[R Course] Data Visualization with R

Learn how to use the ggplot2 package to create visuals in R.

Set up

Data

In this course, we will work with data stored in a Gsheet. Use the following code to load them!

library(gsheet)

dataGraph <- gsheet2tbl("https://docs.google.com/spreadsheets/d/1uLaXke-KPN28-ESPPoihk8TiXVWp5xuNGHW7w7yqLCc/edit?usp=sharing")

date	country	GDP	section
2010	australia	1142250506	a
2011	australia	1389919156	d
2012	australia	1537477830	c
2013	australia	1563950959	e
2014	australia	1454675480	e
2015	australia	1339539063	f
2010	belgium	483577483	c
2011	belgium	526975257	e
2012	belgium	497815990	a
2013	belgium	521370528	f
2014	belgium	531234804	d
2015	belgium	454039037	d
2010	canada	1613406135	c
2011	canada	1788703386	f
2012	canada	1824288757	a
2013	canada	1837443487	a
2014	canada	1783775591	b
2015	canada	1550536520	b

Package

For the examples to work, we need to load the ggplot2 package.

library(ggplot2)

Ggplot

Create a new ggplot

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

ggplot(data = NULL, mapping = aes(), ...,)

ggplot() alone do not work but is necessary to start a new plot. We need to add functions to produce, bar charts, line charts, etc.

The sections above will teach you how to add these functions.

Bar Chart

There are two functions to create bar charts: geom_bar() and geom_col().

Geom_bar

geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x position.

geom_bar(mapping = NULL, data = NULL, stat = "count", position = "stack", ..., width = NULL, 
         binwidth = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Counts

Create bar charts that show counts (or sums of weights):

# Number of times each country is represented in the data frame 
# It represents also the number of year for each country
ggplot(dataGraph, aes(country)) + 
  geom_bar()

Stat identity

Create bar charts that put columns for the x and y axis using the option stat identity:

ggplot(dataGraph, aes(x=country, y=GDP)) + 
  geom_bar(stat = "identity")

It’s the option stat = “identity” that allow the function geom_bar to produce the y axis with the GDP as the value. Without this option, you will receive an error!

Fill

Create bar charts with the fill option:

# Bar charts are automatically stacked when multiple bars are placed at the same location. 
# The order of the fill in the graph is designed to match the legend
# We see here that each color correspond to each year
ggplot(dataGraph, aes(x=country, y=GDP, fill = date)) + 
  geom_bar(stat = "identity")

Dodge2

Create bar charts with the dodge2 option:

# Adding: 
# position = "dodge2": To put each year as separeted column
ggplot(dataGraph, aes(x=country, y=GDP, fill = date)) + 
  geom_bar(stat = "identity", position = "dodge2")

Dodge and Width

Create bar charts with a width option:

# date as the x axis 
# GDP as the y axis
# country as fill
# position = dodge
# the width of the bars as 0.5
ggplot(data = dataGraph, aes(x = date, y = GDP, fill = country)) + 
  geom_bar(stat = "identity", width = 0.5, position = "dodge")

Geom_col

geom_col() uses the option stat_identity by default to leave the data as is.

geom_col(mapping = NULL, data = NULL, position = "stack", ..., width = NULL, 
         na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Bars

# Using geom_col with: 
# Country as x axis 
# GDP as y axis
ggplot(data = dataGraph, aes(x = country, y = GDP)) +
  geom_col()

Width

# Adding: 
# the fill option:  meaning that each bar (each country) has a different colour
# the width option: change the bar size
ggplot(data = dataGraph, aes(x = country, y = GDP, fill = country)) +
  geom_col(width = 0.8)

Line Chart

Geom_line

geom_line() connects them in order of the variable on the x axis.

geom_line(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)

Multiple Lines

geom_line() is suitable for time series:

ggplot(data = dataGraph, aes(x = date, y = GDP, colour = country)) + 
  geom_line()

Unique line

If you wish to show the data for the Canada only:

