Taavi Päll 2019-03-04
We start learning R from data visualisation.
Here we learn:
- how to make basic scatterplot and
- how to map variables to point color, size, or shape.
- This tutorial is based on “Data visualisation with ggplot2” chapter in R4DS by G. Grolemund and H. Wickham.
- lectures/graafilised-lahendused and learn-r/ggplot2 by Ülo Maiväli and Taavi Päll
ggplot2 is an R package for producing statistical graphics based on the grammar of graphics (hence the gg!).
Let’s start by loading tidyverse (meta) library which provides us with set of necessary packages to start with data analysis and visualisations. Importantly, also ggplot2 library is loaded.
# library(ggplot2)
library(tidyverse)## ── Attaching packages ─────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0 ✔ purrr 0.3.0
## ✔ tibble 2.0.1 ✔ dplyr 0.8.0.1
## ✔ tidyr 0.8.2 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
As you can see, running this line of code loads eight different packages (libraries) and warns that some of the functions (filter(), lag()) that were just loaded into namespace have identical name to functions that were already present in namespace.
These new functions masked old ones and, in case you want to use these
masked functions, you need to call them explicitly by using package
where it comes from stats::filter().
If you get error message “there is no package called ‘tidyverse’”, then
you need to install this package and run again library():
install.packages("tidyverse")
library(tidyverse)We need data to create plots! As for start, let’s use ggplot2 built in dataset mpg with fuel economy data from 1999 and 2008 for 38 models of car:
mpg # aka ggplot::mpg## # A tibble: 234 x 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 q… 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 q… 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 q… 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rows
If you worry where this dataset comes from, then there is no magic – it’s bundled with ggplot2 package and will be invisibly loaded every time when ggplot2 library is loaded. mpg invisibility means that, differently from your own R objects, it will not show up in your Environment panel.
For us, key variables in mpg dataset are:
displ– engine displacement (L),hwy– highway miles per gallonclass– class/type of the car
Let’s create simple scatter plot to explore relationship between fuel consumption in highway traffic (hwy) and engine size (displ) is created like this.
We put displ on the x-axis and hwy on the y-axis:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
If plot creation is successful, you can see negative relationship between engine size and fuel consumption (miles per gallon).
It’s probably no news to anyone that cars with big engines use more fuel.
- ggplot2 works iteratively – you start with a layer showing the raw data and then add layers of geoms, annotations, and statistical summaries.
To compose plots, you have to supply minimally:
- Data that you want to visualize and
- aesthetic mappings – what’s on x-axis, what’s on y-axis, and how to you want to group and color your data. Mapped arguments must be found in your data!
- Layers made up of geometric elements: points, lines, boxes, etc. What’s shown on plot.
visualisation of these three components within ggplot context looks like this:
ggplot(data = <DATA>) +
<GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
- Run ggplot(data = mpg). What do you see?
ggplot(data = mpg)
- How many rows are in mpg dataset? How many columns? Run “mpg”
mpg## # A tibble: 234 x 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 q… 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 q… 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 q… 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rows
- What does the drv variable describe? Read help for ?mpg to find out.
?mpg- Make scatterplot of hwy vs cyl using mpg data:
ggplot(data = mpg) +
geom_point(mapping = aes(x = hwy, y = cyl), position = position_jitter(width = 0.1, seed = 11))
What happens if you add some noise to points? Hint: use geom_jitter instead
ggplot(data = mpg) +
geom_jitter(mapping = aes(x = hwy, y = cyl))
- What happens when you make a scatterplot of class versus drv using mpg data:
ggplot(data = mpg) +
geom_point(mapping = aes(x = class, y = drv))
Is such plot useful?
hwy ~ displ scatterplot tells us that there is linear relationship between engine size and fuel consumption: bigger engines use more fuel and are therefore less efficient.
Nevertheless, if we look at the cars with huge engines (>5L), it’s apparent that there are some outliers (plot below) that perform better than cars in this engine class in general.
What are those cars? Do they have something in common?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = (hwy > 20 & displ > 5)))
To better understand the nature of these outliers, we would like add more info present in our mpg dataset to plot. To add more variables to 2D scatterplot, we can use additional aesthetic mappings.
Aesthetics like color, shape, fill, and size can be used to add additional variables to a plot.
By mapping displ to x-axis and hwy to y-axis let’s map additionally color of the points to class variable (color = class) in mpg dataset to reveal the class of each car:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
We can see that most of the cars with large motors and somewhat better fuel efficiency belong to sports cars (“2seaters”).
Recreate previous plot with class mapped to size of each point:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, size = class))## Warning: Using size for a discrete variable is not advised.
We get warning… seems its not a good idea. Why?
What’s wrong with the next plot where we have four categories (“tiny”, “small”, “big”, “very big”) of, let’s say diameters, and we want to map size aesthetic to diameter.
