slides.utf8

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# <div class="line-block">Getting Started in R<br />
an introduction to data analysis and visualisation</div>
## Basics
### Réka Solymosi & Sam Langton
### 2 July 2019

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# R

---

### What is R?

- [R](https://www.r-project.org/) 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](https://bbc.github.io/rcookbook/#how_to_create_bbc_style_graphics) 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

</div>

---

### 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.

--
<br>
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.

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# Example outputs using R

---

### Charts

```r
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

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

<div id="htmlwidget-345a03930e5efc5e4140" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-345a03930e5efc5e4140">{"x":{"filter":"none","fillContainer":false,"data":[["Alabama","Alaska","Arizona","Arkansas","California","Colorado","Connecticut","Delaware","Florida","Georgia"],[13.2,10,8.1,8.8,9,7.9,3.3,5.9,15.4,17.4],[236,263,294,190,276,204,110,238,335,211],[58,48,80,50,91,78,77,72,80,60],[21.2,44.5,31,19.5,40.6,38.7,11.1,15.8,31.9,25.8]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Murder<\/th>\n      <th>Assault<\/th>\n      <th>UrbanPop<\/th>\n      <th>Rape<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

---

### Maps

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

<div id="htmlwidget-838ddf4ff72f82d176ff" style="width:100%;height:360px;" class="leaflet html-widget"></div>
<script type="application/json" data-for="htmlwidget-838ddf4ff72f82d176ff">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]},{"method":"addMarkers","args":[53.466478,-2.236518,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"University of Manchester",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[53.466478,53.466478],"lng":[-2.236518,-2.236518]}},"evals":[],"jsHooks":[]}</script>

---
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# R and RStudio

---
### R and RStudio
It is possible to just use [R](http://www.r-project.org) but there are several reasons why you will prefer to code within the [RStudio](https://www.rstudio.com/products/rstudio/download3/) environment:

- syntax highlighting
- code completion
- [rmarkdown](http://rmarkdown.rstudio.com) integration
- four-pane workspace for managing R windows

---

### RStudio's panes

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# Setting up

---
### 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.

```r
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:

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

On a computer running Windows it would look more like:

```r
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](https://cran.r-project.org/web/packages/available_packages_by_name.html) available to install from CRAN.

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

```r
install.packages("tidyverse")
```

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

```r
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:

```r
?getwd
```

You can also use the `help()` function:

```r
help(getwd)
```

---
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# R basics

---
### Arithmetic
R provides arithmetic operators operators such as

```r
2 + 2 # addition
```

```
## [1] 4
```

```r
9 - 7 # subtraction
```

```
## [1] 2
```

```r
8 * 12 # multiplication
```

```
## [1] 96
```

```r
9 / 3 # division
```

```
## [1] 3
```

```r
10 ^ 2 # exponentiation
```

```
## [1] 100
```

---
### Objects and functions

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

```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.

```r
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.

```r
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.

```r
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,

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

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

whereas the values in 'million_plus' are logical.

```r
# 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).

```r
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
```

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