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

0.1 Expected Learning Outcomes

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

1. Explain the concept of the grammar of graphics when visualizing data with the ggplot2 package.

2. Be familiar with various types of charts.

3. Visualize data in counts and proportions.

4. Select appropriate charts based on strategic considerations (e.g., the characteristics of the data and audience).

5. Create a chart that involves one or two variables with either categorical or continuous data.

6. Create a chart by adding a categorical moderator (3rd variable) to the chart involving two or three variables.

7. Create correlation charts.

8. Read charts and generate insights.

9. Describe three popular packages that allow one to visualize data.

10. Explain the concept of the grammar of graphics when visualizing data with the ggplot2 package.

0.2 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")
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)

1 1. Understand mtcars data

1.1 1.1 Using Help

?mtcars
help(mtcars)

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 Note]

1.2 1.2 Reading data and converting to a tibble (cars)

head(mtcars)

class(mtcars)

[1] "data.frame"

# short cut for pipe operator (%>%, |>) : ctl + shift + M
cars <- mtcars |>
  rownames_to_column() |>
  as_tibble() |>
  rename(model = rowname) |>
  print(n = 20, width = Inf)

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

	mpg <dbl>	cyl <dbl>	disp <dbl>	hp <dbl>	drat <dbl>
Mazda RX4	21.0	6	160	110	3.90
Mazda RX4 Wag	21.0	6	160	110	3.90
Datsun 710	22.8	4	108	93	3.85
Hornet 4 Drive	21.4	6	258	110	3.08
Hornet Sportabout	18.7	8	360	175	3.15
Valiant	18.1	6	225	105	2.76

class(mtcars)

[1] "data.frame"

[1] “data.frame”

cars <- 
  mtcars %>% # piping operator from dplyr (shortcut: Ctrl+Shift+M)
    rownames_to_column() %>% # do this before changing the data to tibble as the conversion will remove rownames in tibble.
    as_tibble() %>%  
    rename(model = rowname) %>% 
    print (n = 20, width = Inf)

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

cars

1.3 1.3 Simple Descriptive Statistics

Note

shortcut for code chunk: ctl + alt + i

summary(cars)

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

glimpse(cars)

Rows: 32
Columns: 12
$ model <chr> "Mazda RX4", "Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "H…
$ 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…

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	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	0	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	0	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	0	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	0	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	0	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	0	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	0	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	0	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	0	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	0	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	0	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

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

glimpse(cars)

Rows: 32
Columns: 12
$ model <chr> "Mazda RX4", "Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "H…
$ 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…

Rows: 32 Columns: 12 $ model <chr> “Mazda RX4”, “Mazda RX4 Wag”, “Datsun 710”, “Hornet 4 Drive”, “H… $ 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…

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	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	0	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	0	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	0	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	0	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	0	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	0	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	0	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	0	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	0	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	0	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	0	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

	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	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	0	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	0	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	0	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	0	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	0	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	0	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	0	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	0	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	0	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	0	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	0	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

2 2. Basic Plotting Methods in Base R

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

#Lattice package

install.packages("lattice")

Error in `contrib.url()`:
! trying to use CRAN without setting a mirror

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

Error:
! object 'mpt' not found

histogram(cars$disp, breaks = 10)

3 ggplot2

• We will use ggplot2 - the best tool in the market for data visualization - from now on.

##4.1 Elaborate Examples

3.0.1 4.1.1 x & y are both continuous with moderator & labeller()

cars |>
  count(cyl)

#barplot will be discussed later

4 3. Lattice package

library(lattice)
xyplot(mpg ~ wt, cars) #xyplot(y~x,data)

histogram(cars$disp, breaks = 10)

