Scatter Plot in R using ggplot2 (with Example)

Details

Graphs are the third part of the process of data analysis. The first part is about data extraction, the second part deals with cleaning and manipulating the data. At last, the data scientist may need to communicate his results graphically.

The job of the data scientist can be reviewed in the following picture

In this tutorial, you will learn-

ggplot2 package

This part of the tutorial focuses on how to make graphs/charts with R.

In this tutorial, you are going to use ggplot2 package. This package is built upon the consistent underlying of the book Grammar of graphics written by Wilkinson, 2005. ggplot2 is very flexible, incorporates many themes and plot specification at a high level of abstraction. With ggplot2, you can't plot 3-dimensional graphics and create interactive graphics.

In ggplot2, a graph is composed of the following arguments:

You will learn how to control those arguments in the tutorial.

The basic syntax of ggplot2 is: 

ggplot(data, mapping=aes()) +
geometric object 

arguments: 
data: Dataset used to plot the graph
mapping: Control the x and y-axis 
geometric object: The type of plot you want to show. The most common object are:
 
- Point: `geom_point()` 
- Bar: `geom_bar()`
- Line: `geom_line()` 
- Histogram: `geom_histogram()`

Scatterplot

Let's see how ggplot works with the mtcars dataset. You start by plotting a scatterplot of the mpg variable and drat variable.

Basic scatter plot

library(ggplot2)
ggplot(mtcars, aes(x = drat, y = mpg)) +
    geom_point()

Code Explanation

Output:

Scatter plot with groups

Sometimes, it can be interesting to distinguish the values by a group of data (i.e. factor level data). 

ggplot(mtcars, aes(x = mpg, y = drat)) +
    geom_point(aes(color = factor(gear)))

Code Explanation

Output:

Change axis

Rescale the data is a big part of the data scientist job. In rare occasion data comes in a nice bell shape. One solution to make your data less sensitive to outliers is to rescale them. 

ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +
    geom_point(aes(color = factor(gear)))

Code Explanation

Note that any other transformation can be applied such as standardization or normalization.

Output:

Scatter plot with fitted values

You can add another level of information to the graph. You can plot the fitted value of a linear regression. 

my_graph <- ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +
    geom_point(aes(color = factor(gear))) +
    stat_smooth(method = "lm",
        col = "#C42126",
        se = FALSE,
        size = 1)
my_graph

Code Explanation

Output:

Note that other smoothing methods are available

Add information to the graph

So far, we haven't added information in the graphs. Graphs need to be informative. The reader should see the story behind the data analysis just by looking at the graph without referring additional documentation. Hence, graphs need good labels. You can add labels with labs()function.

The basic syntax for lab() is : 

lab(title = "Hello gtupapers")
argument:
- title: Control the title. It is possible to change or add title with:			
- subtitle: Add subtitle below title			
- caption: Add caption below the graph			
- x: rename x-axis			
- y: rename y-axis			
Example:lab(title = "Hello gtupapers", subtitle = "My first plot")

Add a title

One mandatory information to add is obviously a title. 

my_graph +
    labs(
        title = "Plot Mile per hours and drat, in log"
         )

Code Explanation

Output:

Add a title with a dynamic name

A dynamic title is helpful to add more precise information in the title.

You can use the paste() function to print static text and dynamic text. The basic syntax of paste() is:

paste("This is a text", A)
arguments
- " ": Text inside the quotation marks are the static text
- A: Display the variable stored in A
- Note you can add as much static text and variable as you want. You need to separate them with a comma

Example: 

A <-2010
paste("The first year is", A)

Output: 

## [1] "The first year is 2010"
B <-2018

paste("The first year is", A, "and the last year is", B)

Output: 

## [1] "The first year is 2010 and the last year is 2018"

You can add a dynamic name to our graph, namely the average of mpg. 

mean_mpg <- mean(mtcars$mpg)
my_graph + labs(
    title = paste("Plot Mile per hours and drat, in log. Average mpg is", mean_mpg)
)

Code Explanation

Output:

Add a subtitle

Two additional detail can make your graph more explicit. You are talking about the subtitle and the caption. The subtitle goes right below the title. The caption can inform about who did the computation and the source of the data. 

my_graph +
    labs(
        title =
        "Relation between Mile per hours and drat",
        subtitle =
        "Relationship break down by gear class",
        caption = "Authors own computation"
    )

Code Explanation

Output:

Rename x-axis and y-axis

Variables itself in the dataset might not always be explicit or by convention use the _ when there are multiple words (i.e. GDP_CAP). You don't want such name appear in your graph. It is important to change the name or add more details, like the units. 

my_graph +
    labs(
        x = "Drat definition",
        y = "Mile per hours",
        color = "Gear",
        title = "Relation between Mile per hours and drat",
        subtitle = "Relationship break down by gear class",
        caption = "Authors own computation"
    )

Code Explanation

Output:

Control the scales

You can control the scale of the axis.

