Expert data wrangling with R : streamline your work with tidyr, dplyr, and ggvis /

"Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work. This video tutorial shows you how to streamline your code-and your thinking-by introducing a set of principles and R packages that make this work much faster and easier....

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Grolemund, Garrett (Orador)
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Place of publication not identified] : O'Reilly Media, 2015.
Temas:
R (Computer program language)
Big data.
Data mining.
Data Mining
R (Langage de programmation)
Données volumineuses.
Exploration de données (Informatique)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

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520 |a "Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work. This video tutorial shows you how to streamline your code-and your thinking-by introducing a set of principles and R packages that make this work much faster and easier. Garrett Grolemund, Data Scientist and Master Instructor at RStudio, demonstrates how R and its packages help you tackle three main issues. Data Manipulation. Data sets contain more information than they display. By transforming your data, you can reveal a wealth of descriptive statistics, group level observations, and hidden variables. R's dplyr package provides optimized functions to help you transform data, as well as a pipe syntax that makes R code more concise and intuitive. Data Tidying. Data sets come in many formats, but R prefers just one. R runs quickly and intuitively when your data is stored in the tidy format, a layout that allows vectorized programming. R's tidyr package reshapes the layout of your data sets, making them tidy while preserving the relationships they contain. Data Visualization. The structure of data visualizations parallels the structure of data sets. Once your data is tidy, visualizations become straightforward: each observation in your dataset becomes a mark on a graph, each variable becomes a visual property of the marks. The result is a grammar of graphics that lets you create thousands of graphs. R's ggvis package implements the grammar, providing a system of data visualization for R."--Resource description page. 
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