ggplot2_old.md

<style> .small-code pre code { font-size: 0.75em; } </style>

ggplot2

author: Etienne Low-Décarie transition: rotate

January 5, 2017
Presentation: https://low-decarie.github.io/ggplot_workshop/#/
Code: https://github.com/low-decarie/ggplot_workshop

Without R

With R

Beautiful and flexible!

Outline (ggplot2)

1. Your first ggplot plot
   • basic scatter plot
   • Exercise 1
2. Grammar of graphics
   • More advanced plots
   • Available plot elements and when to use them
   • Exercise 2

---

Tomorrow: 3. Saving a plot

• Exercise 3
• Challenge

4. Expanding ggplot
5. Fine tuning your plot
   • colours
   • themes
6. Maps

A few pet peeves

• Always work from a script
• Use carriage returns and indentation

object <- function(argument1="value1",
                   argument2="value2",
                   argument3="value3")

• Create your own new script
  • refer to provided code only if needed
  • don't just copy paste from the presentation

Install/load ggplot2

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

Your first ggplot

A basic scatter plot

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

---

Categorical x-axis

qplot(data=iris,
      x=Species,
      y=Sepal.Width)

Less basic scatter plot

?qplot

Arguments

x
y
…
data
xlab
ylab
main

Less basic scatter plot

qplot(data=iris,
      x=Sepal.Length,
      xlab="Sepal Width (mm)",
      y=Sepal.Width,
      ylab="Sepal Length (mm)",
      main="Sepal dimensions")

Exercise 1

produce a basic plot with built in data

CO2
?CO2
BOD
data()

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

<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>

Grammar of graphics (gg)

A graphic is made of elements (layers)

• data
• aesthetics (aes)
• transformation
• geoms (geometric objects)
• axis (coordinate system)
• scales

---

Aesthetics (aes) make data visible:

• x,y : position along the x and y axis
• colour: the colour of the point
• group: what group a point belongs to
• shape: the figure used to plot a point
• linetype: the type of line used (solid, dashed, etc)
• size: the size of the point or line
• alpha: the transparency of the point

geometric objects(geoms)

• point: scatterplot
• line: line plot, where lines connect points by increasing x value
• path: line plot, where lines connect points in sequence of appearance
• boxplot: box-and-whisker plots, for categorical y data
• bar: barplots
• histogram: histograms (for 1-dimensional data)

Single element edit

Editing an element produces a new graph e.g. just change the coordinate system

How it works

• 1. create a simple plot object

plot.object<-qplot()
or
plot.object<-ggplot()

• 2. add graphical layers/complexity

plot.object<-plot.object+layer()

• 3. repeat step 2 until satisfied
• 4. print your object to screen (or to graphical device)

print(plot.object)

Scatter plot as an R object

basic.plot<-qplot(data=iris,
                  x=Sepal.Length,
                  xlab="Sepal Width (mm)",
                  y=Sepal.Width,
                  ylab="Sepal Length (mm)",
                  main="Sepal dimensions")

print(basic.plot)

Using ggplot function

more powerful, more complicated Note: aes() and geom_point()

basic.plot<- ggplot(data=iris)+
               aes(x=Sepal.Length,
                  xlab="Sepal Width (mm)",
                  y=Sepal.Width,
                  ylab="Sepal Length (mm)",
                  main="Sepal dimensions")+
  geom_point()

now required to use stat=""

Scatter plot with colour and shape

basic.plot <- basic.plot+
              aes(colour=Species,
                  shape=Species)

print(basic.plot)

Scatter plot with linear regression

Add a geom (eg. linear smooth)

linear.smooth.plot <- basic.plot+
			  geom_smooth(method="lm", se=F)
                         print(linear.smooth.plot)

Exercise 2

produce a colorful plot containing linear regressions with built in data

CO2
?CO2
msleep
?msleep
OrchardSprays
data()

<script src="countdown.js" type="text/javascript"></script> <script type="application/javascript"> var myCountdown3 = new Countdown({ time: 600, width:150, height:80, rangeHi:"minute" // <- no comma on last item! }); </script>

Available elements

http://docs.ggplot2.org

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

Your browser does not support iframes.

</iframe>

Resources

cheatsheets: https://www.rstudio.com/resources/cheatsheets/

<iframe src="https://www.rstudio.com/resources/cheatsheets/" width="1000" height="800">

Your browser does not support iframes.

