Compact and Flexible Summaries of Data

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.

An API Client for Australian Weather and Climate Data Resources

Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development ('DPIRD') of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science ('DES'). As well as the Bureau of Meteorology ('BOM') of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. 'DPIRD' weather data are accessed through public 'APIs' provided by 'DPIRD', < https://www.agric.wa.gov.au/weather-api-20>, providing access to weather station data from the 'DPIRD' weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology ('BOM') data and accessed through 'SILO' (Scientific Information for Land Owners) Jeffrey et al. (2001) . 'DPIRD' data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license < https://creativecommons.org/licenses/by/3.0/au/deed.en>. SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) < https://creativecommons.org/licenses/by/4.0/>. 'BOM' data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence ('PAL') as appropriate, see < http://www.bom.gov.au/other/copyright.shtml> for further details.

Split, Combine and Compress PDF Files

Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the 'qpdf' C++ library < https://qpdf.sourceforge.io/> and does not require any command line utilities. Note that 'qpdf' does not read actual content from PDF files: to extract text and data you need the 'pdftools' package.

World Vector Map Data from Natural Earth Used in 'rnaturalearth'

Vector map data from < https://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.

Read and Write ODS Files

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

Convert Data from and to 'GeoJSON' or 'TopoJSON'

Convert data to 'GeoJSON' or 'TopoJSON' from various R classes, including vectors, lists, data frames, shape files, and spatial classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'.

Text Extraction, Rendering and Converting of PDF Documents

Utilities based on 'libpoppler' < https://poppler.freedesktop.org> for extracting text, fonts, attachments and metadata from a PDF file. Also supports high quality rendering of PDF documents into PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R.

Fast, Consistent Tokenization of Natural Language Text

Convert natural language text into tokens. Includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, Penn Treebank, regular expressions, as well as functions for counting characters, words, and sentences, and a function for splitting longer texts into separate documents, each with the same number of words. The tokenizers have a consistent interface, and the package is built on the 'stringi' and 'Rcpp' packages for fast yet correct tokenization in 'UTF-8'.

Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ).

Validate 'JSON' Schema

Uses the node library 'is-my-json-valid' or 'ajv' to validate 'JSON' against a 'JSON' schema. Drafts 04, 06 and 07 of 'JSON' schema are supported.