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

Creating new variables

A dataset is not only the data you see. There is a lot of information in it. For example, remember in Chapter 6, when we worked with datetime objects during our data exploration exercise, we took the TIME variable and extracted the year, month, day, and hour from it. This is one of the many ways to create new variables.

Here are some examples of new variables created out of our working dataset:

  • Arithmetical operators: Adding two or more variables to create a total variable.
  • Text extraction: Extracting a meaningful part of a text, for instance, 1234 from ORDER-1234.
  • Custom calculations: Calculating a discount rate based on a business rule.
  • Binarization: Transforming a variable from on and off to 1 and 0. Binary means two options and is commonly associated with 0 and 1 in computer language.
  • Encoding: Transforming a qualitative ordinal variable, such as basic, intermediate, and advanced to 1, 2, and 3.
  • One Hot Encoding: A very common...