How to visualize a linear regression model in R?

# Simple Linear Regression

# Importing the dataset

dataset = read.csv('Salary_Data.csv')

# Splitting the dataset into the Training set and Test set

# install.packages('caTools')

library(caTools)

set.seed(123)

split = sample.split(dataset$Salary, SplitRatio = 2/3)

training_set = subset(dataset, split == TRUE)

test_set = subset(dataset, split == FALSE)

# Feature Scaling

# training_set = scale(training_set)

# test_set = scale(test_set)

# Fitting Simple Linear Regression to the Training set

regressor = lm(formula = Salary ~ YearsExperience,

               data = training_set)

# Predicting the Test set results

y_pred = predict(regressor, newdata = test_set)

# Visualising the Training set results

library(ggplot2)

ggplot() +

  geom_point(aes(x = training_set$YearsExperience, y = training_set$Salary),

             colour = 'red') +

  geom_line(aes(x = training_set$YearsExperience, y = predict(regressor, newdata = training_set)),

            colour = 'blue') +

  ggtitle('Salary vs Experience (Training set)') +

  xlab('Years of experience') +

  ylab('Salary')

# Visualising the Test set results

library(ggplot2)

ggplot() +

  geom_point(aes(x = test_set$YearsExperience, y = test_set$Salary),

             colour = 'red') +

  geom_line(aes(x = training_set$YearsExperience, y = predict(regressor, newdata = training_set)),

            colour = 'blue') +

  ggtitle('Salary vs Experience (Test set)') +

  xlab('Years of experience') +

  ylab('Salary')