MechaCarChallenge.RScript

### CHALLENGE 15: DELIVERABLE 1

# 3. Use the library() function to load the dplyr package
library(dplyr)

#4. Import and read in the MechaCar_mpg.csv file as a dataframe.
library(tidyverse)
mecha_mpg <- read.csv(file='./Resources/MechaCar_mpg.csv',check.names=F,stringsAsFactors = F) 

#5. Perform linear regression using the lm() function
lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD, data=mecha_mpg)

#6. Using the summary() function, determine the p-value and the r-squared value for the linear regression model.
summary(lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD, data=mecha_mpg)) 

#Additional Step: eliminate the independent variables that have little impact on predicting mpg to see impact:
lm(mpg ~ vehicle_length + ground_clearance, data=mecha_mpg)
summary(lm(mpg ~ vehicle_length + ground_clearance, data=mecha_mpg)) 



### CHALLENGE 15: DELIVERABLE 2

#2. Import and read in the Suspension_Coil.csv file as a table
mecha_coil <- read.csv(file='./Resources/Suspension_Coil.csv',check.names=F,stringsAsFactors = F) 

#3. Create a total_summary dataframe using the summarize() function to get the mean, median, variance, and standard deviation of the suspension coil’s PSI column.
total_summary <- mecha_coil %>% summarize(Mean_PSI=mean(PSI),
                                          Median_PSI=median(PSI),
                                          Var_PSI=var(PSI),
                                          Std_Dev_PSI=sd(PSI),
                                          Num_Coil=n(), .groups = 'keep') 
#4. Create a lot_summary dataframe using the group_by() and the summarize() functions to group each manufacturing lot.                                                                
lot_summary <- mecha_coil  %>% group_by(Manufacturing_Lot) %>% summarize(Mean_PSI=mean(PSI),
                                          Median_PSI=median(PSI),
                                          Var_PSI=var(PSI),
                                          Std_Dev_PSI=sd(PSI),
                                          Num_Coil=n(), .groups = 'keep')                                      

#box plot: PSI Whole lot
plt1 <- ggplot(mecha_coil,aes(y=PSI)) #import dataset into ggplot2
plt1 + geom_boxplot() #add boxplot

#box plot: PSI each indicdiual Lot
plt2 <- ggplot(mecha_coil,aes(x=Manufacturing_Lot,y=PSI)) #import dataset into ggplot2
plt2 + geom_boxplot()

### CHALLENGE 15: DELIVERABLE 3

#1. use t.test() to determine if the PSI across ALL lots is statistically different from the pop. mean of 1,500 PSI.
t.test(mecha_coil$PSI,mu=1500)


#2. Use t.test() function 3 more times with subset() to determine if PSI for each manufacturing lot is statistically different from the pop. mean of 1,500 PSI
lot1 <- subset(mecha_coil, Manufacturing_Lot=="Lot1")
lot2 <- subset(mecha_coil, Manufacturing_Lot=="Lot2")
lot3 <- subset(mecha_coil, Manufacturing_Lot=="Lot3")

t.test(lot1$PSI,mu=1500)
t.test(lot2$PSI,mu=1500)
t.test(lot3$PSI,mu=1500)