rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: R Data Science Quick Reference
    sub title: A Pocket Guide to APIs, Libraries, and Packages
  name:
    name part: Mailund, Thomas
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (IX, 246 pages 11 illustrations.)
  abstract: In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you'll learn about the following APIs and packages that deal specifically with data science applications: readr, tibble, forcates, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, broom, knitr, shiny, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. You will: Get started with RMarkdown and notebooks Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot 2 and data fit for models using modelr and broom Report results with markdown, knitr, shiny, and more.
  table of contents: 1. Introduction -- 2. Importing Data: readr -- 3. Representing Tables: tibble -- 4. Reformatting Tables: tidyr -- 5. Pipelines: magrittr -- 6. Functional Programming: purrr -- 7. Manipulating Data Frames: dplyr -- 8. Working with Strings: stringr -- 9. Working with Factors: forcats -- 10. Working with Dates: lubridate -- 11. Working with Models: broom and modelr -- 12. Plotting: ggplot2 -- 13. Conclusions.
  note: by Thomas Mailund.
  note: Electronic resource.
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Computer programming
  subject:
    topic: Big data
  subject:
    topic: Data mining
  subject:
    topic: Programming Languages, Compilers, Interpreters
  subject:
    topic: Programming Techniques
  subject:
    topic: Big Data
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: 005.13
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484248942
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 190807
    record change date: 20230330185325.7
    record identifier: in00004112579