cdrc

This course is brought to you the Consumer Data Research Centre (CDRC) and is funded by the ESRC's (Big Data Network).

See pct.bike

• Developed by statisticians Ross Ihaka and Robert Gentleman
• De-facto standard for advanced statistical analysis
• A programming language in its own right
• The power of the command line
• Used by an increasing number of organisations

• Performace: stable, light and fast
• Support network
• documentation, community, developers
• Reproducibility
• anyone anywhere can reproduce results
• enables dissemination (RPubs, RMarkdown, .RPres) - this presentation is a .Rmd file!
• Versatility: unified solution to almost any numerical problem, graphical capabilities
• Ethics removes economic barrier to statistics, is open and democratic

scholar-searches1

Source: r4stats.com

scholar-searches2

Source: r4stats.com

Source: Data Camp

jobs

Source: revolution analytics

• R's visualisation capabilities have evolved over time
• Used to create plots in the best academic journals
• ggplot2 has revolutionised the visualisation of quantitative information in R, and (possibly) overall
• Thus there are different camps with different preferences when it comes to maps in R

## Loading required package: devtools

## Loading required package: nclRintroduction

5 * 5

1 + 4 * 5

4 * 5 ^ 2

sin(90)

sin(0.5 * pi)

a <- 1
b <- 2
c <- "c"
x_thingy <- 4

a + b
a * b
a + c
a / x_thingy

ls()

## [1] "a"        "b"        "c"        "x_thingy"

x <- x_thingy
rm(x_thingy)
x

## [1] 4

ls()

## [1] "a" "b" "c" "x"

• Open RStudio and have a look around
• Create a new project
• Create a new R Script: pass code to the console with Ctl-Enter
• Use R as a calculator: what is:

\[ \pi * 9.15^2 \]

• Explore each of the 'panes'
• Find and write down some useful shortcuts (Alt-Shift-K on Windows/Linux)

In R:

• Everything that exists is an object
• Everything that happens is a function

# Assignment of x
x <- 5
x

## [1] 5

# A trick to print x
(x <- 5)

(y <- x)

sin(x)

## [1] -0.9589243

exp(x)

## [1] 148.4132

factorial(x)

## [1] 120

sinx <- sin(x)

x = 5 # the same as x <- 5
(x = x + 1)

## [1] 6

x <- c(1, 2, 5)
x

## [1] 1 2 5

x^2

## [1]  1  4 25

x + 2

## [1] 3 4 7

x + rev(x)

x <- c(1, 2, 5)
for(i in x){
  print(i^2)
}

## [1] 1
## [1] 4
## [1] 25

Creating a new vector based on x

for(i in 1:length(x)){
  if(i == 1) x2 <- x[i]^2
  else x2 <- c(x2, x[i]^2)
}
x2

## [1]  1  4 25

R has a hierarchy of data classes, tending to the lowest:

• Binary
• Integer (numeric)
• Double (numeric)
• Character

a <- TRUE
b <- 1:5
c <- pi
d <- "Hello Leeds"

class(a)
class(b)
class(c)
class(d)

ab <- c(a, b)
ab

## [1] 1 1 2 3 4 5

class(ab)

## [1] "integer"

class(c(a, b))

## [1] "integer"

class(c(a, c))

## [1] "numeric"

class(c(b, d))

## [1] "character"

x <- 1:5
y <- 2:6
plot(x, y)

x <- seq(1,2, by = 0.2)
length(x)

## [1] 6

x <- seq(1, 2, length.out = 5)
length(x)

## [1] 5

• Work through Chapter 2 in Colin Gillespie's Introduction to R handout

• Optional: install the R package

install.packages("drat")
drat::addRepo("rcourses")
install.packages("nclRintroduction", type="source")
library(nclRintroduction)
vignette(package = "nclRintroduction", "practical1")

• There are dozens of data formats
• Some data formats require additional packages
• Text encoding can cause additional problems

• Download data from http://www.mas.ncl.ac.uk/~ncsg3/Rcourse/movie.txt
• Open the file with Notepad++
• Read it in with read.csv(ineq.csv).

# Download from the internet:
# url <- "http://www.mas.ncl.ac.uk/~ncsg3/Rcourse/movie.txt"
# download.file(url, "movie.txt")
movie <- read.csv("../movie.txt", header=TRUE, stringsAsFactors=FALSE)

• What is the size of the data saved as a .csv?
• How big is the data save as .Rds?
• What about if you save it as .xlsx?

