R Programming

R Programming - base and advance
Course duration:

45 hours | 2 months (Live online/classroom training+ Projects + assignment/case studies + Interview preparation)

Training Mode:​

Online & Classroom

Course Fees:
  • Group training – INR 15000 | USD 450 (Other countries)
  • Individual training – INR 30000 | USD 650 (Other countries).
Pre-requisites for R Course:

To attend this highly evolved R course, candidates must have basic understanding of maths and computer.

R Programming Certification:

At end of our course, you will be work on various projects. Once you completed assigned projects with expected results we will issue R Certificate.

Syllabus - program of study:

  • How to Install R
  • How to Install R, R Studio, and R Packages?
  • Updating R and R Studio
  • R Packages (Libraries)
  • Recommended Packages
  • Introduction to R Programming
  • History of R language
  • R programming features and limitation
  • Comparison of R with other technologies
  • Working with R scripts
  • R Graphical User Interface (RGUI)
  • Overview of R Studio
  • Components of R Studio - code editor, visualization and debugging tools.
  • Useful R packages
  • Data types
  • Data Structure
  • Object
  • Vectors
  • Data frames
  • Matrices
  • List
  • Importing Data from various sources – CSV, Text, Excel etc.
  • Data frames and data sets in R
  • Expressions, assignment, and arithmetic operations in R
  • Exporting data to various formats
  • Viewing data (viewing partial data and full data)
  • Variable & value labels
  • Data extraction
  • Creating subgroups or bins of data
  • Data type conversions
  • Data formatting
  • Applying transformations
  • Applications of sub-setting data
  • Duplicate data
  • Missing data
  • Sorting and ordering
  • Filtering
  • Creating new categorical variables - binning
  • Reshaping data
  • Sub-setting (rows and columns)
  • Appending
  • Merging/joining (Inner, Full, Left, Right etc.)
  • Grouped expressions
  • Variable and label renaming
  • Conditional execution (if, if else etc.)
  • Sampling data
  • Loops (conditional, iterative loops, apply functions)
  • Computing and adding new variables to a data frame
  • R built-in functions (Text, Numeric, Date etc.)
  • Numerical functions
  • Text functions
  • Date functions
  • Utilities functions and R user defined functions
  • Basics of exploratory data analysis
  • Descriptive statistics - Frequency tables
  • Univariate analysis - Distribution of data and graphical analysis
  • Bivariate analysis - Cross tabulation
  • Packages for exploratory analysis (dplyr, Hmisc, psych, crosstab etc.)
  • About Markdown
  • Chunking
  • Knitr package
  • Exporting (html, pdf, word, jpeg ,excel, csv etc.)
  • Automated reporting
  • Automated email sender
  • Graphs and chart in R
  • Bar graph
  • Pie
  • Histogram
  • Box plot,
  • Scatter plot etc.
  • R packages for modern visualization - (ggplot2, Plotly, lattice etc.)
  • Interactive graphs and charts
  • Case studies
  • Assignments
  • projects with industry data.
  • Case studies
  • Assignments
  • projects with industry data