R Programming

Course Content of Basic Data Analysis with R Programming

Chapter 1 Problem Solving Using Computer

  • Problem Analysis
  • Algorithm Development & Flow charting
  • Compilation and Execution
  • Debugging and Testing
  • Program Documentation

Chapter 2 Introduction to C Programming

  • Character set, Keywords and data types
  • Preprocessor and directives
  • Constants and Variables
  • Operators and statement
  • Formatted I/O
  • Character I/O
  • Programs Using I/O statement

Chapter 3 Control statements

  • Introduction
  • The goto, if, if….else, switch statements
  • The while, do…while, for statements

Chapter 4 User-Defined Functions

  • Introduction
  • Function definition and return statement
  • Function prototypes
  • Function invocation, Call by value & Call by reference, Recursive Functions

Chapter 5 Arrays and Strings

  • Defining an array
  • One dimensional arrays
  • Multi-dimensional arrays
  • Strings and string manipulation
  • Passing array and string to function

Basic Data Analysis with R Programming Course Outline (1.5 Months)

Basic Data Analysis with R

Interface of R and RStudio

Data types and indexing (assignments, objects, vectors, matrices, data frames, lists)

R built in functions and syntax

Working directory


Importing data from CSV, Excel, .txt, SPSS, Stata files etc…

Exporting data as CSV

Preparing data for analysis (renaming variables, variable types, missing values)

Computing, transforming and recoding variables

Subset datasets by row and by columns

Descriptive statistics, frequency tables, cross tabulation tables

Graphics (boxplot, histogram, scatterplot, partitioning window)

Basic analyses (t-test, correlation, ANOVA, regression, chi-square)

Review of data types

Sub-setting datasets

Sorting datasets

Reshaping datasets

Merging and appending datasets

Aggregating datasets (statistics by group, using the suite of apply functions)

User written functions (syntax, if/else statements)

Loops (for loop, while loop, repeat loop)

Base plot function

Graphs for quantitative data (boxplots, scatterplots, bar graphs, histograms etc…)

Changing graph elements: titles, point size, colors

Extensive use of ggplot2