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Data Wrangling with R

Data Wrangling with R

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

Data Wrangling with R

4.9 (7)
By: 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

Loading and Exploring Datasets

Every data exploration begins with data, quite obviously. Thus, it is important for us to know how to load datasets to RStudio before we get to work. In this chapter, we will learn the different ways to load data to an RStudio session. We will begin by importing some sample datasets that come with some preinstalled libraries from R, then move on to reading data from Comma-Separated Values (CSV) files, which turns out to be one of the most used file types in Data Science, given its compatibility with many other programs and data readers.

We will also learn about the basic differences between a Data Frame and a Tibble, followed by a section where we will learn the basics of Web Scraping, which is another good way to acquire data. Later in the chapter, we will learn how to save our data to our local machine and paint a picture of a good workflow for data exploration.

We will cover the following main topics:

  • How to load files to RStudio
  • ...
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Data Wrangling with R
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