It is my pleasure to announce the release of version 0.7.0 of the RQDAassist package. A lot of work has gone into making it even easier for would-be users of RQDA to download and install that package along with its dependencies. In this update, I have focused on making R and Rtools more accessible to users, and as such, if one wants to properly install any of the two, the heavy lifting will be done for you.
Installation of R and Rtools
One of the current problems with installing RQDA is the introduction of UCRT in R 4.2. RQDA dependencies like RGtk2 and cairoDevice cannot be installed as the available binaries were not built with UCRT. They also cannot be built locally with the current version of their source files. So, for now, the use of RQDA will be restricted to R versions lower than 4.2.
RQDAassist was made to work on versions between 4.0 and 4.1, the latest of which is R 4.1.3. Prior to this update, if you were using R 4.2 you would have to separately download the compatible installer from CRAN (using this link), which would then have to be run to install R. Similarly if the user didn’t have Rtools40, that too had to be downloaded by the user from here and installed.
These operations can now be carried out almost automatically using the install function. It will detect if a compatible version of R or Rtools is not on the system, and if necessary, download these for the user to install. The user only needs to accept the Windows Smart Screen prompt to allow the software to be installed. If a previously downloaded copy of any of these is in the default download location, the download step is skipped and that local copy is installed.
Related developments
I’ve been following developments on the RGtk2 repository to see whether there will be a return of RGtk2 to CRAN; this would effectively solve the problem of version compatibility, and even render this package obsolete. There’s been some recent activity there, so I am hopeful that the project will progress beyond this hurdle.
Conclusion
RQDAassist now allows you to set up the R environment as well as Rtools which makes it easier to install RQDA. Visit the GitHub repository and post any issues that may be encountered and the package will be made to serve the community even better.
When it comes to software applications, it is always handy to have more than one way to go about installing them. For R on Windows, there are several ways to accomplish this. In this post, I will be sharing a few that I have encountered and actually used.
1. From CRAN Homepage
The commonest approach to installing R is to visit the Comprehensive R Archive Network (CRAN) homepage at https://cran.r-project.org. On that page, one will see links referring to installation pages for different operating systems. Our interest in this is post is on Windows.
When we click on the link for Windows, we are taken to the R for Windows homepage:
Click on “install R for the first time” and you will be taken to the installation page for the current version of R (version 4.2.2 at the time of writing).
Click on “Download R-x.x.x for Windows…” and the installer will be downloaded to your machine. Save it at a destination of your choice (usually the ‘Downloads’ folder) and then run the file to start the installation proper. This will launch the installation wizard and you can safely use the default options to complete the process as indicated in the next few images:
When setup is complete, R can be launched from the Start Menu
2. From R with the installr package
If R is already installed, then it is easier to install updates with the installr package (this works best with R GUI). This is very useful since R Core is guaranteed to release key updates at least 2-3 times every year. This is the code to use
# install.packages("installr") # => run only if package is not already installed
installr::updateR()
If there’s a more recent version of R available, the following dialog will appear:
This is followed by an offer to see what’s new in R; this can be safely ignored:
Then you’ll be asked if you actually want to install the new R version. Selecting ‘No’ will terminate setup.
Once accepted, a progress bar appears for downloading the installer from CRAN:
This is followed by the wizard prompts as seen in the first section. However, this can be turned off in the function by setting the appropriate arguments. I won’t go into that in this post, but interested readers should look at the help file with ?installr::updateR. (I do have a utility package I created for personal use that has a function, easy_updateR, to do this without the bells and whistles)
After the installation, there will be additional housekeeping prompts. If there’s been a major/minor version change (following the scheme MAJOR.MINOR.PATCH), there may be a need to accept the invitation to update your other packages, but this can always be done later.
3. From the Command Line
By “command line” we are referring to Windows Terminal, PowerShell, or Command Prompt.
Windows now has a package management tool, winget, that eases the installation of software via the command line. Recent builds of Windows 10 and now Windows 11, come with it pre-installed, but if you’re using an older version, it has to be installed from the Microsoft Store. To check whether you have it, simply run winget at the prompt, and you will see a help message:
To install R, open the command line as an administrator (I’m using PowerShell) and run the tool as follows:
winget install Rproject.R
Follow any prompts, accept the defaults, and R will be installed–this time you won’t have to resort to the wizard. The great thing about this method is that it can be fully automated and it’s the most efficient!
Conclusion
There you have it, 3 easy ways to install R on Windows! Do you know of any other ways to do this? If your answer is ‘yes!’, feel free to tell us in the comments.
A new version of the naijR package was released last weekend on CRAN, comprising internal changes that modify certain behaviour. I will try to quickly outline them in this post, but because the update followed soon after the last minor revision, it might be useful to look at the last blog post on v0.5.0.
User-visible Changes
Interactivity on Windows
As stated in previous articles, the function `fix_region()` provides 2 ways (automatic and interactive) to repair misspelt Local Government Areas (LGAs), a problem that is quite common when working with raw data containing this subnational division. Previously, any interactive work was done solely in the R console. Beginning with this version, Windows users have the benefit of using the native windowing system by way of dialogs, text controls, and message boxes, which should be more intuitive. On other platforms, the interaction still defaults to the Console. For details on how this works, read the vignette on the package website or locally via the package documentation.
