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.
Learning Objectives
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 duration
4 Days
Course 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
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
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
Please contact your training representative for more details on having this course delivered onsite or online
