library(openintro);
## Warning: package 'openintro' was built under R version 3.5.2
## Please visit openintro.org for free statistics materials
##
## Attaching package: 'openintro'
## The following objects are masked from 'package:datasets':
##
## cars, trees
data(heartTr)
heartTr
## id acceptyear age survived survtime prior transplant wait
## 1 15 68 53 dead 1 no control NA
## 2 43 70 43 dead 2 no control NA
## 3 61 71 52 dead 2 no control NA
## 4 75 72 52 dead 2 no control NA
## 5 6 68 54 dead 3 no control NA
## 6 42 70 36 dead 3 no control NA
## 7 54 71 47 dead 3 no control NA
## 8 38 70 41 dead 5 no treatment 5
## 9 85 73 47 dead 5 no control NA
## 10 2 68 51 dead 6 no control NA
## 11 103 67 39 dead 6 no control NA
## 12 12 68 53 dead 8 no control NA
## 13 48 71 56 dead 9 no control NA
## 14 102 74 40 alive 11 no control NA
## 15 35 70 43 dead 12 no control NA
## 16 95 73 40 dead 16 no treatment 2
## 17 31 69 54 dead 16 no control NA
## 18 3 68 54 dead 16 no treatment 1
## 19 74 72 29 dead 17 no treatment 5
## 20 5 68 20 dead 18 no control NA
## 21 77 72 41 dead 21 no control NA
## 22 99 73 49 dead 21 no control NA
## 23 20 69 55 dead 28 no treatment 1
## 24 70 72 52 dead 30 no treatment 5
## 25 101 74 49 alive 31 no control NA
## 26 66 72 53 dead 32 no control NA
## 27 29 69 50 dead 35 no control NA
## 28 17 68 20 dead 36 no control NA
## 29 19 68 59 dead 37 no control NA
## 30 4 68 40 dead 39 no treatment 36
## 31 100 74 35 alive 39 yes treatment 38
## 32 8 68 45 dead 40 no control NA
## 33 44 70 42 dead 40 no control NA
## 34 16 68 56 dead 43 no treatment 20
## 35 45 71 36 dead 45 no treatment 1
## 36 1 67 30 dead 50 no control NA
## 37 22 69 42 dead 51 no treatment 12
## 38 39 70 50 dead 53 no treatment 2
## 39 10 68 42 dead 58 no treatment 12
## 40 35 71 52 dead 61 no treatment 10
## 41 37 70 61 dead 66 no treatment 19
## 42 68 72 45 dead 68 no treatment 3
## 43 60 71 49 dead 68 no treatment 3
## 44 62 71 39 dead 69 no control NA
## 45 28 69 53 dead 72 no treatment 71
## 46 47 71 47 dead 72 no treatment 21
## 47 32 69 64 dead 77 no treatment 17
## 48 65 72 51 dead 78 no treatment 12
## 49 83 73 53 dead 80 no treatment 32
## 50 13 68 54 dead 81 no treatment 17
## 51 9 68 47 dead 85 no control NA
## 52 73 72 56 dead 90 no treatment 27
## 53 79 72 53 dead 96 no treatment 67
## 54 36 70 48 dead 100 no treatment 46
## 55 32 71 41 dead 102 no control NA
## 56 98 73 28 alive 109 no treatment 96
## 57 87 73 46 dead 110 no treatment 60
## 58 97 73 23 alive 131 no treatment 21
## 59 37 71 41 dead 149 no control NA
## 60 11 68 47 dead 153 no treatment 26
## 61 94 73 43 dead 165 yes treatment 4
## 62 96 73 26 alive 180 no treatment 13
## 63 90 73 52 dead 186 yes treatment 160
## 64 53 71 47 dead 188 no treatment 41
## 65 89 73 51 dead 207 no treatment 139
## 66 24 69 51 dead 219 no treatment 83
## 67 27 69 8 dead 263 no control NA
## 68 93 73 47 alive 265 no treatment 28
## 69 51 71 48 dead 285 no treatment 32
## 70 67 73 19 dead 285 no treatment 57
## 71 16 68 49 dead 308 no treatment 28
## 