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

Prerequisites

This is a package for use in the R ecosystem. To install R, visit https://cran.r-project.org.

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")

Some simple operations

Maps

A major feature of this version of the packages is the introduction of various map drawing capabilities. To read more about this, read the vignette with this

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

States

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

library(naijR, quietly = TRUE)
states()
#>  [1] "Abia"                      "Adamawa"                  
#>  [3] "Akwa Ibom"                 "Anambra"                  
#>  [5] "Bauchi"                    "Bayelsa"                  
#>  [7] "Benue"                     "Borno"                    
#>  [9] "Cross River"               "Delta"                    
#> [11] "Ebonyi"                    "Edo"                      
#> [13] "Ekiti"                     "Enugu"                    
#> [15] "Federal Capital Territory" "Gombe"                    
#> [17] "Imo"                       "Jigawa"                   
#> [19] "Kaduna"                    "Kano"                     
#> [21] "Katsina"                   "Kebbi"                    
#> [23] "Kogi"                      "Kwara"                    
#> [25] "Lagos"                     "Nasarawa"                 
#> [27] "Niger"                     "Ogun"                     
#> [29] "Ondo"                      "Osun"                     
#> [31] "Oyo"                       "Plateau"                  
#> [33] "Rivers"                    "Sokoto"                   
#> [35] "Taraba"                    "Yobe"                     
#> [37] "Zamfara"

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

states(gpz = "ne")  # i.e. North-East
#> [1] "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_ng("Imo")
#> Warning: The `test` argument of `is_state()` is deprecated as of naijR 0.1.3.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: The `allow.na` argument of `is_state()` is deprecated as of naijR 0.1.3.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#>  [1] "Aboh Mbaise"      "Ahiazu Mbaise"    "Ehime Mbano"      "Ezinihitte"      
#>  [5] "Ideato North"     "Ideato South"     "Ihitte/Uboma"     "Ikeduru"         
#>  [9] "Isiala Mbano"     "Isu"              "Mbaitoli"         "Ngor Okpala"     
#> [13] "Njaba"            "Nkwerre"          "Nwangele"         "Obowo"           
#> [17] "Oguta"            "Ohaji/Egbema"     "Okigwe"           "Orlu"            
#> [21] "Orsu"             "Oru East"         "Oru West"         "Owerri Municipal"
#> [25] "Owerri North"     "Owerri West"      "Unuimo"

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

n <- length(lgas_ng())
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(naijR::lgas_ng(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

Future Work

Some enhancements to expect in future updates:

• Manipulation of phone numbers will provide options for the introduction of separators. Also the function will become more intelligent, pre-empting errors in data entry e.g. accepting the letter ‘O’ as a presumed zero (0).
• fix_mobile() currently works with character vectors. It will be allowed to work with numeric vectors, converting these to character vectors internally.
• Misspelling of Local Government Areas is very common and it is common to find so many variants, especially where compound names are involved. Functionality to address this problem will be introduced.
• A distance matrix for major locations in the country.

Feedback/Contribution

This is an open source project and contributions are welcome. Pull requests for R code or documentation, and any suggestions for making this effort worthwhile will be gladly entertained.

For bug reports or feature requests, kindly submit an issue.