To create a scatterplot with a regression line in R, you first need to enter the data into a data frame, then use the plot function to draw the scatterplot and the abline function to draw the regression line. You can adjust the line to fit the data by specifying the intercept and slope of the line. You can also add a title and labels to the plot and adjust the color and other aesthetic elements.
Often when we perform simple linear regression, we’re interested in creating a to visualize the various combinations of x and y values.
Fortunately, R makes it easy to create scatterplots using the plot() function. For example:
#create some fake data data <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11), y = c(13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33, 35, 40, 41)) #create scatterplot of data plot(data$x, data$y)
It’s also easy to add a regression line to the scatterplot using the abline() function.
For example:
#fit a simple linear regression model model <- lm(y ~ x, data = data) #add the fitted regression line to the scatterplot abline(model)
We can also add confidence interval lines to the plot by using the predict() function:
#define range of x values newx = seq(min(data$x),max(data$x),by = 1) #find 95% confidence interval for the range of x values conf_interval <- predict(model, newdata=data.frame(x=newx), interval="confidence", level = 0.95) #create scatterplot of values with regression line plot(data$x, data$y) abline(model) #add dashed lines (lty=2) for the 95% confidence interval lines(newx, conf_interval[,2], col="blue", lty=2) lines(newx, conf_interval[,3], col="blue", lty=2)
Or we could instead add prediction interval lines to the plot by specifying the interval type within the predict() function:
#define range of x values newx = seq(min(data$x),max(data$x),by = 1) #find 95% prediction interval for the range of x values pred_interval <- predict(model, newdata=data.frame(x=newx), interval="prediction", level = 0.95) #create scatterplot of values with regression line plot(data$x, data$y) abline(model) #add dashed lines (lty=2) for the 95% confidence interval lines(newx, pred_interval[,2], col="red", lty=2) lines(newx, pred_interval[,3], col="red", lty=2)
Lastly, we can make the plot more aesthetically pleasing by adding a title, changing the axes names, and changing the shape of the individual points in the plot.
plot(data$x, data$y,
main = "Scatterplot of x vs. y", #add title
pch=16, #specify points to be filled in
xlab='x', #change x-axis name
ylab='y') #change y-axis name
abline(model, col='steelblue') #specify color of regression line
The following tutorials explain how to perform other common tasks in R: