HW 2: Auto and College Data
by Niko Hellman
Part 1: Auto
library(tidyverse)## ── Attaching packages ───────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ──────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
Auto <- read.table("http://faculty.marshall.usc.edu/gareth-james/ISL/Auto.data",
header=TRUE,
na.strings = "?")
Auto=na.omit(Auto)
head(Auto)## mpg cylinders displacement horsepower weight acceleration year origin
## 1 18 8 307 130 3504 12.0 70 1
## 2 15 8 350 165 3693 11.5 70 1
## 3 18 8 318 150 3436 11.0 70 1
## 4 16 8 304 150 3433 12.0 70 1
## 5 17 8 302 140 3449 10.5 70 1
## 6 15 8 429 198 4341 10.0 70 1
## name
## 1 chevrolet chevelle malibu
## 2 buick skylark 320
## 3 plymouth satellite
## 4 amc rebel sst
## 5 ford torino
## 6 ford galaxie 500A. Looking at our predictors!
str(Auto)## 'data.frame': 392 obs. of 9 variables:
## $ mpg : num 18 15 18 16 17 15 14 14 14 15 ...
## $ cylinders : int 8 8 8 8 8 8 8 8 8 8 ...
## $ displacement: num 307 350 318 304 302 429 454 440 455 390 ...
## $ horsepower : num 130 165 150 150 140 198 220 215 225 190 ...
## $ weight : num 3504 3693 3436 3433 3449 ...
## $ acceleration: num 12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
## $ year : int 70 70 70 70 70 70 70 70 70 70 ...
## $ origin : int 1 1 1 1 1 1 1 1 1 1 ...
## $ name : Factor w/ 304 levels "amc ambassador brougham",..: 49 36 231 14 161 141 54 223 241 2 ...
## - attr(*, "na.action")= 'omit' Named int 33 127 331 337 355
## ..- attr(*, "names")= chr "33" "127" "331" "337" ...The quantitative predictors are mpg, cylinders, displacement, horsepower, weight, acceleration, and year. The qualitative predictors are name and origin. (Note: origin is an int, but when looking up the data itself, we see that it has values 1, 2, and 3 to represent different countries, making it qualitative)
B. Range of quantitive predictors:
range(Auto$mpg)## [1] 9.0 46.6range(Auto$cylinders)## [1] 3 8range(Auto$displacement)## [1] 68 455range(Auto$horsepower)## [1] 46 230range(Auto$weight)## [1] 1613 5140range(Auto$acceleration)## [1] 8.0 24.8range(Auto$year)## [1] 70 82C. Mean and standard deviation:
mean(Auto$mpg)## [1] 23.44592sd(Auto$mpg)## [1] 7.805007mean(Auto$cylinders)## [1] 5.471939sd(Auto$cylinders)## [1] 1.705783mean(Auto$displacement)## [1] 194.412sd(Auto$displacement)## [1] 104.644mean(Auto$horsepower)## [1] 104.4694sd(Auto$horsepower)## [1] 38.49116mean(Auto$weight)## [1] 2977.584sd(Auto$weight)## [1] 849.4026mean(Auto$acceleration)## [1] 15.54133sd(Auto$acceleration)## [1] 2.758864mean(Auto$year)## [1] 75.97959sd(Auto$year)## [1] 3.683737D. Autosub, a subset of Auto excluding observations 10-85:
Autosub<-Auto[-(10:85),]
head(Autosub)## mpg cylinders displacement horsepower weight acceleration year origin
## 1 18 8 307 130 3504 12.0 70 1
## 2 15 8 350 165 3693 11.5 70 1
## 3 18 8 318 150 3436 11.0 70 1
## 4 16 8 304 150 3433 12.0 70 1
## 5 17 8 302 140 3449 10.5 70 1
## 6 15 8 429 198 4341 10.0 70 1
## name
## 1 chevrolet chevelle malibu
## 2 buick skylark 320
## 3 plymouth satellite
## 4 amc rebel sst
## 5 ford torino
## 6 ford galaxie 500Range, mean, and standard deviations of predictors in Autosub:
range(Autosub$mpg)## [1] 11.0 46.6mean(Autosub$mpg)## [1] 24.40443sd(Autosub$mpg)## [1] 7.867283range(Autosub$cylinders)## [1] 3 8mean(Autosub$cylinders)## [1] 5.373418sd(Autosub$cylinders)## [1] 1.654179range(Autosub$displacement)## [1] 68 455mean(Autosub$displacement)## [1] 187.2405sd(Autosub$displacement)## [1] 99.67837range(Autosub$horsepower)## [1] 46 230mean(Autosub$horsepower)## [1] 100.7215sd(Autosub$horsepower)## [1] 35.70885range(Autosub$weight)## [1] 1649 4997mean(Autosub$weight)## [1] 2935.972sd(Autosub$weight)## [1] 811.3002range(Autosub$acceleration)## [1] 8.5 24.8mean(Autosub$acceleration)## [1] 15.7269sd(Autosub$acceleration)## [1] 2.693721range(Autosub$year)## [1] 70 82mean(Autosub$year)## [1] 77.14557sd(Autosub$year)## [1] 3.106217E. Visual representations:
pairs(Auto)
Starting here, we can identify interesting relationships to further investigate.
