Examples of using RCzechia

Jindra Lacko

2018-03-02

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 you can consider using geocode() function from ggmap package, which implies consent with Google terms of use.

library(RCzechia)
library(tidyverse)
library(tmap)
library(sf)

rivers <- reky()

rivers <- rivers[rivers$Major == T, ]

mista <- data.frame(misto = c('kramarova vila', 'arcibiskupske zahrady v kromerizi', 'becov nad teplou'),
                    lon = c(14.41030, 17.39353, 12.83833),
                    lat = c(50.09380, 49.30048, 50.08346))

# to geocode a list of locations consider ggmap::geocode()

POI <- mista %>% # or geocode using ggmap
  st_as_sf(coords = c("lon", "lat"), crs = 4326) # convert plain data to spatial CRS = WGS84, used by Google

tm_plot <- tm_shape(republika()) + tm_borders("grey30", lwd = 1) +
  tm_shape(POI) + tm_symbols(col = "firebrick3", shape = 20, size = 0.5) +
  tm_shape(rivers) + tm_lines(col = 'steelblue', lwd = 1.5)
  

print(tm_plot)

Unemployment in Czech Republic - a chloropleth

A visualization problem: unemployment in the Czech Republic is in general low, but not uniformly low.
What are the hotspots?

library(tidyverse)
library(RCzechia)
library(tmap)
library(sf)

src <- read.csv(url("https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/unempl.csv"), stringsAsFactors = F) 
# open data on unemployment from Czech Statistical Office - https://www.czso.cz/csu/czso/otevrena_data
# lightly edited for size (rows filtered)

src <- src %>%
  mutate(KOD_OBEC = as.character(uzemi_kod))  # keys in RCzechia are of type character

podklad <- obce_polygony() %>% # obce_polygony = municipalities in RCzechia package
  inner_join(src, by = "KOD_OBEC") # linking by key


vystup <- tm_shape(republika()) + tm_borders(col = "grey40") +
  tm_shape(podklad) + tm_fill(col = "hodnota", title = "Unemployment", palette = "YlOrRd") +
  tm_legend(position = c("RIGHT", "top"),
            legend.format = list(fun = function(x) paste0(formatC(x, digits = 0, format = "f"), " %")))

print(vystup)

Distnance between Prague to 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(tidyverse)
library(RCzechia)
library(sf)
library(units)

obce <- obce_polygony()

praha <- obce[obce$NAZ_OBEC == "Praha", ]
brno <- obce[obce$NAZ_OBEC == "Brno", ]

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

print(vzdalenost)
#> Units: kilometers
#>          [,1]
#> [1,] 152.8073

Interactive Map

Interactive maps are powerful tools for data vizualization. They are easy to produce with the tmap package.

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

A map of the whole Czech Republic in original resolution (the accuracy is about 1 meter) would be rather sizeable, and I found it better policy to either
* limit it to a single region (say a NUTS3) or
* to limit the size by applying st_simplify() to the shapefile. Note that RCzechia uses EPSG:4326 projection, with decimal degrees as unit. To simplify to given tolerance in meters you need to first st_transform() it to a different projection, e.g. EPSG:5513 (ing. Křovák).

library(tidyverse)
library(RCzechia)
library(tmap)
library(sf)

src <- read.csv(url("https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/unempl.csv"), stringsAsFactors = F) 
# open data on unemployment from Czech Statistical Office - https://www.czso.cz/csu/czso/otevrena_data
# lightly edited for size (rows filtered)


src <- src %>%
  mutate(KOD_OBEC = as.character(uzemi_kod))  # keys in RCzechia are of type character

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

vystup <- tm_shape(republika()) + tm_borders(col = "grey40") +
  tm_shape(podklad) + tm_fill(col = "hodnota", title = "Unemployment", palette = "YlOrRd", id = "NAZ_OBEC") +
  tm_legend(position = c("RIGHT", "top"),
            legend.format = list(fun = function(x) paste0(formatC(x, digits = 0, format = "f"), " %"))) +
  tm_view(basemaps = "Stamen.Toner")

save_tmap(vystup, filename = "vystup.html")