Generate a list of 10 random numbers between 1 and 100:
sample(1:100, 10)## [1] 69 91 93 16 86 83 34 4 35 96Generate a list of 10 random numbers between 1 and 100 allowing repetitions:
sample(1:100, 10, replace=T)## [1] 1 67 41 80 92 11 13 51 65 35Generate a list of 10 random numbers between 1 and 10 allowing repetitions:
sample(1:10, 10, replace=T)## [1] 1 3 2 5 3 9 10 1 3 8Randomly order the numbers 1 to 10:
sample(1:10, 10, replace=F)## [1] 3 5 10 4 9 8 6 2 7 1Generate 10 random numbers using the numbers 1 and 2:
sample(1:2, 10, replace=T)## [1] 2 1 2 1 1 2 1 2 1 2Load the data set StudentSurvey from the package Lock5Data:
require(Lock5Data)## Loading required package: Lock5Datadata(StudentSurvey)Select 10 random observations:
sample(StudentSurvey, 10)## Year Gender Smoke Award HigherSAT Exercise TV Height Weight
## 129 Sophomore F No Olympic Verbal 2 3 64 140
## 224 Sophomore M No Nobel Verbal 12 10 71 138
## 87 Senior M No Academy Verbal 4 5 72 240
## 11 Sophomore F No Olympic Math 12 6 65 140
## 124 Sophomore M No Olympic Math 21 25 69 171
## 1 Senior M No Olympic Math 10 1 71 180
## 138 FirstYear F No Olympic Math 10 2 63 140
## 26 Sophomore M No Nobel Math 10 5 71 175
## 3 FirstYear M No Nobel Math 14 5 72 208
## 40 Junior F No Nobel Verbal 5 5 64 130
## Siblings BirthOrder VerbalSAT MathSAT SAT GPA Pulse Piercings
## 129 2 3 450 400 850 2.50 65 4
## 224 0 1 730 650 1380 3.56 65 0
## 87 1 1 690 550 1240 3.49 70 0
## 11 1 2 500 670 1170 2.09 63 2
## 124 1 1 480 530 1010 2.70 84 0
## 1 4 4 540 670 1210 3.13 54 0
## 138 3 2 520 540 1060 2.80 68 4
## 26 3 2 700 720 1420 3.70 60 0
## 3 2 1 550 560 1110 2.55 130 0
## 40 0 NA 570 570 1140 2.40 48 4
## orig.ids
## 129 129
## 224 224
## 87 87
## 11 11
## 124 124
## 1 1
## 138 138
## 26 26
## 3 3
## 40 40Select 10 random observations and save them into the data set sample10. This command will also generates a column named origin.ids that gives information on where in the original data set the random observations fall.
sample10<-sample(StudentSurvey, 10)Select 10 random observations and save them into the data set sample10. The option origin.ids=F makes it so that no column named origin.ids is generated, so that the sample has no information on where in the original data set the random observations fall.
sample10<-sample(StudentSurvey, 10, orig.ids=F)Select 5 random observations for which the class year is Junior:
sample(subset(StudentSurvey, Year=="Junior"), 10, orig.ids=F)## Year Gender Smoke Award HigherSAT Exercise TV Height Weight
## 257 Junior F No Olympic Verbal 3 1 66 140
## 140 Junior F No Olympic Math 2 3 67 125
## 220 Junior M No Olympic Math 10 15 71 180
## 331 Junior M Yes Olympic Math 3 3 67 150
## 308 Junior F No Olympic 18 7 67 150
## 251 Junior M No Olympic Math 12 5 70 222
## 53 Junior M No Olympic Math 15 0 72 175
## 4 Junior M No Nobel Math 3 1 63 110
## 335 Junior M Yes Nobel Verbal 3 4 75 175
## 173 Junior F No Nobel Math 5 10 61 110
## Siblings BirthOrder VerbalSAT MathSAT SAT GPA Pulse Piercings
## 257 1 2 580 530 1110 3.30 74 2
## 140 5 4 470 600 1070 3.10 70 2
## 220 1 2 620 640 1260 3.40 54 0
## 331 2 2 620 640 1260 2.90 66 0
## 308 1 1 560 560 1120 3.40 75 5
## 251 2 1 510 730 1240 3.50 66 5
## 53 2 1 650 670 1320 3.60 68 1
## 4 1 1 490 630 1120 3.10 78 0
## 335 0 1 690 500 1190 3.20 61 0
## 173 2 2 660 700 1360 3.83 80 4Select 5 samples of size 2 using the command do to repeat the random sample selection:
do(5)*sample(StudentSurvey, 2, orig.ids=F)## Year Gender Smoke Award HigherSAT Exercise TV Height Weight
## 126 Sophomore M No Olympic Math 3 15 71 188
## 193 Sophomore M No Nobel Verbal 3 5 75 200
## 105 Sophomore M No Olympic Math 5 8 72 165
## 138 FirstYear F No Olympic Math 10 2 63 140
## 24 Sophomore M No Olympic Math 7 10 74 200
## 351 Sophomore F Yes Olympic Math 5 3 66 135
## 297 Senior M No Nobel Math 6 15 65 140
## 231 Sophomore F No Olympic Verbal 25 12 71 193
## 309 Sophomore M No Olympic Math 15 6 71 170
## 225 Senior M No Olympic Math 5 3 71 265
## Siblings BirthOrder VerbalSAT MathSAT SAT GPA Pulse Piercings .row
## 126 3 1 520 600 1120 2.20 66 0 1
## 193 1 1 610 600 1210 3.38 66 0 2
## 105 2 2 640 710 1350 2.80 57 0 1
## 138 3 2 520 540 1060 2.80 68 4 2
## 24 1 2 550 650 1200 3.00 48 0 1
## 351 3 2 570 580 1150 3.25 78 4 2
## 297 3 3 530 640 1170 3.00 88 0 1
## 231 2 2 630 620 1250 3.25 68 0 2
## 309 3 1 580 650 1230 3.25 60 0 1
## 225 1 1 440 460 900 2.70 82 0 2
## .index
## 126 1
## 193 1
## 105 2
## 138 2
## 24 3
## 351 3
## 297 4
## 231 4
## 309 5
## 225 5Select 100 samples of 30 students each, and save the mean SAT score for each sample into a data set called sample30SAT:
sample30SAT<-do(100)*mean(~SAT, data=sample(StudentSurvey, 30, orig.ids=F))Find the mean of the 100 sample mean SAT scores we just created, and compare it to the real mean SAT score of the entire data set:
mean(~result, data=sample30SAT)## [1] 1202.105mean(~SAT, data=StudentSurvey)## [1] 1203.627The two means are very close to each other indicating that the sample distribution is unbiased.