ggplot2 is a system for declaratively creating graphics. You provide:

• the data,
• what graphical primitives to use,
• tell how to map variables to aesthetics,

and it takes care of the details.

The following is a reusable template for making graphs with ggplot2:

ggplot(data = <DATA FRAME>) + 
  <GEOMETRIC OBJECT>(mapping = aes(<MAPPINGS>))

1. the data frame contains the variables we want to display
2. the geometric object is the geometry used by the plot to represent data (scatterplot, barplot, boxplot, and more)
3. the aesthetic mapping associates data frame variables to aesthetics of the plot.
4. An aesthetics is a visual property of the objects in your plot (size, shape, color, x and y positions)

We will work with the mpg dataset that contains fuel economy data and use the following variables:

• displ: engine displacement, in litres
• hwy: highway miles per gallon
• drv drive type: f = front-wheel drive, r = rear wheel drive, 4 = 4wd

library(ggplot2)
library(dplyr)
mpg

## # A tibble: 234 × 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… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # ℹ 224 more rows

Let us stick to the well-known scatterplot geometric object geom_point() for the moment.

library(ggplot2)

# scatteplot of displ and hwy
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

# add drv as color
ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = drv))

We will discover different geometric objects by exploring variation and covariation of qualitative and quantitative variables:

• variation is the tendency of the values of a variable to change from measurement to measurement
• covariation is the tendency for the values of two or more variables to vary together in a related way
• if variation describes the behavior within a variable, covariation describes the behavior between variables

A dataset diamonds contains the prices and other attributes of almost 54,000 diamonds. The main attributes are:

• price: price in US dollars
• carat: weight of the diamond
• cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)
• color: diamond colour, from D (best) to J (worst)
• clarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))

count(diamonds, cut)

## # A tibble: 5 × 2
##   cut           n
##   <ord>     <int>
## 1 Fair       1610
## 2 Good       4906
## 3 Very Good 12082
## 4 Premium   13791
## 5 Ideal     21551

ggplot(data = diamonds) +
  geom_histogram(mapping = aes(x = carat), binwidth = 0.2)

ggplot(data = filter(diamonds, carat <= 2.5)) +
  geom_histogram(mapping = aes(x = carat), binwidth = 0.01)

head(
  sort(
    table(
      cut_interval(diamonds$carat, length = 0.05)), decreasing = TRUE), 10)

## 
## (0.3,0.35]   (1,1.05] (0.5,0.55] (0.25,0.3] (0.35,0.4] (0.7,0.75] (0.4,0.45] 
##       6855       4484       3774       3418       3333       3121       2898 
## (0.65,0.7]   (0.95,1] (0.55,0.6] 
##       2128       1774       1742

diamonds %>% 
  filter(carat <= 2.5) %>% 
  ggplot() +
  geom_histogram(mapping = aes(x = carat), binwidth = 0.01)

ggplot(data = diamonds) +
  geom_freqpoly(mapping = aes(x = carat), binwidth = 0.2)

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = color))

# putting the bars beside one another
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = color), position = "dodge")

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = class, y = hwy))  

# reaorder qualitative variable class
ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), 
                             y = hwy))  

ggplot(data = diamonds) + 
  geom_freqpoly(mapping = aes(x = price, colour = cut), binwidth = 500)

ggplot(data = diamonds) +
  geom_point(mapping = aes(x = carat, y = price))

# use transparency to avoid overlapping of points
ggplot(data = diamonds) +
  geom_point(mapping = aes(x = carat, y = price), alpha = 0.05)

We overlay geom_point and geom_smooth geometries.

geom_smooth uses local polynomial regression, also known as moving regression, a generalization of moving average and polynomial regression.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  geom_smooth()

• global mappings of variables hold for all geometric objects in the plot
• local mappings of variables hold only for the geometric object containing the mapping and can overwrite global mappings

# color is global
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_point() + 
  geom_smooth(se = FALSE)

# color is local to geom_point
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = drv)) + 
  geom_smooth(se = FALSE)

# color is local to geom_smooth
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  geom_smooth(mapping = aes(color = drv), se = FALSE)

ggplot2 contains much more, that you need to exploit when turning an exploratory graphic into an expository graphic:

