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Efficient data processing with R /

What you'll learn--and how you can apply it You'll learn to perform efficient data carpentry--the process of taking rough, raw, and to some extent randomly arranged input data and creating neatly organized and tidy data. Working with clean data will be beneficial for every subsequent stage...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Gillespie, Colin (Colin Stevenson) (Autor), Lovelace, Robin (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly, [2016]
Colección:Lesson
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Gillespie, Colin  |q (Colin Stevenson),  |e author. 
245 1 0 |a Efficient data processing with R /  |c Colin Gillespie and Robin Lovelace. 
264 1 |a [Place of publication not identified] :  |b O'Reilly,  |c [2016] 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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490 0 |a Lesson 
588 0 |a Online resource; title from title page (Safari, viewed January 11, 2017). 
500 |a Date of publication from resource description page. 
500 |a "From Efficient R programming by Colin Gillespie & Robin Lovelace"--Cover 
504 |a Includes bibliographical references. 
520 |a What you'll learn--and how you can apply it You'll learn to perform efficient data carpentry--the process of taking rough, raw, and to some extent randomly arranged input data and creating neatly organized and tidy data. Working with clean data will be beneficial for every subsequent stage of your R project. In this Lesson, readers will learn how to create user-friendly data frames with tibble, reshape data with tidyr operations such as gather and separate, process data efficiently with dplyr's functions, and connect R to a range of database types. This lesson is for you because You are working on a project in R and have reached the data processing stage. You want to clean, manipulate, and tidy your dataset to get it ready for the next stage (typically modeling and visualization). Prerequisites Some knowledge of R Materials or downloads needed in advance Installed RStudio This Lesson relies on a number of packages for data cleaning and processing. Check that they are installed on your computer and load them with: library("tibble") library("tidyr") library("stringr") library("readr") library("dplyr") library("data.table") RSQLite and ggmap are also used in a couple of examples, though they are not central to the Lesson's content 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
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700 1 |a Lovelace, Robin,  |e author. 
700 1 |a Gillespie, Colin  |q (Colin Stevenson).  |t Efficient R programming. 
700 1 |a Lovelace, Robin.  |t Efficient R programming. 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781491980736/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
994 |a 92  |b IZTAP