Introduction to R Programming for Programmers

87 Ratings

Course Number: RPROG-102

Duration: 3 days (19.5 hours)

Format: Live, hands-on

R Training Overview

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.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

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. Extensive prior experience in a modern programming language is required.

Outline

Expand All | Collapse All

Overview
  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation
Introduction
  • Using the R console and RStudio
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Object-oriented programming
  • Introduction to vectorized calculations
  • Introduction to data frames
  • Installing and loading packages
  • Working directory
  • Saving your work
Variable Types and Data Structures in Base R
  • 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 with readr
  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC
Dataframe manipulation with dplyr
  • Introduction to tibbles, enhanced data frames
  • Renaming columns
  • Adding new columns
  • Binning data (continuous to categorical)
  • Combining categorical values
  • Transforming variables
  • Handling missing data
  • Merging datasets together
  • Stacking datasets together (concatenation)
Handling Dates in R using Lubridate
  • Date and date-time classes in R
  • Formatting dates for modeling
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
  • Applying functions across dimensions
    • Sapply, lapply, apply
    • Programming with map and purrr
Advanced R Graphics: ggplot2
  • Understanding the grammar of graphics
  • Quick plots (qplot function)
  • Building graphics by pieces (ggplot function)
  • Understanding geoms (geometries)
  • Linking chart elements to variable values
  • Controlling legends and axes
  • Exporting graphics
General Linear Regression Models in R
  • Understanding formulas
  • Linear and logistic regression models
  • Regression plots
  • Confounding / interaction in regression
  • Evaluating residuals
  • Scoring new data from models (prediction)
  • Useful plots from regression models
Conclusion

Training Materials:

All R Programming training students receive a copy of Addison-Wesley's R for Everyone and related courseware.

Software Requirements:

  • R 3.5 or later with console
  • IDE or text editor of your choice (RStudio recommended)


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