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Advanced Analytics with R and Tableau
Advanced Analytics with R and Tableau

Advanced Analytics with R and Tableau: Advanced analytics using data classification, unsupervised learning and data visualization

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Profile Icon Jen Stirrup Profile Icon Roberto Rösler Profile Icon Ruben Oliva Ramos
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€18.99 per month
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.3 (7 Ratings)
Paperback Aug 2017 178 pages 1st Edition
eBook
€23.99 €26.99
Paperback
€32.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Jen Stirrup Profile Icon Roberto Rösler Profile Icon Ruben Oliva Ramos
Arrow right icon
€18.99 per month
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.3 (7 Ratings)
Paperback Aug 2017 178 pages 1st Edition
eBook
€23.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€23.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m

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Advanced Analytics with R and Tableau

Chapter 1. Advanced Analytics with R and Tableau

Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for a data-savvy user who wants to move into analytics and data science in order to make a difference to their businesses, by harnessing the analytical power of R and the stunning visualization capabilities of Tableau.

Together, Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, and predictive visually appealing analytical solutions can be designed solutions with R and Tableau.

Let's get ready to start our transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. To do this, we need to get the tools ready. In this topic, we will commence our journey of conducting Tableau analytics with the industry-standard, statistical prowess of R. As the first step on our journey, we will cover the installation of R, including key points about ensuring the right bitness before we start. In order to create R scripts easily, we will install RStudio for ease of use.

We need to get R and Tableau to communicate, and to achieve this communication, we will install and configure Rserve.

Installing R for Windows

The following steps shows how to download and install R on windows:

  1. The first step is to download your required version of R from the CRAN website [http://www.rproject.org/].
  2. Go to the official R website, which you can find at https://www.r-project.org/.
  3. The download link can be found on the left-hand side of the page.
  4. The next option is for you to choose the location of the server that holds R. The best option is to choose the mirror that is geographically closest to you. For example, if you are based in the UK, then you might choose the mirror that is located in Bristol.
  5. Once you click on the link, there is a section at the top of the page called Download and Install R. There is a different link for each operating system. To download the Windows-specific version of R, there is a link that specifies Download R for Windows. When you click on it, the download links will appear on the next page to download R.
  6. On the next page, there are a number of options, but it is easier to select the option that specifies install R for the first time.
  7. Finally, there is an option at the top of the page that allows you to download the latest R installation package. The install package is wrapped up in an EXE file, and both 32 bit and 64 bit options are wrapped up in the same file.

    Now that R is downloaded, the next step is to install R. The instructions are given here:

  8. Double-click on the R executable file, and select the language. In this example, we will use English. Choose your preferred language, and click OK to proceed:
    Installing R for Windows
  9. The Welcome page will appear, and you should click Next to continue:
    Installing R for Windows
  10. The next item is the general license agreement. Click Next to continue:
    Installing R for Windows
  11. The next step is to specify the destination location for R's files. In this example, the default is selected. Once the destination has been selected, click Next to proceed:
    Installing R for Windows
  12. In the next step, the components of R are configured. If you have a 32-bit machine, then you will need to select the 32-bit option from the drop-down list.
    Installing R for Windows
  13. In the next screenshot, the 64-bit User Installation option has been selected:
    Installing R for Windows
  14. The next option is to customize the startup options. Here, the default is selected. Click Next to continue.
    Installing R for Windows
  15. The next option is to select the Start Menu folder configuration. Select the default, and click Next:
    Installing R for Windows
  16. Next, it's possible to configure some of R's options, such as the creation of a desktop icon. Here, let's choose the default options and click Next:
    Installing R for Windows
  17. In the next step, the R files are copied to the computer. This step should only take a few moments:
    Installing R for Windows
  18. Finally, R is installed, and you should receive a final window. Click Finish:
    Installing R for Windows
  19. Once completed, launch RGui from the shortcut, or you can locate RGui.exe from your installation path. The default path for Windows is C:\Program Files\R\R- 2.15.1\bin\x64\Rgui.exe.
  20. Type help.start() at the R-Console prompt and press Enter. If you can see the help server page then you have successfully installed and configured your R package.
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Key benefits

  • A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R
  • Combine Tableau analytics and visualization with the power of R using this step-by-step guide
  • Wondering how R can be used with Tableau? This book is your one-stop solution.

Description

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.

Who is this book for?

This book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau. A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book.

What you will learn

  • Integrate Tableau s analytics with the industry-standard, statistical prowess of R.
  • Make R function calls in Tableau, and visualize R functions with Tableau using RServe.
  • Use the CRISP-DM methodology to create a roadmap for analytics investigations.
  • Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.
  • Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 22, 2017
Length: 178 pages
Edition : 1st
Language : English
ISBN-13 : 9781786460110
Vendor :
Tableau
Category :
Languages :
Tools :

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Product Details

Publication date : Aug 22, 2017
Length: 178 pages
Edition : 1st
Language : English
ISBN-13 : 9781786460110
Vendor :
Tableau
Category :
Languages :
Tools :

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Table of Contents

9 Chapters
1. Advanced Analytics with R and Tableau Chevron down icon Chevron up icon
2. The Power of R Chevron down icon Chevron up icon
3. A Methodology for Advanced Analytics Using Tableau and R Chevron down icon Chevron up icon
4. Prediction with R and Tableau Using Regression Chevron down icon Chevron up icon
5. Classifying Data with Tableau Chevron down icon Chevron up icon
6. Advanced Analytics Using Clustering Chevron down icon Chevron up icon
7. Advanced Analytics with Unsupervised Learning Chevron down icon Chevron up icon
8. Interpreting Your Results for Your Audience Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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1 star 71.4%
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GP_user Nov 14, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I used this book for a training. Seems that author or the editors could have spent more time before publishing this book. Texts and codes don’t always match and codes are not even consistent. Best part is on page 124, data was about flower iris, but middle in the page it reads ‘wine’ from some unknown data. You can’t help but thinking how nice it must have been thinking of flowers and wines while writing this part!
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NJ Reader Oct 30, 2017
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Overall, the book is a fantastic overview of using R in tableau. The issue is that you cannot get your hands on the files to go along with the book. In the beginning, they have you going to Packt Publishing and/or Git Hub to get them, but when you do, there are only "read me" files and the CSV data you should have is nowhere to be found.. I contacted Packt and they sent me two emails, telling me that were trying to reach the author. The problem is that I wanted the files in order to teach classes with them. I would have given five stars to the book, if I had the files. But, as is, my students cannot use the book in the way the author says they can.
Amazon Verified review Amazon
Ray Prtha Aug 02, 2020
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This book contains pages and pages of theory with very little practical execution or hands-on exercises. Totally useless. It is like reading a book on swimming when what you really want to do is actually swim.
Amazon Verified review Amazon
Tim Terry Feb 27, 2019
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I don't ever want to write a negative review for a book. However, in this case, it seems warranted. The text of the e-book seems to be incomplete. The chapter summaries read as if large parts of chapters are missing. The packet of supporting files are very confusing. The sample Tableau files are missing the associated databases and have nothing to do with the chapters. I thought maybe the file set was wrong, but the databases are the correct ones. The R examples seem to be well done, but the Tableau examples are pretty poorly documented. I was hoping for a book that talked about integration of R and Tableau more. Perhaps this book does, within the missing sections, but as is, it was not 40 dollars well spent. I'm very dissatisfied.
Amazon Verified review Amazon
Huy Jan 02, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This book has a lot of useful informations if and only if the data they use is not broken/changed.
Amazon Verified review Amazon
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