# Filter to keep the country Canada 
library(dplyr)
dataGraphCanada <- filter(dataGraph, country == "canada")

ggplot(data = dataGraphCanada, aes(x = date, y = GDP)) + 
  geom_line()

Aes Colour

ggplot(data = dataGraphCanada, aes(x = date, y = GDP, colour = date)) + 
  geom_line()

Colour

# Changing the colour parameter to red
ggplot(dataGraphCanada, aes(date, GDP)) +
  geom_line(colour = "red")

Arrow

# Use the arrow parameter to add an arrow to the line
ggplot(dataGraphCanada, aes(date, GDP)) + 
  geom_line(colour = "red", arrow = arrow())

ggplot(dataGraphCanada, aes(date, GDP)) + 
  geom_line(colour = "blue", arrow = arrow(angle = 15, ends = "both", type = "closed"))

Linetype

# Setting line type
ggplot(dataGraphCanada, aes(date, GDP)) + 
  geom_line(colour = "blue", linetype = 2, arrow = arrow(angle = 30, type = "closed"))

# Setting line type
ggplot(dataGraphCanada, aes(date, GDP)) + 
  geom_line(colour = "purple", linetype = 4, arrow = arrow())

Geom_step

geom_step() creates a stairstep plot, highlighting exactly when changes occur. The group aesthetic determines which cases are connected together.

geom_step(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", direction = "hv", na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE, ...)

Steps

geom_step() is useful when you want to highlight exactly when the y value changes:

ggplot(data = dataGraphCanada, aes(x = date, y = GDP)) + 
  geom_step()

Scatterplot

Geom_point

The geom_point() is used to create scatterplots. The scatterplot is most useful for displaying the relationship between two continuous variables. It can be used to compare one continuous and one categorical variable, or two categorical variables, but a variation like geom_jitter(), geom_count(), or geom_bin2d() is usually more appropriate. A bubblechart is a scatterplot with a third variable mapped to the size of points.

geom_point(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., 
           na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Points

ggplot(dataGraph, aes(country, GDP)) + 
  geom_point()

Aes Colour

# Add aesthetic mappings
ggplot(dataGraph, aes(country, GDP, colour = country)) + 
  geom_point()

Aes Size

Set points size by using the option size in the aes() function:

ggplot(dataGraph, aes(country, GDP, colour = country, size = GDP)) + 
  geom_point()

Fixed colour and size

# Set aesthetics to fixed value
ggplot(dataGraph, aes(country, GDP)) + 
  geom_point(colour = "red", size = 3)

Shape, Fill and Stroke

Set up the shape, fill and stroke

# For shapes that have a border (like 21), you can colour the inside and outside separately. 
# Use the stroke aesthetic to modify the width of the border
ggplot(dataGraph, aes(country, GDP)) +
  geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)

Layering Points

# You can create interesting shapes by layering multiple points of different sizes
ggplot(dataGraph, aes(country, GDP, shape = country)) + 
  geom_point(aes(colour = country), size = 4) +
  geom_point(colour = "grey90", size = 1.5)

ggplot(dataGraph, aes(country, GDP, shape = country)) + 
  geom_point(colour = "black", size = 4.5) +
  geom_point(colour = "pink", size = 4) +
  geom_point(aes(shape = factor(country)))

Time series

The use of date as x axis for time series:

# geom_point() works for time series too
ggplot(data = dataGraph, aes(x = date, y = GDP, color = country)) +
  geom_point(aes(size = GDP))

Missing values

Geom_point is capable of handling missing value with the na.rm function:

# Create a column with random data from a sample of 1 to 500
dataGraph$sample <- sample(1:500, 18, replace=F)
dataGraph <- transform(dataGraph, column.na = ifelse(sample(1:500, 18, replace=F) < 150, NA, sample))

A warning message is displayed:

# geom_point warns when missing values have been dropped from the data set and not plotted
# you can turn this off by setting na.rm = TRUE
ggplot(dataGraph, aes(GDP, column.na)) + 
  geom_point()

Warning: Removed 5 rows containing missing values (geom_point).

