1 Introduction to R and RStudio

1.1 What is R?

Tip

Install R

Go here: https://cran.rstudio.com/
Choose the correct “Download R for. . .” option from the top (probably Windows or macOS), then…

If it installs, you should be able to find the R icon in your applications.

Open source (free!) statistical programming language/software
It can be used for:
- Working with data - cleaning, wrangling and transforming
- Conducting analyses including advanced statistical methods
- Creating high-quality tables & figures
- Communicate research with R Markdown
It is constantly growing!
Has a strong online support community
Since it’s one programming language, it is versatile enough to take you from raw data to publishable research using free, reproducible code!

1.2 What is RStudio?

Tip

Install RStudio

Go to https://www.rstudio.com/products/rstudio/download/ to download RStudio Desktop (Open Source License).

If it installs, you should be able to find the RStudio icon in your applications.

RStudio is a free, open source IDE (integrated development environment) for R. (You must install R before you can install RStudio.)

Its interface is organized so that the user can clearly view graphs, tables, R code, and output all at the same time.

It also offers an Import-Wizard-like feature that allows users to import CSV, Excel, SPSS (*.sav), and Stata (*.dta) files into R without having to write the code to do so.

1.3 Why should you learn R?

R is becoming the “lingua franca” of data science
Most widely used and it is rising in popularity
R is also the tool of choice for data scientists at Microsoft, Google, Facebook, Amazon
R’s popularity in academia is important because that creates a pool of talent that feeds industry.
Learning the “skills of data science” is easiest in R

1.3.1 R versus Others Softwares

Excel and SPSS are convenient for data entry, and for quickly manipulating rows and columns prior to statistical analysis.

However, they are a poor choice for statistical analysis beyond the simplest descriptive statistics, or for more than a very few columns.

Some of the reasons for choosing R over others are are:

Missing values are handled inconsistently, and sometimes incorrectly.
Data organisation difficult.
Analyses can only be done on one column at a time.
Output is poorly organised.
No record of how an analysis was accomplished.
Some advanced analyses are impossible

1.3.2 What is Reproducibility?

Reproducibility refers to the ability of a researcher to duplicate the results of a prior study using the same materials as were used by the original investigator.

Note

Reproducibility is a minimum necessary condition for a finding to be believable and informative. - U.S. National Science Foundation (NSF) subcommittee

That is, a second researcher might use the same raw data to build the same analysis files and implement the same statistical analysis in an attempt to yield the same results.

1.3.3 Factors behind irreproducible research

Not enough documentation on how experiment is conducted and data is generated
Data used to generate original results unavailable
Software used to generate original results unavailable
Difficult to recreate software environment (libraries, versions) used to generate original results
Difficult to rerun the computational steps

What is Tidy Data?

Three Rules of Tidy Data

Each variable must have its own column.
Each observation must have its own row.
Each value must have its own cell.

Tidy data is a way to describe data that’s organized with a particular structure – a rectangular structure, where each variable has its own column, and each observation has its own row…………Hadley Wickham, 2014

1.4 Understanding the RStudio environment

1.4.1 Pane layout

The RStudio environment consist of multiple windows. Each window consist of certain Panels which includes:

Source
Console
Environment
History
Files
Plots
Connections
Packages
Help
Build
VCS
Tutorial
Viewer

Note

It is important to understand that not all panels will be used by you in routine as well as by us during the workshop. The workshop focuses on using R for healthcare professionals as a database management, visualization, and communication tool. The most common panels which requires attention are the source, console, environment, history, files, packages, help, tutorial, and viewer panels.

A guided tour

You are requested to make your own notes during the workshop. Let us dive deep into understanding the environment further in the workshop.

1.5 Creating a project.

It is important to understand that good workflows facilitate efficient database management. Lets discuss!

1.6 File types in R.

The most common used file types are

.R : Script file.
.qmd: Quarto Markdown file.
.rds: Single R database file.
.Rproj: R Project file.

1.7 Programming basics.

R is easiest to use when you know how the R language works. This section will teach you the implicit background knowledge that informs every piece of R code. You’ll learn about:

Functions and their arguments.
Objects.
R’s basic data types.
R’s basic data structures including vectors and lists.
R’s package system.

1.7.1 Functions and their arguments.

To do anything in R, we call functions to work for us. Take for example, we want to compute square root of 5197. Now, we need to call a function sqrt() for the same.

sqrt(5197)

[1] 72.09022

Important things to know about functions include:

Code body.

sqrt

function (x)  .Primitive("sqrt")

Typing code body and running it enables us understand what a function does in background.

Run a function.

To run a function, we need to add a parenthesis $()$ after the code body.Within the parenthesis we add the details such as number in the above example.

Help page.

Placing a question mark before the function takes you to the help page. This is the most important aspect we need to understand. When calling help page parenthesis is not placed. This help page will enable you learn about new functions in your journey!

Code

# ?sqrt 
# Tip: Annotations: Annotations are meant for humans to read and not by machines. It enables us take notes as we write. As a result, next time when you open your code even after a long time, you will know what you did last summer :)

Arguments are inputs provided to the function. There are functions which take no arguments, some take a single argument and some take multiple arguments. When there are two or more arguments, the arguments are separated by a comma.

