R Programming Language Training Course Content

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R Programming Language Training Course Content Details

What is R Programming Lang

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R is associate open supply artificial language and software system surroundings for applied math computing and graphics that’s supported by the R Foundation for applied math Computing. The R language is wide used among statisticians and information miners for developing applied math software system and information analysis. Polls, surveys of knowledge miners, and studies of pedantic literature databases show that R’s quality has augmented well in recent years.R programming lang training in hyderabad kukataplly

R may be a wildebeest package. The ASCII text file for the R software system surroundings is written primarily in C, Fortran, and R. R is freely accessible below the wildebeest General Public License, and pre-compiled binary versions area unit provided for varied operational systems. whereas R features a statement interface, there area unit many graphical front-ends accessible.

R is associate implementation of the S artificial language combined with lexical scoping linguistics impressed by theme. S was created by John Chambers whereas at Bell Labs. There area unit some necessary variations, however abundant of the code written for S runs unmovedFor more info click here.




R Programming Language  Course Content

01.Introduction to the R language

  • SAS versus R
  • R, S, and S-plus
  • Obtaining and managing R
  • Objects – types of objects, classes, creating and accessing objects
  • Arithmetic and matrix operations
  • Introduction to functions

02. More details on working with R

  • Reading and writing data
  • R libraries
  • Functions and R programming
  • the if statement
  • looping: for, repeat, while
  • writing functions
  • function arguments and options

03. Graphics

  • Basic plotting
  • Manipulating the plotting window
  • Advanced plotting using lattice library
  • Saving plots




04.Standard statistical models in R

  • Model formulae and model options
  • Output and extraction from fitted models
  • Models considered:
  • Linear regression: lm()
  • Logistic regression: glm()
  • Poisson regression: glm()
  • Survival analysis: Surv(), coxph
  • ()Linear mixed models: lme()

05. Advanced R

  • Extensions of topics discussed in lectures 1, 2 and 3 based on a course surve
  • Data management (importing, subsetting, merging, new variables, missing data
    etc.)
  • Plotting
  • Loops and functions

06.Further topics to be determined by student interest/requirements but may include

  • Migration SAS to R
  • Plotting and Graphics in R
  • Writing R functions, optimizing R code
  • Bioconductor, analysis of gene expression and genomics data.
  • More on linear models
  • Multivariate analysis, Cluster analysis, dimension reduction methods (PCA).

R Programming Language Training Demo






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Updated: May 13, 2017 — 7:07 am

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