Data

For this demonstration, we will use three different databases with a randomly-generated numeric variable associated to each country.

• testdata1 has 90 rows with only a numeric variable (with some missing values)
• testdata1b has 46 rows, with one numeric and one categorical variable (with some missing values)
• testdata1c has 237 rows, with one numeric and one categorical variable (without any missing values)

head(WorldMapR::testdata1)
#>             name countrycode IntVal
#> 1          Aruba          AW  22.82
#> 2    Afghanistan          AF   5.56
#> 3 American Samoa          AS  81.69
#> 4     Antarctica          AQ     NA
#> 5     Azerbaijan          AZ  93.63
#> 6          Benin          BJ  11.45
dim(testdata1)
#> [1] 90  3

head(testdata1b)
#>              Cnames Cshort  VNum     VCat
#> 1         Argentina     AR    NA     <NA>
#> 2 Antigua and Barb.     AG 22.46 2_Medium
#> 3         Australia     AU 13.05    1_Low
#> 4           Austria     AT 26.72 2_Medium
#> 5  Bosnia and Herz.     BA  0.44    1_Low
#> 6     St-Barthélemy     BL 18.91 2_Medium
dim(testdata1b)
#> [1] 46  4

head(testdata1c)
#>        name iso_a2 value   ValCat
#> 1  Zimbabwe     ZW 66.93   3_High
#> 2    Zambia     ZM 65.06   3_High
#> 3     Yemen     YE 69.57   3_High
#> 4   Vietnam     VN 29.26 2_Medium
#> 5 Venezuela     VE 64.59   3_High
#> 6   Vatican     VA 93.03   3_High
dim(testdata1c)
#> [1] 237   4

All these datasets have two variables defining the country for demonstrative purposes (more later);

Displaying a world map for continuous data

As a first step, we may want to plot a map of the world, displaying our data.

We can do this by using the function worldplot(). At its bare minimum, this function takes the name of the dataframe, the name of the column with the values to be plotted, the name of the column with the country names, and the range of the values that we want to be shown.

worldplot(data = testdata1,
          ColName = "IntVal",
          CountryName = "countrycode",
          rangeVal = c(0,100))

By default, the function expects the country name column to be of type ISO 3166-1 alpha-2 (referred as iso-a2 throughout the package). More information about it can be found at (https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2).
It is possible to specify it differently: for example, the following code provides the same result.
Note that it is advisable to use iso-a2 codes, as country names might be ambiguous in some cases.

worldplot(data = testdata1,
          ColName = "IntVal",
          CountryName = "name",
          CountryNameType = "name",
          rangeVal = c(0,100))

We can focus on a subregion of our interest, by specifying a range for latitude and longitude arguments

worldplot(data = testdata1,
          ColName = "IntVal",
          CountryName = "countrycode",
          rangeVal = c(0,100),
          latitude = c(-40,40), longitude = c(-17,50))

Moreover, it is possible to add labels to identify each country present in the database (countries without correspondences in the provided data set or with missing value are not considered). To do this, it is sufficient to add the option annote = TRUE:

worldplot(data = testdata1,
          ColName = "IntVal",
          CountryName = "countrycode",
          rangeVal = c(0,100),
          latitude = c(-40,40), longitude = c(-17,50),
          annote = TRUE)

palette_option allows to change the colour palette, by specifying a letter between “A” and “H”. These are different color maps from the scale_fill_viridis() palette (in a future release, the user will be allowed to define its own palette):

worldplot(data = testdata1,
          ColName = "IntVal",
          CountryName = "countrycode",
          rangeVal = c(0,100),
          latitude = c(-40,40), longitude = c(-17,50),
          annote = TRUE,
          palette_option = "A")

World map for categorical data

The function worldmapCat() deals with categorical data.

The syntax is similar to the previous, with some minor changes.

worldplotCat(data = testdata1b,
             ColName = "VCat",
             CountryName = "Cshort",
             CountryNameType = "isoa2",
             annote = FALSE)

The user is allowed to define the color palette manually: it is simply required to define a colour for each category (plus eventually one for missing data), and provide it in palette_option.

colours <- c("#C3E2EA", "#58C0D0", "#256C91")

worldplotCat(data = testdata1c,
             ColName = "ValCat",
             CountryName = "iso_a2",
             CountryNameType = "isoa2",
             palette_option = colours ,
             Categories = c("Low", "Average", "High"),
             legendTitle = "CAT",
             latitude = c(30,72), longitude = c(-15,40),
             annote = TRUE)

Changing the Coordinates Reference System

The program also allows to use different coordinate systems. By default, the EPSG::4326 (WGS84) reference system is used. This is a nice system if you want to plot the whole world; however, if you are interested in selected regions, other reference systems may be preferable.

For example, the EPSG::3035 is a nice projection specifically thought for Europe maps. The option crs allows to define the coordinate reference system of choice. Keep in mind that, if you change the reference system, you will also need to redefine the values for longitude and latitude accordingly - these may not be limited to (-180,180) and (-90, 90) anymore, and might be counterintuitive. If this is the case, I suggest to start without defining them and then searching for the best values with some trial and error.

Notably, the function automatically updates the coordinates of the labels when a different crs is defined, meaning that these values might help to find the coordinates you want to show (AVAILABLE IN A FUTURE UPDATE, this information can be extracted only by unfolding the function right now).

worldplotCat(data = testdata1c,
             ColName = "ValCat",
             CountryName = "iso_a2",
             CountryNameType = "isoa2",
             palette_option = c("#C3E2EA", "#58C0D0", "#256C91"),
             Categories = c("Low", "Average", "High"),
             legendTitle = "CAT",
             latitude = c(5450000, 1000000), longitude = c(2500000, 6900000),
             crs = 3035, annote = TRUE,
             na.as.category = F)

Saving the plot

The plot can be saved using external functions; for example

figure1 <- worldplot(data = testdata1,
                     ColName = "IntVal",
                     CountryName = "name",
                     CountryNameType = "name",
                     rangeVal = c(0,100))

tiff(filename =  paste(tempdir(), "\\figure.tiff"))

figure1

dev.off()