The other day I got an email from a researcher who is using the naijR package, which, among other things, helps with the drawing of plot maps of Nigeria. He was having trouble drawing maps of the geopolitical zones (GPZ) of the country.
Admittedly, this is an omission from the package, but a deliberate one. I had actually toyed with the idea of baking this feature in, but since the GPZ is not “officially” recognized, I opted for the package to be relatively silent, as only States and Local Government Areas are recognized in the Nigerian Constitution.
Anyway, after a few emails back and forth, I was able to help him resolve the issue. Basically, since the states() function can extract the States by GPZ (via the gpz argument), we can use this little dearie to extract the States of a GPZ and pass it to the region argument of map_ng(). For the Northeast GPZ, this gives us a map that might look like this:
Delineating all the GPZ on a country map is a little trickier and we have to do some functional programming. One approach is to generate a data frame with the contiguous States matched to a factor that represents each GPZ. Then, we draw a choropleth map by passing that data frame to the data argument of map_ng(), while the factor is passed to the x argument. Information for using these arguments can be found in ?map_ng(). In the output below, I chose reddish colouring for the map.
Problem solved. The code for this solution can be found in this GitHub Gist. To get the raw script, follow this link. I think I’m going to have to review the decision on not implementing a native drawing of GPZ maps in naijR.
This post is to announce the arrival of naijR 0.2.2 on CRAN.
New S3 classes
This version of the package introduces the use of an object-oriented style to programming, making available constructors for states and lgas objects. To create instances of both classes, we pass a character vector of States or LGAs as appropriate. These constructors are somewhat permissive and do not perform strict accuracy checks. For that we have the functions is_state and is_lga.
Check for LGAs
There are 774 LGAs in the country and they are pivotal to any analytic tasks done with country data. They are also very often misspelt as any dataset taken from the wild would reveal. I have taken the pains to provide authoritative appellation for this tier of governance using government sources. This can be easily inspected in the inbuilt package dataset lgas_nigeria.
The new function is_lga will scan through a vector to check whether its elements have correctly spelt LGAs. Where poorly spelt ones are found, the function fix_region can be used to correct this. The method for lgas objects will attempt to do this automatically using partial matching. For example
A major addition in the current version is the function fix_region, which helps a user to repair any misspelt adminstrative regions within a dataset. The function has methods for different kinds of regions i.e. States and Local Government Areas, which are optionally represented as the S3 objects states and lgas, respectively. However, the function also has a method for base character vectors, mainly for States, since they are not that many. To repair our vector my_lga, we will create an lgas object first and then pass it as an argument to fix_region.
> fixed <- fix_region(lgas(mylga))
Approximate match(es) not found for the following:
* Bukuru
Warning message:
In lgas(mylga) : One or more elements is not an LGA
> fixed
[1] "Amuwo-Odofin" "Bukuru" "Askira/Uba"
The LGA Askira-Uba has been corrected to its correct spelling, Askira/Uba. However, a match could not be found for the element Bukuru. (Bukuru is actually the name of the headquarters of Jos South LGA of Plateau State). To continue attempting to repair our vector, we run fix_region in interactive mode
> fixed <- fix_region(lgas(mylga), interactive = TRUE)
Approximate match(es) not found for the following:
* Bukuru
Do you want to repair interactively? (Y/N):
The user is prompted to continue interactively. To continue enter something like y.
We are searching for options using the search term buk and only one option was returned i.e. Bukkuyum. Unfortunately, that’s not the one we are looking for so we will enter 2 and run the search again, by passing only bu
Selection: 2
Search pattern: bu
Select the LGA
1: Buruku 2: Akpabuyo
3: Obubra 4: Obudu
5: Burutu 6: Abuja Municipal Area Council
7: Babura 8: Buji
9: Bunkure 10: Sabuwa
11: Bunza 12: Kabba/Bunu
13: Ijebu East 14: Ijebu North
15: Ijebu North East 16: Ijebu Ode
17: Abua/Odual 18: Tambuwal
19: Bursari 20: Bukkuyum
21: Bungudu 22: Retry
23: Skip 24: Quit
The LGA I wanted to select was Buruku, so I pick option 1
Selection: 1
Warning message:
In lgas(mylga) : One or more elements is not an LGA
> fixed
[1] "Amuwo-Odofin" "Buruku" "Askira/Uba"
> is_lga(fixed)
[1] TRUE TRUE TRUE
We’ve fixed the LGAs! At this point, any LGAs that could not be fixed can be treated be directly manipulation of the object,
Maps
This version of the package provides increased granularity for the Nigeria country map, currently going down to LGA levels.
map_ng(lgas())
To know more about drawing Nigeria maps with the package, see the documentation (?map_ng) or read the vignette.
