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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

Technical requirements

The dataset chosen for this project is from American Community Survey 2010-2012 Public Use Microdata Series and can be found in the raw form at this link: https://tinyurl.com/4yx2dhpe. The data is originally from the US census, but FiveThirtyEight pulled it and did some cleanup, creating the final dataset to be used in this chapter. The links to the raw data can also be found in the previously provided URL address. The data is about college majors and there is a lot of information about earnings, employment ratio, and share of enrolled people by sex, thus containing many numbers that we can use to understand how the majors and areas are performing in the job market and in terms of unemployment rates.

All the code can be found in the book’s GitHub repository: https://github.com/PacktPublishing/Data-Wrangling-with-R/tree/main/Part2/Chapter9.

Load the following libraries for this chapter:

library(tidyverse)
library(skimr)
library(statsr)
library...

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