Essential To R Programming

Data Manipulation Techniques Using R Programming

Statistical Applications Using R Programming

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Week 1: Background, Getting Started, and Nuts & Bolts

This week covers the basics to get you started up with R The Background Materials lesson contains information about course mechanics and some videos on installing R The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data I recommend that you watch the videos in the listed order, but watching the videos out of order isn’t going to ruin the story

Week 2: Programming with R

Welcome to Week 2 of R Programming This week, we take the gloves off, and the lectures cover key topics like control structures and functions We also introduce the first programming assignment for the course, which is due at the end of the week

Week 3: Loop Functions and Debugging

We have now entered the third week of R Programming, which also marks the halfway point The lectures this week cover loop functions and the debugging tools in R These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice

Week 4: Simulation & Profiling

This week covers how to simulate data in R, which serves as the basis for doing simulation studies We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed The profiler is a key tool in helping you optimize your programs Finally, we cover the str function, which I personally believe is the most useful function in R

Introduction to R Programming

Introduction to R

Business Analytics

Analytics concepts

The importance of R in analytics

R Language community and
eco-system

Usage of R in industry

Installing R and other packages

Perform basic R operations using
command line

Usage of IDE R Studio and various
GUI

R Programming Concepts

The datatypes in R and its uses

Built-in functions in R

Subsetting methods

Summarize data using functions

Use of functions like head(),
tail(), for inspecting data

Use-cases for problem solving
using R

Data Manipulation in R Programming

Various phases of Data Cleaning

Functions used in Inspection

Data Cleaning Techniques

Uses of functions involved

Use-cases for Data Cleaning using
R

Data Import Techniques in R Programming

Import data from spreadsheets and
text files into R

Importing data from statistical
formats

Packages installation for database
import

Connecting to RDBMS from R using
ODBC and basic SQL queries in R

Web Scraping

Other concepts on Data Import
Techniques

Exploratory Data Analysis (EDA) using R Programming

What is EDA?

Why do we need EDA?

Goals of EDA

Types of EDA

Implementing of EDA

Boxplots, cor() in R

EDA functions

Multiple packages in R for data
analysis

Some fancy plots

Use-cases for EDA using R

Data Visualization in R Programming

Story telling with Data

Principle tenets

Elements of Data Visualization

Infographics vs Data Visualization

Data Visualization & Graphical
functions in R

Plotting Graphs

Customizing Graphical Parameters
to improvise the plots

Various GUIs

Spatial Analysis

Other Visualization concepts

 

R Programming for Data Science

Introduction to the R language

Data Structures

Data Science applications using built-in R functions

Writing R Functions

Data Management

Visualization in R

INTRODUCTION TO R

• History of R
• Features of R
• SAS versus R
• S and S-pus
• Obtaining and managing R
• Instaling R
• Packages
• Input/output
• R interfaces
• R library

FUNCTIONS AND R PROGRAMMING

• Data Types
• Factors
• Numbers
• Attributes
• Entering Inputs
• Evaluation
• Printing
• Missing Objects
• Explicit Coercion
• Data Frame
• Objects

DATA MANAGEMENT

• Reading Data
• Writing data
• Reading data files with tables
• Files connection
• Reading lines of Text files

SORTING DATA

• MERGING DATA
• AGGREGATING DATA
• RESHAPING DATA

CONTROL STRUCTURES

• If
• For
• Repeat
• Next
• Return

WRITING FUNCTIONS

• Lappy
• Tappy
• Split
• Mappy
• Apply

FUNCTION ARGUMENTS AND OPTIONS

LOOPS

DATE AND TIME IN R

• Dates in R
• Times in R
• Operation on Dates and Time on R

GRAPHICS AND PLOTTING USING R

BASIC PLOTTING

• Creating a graph
• Density Pot
• Dot Pot
• Bar Pot
• line charts
• Pie charts
• Box po
• Scatter Pot
• histogram
• Norma

ADVANCE PLOTTING

• Graphical Parameters
• Lattice graphs
• Combining Plot
• Ggplots graph
• Probability graphs
• Correlograms

SAVING PLOTS

SDEBUGGING TOOLS

• Trace
• Debug
• Recover

• DEBUGGING TOOLSDEBUGGING TOOLS

DEBUGGING TOOLS

Module 1- Introduction to Business Analytics

Introduction to Business Analytics

Types of Analytics

Case study on Walmart, Signet Bank

Data Science and its importance

Module 2- Introduction to R

Introduction to R

Installing R

Installing R Studio

Workspace Setup

R Packages

Module 3- R Programming

R Programming

if statements

for statements

while statements

repeat statements

break and next statements

switch statement

scan statement

Executing the commands in a File

Module 4 – R Data Structure

Data structures

Vector

Matrix

Array

Data frame

List

Factors

Module 5 – Apply Functions

DPLYR and apply Function

Import Data File

DPLYP – Selection

DPLYP – Filter

DPLYP – Arrange

DPLYP – Mutate

DPLYP – Summarize

Module 6 – Apply Functions

Data visualization in R

Bar chart, Dot plot

Scatter plot, Pie chart

Histogram and Box plot

Heat Maps

World Cloud

Module 7 – Apply Functions

Introduction to statistics

Type of Data

Distance Measures (Similarity,
dissimilarity, correlation)

Euclidean space.

Manhattan

Minkowski

Cosine similarity

Mahalanobis distance

Pearson’s correlation coefficient

Probability Distributions

Module 8 – Hypothesis Testing I

Hypothesis Testing

Introduction

Hypothesis Testing – T Test, Anova

Module 9 – Hypothesis Testing II

Hypothesis Testing about
population

Chi Square Test

F distribution and F ratio

Module 10 – Regression Analysis

Regression

Linear Regression Models

Non Linear Regression Models

Module 11 – Classification

Classification

Decision Tree

Logistic Regression

Bayesian

Support Vector Machines

Module 12 – Clustering

Clustering

K-means Clustering and Case Study

DBSCAN Clustering and Case study

Hierarchical Clustering

Module 13 – Association

Association

Apriori Algorithm

Candidate Generation

Visualization on Associated Rules

Summary

Conclusion

Week 1: Background, Getting Started, and Nuts & Bolts

This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn’t going to ruin the story.

Week 2: Programming with R

Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.

Week 3: Loop Functions and Debugging

We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.

Week 4: Simulation & Profiling

This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.