7561001964 | 7012999376 | 0484-4860634 # 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.

#### 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
• -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
• -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