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Solving Real World Issues With RCzechia

Jindra Lacko

2024-11-25

Visualizing Czech Population

Population of the Czech Republic from the 2011 census, 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: 2024-11-25 14:10
##   Status: 200
##   Content-Type: application/octet-stream
##   Size: 44.5 kB
## <ON DISK>  /tmp/Rtmpn3okFy/file20e11aee97ca.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 = RCzechia::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 = element_text(hjust = 1),
        legend.title = element_text(hjust = 0.5))
plot of chunk census

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

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])
## 152.4642 [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

plot of chunk brno-center

Interactive Map

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

Since Stamen Toner basemap no longer sparkles joy I have found a new favorite - the Positron by CartoDB.

Note: it is technically impossible to make html in vignette interactive (and for good reasons). 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)

# map metrics - number of unemployed in October 2020
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("CartoDB.Positron") %>%
  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()
plot of chunk ctverce

plot of chunk ctverce

Terrain of the Czech Republic

Understanding the lay of the land is important in many use cases in physical sciences; one of them is interpreting the flow of rivers.

Visualizing the slope & height of terrain is an important first step in understanding it.

Package RCzechia supports two versions of relief visualization:

This example covers the first option.

library(RCzechia)
library(ggplot2)
library(terra)
library(tidyterra)
library(dplyr)

# terrain cropped to "Czechia proper"
relief <- vyskopis("rayshaded", cropped = TRUE)

# report results
ggplot() +
  tidyterra::geom_spatraster(data = relief) +
  scale_fill_gradientn(colors = hcl.colors(50, "Grays"), # 50 shades of Gray...
                       na.value = NA,
                       guide = "none") +
  geom_sf(data = subset(RCzechia::reky(), Major == T), # major rivers
          color = "steelblue", alpha = .5) +
  labs(title = "Czech Rivers & Their Basins",
       fill = "Altitude") +
  theme_bw() +
  theme(axis.title = element_blank(),
        legend.text.align = 1,
        legend.title.align = 0.5)
plot of chunk relief

plot of chunk relief

Senate Elections of 2020

Visualizing election results is one of typical use cases of the RCzechia package.

This example uses {rvest} to scrape the official table of results of the 2020 fall Senate elections from the official site of the Czech Statistical Office, and display a map of the party affiliation of the elected senator.

Since not all districts were up for election in this cycle two thirds of the map contain NA’s; that is expected behavior (the Czech senate elections are staggered, like in the US).


library(RCzechia)
library(ggplot2)
library(dplyr)
library(rvest)

# official result of elections from Czech Statistical Office
vysledky <- "https://www.volby.cz/pls/senat/se1111?xjazyk=CZ&xdatum=20201002&xv=7&xt=2" %>%
  xml2::read_html() %>%  # because rvest::html is deprecated
  html_nodes(xpath = "//*[@id=\"se1111_t1\"]") %>%  # get the table by its xpath
  html_table(fill = T) %>%
  .[[1]] %>%
  dplyr::select(OBVOD = Obvod, strana = `Volebnístrana`) %>%
  # pad OBVOD with zero to 2 places to align to RCzechia data format
  mutate(OBVOD = stringr::str_pad(OBVOD, 2, side = "left", pad = "0"))

podklad <- RCzechia::senat_obvody("low") %>%
  # match by key; left to preserve geometry of off cycle districts (NAs)
  left_join(vysledky, by = "OBVOD")

ggplot() +
  geom_sf(data = RCzechia::republika(), size = .85) + # Czech borders
  geom_sf(data = podklad, aes(fill = strana)) +
  labs(title = "Senate elections 2020") +
  theme_bw()
plot of chunk senat

plot of chunk senat

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.