HW#1_AlexMAtteson
HW 1 - Alex Matteson
This is my first R homework for stats 239
1.) a
Auto <- read.table("http://faculty.marshall.usc.edu/gareth-james/ISL/Auto.data",
header=TRUE,
na.strings = "?")
#head(Auto)
str(Auto)## 'data.frame': 397 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 ...As you can see mpg, displacement, horsepower, weight, acceleration, cylindars and year are quantitative. While name and origin are qualitative.
b
range(Auto$mpg)## [1] 9.0 46.6range(Auto$displacement)## [1] 68 455range(Auto$cylinders)## [1] 3 8range(Auto$horsepower)## [1] NA NArange(Auto$weight)## [1] 1613 5140range(Auto$acceleration)## [1] 8.0 24.8range(Auto$year)## [1] 70 82c
mean(Auto$mpg)## [1] 23.51587sd(Auto$mpg)## [1] 7.825804mean(Auto$cylinders)## [1] 5.458438sd(Auto$cylinders)## [1] 1.701577mean(Auto$displacement)## [1] 193.5327sd(Auto$displacement)## [1] 104.3796mean(Auto$horsepower)## [1] NAsd(Auto$horsepower)## [1] NAmean(Auto$weight)## [1] 2970.262sd(Auto$weight)## [1] 847.9041mean(Auto$acceleration)## [1] 15.55567sd(Auto$acceleration)## [1] 2.749995mean(Auto$year)## [1] 75.99496sd(Auto$year)## [1] 3.690005d
For some reason I do not remember this? And I can’t figure it out based on the class notes/the internet. Is it something like this: Auto[c(10, 85)] ?
e 
- Acceleration does not seem like an effective way of predicting mpg. However, hourse power and weight do. As we increase both of these variables mpg goes down. This makes sense because a heavier car and a more powerful car probably both need more gas. But also I don’t know anything about cars so that may be wrong.
2.) a. b.
# Box office Star Wars (in millions!)
new_hope <- c(460.998, 314.4)
empire_strikes <- c(290.475, 247.900)
return_jedi <- c(309.306, 165.8)
# Vectors region and titles, used for naming
region <- c("US", "non-US")
titles <- c("A New Hope", "The Empire Strikes Back", "Return of
the Jedi")
starWars <- matrix(data = c(new_hope, empire_strikes, return_jedi),
nrow = 3,
byrow = TRUE)
colnames(starWars) = region
rownames(starWars)= titles
starWars## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of\nthe Jedi 309.306 165.8c
worldWide <- rowSums(starWars)
worldWide## A New Hope The Empire Strikes Back Return of\nthe Jedi
## 775.398 538.375 475.106d
BigStarWar <- cbind(starWars, worldWide)
BigStarWar## US non-US worldWide
## A New Hope 460.998 314.4 775.398
## The Empire Strikes Back 290.475 247.9 538.375
## Return of\nthe Jedi 309.306 165.8 475.106e
# Prequels
phantom_menace <- c(474.5, 552.5)
attack_clones <- c(310.7, 338.7)
revenge_sith <- c(380.3, 468.5)
titles2 <- c("phantom_menace", "attack_clones", "revenge_sith")
region2 <- c("US", "non-US")
starWars2 <- matrix(data = c(phantom_menace, attack_clones, revenge_sith),
nrow = 3,
byrow = TRUE)
colnames(starWars2) = region2
rownames(starWars2)= titles2
starWars2## US non-US
## phantom_menace 474.5 552.5
## attack_clones 310.7 338.7
## revenge_sith 380.3 468.5f
allStarWars <- rbind(starWars, starWars2)
allStarWars## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of\nthe Jedi 309.306 165.8
## phantom_menace 474.500 552.5
## attack_clones 310.700 338.7
## revenge_sith 380.300 468.5g
colSums(allStarWars)## US non-US
## 2226.279 2087.8003.)
A
college <- read.csv("http://faculty.marshall.usc.edu/gareth-james/ISL/College.csv",header=TRUE)B
rownames(college) <- college[,1]
college <- college[,-1]
#summary(college)C a
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. :118.00b
pairs(college[,1:10])
c
plot(college$Private, college$Outstate)
d
Elite <- rep("No", nrow(college))
Elite[college$Top10perc > 50] = "Yes"
Elite <- as.factor(Elite)
college <- data.frame(college, Elite)
summary(Elite)## No Yes
## 699 78plot(college$Outstate, college$Elite)