Learning objectives

Install R and RStudio

Install the tidyverse collection of R packages

To introduce Rstudio that we will be using, to write R scripts, in this course

To introduce data types and data structures

Installing software you will need

Before starting this course you will need to ensure that your computer is set up with the required software.

If you have any difficulty installing any of this software then please contact one of the trainers for help.

R and RStudio

R and RStudio are separate downloads and installations.

R is the underlying statistical computing environment. The base R system and a very large collection of packages that give you access to a huge range of statistical and analytical functionality are available from CRAN, the Comprehensive R Archive Network.

However, using R alone is no fun. RStudio is a graphical integrated development environment (IDE) that makes using R much easier and more interactive. You need to install R before you install RStudio.

On this course we will be making use of a brilliant collection of packages designed for data science called the tidyverse that make it much easierand more fun to work with your data. After installing R and RStudio, follow the instructions at the bottom of this page to install the tidyverse.

Windows

If you already have R and RStudio installed

Open RStudio, and click on “Help” > “Check for updates”. If a new version is available, quit RStudio, and download the latest version for RStudio.

To check which version of R you are using, start RStudio and the first thing that appears in the console indicates the version of R you are running. Alternatively, you can type sessionInfo(), which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it. You can check here for more information on how to remove old versions from your system if you wish to do so.

If you don’t have R and RStudio installed

Download R from the CRAN website.
Run the .exe file that was just downloaded
Go to the RStudio download page
Under Installers select RStudio-2023.xx.y-zzz.EXE - Windows 10/11 (where x, y, and z represent version numbers)
Double click the file to install it
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

macOS

If you already have R and RStudio installed

Open RStudio, and click on “Help” > “Check for updates”. If a new version is available, quit RStudio, and download the latest version for RStudio.

To check the version of R you are using, start RStudio and the first thing that appears on the terminal indicates the version of R you are running. Alternatively, you can type sessionInfo(), which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it.

If you don’t have R and RStudio installed

The first thing you should do is check what chip your Mac has
How to check chip type on Mac? Click on apple logo on top left, then chick on About This Mac.

Intel chip

M chip

Download R from the CRAN website.

Select appropriate R .pkg file for the latest R version
- For M type chip: download R-x.y.z-arm64.pkg (where x, y, and z represent version numbers)
- For inter chip: download R-x.y.z.pkg (where x, y, and z represent version numbers)
Double click on the downloaded file to install R
It is also a good idea to install XQuartz (needed by some packages)
Go to the RStudio download page
Under Installers select RStudio-2023.xx.y-zzz.DMG - Mac OS 11+ (64-bit) (where x, y, and z represent version numbers)
Double click the file to install RStudio
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

Linux

Follow the instructions for your distribution from CRAN, they provide information to get the most recent version of R for common distributions. For most distributions, you could use your package manager (e.g., for Debian/Ubuntu run sudo apt-get install r-base, and for Fedora sudo yum install R), but we don’t recommend this approach as the versions provided by this are usually out of date. In any case, make sure you have at least R 3.3.1.
Go to the RStudio download page
Under Installers select the version that matches your distribution, and install it with your preferred method (e.g., with Debian/Ubuntu sudo dpkg -i RSTUDIO-2023.xx.y-zzz-AMD64.DEB at the terminal).
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

Tidyverse

After installing R and RStudio, please install the tidyverse packages.

After starting RStudio, at the console type: install.packages("tidyverse") (look for the ‘Console’ tab and type at the > prompt)
You can also do this by going to Tools -> Install Packages and typing the names of the packages separated by a comma.

Introduction to R and RStudio

Why learn R?

R involves creating & using scripts which makes the steps you used in your analysis clear and can be inspected by someone else for feedback and error-checking.
R code is great for reproducibility. An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.
R integrates with other tools to generate manuscripts from your code. This document (RMarkdown a .Rmd file) is a case in point.
R is interdisciplinary and extensible and has thousands of installable packages to extend its capabilities. R has packages for image analysis, GIS, time series, population genetics, and a lot more.
R scales well to work on data of all shapes and sizes.
R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.
R produces high-quality graphics suitable for publication in journals or the web.
R has a large and welcoming community - Thousands use R daily and many of them are willing to help you through websites such as Stack Overflow or the RStudio community.
Not only is R free, but it is also open-source and cross-platform.

Rstudio a brief tour

Rstudio provides us with a friendly interface to the R statistical progrmming language. It consists of four main “Panes”. These can be re-sized and moved around to suit how you like to work.

