R Programming

Course Title: R Programming

Course No: BDS203

Nature of the Course: Theory + Lab

Semester: 3

Full Marks: 45 + 30

Pass Marks: 18 + 12

Credit Hours: 3

Course Description

This is an introductory course on R programming and statistical analysis by using R programming. This course is intended as a guide to data management, manipulation and analysis with the R programming. This course covers an introduction to R, the R basics, logical operator, data structure, data management in R, matrix operation, data visualization and graphics in R, and basic statistical functions, inferences by using R programming.

Course Objectives

After completion of this course, the students will be able: to understand the basics of R, to operate vector and matrix by using R, to manipulate and transform the data by using R, To illustrate data in graphs and charts by using R, to use R for various statistical computations and inferences, to apply R to compute correlation and regression.

Course Contents

1. An introduction to R and Data Structure in R

7 hrs

1.1. Purpose of using R software

1.2. installing R and RStudio

1.3. The R environment and working with R

1.4. The R packages (Meaning and purpose of packages, installing, loading and learning Packages)

1.5. Basic math

1.6. variables

1.7. data types

1.8. basic R function

1.15. Organizing work in R and RStudio and making it reproducible (R Script, R markdown documents)

2. Conditional and Control Flow in R

3 hrs

2.1. Relational operators and vectors

2.2. Logical operations (AND, OR, NOT)

2.3. Logical operators and vectors

2.4. inbuilt command

2.5. packages

2.6. control statements (conditional statements and loops)

4. Matrix Operation

4 hrs

4.1. Matrix operations

4.2. creating matrix from vector

4.3. combine vectors or matrices

4.4. matrix addition

4.5. transpose matrix

4.6. Find the dimension of a matrix or dataset

4.7. matrix multiplication

4.8. finding the inverse of a matrix

4.9. component-wise multiplication

4.10. create a submatrix

4.11. create a diagonal matrix

4.12. a vector of diagonal elements

4.13. create a vector from a matrix

4.14. calculate the determinant

4.15. find eigenvalues and eigenvectors

4.16. find the singular value decomposition

5. Graphics, Visualization and Summary Measures

8 hrs

5.9. basics of ggplot: introduction to grammar of ggplot, univariate and multivariate charts (bar chart, pie chart, box plot, histogram (along with density plot), line graph, scatter plot by using ggplot

5.10. measures of central tendency

5.11. measures of variation

5.12. measures of skewness and Kurtosis

5.13. apply, lapply and sapply

5.14. summary measure and variability by dplyr

6. Probability and Probability distribution in R

4 hrs

6.1. Random Number

6.2. random number generator

6.3. sample selection

6.4. binomial distribution

6.5. negative binomial distribution

6.6. poisson distribution

6.7. normal distribution

6.8. plot for testing normality

7. Statistical Inference by using R

6 hrs

7.1. Idea about inferential analysis

7.2. hypothesis testing

7.3. null and alternative hypothesis

7.4. decision by critical value and p-value approaches

7.5. inferences in the one sample case

7.6. student's t distribution

7.7. comparing two variances (Fisher's F test)

7.8. comparing two sample means (independent sample t test)

7.9. paired sample t test

7.10. comparing three or more means (F test)

7.11. testing for independence in contingency tables using chi-squared

8. Correlation and Regression Analysis by using R

4 hrs

8.1. correlation coefficient

8.2. test of correlation coefficient

8.3. correlation matrix by using R/R studio

8.4. linear regression

8.5. assumptions

8.6. unstandardized coefficient

8.7. standardized coefficient

8.8. hypothesis testing of individual regression coefficient (t test)

8.9. overall test (f test)

8.10. multiple coefficient of determination

8.11. standard error by using R/R studio

Laboratory Works

The Laboratory work includes solving problems in R Programming covering all the units of the syllabus.

Reference Books

1.Crawley, M.J. (2015). Statistics An Introduction Using R, Second Edition, John Wiley & Sons Ltd London, UK.
2.Dalgaard, P. (2008): Introductory Statistics with R, Second Edition, Springer. Kabacoff, R.I. (2015): R in Action Data analysis and graphics with R, Second Edition, Manning Publications Co.
3.Lander. J.P. (2014). R for Everyone: Advanced Analytics and Graphics. Pearson Education.
4.Thomas, M. (2017): Beginning Data Sciences in R: Data Analysis, Visualization, and Modelling for the Data Scientists. Apress, Denmark.

Notes:

This syllabus follows the official Bachelor in Data Science curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.

R Programming

R Programming

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R Programming

Course Description

Course Objectives

Course Contents

Laboratory Works

Reference Books

Notes: