Introduction to R and RStudio

R Foundations 2024

Ella Kaye, Department of Statistics

October 21, 2024

Overview

  • Why use R?

  • Use RStudio to write and run R programmes

  • Create and start an R project

  • Use install.packages() to install packages

  • How to get help in R

  • See examples of data wrangling and visualisation

Why use R?

What can R do?

  • Data import

  • Data management and wrangling

  • Exploratory data analysis

  • Statistical modelling

  • Advanced statistics

  • Machine learning

  • Data visualisation

  • Reports, articles

  • Dashboards, web apps

  • Integrates well with other languages

  • Packages: share your code and use others

The R Ecosystem

Base R

15 base packages

  • Create R objects
  • Summaries
  • Maths functions
  • Statistics
  • Graphics
  • Datasets
  • Statistics methodology
  • More maths
  • More graphics

The R Ecosystem

Contributed packages

CRAN

Bioconductor

GitHub

  • Packages in development
  • GitHub-only packages

The tidyverse

The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar and data structures.

From https://education.rstudio.com/blog/2020/07/teaching-the-tidyverse-in-2020-part-1-getting-started/

tidyverse 2.0.0. includes lubridate for date-times as well (not shown in image).

palmerpenguins

  • palmerpenguins is a dataset package, designed to be a great example for data exploration and visualisation.

  • It contains measurement data for 344 penguins, from three different species, collected from three islands in the Palmer Archipelago, Antarctica.

Penguin artwork by Allison Horst https://allisonhorst.github.io/palmerpenguins/articles/art.html

The R community

Another reason to love R is the community around it.

It prides itself on being friendly, diverse, helpful, and supportive.

Introducing RStudio

At first

With script

RStudio cheatsheet

Best practice: use R projects

An RStudio project is a contect for working on a specific project

  • Keeps files well-organised

  • Automatically sets working directory to project root

  • Has separate workspace and command history

  • Works well with version control via git or svn

Getting started with projects

  • Create a project from a new or existing directory via the file menu or new project button

  • Switch project, or open a different project in a new RStudio instance via the project menu

RStudio project demo

  • Create R-Foundations project

  • Create first script

Using the console

  • For things that only need doing once, e.g. installing packages

  • For doing things you don’t need to track, e.g. requesting help files

  • To quickly explore new ideas before adding them to a script

Using the console: shortcuts

RStudio provides a few shortcuts to help write code in the R console

  • ↑/↓ go back/forward through history one command at a time
  • Ctrl/⌘ + ↑ review recent history and select command
  • Tab view possible completions for part-written expression

Code completion (using Tab) is also provided in the source editor

Using the console: demo

1 + 1
?log
log(10)
exp(-4 * 4 / 2) / sqrt(2 * pi)
install.packages("tidyverse")
install.packages("palmerpenguins")

Using scripts

Text files saved with an .R suffix are recognised as R code

Code can be sent directly from a script to the console as follows:

  • Ctrl/⌘ + ↵ or Run button run current line
    • Run multiple lines by selecting first
  • Ctrl/⌘ + Shift + ↵ or Source button
    • Run the script from start to finish.

Why R scripts?

Writing an R script for an analysis has several advantages over a graphical user interface (GUI)

  • It provides a record of the exact approach used in an analysis
  • It enables the analysis to be easily reproduced and modified
  • It allows greater control

Good practice for R Scripts

Organising your R script well will help you and others understand and use it.

  • Add comment or two at start to describe purpose of script
    • Use one or more # to start a comment
  • Load required data and packages at the start
  • Avoid hard-coding: define variables such as file paths early on
  • Give functions and variable meaningful names
  • use ### or #--- to separate sections (in RStudio Code > Insert Section)

Installing packages

In console

  • Install a package with install.packages("package_name")

    • Watch out for the plural!

