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

1 Example 01: Math/Comments/Functions

1.1 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

1.2 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

1.3 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

1.4 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

1.5 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)

2 Exercises

2.1 Exercise 01: Edit R script

Let’s try the following steps:

Opening a new script as we just did
Give the script a name by saving the current new unnamed script (ctrl + S for Win and Cmd + S for Mac)
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.
Now we are ready to start editing our first script.
We install a R package called tidyverse in Console.
We add the code to load the tidyverse package in the script
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)

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.

--- title: "Example 01: Basics of R" subtitle: "Getting Started" author: "Jihong Zhang*, Ph.D" institute: | Educational Statistics and Research Methods (ESRM) Program* University of Arkansas date: "2025-01-15" sidebar: false execute: echo: true warning: false eval: true output-location: default code-annotations: below highlight-style: "nord" format: html: page-layout: full toc: true toc-depth: 2 toc-expand: true lightbox: true code-fold: false fig-align: center filters: - quarto - line-highlight --- # 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. ```{r} 1 / 200 * 30 #> [1] 0.15 (59 + 73 + 2) / 3 #> [1] 44.66667 sin(pi / 2) #> [1] 1 ``` ```{r} primes <- c(2, 3, 5, 7, 11, 13) primes * 2 #> [1] 4 6 10 14 22 26 primes - 1 #> [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. ```{r} # 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 ``` ## 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. ```{r} 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. ```{r} #| eval: false function_name(argument1 = value1, argument2 = value2, ...) ``` ```{r} mean(c(1, 2, 3)) ``` ## Install R Package - You can install external package to use more functions ```{r} 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. ```{r} 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. ```{r} #| eval: false 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) ``` 8. Finally, we save the script. ::: callout-note ## 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. :::