Rstudio screenshot

Editing pane

By default the top left-hand pane is one for creating, editing & running R scripts.

Rstudio edit pane screenshot

A script is an R program that you have written. A good practice is for that script to perform only one role in your analysis workflow and so you may have several R scripts which you call, in a particular sequence, to analyse your data.

As you will see, a script is basically a text file that contains R commands and (ideally) comments to explain what the codes function is (as a documentation process).

As well as R scripts, there are many types of Rstudio document including Markdown files which we will use in the teaching of this course. These can provide interactive workbooks or pdf and web documents to name but a few possible outcomes.

Console

Coming down the screen to the bottom left-hand pane we find the console window. This is where we can find output produced by running our R scripts.

Rstudio screenshot

We can also try out snippets of R code here. Those of you who have only used graphical interfaces like Windows or MacOS where you click on commands using a mouse may find this aspect of R somewhat different. We type in commands to R using the command line.

This area can also be used like a calculator. Let’s just type in something like 23 + 45 followed by the return key and see what happens. You should get the following:

> 23 + 45
[1] 68

Now 68 is clearly the answer but what is that 1 in brackets?

Here is another example to explain. If we type 1:36 and press enter, what happens? R generates output counting from 1 to 36 but cannot fit all the output on one line and so starts another like this:

> 1:36
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36

Now we have two lines beginning with a number in square brackets. Note that the number of values displayed on each line may differ on your computer; it largely depends on the width of your console pane and the font size. Try creating a larger sequence of numbers, e.g. 1:100, if all 36 numbers fit on a single line in your case.

This is just R helping us to keep tabs on which number we are looking at. [1] denotes that the line starts with the first result and the [26] denotes that this line starts with the 26th number. Let’s try another one and generate a sequence incrementing in steps of 2:

> 1:36 * 2
 [1]  2  4  6  8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
[26] 52 54 56 58 60 62 64 66 68 70 72

There are other tabs on this pane but we shall not be covering these on this course.

Environment

Next we move to the top right-hand corner pane. Here we have even more tabs (of which we will only consider two Environment and History).

Rstudio screenshot

Environment keeps track on R variables which we create (more on those later) and their contents. History is like a tally role of all the R commands we have entered in our session.

Files, Plots & Help

Our final bottom right-hand pane also has several tabs. The Files tab is a file explorer that enable us to move around our directories and select which files we wish to work on. We can also change the default working directory that Rstudio will use.

Rstudio screenshot

The Plots tab is where any graphs that we create in R will appear. We can move through them using the arrow buttons and the export button will convert them to different graphics formats e.g. for publication in a paper or for the web.

The Packages tab shows which R packages are installed (These expand R’s functionallity and again will be covered later) and can also install new packages.

The Help tab is a massively useful tab which enables you to search R help index to get help pages on R functions and provide example code to help you use them in your R scripts.

Our first look at the R language

Our overall goal for this course is to give you the ability to import your data into R, select a subset of the data most of interest for a given analysis, carry out an analysis to summarize these data and create visualizations of the data. First though, let us consider “What is Data?”

Data comes in many forms: Numbers (Integers and decimal values) or alphabetical (characters or lines of text). Clearly a computer (or R) needs a way of representing this wide range of data with it’s diverse properties.

Data types in R

Image source:https://www.javatpoint.com/r-data-types

R has 6 basic data types
- character
  - character is nothing but a string
  - anything between two quotes (Single or double quotes): “a”, ‘cat’
  - Quoted numbers are character type: ‘3.14’
- numeric or double: Set of all real numbers.
  - A number. Could be a integer or a real number: 3.14, 1.45765, 5, 1000000
- integer: Whole numbers. 5L (the L tells R to store this as an integer)
- logical: TRUE, FALSE
- complex: 1+4i (complex numbers with real and imaginary parts)
- raw:
The last two data types are rarely used in practice
Different types of data are needed in (any) programming for a variety of reasons:
- Processing instructions are different for different data types. For instance mathematical operations (+, -, x and /) are only meaningful for numbers.

11 + 3 # Operation of addition performed correctly
"11" + 3 # gives error

Efficient storage: Integer type uses less memory when compared to decimal (numeric) data type.

R Arithmetic Operators

Mathematical operations such as addition and multiplication are performed using various operators. Here is a list of R’s arithmetic operators.

