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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

Summary

We progressed a lot in this chapter and learned so much about datetime objects and variables. Knowing how to use them will enhance your analytical skills, opening room for better insights.

We started this chapter by learning how to create data objects. Next, we acquired knowledge on how to make good use of the lubridate library, and we are now able to parse dates in many different formats.

After that, the subject changed to math operations with date and time and all the specificities that surround it, including the usage of time zones. That was followed by guidance on how to use the customization power of regexp to parse dates out of texts.

Closing the chapter, we viewed a practical exercise where we used a dataset about songs containing a datetime variable, and how you can use that to extract insight for analysis.

The end of this chapter is also the completion of the building blocks for data wrangling. With it, we now have worked with the three major object types...

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