Introduction to R for Business Intelligence电子书

Introduction to R for Business Intelligence

Introduction to R for Business Intelligence

Credits

About the Author

Acknowledgement

About the Reviewers

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Extract, Transform, and Load

Understanding big data in BI analytics

Extracting data from sources

Importing CSV and other file formats

Importing data from relational databases

Transforming data to fit analytic needs

Filtering data rows

Selecting data columns

Adding a calculated column from existing data

Aggregating data into groups

Loading data into business systems for analysis

Writing data to a CSV file

Writing data to a tab-delimited text file

Summary

2. Data Cleaning

Summarizing your data for inspection

Summarizing using the str() function

Inspecting and interpreting your results

Finding and fixing flawed data

Finding flaws in datasets

Missing values

Erroneous values

Fixing flaws in datasets

Converting inputs to data types suitable for analysis

Converting between data types

Date and time conversions

Adapting string variables to a standard

The power of seven, plus or minus two

Data ready for analysis

Summary

3. Exploratory Data Analysis

Understanding exploratory data analysis

Questions matter

Scales of measurement

R data types

Analyzing a single data variable

Tabular exploration

Graphical exploration

Analyzing two variables together

What does the data look like?

Is there any relationship between two variables?

Is there any correlation between the two?

Is the correlation significant?

Exploring multiple variables simultaneously

Look

Relationships

Correlation

Significance

Summary

4. Linear Regression for Business

Understanding linear regression

The lm() function

Simple linear regression

Residuals

Checking model assumptions

Linearity

Independence

Normality

Equal variance

Assumption wrap-up

Using a simple linear regression

Interpreting model output

Predicting unknown outputs with an SLR

Working with big data using confidence intervals

Refining data for simple linear regression

Transforming data

Handling outliers and influential points

Introducing multiple linear regression

Summary

5. Data Mining with Cluster Analysis

Explaining clustering analysis

Partitioning using k-means clustering

Exploring the data

Running the kmeans() function

Interpreting the model output

Developing a business case

Clustering using hierarchical techniques

Cleaning and exploring data

Running the hclust() function

Visualizing the model output

Evaluating the models

Choosing a model

Preparing the results

Summary

6. Time Series Analysis

Analyzing time series data with linear regression

Linearity, normality, and equal variance

Prediction and confidence intervals

Introducing key elements of time series analysis

The stationary assumption

Differencing techniques

Building ARIMA time series models

Selecting a model to make forecasts

Using advanced functionality for modeling

Summary

7. Visualizing the Datas Story

Visualizing data

Calling attention to information

Empowering user interpretation

Plotting with ggplot2

Geo-mapping using Leaflet

Learning geo-mapping

Extending geo-mapping functionality

Creating interactive graphics using rCharts

Framing the data story

Learning interactive graphing with JavaScript

Summary

8. Web Dashboards with Shiny

Creating a basic Shiny app

The ui.R file

The server.R file

Creating a marketing-campaign Shiny app

Using more sophisticated Shiny folder and file structures

The www folder

The global.R file

Designing a user interface

The head tag

Adding a progress wheel

Using a grid layout

UI components of the marketing-campaign app

Designing the server-side logic

Variable scope

Server components of the marketing-campaign app

Deploying your Shiny app

Located on GitHub

Hosted on RStudio

Hosted on a private web server

Summary

A. References

B. Other Helpful R Functions

Chapter 1 - Extract, Transform, and Load

Chapter 2 - Data Cleaning

C. R Packages Used in the Book

D. R Code for Supporting Market Segment Business Case Calculations