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

The dataset

The dataset to be used in the next exercises can be found in the UCI dataset repository. It was pulled from the popular datasets tab in the repository, and it is named Adults, but it is also known as Census Income dataset (from https://archive.ics.uci.edu/ml/datasets/Adult).

The variables we will be dealing with are listed next and I also invite you to read the adult.names file provided with the dataset in the UCI repository or in the GitHub page for this chapter’s codes (https://tinyurl.com/ywpjj329).

  • Demographics: age, sex, race, marital-status, relationship status, native country.
  • Education: education level and education-num (years of study).
  • Work related: work class, occupation, hours per week.
  • Financial: capital gain and capital loss.
  • fnlwgt: This means final weight, which is a scoring calculation from the Census Bureau based on socio-economic and demographic data. People with similar demographic information should have similar weights...