There is no more warning message:

# Add TRUE to na.rm
ggplot(dataGraph, aes(GDP, column.na)) + 
  geom_point(na.rm = TRUE)

Maps

This set of geom, stat, and coord are used to visualise simple feature (sf) objects. For simple plots, you will only need geom_sf() as it uses stat_sf() and adds coord_sf() for you. geom_sf() is an unusual geom because it will draw different geometric objects depending on what simple features are present in the data: you can get points, lines, or polygons.

Geom_sf

geom_sf(mapping = aes(), data = NULL, stat = "sf", position = "identity", 
        na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)

Setting up the data for the map:

# The package rnaturalearth provides data to create a map of the world.
# Use ne_countries to pull country data and choose the scale. 
# The function ne_countries return sp classes by default. 
# You can choose the sf classe, as defined in the argument returnclass.

library("sf")
library("rnaturalearth")

world <- ne_countries(scale = "medium", returnclass = "sf")

Sf

ggplot(data = world) +
  geom_sf()

Color and fill

ggplot(data = world) + 
  geom_sf(color = "black", fill = "lightgreen")

Coord_sf

coord_sf(xlim = NULL, ylim = NULL, expand = TRUE, crs = NULL, datum = sf::st_crs(4326), 
         label_graticule = waiver(), label_axes = waiver(), ndiscr = 100, default = FALSE, clip = "on")

Crs

ggplot(data = world) +
    geom_sf() +
    coord_sf(crs = "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs ")

Xlim and Ylim

ggplot(data = world) +
    geom_sf() +
    coord_sf(xlim = c(-102.15, -74.12), ylim = c(7.65, 33.97))

Geom_map

geom_map(mapping = NULL, data = NULL, stat = "identity", ..., map,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Map

# Use map_data() to load world data
world2 <- map_data('world')
# Show world2 data
head(world2)

       long      lat group order region subregion
1 -69.89912 12.45200     1     1  Aruba      <NA>
2 -69.89571 12.42300     1     2  Aruba      <NA>
3 -69.94219 12.43853     1     3  Aruba      <NA>
4 -70.00415 12.50049     1     4  Aruba      <NA>
5 -70.06612 12.54697     1     5  Aruba      <NA>
6 -70.05088 12.59707     1     6  Aruba      <NA>

ggplot() + 
  geom_map(data=world2, map=world2, aes(x=long, y=lat, group=group, map_id=region))

Coord_fixed

A fixed scale coordinate system forces a specified ratio between the physical representation of data units on the axes. The ratio represents the number of units on the y-axis equivalent to one unit on the x-axis. The default, ratio = 1, ensures that one unit on the x-axis is the same length as one unit on the y-axis. Ratios higher than one make units on the y axis longer than units on the x-axis, and vice versa.

coord_fixed(ratio = 1, xlim = NULL, ylim = NULL, expand = TRUE, clip = "on")

Ratio

By default the Ration is equal to 1. When using another number the map shrinks.

ggplot() + 
  geom_map(data=world2, map=world2, aes(x=long, y=lat, group=group, map_id=region)) + 
  coord_fixed(1.9)

Xlim and Ylim

You can cut the map by using the xlim and ylim option include in the coord_fixed function:

ggplot() + 
  geom_map(data=world2, map=world2, aes(x=long, y=lat, group=group, map_id=region)) + 
  coord_fixed(1.9, xlim = c(-160,175), ylim = c(-50,80))

Geom_polygon

Polygons are very similar to paths (as drawn by geom_path()) except that the start and end points are connected and the inside is coloured by fill. The group aesthetic determines which cases are connected together into a polygon. From R 3.6 and onwards it is possible to draw polygons with holes by providing a subgroup aesthetic that differentiates the outer ring points from those describing holes in the polygon.

