Example 01: Basics of R

Getting Started

Author
Affiliation

Jihong Zhang*, Ph.D

Educational Statistics and Research Methods (ESRM) Program*

University of Arkansas

Published

January 15, 2025

Example 01: Math/Comments/Functions

Coding Basics

  • Use R for basic math, like 1 / 200 * 30.
  • Create objects using the assignment operator <-, for example, x <- 3 * 4.
  • Vectors can be formed using c(), like primes <- c(2, 3, 5, 7, 11, 13).
  • Utilize keyboard shortcuts in RStudio for efficiency.
1 / 200 * 30
[1] 0.15
#> [1] 0.15
(59 + 73 + 2) / 3
[1] 44.66667
#> [1] 44.66667
sin(pi / 2)
[1] 1
#> [1] 1
primes <- c(2, 3, 5, 7, 11, 13)
primes * 2
[1]  4  6 10 14 22 26
#> [1]  4  6 10 14 22 26
primes - 1
[1]  1  2  4  6 10 12
#> [1]  1  2  4  6 10 12

Comments

  • R ignores text after #, which allows for inline documentation.
  • Comments should explain the purpose and rationale behind code sections.
  • Avoid over-commenting straightforward code; focus on complex logic.
# create vector of primes
primes <- c(2, 3, 5, 7, 11, 13)

# multiply primes by 2
primes * 2
[1]  4  6 10 14 22 26
#> [1]  4  6 10 14 22 26

Name of R object

  • R Object names should start with a letter and can include numbers, _, and ..
  • Use snake_case for clarity in names involving multiple words.
this_is_a_really_long_name <- 2.5
r_rocks <- 2^3

Calling Functions

  • Built-in functions are called by specifying arguments, like function_name(value1, value2).
  • Use RStudio’s auto-completion and tooltips to assist in function usage.
function_name(argument1 = value1, argument2 = value2, ...)
mean(c(1, 2, 3))
[1] 2

Install R Package

  • You can install external package to use more functions
install.packages("ggplot2")
  • Both ggplot() and geom_point() are functions included in the package ggplot2
  • To use them, you need to load the package ggplot2 using library() function
  • mapping, color, and size are called arguments of geom_point() for detailed settings.
library(ggplot2)
ggplot() +
  geom_point(mapping = aes(x = 1:100, y = 100:1), 
             color = "tomato", 
             size = 2)

Exercises

Exercise 01: Edit R script

Let’s try the following steps:

  1. Opening a new script as we just did

  2. Give the script a name by saving the current new unnamed script (ctrl + S for Win and Cmd + S for Mac)

  3. A good convention is to use a descriptive name, with lower case letters, no spaces, only hyphens to separate words, and then followed by the suffix .R. We will call this script my-first-script.R.

  4. Now we are ready to start editing our first script.

  5. We install a R package called tidyverse in Console.

  6. We add the code to load the tidyverse package in the script

  7. We add the following code in the script. Run the whole script.

install.packages("remotes") # install one package called "remotes"
library("remotes") # load the package into your R session
install_github(repo = "JihongZ/ESRM6990V") # install one GitHub package from my GitHub repository
library(ESRM6990V) # load the package into your R session
jihong(details = TRUE) # call one function called "jihong" from the package

# Left
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_smooth()

# Middle
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_smooth(aes(group = drv))

# Right
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_smooth(aes(color = drv), show.legend = FALSE)
  1. Finally, we save the script.
Dataset mpg

  • displ: A car’s engine size, in liters. A numerical variable.

  • hwy: A car’s fuel efficiency on the highway, in miles per gallon (mpg). A car with a low fuel efficiency consumes more fuel than a car with a high fuel efficiency when they travel the same distance. A numerical variable.

  • class: Type of car. A categorical variable.

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