1. Set up R and RStudio
2. Understand the layout and functionality of RStudio
3. Experiment with basic R expressions and data types
4. Try out RStudio.cloud

https://gsu.qualtrics.com/jfe/form/SV_0rKfKuWIRbI3f1k

R is a programming environment

• uses a well-developed but simple programming language
• allows for rapid development of new tools according to user demand
• supports data analytics tasks

1. Visit CRAN: http://cran.r-project.org
   • CRAN = Comprehensive R Archive Network
2. Click a link on the right to download R for your system (Linux, Mac or Windows)
3. Install R (it is safe to accept the default setting and keep clicking “Next”)

Step by step installation guides from YouTube:

• Mac: https://www.youtube.com/watch?v=uxuuWXU-7UQ
• Windows: https://www.youtube.com/watch?v=Ohnk9hcxf9M

RGui is an interactive R environment that comes with R installation, but it is very basic and not so user-friendly.

RStudio is a development environment for R, and provides many advanced features to improve efficiency and ease of use for R users.

1. Visit https://www.rstudio.com/products/rstudio/download/#download
2. Under Installers for Supported Platforms, choose one that fits your system (Linux, Mac or Windows)

This is the most important panel, because this is where R actually does stuff

Collections of commands (scripts) can be edited and saved.

There are ways to speed up the workflow:

• If you don’t select any code, R will just execute the line where the blinking cursor is

• Instead of clicking the “Run” icon, you can just use the keyboard shortcut: Ctrl + Enter

Menu (on the top) → Tools → Global Options

7 + 5

## [1] 12

7 - 5

## [1] 2

7 * 5

## [1] 35

7 / 5

## [1] 1.4

Like a calculator, R also has many functions that let you do more sophisticated manipulations.

round(2.05)

## [1] 2

factorial(3)  # 3! = 3 * 2 * 1

## [1] 6

sqrt(9)       # square root 

## [1] 3

Use # (number sign) to comment your codes

# 2+2
# commented out and will not be evaluated

• R will ignore anything in a line that follows #
• It is a good idea to comment your codes so that others can understand what you are trying to do.

There will be many occasions where you want to learn more about a built-in command or function. Type help(function_name) or ?function_name to get more information. For example:

help(factorial)
?factorial

Use two question marks ?? to search the whole help database, especially when you don’t know exactly the function name. For example,

??read

What do you think this command will return?

factorial(round(2.05) + 1)

R always works from the innermost parentheses to the outermost (just like a calculator)

factorial(round(2.05) + 1)

→ factorial(2 + 1)

→ factorial(3)

→ 6

R can recognize different types of data:

• numbers
• character strings (text)
• logical
• factor

Any number, no quotes.

Appropriate for math.

1 + 1
200000
sqrt(9)

class(0.3)   

## [1] "numeric"

# "class()" is function that shows the data type of an input

Any symbols surrounded by single quotes (’) or double quotes (“)

class("Unstructured Data Management")

## [1] "character"

nchar('Unstructured Data Management')

## [1] 28

toupper("Unstructured Data Management")

## [1] "UNSTRUCTURED DATA MANAGEMENT"

paste("Unstructured", "Data", "Management", sep="_") 

## [1] "Unstructured_Data_Management"

How many characters are in the following strings:

• 123
• Email
• jlee469@gsu.edu

Use paste to join the following words so that the result looks like How#are#you? (hint: ?paste)

• How
• are
• you?

Logical values are either TRUE or FALSE (Note: they are uppercase).

2 + 3 == 5   # use '==' to check whether two values are equal

## [1] TRUE

3 < 2

## [1] FALSE

TRUE == T    # use T as a short hand for TRUE; F for FALSE

## [1] TRUE

R’s form of categorical data. Saved as an integer with a set of labels (e.g. levels)

states <- factor(c("FL", "GA", "AZ"))
states

## [1] FL GA AZ
## Levels: AZ FL GA

class(states)

## [1] "factor"

levels(states)

## [1] "AZ" "FL" "GA"

is.XYZ(x) function returns TRUE/FALSE for whether x is of type XYZ

as.XYZ(x) (tries to) “cast” x to type XYZ — to translate it sensibly into a XYZ-type value

is.integer(1.5)

## [1] FALSE

is.numeric(7)

## [1] TRUE

is.character(7)

## [1] FALSE

is.character("7")

## [1] TRUE

as.character(5/2)

## [1] "2.5"

as.numeric(as.character(5/2))

## [1] 2.5

2 * as.numeric(as.character(5/2))

## [1] 5

We can give names to data objects; these give us variables

Variables are created with the assignment operator, <- or =

Be careful that R is a case sensitive language. FOO, Foo, and foo are three different variables!

x = 2      # use the equal sign to assign value
y <- 3     # you can also use an arrow to assign value
x          # print the value of a variable by typing its name

## [1] 2

x * y

## [1] 6

The assignment operator also changes values:

x

## [1] 2

x <- 8
x

## [1] 8

Using names and variables makes code: easier to design, easier to debug, less prone to bugs, easier to improve, and easier for others to read

Variable names cannot begin with numbers. Wise to avoid special characters, except for period (.) and underline (_)

Example of valid names:

• a
• b
• FOO
• my_var
• .day

Example of invalid names:

• 1
• 2nd
• ^mean
• !bad
• $

A command can spread across multiple lines. This can often improve readability.

x = paste("How", "are", "you?",   # what happens if you only enter this line
          sep=" ")
x

## [1] "How are you?"

• We can put multiple commands in the same line, but they need to be separated by a semicolon (;)

a = 1; b = 2
a + b

## [1] 3

1. Create variables f_name and l_name with value equals to your own first/last names
2. Get the number of characters in f_name and l_name and save them to length_f_name and length_l_name respectively
3. Use the paste() function to get your whole name
4. length_f_name multiplied by length_l_name
5. length_f_name divided by length_l_name
6. Show if length_f_name is greater than length_l_name

RStudio.cloud (https://rstudio.cloud/) provides a similar interface from web browsers. (No installation)

Free tier (Cloud Free) can handle most lightweight operations

1. The R code from this lab will be posted under the “In class R script” section of the content area.

2. No lab assignment for this week.

3. Remember to install R and IDE before next class.

   • The first lab assignments are due by Friday 4:30 pm on the week of the second class.

swirl() package enables you to learn R by following examples inside the console. It may feel a bit retro, but it’s one of the recommended way to onboard R while familiarizing yourself with its interface.

install.packages("swirl")
library("swirl")
swirl()

Workshops on R from GSU Library

• https://research.library.gsu.edu/R/workshop
  • https://rooms.library.gsu.edu/event/8725157
    • Date: Wednesday, January 19, 2022
    • Time: 1:00pm - 3:30pm

Research Data Services (RDS): GSU Data Ready! Badges

• https://research.library.gsu.edu/dataservices/data-ready