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
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

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 https://github.com/scikit-fuzzy/scikit-fuzzy.

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
-fuzzy

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

Type these commands:

$ git clone https://github.com/scikit-fuzzy/scikit-fuzzy
$ cd scikit-fuzzy/
$ sudo python setup.py 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
matplotlib.use('Agg')

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