Additional Layers
1 Introduction
Until now, we have learnt general concepts about geospatial
visualization in independent lessons. The modular
approach of {ggplot2} allows to successively add all of
them in different layers.
However, these enriched thematic maps also require to contain text and labels referring to specific places or regions, and important map elements like scale bars and a north arrow, as will be illustrated in this part.
In this lesson, we will learn to use functions from the
{ggspatial} and {ggsflabel} packages to add
all of these additional elements to our maps!
2 Learning objectives
Access hospital data coordinates using the
{afrihealthsites}package.Add text and labels to ggplot maps using the
{ggsflabel}package.Add arrow and scale annotations in ggplot maps using the
{ggspatial}package.
3 Prerequisites
This lesson requires the following packages:
4 Build an informative map
We will create a map to figure out how Tier 3 Hospitals are distributed in the Western Province of Sierra Leone. For this, we are going to retrieve real Hospital information from a public repository.
4.1 Access hospital sites
with {afrihealthsites}
Instead of looking up coordinates manually, the package
{afrihealthsites} provides a function
afrihealthsites(), which allows to retrieve geographic
coordinates of African health facilities from different sources.
You can run the following code to retrieve geographic coordinates of
all the health facilities in Sierra Leone available in the
who database:
sle_healthsites_all <-
afrihealthsites(country = "Sierra Leone",
datasource = 'who',
plot = FALSE) %>%
janitor::clean_names()
sle_healthsites_all## Simple feature collection with 1120 features and 10 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -13.271 ymin: 6.9679 xmax: -10.3065 ymax: 9.9745
## Geodetic CRS: WGS 84
## # A tibble: 1,120 × 11
## country admin1 facility_name facility_type
## * <chr> <chr> <chr> <chr>
## 1 Sierra Leone Eastern Ahmadiyya Mission … Mission Hospit…
## 2 Sierra Leone Eastern Baama Community He… Community Heal…
## 3 Sierra Leone Eastern Baiama Community H… Community Heal…
## 4 Sierra Leone Eastern Baiima Community H… Community Heal…
## 5 Sierra Leone Eastern Baiwala Community … Community Heal…
## 6 Sierra Leone Eastern Bambara Kaima Comm… Community Heal…
## 7 Sierra Leone Eastern Bambara Maternal a… Maternal & Chi…
## 8 Sierra Leone Eastern Bambawulo Communit… Community Heal…
## 9 Sierra Leone Eastern Bandajuma Communit… Community Heal…
## 10 Sierra Leone Eastern Bandajuma Kpolihun… Community Heal…
## ownership ll_source iso3c facility_type_9 tier
## * <chr> <chr> <chr> <chr> <dbl>
## 1 FBO GPS SLE Hospital 3
## 2 MoH GPS SLE Community Health Unit 2
## 3 MoH GPS SLE Community Health Unit 1
## 4 MoH GPS SLE Community Health Unit 1
## 5 MoH GPS SLE Community Health Unit 1
## 6 MoH GPS SLE Community Health Unit 1
## 7 MoH GPS SLE Health Post 1
## 8 MoH GPS SLE Community Health Unit 1
## 9 MoH GPS SLE Community Health Unit 2
## 10 MoH GPS SLE Community Health Unit 1
## # ℹ 1,110 more rows
## # ℹ 2 more variables: tier_name <chr>, …Access to all the health facilities of Zimbabwe from the
healthsites data source using the
afrihealthsites() function.
According to the three-tier health delivery classification, the highest tier (Tier 3) includes county hospitals in rural regions and city hospitals in urban regions, which are responsible for most inpatient services as well as teaching and research missions (Wang, 2021).}
We can now keep the Tier 3 health facilities inside the Western province:
sle_healthsites_set <-
sle_healthsites_all %>%
filter(admin1 == "Western Area") %>% # 👈👈👈👈👈👈👈👈👈👈👈👈👈👈👈
filter(tier == 3)These sites belong to Sierra Leone. Let’s get their province
boundaries with {rgeoboundaries}:
Now we can make a map with sierra_leone and
sle_healthsites_set objects:
ggplot() +
# country map
geom_sf(data = sierra_leone) +
# hospital points
geom_sf(data = sle_healthsites_set)
We can use coord_sf() after all geom_sf()
calls, to zoom in to our area of interest inside Sierra
Leone:
ggplot() +
# country map
geom_sf(data = sierra_leone) +
# hospital points
geom_sf(data = sle_healthsites_set) +
# map extent
coord_sf(xlim = c(-13.5,-12.7), ylim = c(8.0,8.7)) # 👈👈👈👈👈👈👈
sf objects can handle on the fly the coordinate
system (both projection and extent), which
can be very useful if more than one spatial objects are not in the same
projection.
