R Programming - Class 3: Introduction to ggplot2 and mutate

🎯 Objectives

By the end of this class, you will be able to:

Understand the grammar of graphics using ggplot2.
Create basic plots using ggplot().
Use the mutate() function to create new variables.
Combine data manipulation with plotting for data exploration.

🎨 Introduction to ggplot2

ggplot2 is part of the tidyverse and is used for creating elegant and customizable data visualizations.
Based on “The Grammar of Graphics” concept.
ggplot() works in layers which are added to the main ggplot() function using the ‘+’ symbol instead of a ‘|>’

✨ Basic Structure of a `ggplot`

ggplot(data = <DATA>, aes(x = <X>, y = <Y>)) +
  <GEOM_FUNCTION>()

📊 Common Geoms

Geom Function	Description
`geom_point()`	Scatter plot
`geom_bar()`	Bar plot
`geom_col()`	Bar plot (pre-counted)
`geom_histogram()`	Histogram
`geom_line()`	Line plot

✅ Try it out using the `iris` dataset

🧹 Cleaning the data

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

#Let's clean the data and keep only what we need
iris_clean <- iris |> 
  select(Species, Sepal.Length) |> 
  group_by(Species) |> 
  summarise(mean_length = mean(Sepal.Length),
            sd_length = sd(Sepal.Length),
            min_length = mean_length - sd_length,
            max_length = mean_length + sd_length) |> 
  mutate(Species = fct_reorder(Species, mean_length, .desc = T))

#Look at the summarised data by calling the variable
iris_clean

# A tibble: 3 × 5
  Species    mean_length sd_length min_length max_length
  <fct>            <dbl>     <dbl>      <dbl>      <dbl>
1 setosa            5.01     0.352       4.65       5.36
2 versicolor        5.94     0.516       5.42       6.45
3 virginica         6.59     0.636       5.95       7.22

📊 Plotting a simple bar graph using the clean data

The colour is automatically selected by R because of the fill = Species argument. It colours the bars according to the Species.

iris_clean |> 
  
  #Note: The y-axis equals to the mean_length and not just the Sepal.Length because we want to plot a single count data
  ggplot(aes(x = Species, y = mean_length, fill = Species))+
  geom_col(color = "black")+
  geom_errorbar(aes(ymin = min_length, ymax = max_length), width = 0.3)

📊 Let’s make the graph a little bit more aesthetic

To add the data points to the plot, all we need to do is either use the geom_jitter() or the geom_point() layer. Technically speaking, geom_point(position = "jitter) is geom_jitter() . But, we need to call the non-summarised dataset seperately inside geom_point() or geom_jitter() and specify the aesthetics with aes()

#this is the original code
iris_clean |> 
  ggplot(aes(Species, mean_length, fill = Species))+
  geom_col(color = "black")+
  geom_errorbar(aes(ymin = min_length, ymax = max_length), width = 0.3)+
  
  #adding the data points to the plot
  geom_jitter(data =iris |> 
               select(Species, Sepal.Length) |> 
               group_by(Species), aes(x = Species, y = Sepal.Length), width = 0.3, size = 3, alpha = 0.5)+
  
  #getting rid of the small gap between the x-axis and the start of y-axis. expand does the trick
  scale_y_continuous(expand = c(0,0), limits = c(0, 8), breaks = seq(0,8, 1))+
  theme_classic()+
  
  #making the legend disappear
  theme(
    legend.position = "none"
  )+
  
  #adding labels to x and y axis
  labs(
    x = "",
    y = "Sepal Length"
  )