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Data manipulation with R : efficiently perform data manipulation using the split-apply-combine strategy in R /
This book is for all those who wish to learn about data manipulation from scratch and excel at aggregating data effectively. It is expected that you have basic knowledge of R and have previously done some basic administration work with R.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai [India] :
Packt Publishing,
2015.
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| Edición: | Second edition. |
| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to R Data Types and Basic Operations; Getting different versions of R; Installing R on different platforms; Installing and using R libraries; Manually downloading and installing packages; Installing packages within the R shell; Comparing R with other software; R as an enterprise solution; Writing commands in R; R data types and basic operations; Modes and classes of R objects; The R object structure and mode conversion; Vector; Factor and its types
- Data frameMatrices; Arrays; List; Missing values in R; Summary; Chapter 2: Basic Data Manipulation; Acquiring data; Vector and matrix operations; Factor manipulation; Factors from numeric variables; Date processing using lubridate; Character manipulation; Subscripting and subsetting; Summary; Chapter 3: Data Manipulation using plyr and dplyr; Applying the split-apply-combine strategy; Introducing the plyr and dplyr libraries; plyr's utilities; Intuitive function names in the plyr library; Inputs and arguments; Multiargument functions; Comparing base R and plyr
- Powerful data manipulation with dplyrFiltering and slicing rows; Arranging rows; Selecting and renaming; Adding new columns; Selecting distinct rows; Column-wise descriptive statistics; Group-wise operations; Chaining; Summary; Chapter 4: Reshaping Datasets; Typical layout of a dataset; Long layout; Wide layout; New layout of a dataset; Reshaping the dataset from the typical layout; Reshaping the dataset with the reshape package; Melting data; Missing values in molten data; Casting molten data; The reshape2 package; Summary; Chapter 5: R and Databases; R and different databases; R and Excel
- R and MS AccessRelational databases in R; The filehash package; The ff package; R and sqldf; Data manipulation using sqldf; Summary; Chapter 6: Text Manipulation; Text data and its source; Getting text data; Text processing using default functions; Working with Twitter data; Summary; Index