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

Working with Strings

Strings, in the programming world, are textual information: a single letter, a word, a phrase, or, more generally, anything that comes in between single or double quotes will be understood as a string by the computer once it is assigned to a variable. See the following code and comments:

# If not assigned to a variable, a text is just a comment.
"This is a text."
# These are strings
my_string1 <- "a"
my_string2 <- "Hello, World! I am learning!"
my_string1 <- "42"

The manipulation of strings is a good skill to have due to the amount of good data that is found on the internet in textual format. Natural Language Processing (NLP) is one of the largest areas in data science, and a lot of it relies on wrangling strings.

Most of what can be done with strings involves tasks such as the following:

  • Parsing: Separating parts of the text that are divided by a pattern, extracting parts of it and combining words...