ggplot2 - Introduction to Aesthetics - Rsquared Academy Blog

Introduction

This is the fourth post in the series Elegant Data Visualization with ggplot2. In the previous post, we learnt about geoms and how we can use them to build different plots. In this post, we will focus on the aesthetics i.e. color, shape, size, alpha, line type, line width etc. We can map these to variables or specify values for them. If we want to map the above to variables, we have to specify them within the aes() function. We will look at both methods in the following sections.

Explore aesthetics such as

color
shape
size
fill
alpha
width

Libraries, Code & Data

We will use the following libraries in this post:

All the data sets used in this post can be found here and code can be downloaded from here.

Data

Introduction

ecom <- readr::read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv')
ecom

## # A tibble: 1,000 x 11
##       id referrer device bouncers n_visit n_pages duration country purchase
##    <dbl> <chr>    <chr>  <lgl>      <dbl>   <dbl>    <dbl> <chr>   <lgl>   
##  1     1 google   laptop TRUE          10       1      693 Czech ~ FALSE   
##  2     2 yahoo    tablet TRUE           9       1      459 Yemen   FALSE   
##  3     3 direct   laptop TRUE           0       1      996 Brazil  FALSE   
##  4     4 bing     tablet FALSE          3      18      468 China   TRUE    
##  5     5 yahoo    mobile TRUE           9       1      955 Poland  FALSE   
##  6     6 yahoo    laptop FALSE          5       5      135 South ~ FALSE   
##  7     7 yahoo    mobile TRUE          10       1       75 Bangla~ FALSE   
##  8     8 direct   mobile TRUE          10       1      908 Indone~ FALSE   
##  9     9 bing     mobile FALSE          3      19      209 Nether~ FALSE   
## 10    10 google   mobile TRUE           6       1      208 Czech ~ FALSE   
## # ... with 990 more rows, and 2 more variables: order_items <dbl>,
## #   order_value <dbl>

Data Dictionary

id: row id
referrer: referrer website/search engine
os: operating system
browser: browser
device: device used to visit the website
n_pages: number of pages visited
duration: time spent on the website (in seconds)
repeat: frequency of visits
country: country of origin
purchase: whether visitor purchased
order_value: order value of visitor (in dollars)

Color

In ggplot2, when we mention color or colour, it usually refers to the color of the geoms. The fill argument is used to specify the color of the shapes in certain cases. In this first section, we will see how we can specify the color for the different geoms we learnt in the previous post.

Point

For points, the color argument specifies the color of the point for certain shapes and border for others. The fill argument is used to specify the background for some shapes and will not work with other shapes. Let us look at an example:

ggplot(mtcars, aes(x = disp, y = mpg, color = factor(cyl))) +
  geom_point()

We can map the variable to color in the geom_point() function as well since it inherits the data from the ggplot() function.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(aes(color = factor(cyl)))

If you do not want to map a variable to color, you can specify it separately using the color argument but in this case it should be outside the aes() function.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(color = 'blue')

Now we will change the shape of the points to understand the difference between color and fill arguments. It can be again mapped to variables or values. Let us map shape to variables.

ggplot(mtcars, aes(x = disp, y = mpg, shape = factor(cyl))) +
  geom_point()

Let us map shape to cyl in the geom_point() function. Remember, when you are mapping an aesthetic to a variable, it must be inside aes().

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(aes(shape = factor(cyl)))

Instead of mapping shape to a variable, let us specify a value for shape. In this case, shape is not wrapped inside aes() as we are not mapping it to a variable.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(shape = 5)

Let us specify a color for the point using color argument.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(shape = 5, color = 'blue')

Background color cannot be added for all shapes. In the below example, we try to modify the background color using the fill argument but it does not work.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(shape = 5, fill = 'blue')

Since the shape number is now greater than 21, fill argument will add background color in the below case.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(shape = 22, fill = 'blue')

In shapes greater than number 21, color argument will modify the border of the shape.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(shape = 22, color = 'blue')

Let us map size of points to a variable. It is advised to map size only to continuous variables and not categorical variables.

ggplot(mtcars, aes(x = disp, y = mpg, size = disp)) +
  geom_point()

If you map size to categorical variables, ggplot2 will throw a warning.