4.2 Add a Scale bar and
North arrow with {ggspatial}
Here we introduce the {ggspatial} package, which
provides easy-to-use functions to create a scale bar and north arrow on
a ggplot map:
annotation_north_arrow()allows to add the north symbol andannotation_scale()a scale bar.
ggplot() +
# country map
geom_sf(data = sierra_leone) +
# hospital points
geom_sf(data = sle_healthsites_set) +
# map annotations
annotation_north_arrow(location="tl") + # 👈👈👈👈👈👈👈👈👈👈👈👈
annotation_scale(location="bl") +
# map extent
coord_sf(xlim = c(-13.5,-12.7), ylim = c(8.0,8.7))
The location of the scale bar and north arrow are by
default in the bottom left ("bl") side of the map. They can
be specified using the location argument with
"tr" for top right, "bl" for bottom left,
etc.
The zimbabwe_adm1 object contains the boundaries of all
the provinces in Zimbabwe.
To this ggplot map with the zimbabwe_adm1
object:
Add a Scale bar located in the bottom right of the map,
and a North arrow in the top left.
The North arrow style in
annotation_north_arrow()can also be adjusted using thestyleargument.Note that scale distance is set to
"km"by default inannotation_scale(); you can set it in “m”, “cm”, “mi”, “ft”, or “in”.
4.3 Add hospital names
with geom_sf_text_repel()
We add hospital locations and names as text on the map with
geom_sf_text():
sle_healthsites_set %>%
ggplot() +
# geometry
geom_sf() +
# label
geom_sf_text(mapping = aes(label= facility_name)) # 👈👈👈👈👈👈👈
This is not really satisfactory, as the names overlap on the points,
and they are not easy to read on the grey background. The {ggsflabel}
package offers a very flexible approach (inspired in
{ggrepel}) to deal with label placement in
ggplot maps with sf objects (with
geom_sf_text_repel and geom_sf_label_repel),
including automated movement of labels in case of overlap.
We use it here to “nudge” the labels away, and connect them to the city locations:
sle_healthsites_set %>%
ggplot() +
# geometry
geom_sf() +
# label
geom_sf_text_repel(mapping = aes(label= facility_name)) # 👈👈👈👈
This is zimbabwe_hospitals contains all the
hospital facilities of Zimbabwe:
zimbabwe_hospitals <-
afrihealthsites(country = "Zimbabwe",
datasource = 'healthsites',
plot = FALSE) %>%
filter(amenity == "hospital")
zimbabwe_hospitalsAdd the name of these facilities as text without overlaps using the
geom_sf_text_repel() function.
Additionally, we can manually set {ggrepel}
arguments to improve its output:
- The size (argument
size); - The type of font of the text (
fontface); - The force of repulsion between overlapping text labels
(
force); - The additional padding around the each text label
(
box.padding).
sle_healthsites_set %>%
ggplot() +
# geometry
geom_sf() +
# label
geom_sf_text_repel(mapping = aes(label= facility_name),
size = 2, # 👈👈👈👈👈👈👈👈👈👈👈👈👈👈
fontface = "bold",
force = 40,
box.padding = 0.6)
Adding this layer to the current map, we obtain:
ggplot() +
# country map
geom_sf(data = sierra_leone) +
# hospital points
geom_sf(data = sle_healthsites_set) +
# hospital names with repelled text
geom_sf_text_repel(data = sle_healthsites_set, # 👈👈👈👈👈
mapping = aes(label = facility_name),
size = 2,
fontface = "bold",
force = 0.5,
box.padding = 0.6,
nudge_x = -0.25,
direction = "y",
hjust = 1,
segment.size = 0.2) +
# map annotation
annotation_north_arrow(location="tl") +
annotation_scale(location="bl") +
# map extent
coord_sf(xlim = c(-13.5,-12.7),
ylim = c(8.0,8.7))
4.4 Add province labels
with geom_sf_label_repel()
It would be informative to add finer administrative information on
top of the previous map, starting with administrative borders
(sf object: polygon data) and their names. The package
ggsflabel provides functions to add labels to
sf objects (points and polygons) in maps.
Province names are part of this data, as the shapeName
variable.
sierra_leone %>%
ggplot() +
# country map
geom_sf() +
# province labels
geom_sf_label_repel(aes(label=shapeName)) # 👈👈👈👈👈👈👈👈👈👈👈
The zimbabwe_adm1 object contains the boundaries of all
the provinces in Zimbabwe.
Create a ggplot map of Zimbabwe with labels for the name
of each of its provinces.
