Module 3-1-Principle - Data Visualization with ggplot2 in R

Author

Kelly Liang

Published

February 25, 2026

Expected Learning Outcomes

After taking this workshop, participants should be able to do following:

Explain the concept of the grammar of graphics when visualizing data with the ggplot2 package.
Be familiar with various types of charts.
Visualize data in counts and proportions.
Select appropriate charts based on strategic considerations (e.g., the characteristics of the data and audience).
Create a chart that involves one or two variables with either categorical or continuous data.
Create a chart by adding a categorical moderator (3rd variable) to the chart involving two or three variables.
Create correlation charts.
Read charts and generate insights.
Describe three popular packages that allow one to visualize data.
Explain the concept of the grammar of graphics when visualizing data with the ggplot2 package. ## Loading Packages

#install.packages("ggplot2")
#install.packages('ggrepel')
#install.packages('ggthemes')
#install.packages('scales')
#install.packages('plotly')
#install.packages('lattice')
#install.packages('GGally')
#install.packages("dplyr")
#install.packages("tidyverse")
#install.packages('ggtext')
#install.packages("glue")
#install.packages("tibble")
#install.packages("skimr")
#install.packages("gapminder")
library(ggplot2) #visualization
library(ggrepel) #labels for data
library(ggthemes) #collections of themes
library(scales) # scale
library(plotly) # interactive chart
library(GGally) # correlation
library(dplyr) # data transformation
library(tidyverse) # mega package containing 8 packages
library(ggtext) # for text visualization
library(glue) # combining multiple component
library(gapminder)
library(tibble)
library(skimr)

1. Understand mtcars data

1.1 Using Help

?mtcars
help(mtcars)
data()

A data frame with 32 observations on 11 (numeric) variables.

[, 1]	`mpg`	Miles/(US) gallon
[, 2]	`cyl`	Number of cylinders
[, 3]	`disp`	Displacement (cu.in.)
[, 4]	`hp`	Gross horsepower
[, 5]	`drat`	Rear axle ratio
[, 6]	`wt`	Weight (1000 lbs)
[, 7]	`qsec`	1/4 mile time
[, 8]	`vs`	Engine (0 = V-shaped, 1 = straight)
[, 9]	`am`	Transmission (0 = automatic, 1 = manual)
[,10]	`gear`	Number of forward gears
[,11]	`carb`	Number of carburetors

1.2 Reading data and converting to a tibble (cars)

library(tibble)
head(mtcars)

class(mtcars)

[1] "data.frame"

#ctrl + shift + M = shortcut for pipe operator ( %>% ,|> )
cars <- mtcars
cars$model <- rownames(mtcars)
rownames(cars) <- NULL
cars <- as_tibble(cars)
print(cars, n = 20, width = Inf)

# A tibble: 32 × 12
     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  21       6 160     110  3.9   2.62  16.5     0     1     4     4
 2  21       6 160     110  3.9   2.88  17.0     0     1     4     4
 3  22.8     4 108      93  3.85  2.32  18.6     1     1     4     1
 4  21.4     6 258     110  3.08  3.22  19.4     1     0     3     1
 5  18.7     8 360     175  3.15  3.44  17.0     0     0     3     2
 6  18.1     6 225     105  2.76  3.46  20.2     1     0     3     1
 7  14.3     8 360     245  3.21  3.57  15.8     0     0     3     4
 8  24.4     4 147.     62  3.69  3.19  20       1     0     4     2
 9  22.8     4 141.     95  3.92  3.15  22.9     1     0     4     2
10  19.2     6 168.    123  3.92  3.44  18.3     1     0     4     4
11  17.8     6 168.    123  3.92  3.44  18.9     1     0     4     4
12  16.4     8 276.    180  3.07  4.07  17.4     0     0     3     3
13  17.3     8 276.    180  3.07  3.73  17.6     0     0     3     3
14  15.2     8 276.    180  3.07  3.78  18       0     0     3     3
15  10.4     8 472     205  2.93  5.25  18.0     0     0     3     4
16  10.4     8 460     215  3     5.42  17.8     0     0     3     4
17  14.7     8 440     230  3.23  5.34  17.4     0     0     3     4
18  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
19  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
20  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
   model              
   <chr>              
 1 Mazda RX4          
 2 Mazda RX4 Wag      
 3 Datsun 710         
 4 Hornet 4 Drive     
 5 Hornet Sportabout  
 6 Valiant            
 7 Duster 360         
 8 Merc 240D          
 9 Merc 230           
10 Merc 280           
11 Merc 280C          
12 Merc 450SE         
13 Merc 450SL         
14 Merc 450SLC        
15 Cadillac Fleetwood 
16 Lincoln Continental
17 Chrysler Imperial  
18 Fiat 128           
19 Honda Civic        
20 Toyota Corolla     
# ℹ 12 more rows

1.3 Simple Descriptive Statistics

Note

shortcut for code chunk: ctl + alt + i

summary(cars)

      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb          model          
 Min.   :0.0000   Min.   :3.000   Min.   :1.000   Length:32         
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000   Class :character  
 Median :0.0000   Median :4.000   Median :2.000   Mode  :character  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812                     
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000                     
 Max.   :1.0000   Max.   :5.000   Max.   :8.000

glimpse(cars)

Rows: 32
Columns: 12
$ mpg   <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8…
$ cyl   <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8…
$ disp  <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 1…
$ hp    <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 18…
$ drat  <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92…
$ wt    <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3…
$ qsec  <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 1…
$ vs    <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0…
$ am    <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0…
$ gear  <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3…
$ carb  <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2…
$ model <chr> "Mazda RX4", "Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "H…

skimr::skim(cars)

Data summary
Name	cars
Number of rows	32
Number of columns	12
_______________________
Column type frequency:
character	1
numeric	11
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
model	0	1	7	19	0	32	0

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

2. Basic Plotting Methods in Base R

# Using a built in plotting function
hist(cars$disp, breaks = 10)

3. Lattice package

library(lattice)
xyplot(mpg~wt,cars)

histogram(cars$disp)

4. ggplot 2

we will use ggplot2 – the best tool in the market for data visualization – from now on.

4.1 Elaborate Examples

4.1.1 x & y are both continuous with moderator & labeller()

Wrangling

head(mtcars)
class(mtcars)

cars |> mtcars
  rownames_to_column() |> 
    as_tibble() |> 
    rename(model = rowname)

Error in mtcars: The pipe operator requires a function call as RHS (<input>:4:9)

cars |> 
  dplyr::count(cyl)

Plotting

cars |> 
  mutate(cyl)