72 84 73 42 dead 334 no treatment 37
## 73 91 73 47 dead 340 no control NA
## 74 92 73 44 alive 340 no treatment 310
## 75 58 71 47 dead 342 yes treatment 21
## 76 88 73 54 alive 370 no treatment 31
## 77 86 73 48 alive 397 no treatment 8
## 78 82 71 29 alive 427 no control NA
## 79 81 73 52 alive 445 no treatment 6
## 80 80 72 46 alive 482 yes treatment 26
## 81 78 72 48 alive 515 no treatment 210
## 82 76 72 52 alive 545 yes treatment 46
## 83 64 72 48 dead 583 yes treatment 32
## 84 72 72 26 alive 596 no treatment 4
## 85 71 72 47 alive 630 no treatment 31
## 86 69 72 47 alive 670 no treatment 10
## 87 7 68 50 dead 675 no treatment 51
## 88 23 69 58 dead 733 no treatment 3
## 89 63 71 32 alive 841 no treatment 27
## 90 30 69 44 dead 852 no treatment 16
## 91 59 71 41 alive 915 no treatment 78
## 92 56 71 38 alive 941 no treatment 67
## 93 50 71 45 dead 979 yes treatment 83
## 94 46 71 48 dead 995 yes treatment 2
## 95 21 69 43 dead 1032 no treatment 8
## 96 49 71 36 alive 1141 yes treatment 36
## 97 41 70 45 alive 1321 yes treatment 58
## 98 14 68 53 dead 1386 no treatment 37
## 99 26 69 30 alive 1400 no control NA
## 100 40 70 48 alive 1407 yes treatment 41
## 101 34 69 40 alive 1571 no treatment 23
## 102 33 69 48 alive 1586 no treatment 51
## 103 25 69 33 alive 1799 no treatment 25
# 1.48 Stats scores. Below are the final exam scores of twenty introductory statistics students.
# 57, 66, 69, 71, 72, 73, 74, 77, 78, 78, 79, 79, 81, 81, 82, 83, 83, 88, 89, 94
# Create a box plot of the distribution of these scores. The five number summary provided below
# may be useful.
# Min Q1 Q2 (Median) Q3 Max
# 57 72.5 78.5 82.5 94
stmark<- c(57, 66, 69, 71, 72, 73, 74, 77, 78, 78, 79, 79, 81, 81, 82, 83, 83, 88, 89, 94)
boxplot(stmark, ylab= "Marks" )
#--------------------------------------------------------------------------
table(heartTr$survived,heartTr$transplant)
##
## control treatment
## alive 4 24
## dead 30 45
round(prop.table(table(heartTr$survived,heartTr$transplant)),2)
##
## control treatment
## alive 0.04 0.23
## dead 0.29 0.44
prop.table(table(heartTr$transplant))
##
## control treatment
## 0.3300971 0.6699029
round(prop.table(table(heartTr[which(heartTr$transplant=="treatment"),4])),2)
##
## alive dead
## 0.35 0.65
round(prop.table(table(heartTr[which(heartTr$transplant=="control"),4])),2)
##
## alive dead
## 0.12 0.88
#--------------------------------------------------------------------------
drinks<- c(5,2,2,2,2,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0)
boxplot(drinks)
#------------------------------------------------------------------------
boxplot(
c(64,70,75,78,80,
92,
93,
103,
111,
117,
117,
122,
122,
133,
136,
137,
137,
250,
266,
274,
277,
298
))
round(prop.table(table(heartTr[which(heartTr$transplant=="treatment"),4])),2)
##
## alive dead
## 0.35 0.65
round(prop.table(table(heartTr[which(heartTr$transplant=="control"),4])),2)
##
## alive dead
## 0.12 0.88
table (heartTr$survived)
##
## alive dead
## 28 75
table (heartTr$transplant)
##
## control treatment
## 34 69