# Introduction to R Programming

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,...

Course Code ACCEL-R-INTRO
Duration 4 days
Available Formats Classroom
6119 Reviews star_rate star_rate star_rate star_rate star_half ### Reviews

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.

## 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
• 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

## 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.

### 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
• How to find documentation

Introduction

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

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

Dataframe manipulation with dplyr

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

• 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

FAQ Get immediate answers to our most frequently asked qestions. View FAQs arrow_forward

Close

Close

Close
Close
Close

### SummerSavings

Save up to \$250-\$2500 Use Promo Code: SurfBoard

View Details Register by September 6, 2019

Close

#### Confirm

Close 