5 4. ggplot2

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

5.1 4.1. Elaborate Examples

5.1.1 4.1.1 x & y are both continuous with moderator & labeller()

cars |> 
  count(cyl)

cyl <dbl>	n <int>
4	11
6	7
8	14

3 rows

easy_labels <- c("4" = "4 Cylinder Cars",
                 "6" = "6 Cylinder Cars",
                 "8" = "8 Cylinder Cars"
                 )

cars %>% 
  mutate(cyl = factor(cyl)) %>% 
  ggplot(aes(x = mpg, y = disp, color = cyl)) +
  geom_point(size=3, 
             color='black'
             ) + #geom
 # geom_jitter() +
  geom_smooth(method = lm, se = FALSE) +
 # facet_grid(cols = vars(cyl), 
  facet_wrap(~ cyl, 
             #scales = "free_y", 
             ncol = 1,
             strip.position = "top",
             labeller = labeller(cyl = easy_labels)
             ) + #faceting
  scale_y_continuous(limits = c(0, NA), expand= c(0,0)) +
  coord_flip() + #coordinate
  theme_economist() +  #labels
  labs(title = 'MPG vs Displacement', 
       x = 'Miles Per Gallon', 
       y = 'Displacement') +
  theme(
       strip.placement = "outside", 
       strip.background = element_blank(),
       panel.background = element_blank(),
       panel.grid = element_blank(),
       axis.line = element_line()
        ) +
  guides(color = 'none')

5.1.2 4.1.2 x & y are both continuous with moderator & as_labeller()

easy_labels_n <- as_labeller(c(`4` = "4 Cylinder Cars",
                               `6` = "6 Cylinder Cars",
                               `8` = "8 Cylinder Cars"
                              )
                            )

ggplot(data = cars, aes(x = disp, y = mpg, color = factor(cyl))) + #data
  geom_point(size=3) + #geometry
  facet_grid(~ factor(cyl), 
             labeller =  easy_labels_n 
             ) + #faceting
  theme_bw() + #theme type
  labs(title = 'MPG vs Displacement', #labels
       x = 'Displacement', 
       y = 'Miles Per Gallon',
       color = "# of Cylender"
       ) +
  guides(color = 'none')

5.2 4.2. One continous variable: geom_histogram()

ggplot(data = cars, aes(x = mpg)) +
  geom_histogram(binwidth = 5, color = "red", fill = "blue")

ggplot(data = cars, aes(x = mpg, fill = mpg)) + 
  geom_histogram(binwidth = 3, color = "red", fill = "blue")

5.3 4.3. One categorical variable: geom_bar()

5.3.1 4.3.1 Bar chart

barplot(table(cars$cyl)) # from graphics package

# the same as above in ggplot2
ggplot(data = cars, aes(x = factor(cyl))) + 
  geom_bar(fill = "blue")

ggplot(data = cars, aes(x = factor(cyl), fill = factor(cyl))) + 
  geom_bar() +
  guides(fill = "none") # remove fill legend.

5.3.2 4.3.2 How to reorder bars in Barplot

5.3.2.1 4.3.2.1. mpg data

#help(mpg)

A data frame with 234 rows and 11 variables:

• manufacturer: manufacturer name

• model: model name

• displ: engine displacement, in litres

• year: year of manufacture

• cyl: number of cylinders

• trans: type of transmission

• drv: the type of drive train, where f = front-wheel drive, r = rear wheel drive, 4 = 4wd

• cty: city miles per gallon

• hwy: highway miles per gallon

• fl: fuel type

• class: “type” of car

mpg <- mpg
head(mpg)

manufacturer <chr>	model <chr>	displ <dbl>	year <int>	cyl <int>	trans <chr>	drv <chr>
audi	a4	1.8	1999	4	auto(l5)	f
audi	a4	1.8	1999	4	manual(m5)	f
audi	a4	2.0	2008	4	manual(m6)	f
audi	a4	2.0	2008	4	auto(av)	f
audi	a4	2.8	1999	6	auto(l5)	f
audi	a4	2.8	1999	6	manual(m5)	f

6 rows | 1-7 of 11 columns

5.3.2.2 4.3.2.2. Reorder manufacturers by Count of cars

ggplot(data = mpg, aes(x = manufacturer, fill = manufacturer)) + #not sorted
  geom_bar(stat = "count", show.legend = FALSE) +
  coord_flip()

# use reorder bars in inside ggplot function
mpg %>% 
  count(manufacturer, sort = TRUE) %>% 
  ggplot(aes(x = reorder(manufacturer, n), y = n, fill = manufacturer)) + #reorder() or fct_reorder()/fct_rev()
  geom_bar(stat = "identity", show.legend = FALSE)+
  coord_flip()

