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R-programming-training

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