  • Install multiple packages with install.packages(c("package1", "package2"))

    • The c() function creates a vector

  • Or use install button in packages pane:

Loading packages

In script

  • Load packages with library(package_name)

R script demo

### load packages
library(palmerpenguins)
library(tidyverse)

### Inspect data
View(penguins)
glimpse(penguins)
head(penguins)
summary(penguins)

Getting help in R

Within R: Help with functions

# help with a specific function
help(function_name)
?function_name

# quick reminder of the function arguments
arg(function_name)

# see an example
example(function_name)

# see the source code
## in console
function_name
## in View pane (easier to read, syntax highlighting)
View(function_name)

Within R: help.search

# when ?function_name fails (e.g. don't have package loaded)
help.search("function_name") # note quotation marks
??function_name

# for when you can't quite remember the function name
??something_like_function_name

# R help start page
help.start()
## note that using the RStudio Help home button gives even more resources

Within R: Help with package

# `help`
help(package = "package_name")
# Help panel in RStudio will give links to all documentation
# and help pages for that package

# find/browse vignettes for installed (or specific) packages
browseVignettes()
browseVignettes("package_name")

# use auto-completion in RStudio to see what functions 
# are in a package
?package::

dplyr demo

RStudio Help home demo

Your turn

  • Create an R project called “R-foundations”

  • Install the packages palmerpenguins and tidyverse

  • Find the help page for the penguins dataset

  • Find the help page for the filter function in the dplyr package

  • Experiment typing commands into the console or in an R script.

link to slides

Getting help at Warwick

What can we learn about penguins?

The data

library(palmerpenguins)
library(tidyverse)
penguins
# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

How many of each species?

count(penguins, species)
# A tibble: 3 × 2
  species       n
  <fct>     <int>
1 Adelie      152
2 Chinstrap    68
3 Gentoo      124

How many of each species on each island?

count(penguins, species, island, .drop = FALSE)
# A tibble: 9 × 3
  species   island        n
  <fct>     <fct>     <int>
1 Adelie    Biscoe       44
2 Adelie    Dream        56
3 Adelie    Torgersen    52
4 Chinstrap Biscoe        0
5 Chinstrap Dream        68
6 Chinstrap Torgersen     0
7 Gentoo    Biscoe      124
8 Gentoo    Dream         0
9 Gentoo    Torgersen     0

Show me the bill dimensions of the 5 heaviest female Gentoo penguins

penguins |>
  filter(sex == "female",
         species == "Gentoo") |>
  slice_max(body_mass_g, n = 5) |>
  select(contains("bill"))
# A tibble: 5 × 2
  bill_length_mm bill_depth_mm
           <dbl>         <dbl>
1           46.5          14.8
2           45.2          14.8
3           49.1          14.8
4           44.9          13.3
5           45.1          14.5

The native pipe

|> is a pipe.

It passes what comes before into the first argument of what comes after.

Pipes are a powerful tool for clearly expressing a sequence of multiple operations.

We’ll talk more about pipes in the data wrangling session.

What’s the mean bill length, by species?

penguins |>
  summarise(mean_bill_length = mean(bill_length_mm, na.rm = TRUE),
            .by = species)
# A tibble: 3 × 2
  species   mean_bill_length
  <fct>                <dbl>
1 Adelie                38.8
2 Gentoo                47.5
3 Chinstrap             48.8

What’s the relationship between bill length and depth?

ggplot(data = penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "gray50") +
  labs(title = "Penguin bill dimensions",
       subtitle = "Bill length and depth for Penguins at Palmer Station LTER",
       x = "Bill length (mm)",
       y = "Bill depth (mm)") +
  theme_minimal() + 
  theme(plot.title.position = "plot")

ggplot(data = penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           group = species)) +
  geom_point(aes(color = species, 
                 shape = species),
             size = 3,
             alpha = 0.8) +
  geom_smooth(method = "lm", se = FALSE, aes(color = species)) +
  scale_color_manual(values = c("darkorange","purple","cyan4")) +
  labs(title = "Penguin bill dimensions",
       subtitle = "Bill length and depth for Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER",
       x = "Bill length (mm)",
       y = "Bill depth (mm)",
       color = "Penguin species",
       shape = "Penguin species") +
  theme_minimal() +
  theme(plot.title.position = "plot",
        plot.subtitle = element_text(size = rel(0.95)))

This is an illustration of Simpson’s Paradox.

Simpson’s paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.

Example adapted from https://allisonhorst.github.io/palmerpenguins/articles/examples.html

End matter

Resources

Material inspired by and remixed from:

License

Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).