{"Type":"Package","Package":"skynet","Version":"1.4.3","Title":"Generates Networks from BTS Data","Authors@R":"person(\"Filipe\", \"Teixeira\", email = \"Filipe.MarquesTeixeira@Ugent.be\", role = c(\"aut\", \"cre\"))","Maintainer":"Filipe Teixeira ","URL":"https://github.com/ropensci/skynet","Description":"A flexible tool that allows generating bespoke\nair transport statistics for urban studies based on publicly available\ndata from the Bureau of Transport Statistics (BTS) in the United States\n.","License":"MIT + file LICENSE","BugReports":"https://github.com/ropensci/skynet/issues","Encoding":"UTF-8","LazyData":"true","RoxygenNote":"7.1.1","Collate":"'PowerLaw.R' 'createNodes.R' 'data_ODsample.R'\n'data_airports.R' 'data_carriers.R' 'data_metro.R'\n'disparityfilter.R' 'findAirport.R' 'fromto.R' 'netImport.R'\n'netDir.R' 'netUnd.R' 'netInt.R' 'netPath.R' 'make_net_trip.R'\n'skynet.R' 'nodeStats.R' 'import_t100.R' 'import_db1b.R'\n'import_ontime.R' 'findCarrier.R' 'plotMap.R'\n'skynet_example.R' 'bootnet.R' 'summary.R' 'download_db1b.R'\n'download_t100.R' 'download_t100_int.R' 'download_ontime.R'","Depends":"R (>= 3.1.2)","Imports":"data.table, igraph, dplyr, ggplot2, ggrepel, stringr, maps,\nhttr, geosphere, leaflet, RCurl","Suggests":"knitr, rmarkdown, testthat, kableExtra, covr","VignetteBuilder":"knitr","NeedsCompilation":"no","Packaged":"2022-06-17 11:50:34 UTC; Fil","Author":"Filipe Teixeira [aut, cre]","Repository":"CRAN","Date/Publication":"2022-06-17 13:00:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/skynet_1.4.3.tar.gz","jsonAuthors":[{"name":"Filipe Teixeira","email":"Filipe.MarquesTeixeira@Ugent.be","maintainer":true}],"readme":"\n\n\n# skynet

\n\n![Build Status](https://github.com/ropensci/skynet/workflows/R-CMD-check/badge.svg)\n[![CRAN\\_Status\\_Badge](http://www.r-pkg.org/badges/version/skynet)](https://cran.r-project.org/package=skynet)\n![](https://cranlogs.r-pkg.org/badges/skynet?color=brightgreen)\n[![Coverage\nstatus](https://codecov.io/gh/ropensci/skynet/branch/master/graph/badge.svg)](https://codecov.io/github/ropensci/Skynet?branch=master)\n[![](https://badges.ropensci.org/214_status.svg)](https://github.com/ropensci/software-review/issues/214)\n\n# Overview\n\nThe rationale behind Skynet, is to provide researchers with a unifying\ntool overcoming some of the challenges faced when dealing with the\nBureau of Transport Statistics, DB1B and T100 data. The DB1B data\nconsists of 2 sets of files, Coupon and Ticket. They can be both\ndownloaded at\n and\n\nrespectively while the T100 data can be found here\n.\n\n## Note\n\nTo comply with R syntax guidelines, we changed to a clearer function\nnaming from version 1.2.0. Deprecated functions are still present, but\nwill be removed for the next versions.\n\n## Note on importing from other data sources\n\nWe are constantly working on new functions that allow importing data\nfrom different data sources. However, as we can’t cover them all at\nleast for now, in case you would like to work with a database which is\nnot covered by skynet, simply create a data.frame with the following\nvariables:\n\n`itin_id, mkt_id, seq_num, origin_mkt_id, origin, year, quarter,\ndest_mkt_id, dest, trip_break, op_carrier, distance, gateway, roundtrip,\nitin_yield, passengers, itin_fare, bulk_fare, distance_full`\n\nFor more information on the variables, please visit\n and\n.\n\nSkynet allows that some of this variables have a 0 or NA value, however,\nif you’re working with a specific dataset which doesn’t allow an easy\nconversion to our format, please feel free to create an issue so we can\nlook into it. Please make sure to include at least one small example of\na csv file with the data you’re trying to import.\n\n## Installation\n\nYou can install skynet from github with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"FilipeamTeixeira/skynet\")\n```\n\n## Import Data\n\nTo import data, simply type `import_db1b()` or `import_t100()` including\nthe path to your desired file. \n**Note**: The Coupon file should take the first argument while the\nTicket file should take the second argument.\n\n``` r\n library(skynet)\n import_db1b(\"folder/Coupon 2016Q1.csv\", \"folder/Ticket 2016Q1.csv\")\n import_t100(\"folder/T100_2016.csv\")\n```\n\nThe BTS DB1B data consists of 2 sets of files, `Coupon` and `Ticket`.\nThey can be both downloaded at\n and\n\nrespectively.\n\nDespite being possible to download the complete zipped file, which\nincludes all variables, due to its size, we recommend selecting the\nfollowing set.\n\n| Coupon | Ticket |\n| :------------------------- | :----------------- |\n| Itinerary ID | Itinerary ID |\n| Market ID | Roundtrip |\n| Sequence Number | Itinerary Yield |\n| Origin City Market ID | Passengers |\n| Origin | Itinerary Fare |\n| Year | Bulkfare Indicator |\n| Quarter | Distance |\n| Destination City Market ID | |\n| Destination | |\n| Trip Break | |\n| Operating Carrier | |\n| Distance | |\n| Gateway | |\n\nSince version 1.0.2 that the import method changed being the\n`netimport()` function no longer available. When importing from the\nprezipped DB1B file, just add the argument `zip = TRUE` to the\n`import_db1b()` function. This does not apply to the T100 file which can\nbe simply imported by typing `import_t100()`. In order to save space, it\nis possible as well to import the prezipped file, and convert it to a\nsmaller file with only the necessary variables, with the function\n`convert_raw()`.\n\n## Example\n\nTo generate a directed network, please type:\n\n library(skynet)\n # For DB1B data\n import_db1b(\"folder/Coupon_2011Q1.csv\", \"folder/Ticket_2011Q1.csv\")\n make_net_dir(OD_2011Q1, disp = TRUE, alpha = 0.05)\n \n # For T100 data\n import_t100(\"folder/T100_2011.csv\")\n make_net_dir(T100_2011Q1, disp = TRUE, alpha = 0.05)\n\n[![ropensci\\_footer](https://ropensci.org/public_images/ropensci_footer.png)](https://ropensci.org)","jobInfo":{"package":"skynet","version":"1.4.3","parsingStatus":"success","parserVersion":1,"parsedAt":"2022-06-17T15:30:38+0000"}}

Description

Arguments

Details

References

See Also

Examples