Handling of the Federal Capital Territory
There are peculiarities with data that involve the Federal Capital Territory (FCT). First, it is not a State but is usually lumped along with States in most data sets. Also, it is either used as a whole or as an acronym. Recently, a researcher requested my support in plotting data on a map—one of the issues I realized he was facing was that the word “Abuja” (the capital city) was used to represent FCT, and as a State. Going forward, whenever the term Abuja is used as a State, a warning will be issued to the user.
Suppression of A Superfluous Warning
When the function `lgas` is given a string that is not a valid (i.e. correctly spelled) LGA, a warning is signaled. In a nested call to `fix_region()`, this warning becomes unnecessary and, quite frankly, irritating. So, the function was updated to recognize and suppress the warning whenever these 2 functions are used together. To be clear, in the old version this code signals a warning
lg <- c("Bwari", "Buari", "Kwali", "Kuali")
fix_region(lgas(lg))
## Successful fix(es):
## -------------------
## * Buari => Bwari
## * Kuali => Kwali
## Warning message:
## One or more items is not an LGA. Spelling error?
The warning doesn’t make sense here, since the spelling mistake has been fixed; but it came from the call to lgas(), not fix_region(). In the update, the fix is reported on but without the warning.
Modifying Map Labels
An oversight was spotted, in that the size of region labels in the map could not be changed—a bug that was introduced during an earlier stage of development. This has now been fixed. The default character expansion factor (`cex`) is 0.75, adopted from the `maps` upon which this version of naijR depends. To adjust this, we can now simply use the undocumented `cex` argument which is used by `map_ng()` via its `…` parameter.
map_ng(show.text = TRUE)
We now reduce the text size:
map_ng(show.text = T, cex = .5)
Conclusion
To get more information on how to use the package, visit the GitHub page or the website. Feedback and/or contributions of code are also welcome.
A new version of this R package has been released on CRAN, the Comprehensive R Archive Network. It introduces a number of updates that will be described in somewhat detail in this blog post. For a synopsis, read the release notes.
New methods
Methods for some common generic R functions were included in the update. Some of these include c(), [, [[, na.exclude, to mention a few. This confers consistent behaviour to some of the **naijR** objects. For example, when concatenating 2 objects that represent the Nigerian States, we have the following:
Similarly, when indexing, the output formatting is retained:
s3[1]
## States
## ------
## * Imo
Other methods added are as follows:
head.regions
tail.regions
print.regions
na.exclude.regions
na.omit.regions
Mobile numbers
The function fix_mobile can now repair phone numbers where zeros (0) are mistakenly replaced with the letter O (whether upper or lower case). As an example:
nums <- c("8034510441", "070304", "09O14358956")
# nums[3] has a letter 'O' in it and is fixed by the function
fix_mobile(nums)
[1] "08034510441" NA "09014358956"
Map legends
Changes were made to the arguments that control how legends are displayed in the maps, particularly choropleths. The way the argument legend.text works is similar to that of the barplot function (from the **graphics** package). Only when this argument is TRUE will the legend be plotted. Also, other arguments that were used for fine-tuning were deprecated – leg.x, leg.y, and leg.orient. Going forward, any low-level legend manipulations should be done with graphics::legend().
As an example, let us create a 2-column data frame with hypothetical data on the
# Create a factor
set.seed(12)
x <- states() |>
length() |>
rnorm(100, 10) |>
round() |>
sample() |>
cut(
breaks = c(80, 89, 99, 109, 119, 129),
labels = c("80-89", "90-99", "100-109", "110-119", "120+"),
include.lowest = TRUE
)
# Make a 2-column data frame and take a look at it
df <- data.frame(state = states(), x)
head(df)
Now, we will use this data frame to create a choropleth map. One of the improvements in this version is that the function map_ng(), when given a two-column data frame will inspect each column, and if one column has valid States or LGAs and the other is a factor, it will automatically create the choropleth visualization. So using our data frame df,
map_ng(data = df)
With the new control given in the current version, we can do away with the legend.
Due to the shape of the map, the best place to place any legend is in the bottom right corner. However, if the user wants to place it elsewhere, we can now use the Base R plotting constructs to do fine-grained touch-ups. This, of course, is for users who are more comfortable coding in R. As an example,
# Create objects containing the factor levels and then the colours
# map_ng() uses RColorBrewer internally, so we are using this
# to manually define the colours for the legend itself
categories <- levels(df$x)
colors <- RColorBrewer::brewer.pal(length(categories), "Greys")
# The legend's data, colours, orientation, justification, size and spacing are defined
res <- map_ng(data = df, legend = FALSE)
legend("top", categories, fill = colors, horiz = TRUE, xjust = .8, cex = .55, x.intersp = .5)
So much more can be done with this; details can be found in the documentation via ?legend.
Installation
The new version of the package can be installed from CRAN with the following code:
This is to announce the release of a new version of naijR, a package designed to make it easier to work with data related to Nigeria. In this update, a bug related to the fix_mobile function was fixed. In older versions, whenever a vector made up of only missing values (NA) was passed to it, it failed with the following error:
# The first number is fixed and the missing value untouched
> raw.numbers <- c("8031111111", NA, "07061234567")
> fix_mobile(raw.numbers)
[1] "08031111111" NA "07061234567"
# But if all are missing values, it fails
> all.missing <- rep(NA, 6)
> fix_mobile(all.missing)
Error in fix_mobile(all.missing) :
Objects of type ‘logical’ are not supported
So if, for example, one was trying to clean up column(s) of a data frame that have phone numbers, whenever it encountered one that was empty, it failed. This has now been fixed in the current version: the function now returns such data unchanged.
The Opportunist 