Show the code
library(tidyverse)Mastering ggplot2: A Systemic Guide to the Grammar of Graphics
Introduction
In this tutorial, we will learn how to transform raw data into meaningful visual stories using ggplot2, the most powerful visualization tool in R. We will move away from traditional coding styles and embrace the modern Pipe Operator (%>%) to build graphs step-by-step.
Our Goal: To understand every layer of the “Grammar of Graphics” and progress from an unstructured dataset to a publication-quality visualization.
By the end of this guide, you will be able to: * Understand and analyze data structures. * Build graphs layer by layer. * Use colors and facets to reveal complex relationships. * Export professional-level visualizations.
The Concept: Grammar of Graphics
The Grammar of Graphics is a theoretical framework for data visualization. Just as a sentence is composed of a subject, verb, and object, a graph is composed of specific “grammatical” layers.
- Data: The foundation. Usually a “Tidy” data frame where every column is a variable and every row is an observation.
- Aesthetics (aes): Mapping data variables to visual properties (X-axis, Y-axis, Color, Size).
- Geometries (geom): The visual shape of the data (points, bars, lines).
- Facets: Splitting data into sub-plots to compare groups.
- Statistics (stat): Mathematical transformations (e.g., calculating a mean or a regression line).
- Coordinates (coord): The 2D space of the plot (usually Cartesian).
- Themes: The non-data ink (fonts, background colors, legend positions).
1. Environment Setup
First, we must load the tidyverse suite, which includes ggplot2 and dplyr.
2. Data Inspection (The Inquiry Phase)
Before visualizing, we must perform a “Structural Audit.” We will use the mpg dataset (fuel economy data from the US EPA).
Show the code
# Viewing the first 6 rows
mpg %>% head()# A tibble: 6 × 11
manufacturer model displ year cyl trans drv cty hwy fl class
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compa…
2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compa…
3 audi a4 2 2008 4 manual(m6) f 20 31 p compa…
4 audi a4 2 2008 4 auto(av) f 21 30 p compa…
5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compa…
6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compa…Show the code
# Checking data types and structure
mpg %>% glimpse()Rows: 234
Columns: 11
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "…
$ model <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
$ displ <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.…
$ year <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…
$ cyl <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, …
$ trans <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto…
$ drv <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4…
$ cty <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1…
$ hwy <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2…
$ fl <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p…
$ class <chr> "compact", "compact", "compact", "compact", "compact", "c…Show the code
# Statistical snapshot
mpg %>% summary() manufacturer model displ year
Length:234 Length:234 Min. :1.600 Min. :1999
Class :character Class :character 1st Qu.:2.400 1st Qu.:1999
Mode :character Mode :character Median :3.300 Median :2004
Mean :3.472 Mean :2004
3rd Qu.:4.600 3rd Qu.:2008
Max. :7.000 Max. :2008
cyl trans drv cty
Min. :4.000 Length:234 Length:234 Min. : 9.00
1st Qu.:4.000 Class :character Class :character 1st Qu.:14.00
Median :6.000 Mode :character Mode :character Median :17.00
Mean :5.889 Mean :16.86
3rd Qu.:8.000 3rd Qu.:19.00
Max. :8.000 Max. :35.00
hwy fl class
Min. :12.00 Length:234 Length:234
1st Qu.:18.00 Class :character Class :character
Median :24.00 Mode :character Mode :character
Mean :23.44
3rd Qu.:27.00
Max. :44.00