A **Scatter plot** (also known as **X-Y plot** or **Point graph**) is used to display the relationship between two continuous variables x and y.

By displaying a variable in each axis, it is possible to determine if an association or a *correlation* exists between the two variables.

The correlation can be: positive (values increase together), negative (one value decreases as the other increases), null (no correlation), linear, exponential and U-shaped.

This article describes how to create scatter plots in R using the ggplot2 package.

You will learn how to:

- Color points by groups
- Create bubble charts
- Add regression line to a scatter plot

Contents:

#### Related Book

GGPlot2 Essentials for Great Data Visualization in R## Data preparation

Demo dataset: `mtcars`

. The variable `cyl`

is used as grouping variable.

```
# Load data
data("mtcars")
df <- mtcars
# Convert cyl as a grouping variable
df$cyl <- as.factor(df$cyl)
# Inspect the data
head(df[, c("wt", "mpg", "cyl", "qsec")], 4)
```

```
## wt mpg cyl qsec
## Mazda RX4 2.62 21.0 6 16.5
## Mazda RX4 Wag 2.88 21.0 6 17.0
## Datsun 710 2.32 22.8 4 18.6
## Hornet 4 Drive 3.21 21.4 6 19.4
```

## Loading required R package

Load the ggplot2 package and set the default theme to `theme_bw()`

with the legend at the top of the plot:

```
library(ggplot2)
theme_set(
theme_bw() +
theme(legend.position = "top")
)
```

## Basic scatter plots

- Key functions:
`geom_point()`

for creating scatter plots. - Key arguments:
`color`

,`size`

and`shape`

to change point color, size and shape.

```
# Initiate a ggplot
b <- ggplot(df, aes(x = wt, y = mpg))
# Basic scatter plot
b + geom_point()
# Change color, shape and size
b + geom_point(color = "#00AFBB", size = 2, shape = 23)
```

The different point shapes commonly used in R, include:

## Scatter plots with multiple groups

This section describes how to change point colors and shapes by groups. The functions `scale_color_manual()`

and `scale_shape_manual()`

are used to manually customize the color and the shape of points, respectively.

In the R code below, point shapes and colors are controlled by the levels of the grouping variable *cyl* :

```
# Change point shapes by the levels of cyl
b + geom_point(aes(shape = cyl))
# Change point shapes and colors by the levels of cyl
# Set custom colors
b + geom_point(aes(shape = cyl, color = cyl)) +
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
```

## Add regression lines

- Key R function:
`geom_smooth()`

for adding smoothed conditional means / regression line. - Key arguments:
`color`

,`size`

and`linetype`

: Change the line color, size and type.`fill`

: Change the fill color of the confidence region.

A simplified format of the function `geom_smooth():

`geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95)`

**method**: smoothing method to be used. Possible values are lm, glm, gam, loess, rlm.**method = “loess”**: This is the default value for small number of observations. It computes a smooth local regression. You can read more about**loess**using the R code**?loess**.**method =“lm”**: It fits a**linear model**. Note that, it’s also possible to indicate the formula as**formula = y ~ poly(x, 3)**to specify a degree 3 polynomial.

**se**: logical value. If TRUE, confidence interval is displayed around smooth.**fullrange**: logical value. If TRUE, the fit spans the full range of the plot**level**: level of confidence interval to use. Default value is 0.95

To add a regression line on a scatter plot, the function `geom_smooth()`

is used in combination with the argument `method = lm`

. `lm`

stands for linear model.

```
# Add regression line
b + geom_point() + geom_smooth(method = lm)
# Point + regression line
# Remove the confidence interval
b + geom_point() +
geom_smooth(method = lm, se = FALSE)
# loess method: local regression fitting
b + geom_point() + geom_smooth()
```

**Change point color and shapes by groups**:

```
# Change color and shape by groups (cyl)
b + geom_point(aes(color = cyl, shape=cyl)) +
geom_smooth(aes(color = cyl, fill = cyl), method = lm) +
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
# Remove confidence intervals
# Extend the regression lines: fullrange
b + geom_point(aes(color = cyl, shape = cyl)) +
geom_smooth(aes(color = cyl), method = lm, se = FALSE, fullrange = TRUE) +
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
```