Here we create toy data frame where we have categorical variable diameter for four x values whereas y is all same.
diameter <- tibble(x = 1:4, y = 1, diameter = c("tiny", "small", "big", "very big"))
diameter## # A tibble: 4 x 3
## x y diameter
## <int> <dbl> <chr>
## 1 1 1 tiny
## 2 2 1 small
## 3 3 1 big
## 4 4 1 very big
Create scatterplot y~x and map diameter to point size:
ggplot(data = diameter) +
geom_point(mapping = aes(x = x, y = y, size = diameter))## Warning: Using size for a discrete variable is not advised.
This plot should explain why it’s generally not a good idea to map categorical variable to size aesthetic.
- Update code. Map alpha aesthetic to class:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, alpha = class))## Warning: Using alpha for a discrete variable is not advised.
- Update code. Map shape aesthetic to class. Update code so that there will be 7 shapes instead of default 6:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, shape = class)) +
scale_shape_manual(values = 1:7)
Is everything OK with shapes?
- Using dplyr built-in starwars dataset (hint: starwars) make height~mass scatterplot and map one continuous variable to size:
First print out starwars dataset:
starwars## # A tibble: 87 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Dart… 202 136 none white yellow 41.9 male
## 5 Leia… 150 49 brown light brown 19 female
## 6 Owen… 178 120 brown, gr… light blue 52 male
## 7 Beru… 165 75 brown light blue 47 female
## 8 R5-D4 97 32 <NA> white, red red NA <NA>
## 9 Bigg… 183 84 black light brown 24 male
## 10 Obi-… 182 77 auburn, w… fair blue-gray 57 male
## # … with 77 more rows, and 5 more variables: homeworld <chr>,
## # species <chr>, films <list>, vehicles <list>, starships <list>
Make height~mass scatterplot and map one continuous variable to size:
ggplot(data = starwars) +
geom_point(mapping = aes(x = height, y = mass, size = birth_year))## Warning: Removed 51 rows containing missing values (geom_point).
Extra: there is apparent outlier. Try using geom_label to add name to this point:
ggplot(data = starwars) +
geom_point(mapping = aes(x = height, y = mass, size = birth_year)) +
geom_label(mapping = aes(x = height, y = mass, label = name))## Warning: Removed 51 rows containing missing values (geom_point).
## Warning: Removed 28 rows containing missing values (geom_label).
(We can use a much nicer function geom_label_repel(), and map the birth year to color.)
When you set your aesthetic via mapping, ggplot automatically takes care of the rest: it finds best scale to display selected aesthetic and draws a legend. Note that this happens only when you map aesthetic within aes() function.
You can change the appearance of your plot also manually: change the color or shape of all the points.
For example, let’s suppose you want to make all points in plot blue in displ~hwy scatterplot using mpg data:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "#0000ff", size = 10) # try HEX code "#0000ff" or rgb(0,0,1)
Here, the color is not connected to the variable in your dataset as it is assigned outside aes(), but it just changes the appearance of the plot.
Therefore, to change the appearance of the plot, you need to assign value to aesthetic outside aes() in geom function.
You just have to pick a value that makes sense for that aesthetic:
- the name/code of the color as a character string (“blue”, “#0000ff”)
- size of a point in mm
- shape of a point as a number
Examples!
Update code: change size of all points:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class), size = 0.1)
While colors and sizes are intuitive, it seems impossible to remember available point shape codes in R.
The quickest way out of this is to know how to generate an example plot of the first 25 shapes quickly.
Numbers next to shapes denote R shape number.
ggplot(data = data_frame(x = rep(1:5, 5), y = rep(5:1, each = 5), shape = c(0:24))) +
geom_point(mapping = aes(x = x, y = y, shape = shape), fill = "green", color = "blue", size = 3) +
geom_text(mapping = aes(x = x, y = y, label = shape), hjust = 1.7) +
scale_shape_identity() +
theme(axis.text = element_blank(),
axis.title = element_blank())## Warning: `data_frame()` is deprecated, use `tibble()`.
## This warning is displayed once per session.
Update code:
- Let’s map more than 6 shapes to class and
- use fill = “green”, color = “blue”, to change the appearance of the ALL points, and
- adjust size of the ALL points (size = 3) for better visibility.
ggplot(data = mpg) +
geom_jitter(mapping = aes(x = displ, y = hwy, shape = class), fill = "green", color = "blue", size = 3) +
scale_shape_manual(values = c(16:19, 21:24))
Note the differences how fill and color work on different point shapes! Which is the default point shape in ggplot?
- What’s wrong with this plot? Why are points not blue? Can you fix the code?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
- Which variables in mpg are categorical? Which variables are continuous? (type ?mpg to read the documentation for the dataset). How can you see when you run mpg?