Addition: +
Subtraction: -
Multiplication: *
Division: /
Exponent: ^
Modulus (Remainder from division): %%

5*5 # 5 times 5

## [1] 25

7/3 # 7 divided by 3

## [1] 2.333333

7%%3 # reminder of 7 divided by 3

## [1] 1

Data structures in R

Image source:http://venus.ifca.unican.es/Rintro/dataStruct.html

R has many data structures. These include
- Atomic vector
- data frame
- matrix
- list
- factors

Atomic vector

This is the fundamental data structure in R
All the other data structures built on vectors
Understanding this structure is absolutely essential

Image source:https://www.geeksforgeeks.org/r-vector/

x <- 100 # create first vector

“x” is an object or a variable, both of which are used interchangeably in this course.
<- An assignment operator one can also use = instead of <-
Meaning of above expression is x gets 100

x <- 100 
x = 100

After a variable/object is created, it can be used as many times as needed.

y <- 10
y*y

## [1] 100

y + y

## [1] 20

100 + y

## [1] 110

Variable values can be replaced

x <- "Tom"
x

## [1] "Tom"

x <- "Jerry"
x

## [1] "Jerry"

variable names can not start with a number or a special characters like (_, -, etc)

2x <- 100 # gives error
_x <- 100 # gives error

only allowed special characters in variable names are “_” and “.” any other special character throws an error]

my-name <- "Chandra" # throws error
my_name <- "Chandra" # no error
my.name <- "Chandra" # no error

R is case sensitive
COUNTRY and country are two different variables

COUNTRY <- "United Kingdom" 
country <- "India"

c() function should be used to create a vector that holds more than one value
c stands for combine
values are separated by “,”
When creating single or multiple values as a beginner, it would be advisable to use the function c()

x <- 100
x <- c(100) # same like above
x <- 100, 200 # gives error
x <- c(100, 200) # no error

“:” operator that generates a range of values

x <- c(1:100) # create values from 1 to 100

The values in a vector can be of one and only one type, such as numerics, integers, characters, logic, complexes, or raw data.
When we attempt to mix different data types in a single vector, R automatically converts the data types, this phenomenon is called Coercion

x <- c(1,2,3,4)
typeof(x)

## [1] "double"

x <- c(1,"2",3,4)
typeof(x)

## [1] "character"

y <- c(TRUE, FALSE, TRUE, 1L)
y

## [1] 1 0 1 1

typeof(y)

## [1] "integer"

z <- c(TRUE, FALSE, FALSE)
typeof(z)

## [1] "logical"

z <- c(TRUE, FALSE, "FALSE")
z

## [1] "TRUE"  "FALSE" "FALSE"

typeof(z)

## [1] "character"

Logical values TRUE and FALSE are internally represented as 1 and 0. Therefore mathematical operations can be performed on these vectors.

y <- c(TRUE, FALSE, TRUE)
y

## [1]  TRUE FALSE  TRUE

sum(y)

## [1] 2

mean(y)

## [1] 0.6666667

Vectorization in R
- It is important to note that most of R’s functions are vectorized, which means that they operate on all elements of a vector without looping through each element one by one. Coding becomes more concise, easier to read, and less prone to errors as a result.

x <- c(1,2,3,4,5) 
x * 5 # same as x * c(5)

## [1]  5 10 15 20 25

x + 1 # same as x + c(1)

## [1] 2 3 4 5 6

Vectorization is powerful, quick and concise, but leads to confusion when vector lengths are different.

x <- c(1,2,3,4,5,6)
y <- c(1,2,3,4,5,6)
x + y

## [1]  2  4  6  8 10 12

x <- c(1,2,3,4,5)
y <- c(1,2)
x + y

## Warning in x + y: longer object length is not a multiple of shorter object
## length

## [1] 2 4 4 6 6

How to access values from a vector?
- Use [] subscript operator
- within [] one can give any of the following
  - Vector of index numbers
  - Vector of logical values

vec <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
vec

##  [1]  10  20  30  40  50  60  70  80  90 100

# extract 4th value from the vector var
vec[4]

## [1] 40

vec[c(4)] # good idea to use c() function even for single value

## [1] 40

# extract 4th and 7th values from the vector var
vec[c(4,7)]

## [1] 40 70

One can inverse using “-” symbol

# extract all the values except 4th and 7th value
vec[c(-4,-7)]

## [1]  10  20  30  50  60  80  90 100

Extracting values using logical vectors

y <- c( 5, 8, 10) 
y[c(FALSE, TRUE, FALSE)] # extract second element

## [1] 8

Logic vectors are not created manually in reality
Logic vectors are the outputs of comparison operators
comparison operators
- Equal to ==
- Not equal to !=
- Greater than >
- Less than <
- Greater than or equal to >=
- Less than or equal to <=

x <- c(10, 20, 30, 40)
x == 20

## [1] FALSE  TRUE FALSE FALSE

x > 20

## [1] FALSE FALSE  TRUE  TRUE

keep <- x > 20
keep

## [1] FALSE FALSE  TRUE  TRUE

x[keep]

