A crash course in R

Fall 2023

Julien Arino

Department of Mathematics & Data Science Nexus
University of Manitoba*

Canadian Centre for Disease Modelling

* The University of Manitoba campuses are located on original lands of Anishinaabeg, Cree, Oji-Cree, Dakota and Dene peoples, and on the homeland of the Métis Nation.


  • Foreword: the R language
  • Data types and data structures
  • Flow control
  • Functions
  • Rmarkdown, Sweave and Quarto

Foreword: the R language

R was originally for stats but is now more

  • Open source version of S
  • Appeared in 1993
  • Now (Fall 2023) version 4.3
  • One major advantage in my view: uses a lot of C and Fortran code. E.g., deSolve:

The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps

  • Very active community on the web, easy to find solutions (same true of Python, I just prefer R)

Development environments

  • Terminal version, not very friendly
  • Nicer terminal: radian
  • Execute R scripts by using Rscript name_of_script.R. Useful to run code in cron, for instance
  • Use IDEs:
    • RStudio has become the reference
    • RKWard is useful if you are for instance using an ARM processor (Raspberry Pi, some Chromebooks..)
  • Integrate into jupyter notebooks

Going further

  • RStudio server: run RStudio on a Linux server and connect via a web interface
  • Shiny: easily create an interactive web site running R code
  • Shiny server: run Shiny apps on a Linux server
  • Rmarkdown: markdown that incorporates R commands. Useful for generating reports in html or pdf, can make slides as well..
  • RSweave: LaTeX incorporating R commands. Useful for generating reports. Not used as much as Rmarkdown these days but very useful if you are a user, e.g., to make Beamer slides
  • Quarto: intended successor to RSweave and Rmarkdown; personally, I am not yet sure what the intent is...

R is a scripted language

  • Interactive
  • Allows you to work in real time
    • Be careful: what is in memory might involve steps not written down in a script
    • If you want to reproduce your steps, it is good to write all the steps down in a script and to test from time to time running using Rscript: this will ensure that all that is required to run is indeed loaded to memory when it needs to, i.e., that it is not already there..

Data types and data structures

Data types

  • character: "a", "truc"
  • numeric (real or decimal): 3, 12.5
  • integer: 3L (the L forces the type to be integer)
  • logical: TRUE, FALSE (or T, F)
  • complex: 1-2i


Two ways:

X <- 10


X = 10

First version is preferred by R purists.. I don't really care


x = 1:10
y <- c(x, 12)
> y
 [1]  1  2  3  4  5  6  7  8  9 10 12
z = c("red", "blue")
> z
[1] "red"  "blue"
z = c(z, 1)
> z
[1] "red"  "blue" "1"

Note that in z, since the first two entries are characters, the added entry is also a character. Vectors have all entries of the same type, so whatever you put in there first is what it is

Adding entries to a vector

Let us do something inefficacious but illustrative

x = c()
for (i in 1:10) {
    x = c(x, i)

would give us the same as x = 1:10

Vector operations

Vector addition can be frustrating. Say you write x=1:10, i.e., make the vector

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

Then x+1 gives

> x+1
 [1]  2  3  4  5  6  7  8  9 10 11

i.e., adds 1 to all entries in the vector

Beware of this in particular when addressing sets of indices in lists, vectors or matrices


Matrix (or vector) of size full of zeros

A <- mat.or.vec(nr = 2, nc = 3)

Matrix with prescribed entries

B <- matrix(c(1,2,3,4), nr = 2, nc = 2)
> B
     [,1] [,2]
[1,]    1    3
[2,]    2    4
C <- matrix(c(1,2,3,4), nr = 2, nc = 2, byrow = TRUE)
> C
     [,1] [,2]
[1,]    1    2
[2,]    3    4

Remark that here and elsewhere, naming the arguments (e.g., nr = 2) allows to use arguments in any order

