Data wrangling with R / Bradley C. Boehmke.  (Text) (Text)

Boehmke, Bradley C
Call no.: QA276.45.R3 B64 2016Series: Use R!: Publication: Cham, Switzerland : Springer, c2016Description: xii, 238 p. : ill. (some col.)ISBN: 9783319455983; 3319455982Subject(s): Multivariate analysisR (Computer program language)Statistics -- Data processingLOC classification: QA276.45.R3 | B64 2016
Contents:The role of data wrangling -- Introduction to R -- The basics -- Dealing with numbers -- Dealing with character strings -- Dealing with regular expressions -- Dealing with factors -- Dealing with dates -- Data structure basics -- Managing vectors -- Managing lists -- Managing matrices -- Managing data frames -- Dealing with missing values -- Importing data -- Scraping data -- Exporting data -- Functions -- Loop control statements -- Simplify your code with %>% -- Reshaping your data with tidyr -- Transforming your data with dplyr.
Summary: This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.
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Includes bibliographical references and index.

The role of data wrangling -- Introduction to R -- The basics -- Dealing with numbers -- Dealing with character strings -- Dealing with regular expressions -- Dealing with factors -- Dealing with dates -- Data structure basics -- Managing vectors -- Managing lists -- Managing matrices -- Managing data frames -- Dealing with missing values -- Importing data -- Scraping data -- Exporting data -- Functions -- Loop control statements -- Simplify your code with %>% -- Reshaping your data with tidyr -- Transforming your data with dplyr.

This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.

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