Basics

ggplot2 is based on the grammar of graphics, a coherent system for describing and building graphs.

With ggplot2, you can build every graph from the same components:

• a data set
• a coordinate system
• geoms—visual marks that represent data points.

To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations.

Complete the template below to build a graph.

A graphing template

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

ggplot(data = mpg, aes(x = cty, y = hwy))

qplot creates a complete plot with given data, geom, and mappings. Supplies many useful defaults.

last_plot() returns the last plot

ggsave(“plot.png”, width = 5, height = 5) saves last plot as 5’ x 5’ file named “plot.png” in working directory.

Aesthetic mappings

You can add a third varible, like class, to a two dimensional scatterplot by mapping it to an aesthetic. An aesthetic is a visual property of the objects in your plot.

Aesthetics include things like:

• the size,
• the shape,
• or the color of your points.

class

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

size

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

## Warning: Using size for a discrete variable is not advised.

# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

set the aesthetic properties manually - outside of aes()

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

Facets - categorical variables

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

Geometric objects

A geom is the geometrical object that a plot uses to represent data.

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

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

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

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

ggplot2 provides over 30 geoms, and extension packages provide even more (see https://www.ggplot2-exts.org for a sampling). The best way to get a comprehensive overview is the ggplot2 cheatsheet, which you can find at http://rstudio.com/cheatsheets. To learn more about any single geom, use help: ?geom_smooth.

In practice, ggplot2 will automatically group the data for these geoms whenever you map an aesthetic to a discrete variable (as in the linetype example).

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

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

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, color = drv),
    show.legend = FALSE
  )

To display multiple geoms:

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

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

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)

Statistical transformations

The diamonds dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and cut of each diamond.

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

The algorithm used to calculate new values for a graph is called a stat, short for statistical transformation. The figure below describes how this process works with geom_bar().

?geom_bar shows that the default value for stat is “count”, which means that geom_bar() uses stat_count().

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

    demo <- tribble(
      ~cut,         ~freq,
      "Fair",       1610,
      "Good",       4906,
      "Very Good",  12082,
      "Premium",    13791,
      "Ideal",      21551
    )
    
    ggplot(data = demo) +
      geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")

    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))

    ggplot(data = diamonds) + 
      stat_summary(
        mapping = aes(x = cut, y = depth),
        fun.ymin = min,
        fun.ymax = max,
        fun.y = median
      )

Position adjustments

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

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

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

The stacking is performed automatically by the position adjustment specified by the position argument. If you don’t want a stacked bar chart, you can use one of three other options:

• "identity",
• "dodge"
• or "fill".

    ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
      geom_bar(alpha = 1/5, position = "identity")

    ggplot(data = diamonds, mapping = aes(x = cut, colour = clarity)) + 
      geom_bar(fill = NA, position = "identity")

    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")

    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

The values of hwy and displ are rounded so the points appear on a grid and many points overlap each other. This problem is known as overplotting.

position = "jitter" adds a small amount of random noise to each point.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")

Coordinate systems

The default coordinate system is the Cartesian coordinate system where the x and y positions act independently to determine the location of each point

• coord_flip() switches the x and y axes.

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

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

• coord_quickmap() sets the aspect ratio correctly for maps.

#install.packages("maps")    
nz <- map_data("nz")

ggplot(nz, aes(long, lat, group = group)) +
      geom_polygon(fill = "white", colour = "black")

ggplot(nz, aes(long, lat, group = group)) +
      geom_polygon(fill = "white", colour = "black") +
      coord_quickmap()

• coord_polar() uses polar coordinates.

    bar <- ggplot(data = diamonds) + 
      geom_bar(
        mapping = aes(x = cut, fill = cut), 
        show.legend = FALSE,
        width = 1
      ) + 
      theme(aspect.ratio = 1) +
      labs(x = NULL, y = NULL)
    
    bar + coord_flip()

    bar + coord_polar()

Eurostat

R tools to access open data from Eurostat database

# install.packages("eurostat")
library(eurostat)
query <- search_eurostat("road", type = "table")
query[3:100,1:2]

dat <- get_eurostat(id = "sdg_11_40", time_format = "num")

## Table sdg_11_40 cached at C:\Users\ncloud\AppData\Local\Temp\2\RtmpmmhtcB/eurostat/sdg_11_40_num_code_TF.rds

head(dat)

dat <- label_eurostat(dat)
head(dat, 10)

t1 <- get_eurostat("sdg_11_40", filters = list(geo = c("UK", "FR", "PL", "ES", "PT"), unit = c("NR")))
t1

library(ggplot2)
ggplot(t1, aes(x = time, y = values, color = geo, group = geo, shape = geo)) +
    geom_point(size = 2) +
    geom_line() +
    theme_bw() +
    labs(title="Road accidents", x = "Year", y = "Victims")

library("dplyr")
t2 <- t1 %>% filter(time == "2014-01-01")
ggplot(t2, aes(geo, values, fill=geo)) +
geom_bar(stat = "identity") + theme_bw() +
theme(legend.position = "none")+
labs(title="Road accidents in 2014", x="", y="Victims")