nigeria-maps

The need for relatively easy-to-use tools in the R statistical environment for mapping data on Nigeria was a motivation for the development of this package. Not so much of low-level programming needs to be done by the user; rather a collection of pre-existing packages and idioms are brought together to enable a focus on actual data visualization.

The main function for drawing Nigeria maps is map_ng. It uses data from the CIA World Database provided by the {mapdata} package as well as ESRI shapefiles from the World Bank to draw simple maps of Nigeria.

library(naijR)

map_ng()

To show the map without the state borders, set the first argument (region) to NULL. Thus, with all other arguments set to default values, that would be

map_ng("Nigeria")

All the Local Government Areas can equally be displayed, using a map that, by default, does not display text, so as to avoid cluttering. This kind of map is useful for charting choropleth maps, as shown below for the States. To this, we pass the value produced by lgas().

map_ng(lgas())

It is also possible to make maps of sub-national divisions by defining them via region. For example, states("sw") returns a character vector of the states in the South-West geopolitical zone (see ?states). To display them on a map with a few embellishments like labelling and colour, write

map_ng(states(gpz = "sw"), show.text = TRUE, col = 4)

A single state can also be drawn:

kk <- "Kebbi"
map_ng(kk, col = 6, title = paste(kk, "State"))

Choropleth maps

Choropleth maps that display differences among regions can also be created with map_ng. There are a number of ways this can be done, depending on the kind of arguments supplied to the function:

A data frame containing a column for the regions and at least one other for the data to be displayed, or
An atomic vector for the regions and another for the data to be displayed.

This map is built on categorical data (i.e. factors); atomic vectors that are coercible to this class are also accepted. In the case of numeric data, notably those of type double, they can be coaxed into categories only if class boundaries are provided via the breaks argument.

To demonstrate this, we will create a data frame with some meaningless data:

# Create variables
ss <- states()
numStates <- length(ss)
vv <- sample(LETTERS[1:5], numStates, TRUE)

# Create a data frame and view top rows
dd <- data.frame(state = ss, letter = vv)
head(dd)
#>       state letter
#> 1      Abia      A
#> 2   Adamawa      C
#> 3 Akwa Ibom      C
#> 4   Anambra      B
#> 5    Bauchi      D
#> 6   Bayelsa      C

Using a data frame

The easiest way to draw the choropleth map is to pass a two-column data frame to the data argument. Thus if the data frame has more columns, it is trivial to subset it and extract the 2 columns of interest. Continuing with our example,

map_ng(data = dd)

Note the following:

The argument var is the name of the column of our data frame that has the variable to be mapped. It is found via the mechanism called quasiquotation; to explore this further see ?rlang::quasiquotation.
The colours darken sequentially from A through E and it follows the natural (alphabetical) ordering of the factor levels (i.e. category labels). To control this order upfront, create an ordered factor using any the various ways available in R. (See ?ordered)
Our variable var is actually a character vector, but internally is converted to a factor.

Using atomic vectors

We can use the same data to make the same map, but with a different coding approach:

map_ng(region = states(), x = vv, col = "red", show.text = FALSE)

It’s exactly same map as before, but this time, we used 2 vectors to provide the data – one for the regions (in this case the 36 States and FCT) and another for the categorization. We have also introduced some colouring with the col argument. Although it has the same data, if we try to use the column name from dd, we have an error.

map_ng(region = states(), x = var)
#> Error in `.prep_choropleth_opts()`:
#> ! One or more inputs for generating choropleth options are invalid

Numerical values

When the data to be categorized are numeric vectors, the categories have to be defined. To achieve this, the breaks argument has to be supplied. This is a numeric vector that describes class limits for grouping the values. The number of classes is length(breaks) - 1.

nn <- runif(numStates, max = 100)  # random real numbers ranging from 0 - 100
bb <- c(0, 40, 60, 100)

map_ng(
  region = states(),
  x = nn,
  breaks = bb,
  col = 'YlOrRd',
  show.text = FALSE
)

To get a more meaningful and interpretable legend, use the categories argument.

map_ng(
  region = states(),
  x = nn,
  breaks = bb,
  categories = c("Low", "Medium", "High"),
  col = 3L, 
  show.text = FALSE
)

Note that various colour arguments have been used, some of them kind of cryptic. More details on how to use the colouring schemes, check the help page of ?map_ng.

Mapping of point data

To show the distribution of point data on any the maps such as GIS location data, a different set of arguments is employed. Since point data requires a pair of coordinates to be accurate, both the x and y arguments MUST be provided. Once the function receives both coordinates, either as separate atomic vectors or as columns from a data frame, map_ng defaults to the mapping of points only in within the boundaries indicated by region. It looks like this:

x <- c(3.000, 4.000, 6.000, 5.993, 5.444, 6.345, 5.744)
y <- c(8.000, 9.000, 9.300, 10.432, 8.472, 6.889, 9.654)

map_ng("Nigeria", x = x, y = y)

The function map_ng carries out a bounds check on point data and returns an error, and the points are not plotted, when the coordinates are beyond the region. For instance, using the same points provided earlier, if we try to plot them on a map of Kwara State,

map_ng("Kwara", x = x, y = y)

#> Error in `map_ng()`:
#> ! Coordinates are beyond the bounds of the plotted area

Conclusion

This vignette provided an initial look at the mapping facilities currently available in the naijR package. Additional development is going on to provide the user with more granular control, as well make the maps to be extendible by more established geospatial R packages.