To continue building the complex map introduced at the beginning of
the lesson, province data is directly plotted as an additional
sf layer using geom_sf(). In addition,
province names will be added using geom_sf_label_repel(),
as well as the label (from shapeName), and a relatively big
font size.
Since the Eastern province is not showed in the zoomed map, we
dropped it using filter() from the {dplyr}
package:
ggplot() +
# country map
geom_sf(data = sierra_leone) +
# province labels
geom_sf_label_repel(data = sierra_leone %>% # 👈👈👈👈👈👈
filter(shapeName!="Eastern"),
mapping = aes(label=shapeName)) +
# hospital points
geom_sf(data = sle_healthsites_set) +
# hospital names with repelled text
geom_sf_text_repel(data = sle_healthsites_set,
mapping = aes(label = facility_name),
size = 2,
fontface = "bold",
force = 0.5,
box.padding = 0.6,
nudge_x = -0.25,
direction = "y",
hjust = 1,
segment.size = 0.2) +
# map annotation
annotation_north_arrow(location="tl") +
annotation_scale(location="bl") +
# map extent
coord_sf(xlim = c(-13.5,-12.7), ylim = c(8.0,8.7))
5 Final map
For the final map, we put everything together, having a country boundary map based on the Sierra Leona map, province labels, main hospital names and locations, custom GPS collected field sites, as well as a theme adjusted with axis labels, and a north arrow and scale bar.
We also added District boundaries from a local shapefile data to fill each polygon with respect to district area, and field sited recorded by a GPS device:
# districts
sierra_leone_shp <-
sf::read_sf(here("data/boundaries/sle_adm3.shp")) %>%
filter(admin1Name=="Western")
# field site points
sites_sf <- readr::read_csv(
here("data/gps_healthsites.csv")) %>%
st_as_sf(coords = c("gps_longitude","gps_latitude"), crs = 4326)ggplot() +
# country map
geom_sf(data = sierra_leone) +
# districts filled by area
geom_sf(data = sierra_leone_shp, # 👈👈👈👈👈👈👈👈👈👈👈👈👈👈👈
mapping = aes(fill = area_km2)) +
# color scale for fill
scale_fill_continuous_sequential(palette="Reds 3",
alpha = 0.8) +
# field site points
geom_sf(data = sites_sf, # 👈👈👈👈👈👈👈👈👈👈👈👈👈
mapping = aes(color = gps_name),
shape = 18, size = 4) +
# province names with repelled labels
geom_sf_label_repel(data = sierra_leone %>%
filter(shapeName!="Eastern"),
mapping = aes(label=shapeName)) +
# hospital points
geom_sf(data = sle_healthsites_set) +
# hospital names with repelled text
geom_sf_text_repel(data = sle_healthsites_set,
mapping = aes(label = facility_name),
size = 2,
fontface = "bold",
force = 0.5,
box.padding = 0.6,
nudge_x = -0.25,
direction = "y",
hjust = 1,
segment.size = 0.2) +
# map annotation
annotation_north_arrow(location="tl") +
annotation_scale(location="bl") +
# map extent
coord_sf(xlim = c(-13.5,-12.7),
ylim = c(8.0,8.7)) +
# ggplot labels
labs(x = "Longitude",
y = "Latitude",
fill = expression(Area~km^2),
color = "GPS data",
title = "How are Tier 3 Hospitals distributed in the Western Province of Sierra Leone?")
6 Wrap up
This example fully demonstrates that adding layers
on ggplot2 is relatively straightforward, as long as the
data is properly stored as an sf object. Adding additional
layers like external data, color
palettes, point or polygon labels and
map annotations would simply follow the same logic,
with additional calls after geom_sf() and at the
right place in the ggplot2 sequence.
Contributors
The following team members contributed to this lesson:
ANDREE VALLE CAMPOS
Motivated by reproducible science and education
LAURE VANCAUWENBERGHE
A firm believer in science for good, striving to ally programming, health and education
References
Some material in this lesson was adapted from the following sources:
Moreno, M., Basille, M. Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 1: Basics. (2018). Retrieved 01 June 2022, from https://r-spatial.org/r/2018/10/25/ggplot2-sf.html
Moreno, M., Basille, M. Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 2: Layers. (2018). Retrieved 01 June 2022, from https://r-spatial.org/r/2018/10/25/ggplot2-sf-2.html
Wilke, Claus O. Fundamentals of Data Visualization. Chapter 4: Color scales. (2020). Retrieved 01 June 2022, from https://clauswilke.com/dataviz/color-basics.html#color-to-represent-data-values
This work is licensed under the Creative Commons Attribution Share Alike license. 