{"Package":"naijR","Type":"Package","Title":"Operations to Ease Data Analyses Specific to Nigeria","Version":"0.3.2","Date":"2021-10-07","Depends":"R (>= 3.6), methods, utils","Imports":"RColorBrewer (>= 1.1.2), lifecycle (>= 0.2.0), magrittr (>=\n1.5), mapdata (>= 2.3.0), maps (>= 3.3.0), rgdal (>= 1.4.4),\nrlang (>= 0.4.0)","Suggests":"covr, here, knitr, readxl, rmarkdown, sp (>= 1.4.2),\ntestthat, usethis","Authors@R":"person(\"Victor\", \"Ordu\",\nemail = \"victorordu@outlook.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-3716-0668\"))","Description":"A set of convenience functions as well as geographical/political\ndata about Nigeria, aimed at simplifying work with data and information that\nare specific to the country.","License":"GPL-3","LazyData":"TRUE","Encoding":"UTF-8","RoxygenNote":"7.1.1","VignetteBuilder":"knitr","RdMacros":"lifecycle","URL":"https://brovic.github.io/naijR/","BugReports":"https://github.com/BroVic/naijR/issues","NeedsCompilation":"no","Packaged":"2021-10-19 17:09:19 UTC; Admn","Author":"Victor Ordu [aut, cre] ()","Maintainer":"Victor Ordu ","Repository":"CRAN","Date/Publication":"2021-10-20 15:00:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/naijR_0.3.2.tar.gz","jsonAuthors":[{"name":"Victor Ordu","email":"victorordu@outlook.com","maintainer":true}],"readme":"\n\n\n# naijR

\n\nAn R package on Nigeria and for Nigeria\n\n\n\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/naijR)](https://cran.r-project.org/package=naijR)\n[![Codecov test\ncoverage](https://codecov.io/gh/BroVic/naijR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/BroVic/naijR?branch=master)\n[![R build\nstatus](https://github.com/BroVic/naijR/workflows/R-CMD-check/badge.svg)](https://github.com/BroVic/naijR/actions)\n[![Lifecycle:\nstable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n\n\nThe goal of **naijR** is to make it easier for R users to work with data\nrelated to Nigeria.\n\n## Usage\n\n### Some simple operations\n\n#### Maps\n\nA major feature of this version of the packages is the introduction of\nvarious map drawing capabilities. To find out more about this, read the\nvignette. It can be accessed from within R with this line of code:\n\n``` r\nvignette('nigeria-maps', 'naijR')\n```\n\n#### States\n\nTo create a list of all the States of the Nigerian Federation, simply\ncall `states()`.\n\n``` r\nlibrary(naijR, quietly = TRUE)\nss <- states()\nhead(ss)\ncat(sprintf(\"\\n...but Nigeria has %i States.\", length(ss)))\n## Abia\n\n## Adamawa\n\n## Akwa Ibom\n\n## Anambra\n\n## Bauchi\n\n## Bayelsa\n## \n## ...but Nigeria has 37 States.\n```\n\nStates from a given geo-political zone can also be selected:\n\n``` r\nstates(gpz = \"ne\") # i.e. North-East\n## Adamawa\n\n## Bauchi\n\n## Borno\n\n## Gombe\n\n## Taraba\n\n## Yobe\n```\n\nFor other capabilities of this function, see `?states()`.\n\n#### Local Government Areas\n\nThis is a basic example that shows how to very quickly fetch the names\nof Local Government Areas within a given State:\n\n``` r\nlgas(\"Imo\")\n## Aboh Mbaise\n\n## Ahiazu Mbaise\n\n## Ehime Mbano\n\n## Ezinihitte\n\n## Ideato North\n\n## Ideato South\n\n## Ihitte/Uboma\n\n## Ikeduru\n\n## Isiala Mbano\n\n## Isu\n\n## Mbaitoli\n\n## Ngor Okpala\n\n## Njaba\n\n## Nkwerre\n\n## Nwangele\n\n## Obowo\n\n## Oguta\n\n## Ohaji/Egbema\n\n## Okigwe\n\n## Orlu\n\n## Orsu\n\n## Oru East\n\n## Oru West\n\n## Owerri Municipal\n\n## Owerri North\n\n## Owerri West\n\n## Unuimo\n```\n\nTo list all the LGAs in Nigeria, call the same function without any\nparameters:\n\n``` r\nn <- length(lgas())\nsprintf(\"Nigeria has a total of %i Local Government Areas\", n)\n## [1] \"Nigeria has a total of 774 Local Government Areas\"\n```\n\nWant to create a function to check how many LGAs a particular State has?\n\n``` r\nhow_many_lgas <- function(state) {\n n <- length(lgas(state))\n cat(state, \"State has\", n, \"LGAs\\n\")\n}\n\nhow_many_lgas(\"Sokoto\")\n## Sokoto State has 23 LGAs\nhow_many_lgas(\"Ekiti\")\n## Ekiti State has 16 LGAs\n```\n\n#### Working with phone numbers\n\nIt is common to come across datasets where phone numbers are wrongly\nentered or misinterpreted by software like MS Excel. The function\n`fix_mobile()` helps with this.\n\n``` r\nfix_mobile(\"8032000000\")\n## [1] \"08032000000\"\n```\n\nThe function works on vectors; thus an entire column of a table with\nphone numbers can be quickly processed. Illegible or irreparable numbers\nare turned into missing values, e.g.\n\n``` r\n(dat <- data.frame(\n serialno = 1:8,\n phone = c(\n \"123456789\",\n \"0123456789\",\n \"8000000001\",\n \"9012345678\",\n \"07098765432\",\n \"08123456789\",\n \"09064321987\",\n \"O8055577889\"\n )\n))\n## serialno phone\n## 1 1 123456789\n## 2 2 0123456789\n## 3 3 8000000001\n## 4 4 9012345678\n## 5 5 07098765432\n## 6 6 08123456789\n## 7 7 09064321987\n## 8 8 O8055577889\n```\n\n``` r\nfix_mobile(dat$phone)\n## [1] NA NA \"08000000001\" \"09012345678\" \"07098765432\"\n## [6] \"08123456789\" \"09064321987\" NA\n```\n\n## Installation\n\nTo download and install the current stable version of this package from\nCRAN:\n\n``` r\ninstall.packages(\"naijR\")\n```\n\nThe development version can be obtained from\n[GitHub](https://github.com/BroVic/naijR) with:\n\n``` r\n# If necessary, 'install.packages(\"remotes\")' first\nremotes::install_github(\"BroVic/naijR\")\n```\n\n## Feedback/Contribution\n\nContributions are welcome and pull requests for R code or documentation\nwill be gladly entertained. For bug reports or feature requests, kindly\nsubmit an [issue](https://github.com/BroVic/naijR/issues/new).","jobInfo":{"package":"naijR","version":"0.3.2","parsingStatus":"success","parserVersion":1,"parsedAt":"2021-11-05T18:29:59+0000"}}

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