How to Create Interaction Plot in R?

In this article, we will discuss how to create an interaction plot in the R Programming Language.

The interaction plot shows the relationship between a continuous variable and a categorical variable in relation to another categorical variable. It lets us know whether two categorical variables have any interaction in response to a common continuous variable. If there are two parallel lines in the interaction plot, it means those two categorical variables have no interaction. Otherwise, if both lines intersect at a point that means there is an interaction between those two categorical variables. 

Create a basic Interaction Plot:

To create a basic interaction plot in the R language, we use interaction.plot() function. The interaction.plot() function helps us visualize the mean/median of the response for two-way combinations of factors. This helps us in illustrating the possible interaction. The interaction.plot() function takes x.factor, trace.factor, response, and fun as arguments and returns an interaction plot layer.

Syntax:

interaction.plot( x.factor, trace.factor, response, fun )

Parameters:

• x.factor: determines the variable whose levels will form the x-axis.
• trace.factor: determines another factor whose levels will form the traces.
• response: determines a numeric variable giving the response.
• fun: determines the statistical summary element according to which trace will be made.

Example 1: Basic interaction plot

Here, is a basic interaction plot. The CSV file used in the example can be downloaded here.

R

# import sample data to data frame
sample_data <- read.csv("Sample_interaction.CSV")

# Basic Interaction Plot
interaction.plot(x.factor = sample_data$Effort,
                 trace.factor = sample_data$gender,
                 response = sample_data$Result, fun = median)

Output:

Example 2: Label Customization

To customize the x-axis and y-axis labels in the interaction plot, we use the xlab and ylab arguments of the interaction.plot() function in the R Language. To change the label of the variable in the legend of the plot, we use the trace.label argument of the interaction.plot() function in the R Language.

Syntax: interaction.plot( x.factor, trace.factor, response, fun, xlab, ylab, trace.label )

Parameters:

• xlab: determines the label for the x-axis variable.
• ylab: determines the label for the y-axis variable.
• trace.label: determines the label for the trace factor variable in legend.

Here, is a basic interaction plot with custom labels.

R

# import sample data to data frame
sample_data <- read.csv("Sample_interaction.CSV")

# Basic Interaction Plot with custom labels
interaction.plot(x.factor = sample_data$Effort,
                 trace.factor = sample_data$gender,
                 response = sample_data$Result,
                 fun = median, xlab="Effort",
                 ylab="Result", trace.label="Gender")

Output:

Example 3: Color and Shape Customization

To customize the color of the lines, we use the col parameter of the interaction.plot() function which takes a color vector as an argument. To customize the width and shape of the line, we use the lwd and lty parameters of the interaction.plot() function.

Syntax: interaction.plot( x.factor, trace.factor, response, fun, col, lwd, lty )

Parameters:

• col: determines the colors of the lines in the plot.
• lty: determines the type of line for example dashed, wedged,etc.
• lwd: determines the width of the plotline.

Here, is a basic interaction plot with custom labels, color, and shape.

R

# import sample data to data frame
sample_data <- read.csv("Sample_interaction.CSV")

# Basic Interaction Plot with custom labels
interaction.plot(x.factor = sample_data$Effort,
                 trace.factor = sample_data$gender,
                 response = sample_data$Result,
                 fun = median, xlab="Effort",
                 ylab="Result", trace.label="Gender",
                 col=c("green","red"),
                 lty=4, lwd=2.5 )

Output: