Summary

• What does this package do? (explain in 50 words or less):
  Creates compact and flexible data summaries that are pipeable and display nicely in the console. For a given data frame or vector the skim function provides a useful set of summary statistics (based on the class of the individual vector/column) that allows users to skim their data to get an overall sense of what is included, extent of missing values, and similar information. The skim generic can be extended by users to other data structures.

• Paste the full DESCRIPTION file inside a code block below:

Package: skimr
Title: Compact and Flexible Summaries of Data 
Version: 0.9.92
Authors@R: c(
  person("Amelia", "McNamara", email="[email protected]", role = "aut"),
  person("Eduardo", "Arino de la Rubia", email="[email protected]", role = "aut"),
  person("Hao", "Zhu", email="[email protected]", role = "aut"),
  person("Julia", "Lowndes", email="[email protected]", role = 'ctb'),
  person("Shannon", "Ellis", email="[email protected]", role = "aut"),
  person("Elin", "Waring", email="[email protected]", role = "cre"),
  person("Michael", "Quinn", email="[email protected]", role = "aut"),
  person("Hope", "McLeod", email="[email protected]", role = 'ctb'),
  person("Hadley", "Wickham", email="[email protected]", role = 'ctb'),
  person("Connor", "Kirkpatrick", email="[email protected]", role = 'ctb')
  )
Maintainer: Elin Waring <[email protected]>
Description: A simple to use summary function that can be used with pipes
    and displays nicely in the console. The default summary statistics may be 
    modified by the user as can the default formatting. Support for data frames 
    and vectors is included, and users can implement their own skim methods for
    specific object types as described in a vignette. Default summaries include
    support for inline spark graphs. Instructions for managing these on 
    specific operating systems are given in the Using skimr vignette and the 
    README.
Depends:
    R (>= 3.1.2)
Imports:
    dplyr (>= 0.7),
    magrittr,
    pander,
    purrr,
    rlang,
    stats,
    stringr,
    knitr,
    tibble (>= 0.6),
    tidyr (>= 0.7),
    tidyselect
Suggests:
    extrafont,
    rmarkdown,
    testthat,
    withr
License: GPL-3 + file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://github.com/ropenscilabs/skimr
BugReports: https://github.com/ropenscilabs/skimr/issues
VignetteBuilder: knitr
RoxygenNote: 6.0.1
Collate: 
    'skimr-package.R'
    'formats.R'
    'stats.R'
    'functions.R'
    'options.R'
    'skim.R'
    'skim_print.R'
    'skim_v.R'
    'summary.R'

• URL for the package (the development repository, not a stylized html page):
  https://github.com/ropenscilabs/skimr

• Please indicate which category or categories from our package fit policies this package falls under *and why(? (e.g., data retrieval, reproducibility. If you are unsure, we suggest you make a pre-submission inquiry.):

"tools" Because skimr provides users with a way to get started with a new, unknown data set (by getting a quick overview (or skim) of the data that is readable and compact. It also serves as a good tool for reporting summary information about data.

•   Who is the target audience and what are scientific applications of this package?  

• People who want to get a quick overview of a data set while initially reviewing, iteratively cleaning, or sharing with others.
• Beginning statistics students who need simple access to a set of basic descriptive statistics.

• Are there other R packages that accomplish the same thing? If so, how does
  yours differ or meet our criteria for best-in-category?

This is intended as an improved version of the r core summary functions available. Like summary, skim is a generic that users can extend to any R data object. It is designed to be somewhat like a more flexible version of fivenums() from stats or favstats() from the Mosaic package.

•   If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.

Requirements

Confirm each of the following by checking the box. This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, including reporting of test coverage, using services such as Travis CI, Coeveralls and/or CodeCov.
• I agree to abide by ROpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

Publication options

• Do you intend for this package to go on CRAN?
• Do you wish to automatically submit to the Journal of Open Source Software? If so:
  • The package has an obvious research application according to JOSS's definition.
  • The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
  • The package is deposited in a long-term repository with the DOI:
  • (Do not submit your package separately to JOSS)
• Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:
  • The package is novel and will be of interest to the broad readership of the journal.
  • The manuscript describing the package is no longer than 3000 words.
  • You intend to archive the code for the package in a long-term repository which meets the requirements of the journal.
  • (Please do not submit your package separately to Methods in Ecology and Evolution)

Detail

• Does R CMD check (or devtools::check()) succeed? Paste and describe any errors or warnings:

• Does the package conform to rOpenSci packaging guidelines? Please describe any exceptions:

• If this is a resubmission following rejection, please explain the change in circumstances:

• If possible, please provide recommendations of reviewers - those with experience with similar packages and/or likely users of your package - and their GitHub user names: