# Scatterplot in R (10 Examples) | Create XYplot in Base R, ggplot2 & lattice

In this R programming tutorial you’ll learn how to draw scatterplots.

Let’s dive right in:

## Exemplifying Data

We’ll use the following two numeric vectors for the following examples of this R (or RStudio) tutorial:

```set.seed(42424) # Create random data x <- rnorm(500) y <- x + rnorm(500)```

Our vectors contain 500 values each and are correlated. Now let’s plot these data!

## Example 1: Basic Scatterplot in R

If we want to create a scatterplot (also called XYplot) in Base R, we need to apply the plot() function as shown below:

`plot(x, y) # Basic scatterplot` Figure 1: Scatterplot with Default Specifications in Base R.

Figure 1 shows an XYplot of our two input vectors. As you can see, our vectors are correlated.

However, the scatterplot is relatively plain and simple. In the next examples you’ll learn how to adjust the parameters of our scatterplot in R.

## Example 2: Scatterplot with User-Defined Title & Labels

In Example 2, we’ll create a main title and change the axis labels of both axes:

```plot(x, y, # Scatterplot with manual text main = "This is my Scatterplot", xlab = "My X-Values", ylab = "My Y-Values")``` Figure 2: Scatterplot with User-Defined Main Title & Axis Labels.

If you compare Figure 1 and Figure 2, you will see that the title and axes where changed.

## Example 3: Add Fitting Line to Scatterplot (abline Function)

Quite often it is useful to add a fitting line (or regression slope) to a XYplot to show the correlation of the two input variables.

In the R programming language, we can do that with the abline function:

```plot(x, y) # Scatterplot with fitting line abline(lm(y ~ x), col = "red")``` Figure 3: Scatterplot with Straight Fitting Line.

In Figure 3 you can see a red regression line, which overlays our original scatterplot.

## Example 4: Add Smooth Fitting Line to Scatterplot (lowess Function)

In Example 3, we added a straight fitting line. However, it is also possible to draw a smooth fitting line with the lowess function.

```plot(x, y) # Scatterplot with smooth fitting line lines(lowess(x, y), col = "green")``` Figure 4: Scatterplot with Smooth Fitting Line.

Have a close look at the green line in Figure 4. It is not perfectly straight due to the random variation in our data.

## Example 5: Modify Color & Point Symbols in Scatterplot

The plot function provides several options to change the design of our XYplot. For instance, we can use the pch argument to adjust the point symbols or the col argument to change the color of the points:

```plot(x, y, # Scatterplot with color & symbols pch = 16, col = "#1b98e0")``` Figure 5: Scatterplot with Different Color & Point Symbols.

## Example 6: Create Scatterplot with Multiple Groups

We can also use the design features of the plot function to represent different groups in a single scatterplot. Consider the following grouping variable:

`group <- rbinom(500, 1, 0.3) + 1 # Create grouping variable`

Now, we can use our grouping variable to specify a point symbol for each point…

```group_pch <- group # Create variable for symbols group_pch[group_pch == 1] <- 16 group_pch[group_pch == 2] <- 8```

…and to create an indicator for the color of each point:

```group_col <- group # Create variable for colors group_col[group_col == 1] <- "red" group_col[group_col == 2] <- "green"```

If we now use our symbol- and color-indicators within the plot function, we can draw multiple scatterplots in the same graphic:

```plot(x, y, # Scatterplot with two groups pch = group_pch, col = group_col)``` Figure 6: Multiple Scatterplots in Same Graphic.

Looks good, but at this point the reader of our graph cannot know which color represents which group… Let’s add a legend!

## Example 7: Add Legend to Scatterplot

We can add a legend to our graph, which we have created in Example 6, with the legend function:

```legend("topleft", # Add legend to scatterplot legend = c("Group 1", "Group 2"), col = c("red", "green"), pch = c(16, 8))``` Figure 7: Scatterplot with Legend.

Figure 7 is exactly the same as Figure 6, but this time it’s visualizing the two groups in a legend.

## Example 8: Matrix of Scatterplots

If we want to visualize several XYplots at once, we can also create a matrix of scatterplots. In Base R, we can do this based on the pairs function.

However, first we need to extend our example data. With the following R syntax, we can create a uniformly distributed random vector and store this vector together with our two example vectors x and y in the same data frame:

```z <- runif(500) # Create third random variable data <- data.frame(x, y, z) # Add all vectors to data frame```

Now, we can apply the pairs function in order to draw a scatterplot matrix:

`pairs(data) # Create matrix of scatterplots` Figure 8: Scatterplot Matrix Created with pairs() Function.

As you can see based on Figure 8, each cell of our scatterplot matrix represents the dependency between two of our variables.

## Example 9: Scatterplot in ggplot2 Package

So far, we have created all scatterplots with the base installation of R. However, there are several packages, which also provide functions for the creation of scatterplots.

In this example, I’ll show you how to draw a scatterplot with the ggplot2 package. Let’s install and load the package:

```install.packages("ggplot2") # Install ggplot2 package library("ggplot2") # Load ggplot2 package```

Now, we can use the ggplot and geom_point functions to draw a ggplot2 scatterplot in R:

```ggplot(data, aes(x = x, y = y)) + # Scatterplot in ggplot2 geom_point()``` Figure 9: Scatterplot Created with the ggplot2 Package.

Figure 9 contains the same XYplot as already shown in Example 1. This time, however, the scatterplot is visualized in the typical ggplot2 style.

## Example 10: Scatterplot in lattice Package

Another popular package for the drawing of scatterplots is the lattice package. First, we need to install and load the lattice package:

```install.packages("lattice") # Install lattice package library("lattice") # Load lattice package```

The lattice package contains the xyplot command, which is used as follows:

`xyplot(y ~ x, data) # Scatterplot in lattice` Figure 10: Scatterplot Created with the lattice Package.

Again the same picture as in Examples 1 and 9, but this time with a lattice design.

## Video & Further Resources

Have a look at the following video of my YouTube channel. In the video, I’m showing the R programming syntax of this tutorial:

Furthermore, you could read the related tutorials on my website. You can find some other tutorials about the plotting of data here.

In this tutorial you learned how to make a scatterplot in RStudio, i.e. the R programming language. If you have additional questions or comments, let me know in the comments section.