## Add marginal rugs to a scatter plot

The function `geom_rug()`

is used to display display individual cases on the plot.

```
# Add marginal rugs
b + geom_point() + geom_rug()
# Change colors by groups
b + geom_point(aes(color = cyl)) +
geom_rug(aes(color = cyl))
# Add marginal rugs using faithful data
data(faithful)
ggplot(faithful, aes(x = eruptions, y = waiting)) +
geom_point() + geom_rug()
```

## Jitter points to reduce overplotting

The `mpg`

data set [in **ggplot2**] is used in the following examples.

To reduce overplotting, the option `position = position_jitter()`

with the arguments *width* and *height* are used:

*width*: degree of jitter in x direction.*height*: degree of jitter in y direction.

```
# Default plot
ggplot(mpg, aes(displ, hwy)) +
geom_point()
# Use jitter to reduce overplotting
ggplot(mpg, aes(displ, hwy)) +
geom_point(position = position_jitter(width = 0.5, height = 0.5))
```

## Add point text labels

Key functions:

`geom_text()`

and`geom_label()`

: ggplot2 standard functions to add text to a plot.`geom_text_repel()`

and`geom_label_repel()`

[in ggrepel package]. Repulsive textual annotations. Avoid text overlapping.

First install `ggrepel`

(`ìnstall.packages("ggrepel")`

), then type this:

```
library(ggrepel)
# Add text to the plot
.labs <- rownames(df)
b + geom_point(aes(color = cyl)) +
geom_text_repel(aes(label = .labs, color = cyl), size = 3)+
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
```

```
# Draw a rectangle underneath the text, making it easier to read.
b + geom_point(aes(color = cyl)) +
geom_label_repel(aes(label = .labs, color = cyl), size = 3)+
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
```

## Bubble chart

In a bubble chart, points `size`

is controlled by a continuous variable, here `qsec`

. In the R code below, the argument alpha is used to control color transparency. alpha should be between 0 and 1.

```
b + geom_point(aes(color = cyl, size = qsec), alpha = 0.5) +
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
scale_size(range = c(0.5, 12)) # Adjust the range of points size
```

## Color by a continuous variable

- Color points according to the values of the continuous variable: “mpg”.
- Change the default blue gradient color using the function
`scale_color_gradientn()`

[in ggplot2], by specifying two or more colors.

```
b + geom_point(aes(color = mpg), size = 3) +
scale_color_gradientn(colors = c("#00AFBB", "#E7B800", "#FC4E07")) +
theme(legend.position = "right")
```

## Recommended for you

This section contains best data science and self-development resources to help you on your path.

### Coursera - Online Courses and Specialization

#### Data science

- Course: Machine Learning: Master the Fundamentals by Standford
- Specialization: Data Science by Johns Hopkins University
- Specialization: Python for Everybody by University of Michigan
- Courses: Build Skills for a Top Job in any Industry by Coursera
- Specialization: Master Machine Learning Fundamentals by University of Washington
- Specialization: Statistics with R by Duke University
- Specialization: Software Development in R by Johns Hopkins University
- Specialization: Genomic Data Science by Johns Hopkins University

#### Popular Courses Launched in 2020

- Google IT Automation with Python by Google
- AI for Medicine by deeplearning.ai
- Epidemiology in Public Health Practice by Johns Hopkins University
- AWS Fundamentals by Amazon Web Services

#### Trending Courses

- The Science of Well-Being by Yale University
- Google IT Support Professional by Google
- Python for Everybody by University of Michigan
- IBM Data Science Professional Certificate by IBM
- Business Foundations by University of Pennsylvania
- Introduction to Psychology by Yale University
- Excel Skills for Business by Macquarie University
- Psychological First Aid by Johns Hopkins University
- Graphic Design by Cal Arts

### Books - Data Science

#### Our Books

- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)

#### Others

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet

Version: Français

## No Comments