naijR

An R package on Nigeria and for Nigeria

The goal of naijR is to make it easier for R users to work with data related to Nigeria.

Usage

Some simple operations

Maps

A major feature of this version of the packages is the introduction of various map drawing capabilities. To find out more about this, read the vignette. It can be accessed from within R with this line of code:

vignette('nigeria-maps', 'naijR')

States

To create a list of all the States of the Nigerian Federation, simply call states().

library(naijR, quietly = TRUE)
ss <- states()
head(ss)
cat(sprintf("\n...but Nigeria has %i States.", length(ss)))
## Abia

## Adamawa

## Akwa Ibom

## Anambra

## Bauchi

## Bayelsa
## 
## ...but Nigeria has 37 States.

States from a given geo-political zone can also be selected:

states(gpz = "ne")  # i.e. North-East
## Adamawa

## Bauchi

## Borno

## Gombe

## Taraba

## Yobe

For other capabilities of this function, see ?states().

Local Government Areas

This is a basic example that shows how to very quickly fetch the names of Local Government Areas within a given State:

lgas("Imo")
## Aboh Mbaise

## Ahiazu Mbaise

## Ehime Mbano

## Ezinihitte

## Ideato North

## Ideato South

## Ihitte/Uboma

## Ikeduru

## Isiala Mbano

## Isu

## Mbaitoli

## Ngor Okpala

## Njaba

## Nkwerre

## Nwangele

## Obowo

## Oguta

## Ohaji/Egbema

## Okigwe

## Orlu

## Orsu

## Oru East

## Oru West

## Owerri Municipal

## Owerri North

## Owerri West

## Unuimo

To list all the LGAs in Nigeria, call the same function without any parameters:

n <- length(lgas())
sprintf("Nigeria has a total of %i Local Government Areas", n)
## [1] "Nigeria has a total of 774 Local Government Areas"

Want to create a function to check how many LGAs a particular State has?

how_many_lgas <- function(state) {
  n <- length(lgas(state))
  cat(state, "State has", n, "LGAs\n")
}

how_many_lgas("Sokoto")
## Sokoto State has 23 LGAs
how_many_lgas("Ekiti")
## Ekiti State has 16 LGAs

Working with phone numbers

It is common to come across datasets where phone numbers are wrongly entered or misinterpreted by software like MS Excel. The function fix_mobile() helps with this.

fix_mobile("8032000000")
## [1] "08032000000"

The function works on vectors; thus an entire column of a table with phone numbers can be quickly processed. Illegible or irreparable numbers are turned into missing values, e.g.

(dat <- data.frame(
  serialno = 1:8,
  phone = c(
    "123456789",
    "0123456789",
    "8000000001",
    "9012345678",
    "07098765432",
    "08123456789",
    "09064321987",
    "O8055577889"
  )
))
##   serialno       phone
## 1        1   123456789
## 2        2  0123456789
## 3        3  8000000001
## 4        4  9012345678
## 5        5 07098765432
## 6        6 08123456789
## 7        7 09064321987
## 8        8 O8055577889

fix_mobile(dat$phone)
## [1] NA            NA            "08000000001" "09012345678" "07098765432"
## [6] "08123456789" "09064321987" NA

Installation

To download and install the current stable version of this package from CRAN:

install.packages("naijR")

The development version can be obtained from GitHub with:

# If necessary, 'install.packages("remotes")' first
remotes::install_github("BroVic/naijR")

Feedback/Contribution

Contributions are welcome and pull requests for R code or documentation will be gladly entertained. For bug reports or feature requests, kindly submit an issue.