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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

Preface 1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

Familiarizing yourself with RStudio


Compared to RStudio, the Python development process that has been used in previous chapters has been a bit indirect. Code has been written in a text editor to perform a particular function and then executed as a whole through a separate interface.

Writing code in RStudio is more of an iterative process. Code can be run line by line from the editor, and data and variables are stored continuously within the environment. This means that you can conduct analysis, observe the data, and verify the correctness of your code as you go. The following steps can be used to create an R script in RStudio.

  1. To begin with, open the RStudio program:
  1. From RStudio, you can create a new R script by selecting File | New | Rscript. This will create and open a .R file in the text editor:
  1. This will open the script for editing. You can save the script to your ch6 folder by selecting File | Save. The name of the script is not that important here, but I will name mine r_intro. Note that...
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