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Introduction to R Programming for Programmers

Accelebrate's Introduction to R course teaches programmers how to use the R programming language to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations. Skills Gained All students will: Master the use of the R...

Course Code ACCEL-R-PROG
Duration 3 days
Available Formats Classroom
5090 Reviews star_rate star_rate star_rate star_rate star_half
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Accelebrate's Introduction to R course teaches programmers how to use the R programming language to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.

Skills Gained

All students will:

  • Master the use of the R 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 use dates in R
  • Using R for mathematical operations
  • Use of vectorized calculations
  • Write user-defined R functions
  • How and when to use control statements
  • Looping constructs in R
  • Use Apply to iterate functions across data
  • Reshape data to support different analyses
  • Understand split-apply-combine (group-wise operations) in R
  • Deal with missing data
  • Manipulate strings in R
  • Understand basic regular expressions in R
  • Understand base R graphics
  • Focus on GGplot2 graphics for R
  • Be familiar with trellis (lattice) graphics
  • Use R for descriptive statistics
  • Use R for inferential statistics
  • Write multivariate models in R
  • 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. Extensive prior experience in a modern programming language is required.

Course Details

Software Requirements

  • R 3.0 or later with console
  • IDE or text editor of your choice (RStudio recommended)

Outline

Overview

  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation

Introduction

  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Object oriented programming
  • Introduction to vectorized calculations
  • Introduction to data frames
  • Installing packages
  • Working directory
  • Saving your work

Variable types and data structures

  • Variables and assignment
  • Data types
  • Numeric, character, boolean, and factors
  • Data structures
  • Vectors, matrices, arrays, dataframes, lists
  • Indexing, subsetting
  • Assigning new values
  • Viewing data and summaries
  • Naming conventions
  • Objects

Getting data into the R environment

  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC

Dataframe manipulation with dplyr

  • Renaming columns
  • Adding new columns
  • Binning data (continuous to categorical)
  • Combining categorical values
  • Transforming variables
  • Handling missing data
  • Long to wide and back
  • Merging datasets together
  • Stacking datasets together (concatenation)

Handling dates in R

  • Date and date-time classes in R
  • Formatting dates for modeling

Control flow

  • Truth testing
  • Loops

Intro to functions

  • Parameters
  • Return values
  • Variable scope

Applying functions across dimensions

  • Sapply, lapply, apply

Exploratory data analysis (descriptive statistics)

  • Continuous data
  • Distributions
  • Quantiles, mean
  • Bi-modal distributions
  • Histograms, box-plots
  • Categorical data
  • Tables
  • Barplots
  • Group by calculations with dplyr
  • Split-apply-combine

Inferential statistics

  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test

Intro to base graphics

  • Base graphics system in R
  • Scatterplots, histograms
  • Exporting graphics to different formats

Advanced R graphics: ggplot2

  • Understanding the grammar of graphics
  • Quick plots (qplot function)
  • Building graphics by pieces (ggplot function

General linear regression

  • Linear and logistic models
  • Regression plots
  • Confounding / interaction in regression
  • Scoring new data from models (prediction)

Conclusion

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