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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

Slicing and filtering

When you have a table as large as the dataset we are working with, it is very hard to look at all the observations one by one. Look how many rows and columns this dataset has:

# Dataset dimensions
dim(df)
[1] 32561    15

The dim() function shows the number of rows first, then the number of columns, or variables. It’s easy to see that it would take us too much time – not to mention that it is not productive as well – to look at 32,561 observations. Therefore, the tasks of slicing and filtering play a major role, acting like a magnifying glass for us to zoom in on specific parts of the data.

These tasks can sound like they’re the same, but there is a slight difference between them.

Slicing

Slicing means cutting and displaying a slice, a piece, of the dataset. A good application of this task is when we need to look at the errors of a model. In this case, it is possible to take only the observations where...

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