Introduction to R

Nihil Kaza

2024-08-17

Agenda

Working with R & Rstudio
R Basics
Built-in Functions
Working with Vectors
Sneak Peak

What is R? What is Rstudio ?

1 2 3 4 5

What is R?

1 2 3 4 5

R is an open-source (free!) scripting language for working with data.

It started out as a statistical analysis language but is so much more now.

Getting R & Rstudio

1 2 3 4 5

You need the R language

And also the Integrated Development Environment

Navigating RStudio

1 2 3 4 5

project files are here.
Visualisations show up here

imported data shows up here

code can go here

Navigating RStudio

1 2 3 4 5

project files are here

imported data shows up here

code can also
go here

Organizing with projects

1 2 3 4 5

highly recommend nay require using projects to stay organized

keeps code files and data files together, allowing for easier file path navigation and better reproducible work habits

File -> New Project

more guidance: here and here

Organizing with projects

1 2 3 4 5

project files are here

imported data shows up here

code can also
go here

Organizing with projects

1 2 3 4 5

.
└── my_awesome_project
    ├── my_awesome_project.Rproj
    ├── data
        ├── raw
        └── temp
        └── processed
    ├── src
        ├── 01_first_script.R
        └── 02_second_script.R
    ├── R 
        ├── useful_function_1.R
        └── useful_function_2.R
    ├── writing
        ├── 01_chapter.Rmd
    ├── output

   └──README.md

Organizing with projects

1 2 3 4 5

# create directory called 'data'
dir.create("data")

# create subdirectory raw in the data directory
dir.create("data/raw")

# create subdirectory processed in the data directory
dir.create("data/processed")

# list the files and directories
list.files(recursive = TRUE, include.dirs = TRUE)

# [1] "data"  "data/raw" "data/processed"  "my_awesome_project.Rproj"

Exercise 1

R Basics

1 2 3 4 5

Tentative steps

1 2 3 4 5

project files are here.
Visualisations show up here

imported data shows up here

code can go here

Tentative steps

1 2 3 4 5

2 + 2

[1] 4

sin(pi/2)

[1] 1

log10(3)

[1] 0.4771213

log(3)

[1] 1.098612

sqrt(4)

[1] 2

4^2

[1] 16

exp(4)

[1] 54.59815

Assign to an object

1 2 3 4 5

<- is the assignment operator¹

my_obj <- 48

my_obj

[1] 48

my_obj <- exp(4) * sin(2)^3

my_obj

[1] 41.04836

my_obj <- (exp(4)*sin(2))^3

my_obj

[1] 122363.4

my_obj <- "test"

my_obj

[1] "test"

Manipulate objects

1 2 3 4 5

my_obj1 <- 48

my_obj1

[1] 48

my_obj2 <- exp(4) * sin(2)^3

my_obj2

[1] 41.04836

my_obj3 <- my_obj1 + my_obj2

my_obj3

[1] 89.04836

my_obj4  <- "test"

my_obj5 <- my_obj3 + my_obj4

Error in my_obj3 + my_obj4: non-numeric argument to binary operator

Google is your friend to decipher this error!

Objects may be modified

1 2 3 4 5

my_obj4

[1] "test"

my_obj4 <- my_obj3

my_obj4

[1] 89.04836

my_obj5 <- my_obj3 + no_obj

Error in eval(expr, envir, enclos): object 'no_obj' not found

my_obj5

Error in eval(expr, envir, enclos): object 'my_obj5' not found

Pay attention to the errors!

Built-in Functions

1 2 3 4 5

Functions

1 2 3 4 5

my_vec <- c(2, 3, 1, 6, 4, 3, 3, 7)
my_vec

[1] 2 3 1 6 4 3 3 7

length(my_vec)

[1] 8

mean(my_vec)

[1] 3.625

sd(my_vec)

[1] 1.995531

my_vec[5]

[1] 4

c is built-in R function. short for concatenate
Likewise length, mean, sd, [ are other built-in functions in R.

Anatomy of Functions

1 2 3 4 5

Functions can be broken down into three components: arguments (formals), body, and environment.

formals(sd) # What arguments can you pass to the function `sd`

$x


$na.rm
[1] FALSE

body(sd)

sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x), 
    na.rm = na.rm))

environment(sd)

<environment: namespace:stats>

Anatomy of Functions

1 2 3 4 5

Functions are objects, just as vectors are objects. Be very careful!

sd(my_vec)

[1] 1.995531

sd <- function(x){x^2}

sd

function(x){x^2}

sd(my_vec)

[1]  4  9  1 36 16  9  9 49

stats::sd(my_vec)

[1] 1.995531

sd <- 8

sd

[1] 8

sd(my_vec)

[1] 1.995531

Getting Help

1 2 3 4 5

?mean

Ask for help!

