Getting Started With R

Installing `R` and RStudio

If you have not installed R and RStudio, refer to the study guide for some instructions.

To start this workshop download the source from the class website. You can now save this to your computer and make notes in this document.

Some `R` basics

Working directory

In RStudio, set the working directory under the Session menu. It is a good idea to start your analysis as a new project in the File menu so that the entire work and data files can be saved and re-opened easily later on.

Code

getwd() # check your working directory

[1] "/Users/mmarraff/Documents/Classes/161.250/Data_Analysis_Course/workshops"

Code

# setwd() # if you know the directory you want R to look in

R/RStudio as a calculator

In RStudio, use the File >> New File >> R Script menu to type or copy and paste the commands and execute them

Type 1+1 to see 2 on the console (or ->Run the code in RStudio).

Code

1+1

[1] 2

Type a=1;b=2;a/b to see 0.5.

Code

a=1;b=2;a/b

[1] 0.5

Note that semicolon separates various commands. It is optional to use them as long as you type the commands one by one as follows:

Code

a=1
b=2
a/b

[1] 0.5

Exercise 1.1

There are many built-in functions. Try the following.

27^3 sqrt(10) round(sqrt(10),2) abs(-4) log(10) exp(10) rnorm(100) mean(rnorm(100)) sd(rnorm(100))

Code

# your code goes here
# make yourself notes about what each of these functions does

You may wonder what was the base used for log(10). A help on this can be obtained by placing a question mark (?) before log as ?log or by help(log)

There are a few exceptions. The command ?if wont work but ?"if" will. In other words, ?"log" or help("log") are safer ways of getting help on “built-in” functions.

In RStudio, use the R Editor (menu File > New Script) to type the commands and submit them (shortcut: CNTRL+R).`

Exercise 1.2

Why does this code not work?

Code

my_variable <- 10
my_varıable

Exercise 1.3

Make a variable with a sequence of numbers between 1 and 10. Look up the function seq() in the help menu for tips.

Code

# your code goes here

Default examples

The command example() will produce the available HELP examples, and will work for most functions. For example, try example(boxplot). You will see many boxplot examples such as the following:

Code

boxplot(count ~ spray, data = InsectSprays, col = "lightgray")

There are also demos available, explore using the command demo(). The basic R system produces somewhat old style graphs.

So we will be largely using the newer plotting system ggplot which is part of the tidyverse suite of packages; see https://www.tidyverse.org/.

Let’s load that package now:

Code

# install.packages('tidyverse') # if you do not have tidyverse you will need to install it first
# you only have to install packages once per machine (computer)
library(tidyverse) # you need to load a library every R session

A huge number of other dedicated packages are available to improve the power of R. Many R packages are hosted at a repository called CRAN (Comprehensive R Archive Network). The package install option within RStudio can download and install these optional packages under the menu Packages >> Install. You can also do this using the command install.packages. For example

install.packages(c("tidyverse", "car"), dependencies = TRUE)

This command installs two packages tidyverse and car in one go.

Contributed R packages are grouped in various headings at https://cran.r-project.org/web/views/. They can be installed in bulk using the ctv package command install.views().

You might have to install quite a few packages as you work through this course.

Loading/Importing Data

If the data file is stored locally, you should put the data into the same directory as your Quarto or R markdown script. That way, you can (usually) load it easily without having to type the full pathway (e.g., mydata.csv rather than C:/Users/anhsmith/Work/Project1/data/mydata.csv). Better yet, Projects make this much easier.

You can also load data from the web using a URL. For example,

Code

read_csv("https://www.massey.ac.nz/~anhsmith/data/rangitikei.csv")

Rows: 33 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (10): id, loc, time, w.e, cl, wind, temp, river, people, vehicle

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# A tibble: 33 × 10
      id   loc  time   w.e    cl  wind  temp river people vehicle
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>
 1     1     1     2     1     1     2     2     1     37      15
 2     2     1     1     1     1     2     1     2     23       6
 3     3     1     2     1     1     2     2     3     87      31
 4     4     2     2     1     1     2     1     1     86      27
 5     5     2     1     1     1     2     2     2     19       2
 6     6     2     2     1     2     1     3     3    136      23
 7     7     1     2     2     2     2     2     3     14       8
 8     8     1     2     1     2     2     2     3     67      26
 9     9     1     1     2     1     3     1     2      4       3
10    10     2     2     1     2     2     2     3    127      45
# ℹ 23 more rows

We’d usually want to store the data as an object though, like so:

Code

rangitikei <- read_csv("https://www.massey.ac.nz/~anhsmith/data/rangitikei.csv")

Rows: 33 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (10): id, loc, time, w.e, cl, wind, temp, river, people, vehicle

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Now the data are available in R as an object.

