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Data Wrangling with R

Data Wrangling with R

By : Gustavo R Santos, Gustavo Santos
4.9 (7)
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Data Wrangling with R

Data Wrangling with R

4.9 (7)
By: Gustavo R Santos, Gustavo 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)
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1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Math operations with variables

As part of a data wrangling process, there will be tasks involving mathematical operations with variables, where there will be a need to add, multiply, or even calculate the log of numbers, for example. Ergo, working with a data frame or a Tibble object is recommended, due to the facilities to perform those operations with variables.

The most common math operators in R are as follows:

Figure 5.5 – A table with the R language’s math operators

If we still use the data frame with names and grades, just created for the last exercise, let’s imagine that the professor offered one extra point for those who wrote a paper. Let’s suppose everyone delivered it; here is how we can add a new column with the extra point:

# Extra point
# Scenario: everyone delivered
df$new_grade = df$grade + 1

Figure 5.6 – One point added to all the students

If the professor wants to normalize...

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