pa00gz/colmaps2

colmaps

colmaps2 - changes to the original colmaps (https://github.com/nebulae-co/colmaps) for better use with plotly and other packages.

colmaps - colombian maps or color maps (your choice) - is an R package with current boundaries data of Colombia plus a minimal ggplot2::geom_map() wrapper to make choropleth maps with selected default color scales.

Installation

You can install colmaps from GitHub with devtools:

# install.packages("devtools")
devtools::install_github("nebulae-co/colmaps")

Usage

There are two SpatialPolygonsDataFrames provided in the package, one for municipalities and one for departments, municipios and departamentos respectively. You can see details of each at the documentation: ?municipios ?departamentos.

The data frame part looks like:

library("colmaps")
head(municipios@data)

#>      id id_depto            municipio        depto
#> 1 20001       20           Valledupar        Cesar
#> 2 68217       68             Coromoro    Santander
#> 3 68229       68               Curití    Santander
#> 4 68235       68 El Carmen De Chucurí    Santander
#> 5 68245       68         El Guacamayo    Santander
#> 6 25823       25              Topaipí Cundinamarca

The wrapper around ggplot2::geom_map() is colmap(), it will take a map and some data as arguments and ggplot it with a selected color scale and a good theme for maps taken from ggmap::theme_nothing(). Since it is build with ggplot it also returns a ggplot object so one can further alter the plot in ggplot idiom, note however that you need to explicitly load ggplot2 to add further layers and modifications.

By default the id of each area will be used as a variable:

library("ggplot2")

colmap(municipios) +
  ggtitle("Colombia - Fronteras Municipales")

But if you have data of each geographical unit you can pass it, specifying how to relate the map and the data frame. For example, from the homicidios package we have historical population, homicides and homicide rates data for municipalities:

# devtools::install_github("nebulae-co/homicidios")
library("homicidios")

head(homicidios)

#>      id id_depto  municipio     depto  año poblacion homicidios     tasa
#> 1 05001       05   Medellín Antioquia 1990   1688394       3924 232.4102
#> 2 05002       05  Abejorral Antioquia 1990     24789         33 133.1236
#> 3 05004       05   Abriaquí Antioquia 1990      3201          5 156.2012
#> 4 05021       05 Alejandría Antioquia 1990      4851          7 144.3001
#> 5 05030       05      Amagá Antioquia 1990     23606         31 131.3225
#> 6 05031       05     Amalfi Antioquia 1990     18535         25 134.8800

And we can easily plot a thematic map of the data:

colmap(municipios, subset(homicidios, año == 2000), var = "tasa")

Tragically (and also ironically?!) on the year 2000, the municipality of Matanza, Santander had a homicide rate of over 1200 murders per 100.000 inhabitants, which distorts the default color scale dramatically, but we can manually re-scale the variable to be mapped into the color aesthetic, for example we can plot log(tasa + 1) instead of tasa and also we can override the default color scale in ggplot idiom. We will use dplyr and the pipe operator (%>%) from here on to handle the data with ease:

library("ggplot2")
library("dplyr")

homicidios %>%
  filter(año == 2000) %>%
  mutate(log_tasa = log1p(tasa)) %>%
  colmap(municipios, data = ., var = "log_tasa") +
  scale_fill_continuous(low = "#bcbddc", high = "#3f007d", na.value = "wheat")

Lets make the same map but by department to see other arguments:

homicidios %>%
  filter(año == 2000) %>%
  group_by(id_depto) %>%
  summarise(tasa = 10^5 * sum(homicidios, na.rm = TRUE) /
                          sum(poblacion, na.rm = TRUE)) %>%
  colmap(departamentos, data = ., data_id = "id_depto")

Here we note two things, var by default is the first non data_id column present on data so we may ommit the argument. Second we use data_id to specify the key by which the map is to be joined.

Color scales

The color scales depend on the class of the variable the map is being colored by:

• For numeric data it is a continous scale with lower limit set on "#fee8c8" and high limit on "#b30000":

  ggplot2::scale_fill_continuous(low = "#fee8c8", high = "#b30000")

  These were taken from the limits of the three sequential category multi-hue red pallete sugested in http://colorbrewer2.org/.

• For factors with less than 10 levels the color scale used is:

  ggplot2::scale_fill_brewer(type = "qual", palette = "Set1")`

• For factors with n => 10 levels the color scale used is:

  ggplot2::scale_fill_manual(values = rainbow(n = n, v = 0.3 * sin(seq_len(n)) + 0.7))

  Which was designed by Julian (in his own way).

• For ordered factors a manual scale that defines a discrete version of the numeric scale is used, defined as:

  ggplot2::scale_fill_manual(values = colorRampPalette(c("#fee8c8", "#b30000"))(n))

  Where n is the number of levels of the factor.

• Finally integer data uses the ordered factor scale for less than 20 different values and the numeric scale otherwise.

• In all cases the default color for missing (NA) data is "grey50" which is the default ggplot2 has for scale_fill_continous(). Note that it can be changed with the scale as shown in the third map.

We have already seen numeric data and factors (with more than ten levels in the first plot). To ilustrate ordered factors we will make again the homicides map by municipality but first transforming the homicide rate to an ordered factor defining groups by deciles:

homicidios %>%
  filter(año == 2000) %>%
  mutate(tasa = round(tasa, 2),
         Tasa = cut(x = tasa,
                     breaks = c("0%" = -Inf,
                                quantile(tasa, seq(0.1, 1, 0.1), na.rm = TRUE)),
                     ordered_result = TRUE,
                     dig.lab = 6)) %>%
  colmap(municipios, data = ., var = "Tasa")

Using ggplot2

As shown above, with ggplot, you can add components (e.g. ggtitle) to the plot or alter them (e.g. scale_fill_*). A nice example of this is facetting. Here we use the year (año) variable passed in data to make a grid of plots, trellis style: one per level of the variable - in this case one per year, from 2008 to 2013.

gg <- homicidios %>%
  filter(año >= 2008) %>%
  colmap(municipios, data = ., var = "tasa")

gg + facet_wrap(~ año, nrow = 2)

(It is encouraging to see how this particular map has been getting somewhat paler with time.)

About

This is the result of a sub-project we started with various goals:

• learn how to make R packages (in particular data packages) and improving our git skills.
• distribute some data we think should be easier to access in a simple way - of course this is mainly targeted for R users, including ourselves.
• enable us to do some map visualizations in a simpler way. We used this data to prepare the vignette: Un micro-mapa de Colombia which shows an example of a linked micromap with colombian data. That vizualisaton was presented in a random academic event.