F Correlation and Regression code

Video link: https://youtu.be/8lNF6PfkS2Y

################################################################################
#                        Correlation and Regression
################################################################################

#Load the full dataset
data <- mtcars

#Look at the included variables
head(data)

#Load tidyverse package
library(tidyverse)

#Only keep 3 variables: mpg, hp, and wt
data1 <- data %>%
  select(c(mpg, hp, wt))

#Check once more that our data is how we'd like it
head(data1)

#Load ggplot package
library(ggplot2)

#mpg-hp scatterplot
ggplot(data1, aes(x = mpg, y = hp)) +
  geom_point()

#hp-wt scatterplot
ggplot(data1, aes(x = hp, y = wt)) +
  geom_point()

#mpg-wt scatterplot
ggplot(data1, aes(x = mpg, y = wt)) +
  geom_point()

#Creating multiple scatterplots
pairs(data1[, c("mpg", "hp", "wt")])

#Run the test
cor.test(data1$mpg, data1$hp)

#Load the Hmisc package
library(Hmisc)

#Run the test; saving out results
c <- rcorr(as.matrix(data1)) 

#View results
c

#Load the package
library(correlation)

#Run the correlation
correlation(data1)

###### REGRESSION ######

#Assign our data to a dataframe
df_t <- trees

#Look at the data
head(df_t)

#ggplot2 is already loaded from above, so we don't need to do it again
ggplot(data = df_t, aes(x = Height, y = Girth)) +
  geom_point()

#Make the linear regression model
model <- lm(Girth ~ Height, data = df_t)

#Look at model output
model

#Ask for more model information
summary(model)