# Use reorder bars inside mutate function during wrangling
mpg %>% 
  count(manufacturer, sort = TRUE) %>% 
  mutate(manufacturer = fct_reorder(manufacturer, n)
         ) %>% 
  ggplot(aes(x = manufacturer, y = n, fill = manufacturer)) +
  geom_bar(stat = "identity", show.legend = FALSE)+
  coord_flip()

5.3.2.3 4.4. Two Variables

5.3.3 4.4.1. Cutting a Categorical Var by a Categorical Var: Stacked vs. Dodged vs. Filled Barplots

5.3.3.1 4.4.1.1. Stack vs. dodged barplots

ggplot(data = cars, aes(x = factor(cyl), fill = factor(am))) +
  geom_bar(stat = "count", position = "stack") +
  labs(x = "Cylinder Size",
       y = "# of Cars",
       fill = "Automatic vs. Manual"
       )

ggplot(data = cars, aes(x = factor(cyl), fill = factor(am))) +
  geom_bar(position = "dodge")+
  labs(x = "Cylinder Size",
       y = "# of Cars",
       fill = "Automatic vs. Manual"
       )

5.3.3.2 4.4.1.2. Stack vs. Filled barplots

# stack.
ggplot(data = mpg, aes(x = manufacturer, fill = class)) + 
  geom_bar(stat = "count", position = "stack") +
  theme_economist() +
  theme(axis.text.x = element_text(angle = 90, hjust = 0)) +
  labs(title = "# of Cars by Class for Each Manufacturer",
       x = NULL,
       y = "# of cars",
              ) +
  coord_flip()

# fill.
ggplot(data = mpg, aes(x = manufacturer, fill = class)) + 
  geom_bar(stat = "count", position = "fill") + #fill converts count to fraction.
  scale_y_continuous(labels = percent) +
  theme_economist() +
  theme(axis.text.x = element_text(angle = 0, hjust = 0)) +
  labs(title = "Percent of cars by class for each Manufacturer",
       x = "",
       y = NULL,
       ) + 
  coord_flip()

5.3.3.3 4.4.1.3. faceted barplots

# manufacturer by class
ggplot(data = mpg, aes(x = manufacturer, fill = class)) + 
  geom_bar(stat = "count") +
  facet_wrap(~ class) +
  theme_bw() +
  labs(title = "# of Cars for Manufacturer by class",
       x = NULL,
       y = "# of cars",
              ) +
  coord_flip() +
  guides(fill = "none")

# class by manufacturer
ggplot(data = mpg, aes(x = class, fill = manufacturer)) + 
  geom_bar(stat = "count") +
  facet_wrap(~ manufacturer) +
  theme_bw() +
  labs(title = "# of Cars for Class by Manufacturer",
       x = NULL,
       y = "# of cars",
              ) +
  coord_flip()+
  guides(fill = "none")

5.3.4 4.4.2. Scatter plot (x: continuous, y:continuous)

mpg %>% 
  ggplot(aes(cyl, cty))+
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) + #lm = linear model; se = standard error
  geom_jitter()+
  theme_economist_white()+
  labs(title = "City Mileage versus Cylinders",
       subtitle = "Scatter and jitter plot with regression line",
       caption = "Source: mpg dataset",
       x = "# of Cylnders",
       y = "City mileage", 
       )

5.3.5 4.4.3. x = categorical, y = continous

• A box plot is best for this situation. A box plot will produce an average of y variable for each level of x.

• To use a bar plot for this situation, one needs to average y variable per each level of x variable first before drawing a barplot.

5.3.5.1 4.4.3.1 Boxplot

ggplot(data=cars, aes(x=cyl, y=mpg, group=cyl))+ # the same as below
  geom_boxplot(aes(fill = cyl)) # different hues of the same color since cyl is continuous

ggplot(data=cars, aes(x=cyl, y=mpg, group=cyl))+ # the same as below
  geom_boxplot(aes(fill = factor(cyl))) # different color since cyl has been factorized and became categorical.

