R-Programming
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Course Curriculum
Lesson 01 - Getting Started with R
- -Installing R
- -Writing Code / Setting Your Working Directory
- -R Console Input and Evaluation
- -Data Types - R Objects and Attributes
- -Data Types - Vectors and Lists
- -Data Types - Matrices
- -Data Types - Factors
- -Data Types - Missing Values
- -Data Types - Data Frames
- -Data Types - Names Attribute
- -Data Types - Summary
- -Reading Tabular Data
- -Reading Large Tables
- -Textual Data Formats
- -Connections: Interfaces to the Outside World
- -Subsetting - Basics
- -Vectorized Operations
- -Introduction to swirl
Lesson 02 - Programming With R
- -Control Structures - Introduction
- -Control Structures - If-else
- -Control Structures - For Loops
- -Control Structures - While Loops
- -Control Structures - Repeat, Next, Break
- -Your First R Function
- -Functions
- -Scoping Rules - Symbol Binding
- -Scoping Rules - R Scoping Rules
- -Coding Standards
- -Date and Times
Lesson 03 - Loop Functions and Debugging
- -Loop Functions - lapply
- -Loop Functions - apply
- -Loop Functions - mapply
- -Loop Functions - tapply
- -Loop Functions - split
- -Debugging Tools - Diagnosing the Problem
- -Debugging Tools - Basic Tools
- -Loop Functions and Debugging
Lesson 04 - Simulation and Profiling
- -The str Function
- -Simulation - Generating Random Numbers
- -Simulation - Simulating a Linear Model
- -Simulation - Random Sampling
- -R Profiler