Solving Real World Issues With RCzechia

Jindra Lacko

2021-12-01

Visualizing Czech Population

Population of the Czech Republic as per the latest census in 2011, per district (okres). The results can be easily accessed from the comfort of your R session using the excellent package {czso} by Petr Bouchal.

As the population distributed highly unevenly a log scale is used.

library(RCzechia)
library(ggplot2)
library(readxl)
library(dplyr)
library(httr)

tf <- tempfile(fileext = ".xls") # a temporary xls file
GET("https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/zvcr034.xls",
    write_disk(tf))
## Response [https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/zvcr034.xls]
##   Date: 2021-12-01 13:37
##   Status: 200
##   Content-Type: application/octet-stream
##   Size: 44.5 kB
## <ON DISK>  /tmp/RtmpdnsylJ/filed8a33452c064.xls
src <- read_excel(tf, range = "Data!B5:C97") # read in with original column names

colnames(src) <- c("NAZ_LAU1", "obyvatel") # meaningful names instead of the original ones
src <- src %>%
  mutate(obyvatel = as.double(obyvatel)) %>%
    # convert from text to number
  mutate(NAZ_LAU1 = ifelse(NAZ_LAU1 == "Hlavní město Praha", "Praha", NAZ_LAU1))
    # rename Prague (from The Capital to a regular city)

okresni_data <- RCzechia::okresy("low") %>% # data shapefile
  inner_join(src, by = "NAZ_LAU1")
    # key for data connection - note the use of inner (i.e. filtering) join

# report results
ggplot(data = okresni_data) +
  geom_sf(aes(fill = obyvatel), colour = NA) +
  geom_sf(data = republika("low"), color = "gray30", fill = NA) +
  scale_fill_viridis_c(trans = "log", labels = scales::comma) +
  labs(title = "Czech population",
       fill = "population\n(log scale)") +
  theme_bw() +
  theme(legend.text.align = 1,
        legend.title.align = 0.5)

plot of chunk census

Geocoding Locations & Drawing them on a Map

Drawing a map: three semi-random landmarks on map, with rivers shown for better orientation.

To get the geocoded data frame function RCzechia::geocode() is used.

library(RCzechia)
library(ggplot2)
library(sf)

borders <- RCzechia::republika("low")

rivers <- subset(RCzechia::reky(), Major == T)

mista <- data.frame(misto =  c("Kramářova vila",
                               "Arcibiskupské zahrady v Kroměříži",
                               "Hrad Bečov nad Teplou"),
                    adresa = c("Gogolova 212, Praha 1",
                               "Sněmovní náměstí 1, Kroměříž",
                               "nám. 5. května 1, Bečov nad Teplou"))

# from a string vector to sf spatial points object
POI <- RCzechia::geocode(mista$adresa)

class(POI) # in {sf} package format = spatial and data frame
## [1] "sf"         "data.frame"

# report results
ggplot() +
  geom_sf(data = POI, color = "red", shape = 4, size = 2) +
  geom_sf(data = rivers, color = "steelblue", alpha = 0.5) +
  geom_sf(data = borders, color = "grey30", fill = NA) +
  labs(title = "Very Special Places") +
  theme_bw()

plot of chunk geocode

Distance Between Prague and Brno

Calculate distance between two spatial objects; the sf package supports (via gdal) point to point, point to polygon and polygon to polygon distances.

Calculating distance from Prague (#1 Czech city) to Brno (#2 Czech city).

library(dplyr)
library(RCzechia)
library(sf)
library(units)

obce <- RCzechia::obce_polygony()

praha <- subset(obce, NAZ_OBEC == "Praha")

brno <- subset(obce, NAZ_OBEC == "Brno")

vzdalenost <- sf::st_distance(praha, brno) %>%
  units::set_units("kilometers") # easier to interpret than meters, miles or decimal degrees..

# report results
print(vzdalenost[1])
## 152.4636 [kilometers]

Geographical Center of the City of Brno

The metaphysical center of the Brno City is well known. But where is the geographical center?

The center is calculated using sf::st_centroid() and reversely geocoded via RCzechia::revgeo().

