Introduction to R for business intelligence : learn how to leverage the power of R for business intelligence
Learn how to leverage the power of R for Business Intelligence About This Book Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful. This highly practical guide teaches you how to develop dashboards that help you make informed decisions using...
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| Main Author | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Birmingham :
Packt Publishing,
2016.
|
| Series | Community experience distilled.
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| Subjects | |
| Online Access | Full text |
| ISBN | 9781785286513 178528651X 9781785280252 1785280252 1523106204 9781523106202 |
| Physical Description | 1 online resource : illustrations. |
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| 003 | OCoLC | ||
| 005 | 20240717213016.0 | ||
| 006 | m o d | ||
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| 008 | 160913s2016 enka ob 000 0 eng d | ||
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| 020 | |a 9781785286513 |q (electronic bk.) | ||
| 020 | |a 178528651X |q (electronic bk.) | ||
| 020 | |z 9781785280252 | ||
| 020 | |a 1785280252 | ||
| 020 | |a 9781785280252 | ||
| 020 | |a 1523106204 | ||
| 020 | |a 9781523106202 | ||
| 035 | |a (OCoLC)958301035 |z (OCoLC)1259164720 | ||
| 100 | 1 | |a Gendron, Jay, |e author. | |
| 245 | 1 | 0 | |a Introduction to R for business intelligence : |b learn how to leverage the power of R for business intelligence / |c Jay Gendron. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, |c 2016. | |
| 300 | |a 1 online resource : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 504 | |a Includes bibliographical references. | ||
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a Learn how to leverage the power of R for Business Intelligence About This Book Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful. This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R. Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide. Who This Book Is For This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected. What You Will Learn Extract, clean, and transform data Validate the quality of the data and variables in datasets Learn exploratory data analysis Build regression models Implement popular data-mining algorithms Visualize results using popular graphs Publish the results as a dashboard through Interactive Web Application frameworks In Detail Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence. Style and approach This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions - and all of this with the help of real-life examples. | ||
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 4: Linear Regression for Business -- Understanding linear regression -- The lm() function -- Simple linear regression -- Residuals. | |
| 505 | 8 | |a 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 8: Web Dashboards with Shiny -- Creating a basic Shiny app -- The ui.R file -- The server.R file -- Creating a marketing-campaign Shiny app. | |
| 505 | 8 | |a 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 -- Appendix A: References -- Appendix B: Other Helpful R Functions -- Chapter 1 -- Extract, Transform, and Load -- Chapter 2 -- Data Cleaning -- Appendix C: R Packages Used in the Book -- Appendix D: R Code for Supporting Market Segment Business Case Calculations -- Index. | |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Business planning |x Data processing. | |
| 650 | 0 | |a R (Computer program language) | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 776 | |z 1-78528-025-2 | ||
| 830 | 0 | |a Community experience distilled. | |
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