ggplot(data=cars, aes(x=factor(cyl), y=mpg, group = factor(cyl)))+ # group argument doesn't add color.
  geom_boxplot()

ggplot(data=cars, aes(x=factor(cyl), y=mpg, fill = factor(cyl)))+ # use fill for coloring inside the box.
  geom_boxplot() + 
    labs(x = "# of Cylinder",
         y = "Average Mile Per a Galon of Gasoline",
         title = "Fuel Efficiency by Cylinder Size",
        ) +
  guides(fill = "none")

ggplot(data=cars, aes(x=factor(cyl), y=mpg, color = factor(cyl)))+ # use color to add color to the borderlines.
  geom_boxplot()

5.3.5.2 4.4.3.2. geom_bar(stat = “identity”)

Tip

geom_bar(stat = "identity") = geom_col()(1) Wrong visualization

# when y is not a count but another continuous variable, geom_bar() needs stat = 'identity' as below. In this case, just use geom_col().
ggplot(data=cars, aes(x=factor(cyl), y=mpg, fill = factor(cyl))) +
  geom_bar(stat = 'identity') # the same as below

ggplot(data=cars, aes(x= cyl, y=mpg, fill = cyl)) +
  geom_bar(stat = 'identity') # similar to the above but different hues of the same color.

ggplot(data=cars, aes(x=factor(cyl), y=mpg, fill = factor(cyl))) +
  geom_col() # simpler than geom_bar

Warning

However, notice the mpg in the charts created above. mpg for each level of x variable is too high? Would it be sum or average of y for each level of x? Let’s wrangle data first before visualization. You will learn how to wrangle data in another module. Please bear with me for now.

sum: The result below shows the same y variable as the one for the two charts above.

cars %>% 
  group_by(cyl) %>% # group_by() is from dplyr
  summarize(mpg_sum = sum(mpg)) %>% # summarize() is from dplyr
  ggplot(aes(x=factor(cyl), y=mpg_sum, fill = factor(cyl))) +
  geom_col()

• average: The result below shows the same y variable as the one for boxplot.

cars %>% 
  group_by(cyl) %>% 
  summarize(mpg_mean = mean(mpg), .groups = "drop") %>% 
  ggplot(aes(x=factor(cyl), y=mpg_mean, fill = factor(cyl))) +
  geom_col()

Caution

• After observing the two charts above, we can conclude that geom_bar or geom col uses a sum of y for each level of x variable, which is not what we want.

• Thus, to use geom_bar or geom_col for categorical x and continuous y, we need to average y for each level of categorical x first before we use goem_bar() or geom_col.

5.3.5.2.1 (2) Correct visualization

• calculating mean first during wrangling and use stat=“identity” in geom_bar()

cars %>% 
  group_by(cyl) %>% 
  summarize(mpg_mean = mean(mpg), .groups = "drop") %>% 
  ggplot(aes(x = factor(cyl), y = mpg_mean, fill = factor(cyl))) +
  geom_bar(stat = 'identity') # the same as above chart

• Using stat = “summary” with fun without wrangling

cars %>% 
  ggplot(aes(factor(cyl), mpg, fill = factor(cyl))) +
  geom_bar(stat = "summary", fun = mean)

cars %>% 
  ggplot(aes(factor(cyl), mpg, fill = factor(cyl))) +
  geom_bar(stat = "summary", fun = median)

cars %>% 
  ggplot(aes(factor(cyl), mpg, fill = factor(cyl))) +
  geom_bar(stat = "summary", fun = max)

5.3.5.2.2 (3) Exercise with geom_col()

Task: show average of highway mileage by each car class sorted by the performance of the car from best to the worst.

head(mpg)

manufacturer <chr>	model <chr>	displ <dbl>	year <int>	cyl <int>	trans <chr>	drv <chr>
audi	a4	1.8	1999	4	auto(l5)	f
audi	a4	1.8	1999	4	manual(m5)	f
audi	a4	2.0	2008	4	manual(m6)	f
audi	a4	2.0	2008	4	auto(av)	f
audi	a4	2.8	1999	6	auto(l5)	f
audi	a4	2.8	1999	6	manual(m5)	f

6 rows | 1-7 of 11 columns

#correct
mpg %>% 
  group_by(class) %>% 
  summarise(hwy_mean = mean(hwy)) %>% 
  ggplot(aes(x = reorder(class, hwy_mean), y=hwy_mean, fill=class)) +
  geom_col() +
  labs(x = "Class",
       y = "AVG. Highway MPG",
       title = "Average Highway Mile Per Gas by Class"
       ) +
  coord_flip() +
  guides(fill = "none")

