In RamiKrispin/covid19Italy: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset

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The goal of this vignette is to demonstrate methods for creating choropleth maps for the covid19 cases in Italy by province and region. We will use the rnaturalearth package to extract the geometry spital data of Italy, and mapview, ggplot2 packages to create choropleth maps of Italy province and regions.

Getting the geometry spital data

The ne_state function from the rnaturalearth package returns the geometry spital data of Italy's province. We will set the return class to sf (Simple Features) object:

library(covid19italy)
library(rnaturalearth)
library(dplyr)

italy_map <- ne_states(country = "Italy", returnclass = "sf") 

str(italy_map)

The function returns many features, such as a different naming convention. However, for the purpose of this vignette, we will only need the following columns:

• name - the province name
• region - the region name
• geometry - the geometry spital data of the provinces

italy_map <- italy_map %>% 
  select(province = name, region, geometry) 

head(italy_map)

The italy_map object, as it is, can now use to create a choropleth map plot for the province level. To get a similar object to the regional level, we will use the dplyr package to group the data by the region column. This will automatically will group the geometry objects of the italy_map to the region level:

italy_map_region <- italy_map %>%
  group_by(region) %>%
  summarise(n = n())

head(italy_map_region)

Note that since we had to feed some variable to the summarise function, we used the n function as a dummy variable.

Now, we have a geometric object for the provinces and regions levels, we can merge them with the corresponding datasets - italy_province and italy_region. Each dataset ha

The province_spatial and region_spatial columns on the italy_province and italy_region datasets are the default provinces and regions names as used in the rnatualearth package spatial objects, and we will use them to merge the objects:

italy_map_region <- italy_map_region %>%
  left_join(italy_region %>% 
              filter(date == max(date)), # subseting for the most recent day
            by = c("region" = "region_spatial"))


italy_map_province <- italy_map %>%
  left_join(italy_province %>% 
              filter(date == max(date)), # subseting for the most recent day
            by = c("province" = "province_spatial"))

Now that we have one object for the province level and one for the regional level, we can start to plot them.

Choropleth maps with the mapview package

The mapview package provides interactive visualisations of spatial data based on the Leaflet JavaScript library. In the following example we will plot the total number of covide19 cases in Italy as of r max(italy_province$date), using the total_cases variable:

library(mapview)

italy_map_province %>%
  mapview(zcol = "total_cases")

Note: the Italy province spatial data on the rnaturalearth package is not updated with changes in the provinces of the Sardegna region that occurred in 2016. Therefore, some of the provinces in that region is missing from the plot. Alternatively, you can download Italy shapefile from the European Environment Agency website. However, the use of shapefiles is not cover on this vignette.

The col.regions argument enables to set the color pallet of the choropleth map. For example, we will use the plasma color pallet from the viridisLite package:

italy_map_province %>%
  mapview(zcol = "total_cases", col.regions = viridisLite::plasma)

Similarly, we can plot the region level total number of tests conduct as of r max(italy_province$date):

italy_map_region %>% 
  mapview(zcol = "total_tests")

Additional customization options can be found on the mapview package site

Choropleth maps with the ggplot2 package

Once the geometric objects of the province and region levels are ready, it is straightforward to plot the mops with the ggplot2 package. For example, let's plot the total number of testing again, this time with ggplot2:

library(ggplot2)
ggplot(data = italy_map_province, aes(fill = `total_cases`)) + 
  geom_sf() + 
  scale_fill_viridis_b()

Customizations of the plot color palette can be done with the scale_fill_viridis_b argument. For example, by default, the function uses the viridis palette from the viridis package. We can modify the palette to magma, by setting the option argument to A:

ggplot(data = italy_map_province, aes(fill = `total_cases`)) + 
  geom_sf() + 
    scale_fill_viridis_b(option = "A",
                       begin = 0.2,
                       end = 0.7) + 
   theme_void()

The theme_void remove the axis grids and the gray background.

RamiKrispin/covid19Italy documentation built on April 8, 2023, 10:17 a.m.