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Introduction to R Programming

RPROG-100 (4 Days)
4.45 out of 5 (579 reviews)  

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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.

Location and Pricing

Most Accelebrate courses are delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, please contact us.

R Programming Training Objectives

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
  • Use R for mathematical operations
  • Use of vectorized calculations
  • Write user-defined R functions
  • Use control statements
  • Write Loop 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

R Programming Training Outline

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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
  • Data structures
  • 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
  • Branching
  • Looping
Functions in depth
  • Parameters
  • Return values
  • Variable scope
  • Exception handling
Applying functions across dimensions
  • Sapply, lapply, apply
Exploratory data analysis (descriptive statistics)
  • Continuous data
  • Categorical data
  • Group by calculations with dplyr
  • Melting and casting data
Inferential statistics
  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test
Base graphics
  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, titles, axes
  • 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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Lecture percentage

40%

Lecture/Demo

Lab percentage

60%

Lab

Course Number:

RPROG-100

Duration:

4 Days

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.

Training Materials:

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

Software Requirements:

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

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

Contact Us

Toll-free in US/Canada:
877 849 1850
International:
+1 678 648 3113

Toll-free in US/Canada:
866 566 1228
International:
+1 404 420 2491

925B Peachtree Street, NE
PMB 378
Atlanta, GA 30309-3918
USA

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