R-Program: Syllabus

About this Course

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

The detailed syllabus of the course is as below.

Week	Module	Details
Week-1	Module 1 : Basic R	Download and installation of console R and R-studio Familiarization to R-studio environment Setting up working directory Getting started with R: Basic arithmetic and coding in R How to get help in R: stack overflow, books Installation of required packages and using packages
Week-2 and Week-3	Module 2: R for data management and cleaning	Introduction to data objects Import datasets in R  (from csv, dta, xls, sav) Convert data types between integer, factor, numeric, date and character Labeling factor variables  Compute variables and re-coding  Filter, select, group_by, mutate, tunneling Conditional, looping (if_else, if, case_when, for loop) and apply function Join (inner join, left join, right join, full join) and append datasets Change column name, convert row name to column and vice versa Export datasets from R to excel Handling dates Aggregation of data and pivot table Arrange the column in ascending and descending order Randomization
Week-4	Module 3 : R for descriptive analysis	Introduction to basic statistics Numerical data presentation:  mean, sd, median, quartiles, max, min Categorical data presentation: table (number, proportion), cross table with row and column wise percentages Generate summary tables (table1 package or other best packages) Calculate Confidence interval around proportion of binary and multi nominal variables
Week-5	Module 4: R for graphics or plot	to be added
Week-6	Module 5: R for basic statistical tests	Introduction to different types of tests and their implication Independent sample t-test, Mann-Whitney U test Paired sample t-test, Wilcoxon Signed Rank test Chi-square test and odds ratio calculation for 2x2 tables One-way ANOVA and Friedman test
Week-7	Module 6: R for linear regression (simple and multiple)	Concept on Linear regression Linear regression in R and interpretation Data presentation in report
Week-8	Module 7: R for logistic regression (simple and multiple)	Concept on Logistic regression Concept on reference group and contrasts Logistic regression in R and interpretation Ordinal logistic regression in R and interpretation Data presentation in report
Week-9	Module 8: Survival Analysis	Concept on survival analysis Survival analysis in R and interpretation Data presentation in report
Week-10	Module 9 : Time series data analysis	Concept on time series data analysis Time series data analysis in R and interpretation Data presentation in report
Week-11	Final project	Write result section on the research data