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

Author

Janely Rosales

Published

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1 Overview

1.1 Expected Learning Outcomes

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

  1. Explain the concepts of the grammar of graphics when visualizing data with ggplot2 package.
  2. Be familiar with various types of charts.
  3. Visualize data in counts and proportions.
  4. Select appropiate charts based on straight consideration (e.g, the characteristics of the data and audience)
  5. Create a chart that involves one or two variables with either categorial or continuos data.
  6. Create a chart by adding categorical moderator (3rd variable) to the chart involving two or three variables. 7.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. #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”) 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)

2 1. Understand mtcars data

2.1 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]

2.2 1.2 Reading data and converting to a tibble (cars)

head(mtcars)

class(mtcars)

[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

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

3 Using a built in plotting function

hist(cars$disp, breaks = 10)

#barplot will be discussed later

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

histogram(cars$disp, breaks = 10)

4 4. ggplot2

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

4.1 4.1. Elaborate Examples

4.1.1 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')

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’)

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”)

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

5 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.

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

6 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()

7 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()

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” )

8 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()

9 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()

10 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”)

11 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”)

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”, )