R: Plotting with Base R & ggplot2

What you will learn

R has powerful built-in plotting (base R graphics) and an even more popular package, ggplot2. You'll also learn the tidyverse — a collection of packages for modern R.

Base R plotting

x <- 1:10
y <- x^2

plot(x, y, type = "l", col = "blue", main = "Square Function")

# Histogram
hist(rnorm(100), main = "Normal Distribution", col = "lightblue")

# Boxplot
boxplot(mtcars$mpg ~ mtcars$cyl,
        main = "MPG by Cylinders",
        xlab = "Cylinders", ylab = "Miles Per Gallon")

The grammar of graphics: ggplot2

ggplot2 is the most popular R graphics package. It uses a layered approach:

library(ggplot2)

ggplot(mtcars, aes(x = wt, y = mpg)) +
    geom_point() +
    geom_smooth(method = "lm") +
    labs(title = "Weight vs MPG", x = "Weight", y = "MPG")

The tidyverse

The tidyverse is a collection of R packages designed for data science:

Package	Purpose
`dplyr`	Data manipulation (filter, select, mutate, summarize)
`tidyr`	Data tidying (pivot, separate, unite)
`ggplot2`	Data visualization
`readr`	Reading data (CSV, TSV, etc.)
`purrr`	Functional programming
`tibble`	Modern data frames

library(dplyr)

mtcars %>%
    filter(mpg > 20) %>%
    select(mpg, hp, wt) %>%
    summarise(avg_mpg = mean(mpg))

The %>% (pipe) operator passes the result on the left as the first argument of the function on the right.

Common mistakes

Forgetting library(ggplot2) before using ggplot functions.
Using = inside aes() instead of == for conditions — aes() expects column names, not logical expressions.
Not installing packages first — install.packages("ggplot2") before library(ggplot2).
Mixing base R and tidyverse pipe operators — |> (base R 4.1+) vs %>% (magrittr/tidyverse).

Quick check below!