Code

glimpse(rangitikei)

Rows: 33
Columns: 10
$ id      <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,…
$ loc     <dbl> 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1,…
$ time    <dbl> 2, 1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2,…
$ w.e     <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2,…
$ cl      <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2,…
$ wind    <dbl> 2, 2, 2, 2, 2, 1, 2, 2, 3, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 3, 2,…
$ temp    <dbl> 2, 1, 2, 1, 2, 3, 2, 2, 1, 2, 2, 3, 1, 2, 2, 2, 3, 2, 2, 2, 2,…
$ river   <dbl> 1, 2, 3, 1, 2, 3, 3, 3, 2, 3, 1, 3, 1, 3, 1, 1, 2, 1, 2, 2, 3,…
$ people  <dbl> 37, 23, 87, 86, 19, 136, 14, 67, 4, 127, 43, 190, 50, 47, 32, …
$ vehicle <dbl> 15, 6, 31, 27, 2, 23, 8, 26, 3, 45, 7, 53, 22, 18, 10, 3, 11, …

Exercise 1.4

Try importing the Telomeres data file (in Excel format) available at

https://rs.figshare.com/ndownloader/files/22850096

Code

# your code goes here

Note that Excel files usually contain blanks for missing or unreported data or allocate many rows for variable description, which can cause issues while importing them.

Consider the study guide dataset rangitikei.txt (Recreational Use of the Rangitikei river) again. The first 10 rows of this dataset are shown below:

   id loc time w.e cl wind temp river people vehicle
1   1   1    2   1  1    2    2     1     37      15
2   2   1    1   1  1    2    1     2     23       6
3   3   1    2   1  1    2    2     3     87      31
4   4   2    2   1  1    2    1     1     86      27
5   5   2    1   1  1    2    2     2     19       2
6   6   2    2   1  2    1    3     3    136      23
7   7   1    2   2  2    2    2     3     14       8
8   8   1    2   1  2    2    2     3     67      26
9   9   1    1   2  1    3    1     2      4       3
10 10   2    2   1  2    2    2     3    127      45

The description of the variables is given below:

loc - two locations were surveyed, coded 1, 2
time - time of day, 1 for morning, 2 for afternoon
w.e - coded 1 for weekend, 2 for weekday
cl- cloud cover, 1 for >50%, 2 for <50%
wind- coded 1 through 4 for increasing wind speed
temp - temperature, 1, 2 or 3 increasing temp
river- murkiness of river in 3 increasing categories
people - number of people at that location and time
vehicle- number of vehicles at that location at that time

Exercise 1.5

How many rows are in my.data? How many columns? How can you find that in R?

Exercise 1.6

Find the mean of vehicle and the median of people using built in R functions.

Graphing with ggplot2

If have not installed ggplot2 or tidyverse, install it with the following commands.

Code

install.packages("ggplot2")
install.packages("tidyverse")

We can now load the ggplot2 library with the commands:

Code

library(ggplot2)

In order to work with ggplot2, we must have a data frame or a tibble containing our data. We need to specify the aesthetics or how the columns of our data frame can be translated into positions, colours, sizes, and shapes of graphical elements.

Code

ggplot(my.data) +
  aes(x = vehicle, y = people) +
  geom_point()

The aes part defines the “aesthetics”, which is how columns of the dataframe map to graphical attributes such as x and y position, colour, size, etc. An aesthetic can be either numeric or categorical and an appropriate scale will be used. After this, we add layers of graphics. geom_point layer is employed to map x and y and we need not specify all the options for geom_point.

Exercise 1.6

Why does the following give an error and how would you fix it?

Code

ggplot(data = my.data) + 
  geom_point()

We can add a title using labs() or ggtitle() functions. Try-

Code

ggplot(my.data) +
  aes(x = vehicle, y = people, color = river) +
  geom_point() + 
  ggtitle("No. of people vs No. of vehicles")

Code

ggplot(my.data)+
  aes(x = vehicle, y = people) +
  geom_point() + 
  labs(title = "No. of people vs No. of vehicles")

Note that labs() allows captions and subtitles.

Exercise 1.7

Remake the above graph, adjusting the x axis to say Vehicles using the labs() function.

geom_smooth is additionally used to show trends.

Code

ggplot(my.data) +
  aes(x = vehicle, y = people) +
  geom_point() + 
  geom_smooth()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Exercise 1.8

Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

Code

ggplot(my.data) +
  aes(x = vehicle, y = people) +
  geom_point()+
  geom_smooth(se = FALSE)

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Similar to geom_smooth, a variety of geoms are available.