Matrix multiplication

Probably the biggest annoyance in R compared to other languages

  • The notation A*B is the Hadamard product (what would be denoted A.*B in matlab), not the standard matrix multiplication
  • Matrix multiplication is written A %*% B

For the matlab-ers here

  • R does not have the keyword end to access the last entry in a matrix/vector/list..
  • Use length (lists or vectors), nchar (character chains), dim (matrices.. careful, of course returns 2 values)

Accessing entries in a matrix

A[i,j]                 # Entry (i,j)
A[i,]                  # Row i
A[,j]                  # Column j
A[i,dim(A)[2]]         # Last entry in row i
A[dim(A)[1],dim(A)[2]] # Last entry in matrix

Adding/replacing rows/columns in a matrix

A[i,] <- c(1,2,3) # Replace row i by 1,2,3
B[,j] <- c(1,2)   # Replace column j by 1,2

Beware, in this case, the dimensions must make sense.. the above operations will fail if is not and is not

Data frames

  • Data frames are matrices on steroids..
  • Matrices have all entries of the same type, data frames do not
  • This leads to quite a lot of frustration, e.g., when you want to make a matrix out of a data frame or if you want to add a column to a matrix with a different entry type and forget about this difference...

Useful functions for data frames and matrices

  • head() - shows first 6 rows; override with, e.g., head(dataframe, n = 10)
  • tail() - shows last 6 rows
  • dim() - returns number of rows and number of columns
  • nrow() - number of rows
  • ncol() - number of columns
  • str() - structure of data frame - name, type and preview of data in each column
  • names() or colnames() - show the names attribute for a data frame
  • sapply(dataframe, class) - shows the class of each column in the data frame


A very useful data structure, quite flexible and versatile. Empty list: L <- list(). Convenient for things like parameters. For instance

L <- list()
L$a <- 10
L$b <- 3
L[["another_name"]] <- "Plouf plouf"
> L[1]
[1] 10
> L[[2]]
[1] 3
> L$a
[1] 10
> L[["b"]]
[1] 3
> L$another_name
[1] "Plouf plouf"

Checking data types and data typing

is.type functions (e.g., is.numeric, is.character, is.matrix, is.list) allow to check the type of an object, as.type functions (e.g., as.numeric, as.character, as.matrix, as.list) allow to convert an object to a given type

The result of data typing can be weird, so it may take a few tries to get things right

Logical statements

In if statements (see later) and many other places, you need to evaluate the truth value of a statement. Use == for equality, != for inequality, >, <, >=, <= for the obvious. & is the logical and, | is the logical or, ! is the logical not

Logical tests on vector entries and which

Make a vector of 5 uniformly distributed numbers (by default in )

> v = runif(5)
> v
[1] 0.682311734 0.612788785 0.681121278 0.003132367 0.842270188

Then using logical statements and which helps for selection

> v <= 0.5
> which(v <= 0.5)
[1] 4

(which returns indices for which the statement is TRUE)

Logical tests on matrix entries and which (1)

Make a matrix of 9 uniformly distributed numbers

> A = matrix(data = runif(9), nr = 3)
> A
          [,1]       [,2]      [,3]
[1,] 0.1605460 0.18508003 0.6043105
[2,] 0.7762981 0.02225763 0.3739177
[3,] 0.8170578 0.88845646 0.5842683

Then using logical statements and which helps for selection

> A <= 0.5
      [,1]  [,2]  [,3]

Logical tests on matrix entries and which (2)

Note that by default, which returns indices of the matrix enumerated column-wise (1-3 are first column, 4-6 are second, etc.)

