ggplot2

To view presentations:
search: low-decarie wrangling github

http://low-decarie.github.io/Data_wrangling_and_plotting/#/

To view code that generated presentations:
https://github.com/low-decarie/Data_wrangling_and_plotting
You can look at the .Rpres files that generated these presentations
You can run the code in these presentation (even all of it, using Chunks>Run All)

There is a folder called ./Data/ that contains data relavant to some of the exercises

• 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

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

?qplot

Arguments

x
y
…
data
xlab
ylab
main

produce a basic plot with built in data

CO2
?CO2
BOD
data()

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

• 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

• 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)

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

• 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)

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=“”

produce a colorful plot containing linear regressions with built in data

CO2
?CO2
msleep
?msleep
OrchardSprays
data()

http://docs.ggplot2.org

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

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

msleep
?msleep
OrchardSprays
data()

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

ggsave: saves last plot and guesses format from file name

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

base R plot function has methods for many different object types

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

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!)

ggplot can be extended for plotting specific classes of objects

autoplot and fortify

if(!require(devtools)) {install.packages("devtools")}
require(devtools)
if(!require(ggvegan)) {install_github("gavinsimpson/ggvegan")}
require(ggvegan)
data(dune)
data(dune.env)

Bonus!!! Wes Anderson colour palette

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

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