</iframe>

Exercise 3

Explore geoms and other plot elements with the data you have used and/or your own data

msleep
?msleep
OrchardSprays
data()

<script src="countdown.js" type="text/javascript"></script> <script type="application/javascript"> var myCountdown3 = new Countdown({ time: 300, width:150, height:80, rangeHi:"minute" // <- no comma on last item! }); </script>

Tomorrow

3. Saving a plot

• Exercise 3
• Challenge

4. Expanding ggplot
5. Fine tuning your plot
   • colours
   • themes
6. Maps (time permitting)

Saving plots

pdf("./Plots/todays_plots.pdf")
  print(basic.plot)
  print(plot.with.linear.smooth)
  print(categorical.plot)
  print(CO2.plot)
graphics.off()

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

Saving plots

ggsave: saves last plot and guesses format from file name

ggsave("./Plots/todays_plots.jpeg", basic.plot)

Using facets and groups: the basic plot

CO2.plot<-qplot(data=CO2,
                x=conc,
                y=uptake,
                colour=Treatment)

print(CO2.plot)

Facets

plot.object<-plot.object + facet_grid(rows~columns)

CO2.plot<-CO2.plot+facet_grid(.~Type)
print(CO2.plot)

Groups

Problems when adding the geom_line

print(CO2.plot+geom_line())

Groups

Solution: specify groups

CO2.plot<-CO2.plot+geom_line(aes(group=Plant))
print(CO2.plot)

Using the right tool for the right situation

base R plot function has methods for many different object types

plot(iris)

Using the right tool for the right situation

base R plot function has methods for many different object types

lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi,
            data = LifeCycleSavings)
plot(lm.SR)

Challenge

Find an interesting data set on Dryad.org, reproduce a figure from the article using ggplot2

Example: try to reproduce figure 1 and 4 from Low-Décarie, E., Fussmann, G. F., Bell, G., Low-Decarie, E., Fussmann, G. F., Bell, G., Low-Décarie, E., Fussmann, G. F. & Bell, G. 2014 Aquatic primary production in a high-CO2 world. Trends Ecol. Evol. 29, 1–10.
paper
data
full scripts also available on github (old ugly code!)

Extending ggplot

ggplot can be extended for plotting specific classes of objects

autoplot and fortify

Extending ggplot

ggfortify provides autoplotand
fortify for common models

require(ggfortify)
autoplot(lm.SR)

---

===

help(package=ggfortify)

Fine tunning: Scales

class: small-code

CO2.plot +
  scale_y_continuous(name = "CO2 uptake rate",
                     breaks = seq(5,50, by= 10),
                     labels = seq(5,50, by= 10), 
                     trans="log10")

Fine tunning: Scales

CO2.plot+
  scale_colour_brewer()

Fine tunning: Scales

CO2.plot+
  scale_colour_manual(values=c("nonchilled"="red",
                               "chilled"="blue"))

Fine tunning: Scales

Bonus!!! Wes Anderson colour palette

Fine tunning: Scales

Bonus!!! Wes Anderson colour palette

if(!require(devtools)) {install.packages("devtools")}
require(devtools)
if(!require(wesanderson)){
devtools::install_github("karthik/wesanderson")}
require(wesanderson)

Fine tunning: Scales

Bonus!!! Wes Anderson colour palette

require(wesanderson)
basic.plot + 
  scale_color_manual(values = wesanderson::wes_palette("Darjeeling",3)) 

Fine tuning: Multiple plots

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

grid.arrange(basic.plot, CO2.plot)

Fine tuning: Multiple plots

class: small-code

Sub-plots can be aligned and matched in size

basic.plot.table <- ggplot_gtable(ggplot_build(basic.plot))
CO2.plot.table <- ggplot_gtable(ggplot_build(CO2.plot))
maxWidth = grid::unit.pmax(basic.plot.table$widths[2:3],
                           CO2.plot.table$widths[2:3])
basic.plot.table$widths[2:3] <- as.list(maxWidth)
CO2.plot.table$widths[2:3] <- as.list(maxWidth)

Fine tuning: Multiple plots

Sub-plots can be aligned and matched in size

grid.arrange(basic.plot.table, 
             CO2.plot.table,
             ncol=1)

Fine tuning: Themes

theme_set(theme())
or
plot+theme()

dark <- basic.plot+theme_dark()
minimal <- basic.plot+theme_minimal()

grid.arrange(basic.plot, dark, minimal, nrow=1)

Fine tuning: Themes

class: small-code

mytheme <- theme_grey() +
 theme(plot.title = element_text(colour = "red"))
mytheme_plot <- basic.plot + mytheme

grid.arrange(basic.plot, mytheme_plot, nrow=1)

Bonus: xkcd

<iframe src="http://xkcd.com" width="1000" height="800">

Your browser does not support iframes.

</iframe>

Bonus: xkcd

#install.packages("xkcd")
#install xkcd font: "http://simonsoftware.se/other/xkcd.ttf"
#import font : font_import(pattern = "[X/x]kcd", prompt=FALSE)
#loadfonts()
require(xkcd)
require(extrafont)

xrange <- range(iris$Sepal.Length)
yrange <- range(iris$Sepal.Width)

print(basic.plot+
  xkcdaxis(xrange,yrange)+
    theme(text=element_text(family = "xkcd")))

Bonus: xkcd

Challenge

Using figure from previous challenge (or other dryad.org paper/data), edit figure to match a journal's style requirements

Example: try to reproduce Figure 3 in: Lucek K, Sivasundar A, Roy D, Seehausen O (2013) Repeated and predictable patterns of ecotypic differentiation during a biological invasion: lake-stream divergence in parapatric Swiss stickleback. Journal of Evolutionary Biology 26(12): 2691–2709.

paper
data