> which(A <= 0.5)
[1] 1 4 5 8

If you want "proper" matrix indices, use

> which(A <= 0.5, arr.ind = TRUE)
     row col
[1,]   1   1
[2,]   1   2
[3,]   2   2
[4,]   2   3

Using which to set vector/matrix entries

Make a vector of 5 / matrix of 9 uniformly distributed numbers (by default in ) and set all those with value to zero

> v = runif(5)
> v[which(v<0.5)] = 0
> v
[1] 0.8877751 0.9500462 0.0000000 0.0000000 0.0000000

> A = matrix(data = runif(9), nr = 3)
> A[which(A<0.5)] = 0
> A
          [,1]      [,2]      [,3]
[1,] 0.9365261 0.0000000 0.0000000
[2,] 0.8927255 0.0000000 0.0000000
[3,] 0.7267821 0.8341371 0.6286996

Flow control

If statements

if (condition is true) {
  list of stuff to do

Even if list of stuff to do is a single instruction, best to use curly braces

if (condition is true) {
  list of stuff to do
} else if (another condition) {
} else {
    # This is the default if none of the above conditions are true

For loops

for applies to lists or vectors

for (i in 1:10) {
  something using integer i
for (j in c(1,3,4)) {
  something using integer j
for (n in c("truc", "muche", "chose")) {
  something using string n
for (m in list("truc", "muche", "chose", 1, 2)) {
  something using string n or integer n, depending


Very useful function (a few others in the same spirit: sapply, vapply, mapply)

Applies a function to each entry in a list/vector/matrix

Because there is a parallel version (parLapply) that we will see later, worth learning

l = list()
for (i in 1:10) {
        l[[i]] = runif(i)
lapply(X = l, FUN = mean)

or, to make a vector

unlist(lapply(X = l, FUN = mean))

or sapply(X = l, FUN = mean)

"Advanced" lapply

Can "pick up" nontrivial list entries

l = list()
for (i in 1:10) {
        l[[i]] = list()
        l[[i]]$a = runif(i)
        l[[i]]$b = runif(2*i)
sapply(X = l, FUN = function(x) length(x$b))


[1]  2  4  6  8 10 12 14 16 18 20

Just recall: the argument to the function you define is a list entry (l[[1]], l[[2]], etc., here)

Avoid parameter variation loops with expand.grid

# Suppose we want to vary 3 parameters
variations = list(
    p1 = seq(1, 10, length.out = 10),
    p2 = seq(0, 1, length.out = 10),
    p3 = seq(-1, 1, length.out = 10)

# Create the list
tmp = expand.grid(variations)
PARAMS = list()
for (i in 1:dim(tmp)[1]) {
    PARAMS[[i]] = list()
    for (k in 1:length(variations)) {
        PARAMS[[i]][[names(variations)[k]]] = tmp[i, k]     

There is still a loop, but you can split this list, use it on different machines, etc. And can use parLapply


Why make your own functions?

  • Like most programming languages, additionally to built-in functions and functions provided by libraries, you can make your own functions
  • This is useful to avoid duplicated (or multiplicated) code when you are performing the same type of operation repeatedly
  • Allows also to have one place debugging and editing

Defining a function

A function needs three things

  • A name by which to be called
  • Argument(s) on which to operate (optional)
  • Something to return (optional)

Generic form ([ ] indicates something optional)

function_name <- function([arguments]) {
  set of instructions
  [return value]

Function (and variables) naming conventions

  • No super specific instructions, except that the name cannot contain dashes and other special characters. _ is allowed and often used for space
  • Read a bit about naming conventions for instance here. The main ones:
    • camel case: firstName, lastName
    • pascal case: FirstName, LastName
    • snake case: first_name, last_name
    • kebab case: first-name, last-name. Not allowed in R!
  • The same is true for variables
  • Not important which one you choose (unless specific instructions are given), but it is good to try to stick to one form

What's a good function name?

