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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 began Chapter 2 by learning how to acquire data, using native R datasets or loading it from the popular CSV format, and how to customize the dataset even during the importing data phase, such as deciding on the number of rows to load. Then we briefly explained the difference between a data frame and Tibble format. They serve the same purpose and basically do the same things, but Tibbles bring some enhancements and are more suited to the modern world, and work much better with the tidyverse package in R.

Next, we advanced to more sophisticated ways to bring data to your R session by using web scraping or capturing datasets from a public API. As we live in a world where many businesses and salespeople work with Microsoft Excel, it is important to know how to save a file as a CSV. That was also covered in this chapter.

Coming to a close, we went over the basic steps of EDA: loading and viewing data, calculating descriptive statistics, handling missing values and outliers...

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