http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
efficiently perform data manipulation using the split-apply-combine strategy in R
1 online resource (130 pages) : illustrations, tables
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.
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
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Birmingham, England ; Mumbai, [India] : Packt Publishing, 2015