Note the use of reky("Brno") to provide the parts of Svitava and Svratka relevant to a map of Brno city.

library(dplyr)
library(RCzechia)
library(ggplot2)
library(sf)

# all districts
brno <- RCzechia::okresy() %>%
  dplyr::filter(KOD_LAU1 == "CZ0642")

# calculate centroid
pupek_brna <- brno %>%
  sf::st_transform(5514) %>% # planar CRS (eastings & northings)
  sf::st_centroid(brno) # calculate central point of a polygon

# the revgeo() function takes a sf points data frame and returns it back
# with address data in "revgeocoded" column
adresa_pupku <- RCzechia::revgeo(pupek_brna) %>%
  pull(revgeocoded)

# report results
print(adresa_pupku)
## [1] "Žižkova 513/22, Veveří, 61600 Brno"

ggplot() +
  geom_sf(data = pupek_brna, col = "red", shape = 4) +
  geom_sf(data = reky("Brno"), color = "skyblue3") +
  geom_sf(data = brno, color = "grey50", fill = NA) +
  labs(title = "Geographical Center of Brno") +
  theme_bw()

plot of chunk brno-center

Interactive Map

Interactive maps are powerful tools for data visualization. They are easy to produce with the leaflet package.

I found the stamen toner basemap a good company for interactive chloropleths - it gives enough context without distracting from the story of your data.

Note: it is technically impossible to make html in vignette interactive. As a consequence the result of code shown has been replaced by a static screenshot; the code itself is legit.

library(dplyr)
library(RCzechia)
library(leaflet)
library(czso)

# metrika pro mapování - uchazeči za říjen
metrika <- czso::czso_get_table("250169r20") %>%
   filter(obdobi == "20201031" & vuk == "NEZ0004")

podklad <- RCzechia::obce_polygony() %>% # obce_polygony = municipalities in RCzechia package
  inner_join(metrika, by = c("KOD_OBEC" = "uzemi_kod")) %>% # linking by key
  filter(KOD_CZNUTS3 == "CZ071") # Olomoucký kraj

pal <- colorNumeric(palette = "viridis",  domain = podklad$hodnota)

leaflet() %>%
  addProviderTiles("Stamen.Toner") %>%
  addPolygons(data = podklad,
              fillColor = ~pal(hodnota),
              fillOpacity = 0.75,
              color = NA)

This is just a screenshot of the visualization, so it's not interactive. You can play with the interactive version by running the code shown.

KFME Grid Cells

The Kartierung der Flora Mitteleuropas (KFME) grid is a commonly used technique in biogeography of the Central Europe. It uses a grid of 10×6 arc-minutes (in Central European latitudes this translates to near squares), with cells numbered from north to south and west to east.

A selection of the grid cells relevant for faunistical mapping of the Czech Republic is available in the RCzechia package.

This example covers a frequent use case:

library(RCzechia)
library(ggplot2)
library(dplyr)
library(sf)

obec <- "Humpolec" # a Czech location, as a string

# geolocate the place
place <- RCzechia::geocode(obec) %>%
  filter(type == "Obec")

class(place) # a spatial data frame
## [1] "sf"         "data.frame"

# ID of the KFME square containg place geocoded (via spatial join)
ctverec_id <- sf::st_join(RCzechia::KFME_grid(),
                          place, left = FALSE) %>% # not left = inner (filtering) join
  pull(ctverec)

print(paste0("Location found in grid cell number ", ctverec_id, "."))
## [1] "Location found in grid cell number 6458."

# a single KFME square to be highlighted as a polygon
highlighted_cell <- KFME_grid() %>%
  filter(ctverec == ctverec_id)

# report results
ggplot() +
  geom_sf(data = RCzechia::republika(), size = .85) + # Czech borders
  geom_sf(data = highlighted_cell, # a specific KFME cell ...
          fill = "limegreen", alpha = .5) +  # ... highlighted in lime green
  geom_sf(data = KFME_grid(), size = .33, # all KFME grid cells, thin
          color = "gray80", fill = NA) + # in gray and without fill
  geom_sf(data = place,  color = "red", pch = 4) +  # X marks the spot!
  labs(title = paste("Location", obec, "in grid cell number", ctverec_id)) +
  theme_bw()