Hello!

We aren’t computer scientists and that’s okay!

We make lots of mistakes. Mistakes are funny. You can laugh with us.

Let’s go, Simba, Pumbaa, and Timon!

1 Overview of R

1.1 What’s R?

R is a programming language developed by Ross Ihaka and Robert Gentleman in the Department of Statistics at the University of Auckland, New Zealand.

R has comprehensive statistical analysis packages. It is open-source, free, and dynamic software environment. It has been used for data analysis, data manipulation, calculation, and graphical display.

Pros:

R’s biggest advantage is the vast array of packages, over 10,000 packages and the number is growing. Unmatched quality plotting and graphing.

Cons:

Memory management, speed, and security.

1.2 Who uses R

https://stackoverflow.blog/2017/10/10/impressive-growth-r/

1.3 When we use R

• To connect to databases
• To read/download data from websites
• To document and share methods
• When data will have frequent updates
• When we want to improve a process over time

2 RStudio - A Grand Tour

RStudio is the integrated development environment (IDE) for R

2.1 Customize R studio

Let’s add a little style so R Studio feels like home. Follow these steps to change the font-size and color scheme:

Go to Tools on the top navigation bar.
Choose Global Options…
Choose Appearance with the paint bucket.
Increase the Editor Font size
Pick an Editor theme you like. The default is Textmate if you want to go back

2.2 Make a R project

Step 1: Start a new project

In Rstudio select File from the top menu bar

Choose New Project…

Choose New Directory

Choose New Project

Enter a project name such as “training”

Select Browse… and choose a folder where you normally perform your work

Click Create Project

In the Plots and files panel, click Files, then New Folder to create subfolders, e.g. input, script, output, etc.

Step 2: Open a new script

File > New File > R Script

Click the floppy disk save icon

Give it a name: Simba.R or session1_training.R will work well

Exercise 1

Create a R project

3 R coding basics

3.1 Create an object

All R statements where you create objects have the same form:

object_name <- value

You can create new objects using the “left arrow” <-. It’s typed with a less-than sign < followed by a hyphen -. It’s more officially known as the assignment operator.

RStudio’s keyboard shortcut: Alt + - (the minus sign)

3.2 Name an object

Object names must start with a letter, and can only contain letters, numbers, _ and .

Exercise 2

What happens when you add these to your R script?

To run a line of code in your script, click the cursor anywhere on that line and press CTRL+ENTER

n data <- 5

n*data <- 5

n_data <- 5

n.data <- 5

data! <- 5

1st_data <- 5

3.3 Calling functions

R has a large collection of built-in functions that are called like this:

function_name(arg1 = val1, arg2 = val2, ...)

For example:

using sum() to take summation of all numbers in its arguments

a <- 5
b <- 6
c <- 10
sum(a,b,c)

To get the help of a function, type ?function_name or search from the ‘Plots and files’ Panel

Exercise 3

Use function seq() to create a sequence of numbers from 0 to 20 with increment 5.

3.4 Basic data types

Four common data types:

integer: numbers that do not contain decimal values, 2L
as.integer(), integer(), is.integer()

double or numeric: numbers with decimal values, 2.0, 5.2
as.double(), double(), is.double()

character or string: alphabets or numbers enclosed by quotes, “hello”, “a”
as.character(), character(), is.character()

logical: a variable that can have a value of TRUE/T and FALSE/F
as.logical(), logical(), is.logical

R provides several functions to examine the type of objects, for example

• class()

• typeof()

• str()

3.5 Data structure

Commonly used data structures in R:

• vector

• data frame

• list

• matrix

3.5.1 Vector

Vector is the simplest type of data structure in R. It is a sequence of data elements of the same basic type.

Create a vector using function c(): place the elements seperated by a comma between parentheses.

conc <- c(80, 40, 8) # numeric vector (use hash sign to add comments)

conc

is.vector(conc) # check if conc is a vector

length(conc) # print the length of conc

str(conc) # display overall info of conc

View(conc) # invoke a spreadsheet-style data viewer

pollutants <- c("ozone", "no2", "pm2.5") # character vector

Exercise 4

Create a logical vector with elements of TRUE, FALSE, FALSE and check its info

3.5.2 Data frame

A table in R is known as a data frame. We can think of it as a group of columns, where each column is made from a vector. Data frames in R have columns of data that are all the same length.

Let’s make a data frame with two columns using data.frame():

#create a table with two columns: item & value
monitor_df <- data.frame(pollutant = pollutants,
                          design_value = conc)

# print the monitor_df data frame to the console
monitor_df

View your data frame using: View()

Use the $ sign after the name of your table to see the values in a column

monitor_df$pollutant

Exercise 5

Add a third column (above_naaqs) to monitor_df using the logical vector created in Exercise 4. View your data frame using str() and View()

3.5.3 Matrix

A matrix is two-dimensional data with same types

3.5.4 List

A list is a combination of elements with different types and lengths.

4 Recap

creating an R project
basic data types
basic data structures