R Programming - Week 1 Learning Material

1. What is R?

R is an open-source programming language and software environment developed for statistical computing, graphics, reporting and data analysis. It was created by Ross Ihaka and Robert Gentleman and is maintained by the R Development Core Team.

Statistical analysis and reporting
Data cleaning and visualization
Cross-platform (Windows, Linux, macOS)
Extensible through thousands of CRAN packages
Integrates with C, C++, Python, Java and others

Learning Objectives
Define R, explain its features and describe why it is widely used in research and industry.

Example

2 + 3

marks <- c(78,85,90,67)
marks
mean(marks)

x <- c(1,2,3,4)
y <- c(2,4,6,8)
plot(x,y,type="b",main="Simple Line Plot")

2. Why Use R?

Free and open source (GNU GPL)
Excellent statistical computing
Professional graphics
Interactive data exploration
Large package ecosystem
Object-oriented programming support

Industry Usage

Twitter – user experience analytics
Ford – social media analysis
The New York Times – data journalism
Google – internal analytics and style guide
Microsoft – enterprise R distributions

Common Misconceptions

R is more than a statistics package.
Beginners can learn R easily.
Vectors and data frames reduce the need for complex loops.

3. Installing R and RStudio

Visit https://www.r-project.org
Open CRAN.
Download R for Windows.
Install using default settings.
Download RStudio (Posit).
Install and launch RStudio.

print("R is installed and working!")
R.version.string
2+2
getwd()

Remember: RStudio is an IDE. R must be installed first.

4. Student Survey Analysis Workflow

Workflow:

Load Data → Clean Data → Calculate Statistics → Create Graphs → Interpret Results

Sample Dataset

ID	Gender	Attendance	Theory	Lab	Rating
101	Male	92	84	90	5
102	Female	88	76	82	4
103	Male	65	58	62	3
104	Female	NA	81	85	5
105	Male	95	91	94	5

Step 0 - Load Data

setwd("C:/Users/Student/Documents")
getwd()
survey <- read.csv("student_survey.csv")
head(survey)

Step 1 - Clean Data

is.na(survey$Attendance)
sum(is.na(survey$Attendance))
avg_attendance <- mean(survey$Attendance,na.rm=TRUE)
survey$Attendance[is.na(survey$Attendance)] <- avg_attendance

Step 2 - Statistics

mean(survey$TheoryMarks)
mean(survey$LabMarks)
mean(survey$CourseRating)
sd(survey$TheoryMarks)
summary(survey)

Step 3 - Visualization (ggplot2)

install.packages("ggplot2")
library(ggplot2)

ggplot(survey,aes(x=Gender,y=TheoryMarks))+
stat_summary(fun=mean,geom="bar")

ggplot(survey,aes(x=TheoryMarks))+geom_histogram(binwidth=5)

ggplot(survey,aes(x=Gender,y=TheoryMarks))+geom_boxplot()

ggplot(survey,aes(x=Attendance,y=TheoryMarks))+geom_point(size=3)

Step 4 - Interpretation

Missing attendance replaced using average.
Average theory marks ≈ 80.
Average course rating = 4.3/5.
Higher attendance generally corresponds to higher theory marks.
No major outliers observed.

Summary

Week 1 introduced R, explained why it is popular, demonstrated installation of R and RStudio, and completed a practical data analysis workflow including importing CSV data, cleaning missing values, computing statistics, visualizing results with ggplot2, and interpreting findings.