datasets (CASE SENSITIVE, use capitals)

Aesthetics

set with the aes() function

• color ("outside" color)
• fill ("inside" color)
• shape (of points)
• linetype
• size (of points or line)
• position (i.e., on the x and y axes)
• group (that a point belongs to)
• alpha (transparency of the point)

Scatter plot with colour and shape

basic_graph_axis_cs <- qplot(data=iris,
      x=Sepal.Length,
      y=Sepal.Width,
      xlab="Sepal length (mm)",
      ylab="Sepal width (mm)")+
  aes(colour=Species,
      shape=Species)

print (basic_graph_axis_cs)

Same thing as:

basic_graph_axis_cs <- basic_graph_axis +
  aes(colour=Species,
      shape=Species)

print (basic_graph_axis_cs)

---

Geometic Objects

set with the "geom_..." command

Ex:

• points (geom_point, for scatter plots, dot plots, etc)
• lines (geom_line, for time series, trend lines, etc)
• boxplot (geom_boxplot, for, well, boxplots!)
• violins (geom_violin, region inside the violin contains all of the observed data)

A plot must have at least one geom; there is no upper limit. You can add a geom to a plot using the + operator

Geometric example

Categorical x-axis

basic_plot_category <- qplot(data=iris,
                              x=Species,
                              y=Sepal.Width)
  
print (basic_plot_category)

.
.
.

basic_plot_category_bx <- basic_plot_category+
                          geom_boxplot ()

print (basic_plot_category_bx)

---

Geometric example

Categorical x-axis, geom_violin

Aesthetic and geometric example

Give new color and shape to each iris species

Note: here aes() and geom_...() with ggplot function

iris_color_graph <- ggplot (data=iris, aes (x= Sepal.Length, y=Sepal.Width, color=Species, shape=Species))+
  xlab("Sepal lenght (mm)")+
  ylab("Sepal width (mm)")+
  geom_point(size = 3)+
  geom_smooth(method="lm", se=F)+
  scale_color_manual(breaks=c("setosa", "versicolor", "virginica"), 
                     values=c("#6600CC", "#990099", "#FF3399"), 
                     labels=c("Setosa", "Versicolor", "Virginica"))+
  scale_shape_manual(breaks=c("setosa", "versicolor", "virginica"), 
                     values=c(16, 17, 18), 
                     labels=c("Setosa", "Versicolor", "Virginica"))

print (iris_color_graph)

Help

• R-help
• R Cookbook (ex color choices: http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/)
• Cheatsheets (see https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
• Stackoverflow (google your question with ggplot2)

Available elements

http://ggplot2.tidyverse.org/reference/

<iframe src="http://ggplot2.tidyverse.org/reference/" width="1000" height="800">

Your browser does not support iframes.

</iframe>

Exercise 2

Fine tune your graph using built in data

• play with aesthetics (aes)
• use geometrics (geom_...)

CO2
?CO2
data()

WARNING: THERE ARE MULTIPLE CO2/co2 datasets (CASE SENSITIVE, use capitals)

<script src="countdown.js" type="text/javascript"></script> <script type="application/javascript"> var myCountdown2 = new Countdown({ time: 300, width:150, height:80, rangeHi:"minute" // <- no comma on last item! }); </script>

Theme

Changing font size

iris_color_graph_fs <- iris_color_graph+
  theme(axis.title.x=element_text(size=30),
        axis.text.x=element_text(size=25))+
  theme(axis.title.y=element_text(size=30),
        axis.text.y=element_text(size=25))

print (iris_color_graph_fs)

---

Facets

Divide the data graphically

facet_grid(rows~columns)

iris_facets <- iris_color_graph + 
              facet_grid(. ~ Species)

print (iris_facets)

---

Coordinates

Change the coordinate system

DF <- data.frame(variable = LETTERS[1:10], value = sample(10, replace = TRUE))

bar_graph <- ggplot(data=DF, 
                aes(x=variable,
                    y=value,
                    fill=variable))+
                geom_bar(stat="identity")

print(bar_graph)

polar_graph <- ggplot(data=DF, 
                aes(x=variable,
                fill=variable,
                y=value))+
                geom_bar(stat="identity") + 
                coord_polar()

print(polar_graph)

---

Exercise 3

Explore graph elements with the data you have used and/or your own data

• theme
• facets
• coordinates

Graph for publication

B&W background using theme_bw ()

iris_bw <- iris_color_graph+
  theme_bw()
  
print(iris_bw)

---

Graph for publication

B&W using theme_classic ()

iris_classic <- iris_color_graph+
           theme_classic()

print (iris_classic)

---

Graph for publication with cowplot

• simple add-on to ggplot2
• provide a publication-ready theme for ggplot2
• minimum amount of fiddling with sizes of axis labels, plot backgrounds, etc

see https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html

if(!require(cowplot)){install.packages("cowplot")}
require(cowplot)

Cowplot, B&W graph

Once cowplot package is installed and read, your graph background is B&W by default

iris_cowplot <- iris_color_graph

print (iris_cowplot)

---

Cowplot, multiple graphs

Label and align multiple graphs

fig.1 <- plot_grid(iris_bw, iris_cowplot, labels = c("a)", "b)"), nrow = 2, align = "v")
print (fig.1)

---

Saving plots

pdf("./Plots/todays_plots.pdf")
  print(basic_graph_cs)
  print(iris_color_graph_fs)
  print(polar_graph)
  print(bar_graph)
  print (iris_cowplot)
graphics.off()

all other R-base save functions available:
bmp(), jpeg(), etc

Saving plots

ggsave: saves last plot and guesses format from file name

ggsave("./Plots/todays_plots.jpeg", iris_facets)

Exercise 4

Make a graph in B&W background and save your favorite graphs of the day