# Wrong: why? see the magnitudes of hwy on x-axis.
mpg %>% 
  ggplot(aes(x = reorder(class, hwy), y = hwy, fill=class)) +
  geom_col() +
  coord_flip()

# Fix the problem
mpg %>% 
  ggplot(aes(x = reorder(class, hwy), y = hwy, fill=class)) +
  geom_bar(stat = "summary", fun = mean) +
  labs(x = "Class",
       y = "AVG. Highway MPG",
       title = "Average Highway Mile Per Gas by Class"
       ) +
  coord_flip() +
  guides(fill = "none")

5.4 4.5. Three variables (x=Categorical, y=continuous, moderator = categorical)

In this case, both boxplots and barplots work well. With barplot, we don’t need to calculate average first as R will automatically do so across all levels of the categorical moderator.

5.4.1 4.5.1. Boxplot (with facet_grid)

5.4.1.1 (1) Basic with faceting

?mtcars

ggplot(data=cars, aes(x=factor(cyl), y=mpg, fill = factor(cyl)))+ 
  geom_boxplot() +
  facet_grid(~ factor(am)) + #am    Transmission (0 = automatic, 1 = manual) 
  labs(title = "Impact of Cylinder size on mpg across am",
       x = "Number of Cylinder",
       y = "Average MPG"
       )

5.4.1.2 (2) Changing facet label with labeller argument

# change facet label
to_string <- as_labeller(c(`0` = "Automatic", `1` = "Manual")) # for numbers, use ``

ggplot(data=cars, aes(x=factor(cyl), y=mpg, fill = factor(cyl)))+ 
  geom_boxplot(show.legend = FALSE) +
  facet_grid(~ factor(am), labeller = to_string) + 
  labs(title = "Impact of Cylinder size on mpg across am",
       x = "Number of Cylinder",
       y = "Average MPG"
       )

5.4.2 4.5.2. Barplot

Note We know barplot doesn’t work when y is continuous, but it works okay when we cut it by another categorical variable (moderator) as you will see below.

5.4.2.1 4.5.2.1 Dodged Barplot (Changing fill labels)

5.4.2.1.1 (1) Using mutate()

# change moderator's factor level prior to visualization

mtcars %>% as_tibble() %>% 
  mutate(am = factor(am), # change the labels of the factor levels in wrangling
         am = case_when(am == "0" ~ "Automatic",
                        am == "1" ~ "Manual"
                        )
         ) %>% 
ggplot(aes(x = factor(cyl), y = mpg, fill = am)) +
  geom_col(position = "dodge") + 
  labs(title = "Impact of the number of cylinder on MPG across AM",
       subtitle = "An example of barplot when we don't need to average y varaible",
       x = "The number of cylinder",
       y = "Average MPG",
       fill = "Automatic vs. Manual"
      )

5.4.2.1.2 (2) Using scale_fill_discrete(labels = xxx)

# change moderator's factor level using scale_fill_discrete()

ggplot(data = mtcars, aes(x = factor(cyl), y = mpg, fill = factor(am))) +
  geom_bar(stat = 'identity', position = "dodge") + #am Transmission (0 = automatic, 1 = manual)
  scale_fill_discrete(labels = to_string) + # change the labels of the factor levels in visualization stage
  labs(title = "Impact of Cylinder size on mpg across am",
       x = "Number of Cylinder",
       y = "Average MPG",
       fill = "Automatic vs. Manual"
       )

5.4.2.2 4.5.2.2 Stacked vs. Filled Barplot

• Calculate numbers and pass it via stat = “identity” in Barplot

5.4.2.2.1 (1) Adding % inside Stacked Barplot

# stacked bar chart
mpg %>% 
  group_by(manufacturer, class) %>% 
  tally() %>% 
  mutate(percent = n/sum(n), .group = "drop") %>% 
  ggplot(aes(x = reorder(manufacturer, n), y = n, fill = class)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = paste0(sprintf("%1.1f", percent*100),"%")),
                position = position_stack(vjust = 0.5), 
                color = "white") +
  theme_minimal() +
  coord_flip()

