Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

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.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
About the Authors
About the Reviewers
Customer Feedback

Fuzzy logic

One of the famous Python libraries for fuzzy logic is scikit-fuzzy. Several fuzzy logic algorithms have already been implemented on this library. Since scikit-fuzzy is an open source library, you can review the source code at

Before you install this library, you should already have installed NumPy and SciPy libraries. You can install scikit-fuzzy using pip, by typing the following command:

$ sudo pip install scikit

As another option, you can install the scikit-fuzzy library from source code.

Type these commands:

$ git clone
$ cd scikit-fuzzy/
$ sudo python install

After completing the installation, you can use scikit-fuzzy. To test how to work with scikit-fuzzy, we will build a fuzzy membership for temperature using the fuzz.trimf() function. You can write the following scripts:

import matplotlib

import numpy as np
import skfuzzy as fuzz
import matplotlib...