ggplot(Auto, aes(x=year, y=mpg, color=origin)) +
geom_point() 
In this plot, we see an upward trend in mpg over the years, generally with Japanese cars (3) having highest and American cars (1) having lowest in each year.
ggplot(Auto, aes(x=mpg))+
geom_bar()
This plot gives us a sense of most common mpg values for cars in our data set. We see that 13 mpg is most common with 20 cars, followed by 14 mpg and 18mpg.
ggplot(Auto, aes(x=weight, y=displacement)) +
geom_point()
Here we see an upward trend where displacement increases as weight increases.
F. Based on our first pair() plot, we are able to view the relationships mpg holds with all other variables. We see clear negative relationships with displacement, horsepower, and weight.There appears to be more positive relationships with acceleration and year. In my first ggplot() you see the clear relationship between mpg over time. This relationship would lead us to predict a higher mpg as time increases. The representation of countries of origin (by color) would also lead us to predict a higher mpg for Japanese cars and lower for American.
Part 2: College
A. Loading our data set:
college<-read.csv("http://faculty.marshall.usc.edu/gareth-james/ISL/College.csv",header=TRUE)B. Viewing and adjusting (note: View() replaced by head() for Rpub)
head(college)## X Private Apps Accept Enroll Top10perc Top25perc
## 1 Abilene Christian University Yes 1660 1232 721 23 52
## 2 Adelphi University Yes 2186 1924 512 16 29
## 3 Adrian College Yes 1428 1097 336 22 50
## 4 Agnes Scott College Yes 417 349 137 60 89
## 5 Alaska Pacific University Yes 193 146 55 16 44
## 6 Albertson College Yes 587 479 158 38 62
## F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal
## 1 2885 537 7440 3300 450 2200 70 78
## 2 2683 1227 12280 6450 750 1500 29 30
## 3 1036 99 11250 3750 400 1165 53 66
## 4 510 63 12960 5450 450 875 92 97
## 5 249 869 7560 4120 800 1500 76 72
## 6 678 41 13500 3335 500 675 67 73
## S.F.Ratio perc.alumni Expend Grad.Rate
## 1 18.1 12 7041 60
## 2 12.2 16 10527 56
## 3 12.9 30 8735 54
## 4 7.7 37 19016 59
## 5 11.9 2 10922 15
## 6 9.4 11 9727 55rownames(college)<- college[,1]
head(college)## X Private Apps Accept
## Abilene Christian University Abilene Christian University Yes 1660 1232
## Adelphi University Adelphi University Yes 2186 1924
## Adrian College Adrian College Yes 1428 1097
## Agnes Scott College Agnes Scott College Yes 417 349
## Alaska Pacific University Alaska Pacific University Yes 193 146
## Albertson College Albertson College Yes 587 479
## Enroll Top10perc Top25perc F.Undergrad P.Undergrad
## Abilene Christian University 721 23 52 2885 537
## Adelphi University 512 16 29 2683 1227
## Adrian College 336 22 50 1036 99
## Agnes Scott College 137 60 89 510 63
## Alaska Pacific University 