R Programming Training Overview

by Accelebrate Claim Listing

Accelebrate's Introduction to R Programming training course teaches attendees how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.

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img Duration

4 Days

Course Details

R Programming Training Overview

  • Accelebrate's Introduction to R Programming training course teaches attendees how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.

 

Objectives

  • Master the use of the R and RStudio interactive environment

  • Expand R by installing R packages

  • Explore and understand how to use the R documentation

  • Read Structured Data into R from various sources

  • Understand the different data types in R

  • Understand the different data structures in R

  • Understand how to create and manipulate dates in R

  • Use the tidyverse collection of packages to manipulate dataframes

  • Write user-defined R functions

  • Use control statements

  • Write Loop constructs in R

  • Use the apply family of functions to iterate functions across data

  • Expand iteration and programming through the Purrr package

  • Reshape data from long to wide and back to support different analyses

  • Perform merge operations with R

  • Understand split-apply-combine (group-wise operations) in R

  • Identify and deal with missing data

  • Manipulate strings in R

  • Understand basic regular expressions in R

  • Understand base R graphics

  • Focus on GGplot2 graphics for R for generating charts

  • Use RMarkdown to programmatically generate reproducible reports

  • Use R for descriptive statistics

  • Use R for inferential statistics

  • Write multivariate models in R (general linear models)

  • Understand confounding and adjustment in multivariate models

  • Understand interaction in multivariate models

  • Predict/Score new data using models

  • Understand basic non-linear functions in models

  • Understand how to link data, statistical methods, and actionable questions

 

Prerequisites

  • Students should have knowledge of basic statistics (t-test, chi-square-test, regression) and know the difference between descriptive and inferential statistics. No programming experience is needed.

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