Enkhjin/ch2.9

{r} install.packages("ISLR")

{r} library(ISLR) data("Auto") head(Auto)

{r} url <- "https://www.statlearning.com/s/Auto.csv" auto_data <- read.csv(url, header = TRUE, na.strings = "?") head(auto_data)

’’(a) Which of the predictors are quantitative, and which are quali-tative?

Quantitative:

mpg - Miles per gallon cylinders - Number of cylinders between 4 and 8 displacement - Engine displacement (cu. inches) horsepower - Engine horsepower weight - Vehicle weight (lbs.) acceleration - Time to accelerate from 0 to 60 mph (sec.) year - Model year (modulo 100)

Qualitative:

origin - Origin of car (1. American, 2. European, 3. Japanese) name - Vehicle name’’

2. What is the range of each quantitative predictor?

{r} range_Auto <- data.frame(sapply(Auto[ ,1:7], range)) rownames(range_Auto) <- c("min:", "max:") range_Auto

3. What is the mean and standard deviation of each quantitative predictor? {r} sapply(Auto[ ,1:7], mean)

{r} sapply(Auto[ ,1:7], sd)

4. Now remove the 10th through 85th observations. What is the range, mean, and standard deviation of each predictor in the subset of the data that remains?

{r} Auto_2 <- Auto[-c(10:85), ]

{r} range_Auto_2 <- data.frame(sapply(Auto_2[ ,1:7], range)) rownames(range_Auto_2) <- c("min:", "max:") range_Auto_2

{r} sapply(Auto_2[ ,1:7], mean)

{r} sapply(Auto_2[ ,1:7], sd)

5. Using the full data set, investigate the predictors graphically, using scatterplots or other tools of your choice. Create some plots highlighting the relationships among the predictors. Comment on your findings.

{r} pairs(Auto[ ,1:7])

{r} summary(lm(displacement ~ year, data = Auto))

{r} summary(lm(displacement ~ year * origin, data = Auto))

6. Suppose that we wish to predict gas mileage (mpg) on the basis of the other variables. Do your plots suggest that any of the other variables might be useful in predicting mpg? Justify your answer.

```{r} Auto\(brand <- sapply(strsplit(as.character(Auto\)name), split = ” “), function(x) x[1]) # extract the first item from each list element

Auto\(brand <- factor(ifelse(Auto\)brand %in% c(“vokswagen”, “vw”), “volkswagen”, ifelse(Auto\(brand == "toyouta", "toyota", ifelse(Auto\)brand %in% c(“chevroelt”, “chevy”), “chevrolet”, ifelse(Auto\(brand == "maxda", "mazda", Auto\)brand))))) # fixing typo’s

table(Auto$brand)

```{r}
Auto$brand <- sapply(strsplit(as.character(Auto$name), split = " "),
                     function(x) x[1]) # extract the first item from each list element

Auto$brand <- factor(ifelse(Auto$brand %in% c("vokswagen", "vw"), "volkswagen", 
                     ifelse(Auto$brand == "toyouta", "toyota", 
                            ifelse(Auto$brand %in% c("chevroelt", "chevy"), "chevrolet", 
                                   ifelse(Auto$brand == "maxda", "mazda", 
                                          Auto$brand))))) # fixing typo's
library(forcats)

Auto$brand <- fct_lump(Auto$brand, 
                       n = 9, 
                       other_level = "uncommon") # collapse into 10 categories

table(Auto$brand)

{r} library(ggplot2) ggplot(Auto, aes(x = brand, y = mpg, fill = brand)) + geom_boxplot() + theme(legend.position = "none") + labs(title = "Brand vs Mpg - Boxplot", subtitle = "Engineered feature", x = "Brand", y = "MPG")

{r} ggplot(Auto, aes(x = origin, y = mpg, fill = origin)) + geom_boxplot() + theme(legend.position = "none") + labs(title = "Origin vs Mpg - Boxplot", x = "Origin", y = "MPG")