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# Introduction to R Programming

• Reviews star_rate star_rate star_rate star_rate star_half 4119 Ratings
• Course Code ACCEL-R-INTRO
• Duration 4 days
• Available Formats Classroom

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

### When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.

### Does the course schedule include a Lunchbreak?

Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

### How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

### What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

### What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

### Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

### Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals, and private on sites. View our group training page for more information.

it was good and very informative. Instructure covered everything in detail.

Although there seemed to be too many links for the course, everything worked smoothly.

Fantastic and great training. Tons of hands-on labs to really make you understand the material being thought.

This was effective way to provide a ton of information in a short time period.

Great instructor, clear and concise course. Labs were easy to follow and worked perfectly.

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