  • Everyone has different ideas about this
  • In an advanced project, you will have multiple functions, so a function name should be informative
  • However, the name should also not be too long

Arguments - No arguments

A function can have no arguments, in which case it looks like this

function_name <- function() {
  set of instructions
  [return value]

and is used by calling as function_name(). E.g.,

print_date = function() {

is used as

> print_date()
[1] "2023-10-13"


  • Arguments can be grouped together in vector or a list, or enumerated individually
  • Each has pros and cons

Default values for arguments

You can (and should when possible) set default values for arguments to a function

print_date = function(date_format = "YYYY-MM-DD") {
  date = as.character(Sys.Date())
  tmp = strsplit(date, "-")
  YYYY = tmp[[1]][1]
  MM = tmp[[1]][2]
  DD = tmp[[1]][3]
  if (date_format == "MM-DD-YYYY") {
    OUT = sprintf("%s-%s-%s", MM, DD, YYYY)
  } else if (date_format == "DD-MM-YYYY") {
    OUT = sprintf("%s-%s-%s", DD, MM, YYYY)
  } else {
    OUT = date
> print_date()
[1] "2023-10-13"
> print_date("DD-MM-YYYY")
[1] "13-10-2023"

Using a function of several variables as a function of one variable

Often, you will create a function of several variables, but will want to use it as a function of fewer, e.g., in a minimisation routine

my_silly_function = function(x,y) {

To use as a function of, say, x with y=5,

function(t) my_silly_function(x = t, y = 5)

whereas to use as a function of y with x=2

function(t) my_silly_function(x = 2, y = t)

You can use any letter in the call to function; I am not using x here to make it obvious, but you could do function(x) my_silly_function(x = x, y = 5)

Rmarkdown, Sweave and Quarto

  • Rmarkdown, Sweave and Quarto are notebook-type document generating mechanisms
  • The idea is to weave together "regular text" and R commands
  • When R is run, it goes through the document.. text is formated as prescribed by the type of program used (markdown for Rmarkdown and Quarto, for Sweave), and R commands are run, with the output incorporated to the text
  • This is a good way to produce dynamic documents, if for instance you are getting some of the data used for your computations from a dynamic website


  • Uses markdown to typeset text. Markdown is a very simple text formatting language. See, e.g., here for a summary of commands. The most common
# Section
## Subsection
### Subsubsection
**bold text**
*italicised text*
[linked text](https://www.google.ca/)

R code within Rmarkdown

  • R code chunks are included in the text as
Some R code

You must use {r} after the first three backticks. Blocks like

Some R code


Some R code

are pure markdown code blocks, R does not execute the R commands there

Chunk names

It is a good idea to name chunks: when your code gets lengthy or complicated, debugging is greatly facilitated with named chunks, since errors will refer to the chunk name; with unnamed chunks, they will just refer to chunk number... RStudio also shows chunk names in the quick selection box

Chunk names appear in the {r} statement at the beginning of a chunk, e.g., in the RStudio Rmd skeleton file, the first chunk

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

is named setup

Chunk options

Chunk options follow the chunk name, if any, separated with commas. For instance, in the RStudio Rmd skeleton file, the first chunk

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

is set with include=FALSE, which prevents the code and results to show in the rendered file. See a list of options here

Global chunk options

Note that the RStudio skeleton file includes the statement

knitr::opts_chunk$set(echo = TRUE)

which sets the chunk options globally (unless overridden in a specific chunk). For instance, if you want the default behaviour to be that your code is not shown, you could do

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)

and override this in a specific chunk with

```{r some name, echo=TRUE}
Some commands

Inline R code within Rmarkdown

  • The syntax on the previous slides will display your R code as a code block (unless you choose to hide the code or the output)
  • You can also use R "inline", that is, within a regular markdown statement instead of a code chunk, using `r r-command`, where r-command is the R command you want to use

For example, the default Rmd file generated by RStudio uses the R example dataset cars. To show the number of rows in a regular sentence, outside of a code chunk, you could write

The cars dataset contains `r dim(cars)[1]` rows.

which renders as

The cars dataset contains 50 rows.

Output type

  • Rmarkdown can render your file in html, pdf or as a Word file
  • To generate a pdf, you will need to have installed