R Programming

R Programming Training 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

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


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)