Code

ggplot(my.data) + 
  aes(x = factor(wind), y = people) +
  geom_boxplot()

Each geom accepts a particular set of mappings;for example geom_text() accepts a labels mapping. Try-

Code

ggplot(my.data) +
  aes(x = vehicle, y = people) +
  geom_point() + 
  geom_text(aes(label = w.e), 
            size = 5)

Exercise 1.9

Will these two graphs look different? Why/why not?

Code

ggplot(
  data = my.data,
  mapping = aes(x = vehicle, y = people)
) +
  geom_point() +
  geom_smooth()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Code

ggplot() +
  geom_point(
    data = my.data,
    mapping = aes(x = vehicle, y = people)
  ) +
  geom_smooth(
    data = my.data,
    mapping = aes(x = vehicle, y = people)
  )

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

The faceting option allows a collection of small plots with the same scales. Try-

Code

ggplot(my.data) +
  aes(x=vehicle, y=people) + 
  geom_point() +
  facet_wrap(~ river)

Faceting is the ggplot2 option to create separate graphs for subsets of data. ggplot2 offers two functions for creating small multiples:

facet_wrap(): define subsets as the levels of a single grouping variable
facet_grid(): define subsets as the crossing of two grouping variables

The following arguments are common to most scales in ggplot2:

name: the first argument gives the axis or legend title
limits: the minimum and maximum of the scale
breaks: the points along the scale where labels should appear
labels: the labels that appear at each break

Specific scale functions may have additional arguments. Some of the available Scales are:

Scale	Examples
`scale_color_`	`scale_color_discrete`
`scale_fill_`	`scale_fill_continuous`
`scale_size_`	`scale_size_manual`
	`scale_size_discrete`

`scale_shape_`	`scale_shape_discrete`
	`scale_shape_manual`
`scale_linetype_`	`scale_linetype_discrete`

`scale_x_`	`scale_x_continuous`
	`scale_x_log`
	`scale_x_date`
`scale_y_`	`scale_y_reverse`
	`scale_y_discrete`
	`scale_y_datetime`

In RStudio, we can type scale_ followed by TAB to get the whole list of available scales.

Try-

Code

ggplot(my.data) + 
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point() + 
  scale_x_continuous(name = "No. of Vehicles") + 
  scale_y_continuous(name = "No. of people") + 
  scale_color_discrete(name = "Temperature")

The other coding option is shown below:

Code

ggplot(my.data) +
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point() + 
  xlab("No. of Vehicles") + 
  ylab("No. of people") + 
  labs(colour="Temperature")

Note that a desired graph can be obtained in more than one way.

The ggplot2 theme system handles plot elements (not data based) such as

Axis labels
Plot background
Facet label background
Legend appearance

Built-in themes include:

theme_gray() (default)
theme_bw()
theme_minimal()
theme_classic()

Code

p1 <- ggplot(my.data) + 
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point()

Note that the graph is assigned an object name p1 and nothing will be printed unless we then print the object p1.

Code

p1 <- ggplot(my.data) + 
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point()

p1

Try-

Code

p1 + theme_light()

Code

p1 + theme_bw()

Specific theme elements can be overridden using theme(). For example:

Code

p1 + theme_minimal() +
  theme(text = element_text(color = "red"))

All theme options can be seen with ?theme.

To specify a theme for a whole document, use

Code

theme_set(theme_minimal())

Minimal graphing can be done using the qplot option that will produce a few standard formatted graphs quickly.

Code

qplot(people, vehicle, data = my.data, colour = river)

Warning: `qplot()` was deprecated in ggplot2 3.4.0.

Try-

qplot(people, data = my.data)

qplot(people, fill=factor(river), data=my.data)

qplot(people, data = my.data, geom = "dotplot")

qplot(factor(river), people, data = my.data, geom = "boxplot")

A cheat sheet for ggplot2 is available at https://www.rstudio.com/resources/cheatsheets/ (optional to download). There are many other packages which incorporate ggplot2 based graphs or dependent on it.

The library patchwork allows complex composition arbitrary plots, which are not produced using the faceting option. Try

Code

library(patchwork)

p1 <- qplot(people, data = my.data, geom = "dotplot")
p2 <- qplot(people, data = my.data, geom = "boxplot")
p3 <- ggplot(my.data, aes(x = vehicle, y = people)) + geom_point()

(p1 + p2) / p3 + 
  plot_annotation("My title", caption = "My caption")

Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.

Quarto

Demonstrate how to open a new Quarto document

Installing R and RStudio

Some R basics

Exercise 1.1

Exercise 1.2

Exercise 1.3

Loading/Importing Data

Exercise 1.4

Exercise 1.5

Exercise 1.6

Graphing with ggplot2

Exercise 1.6

Exercise 1.7

Exercise 1.8

Exercise 1.9

Quarto

Installing `R` and RStudio

Some `R` basics