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Data Wrangling with R

Data Wrangling with R

By : Gustavo R Santos, Gustavo Santos
4.9 (7)
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Data Wrangling with R

Data Wrangling with R

4.9 (7)
By: Gustavo R Santos, Gustavo Santos

Overview of this book

In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you’ll need plenty of tools that enable you to extract the most useful knowledge from data. Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization. The book begins by teaching you how to load and explore datasets. Then, you’ll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you’ll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards. By the end of this book, you’ll have learned how to create your first data model and build an application with Shiny in R.
Table of Contents (21 chapters)
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1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Working with regular expressions

Regexps are textual expressions – or codes – that empower programmers to create highly customized parsers for strings. Pretend that your client has a document with thousands of customer comments in it and they only want to extract the serial numbers of defective products. If you have ever seen a product serial number, you will know that it is a unique identifier of a product, generally composed of letters and numbers – for example, XYW-001AB. Well, using regexps, this task becomes easy, as it is possible to create a pattern that will fit that combination of letters and numbers for your client’s products exactly, making parsing a quick task.

At first, when you look at a regexp, it will look like a weird code or something hard to learn, but as you begin practicing and using it more, the patterns naturally start to look easier to read and write. We will go step by step from zero to build our knowledge about regexps.

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