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

Replacing and filling data

A dataset can and certainly will be acquired with imperfections. An example of imperfection is the use of the ? sign instead of the default NA for missing values for the Census Income dataset. This problem will require the question mark to be replaced with NA first, and then filled with another value, such as the mean, the most frequent observation, or using more complex methods, even machine learning.

This case clearly illustrates the necessity of replacing and filling data points from a dataset. Using tidyr, there are specific functions to replace and fill in missing data.

First, the ? sign needs to be replaced with NA, before we can think of filling the missing values. As seen in Chapter 7, there are only missing values for the workclass (1836), occupation (1843), and native_country (583) columns. To confirm that, a loop through the variables searching for ? would be the fastest resource:

# Loop through variables looking for cells == "...