Data Visualization - principles

Table of contents

• Overview

  • Expected Learning Outcomes

  • Loading Packages

• 1. Understand mtcars data

  • 1.1 Using Help

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

  • 1.3 Simple Descriptive Statistics

• 2. Basic Plotting Methods in Base R

• 3. Lattice package

• 4. ggplot2

  • 4.1. Elaborate Examples

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

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

  • 4.2. One continous variable: geom_histogram()

  • 4.3. One categorical variable: geom_bar()

    • 4.3.1 Bar chart

    • 4.3.2 How to reorder bars in Barplot

      • 4.3.2.1. mpg data

      • 4.3.2.2. Reorder manufacturers by Count of cars

  • 4.4. Two Variables

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

      • 4.4.1.1. Stack vs. dodged barplots

      • 4.4.1.2. Stack vs. Filled barplots

      • 4.4.1.3. faceted barplots

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

    • 4.4.3. x = categorical, y = continous

      • 4.4.3.1 Boxplot

      • 4.4.3.2. geom_bar(stat = “identity”)

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

    • 4.5.1. Boxplot (with facet_grid)

      • (1) Basic with faceting

      • (2) Changing facet label with labeller argument

    • 4.5.2. Barplot

      • 4.5.2.1 Dodged Barplot (Changing fill labels)

      • 4.5.2.2 Stacked vs. Filled Barplot

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

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

• 5. Correlations with ggpairs()

  • 5.1. Simple chart

  • 5.2. Correlations groped by categorical variables

• 6. Interactive plots

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

  • 7.1. Additive method

  • 7.2 Piping method

• 8. Saving plots

• 9. References

Overview 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.

Introduction

Library and Packages

#install.packages("ggplot2")
#install.packages('ggrepel')
#install.packages('ggthemes')
#install.packages('scales')
#install.packages('plotly')
#install.packages('lattice')
#install.packages('GGally')
#install.packages("tidyverse")
#install.packages('ggtext')
#install.packages("glue")
#install.packages("gapminder")
#install.packages("dplyr")
library(ggplot2) #visualization
library(ggrepel) #labels for data
library(ggthemes) #collections of themes
library(scales) # scale
library(plotly) # interactive chart

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library(GGally) # correlation
library(tidyverse) # mega package containing 8 packages

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ lubridate 1.9.4     ✔ tibble    3.3.1
✔ purrr     1.2.1     ✔ tidyr     1.3.2

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ readr::col_factor() masks scales::col_factor()
✖ purrr::discard()    masks scales::discard()
✖ dplyr::filter()     masks plotly::filter(), stats::filter()
✖ dplyr::lag()        masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggtext) # for text visualization
library(glue) # combining multiple component
library(gapminder)
library(dplyr)

Understand mtcars data

Using help

1. Understand mtcars data

1.1 Using 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 Reading data and converting to a tibble (cars)

head(mtcars)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

class(mtcars)

[1] "data.frame"

mtcars

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

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

mtcars |>
  rownames_to_column()

               rowname  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
21       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
23         AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
24          Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
26           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
27       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

cars

# A tibble: 32 × 12
   model         mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
   <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 Mazda RX4    21       6  160    110  3.9   2.62  16.5     0     1     4     4
 2 Mazda RX4 …  21       6  160    110  3.9   2.88  17.0     0     1     4     4
 3 Datsun 710   22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
 4 Hornet 4 D…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
 5 Hornet Spo…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
 6 Valiant      18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
 7 Duster 360   14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
 8 Merc 240D    24.4     4  147.    62  3.69  3.19  20       1     0     4     2
 9 Merc 230     22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
10 Merc 280     19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
# ℹ 22 more rows

1.3 simple descriptive statistics

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	▇▂▅▁▁

2. Basic plottinf methods in base R

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

3. lattice package

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

4. GG plot 2

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

4.1 Elaborate Examples

cars |>
  count(cyl)

# A tibble: 3 × 2
    cyl     n
  <dbl> <int>
1     4    11
2     6     7
3     8    14

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')

`geom_smooth()` using formula = 'y ~ x'

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')

4.2. One continous variable: geom_histogram()

ggplot(data = cars, aes(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")