R Progragramming

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys, and studies of scholarly literature databases show substantial increases in popularity; as of June 2019, R ranks 22nd in the TIOBE index, a measure of the popularity of programming languages.

 

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.

Curriculum

  • History of R
  • Features of R
  • SAS versus R
  • S and S-pus
  • Obtaining and managing R
  • Instaing R
  • Packages
  • Input/output
  • R interfaces
  • R ibrary

  • Data Types
  • Factors
  • Numbers
  • Attributes
  • Entering Inputs
  • Evauation
  • Printing
  • Missing Objects
  • Expicit Coercion
  • Data Frame
  • Objects

  • Reading Data
  • Writing data
  • Reading data files with tables
  • Files connection
  • Reading lines of Text files

  • MERGING DATA
  • AGGREGATING DATA
  • RESHAPING DATA

  • If
  • For
  • Repeat
  • Next
  • Return

  • Lappy
  • Tappy
  • Split
  • Mappy
  • Apply

  • Dates in R
  • Times in R
  • Operation on Dates and Time on R

  • Creating a graph
  • Density Pot
  • Dot Pot
  • Bar Pot
  • ine charts
  • Pie charts
  • Box po
  • Scatter Pot
  • istogram
  • Norma

  • Graphical Parameters
  • Lattice graphs
  • Combining Plot
  • Ggplots graph
  • Probability graphs
  • Correlograms

  • Trace
  • Debug
  • Recover

  • Creating Random Numbers
  • Generating Random Numbers
  • Random Sampling

CAREER GUIDANCE


Course Duration:

R Programming is a  course, depending on the specialisation chosen & the number of classes held per week. Classes are typically held 2 hours a day/ 3 days a week

Eligibility

graduates/ undergraduates/Working professionals

Job Opportunities:

R programmer