Excersice 0
Install LearnBayes package in R/Rstudio and then access studentdata
#Install the LearnBayes package
#Keep in mind that R is case-sensitive
#install.packages('LearnBayes')
#You just need to install once and then you can directly use
#so long as you access the LearnBayes package
library(LearnBayes)
#Access studentdata from the LearnBayes package
data(studentdata)
attach(studentdata)
#show part of data
head(studentdata)## Student Height Gender Shoes Number Dvds ToSleep WakeUp Haircut Job Drink
## 1 1 67 female 10 5 10 -2.5 5.5 60 30.0 water
## 2 2 64 female 20 7 5 1.5 8.0 0 20.0 pop
## 3 3 61 female 12 2 6 -1.5 7.5 48 0.0 milk
## 4 4 61 female 3 6 40 2.0 8.5 10 0.0 water
## 5 5 70 male 4 5 6 0.0 9.0 15 17.5 pop
## 6 6 63 female NA 3 5 1.0 8.5 25 0.0 water\(\color{red}{\text{Q1}}\) The variable Dvds in the student dataset contains the number of movie DVDs owned by
students in the class
- Construct a histogram of this variable using the hist command in R
# Histogram of Dvds
hist(studentdata$Dvds, prob=T)
- Summarize this variable using the summary command in R
# Histogram of Dvds
summary(studentdata)## Student Height Gender Shoes Number
## Min. : 1 Min. :54.0 female:435 Min. : 0.00 Min. : 1.00
## 1st Qu.:165 1st Qu.:64.0 male :222 1st Qu.: 6.00 1st Qu.: 4.00
## Median :329 Median :66.0 Median : 12.00 Median : 6.00
## Mean :329 Mean :66.7 Mean : 15.42 Mean : 5.67
## 3rd Qu.:493 3rd Qu.:70.0 3rd Qu.: 20.00 3rd Qu.: 7.00
## Max. :657 Max. :84.0 Max. :164.00 Max. :10.00
## NA's :10 NA's :22 NA's :2
## Dvds ToSleep WakeUp Haircut
## Min. : 0.00 Min. :-2.500 Min. : 1.000 Min. : 0.00
## 1st Qu.: 10.00 1st Qu.: 0.000 1st Qu.: 7.500 1st Qu.: 10.00
## Median : 20.00 Median : 1.000 Median : 8.500 Median : 16.00
## Mean : 30.93 Mean : 1.001 Mean : 8.383 Mean : 25.91
## 3rd Qu.: 30.00 3rd Qu.: 2.000 3rd Qu.: 9.000 3rd Qu.: 30.00
## Max. :1000.00 Max. : 6.000 Max. :13.000 Max. :180.00
## NA's :16 NA's :3 NA's :2 NA's :20
## Job Drink
## Min. : 0.00 milk :113
## 1st Qu.: 0.00 pop :178
## Median :10.50 water:355
## Mean :11.45 NA's : 11
## 3rd Qu.:17.50
## Max. :80.00
## NA's :32- Use the table command in R to construct a frequency table of the individual values of Dvds that were observed. If one constructs a barplot of these tabled values using the command
# Barplot of Dvds
barplot(table(Dvds),col='red') 
We observe from the barplot of Dvds (name of movie dvds owned) that the popular response values are 10 and 20.
\(\color{red}{\text{Q2}}\) The variable Height contains the height (in inches) of each student in the class.
- Construct parallel boxplots of the heights using the Gender variable. Hint: boxplot(Height~Gender)
# Barplot of Dvds
boxplot(Height~Gender)
- If one assigns the boxplot output to a variable, then output is a list that contains statistics used in constructing the boxplots. Print output to see the statistics that are stored.
# Assign boxplot to a variable named output
output=boxplot(Height~Gender)
print(output)## $stats
## [,1] [,2]
## [1,] 57.75 65
## [2,] 63.00 69
## [3,] 64.50 71
## [4,] 67.00 72
## [5,] 73.00 76
##
## $n
## [1] 428 219
##
## $conf
## [,1] [,2]
## [1,] 64.19451 70.6797
## [2,] 64.80549 71.3203
##
## $out
## [1] 56 76 55 56 76 54 54 84 78 77 56 63 77 79 62 62 61 79 59 61 78 62
##
## $group
## [1] 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
##
## $names
## [1] "female" "male"- On average, how much taller are male students than female students?
# Method: using aggregate()
group_means <- aggregate(Height~Gender, data = studentdata, FUN = mean)
print(group_means)## Gender Height
## 1 female 64.75701
## 2 male 70.50767#Calculate the mean difference of heights between male and female students
# Using the results from aggregate()
mean_diff <- group_means[2,2] - group_means[1,2]
print(mean_diff) # Output: 5.750657## [1] 5.750657On average, the height of male students is 5.750657 inches taller than female students.
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