- Pleleminary tasks
- R base scatter plot: plot()
- Enhanced scatter plots: car::scatterplot()
- 3D scatter plots
- Summary
- Related articles
- See also
- Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into **R**.

Here, we’ll describe how to make a **scatter plot**. A **scatter plot** can be created using the function **plot**(x, y).The function **lm**() will be used to fit linear models between y and x. A **regression line** will be added on the plot using the function **abline**(), which takes the output of **lm**() as an argument. You can also add a smoothing line using the function **loess**().

**Launch RStudio**as described here: Running RStudio and setting up your working directory**Prepare your data**as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files**Import your data**into**R**as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in mtcars data set.

`x <- mtcars$wty <- mtcars$mpg# Plot with main and axis titles# Change point shape (pch = 19) and remove frame.plot(x, y, main = "Main title", xlab = "X axis title", ylab = "Y axis title", pch = 19, frame = FALSE)# Add regression lineplot(x, y, main = "Main title", xlab = "X axis title", ylab = "Y axis title", pch = 19, frame = FALSE)abline(lm(y ~ x, data = mtcars), col = "blue")`

`# Add loess fitplot(x, y, main = "Main title", xlab = "X axis title", ylab = "Y axis title", pch = 19, frame = FALSE)lines(lowess(x, y), col = "blue")`

The function **scatterplot**() [in **car** package] makes enhanced scatter plots, with box plots in the margins, a non-parametric regression smooth, smoothed conditional spread, outlier identification, and a regression line, …

- Install
**car**package:

`install.packages("car")`

- Use
**scatterplot**() function:

`library("car")scatterplot(wt ~ mpg, data = mtcars)`

The plot contains:

- the points
- the regression line (in green)
- the smoothed conditional spread (in red dashed line)
- the non-parametric regression smooth (solid line, red)

`# Suppress the smoother and framescatterplot(wt ~ mpg, data = mtcars, smoother = FALSE, grid = FALSE, frame = FALSE)`

`# Scatter plot by groups ("cyl")scatterplot(wt ~ mpg | cyl, data = mtcars, smoother = FALSE, grid = FALSE, frame = FALSE)`

It’s also possible to add labels using the following arguments:

**labels**: a vector of point labels**id.n**,**id.cex**,**id.col**: Arguments for labeling points specifying the number, the size and the color of points to be labelled.

`# Add labelsscatterplot(wt ~ mpg, data = mtcars, smoother = FALSE, grid = FALSE, frame = FALSE, labels = rownames(mtcars), id.n = nrow(mtcars), id.cex = 0.7, id.col = "steelblue", ellipse = TRUE)`

`## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant ## 1 2 3 4 5 6 ## Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE ## 7 8 9 10 11 12 ## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128 ## 13 14 15 16 17 18 ## Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 ## 19 20 21 22 23 24 ## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino ## 25 26 27 28 29 30 ## Maserati Bora Volvo 142E ## 31 32`

Other arguments can be used such as:

**log**to produce log axes. Allowed values are log = “x”, log = “y” or log = “xy”**boxplots**: Allowed values are:- “x”: a box plot for x is drawn below the plot
- “y”: a box plot for y is drawn to the left of the plot
- “xy”: both box plots are drawn
- “” or FALSE to suppress both box plots.

**ellipse**: if TRUE data-concentration ellipses are plotted.

To plot a 3D scatterplot the function **scatterplot3D** [in **scatterplot3D** package can be used].

The following R code plots a 3D scatter plot using *iris* data set.

`head(iris)`

`## Sepal.Length Sepal.Width Petal.Length Petal.Width Species## 1 5.1 3.5 1.4 0.2 setosa## 2 4.9 3.0 1.4 0.2 setosa## 3 4.7 3.2 1.3 0.2 setosa## 4 4.6 3.1 1.5 0.2 setosa## 5 5.0 3.6 1.4 0.2 setosa## 6 5.4 3.9 1.7 0.4 setosa`

`# Prepare the data setx <- iris$Sepal.Lengthy <- iris$Sepal.Widthz <- iris$Petal.Lengthgrps <- as.factor(iris$Species)# Plotlibrary(scatterplot3d)scatterplot3d(x, y, z, pch = 16)`

`# Change color by groups# add grids and remove the box around the plot# Change axis labels: xlab, ylab and zlabcolors <- c("#999999", "#E69F00", "#56B4E9")scatterplot3d(x, y, z, pch = 16, color = colors[grps], grid = TRUE, box = FALSE, xlab = "Sepal length", ylab = "Sepal width", zlab = "Petal length")`

- Read more about static and interactive 3D scatter plot:
- R base scatterplot3D
- Amazing interactive 3D scatter plots
- Impressive package for 3D and 4D graph
- A complete guide to interactive 3D visualization device system in R

**Create a scatter plot**:

- Using R base function:

`with(mtcars, plot(wt, mpg, frame = FALSE))`

- Using
**car**package:

`car::scatterplot(wt ~ mpg, data = mtcars, smoother = FALSE, grid = FALSE)`

- 3D scatter plot:

`library(scatterplot3d)with(iris, scatterplot3d(x = Sepal.Length, y = Sepal.Width, z = Petal.Length, pch = 16, grid = TRUE, box = FALSE))`

- Creating and Saving Graphs in R
- Scatter Plot Matrices
- Box Plots
- Strip Charts: 1-D scatter Plots
- Bar Plots
- Line Plots
- Pie Charts
- Histogram and Density Plots
- Dot Charts
- Plot Group Means and Confidence Intervals
- Graphical Parameters

- Lattice Graphs
- ggplot2 Graphs

This analysis has been performed using **R statistical software** (ver. 3.2.4).