geom_polygon(mapping = NULL, data = NULL, stat = "identity", position = "identity", rule = "evenodd", ..., 
             na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Polygon

Let’s load US data with the function map_data().

library(maps)
library(ggmap)

usa <- map_data("usa")

long	lat	group	order	region	subregion
-101.4078	29.74224	1	1	main	NA
-101.3906	29.74224	1	2	main	NA
-101.3620	29.65056	1	3	main	NA
-101.3505	29.63911	1	4	main	NA
-101.3219	29.63338	1	5	main	NA
-101.3047	29.64484	1	6	main	NA

ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() 

Coord_map

coord_map projects a portion of the earth, which is approximately spherical, onto a flat 2D plane using any projection defined by the mapproj package. Map projections do not, in general, preserve straight lines, so this requires considerable computation.

coord_map(projection = "mercator", ..., parameters = NULL, orientation = NULL, 
          xlim = NULL, ylim = NULL, clip = "on")

Coord

ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  coord_map() 

Projection

ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  coord_map("gilbert") 

ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  coord_map("orthographic")

ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  coord_map("azequalarea") 

ggplot(usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  coord_map("conic", lat0 = 30)

Now, let’s load the US states data.

state <- map_data("state")

long	lat	group	order	region	subregion
-87.46201	30.38968	1	1	alabama	NA
-87.48493	30.37249	1	2	alabama	NA
-87.52503	30.37249	1	3	alabama	NA
-87.53076	30.33239	1	4	alabama	NA
-87.57087	30.32665	1	5	alabama	NA
-87.58806	30.32665	1	6	alabama	NA

ggplot(state, aes(x = long, y = lat, fill = region, group = group)) +
  geom_polygon(col = "white") +
  coord_map() + 
  theme(legend.position = "none")

Points

For the following map we need data on cities. Let’s load them!

library(readr)
cities <- read_tsv('https://assets.datacamp.com/production/course_862/datasets/US_Cities.txt')

City	State	Pop_est	lat	long
Eugene	Oregon	163460	44.0567	-123.1162
Salem	Oregon	164549	44.9237	-123.0231
Hillsboro	Oregon	102347	45.5167	-122.9833
Santa Rosa	California	174972	38.4468	-122.7061
Portland	Oregon	632309	45.5370	-122.6500
Vancouver	Washington	172860	45.6372	-122.5965

ggplot(data = usa, aes(x = long, y = lat, group = group)) +
  geom_polygon() +
  geom_point(data = cities, aes(group = State, size = Pop_est),
             col = "red", shape = 19, alpha = 0.6) +
  coord_map()

We need to make some data wrangling on cities data.

First, we want the population by states.

pop <- aggregate(Pop_est ~ State, data = cities, sum)

Check the course on Data Wrangling with R: Computing Summary Statistics to understand this block of code!

Second, we need to rename the column state by region.

names(pop)[names(pop) == "State"] <- "region"

Check the course on Data Wrangling with R section Renaming Variables to understand this block of code!

Third, we want to put in lowercase the column region

pop$region <- stringr::str_to_lower(pop$region)

Check the course on Data Wrangling with R section Converting Text Case to understand this block of code!

Next, we want to join the dataframe “state” and “pop” by the column “region” into a new dataframe called “state2”.

state2 <- dplyr::left_join(state, pop, by="region")

Check the course on Data Wrangling with R: Combining Tables section Mutating Join to understand this block of code!

long	lat	group	order	region	subregion	Pop_est
-87.46201	30.38968	1	1	alabama	NA	797933
-87.48493	30.37249	1	2	alabama	NA	797933
-87.52503	30.37249	1	3	alabama	NA	797933
-87.53076	30.33239	1	4	alabama	NA	797933
-87.57087	30.32665	1	5	alabama	NA	797933
-87.58806	30.32665	1	6	alabama	NA	797933

Now, let’s produce a map!

ggplot(state2, aes(x = long, y = lat, fill = Pop_est, group = group)) +
  geom_polygon(col = "white") +
  coord_map()