fm <- lm(formula = y ~ x, data = d)
# This is the key formula for regression. lm stands for linear model
# it has the parameters formula e.g. y~x (reads is in proportion to)
# and data containing the data.frame with y,x values

# The model yields a result that is stored in fm
# from fm we extract the predicted values with predict(fm)
d$predicted = predict(fm)

# We can also extract the residuals (erros) with residuals(fm)
d$residuals = residuals(fm)

# knitr::kable plots the values as a table
knitr::kable(head(d))