Creating and Editing Interaction Plots in R Studio

In this tutorial, I am going to show you how to create and edit interaction plots in R studio. Below is all the R code I used in this video. Please note that angle brackets are not allowed in youtube video descriptions, so I left notes below where the angle brackets need to be inserted within the code. ### R Studio Tutorial: Creating and Editing Interaction Plots ### # Research Question: Is there a two-way interaction between men and women employees in the amount of close friends they report at work and their overall job satisfaction? # Step 1: Upload dataset Data1 (insert angled bracket here)- read.csv(file.choose()) # Step 2: Examine dataset head(Data1) # Step 3: Run a standard "interaction.plot" #interaction.plot(dataset$var1, dataset$var2, dataset$response) interaction.plot(Data1$Friend, Data1$Sex, Data1$Job_Satisfaction) # x.factor = "variable for your x-axis" # trace.factor = "grouping variable" # response = "variable for your y-axis" with(Data1,{interaction.plot(x.factor = Friend, trace.factor = Sex, response = Job_Satisfaction)}) # This cleans up the labels slightly # OR... you can remove the subheadings with(Data1,{interaction.plot(Friend, Sex, Job_Satisfaction)}) # Step 4: Start editing your "interaction.plot" # Additional options to change # xlab = "label your x-axis" # ylab = "label your y-axis" # main = "title for your plot" # ylim = "range of values along y-axis" # trace.label = "label your legend" # type = "puts markers on your plot" # pch = "customize markers on your plot" # col = "adds colour to your plot" # fixed = "orders your factors based on your dataset" interaction.plot(Data1$Friend, Data1$Sex, Data1$Job_Satisfaction, xlab = "Close Friends at Work", ylab = "Overall Job Satisfaction", main = "Employees' Overall Job Satisfaction and Close Friends at Work", ylim = c(1,10), trace.label = "Gender", type = "b", col=c("red","green"), pch = c(19,17), fixed = TRUE) # Step 5: Properly label your legend and x-axis # This is going to involve recoding your variables # So create a copy of the dataset just so you do not overwrite the original Data2 (insert angled bracket here)- Data1 library(car) Data2$Sex (insert angled bracket here)- recode(Data2$Sex, '0 = "Male"; 1 = "Female";', as.factor.result = FALSE) Data2$Friend (insert angled bracket here)- recode(Data2$Friend, '0 = "low"; 1 = "medium"; 2 = "high"; 3 = "very high";', as.factor.result = FALSE) # Step 6: Rerun your "interaction.plot" interaction.plot(Data2$Friend, Data2$Sex, Data2$Job_Satisfaction, xlab = "Close Friends at Work", ylab = "Overall Job Satisfaction", main = "Employees' Overall Job Satisfaction and Close Friends at Work", ylim = c(1,10), trace.label = "Gender", type = "b", col=c("red","green"), pch = c(19,17), fixed = TRUE) # Step 7: Reorder the labels on your x-axis # Create a factor with four levels to reorder the labels that will display on the x-axis x1 = factor(Data2$Friend, levels=c("low", "medium", "high", "very high")) # Step 8: Run your finalized "interaction.plot" interaction.plot(x1, Data2$Sex, Data2$Job_Satisfaction, xlab = "Close Friends at Work", ylab = "Overall Job Satisfaction", main = "Employees' Overall Job Satisfaction and Close Friends at Work", ylim = c(1,10), trace.label = "Gender", type = "b", col=c("red","green"), pch = c(19,17), fixed = TRUE)