• Identify a medium/large dataset of interest
• Read it into R
• If you finish early: go back to the subsetting stage
• If you finish that: find more datasets!
• Try saving the data with different file formats

• It's good to start small before going BIG
• A quick exploration with the diamonds dataset:

library(ggplot2)
diamonds[1:3,]

##   carat     cut color clarity depth table price    x    y    z
## 1  0.23   Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21 Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23    Good     E     VS1  56.9    65   327 4.05 4.07 2.31

The base R way

summary(diamonds$color)

##     D     E     F     G     H     I     J 
##  6775  9797  9542 11292  8304  5422  2808

head(diamonds[diamonds$color == "I",])

##    carat       cut color clarity depth table price    x    y    z
## 4   0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 7   0.24 Very Good     I    VVS1  62.3    57   336 3.95 3.98 2.47
## 17  0.30     Ideal     I     SI2  62.0    54   348 4.31 4.34 2.68
## 21  0.30      Good     I     SI2  63.3    56   351 4.26 4.30 2.71
## 27  0.24   Premium     I     VS1  62.5    57   355 3.97 3.94 2.47
## 40  0.33     Ideal     I     SI2  61.8    55   403 4.49 4.51 2.78

The dplyr way

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

filter(diamonds, color == "I")

## Source: local data frame [5,422 x 10]
## 
##    carat       cut  color clarity depth table price     x     y     z
##    (dbl)    (fctr) (fctr)  (fctr) (dbl) (dbl) (int) (dbl) (dbl) (dbl)
## 1   0.29   Premium      I     VS2  62.4    58   334  4.20  4.23  2.63
## 2   0.24 Very Good      I    VVS1  62.3    57   336  3.95  3.98  2.47
## 3   0.30     Ideal      I     SI2  62.0    54   348  4.31  4.34  2.68
## 4   0.30      Good      I     SI2  63.3    56   351  4.26  4.30  2.71
## 5   0.24   Premium      I     VS1  62.5    57   355  3.97  3.94  2.47
## 6   0.33     Ideal      I     SI2  61.8    55   403  4.49  4.51  2.78
## 7   0.33     Ideal      I     SI2  61.2    56   403  4.49  4.50  2.75
## 8   0.32     Ideal      I     SI1  60.9    55   404  4.45  4.48  2.72
## 9   0.30     Ideal      I     SI2  61.0    59   405  4.30  4.33  2.63
## 10  0.30 Very Good      I     SI1  62.6    57   405  4.25  4.28  2.67
## ..   ...       ...    ...     ...   ...   ...   ...   ...   ...   ...

sum(diamonds$color == "I" | diamonds$color == "J")

## [1] 8230

dfs <- filter(diamonds, color == "I" | color == "J")
nrow(dfs)

## [1] 8230

arrange(diamonds, desc(z))

## Source: local data frame [53,940 x 10]
## 
##    carat       cut  color clarity depth table price     x     y     z
##    (dbl)    (fctr) (fctr)  (fctr) (dbl) (dbl) (int) (dbl) (dbl) (dbl)
## 1   0.51 Very Good      E     VS1  61.8  54.7  1970  5.12  5.15 31.80
## 2   2.00   Premium      H     SI2  58.9  57.0 12210  8.09 58.90  8.06
## 3   5.01      Fair      J      I1  65.5  59.0 18018 10.74 10.54  6.98
## 4   4.50      Fair      J      I1  65.8  58.0 18531 10.23 10.16  6.72
## 5   4.13      Fair      H      I1  64.8  61.0 17329 10.00  9.85  6.43
## 6   3.65      Fair      H      I1  67.1  53.0 11668  9.53  9.48  6.38
## 7   4.00 Very Good      I      I1  63.3  58.0 15984 10.01  9.94  6.31
## 8   3.40      Fair      D      I1  66.8  52.0 15964  9.42  9.34  6.27
## 9   4.01   Premium      J      I1  62.5  62.0 15223 10.02  9.94  6.24
## 10  4.01   Premium      I      I1  61.0  61.0 15223 10.14 10.10  6.17
## ..   ...       ...    ...     ...   ...   ...   ...   ...   ...   ...

Work through the worked example here:

http://r4ds.had.co.nz/transform.html

Your feedback is greatly appreciated.

Please complete the online form here:

https://leeds.onlinesurveys.ac.uk/rstudiojan16