# 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

## 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])
    ## 152.4642 [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

## 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)

## 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:

- geocoding a location (via `RCzechia::geocode()`)
- assigning it to a KFME grid cell (spatial join via `sf::st_join`)
- plotting the outcome – both as a grid cell and exact location – on a map

    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

## 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:

- actual elevation model (meters above sea level)
- shaded relief

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

## 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