---
title: "Solving Real World Issues With RCzechia"
author: "Jindra Lacko"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
self_contained: no
vignette: >
  %\VignetteIndexEntry{Solving real world issues with RCzechia}
  %\VignetteDepends{readxl}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = T, fig.align="center", dpi = 150, out.width = "100%", fig.width = 8, fig.height = 4)
library(httr)
library(roxygen2)
```
### Visualizing Czech Population

Population of the Czech Republic as per the [latest census](https://www.czso.cz/csu/czso/zakladni-vysledky-scitani-lidu-domu-a-bytu-2011-ceska-republika-2011-eihs9d4gp3) in 2011, per district (okres).

As the population distributed unevenly a log scale is used.
  
```{r census, echo = T, eval = T, message = F}
library(RCzechia)
library(ggplot2)
library(readxl)
library(dplyr)
library(httr)
library(sf)

tf <- tempfile(fileext = ".xls") # a temporary xls file

GET("https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/zvcr034.xls", 
    write_disk(tf))

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)




```

## 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.

```{r geocode, echo = T, eval = T, message = F, warning = F,fig.width = 8, fig.height = 5}
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 Kromeříži", 
                               "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

# 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()

```


## 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).

```{r distance, echo = T, eval = T, message = F}
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])

```

## Geographical Center of the City of Brno

The *metaphysical* center of the Brno City is [well known](https://en.wikipedia.org/wiki/Brno_astronomical_clock). 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.

```{r brno-center, echo = T, eval = T, message = F, fig.width = 6, fig.height = 6}
library(dplyr)
library(RCzechia)
library(ggplot2)
library(sf)

# polygon of Brno city
brno <- dplyr::filter(RCzechia::okresy(), KOD_LAU1 == "CZ0642")

# calculate centroid
pupek_brna <- brno %>%
  st_transform(5514) %>% # planar CRS (eastings & northings)
  st_set_agr('constant') %>%  # not strictly necessary, but avoids error message
  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)$revgeocoded

# report results
print(adresa_pupku)

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()



```


## 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.

```{r interactive, echo = T, eval = F}
library(dplyr)
library(RCzechia)
library(leaflet)
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

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

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

```
<p align="center">
<img src="https://raw.githubusercontent.com/jlacko/RCzechia/master/data-raw/interactive-screenshot.png" alt="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.">
</p>

## Dissolving `sf` Polygons

Creating custom polygons by aggregating administrative units is a common use case in sales reporting and analysis.

In many use cases a simple `dplyr::group_by() %>% dplyr::summarize()` call will do. Some shapefiles (unfortunately including the ArcČR®500, on which `{RCzechia}` is based) are not that well behaved, and require repairing of faulty geometry.

Function `RCzechia::union_sf()` makes this task easier by automating some of the more common polygon repair tricks.

In this demonstration the Czech LAU1 units are grouped into two categories: those with odd lettered names, and those with even letters. They are then dissolved into two multipolygons.

```{r union,  echo = T, eval = T, message = F}
library(RCzechia)
library(ggplot2)
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

# Structure of the "poly" object:
head(poly)

# report results
ggplot(data = poly, aes(fill = oddeven)) +
  geom_sf() +
  scale_fill_viridis_d() +
  labs(title = "Number of characters in names of Czech districts",
       fill = "Odd or even?") +
  theme_bw()

```

## 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 (via `sf::st_intersection`)
* plotting the outcome – both as a grid cell and exact location – on a map


```{r ctverce, echo = T, eval = T, message = F, warning = F,fig.width = 8, fig.height = 5}
library(RCzechia)
library(ggplot2)
library(dplyr)
library(sf)

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

# geolocate centroid of a place
place <- RCzechia::geocode(obec) %>% 
  filter(typ == "Obec") 

class(place) # a spatial data frame

# ID of the KFME square containg place geocoded (via spatial join)
ctverec_id <- sf::st_join(RCzechia::KFME_grid(), 
                          place, left = F)$ctverec

print(paste0("Location found in grid cell number ", ctverec_id, "."))

# 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!
  ggtitle(paste("Location", obec, "in grid cell number", ctverec_id)) +
  theme_bw()

```

    
## 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 second option.

```{r relief, echo = T, eval = T, message = F, warning = F,fig.width = 8, fig.height = 5}
library(RCzechia)
library(ggplot2)
library(dplyr)
library(raster)

# ggplot does not play nice with {raster} package; a data frame is required
relief <- vyskopis("rayshaded") %>% 
  as("SpatialPixelsDataFrame") %>% # old style format - {sp}
  as_tibble()

# report results
ggplot() +
  geom_raster(data = relief, aes(x = x, y  = y, alpha = -raytraced), # relief
              fill = "gray30",  show.legend = F) + # no legend is necessary
  geom_sf(data = subset(RCzechia::reky(), Major == T), # major rivers
          color = "steelblue", alpha = .7) +
  labs(title = "Czech Rivers & Their Basins") +
  theme_bw() +
  theme(axis.title = element_blank())

```