The function seq() is convenient when you need to create a sequence of number. The basic syntax is: 

seq(begin, last, by = x)
arguments:
- begin: First number of the sequence
- last: Last number of the sequence
- by= x: The step. For instance, if x is 2, the code adds 2 to `begin-1` until it reaches `last`

For instance, if you want to create a range from 0 to 12 with a step of 3, you will have four numbers, 0 4 8 12 

seq(0, 12,4)

Output:

## [1]  0  4  8 12

You can control the scale of the x-axis and y-axis as below 

my_graph +
    scale_x_continuous(breaks = seq(1, 3.6, by = 0.2)) +
    scale_y_continuous(breaks = seq(1, 1.6, by = 0.1)) +
    labs(
        x = "Drat definition",
        y = "Mile per hours",
        color = "Gear",
        title = "Relation between Mile per hours and drat",
        subtitle = "Relationship break down by gear class",
        caption = "Authors own computation"
    )

Code Explanation

Output:

Theme

Finally, R allows us to customize out plot with different themes. The library ggplot2 includes eights themes: 

my_graph +
    theme_dark() +
    labs(
        x = "Drat definition, in log",
        y = "Mile per hours, in log",
        color = "Gear",
        title = "Relation between Mile per hours and drat",
        subtitle = "Relationship break down by gear class",
        caption = "Authors own computation"
    )

Output:

Save Plots

After all these steps, it is time to save and share your graph. You add ggsave('NAME OF THE FILE) right after you plot the graph and it will be stored on the hard drive.

The graph is saved in the working directory. To check the working directory, you can run this code: 

directory <-getwd()
directory

Let's plot your fantastic graph, saves it and check the location 

my_graph +
    theme_dark() +
    labs(
        x = "Drat definition, in log",
        y = "Mile per hours, in log",
        color = "Gear",
        title = "Relation between Mile per hours and drat",
        subtitle = "Relationship break down by gear class",
        caption = "Authors own computation"
    )

Output: 

ggsave("my_fantastic_plot.png")

Output: 

## Saving 5 x 4 in image

Note: For pedagogical purpose only, we created a function called open_folder() to open the directory folder for you. You just need to run the code below and see where the picture is stored. You should see a file names my_fantastic_plot.png. 

# Run this code to create the
function
open_folder <- function(dir) {
    if (.Platform['OS.type'] == "windows") {
        shell.exec(dir)
    } else {
        system(paste(Sys.getenv("R_BROWSER"), dir))
    }
}

# Call the
function to open the folder open_folder(directory)

Summary

You can summarize the arguments to create a scatter plot in the table below:

Objective

Code

Basic scatter plot 

ggplot(df, aes(x = x1, y = y)) + geom_point()

Scatter plot with color group 

ggplot(df, aes(x = x1, y = y)) + geom_point(aes(color = factor(x1)) + stat_smooth(method = "lm")

Add fitted values 

ggplot(df, aes(x = x1, y = y)) + geom_point(aes(color = factor(x1))

Add title 

ggplot(df, aes(x = x1, y = y)) + geom_point() + labs(title = paste("Hello gtupapers"))

Add subtitle 

ggplot(df, aes(x = x1, y = y)) + geom_point() + labs(subtitle = paste("Hello gtupapers"))

Rename x 

ggplot(df, aes(x = x1, y = y)) + geom_point() + labs(x = "X1")

Rename y 

ggplot(df, aes(x = x1, y = y)) + geom_point() + labs(y = "y1")

Control the scale 

ggplot(df, aes(x = x1, y = y)) + geom_point() + scale_y_continuous(breaks = seq(10, 35, by = 10)) + scale_x_continuous(breaks = seq(2, 5, by = 1)

Create logs 

ggplot(df, aes(x =log(x1), y = log(y))) + geom_point()

Theme 

ggplot(df, aes(x = x1, y = y)) + geom_point() + theme_classic()

Save 

ggsave("my_fantastic_plot.png")

 

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