Solving Real World Issues With RCzechia

library(RCzechia)
library(dplyr)
library(readxl)
library(httr)
library(tmap)
library(sf)

GET("https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/zvcr034.xls", 
    write_disk(tf <- tempfile(fileext = ".xls")))
## Response [https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/zvcr034.xls]
##   Date: 2019-04-05 08:07
##   Status: 200
##   Content-Type: application/octet-stream
##   Size: 44.5 kB
## <ON DISK>  /tmp/RtmpVcGE4Z/filec41cfd46e5.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

vystup <- tm_shape(okresni_data) + tm_fill(col = "obyvatel", title = "Population", 
                                           palette = "Blues", style = "quantile", n = 5) +
  tm_shape(okresni_data) + tm_borders("grey40", lwd = 0.5) + # thin edges of districts
  tm_shape(republika("low")) + tm_borders("grey30", lwd = 1.5) + # thick national borders
  tm_layout(frame = F) # clean does it

print(vystup)

library(RCzechia)
library(dplyr)
library(tmap)
library(sf)

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

rivers <- RCzechia::reky()

rivers <- rivers %>%
  filter(Major == T)

mista <- data.frame(misto =  c("Kramářova vila", 
                               "Arcibiskupské zahrady v Kromeříži", 
                               "Hrad Bečov nad Teplou"),
                    adresa = c("Gogolova 1, 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) 


tm_plot <- tm_shape(borders) + 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, alpha = 0.5) +
  tm_legend(title = "Very Special Places") + # ... or whatever :)
  tm_layout(frame = F)
  

print(tm_plot)

library(dplyr)
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 <- RCzechia::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(legend.format = list(fun = function(x) paste0(formatC(x, digits = 0, format = "f"), " %"))) +
  tm_layout(frame = F)

print(vystup)

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

obce <- RCzechia::obce_polygony()

praha <- obce %>%
  filter(NAZ_OBEC == "Praha")

brno <- obce %>%
  filter(NAZ_OBEC == "Brno")

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

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

library(dplyr)
library(RCzechia)
library(tmap)
library(sf)

brno <- RCzechia::obce_polygony() %>%
  filter(NAZ_OBEC == "Brno") 

pupek_brna <- sf::st_centroid(brno) # calculate central point of a polygon
## Warning in st_centroid.sf(brno): st_centroid assumes attributes are
## constant over geometries of x
## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon =
## of_largest_polygon): st_centroid does not give correct centroids for
## longitude/latitude data

# 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)$revgeocoded

tm_plot <- tm_shape(brno) + tm_borders(col = "grey40") +
  tm_shape(pupek_brna) + tm_dots(size = 1/3, col = "red", shape = 4) +
  tm_legend(title = "Center of Brno") + 
  tm_layout(frame = F)
  
print(adresa_pupku)
## [1] "Žižkova 513/22, Veveří, 61600 Brno"

print(tm_plot)

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

tmap_mode("view")

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

print(vystup)

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


poly <- RCzechia::okresy("low") %>% # Czech LAU1 regions as sf data frame
  mutate(oddeven = ifelse(nchar(NAZ_LAU1) %% 2 == 1, "odd", "even" )) %>% # odd or even?
  RCzechia::union_sf("oddeven") # ... et facta est lux

plot(poly, key.pos = 1)