This document discusses importing data from CSV files into R and performing calculations and analysis on the data. It covers changing the working directory in R, importing CSV data and assigning column comments, and calculating summary statistics like the mean, median, and standard deviation of columns of data. It also demonstrates creating bar plots, pie charts, histograms and calculating the correlation between variables and fitting a linear regression model.

1. Importing data Files of Other
Software and Redirecting Output

2. First step:
>getwd()
>
(To know where is R software located in computer)
Second step:
>setwd
>setwd(“E/Rcourse/”)
(To change work location of directory)
Third step
Save file in CSV format in new work location ( In Rcourse -E drive)

3. Calculation in R by importing data in CSV
format
Mean
Median
SD
Correlation
Regression

4. Calculation of MEAN
>setwd(“E/Rcourse/”)
>data<-read.csv(“file name.csv")
>data
(incomplete data)
>data<-read.csv(“file name.csv", header=FALSE)
(complete data with proper heading)
>data
(give comments to each column by using # key)
> #V1 EMPLOYEE NUMBERS
> #V2 NUMBERS OF HOURS WORKED
> #V3 TOTAL REMUNERATIONS
> #V4 NUMBER OF DAYS ABSENT

5. Check data column wise
> data[,1]
> data[,2]
> data[,3]
> data[,4]
Find out mean
> mean(data[,1])
> mean(data[,2])
> mean(data[,3])
> mean(data[,4])

6. Calculation of MEDIAN
>median(data[,1])
>median(data[,2])
> median(data[,3])
> median(data[,4])

7. Calculation of SD
>sd(data[,1])
>sd(data[,2])
> sd(data[,3])
> sd(data[,4])

8. Absolute and relative frequencies:
Absolute frequency: Actual frequency
Relative frequency: Proportion
Data on gender:
M,F,F,M,F,M,M,F,F,M
1,2,2,1,2,1,1,2,2,1
Absolute frequency:
Total Male:5
Total Female:5
Relative Frequency:
Total male/Total persons= 05/10= 0.5
Total Female/Total persons= 05/10= 0.5

9. > gender<-c(1,2,1,1,2,1,2,1,2,1,2)
> gender
[1] 1 2 1 1 2 1 2 1 2 1 2
> table(gender)
gender
1 2
6 5
> table(gender)/length(gender)
gender
1 2
0.5454545 0.4545455

10. Graphics and Plots
> gender<-c(1,2,1,2,1,2,2,1,2,1,1)
> gender
[1] 1 2 1 2 1 2 2 1 2 1 1
> table(gender)
gender
1 2
6 5
> barplot(table(gender))

11. > barplot(gender)

12. > barplot(table(gender))

13. >barplot(table(gender)/length(gender))

14. Pie diagram
>pie(table(gender))

15. Histogram
> hist(gender)

16. Correlation
> cor(c(100,200,300,400),c(100,200,300,400))
[1] 1

17. Graph
> cor(c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8))
[1] 1
> plot(c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8))

18. > scatter.smooth(c(1,2,3,4,5,6,7,8),c(1,2,3,4,5,6,7,8))