# No argument
Sys.Date()

[1] "2024-10-22"

# One argument
sqrt(5197)

[1] 72.09022

# Two arguments
sum(2,3)

[1] 5

# Multiple arguments
seq(from=1,
    to = 10, 
    by  = 2)

[1] 1 3 5 7 9

Matching arguments: Some arguments are understood as such by the software. Take for example, generating a sequence includes three arguments viz: from, to, by. The right inputs are automatically matched to the right argument.

seq(1,10,2)

[1] 1 3 5 7 9

Warning

The wrong inputs are also matched. Best practice is to be explicit at early stages. Use argument names!

seq(2,10,1)

[1]  2  3  4  5  6  7  8  9 10

seq(by = 2,
    to = 10,
    from = 1)

[1] 1 3 5 7 9

Optional arguments: Some arguments are optional. They may be added or removed as per requirement. By default these optional arguments are taken by R as default values. Take for example, in sum() function, na.rm = FALSE is an optional argument. It ensures that the NA values are not removed by default and the sum is not returned when there are NA values. These optional arguments can be override by mentioning them explicitly.

sum(2,3,NA)

[1] NA

sum(2,3,NA, na.rm = T)

[1] 5

In contrast, the arguments which needs to be mentioned explicitly are mandatory! Without them, errors are returned as output.

sqrt()

1.7.2 Objects.

If we want to use the results in addition to viewing them in console, we need to store them as objects. To create an object, type the name of the object (Choose wisely, let it be explicit and self explanatory!), then provide an assignment operator. Everything to the right of the operator will be assigned to the object. You can save a single value or output of a function or multiple values or an entire data set in a single object.

# Single value
x <- 3
x

[1] 3

# Output from function
x <- seq(from=1,
    to = 10, 
    by  = 2)
# Better name:
sequence_from_1_to_10 <- seq(from=1,
    to = 10, 
    by  = 2)

Creating an object helps us in viewing its contents as well make it easier to apply additional functions

Tip

While typing functions/ object names, R prompts are provided. Choose from the prompts rather than typing the entire thing. It will ease out many things later!

sequence_from_1_to_10

[1] 1 3 5 7 9

sum(sequence_from_1_to_10)

[1] 25

1.7.2.1 Vectors

R stores values as a vector which is one dimensional array. Arrays can be two dimensional (similar to excel data/ tabular data), or multidimensional. Vectors are always one dimensional!

Vectors can be a single value or a combination of values. We can create our own vectors using c() function.

single_number <- 3
single_number

[1] 3

number_vector <- c(1,2,3)
number_vector

[1] 1 2 3

Creating personalized vectors is powerful as a lot of functions in R takes vectors as inputs.

mean(number_vector)

[1] 2

Vectorized functions: The function is applied to each element of the vector:

sqrt(number_vector)

[1] 1.000000 1.414214 1.732051

If we have two vectors of similar lengths (such as columns of a research data), vectorised functions help us compute for new columns by applying the said function on each element of both the vectors and give a vector of the same length (Consider this as a new column in the research data)

number_vector2 <- c(3,-4,5.4)
number_vector + number_vector2

[1]  4.0 -2.0  8.4

1.7.2.2 Data Types

R recognizes different types of vectors based on the values in the vector.

If all values are numbers (positive numbers, negative numbers, decimals), R will consider that vector as numerical and allows you to carry out mathematical operations/ functions. You can find the class of the vector by using class() function.R labels these vectors as “double”, “numeric”, or “integers”.

class(number_vector)

[1] "numeric"

class(number_vector2)

[1] "numeric"

If the values are within quotation marks, it is character variable by default. It is equivalent to nominal variable.

alphabets_vector <- c("a", "b", "c")
class(alphabets_vector)

[1] "character"

integer_vector <- c(1L,2L)
class(integer_vector)

[1] "integer"

Logical vectors contain TRUE and FALSE values

logical_vector <- c(TRUE, FALSE)
class(logical_vector)

[1] "logical"

One vector = One type. For example: When there is mix of numbers and characters, R will consider all as character.

mix_vector <- c(1,"a")
class(mix_vector)

[1] "character"

Double, character, integer, logical, complex, raw, dates, etc… There are many other data types and objects but for now, lets start with these. You will understand additional types as you will proceed in your R journey!

1.7.2.3 Lists

In addition to vectors, lists are another powerful objects. A list can be considered as a vector of vectors!! They enable you to store multiple types of vectors together. A list can be made using a list() function. It is similar to c() function but creates a list rather than a vector. It is a good practice to name the vectors in the list.

example_list <- list(numbers = number_vector, 
                     alphabets = alphabets_vector)
class(example_list)

[1] "list"

example_list

$numbers
[1] 1 2 3

$alphabets
[1] "a" "b" "c"

The elements of a named list/ a named vector can be called by using a $.

example_list$numbers

[1] 1 2 3

Important: Dataframes/ excelsheet we use are special type of lists. Each column is named and is a vector!!

1.7.2.4 Packages

There are thousands of functions in R. To be computationally efficient, R do not load all functions on start. It loads only base functions. As you want to use additional functions, we need to load the packages using library() function.

Important: The additional packages are installed once but loaded everytime you start R sessions.