## [1] 30 40

x[ x > 20 ] # The equivalent of x[keep]

## [1] 30 40

By using logical operators one can create complex expression for more complex subsetting
- There are 3 logical operators in R
  - & : logical and
  - : logical or
  - ! : logical not

For instance if you want to extract all the values in a vector that are greater than 10 but less than 40

x <- c(10, 20, 30, 40)
x[x > 10] # get all the values > 10

## [1] 20 30 40

x[x < 40] # get all the values < 40

## [1] 10 20 30

x[ x > 10 & x < 40] # get all the values > 10 and < 40

## [1] 20 30

x[ x == 20] # get values that are equal to 20

## [1] 20

x[ !x == 20] # equivalent to x[ x != 20]

## [1] 10 30 40

It is possible to selectively replace vector values.

x <- c(10, 20, 30)
x[2]

## [1] 20

x[2] <- 1000
x

## [1]   10 1000   30

Functions and their arguments

Functions are a fundamental building block of R. Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc. Many functions are predefined, or can be made available by importing R packages (more on that later). A function usually takes one or more inputs called arguments. Functions often (but not always) return a value. A typical example would be the function round(). The input (the argument) must be a number, and the return value (in fact, the output) is the rounded number. Executing a function (‘running it’) is called calling the function. An example of a function call is:

pi <- 3.141593
round(pi)

## [1] 3

round is a function that takes at lest one number and returns a number that rounded to the nearest integer.
In R, all functions have the same syntax, which is function name followed by ()
Depends on a function within “()” you supply zero to many arguments
The names and numbers of arguments vary from function to function
How to know what augments function has?
- Get help of that function
- use args() function
How to get help in R?
- How to get help in R details: https://www.r-project.org/help.html
- By typing ? or help() followed by the name of the function in the console, for example to get help with the round function, type “?round” in the console
- In Rstudio under the help tab one can search for a given function
- On general Google search, for instance “round + r function + documentation”

?round 
help(round) # equivalent to ?round

Use args() to view the arguments of a function

args(round)

## function (x, digits = 0) 
## NULL

Using the help or the args function, you can see that round() takes exactly two arguments
- x: a numeric vector
- digits: integer indicating the number of decimal places to round

round(x=pi, digits = 0)

## [1] 3

round(x=pi, digits = 2)

## [1] 3.14

round(x=pi, digits = 4)

## [1] 3.1416

As long as you use argument names order of the arguments does not matter.

round( digits = 4, x=pi)

## [1] 3.1416

Some useful math/stat functions in R
- max(): maximum value in a numeric vector
- min(): minimum value in a numeric vector
- range(): vector of min and max
- sum(): sum of a vector
- mean(): mean of a vector
- median(): median of a vector
- var(): variance of a vector
- sd(): standard deviation of a vector
- sort(): sorted version of a vector
- length(): length of an object
- cor(): correlation of x and y
data type conversion functions: as.”datatype” family of functions are useful for converting one data type to other
- as.numeric()
- as.character()
- as.integer()

Missing data

As R was designed to analyze datasets, it includes the concept of missing data (which is uncommon in other programming languages). Missing data are represented in vectors as NA.

When doing operations on numbers, most functions will return NA if the data you are working with include missing values. This feature makes it harder to overlook the cases where you are dealing with missing data. You can add the argument na.rm = TRUE to calculate the result while ignoring the missing values.

heights <- c(2, 4, 4, NA, 6)
mean(heights)

## [1] NA

max(heights)

## [1] NA

mean(heights, na.rm = TRUE)

## [1] 4

max(heights, na.rm = TRUE)

## [1] 6

If your data include missing values, you may want to become familiar with the function is.na() See below for examples.

## Extract those elements which are not missing values.
heights[!is.na(heights)]

## [1] 2 4 4 6

Challenges

You have given a list of tumour volumes from 5 patients, 2.1, 1.9, 2.6, 1.8, and 3 \(cm^3\). Convent this data into a R vector and get the following characteristics of data by applying some of the functions listed above.
1. How many observations we have?
2. What is the mean tumour volume?
3. How many patients has tumour volume less than 2 \(cm^3\)?

