Intro to R with College data
David Robinson
January 23, 2016
library(ISLR)
library(ggplot2)
data(College)What are the variables?
?CollegeWhat does the data look like?
head(College)## Private Apps Accept Enroll Top10perc
## Abilene Christian University Yes 1660 1232 721 23
## Adelphi University Yes 2186 1924 512 16
## Adrian College Yes 1428 1097 336 22
## Agnes Scott College Yes 417 349 137 60
## Alaska Pacific University Yes 193 146 55 16
## Albertson College Yes 587 479 158 38
## Top25perc F.Undergrad P.Undergrad Outstate
## Abilene Christian University 52 2885 537 7440
## Adelphi University 29 2683 1227 12280
## Adrian College 50 1036 99 11250
## Agnes Scott College 89 510 63 12960
## Alaska Pacific University 44 249 869 7560
## Albertson College 62 678 41 13500
## Room.Board Books Personal PhD Terminal
## Abilene Christian University 3300 450 2200 70 78
## Adelphi University 6450 750 1500 29 30
## Adrian College 3750 400 1165 53 66
## Agnes Scott College 5450 450 875 92 97
## Alaska Pacific University 4120 800 1500 76 72
## Albertson College 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 55# or View(College) interactivelyWhat are the top schools by some of these metrics? (I would use dplyr here, I think it’s easier to read than using order and dollar signs).
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# smallest schools
head(arrange(College, Enroll))## Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad
## 1 Yes 100 90 35 10 52 282 331
## 2 Yes 167 130 46 16 50 199 676
## 3 Yes 81 72 51 33 71 139 3
## 4 Yes 193 146 55 16 44 249 869
## 5 Yes 141 118 55 12 21 201 173
## 6 Yes 191 165 63 5 25 494 574
## Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni
## 1 8400 2812 300 2134 10 50 12.1 24
## 2 11428 5084 450 475 67 76 8.3 43
## 3 8730 3600 400 800 92 92 9.3 17
## 4 7560 4120 800 1500 76 72 11.9 2
## 5 8300 4850 450 1300 53 53 9.5 19
## 6 11550 4270 300 500 43 77 14.5 8
## Expend Grad.Rate
## 1 7976 52
## 2 10291 67
## 3 10922 58
## 4 10922 15
## 5 6936 76
## 6 9209 40# largest schools
head(arrange(College, desc(Enroll)))## Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad
## 1 No 14474 10519 6392 49 85 31643 2798
## 2 No 18114 15096 6180 23 57 26640 4120
## 3 No 21804 18744 5874 29 60 26213 4065
## 4 No 16587 13243 5873 25 72 24763 2717
## 5 No 14939 11652 5705 52 88 25422 911
## 6 No 14752 9572 5329 48 85 30017 5189
## Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni
## 1 5130 3412 600 2144 89 91 23.1 29
## 2 10658 3734 504 600 93 95 14.0 9
## 3 9556 3990 570 1060 86 86 18.2 15
## 4 9766 3990 600 2000 77 88 21.3 24
## 5 7560 4574 500 1982 87 90 17.4 13
## 6 5130 3309 650 3140 91 99 19.7 11
## Expend Grad.Rate
## 1 8471 69
## 2 10520 71
## 3 8604 67
## 4 8686 68
## 5 8559 81
## 6 7837 65# highest % of PhDs
head(arrange(College, desc(PhD)))## Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad
## 1 No 529 481 243 22 47 1206 134
## 2 Yes 1465 810 313 71 95 1088 16
## 3 Yes 1377 572 178 95 100 654 5
## 4 Yes 1133 630 220 37 73 750 30
## 5 Yes 12586 3239 1462 87 95 5643 349
## 6 Yes 1860 767 227 71 93 887 1
## Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni
## 1 4860 3122 600 650 103 88 17.4 16
## 2 18165 6750 500 1200 100 100 12.3 49
## 3 17230 6690 700 900 100 100 8.2 46
## 4 17688 5900 650 850 100 100 10.4 11
## 5 19528 5926 720 1100 99 100 7.6 39
## 6 17000 6010 500 850 99 99 9.6 52
## Expend Grad.Rate
## 1 6415 43
## 2 17449 89
## 3 21569 100
## 4 14820 73
## 5 20440 97
## 6 18443 87Hey, does it really think one school has a 103% PhDs? This shows sometimes data is bad. We’ll get into data cleaning in a future class, etc.
How are the number of applications and enrollments related?
ggplot(College, aes(Apps, Enroll)) +
geom_point()
How does size differ between private and public schools?
ggplot(College, aes(Private, Enroll)) +
geom_boxplot()
Does the percentage of faculty with PhDs predict the (out-of-state) tuition?
ggplot(College, aes(PhD, Outstate)) +
geom_point()
Does the fraction of students from the top 10% of the class predict what fraction graduates?
ggplot(College, aes(Top10perc, Grad.Rate)) +
geom_point()