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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

Getting started with dplyr


To start off with, I will create an R script called dplyr_intro.R and set up my R environment. First, you should set your working directory to the ch7 project folder. Next, you should read the fuel economyhttps://catalog.data.gov/dataset/consumer-price-index-average-price-data dataset into a dataframe as follows:

setwd("path/to/your/project/folder")
vehicles<-read.csv("data/vehicles.csv")

The next step is to import the dplyr and tibble packages. In R, you can import a package using the library() function. The following lines import the dplyr package and the tibblepackage:

library('dplyr')
library('tibble')

I will start with the select() function. The select() function allows you to select a certain number of columns from a dataframe and returns another dataframe containing only those selected columns. As its first argument, the select() function takes a dataframe. The following arguments to the select() function after the first argument are the names of the columns...

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