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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

Creating single-variable plots

Single-variable plots are mostly used to visualize the distribution of a variable. Using these kinds of graphics, it is possible to understand more of your dataset, evaluate where there is data concentration, data symmetry, or skewness, and visualize how the data behaves in comparison to the mean and detect patterns.

Dataset

The dataset chosen for this chapter comes from the datasets library; it is named mtcars. It is a widely known toy dataset for you to play with to learn coding and Data Science. For our goal here, which is understanding how to create each graphic, it presents itself as one of the best options because it is about a common subject (cars) and it has many variable numerical and categorical types for us to create different visualizations. If you want to know more about the variables, feel free to write help("mtcars") on your console in R. To load the dataset into an R session, just use the code that follows:

data(&quot...

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