What is R?

• R is a programming language for exploring, visualising and analysing data.

• Created by Ross Ihaka and Robert Gentleman (University of Auckland)

• Released in 1995

• Implements the S programming language created at Bell Labs

• Companies like Google, Facebook and the Financial Times use it. The BBC have also recently made a big push to use it for their visualisations.

Why use R?

• Cutting edge analytics: over 10,000 user-contributed packages available on finance, genomics, animal tracking, crime analysis, and much more

• Powerful graphics and data visualisations: used by the New York Times and FiveThirtyEight

• Open source: free and customisable

• Reproducibility: code can be shared and the results repeated

• Transparency: explicitly documents all the steps of your analyses

• Automation: analyses can be run and re-run with new and existing datasets

• Support network: worldwide community of developers and users

Why wouldn't you use it?

• Anxiety about 'hard' skills
• "I am just not good at that
  kind of thing"
• "I have to be good at maths"
• Experts putting you off

Are there any disadvantages?

Learning R can be a steep learning curve and the transition from a graphical user interface like Excel or SPSS to one that is command driven can be unsettling.

However, you’ll soon find that working with a command line is much more efficient than pointing and clicking. After all, you can replicate, automate and share your R scripts.

Charts

library(ggplot2) 
p <- ggplot(mpg, aes(x = displ, y = hwy)) +  geom_point() +
  geom_smooth(method = "loess", se = FALSE) +
  labs(x = "Engine size", y = "Miles per gallon", 
       title = "Fuel efficiency decreases with engine size") +
  theme_minimal()
p

Tables

library(DT)
DT::datatable(
  head(USArrests, 10),
  fillContainer = FALSE, options = list(pageLength = 5)
)

Maps

library(leaflet)
leaflet() %>% addTiles() %>% 
  addMarkers(-2.236518, 53.466478, 
             popup = "University of Manchester")

R and RStudio

It is possible to just use R but there are several reasons why you will prefer to code within the RStudio environment:

• syntax highlighting
• code completion
• rmarkdown integration
• four-pane workspace for managing R windows

RStudio's panes

The working directory

The working directory is where everything created in your R session is stored. The function getwd() returns the filepath to the current working directory.

getwd()

To change the working directory you have two options:

1. Use the dropdown menus: Session -> Set Working Directory -> Choose Directory
2. Use the command setwd() in the console

For example, to change the working directory on a computer running macOS you might enter:

setwd("/Users/your_name/Documents/GSinR")

On a computer running Windows it would look more like:

setwd("C:/Users/your_name/Documents/GSinR")

Installing and loading packages

Packages are collections of R functions and data. There are over 10,000 user-contributed packages available to install from CRAN.

Just type install.packages() in the console with the name of the package in inverted commas:

install.packages("tidyverse")

Once installed you can load the package using the library() function like this:

library(tidyverse)

Finding help

You can find help within R using the ? function. For example, if you want to learn more about a function just place a ? before the function name.

For example, if you wanted to read up on the getwd() function you'd enter:

?getwd

You can also use the help() function:

help(getwd)

Arithmetic

R provides arithmetic operators operators such as

2 + 2 # addition

## [1] 4

9 - 7 # subtraction

## [1] 2

8 * 12 # multiplication

## [1] 96

9 / 3 # division

## [1] 3

10 ^ 2 # exponentiation

## [1] 100

Objects and functions

Objects are information (e.g. values, model coefficients, plots etc) stored in R.

x <- c(11, 19, 13, 16, 12, 12, 18, 14, 20, NA)
x

##  [1] 11 19 13 16 12 12 18 14 20 NA

Functions are collections of R commands that do a specific task. Many functions have arguments which provide extra information to the function.

mean(x, na.rm = TRUE)

## [1] 15

Creating variables

The assignment operator (<-) is used to assign the value of an expression to a variable. The object is being 'given' the whatever the value is.

For example, the object 'population' is created by using the c() or concatenate function to combine values.

pop <- c(9896000, 2488200, 2434100, 980800, 845000)
pop

## [1] 9896000 2488200 2434100  980800  845000

Data Types

The object 'pop' is a vector, a one-dimensional array with each element separated by a comma. Each element in the vector is a numeric value.

class(pop)

## [1] "numeric"

R support other data types: integer, character, logical, factor, and complex.

Each element in the vector 'city' is a string of one or more characters,

# character
city <- c("London", "Birmingham", "Manchester", "Glasgow", "Newcastle")
city

## [1] "London"     "Birmingham" "Manchester" "Glasgow"    "Newcastle"

whereas the values in 'million_plus' are logical.

# logical
million_plus <- c(TRUE, TRUE, TRUE, FALSE, FALSE)
million_plus

## [1]  TRUE  TRUE  TRUE FALSE FALSE

Creating data frames

Like vectors, data frames are another data structure used in R. Similar to spreadsheets a data frame stores variables in columns and observations in rows. Unlinke matrices, data frames can store variables of different types (e.g. numeric, character).

city_pop <- data.frame(city, population = pop, million_plus)
city_pop

##         city population million_plus
## 1     London    9896000         TRUE
## 2 Birmingham    2488200         TRUE
## 3 Manchester    2434100         TRUE
## 4    Glasgow     980800        FALSE
## 5  Newcastle     845000        FALSE

Data source: http://www.centreforcities.org/data-tool/