Answer

# Challenge 1 a: How many observations we have?
tumour_vol <- c(2.1, 1.9, 2.6, 1.8,3)
length(tumour_vol)

## [1] 5

# Challenge 1 b:  What is the mean tumour volume?
mean(tumour_vol)

## [1] 2.28

# Challenge 1 c: How many patients has tumour volume less than 2
sum(c(2.1, 1.9, 2.6, 1.8,3) < 2)

## [1] 2

You have given two vectors of observations data1 <- c(10, 9, 7, 6, 7, 3, 7, 5, 6, 6) and data2 <- c(5, 2, 10, 7, 2, 5, 1, 5, 3, 4).
1. use the function cor() to get the correlation coefficient
2. Can you identify the default correlation method the cor() function uses? For help use help() or ?
3. Can you get Spearman correlation coefficient for these two vectors?

Answer

# Challenge 2 a: use the function `cor()` to get the correlation coefficient
data1 <- c(10, 9, 7, 6, 7, 3, 7, 5, 6, 6)
data2 <- c(5, 2, 10, 7, 2, 5, 1, 5, 3, 4)
cor(x=data1, y=data2)

## [1] -0.1572206

# Challenge 2 b: Can you identify the default correlation method the `cor()` function uses?
?cor
# According to the help in the 'cor' file, if no method is specified by default the function will use the 'pearson' method.

# Challenge 2 c: Can you get Spearman correlation coefficient for these two vectors?
cor(x=data1, y=data2, method = "spearman")

## [1] -0.2839244

You have given a logical vector, logi_vec <- c(TRUE, FALSE, TRUE, TRUE). Can you apply math functions like sum(), mean() on this logical vector, if so what is the output of sum() and mean()?

Answer

# Challenge 3: 
logi_vec <- c(TRUE, FALSE, TRUE, TRUE)
# Mathematical functions can be applied to logical vectors. Internally, the logical values TURE and FALSE are represented as 1 and 0, respectively.
sum(logi_vec)

## [1] 3

mean(logi_vec)

## [1] 0.75

What is the output of following operations and explain your logic behind it.
1. c(5, 2, 9, 1, 13) * c(2)
2. c(5, 2, 9, 1, 13) * c(1,2)
3. c(5, 2, 9, 1, 13) + c(1,2,3,4,5)

Answer

# Challenge 4 a: c(5, 2, 9, 1, 13) * c(2)
c(5, 2, 9, 1, 13) * c(2)

## [1] 10  4 18  2 26

# Since the shorter vector has only one value, every value of the longer vector is multiplied by the value of the shorter vector.

# Challenge 4 b: c(5, 2, 9, 1, 13) * c(1,2)
c(5, 2, 9, 1, 13) * c(1,2)

## Warning in c(5, 2, 9, 1, 13) * c(1, 2): longer object length is not a multiple
## of shorter object length

## [1]  5  4  9  2 13

# Since the shorter vector has only two values, these two values are sequentially recycled to multiply the longer vector's values.

# Challenge 4 c: c(5, 2, 9, 1, 13) + c(1,2,3,4,5)
c(5, 2, 9, 1, 13) + c(1,2,3,4,5)

## [1]  6  4 12  5 18

# Due to the fact that both vectors have the same length, values are sequentially added together

From the vector c(23, 12, 41, 65, 23, 6), can you extract those values that are equal to 23 or less than 15?

Answer

vec <- c(23, 12, 41, 65, 23, 6)
vec[ vec == 23 | vec < 15]

## [1] 23 12 23  6

“month.name” is a in-built R vector
1. What is the index number of “April” in month.name vector? hist: “which” function may help you.
2. Extract all the months from April to December

Answer

# Challenge 6 a: What is the index number of "April" in month.name vector? hist: "which" function may help you.
which(month.name == "April")

## [1] 4

# Challenge 6 b:Extract all the months from April to December
month.name[which(month.name == "April"):length(month.name)]

## [1] "April"     "May"       "June"      "July"      "August"    "September"
## [7] "October"   "November"  "December"

Credit

These instructions were adapted from Data Carpentry course materials.

Data types and structures

Learning objectives

Installing software you will need

R and RStudio

Windows

If you already have R and RStudio installed

If you don’t have R and RStudio installed

macOS

If you already have R and RStudio installed

If you don’t have R and RStudio installed

Linux

Tidyverse

Introduction to R and RStudio

Why learn R?

Rstudio a brief tour

Editing pane

Console

Environment

Files, Plots & Help

Our first look at the R language

Data types in R

R Arithmetic Operators

Data structures in R

Atomic vector

Functions and their arguments

Missing data

Challenges

Credit