Conclusion
This version of naijR brings some new functionality to aid with data cleaning and validation of LGA names, as well as LGA level mapping. I would like you to try it out and give me some feedback.
This is to announce a new version of the R package RQDAassist, a package whose goal is to make working with RQDA much easier.
This version principally adds new functionality in the retrieval of codings from a project database. The function takes as arguments the file path to an RQDA project and a string containing a valid SQL query (SQLite flavour). As a default, one does not need to specify the query. The function does this internally to fetch data from relevant tables in the .rqda file. Thus, for a project MyProject.rqda, one can simply call
retrieve_codingtable("path/to/MyProject.rqda")
The default query that is run internally by this function is as follows:
SELECT treecode.cid AS cid, codecat.name AS codecat
FROM treecode, codecat
WHERE treecode.catid=codecat.catid AND codecat.status=1;
The user is at liberty to form their own queries; a reference for the database tables is in the RQDA package and the documentation for this function (accessed with ?retrieve_codingtable) provides a quick link to that help page. For example, if we want to just collect the filenames of the transcripts used in an analysis, we can use a different query. Note that the data are returned invisibly, to prevent cluttering of the console, so it’s better to bind it to a variable.
qry <- "SELECT DISTINCT name FROM source WHERE status=1;"
tbl <- retrieve_codingtable("path/to/MyProject.rqda", qry)
tbl
If we check the working directory with list.files, we should see the project there. Next, using our package’s function, we can get a data frame with information on codings.
We see that we now created a data frame with 9 columns, with interesting data in them. Note particularly the variables codename, filename, and codecat. Let us now carry out the other query we gave as an example – to get the filenames of all the transcripts in the project:
> qry <- "SELECT DISTINCT name FROM source WHERE status=1;"
> tbl <- retrieve_codingtable(project, qry)
> tbl
name
1 AEA2012 - Post-Program Interview1
2 AEA2012 - Post-Program Interview2
3 AEA2012 - Post-Program Focus Group
4 AEA2012 - Pre-Program Focus Group
This project contains only 4 active files from which all the codings are derived!
A practical point
This function is useful for developing qualitative codebooks, and particularly when coding is carried out inductively and as has been demonstrated, can be extended to other uses, depending on the kind of data that are retrieved.
Installation
The easiest way to install the package is from an R session with
A few weeks aga, I published a package on GitHub, which I called RQDAassist. The package was inspired by a script I wrote to help RQDA users, myself included, to install the package after it was archived on CRAN when R 4.0 arrived on the scene. So, when RQDAassist was first published, that was its only real functionality.
The package RQDAassist is now (pre-)released on #GitHub. Its first purpose is to help #rstats researchers doing qualitative data analysis with RQDA to install the damn thing! Please try it out.https://t.co/81kZifGqpV
Today, I am releasing a minor update (v. 0.2.0) that has a few functions added. It can now convert transcripts written in Word into plain text files – a desired format for RQDA projects – and it can prepare those test files into objects that can be read, in bulk, into an RQDA database. Another thing I personally needed for my work was the ability to seaarch qualitative codes using R scripts rather than the graphics user interface; so I wrote a search function, which currently works for active RQDA projects.
This package has so far been tested on Windows 10 (x64) but it should work fine on other major platforms (any subequent update will include the relevant tests for Linux and Mac OS).
There are no plans to take this package to CRAN and indeed there should be no need to do so once RQDA installation from that repository is fully restored. But I find the prospect of additional helper functions to be quite useful in my work and hope others do too. I hope to see these functionalities expand over time.
You are welcome to check out this project at the GitHub repository or try it out using the instructions in the README.
As a starter in programming, once one encounters the world of “open source”, it can be daunting, if not impossible to contribute to projects. Of course, you’re just starting out and can barely construct a working program in the language you are currently learning.
So, do I have to wait until I am proficient or an expert in my favourite programming language, before I can contribute to an open source project? How can I be a active in the community, and not an onlooker, from the very start?
Easy. Documentation.
I don’t know about others but from my experience, software documentation is often lacking in quantity and quality. I guess because programmers are focused first and foremost on developing working programs, the documentation, manuals, help files, etc. end up having quite a few mistakes, errors and inconsistencies.
So, if you’re new to programming, you may not be able to immediately submit code to that project, but you can always help to improve the documentation. I assure you, this is one area where you can really really make yourself useful, and distinguish yourself as one who brings some value to the table. ‘Cos the documentation is a very important part of any good project.
So, dig in. Clone that GitHub repository and fix any problems you find in the manual.