# The chart below is identical to the first with a one exception. ordering of manufacturer is dropped here. Compare both charts and see the difference.
mpg %>% 
  group_by(manufacturer, class) %>% 
  tally() %>% 
  mutate(percent = n/sum(n), .groups = 'drop') %>% 
  ggplot(aes(x = manufacturer, y = n, fill = class)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = paste0(sprintf("%1.1f", percent*100),"%")),
                position = position_stack(vjust = 0.5), 
                color = "white") +
  theme_minimal() +
  coord_flip()  

5.4.2.2.2 (2) Adding % inside Filled Barplot

# filled bar chart

mpg %>% 
  group_by(manufacturer, class) %>% 
  tally() %>% 
  mutate(percent = n/sum(n)) %>% 
  ggplot(aes(x = manufacturer, y = n, fill = class)) +
  geom_bar(stat = "identity", position = "fill") +
  geom_text(aes(label = paste0(sprintf("%1.1f", percent*100),"%")),
            position = position_fill(vjust = 0.5), 
            color = "white"
            ) +
  theme_minimal() +
  labs(title = "Percent of Cars by class across manufacturers",
       x = "Car Brand",
       y = "Percentage",
       ) +
  coord_flip()

5.4.3 4.5.3. Scatterplot (two continuous variable cut by a categorical variable)

mpg %>% 
  ggplot(aes(x = factor(cyl), y = cty))+
  geom_point(alpha = .5) +
  geom_smooth(method = "lm", se = FALSE) + #lm = linear model; se = standard error
  geom_jitter()+
  facet_wrap(~ drv)+ 
  #facet_grid(cols = vars(trans)) +
  theme_economist_white()+
  labs(title = "Impact of Cylinders on City Mileage across Type of Drive Train",
       subtitle = "Scatter and jitter plot with regression line",
       caption = "Source: mpg dataset",
       x = "# of Cylnders",
       y = "City mileage", 
       )

5.5 4.6. Five variables (x=Continuous, y=continuous, color, size, facet)

mpg %>% 
  ggplot(aes(displ, cty)) +
  geom_point(aes(color = drv, size = cyl),
             alpha = 0.5)+
  geom_smooth() +
  scale_size_continuous(range = c(3, 8))+
  facet_wrap(~ year)

mpg %>% 
  ggplot(aes(displ, cty)) +
  geom_point(aes(color = drv, size = cyl),
             alpha = 0.5)+
  geom_smooth(method = lm) +
  facet_wrap(~ year) +
  scale_size_continuous(range = c(3, 8))+
  labs(x = "Engine Size",
       y = "City MPG",
       title = "Fuel Efficiency") +
  theme_bw()

6 5. Correlations with ggpairs()

6.1 5.1. Simple chart

# simple
ggpairs(cars %>% select(cyl, mpg, disp, wt, qsec))

6.2 5.2. Correlations groped by categorical variables

ggpairs(cars, columns = 2:5,
        aes(color = factor(am))
        )

ggpairs(iris, columns = 1:4,
        aes(color = Species)
        )

7 6. Interactive plots

# library(plotly)
plot <- ggplot(data = cars, aes(x = hp, y = qsec)) + 
  geom_point() + 
  geom_smooth()
ggplotly(plot)

8 7. Labeling with ggrepel::geom_label()/ geom_label_repel()

8.1 7.1. Additive method

library(ggrepel)
plot + 
  geom_label(aes(label=model))

plot + 
  geom_label_repel(aes(label = model))

8.2 7.2 Piping method

cars %>% 
  ggplot(aes(x = hp, y = qsec)) + 
  geom_point() + 
  geom_smooth() +
  geom_label_repel(aes(label=model))

9 8. Saving plots

ggsave(filename = "mygraph.png", plot, dpi = 300)

10 9. References

• R Studio Cheatsheets: https://posit.co/resources/cheatsheets/

• R4DS Book: https://r4ds.hadley.nz/data-visualize

• ggplot2: elegant graphics for data analysis by Hadley Wickham: https://ggplot2-book.org/index.html

• The R Graph Gallery: https://r-graph-gallery.com/index.html