1. facets
2. labels
3. annotations
4. scales
5. zooming
6. themes

# filter best model in each class
best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

# or
best_in_class <- mpg %>%
  group_by(class) %>%
  arrange(desc(hwy)) %>% 
  filter(row_number() == 1)


ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(data = best_in_class, size = 3, shape = 1) +
  ggrepel::geom_label_repel(data = best_in_class, 
                            mapping = aes(label = model)) +
  geom_smooth(se = FALSE) +
  labs(
    title = "Fuel efficiency generally decreases 
    with engine size",
    subtitle = "Two seaters (sports cars) are an exception 
    because of their light weight",
    caption = "Data from fueleconomy.gov",
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    colour = "Car class"
  ) +
  theme_classic()

The presidential data set in ggplot2 package contains the names of each president, the start and end date of their term, and their party of 11 US presidents from Eisenhower to Obama.

presidential

## # A tibble: 11 × 4
##    name       start      end        party     
##    <chr>      <date>     <date>     <chr>     
##  1 Eisenhower 1953-01-20 1961-01-20 Republican
##  2 Kennedy    1961-01-20 1963-11-22 Democratic
##  3 Johnson    1963-11-22 1969-01-20 Democratic
##  4 Nixon      1969-01-20 1974-08-09 Republican
##  5 Ford       1974-08-09 1977-01-20 Republican
##  6 Carter     1977-01-20 1981-01-20 Democratic
##  7 Reagan     1981-01-20 1989-01-20 Republican
##  8 Bush       1989-01-20 1993-01-20 Republican
##  9 Clinton    1993-01-20 2001-01-20 Democratic
## 10 Bush       2001-01-20 2009-01-20 Republican
## 11 Obama      2009-01-20 2017-01-20 Democratic

Draw the following plot:

using the following scaffolding:

presidential %>%
  # add id of the president starting from 34
  mutate(id = 33 + ___) %>% 
  # map start, id, and party to x, y, and colour
  ggplot(aes(___, ___, colour = ___)) + 
  # add a point marking the start of the term
  geom_point() + 
  # add a segment with length the duration of the presidential term
  geom_segment(aes(xend = ___, yend = ___)) + 
  # set manually the colors (Republican = red, Democratic = blue)
  scale_colour_manual(values = c(___ = ___, ___ = ___) + 
  # add black and white theme                        
  ___ 

presidential %>%
  mutate(id = 33 + 1:nrow(presidential)) %>%
  ggplot(aes(start, id, colour = party)) +
  geom_point() +
  geom_segment(aes(xend = end, yend = id)) +
  scale_colour_manual(values = c(Republican = "red", Democratic = "blue")) +
  theme_bw()

The gapminder dataset is an excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country.

library(gapminder)
gapminder

## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # … with 1,694 more rows

1. using the gapminder dataset write a function that plots the scatterplot of GDP and life expectancy using population as size for a given continent and temporal span
2. plot the scatteplot for the same continent increasing the temporal span and for different continents with the same temporal span

gaplot = function(cont, year1, year2) {
  gapminder %>% 
  filter(___) %>%
  ggplot(aes(___, ___, size = ___, colour = country)) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 20)) +
  scale_x_log10() +
  labs(title = ___) +    
  theme_minimal() 
}

country_colors[names(country_colors) == "Italy"]

##     Italy 
## "#3B7D1D"

gaplot = function(cont, year1, year2) {
  gapminder %>% 
  filter(continent == cont, year >= year1, year <= year2) %>%
  ggplot(aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 20)) +
  scale_x_log10() +
  labs(title = paste(cont, year1, "-", year2)) +    
  theme_minimal() 
}

gaplot("Africa", 1960, 1970)

gaplot("Africa", 1971, 1980)

gaplot("Africa", 1981, 1990)

gaplot("Africa", 1991, 2000)

gaplot("Asia", 1991, 2000)

gaplot("Europe", 1991, 2000)

gaplot("Americas", 1991, 2000)

gaplot("Oceania", 1991, 2000)

• ggplot2 cheat sheet
• Data visualization with ggplot
• Exploratory Data Analysis with dplyr and ggplot
• Graphics for communication
• Animated graphics with gganimate
• Interactive graphics with HTML widgets and Shiny