This is an R Markdown presentation for Homework 1
#loading library and data dimension
#install.packages("LearnBayes")
library("LearnBayes")#1. What is the size of the data? (data dimension)
dim(studentdata)## [1] 657 11#2. Show the top six rows of the data. data(studentdata)
head(studentdata)## Student Height Gender Shoes Number Dvds ToSleep WakeUp Haircut Job
## 1 1 67 female 10 5 10 -2.5 5.5 60 30.0
## 2 2 64 female 20 7 5 1.5 8.0 0 20.0
## 3 3 61 female 12 2 6 -1.5 7.5 48 0.0
## 4 4 61 female 3 6 40 2.0 8.5 10 0.0
## 5 5 70 male 4 5 6 0.0 9.0 15 17.5
## 6 6 63 female NA 3 5 1.0 8.5 25 0.0
## Drink
## 1 water
## 2 pop
## 3 milk
## 4 water
## 5 pop
## 6 water#3. Pull out the complete data of Students 4, 22, 517, and 533.
sd1 <- studentdata[c(4, 22, 517, 533),]
sd1## Student Height Gender Shoes Number Dvds ToSleep WakeUp Haircut Job
## 4 4 61 female 3 6 40 2.0 8.5 10 0
## 22 22 61 female 20 5 10 1.5 7.5 20 5
## 517 517 71 male 5 7 15 1.5 8.0 15 20
## 533 533 70 male 3 5 10 0.0 7.0 9 12
## Drink
## 4 water
## 22 water
## 517 water
## 533 pop#4. Using the documentation command, describe each variable in the data.
?studentdata#6. What is the nature of each variable?
sapply(studentdata, class)## Student Height Gender Shoes Number Dvds ToSleep
## "integer" "numeric" "factor" "numeric" "integer" "numeric" "numeric"
## WakeUp Haircut Job Drink
## "numeric" "numeric" "numeric" "factor"#7. Show the summary statistics of each variable.
summary(studentdata)## Student Height Gender Shoes
## Min. : 1 Min. :54.0 female:435 Min. : 0.00
## 1st Qu.:165 1st Qu.:64.0 male :222 1st Qu.: 6.00
## Median :329 Median :66.0 Median : 12.00
## Mean :329 Mean :66.7 Mean : 15.42
## 3rd Qu.:493 3rd Qu.:70.0 3rd Qu.: 20.00
## Max. :657 Max. :84.0 Max. :164.00
## NA's :10 NA's :22
## Number Dvds ToSleep WakeUp
## Min. : 1.00 Min. : 0.00 Min. :-2.500 Min. : 1.000
## 1st Qu.: 4.00 1st Qu.: 10.00 1st Qu.: 0.000 1st Qu.: 7.500
## Median : 6.00 Median : 20.00 Median : 1.000 Median : 8.500
## Mean : 5.67 Mean : 30.93 Mean : 1.001 Mean : 8.383
## 3rd Qu.: 7.00 3rd Qu.: 30.00 3rd Qu.: 2.000 3rd Qu.: 9.000
## Max. :10.00 Max. :1000.00 Max. : 6.000 Max. :13.000
## NA's :2 NA's :16 NA's :3 NA's :2
## Haircut Job Drink
## Min. : 0.00 Min. : 0.00 milk :113
## 1st Qu.: 10.00 1st Qu.: 0.00 pop :178
## Median : 16.00 Median :10.50 water:355
## Mean : 25.91 Mean :11.45 NA's : 11
## 3rd Qu.: 30.00 3rd Qu.:17.50
## Max. :180.00 Max. :80.00
## NA's :20 NA's :32#8. What is the gender distribution?
table(studentdata$Gender)##
## female male
## 435 222#9. What is the most favorite number of the students?
table(studentdata$Number)##
## 1 2 3 4 5 6 7 8 9 10
## 9 57 76 60 78 66 191 69 42 7##Most favorite number is 7
#10. What is the second most favorite number of the students?
##Second most favorite number is 5
#11. What is the least favorite number of students?
##Least favorite number is 10#12. Use the 'table' command on 'studentdata$Shoes' and show the output.
table(studentdata$Shoes)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 13.5
## 1 2 16 34 44 51 35 22 30 13 63 5 20 8 1
## 14 15 15.5 16 17 17.5 18 20 21 22 23 24 25 26 27
## 5 67 1 3 4 2 7 60 4 2 4 4 30 2 2
## 27.5 28 29 30 32 33 34 35 36 37 38 40 44 45 47
## 2 1 1 32 2 1 1 16 1 1 1 11 1 1 1
## 49 50 53 54 55 60 63 65 70 100 164
## 1 8 1 1 1 1 1 1 3 1 1# 13. What is unusual about the output in question 11?
sd2 <- subset(studentdata, Shoes== 13.5 | Shoes ==15.5 | Shoes==17.5 | Shoes==27.5, select=c(Student, Shoes))
sd2## Student Shoes
## 282 282 17.5
## 296 296 13.5
## 377 377 17.5
## 397 397 27.5
## 483 483 27.5
## 618 618 15.5##Some of the values are not integers e.g., 17.5, 15.5#14. Use the 'table' command on 'studentdata$Drink.'
table(studentdata$Drink)##
## milk pop water
## 113 178 355#15. Cross-tabulate 'Gender' and 'Drink.'
genderdrink<-table(studentdata$Gender, studentdata$Drink)
genderdrink##
## milk pop water
## female 63 110 256
## male 50 68 99#16. Calculate proportions row-wise and column-wise as well correct to two decimal places.
round(prop.table(genderdrink),2)##
## milk pop water
## female 0.10 0.17 0.40
## male 0.08 0.11 0.15