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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

Preparing data for modeling in R

We must wrangle the data to prepare it for modeling. Since we know where we want to go at the end of this project, the next step is a matter of finding a way to get there.

The first thing we must do is load the libraries to be used for wrangling and modeling the data. We will use tidyverse to perform data wrangling and visualization, skimr to create a descriptive statistics summary, patchwork, a great library to put graphics side by side, randomForest to create the model, caret to create the confusion matrix, and ROCR to plot the ROC curve of model performance.

To load the dataset, the best option is to pull it directly from the internet, without the need to save it locally on our machine. Just use the read_csv() function and point to the web address where the raw dataset is located, as we’ve done previously in this book. Here, we are using the trim_ws=TRUE argument to trim any unwanted white spaces and the col_names=headers argument, where...

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