55 16 44 249 869
## Albertson College 158 38 62 678 41
## Outstate Room.Board Books Personal PhD Terminal
## Abilene Christian University 7440 3300 450 2200 70 78
## Adelphi University 12280 6450 750 1500 29 30
## Adrian College 11250 3750 400 1165 53 66
## Agnes Scott College 12960 5450 450 875 92 97
## Alaska Pacific University 7560 4120 800 1500 76 72
## Albertson College 13500 3335 500 675 67 73
## S.F.Ratio perc.alumni Expend Grad.Rate
## Abilene Christian University 18.1 12 7041 60
## Adelphi University 12.2 16 10527 56
## Adrian College 12.9 30 8735 54
## Agnes Scott College 7.7 37 19016 59
## Alaska Pacific University 11.9 2 10922 15
## Albertson College 9.4 11 9727 55college<- college[,-1]
head(college)## Private Apps Accept Enroll Top10perc Top25perc
## Abilene Christian University Yes 1660 1232 721 23 52
## Adelphi University Yes 2186 1924 512 16 29
## Adrian College Yes 1428 1097 336 22 50
## Agnes Scott College Yes 417 349 137 60 89
## Alaska Pacific University Yes 193 146 55 16 44
## Albertson College Yes 587 479 158 38 62
## F.Undergrad P.Undergrad Outstate Room.Board Books
## Abilene Christian University 2885 537 7440 3300 450
## Adelphi University 2683 1227 12280 6450 750
## Adrian College 1036 99 11250 3750 400
## Agnes Scott College 510 63 12960 5450 450
## Alaska Pacific University 249 869 7560 4120 800
## Albertson College 678 41 13500 3335 500
## Personal PhD Terminal S.F.Ratio perc.alumni Expend
## Abilene Christian University 2200 70 78 18.1 12 7041
## Adelphi University 1500 29 30 12.2 16 10527
## Adrian College 1165 53 66 12.9 30 8735
## Agnes Scott College 875 92 97 7.7 37 19016
## Alaska Pacific University 1500 76 72 11.9 2 10922
## Albertson College 675 67 73 9.4 11 9727
## Grad.Rate
## Abilene Christian University 60
## Adelphi University 56
## Adrian College 54
## Agnes Scott College 59
## Alaska Pacific University 15
## Albertson College 55C. Additional tasks:
- Numerical summary of variables
summary(college)## Private Apps Accept Enroll Top10perc
## No :212 Min. : 81 Min. : 72 Min. : 35 Min. : 1.00
## Yes:565 1st Qu.: 776 1st Qu.: 604 1st Qu.: 242 1st Qu.:15.00
## Median : 1558 Median : 1110 Median : 434 Median :23.00
## Mean : 3002 Mean : 2019 Mean : 780 Mean :27.56
## 3rd Qu.: 3624 3rd Qu.: 2424 3rd Qu.: 902 3rd Qu.:35.00
## Max. :48094 Max. :26330 Max. :6392 Max. :96.00
## Top25perc F.Undergrad P.Undergrad Outstate
## Min. : 9.0 Min. : 139 Min. : 1.0 Min. : 2340
## 1st Qu.: 41.0 1st Qu.: 992 1st Qu.: 95.0 1st Qu.: 7320
## Median : 54.0 Median : 1707 Median : 353.0 Median : 9990
## Mean : 55.8 Mean : 3700 Mean : 855.3 Mean :10441
## 3rd Qu.: 69.0 3rd Qu.: 4005 3rd Qu.: 967.0 3rd Qu.:12925
## Max. :100.0 Max. :31643 Max. :21836.0 Max. :21700
## Room.Board Books Personal PhD
## Min. :1780 Min. : 96.0 Min. : 