• Why now?
• Why R?
• General tips
• Recommended packages
• Recommended resources

• Point-and-click software just isn't time efficient
• Automating tasks will pay off within the time frame of a PhD and thereafter

• There is an increasing expectation that materials, data, and analysis details are provided alongside research to ensure it is reproducible
  • This is easier when things are script based
• Peer Reviewers' Openness Initiative

• R is increasingly taught in Psychology departments, including at undergraduate level
• Useful skill for jobs outside academia
• Makes you a more efficient academic

• There are R packages for a huge range of analyses
• Great data manipulation packages
• Slides
• Documents
  • Including books
• Interactive HTML applications

• R projects
• R Markdown

• It's free
• Big community
• R has the happiest commenters

• Given the age of R there are many ways to complete a task
• Most data manipulation tasks can be done with 'base R'
  • However, this often isn't the most efficient or readable approach

• A collection of packages by Hadley Wickham for:
  • Data visualisation (ggplot2)
  • Data manipulation (dplyr)
  • Data tidying (tidyr)
  • Importing data (readr)
  • Functional programming (purrr)
    • See here for a full list of the included packages
• These packages are all designed to work nicely together
• More readable by people than most R code

• To install and load any package you just do:

install.packages("tidyverse")
library(tidyverse)

• You need to load a package in with library() for any new R session you want to use it with
• Loading tidyverse loads all the packages described previously

• The pipe operator is key to why the tidyverse packages are so usable and readable
• It passed the thing on its left as the first argument to a function on its right
  • x %>% f() is equivalent to f(x)

x <- c(10, 5, 15)

mean_x <- x %>% mean()

• This is amazing for chaining together the various steps some data goes through without needing to create intermediary objects
• Doesn't work so smoothly with some packages

• Build graphs by specifying:
  • Aesthetics: physical properties of the plot mapped to variables in the data (x & y positions, size, shape, colour etc.)
  • Geometries: what to actually use to represent the data (lines, bars, points etc.)

qplot(x = df$x, y = df$y)

qplot(df$x, df$y) + 
  geom_smooth(method = "lm")

ggplot(data = df, mapping = aes(x = x, y = y)) +
  geom_point() +
  geom_smooth(method = "lm")

ggplot(data = df, mapping = aes(x = x, y = y, colour = treatment)) +
  geom_point() +
  geom_smooth(method = "lm")

• The package ggthemes is good for providing premade plot 'styles'
• RColorBrewer is useful for colours
  • Useful info on colour in ggplot2 here
• cowplot is good for creating grids of labelled plots for papers
  • cowplot vignette

ggplot(df, aes(x, y, colour = treatment)) +
  geom_point() +
  geom_smooth(method = "lm") +
  theme_few() +
  scale_color_brewer(palette = "Set1")