Book Image

Data Wrangling with R

By : Gustavo R Santos
Book Image

Data Wrangling with R

By: Gustavo R 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)
1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Summary

In this chapter, we went over an EDA project, beginning with the load of the data to RStudio up to an analysis report.

After loading the data, we started to understand the shape of the dataset and the data types, and we did a transformation of some variables to factor. Moving on, we cleaned the data of missing values and started the exploration and visualization part. This began with a checkup of the descriptive statistics, then we looked at the distributions of the data and outlier detection. The sequence was to look at a bivariate chart and a pair plot that shows the correlations and scatterplots, allowing one to understand the relationship between the variables and start to get a feel of the best ones for modeling.

Next, we started to ask questions to lead our exploration, always answering them with data and statistical tests. Finally, closing the chapter, we presented an analysis report example, highlighting the findings in text form.