Specify value for size.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(size = 4)

To modify the opacity of the color, use the alpha argument.

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point(aes(alpha = factor(cyl)), color = 'blue')

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

So far we have focussed on geom_point() to learn how to map aesthetics to variables. To explore line type and line width, we will use geom_line(). In the previous post, we used geom_line() to build line charts. Now we will modify the appearance of the line. In the section below, we will specify values for color, line type and width. In the next section, we will map the same to variables in the data. We will use a new data set. You can download it from here. It contains GDP (Gross Domestic Product) growth data for the BRICS (Brazil, Russia, India, China, South Africa) for the years 2000 to 2005.

Data

gdp <- readr::read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/gdp.csv')

## Warning: Missing column names filled in: 'X1' [1]

A line chart can be created using geom_line(). In the below example, we examine the GDP trend of India and modify the color of the line to 'blue'.

ggplot(gdp, aes(year, india)) +
  geom_line(color = 'blue')

To modify the line type, use the linetype argument. It can take values between 1 and 5.

ggplot(gdp, aes(year, india)) +
  geom_line(linetype = 2)

The line type can also be mentioned in the following way:

ggplot(gdp, aes(year, india)) +
  geom_line(linetype = 'dashed')

The width of the line can be modified using the size argument.

ggplot(gdp, aes(year, india)) +
  geom_line(size = 2)

Now let us map the aesthetics to the variables. The data used in the above example cannot be used as we need a variable with country names. We will use gather() function from the tidyr package to reshape the data.

gdp2 <- 
  gdp %>% 
  select(year, growth, india, china) %>% 
  gather(key = country, value = gdp, -year)

gdp2

## # A tibble: 18 x 3
##    year       country   gdp
##    <date>     <chr>   <dbl>
##  1 2000-01-01 growth      6
##  2 2001-01-01 growth      9
##  3 2002-01-01 growth      8
##  4 2003-01-01 growth      9
##  5 2004-01-01 growth      9
##  6 2005-01-01 growth      8
##  7 2000-01-01 india       5
##  8 2001-01-01 india       9
##  9 2002-01-01 india       8
## 10 2003-01-01 india       8
## 11 2004-01-01 india       5
## 12 2005-01-01 india       7
## 13 2000-01-01 china       8
## 14 2001-01-01 china       5
## 15 2002-01-01 china       6
## 16 2003-01-01 china       8
## 17 2004-01-01 china       9
## 18 2005-01-01 china       8

To map the aesthetics to a variable, we must use the group argument. In the below example, we map the aesthetics to country. But we cannot distinguish between the lines as their color, width and line type are the same. We have easily plotted the GDP trend of all countries using the group argument. Now, let us ensure that we can distinguish and identidy them using different aesthetics.

ggplot(gdp2, aes(year, gdp, group = country)) +
  geom_line()

Let us begin by ensuring that the lines have different color using the color argument within aes() and assigning it the variable country.

ggplot(gdp2, aes(year, gdp, group = country)) +
  geom_line(aes(color = country))

Instead of color, now we modify the line type using the linetype argument.

ggplot(gdp2, aes(year, gdp, group = country)) +
  geom_line(aes(linetype = country))

In the below instance, we assign different width to the lines using the size argument.

ggplot(gdp2, aes(year, gdp, group = country)) +
  geom_line(aes(size = country))

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

Before we wrap up, let us quickly see how we can map aesthetics to variables for different plots.

Bar Plots

Here we create a stacked bar plot by mapping fill to purchase.

ggplot(ecom, aes(device, fill = purchase)) +
  geom_bar()

Histograms

Instead of a bar chart, we create a histogram and again map fill to purchase.

ggplot(ecom) +
  geom_histogram(aes(duration, fill = purchase), bins = 10)

Box Plots

We repeat the same exercise below, but replace the bar plot with a box plot.

ggplot(ecom) +
  geom_boxplot(aes(device, duration, fill = purchase))

In all the above cases, you can observe that when we are mapping aesthetics such as color, fill, shape, size or linetype to variables, they are all wrapped inside aes().

Summary

In this post, we learnt about aesthetics i.e. how to modify the properties of geoms such as:

color
shape
size
fill
alpha
width

Up Next..

In the next post, we will learn to modify the axis and labels of a plot.