Experiment-2
Computing Summary Statistics /plotting and visualizing
data using Tabulation and Graphical Representations
Aim:
The purpose of this experiment is to learn the different alignment of data set and various
graphical representations in R
Procedure:
Step by step procedure to conduct the required experiment –
1. Arrangement of data using various R functions
2. Visualize the data set using various R functions
Code and Results:
empid=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)
empid
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
age=c(30,37,45,32,50,60,35,32,34,43,32,30,43,50,60)
age
## [1] 30 37 45 32 50 60 35 32 34 43 32 30 43 50 60
gender=c(0,1,0,1,1,1,0,0,1,0,0,1,1,0,0)
gender
## [1] 0 1 0 1 1 1 0 0 1 0 0 1 1 0 0
status=c(1,1,2,2,1,1,1,2,2,1,2,1,2,1,2)
status
## [1] 1 1 2 2 1 1 1 2 2 1 2 1 2 1 2
## empid age gender status
## 1 1 30 0 1
## 2 2 37 1 1
## 3 3 45 0 2
## 4 4 32 1 2
## 5 5 50 1 1
## 6 6 60 1 1
## 7 7 35 0 1
## 8 8 32 0 2
## 9 9 34 1 2
## 10 10 43 0 1
## 11 11 32 0 2
## 12 12 30 1 1
## 13 13 43 1 2
## 14 14 50 0 1
## 15 15 60 0 2
# labeling character to numeric
empinfo$gender=factor(empinfo$gender,labels=c("male","female")) empinfo$gender
## [1] male female male female female female male male female male
## [11] male female female male male
## Levels: male female
empinfo$status=factor(empinfo$status,labels=c("staff","faculty"))
empinfo$status
## [1] staff staff faculty faculty staff staff staff faculty
faculty
## [10] staff faculty staff faculty staff faculty
## Levels: staff faculty
empinfo
## empid age gender status
## 1 1 30 male staff
## 2 2 37 female staff
## 3 3 45 male faculty
## 4 4 32 female faculty
## 5 5 50 female staff
## 6 6 60 female staff
## 7 7 35 male staff
## 8 8 32 male faculty
## 9 9 34 female faculty
## 10 10 43 male staff
## 11 11 32 male faculty
## 12 12 30 female staff
## 13 13 43 female faculty
## 14 14 50 male staff
## 15 15 60 male faculty
# Extract male data
male=subset(empinfo,empinfo$gender=="male") male
## empid age gender status
## 1 1 30 male staff
## 3 3 45 male faculty
## 7 7 35 male staff
## 8 8 32 male faculty
## 10 10 43 male staff
## 11 11 32 male faculty
## 14 14 50 male staff
## 15 15 60 male faculty
# Extract female data
female=subset(empinfo, empinfo$gender=='female') female
## empid age gender status
## 2 2 37 female staff
## 4 4 32 female faculty
## 5 5 50 female staff
## 6 6 60 female staff
## 9 9 34 female faculty
## 12 12 30 female staff
## 13 13 43 female faculty
# summary statistics for empinfo data
summary(empinfo)
## empid age gender status
## Min. : 1.0 Min. :30.00 male :8 staff :8
## 1st Qu.: 4.5 1st Qu.:32.00 female:7 faculty:7
## Median : 8.0 Median :37.00
## Mean : 8.0 Mean :40.87
## 3rd Qu.:11.5 3rd Qu.:47.50
## Max. :15.0 Max. :60.00
# summary statistics of male,female and age
summary(male)
## empid age gender status
## Min. : 1.000 Min. :30.00 male :8 staff :4
## 1st Qu.: 6.000 1st Qu.:32.00 female:0 faculty:4
## Median : 9.000 Median :39.00
## Mean : 8.625 Mean :40.88
## 3rd Qu.:11.750 3rd Qu.:46.25
## Max. :15.000 Max. :60.00
summary(female)
## empid age gender status
## Min. : 2.000 Min. :30.00 male :0 staff :4
## 1st Qu.: 4.500 1st Qu.:33.00 female:7 faculty:3
## Median : 6.000 Median :37.00
## Mean : 7.286 Mean :40.86
## 3rd Qu.:10.500 3rd Qu.:46.50
## Max. :13.000 Max. :60.00
summary(age)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.00 32.00 37.00 40.87 47.50 60.00
# creating table (one-way)
table1=table(empinfo$gender) table1
##
## male female
## 8 7
table2=table(empinfo$status) table2
##
## staff faculty
## 8 7
# creating table (two-way)
table3=table(empinfo$gender, empinfo$status) table3
##
## staff faculty
## male 4 4
## female 4 3
plot(empinfo$age,type="l",main="Age
of employees",xlab="empid",ylab="age in
pie(table1)
# Graphical representation (Bar plot)
barplot(table3,beside=T,xlim=c(1,15),ylim=c(0,5),col=c("blue", "red"))
legend("topright",legend=rownames(table3),fill=c('blue','red'),bty="n")
# Graphical representation (Box plot)
boxplot(empinfo$age~empinfo$status,col=c('red','blue'))
Conclusion:
Different alignment of data set and various graphical representations in R have been explored
and executed.
Exercise
1. Import a data set from online and draw histogram by using R.
2. Import the given data set (excel sheet is attached) and do the following by using R codes
(i) Summary statistics
(ii) Frequency table
(iii) Cross tabulation
(iv) All possible plots