250 Min. : 8.00
## 1st Qu.:3597 1st Qu.: 470.0 1st Qu.: 850 1st Qu.: 62.00
## Median :4200 Median : 500.0 Median :1200 Median : 75.00
## Mean :4358 Mean : 549.4 Mean :1341 Mean : 72.66
## 3rd Qu.:5050 3rd Qu.: 600.0 3rd Qu.:1700 3rd Qu.: 85.00
## Max. :8124 Max. :2340.0 Max. :6800 Max. :103.00
## Terminal S.F.Ratio perc.alumni Expend
## Min. : 24.0 Min. : 2.50 Min. : 0.00 Min. : 3186
## 1st Qu.: 71.0 1st Qu.:11.50 1st Qu.:13.00 1st Qu.: 6751
## Median : 82.0 Median :13.60 Median :21.00 Median : 8377
## Mean : 79.7 Mean :14.09 Mean :22.74 Mean : 9660
## 3rd Qu.: 92.0 3rd Qu.:16.50 3rd Qu.:31.00 3rd Qu.:10830
## Max. :100.0 Max. :39.80 Max. :64.00 Max. :56233
## Grad.Rate
## Min. : 10.00
## 1st Qu.: 53.00
## Median : 65.00
## Mean : 65.46
## 3rd Qu.: 78.00
## Max. :118.00- Scatterplot of first 10 variables
pairs(college[,1:10])
- Side-by-side boxplot of Outstate vs. Private
ggplot(college, aes(x=Private, y=Outstate))+
geom_boxplot() 
- New variable “Elite”
Elite<-rep("No", nrow(college))
Elite[college$Top10perc>50]="Yes"
Elite<- as.factor(Elite)
college<-data.frame(college, Elite)
summary(college)## Private Apps Accept Enroll Top10perc
## No :212 Min. : 81 Min. : 72 Min. : 35 Min. : 1.00
## Yes:565 1st Qu.: 776 1st Qu.: 604 1st Qu.: 242 1st Qu.:15.00
## Median : 1558 Median : 1110 Median : 434 Median :23.00
## Mean : 3002 Mean : 2019 Mean : 780 Mean :27.56
## 3rd Qu.: 3624 3rd Qu.: 2424 3rd Qu.: 902 3rd Qu.:35.00
## Max. :48094 Max. :26330 Max. :6392 Max. :96.00
## Top25perc F.Undergrad P.Undergrad Outstate
## Min. : 9.0 Min. : 139 Min. : 1.0 Min. : 2340
## 1st Qu.: 41.0 1st Qu.: 992 1st Qu.: 95.0 1st Qu.: 7320
## Median : 54.0 Median : 1707 Median : 353.0 Median : 9990
## Mean : 55.8 Mean : 3700 Mean : 855.3 Mean :10441
## 3rd Qu.: 69.0 3rd Qu.: 4005 3rd Qu.: 967.0 3rd Qu.:12925
## Max. :100.0 Max. :31643 Max. :21836.0 Max. :21700
## Room.Board Books Personal PhD
## Min. :1780 Min. : 96.0 Min. : 250 Min. : 8.00
## 1st Qu.:3597 1st Qu.: 470.0 1st Qu.: 850 1st Qu.: 62.00
## Median :4200 Median : 500.0 Median :1200 Median : 75.00
## Mean :4358 Mean : 549.4 Mean :1341 Mean : 72.66
## 3rd Qu.:5050 3rd Qu.: 600.0 3rd Qu.:1700 3rd Qu.: 85.00
## Max. :8124 Max. :2340.0 Max. :6800 Max. :103.00
## Terminal S.F.Ratio perc.alumni Expend
## Min. : 24.0 Min. : 2.50 Min. : 0.00 Min. : 3186
## 1st Qu.: 71.0 1st Qu.:11.50 1st Qu.:13.00 1st Qu.: 6751
## Median : 82.0 Median :13.60 Median :21.00 Median : 8377
## Mean : 79.7 Mean :14.09 Mean :22.74 Mean : 9660
## 3rd Qu.: 92.0 3rd Qu.:16.50 3rd Qu.:31.00 3rd Qu.:10830
## Max. :100.0 Max. :39.80 Max. :64.00 Max. :56233
## Grad.Rate Elite
## Min. : 10.00 No :699
## 1st Qu.: 53.00 Yes: 78
## Median : 65.00
## Mean : 65.46
## 3rd Qu.: 78.00
## Max. :118.00ggplot(college, aes(x=Elite, y=Outstate))+
geom_boxplot() 