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

Working with dirty data

The process of cleaning data involves tidying the data, which usually results in making the dataset smaller because we have cleaned out some of the dirty data. What makes data dirty?

Dirty data can be due to invalid data, which is data that is false, incomplete, or doesn't conform to the accepted standard. An example of invalid data could be formatting errors, or data that is out of an acceptable range. Invalid data could also have the wrong type. For example, the Asterix is invalid because the acceptable formatted data is for letters only, so it can be removed.

Dirty data can be due to missing data, which is data where no value is stored. An example of missing data is data that has not been stored due to a faulty sensor. We can see that some data is missing, so it is removed from consideration.

Dirty data could also have null values. If data has null values, then programs may respond differently to the data on that basis. The nulls will need to be considered in order...