The code presents a sample data set, plots it, calls the linear model of regression, calculates predicted values and plots the residuals.

| Regression.r

library(ggplot2)
x=c(7,8,10,7,6,10,11,4)
y=c(5,8,18,10,7,12,16,2)
d = data.frame(x,y)
knitr::kable(head(d))
plot(x,y,xlab = "x", ylab = "y", pch = 19, lwd=1.5, cex=1.5)
fm <- lm(formula = y ~ x, data = d)
d$predicted = predict(fm)
d$residuals = residuals(fm)
knitr::kable(head(d))
ggplot(d, aes(x = x, y = y)) +
  geom_smooth(method = "lm", se = FALSE, color = "red") +  
  geom_segment(aes(xend = x, yend = predicted), alpha = .2) +  
  geom_point() +
  geom_point(aes(y = predicted), shape = 1) +
  theme_bw()