1 2 3 4 5

Help Me Help You!

1 2 3 4 5 6

I am importing fairly large csv’s (2 - 3 million rows). When I import these using read_csv, it fails. Anyone know why?

This question is missing the key information required to reproduce and troubleshoot the problem:

  How is the datafile being imported delimited(csv(comma-delimited), other)?

  What operating system is involved(Linux, Windows)? What locale?

  What version of R running? What functions and libraries are being used?

  The post does not provide an example of the R code, with the data that led to the problem

Working with Vectors

1 2 3 4 5

Extract Elements from Vectors

1 2 3 4 5

[ is very useful extractor function.
Do not confuse with (.
Extract based on position

my_vec

[1] 2 3 1 6 4 3 3 7

my_vec[3]

[1] 1

my_vec[c(3,5,7)]

[1] 1 4 3

Extract Elements from Vectors

1 2 3 4 5

my_vec

[1] 2 3 1 6 4 3 3 7

my_vec > 4

[1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE

my_vec[my_vec > 4]

[1] 6 7

# this is same as
my_vec[c(FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE)]

[1] 6 7

my_vec[my_vec >= 4]        # values greater or equal to 4

[1] 6 4 7

my_vec[my_vec == 4]        # values equal to 4

[1] 4

my_vec[my_vec != 4]         # values not equal to 4

[1] 2 3 1 6 3 3 7

Replace Element(s)

1 2 3 4 5

my_vec[4] <- 500
my_vec

[1]   2   3   1 500   4   3   3   7

my_vec[c(6, 7)] <- 100
my_vec

[1]   2   3   1 500   4 100 100   7

my_vec[my_vec <= 4] <- 1000
my_vec

[1] 1000 1000 1000  500 1000  100  100    7

Ordering Vectors

1 2 3 4 5

vec_sort <- sort(my_vec)
vec_sort

[1]    7  100  100  500 1000 1000 1000 1000

vec_sort2 <- sort(my_vec, decreasing = TRUE)
vec_sort2

[1] 1000 1000 1000 1000  500  100  100    7

height <- c(180, 155, 160, 167, 181)
p.names <- c("Joanna", "Charlotte", "Helen", "Karen", "Amy")
height_ord <- order(height)
height_ord

[1] 2 3 4 1 5

p.names[height_ord]

[1] "Charlotte" "Helen"     "Karen"     "Joanna"    "Amy"

Vectorisation

1 2 3 4 5

my_vec * 5

[1] 5000 5000 5000 2500 5000  500  500   35

my_vec3 <- c(17, 15, 13, 19, 11, 0)
my_vec + my_vec3

[1] 1017 1015 1013  519 1011  100  117   22

Beware though of recycling

my_vec4 <- c(5,10)

my_vec + my_vec4

[1] 1005 1010 1005  510 1005  110  105   17

Beware of Missing Data

1 2 3 4 5

temp  <- c(7.2, NA, 7.1, 6.9, 6.5, 5.8, 5.8, 5.5, NA, 5.5)
temp

 [1] 7.2  NA 7.1 6.9 6.5 5.8 5.8 5.5  NA 5.5

mean(temp)

[1] NA

mean(temp, na.rm = TRUE)

[1] 6.2875

Everything Printed is not True

1 2 3 4 5

sd_temp <- sd(temp, na.rm = TRUE)
sd_temp

[1] 0.719995

sd_temp == 0.719995

[1] FALSE

options(digits = 10)
sd_temp

[1] 0.7199950397

options(digits = 20)
sd_temp

[1] 0.71999503966545297384

Exercise 2

Sneak Peak

1 2 3 4 5

Rectangular Data

1 2 3 4 5

Introduction to R

Agenda

What is R? What is Rstudio ?

What is R?

Getting R & Rstudio

Navigating RStudio

Navigating RStudio

Organizing with projects

Organizing with projects

Organizing with projects

Organizing with projects

Exercise 1

R Basics

Tentative steps

Tentative steps

Assign to an object

Manipulate objects

Objects may be modified

Built-in Functions

Functions

Anatomy of Functions

Anatomy of Functions

Getting Help

Ask for help!

Help Me Help You!

Working with Vectors

Extract Elements from Vectors

Extract Elements from Vectors

Replace Element(s)

Ordering Vectors

Vectorisation

Beware of Missing Data

Everything Printed is not True

Exercise 2

Sneak Peak

Rectangular Data

Thank You!