# INBO CODING CLUB
21 March 2019

Welcome!

## A few more resources

- Overview Belgian WMS services: https://wms.michelstuyts.be/
- Book 'Geocomputing with R': https://geocompr.robinlovelace.net/
- [Notes and material from the tutorial](https://drive.google.com/drive/folders/0B-DYcTNHFnIXeUhfOFYtZEw4LUU?usp=sharing) on geospatial data visualization on the useR!2017 conference.

## Share your code snippet

If you want to share your code snippet, copy paste your snippet within a section of three backticks (```):

As an **example**:

library(tidyverse)

...

(*you can copy paste this example and add your code further down, but do not fill in your code in this section*)

*Your snippets:*

## Challenge 1

There is no data for Limburg in 2016. You can add it to still have it in the map.

First option: use `spread` and `gather`

n_crab_provinces <- n_crab_provinces %>% 
  spread(year, n) %>% 
  gather(key = "year", value = "n", `2015`, `2016`)

Second option by adding a new row:

n_crab_provinces_2016_limburg <- 
  n_crab_provinces %>%
  filter(TX_PROV_DESCR_NL == "Provincie Limburg") %>%
  mutate(year = 2016, n = NA_integer_)

n_crab_provinces <- 
  n_crab_provinces %>%
  bind_rows(n_crab_provinces_2016_limburg)

### Poging Britt

provincecrabs <- left_join(
  provinces, n_crab_provinces, 
  by = c('CD_PROV_REFNIS' = 'CD_PROV_REFNIS'))

provincecrabs2016 <- filter(provincecrabs, year == 2016)

ggplot(provincecrabs2016) +
  geom_sf(aes(fill = n))

### Toon

n_crab_provinces_2016 <- n_crab_provinces %>%
  rename(province = TX_PROV_DESCR_NL) %>%
  group_by(province) %>%
  summarise(n = sum(n * (year == 2016))) %>% #geeft een 0 voor Limburg
  ungroup()

provinces_crab <- provinces %>%
  rename(province = TX_PROV_DESCR_NL) %>%
  left_join(n_crab_provinces_2016, by = "province")

provinces_crab %>%
  ggplot(aes(fill = n)) +
  geom_sf()

# Jeroen

n_crab_2016 <- n_crab_provinces %>%
  filter(year == 2016)
crab_prov <- left_join(provinces, n_crab_2016, by = "TX_PROV_DESCR_NL") %>%
  mutate(n2016 = ifelse(is.na(n), 0, n))
ggplot(crab_prov) + geom_sf(aes(fill = n2016))

### Google resultaat om NA toe te voegen aan de legende - Ivy

crab_2016 <- n_crab_provinces %>%
  filter(year == 2016)

provinces_2016 <- provinces %>%
  left_join(crab_2016, by = "TX_PROV_DESCR_NL") %>%
  mutate(discrete_n = cut(.$n, breaks = seq(from = 20, to = 100, by = 10)))

ggplot(provinces_2016) +
  geom_sf(aes(fill = discrete_n)) +
  scale_fill_brewer(na.value = "black")

## Challenge 2

### Poging Britt (challenge )

binpal <- colorBin("RdPu", 
                   provincecrabs2016$n, 
                   6, 
                   pretty = TRUE)

m <- leaflet() %>% 
  addTiles() %>% 
  addPolygons(data = provincecrabs2016,
              fillColor = ~binpal(n),
              stroke = FALSE,
              fillOpacity = 0.8) %>% 
  addLegend("bottomright",
            pal = binpal,
            values = provincecrabs2016$n,
            title = "Number of mitten crabs per province (2016)",
            opacity = 1
  )
m

### Emma

pal <- colorBin(palette = rev(heat.colors(11)), bins = c(1,10,20,30,40,50,60,70,80,90,100, +Inf),
                na.color="#cecece")

map <- leaflet(n_crab_provinces2016) %>%
  addTiles() %>% 
  addLegend(title = "Aantal krabben",
            pal = pal, values = ~n, 
            position = "bottomleft") %>%
  addPolygons(color = ~pal(n), 
              stroke = FALSE, smoothFactor = 0.2, fillOpacity = 0.85,
              popup = sprintf("%s<br>%s",n_crab_provinces2016$TX_PROV_DESCR_NL,
                            n_crab_provinces2016$n)) %>%
  addPolylines(weight = 1.5, color = "black")
print(map)

### Floris

pal <- colorNumeric(
    palette = "Blues",
    domain = prov_crab$n)

prov_crab %>%
    leaflet() %>%
    addTiles() %>%
    addPolygons(weight = 1,
                smoothFactor = 0.2,
                fillOpacity = 0.5,
                color = ~pal(n))

# Challenge 3

### Britt (challenge 3)

# VMM WMS is precies down, maar gebruik gerust de Scheldemonitorlayers van VLIZ :)
# "http://geo.vliz.be/geoserver/wms?&request=getcapabilities"
crabs2015_spatial <- st_as_sf(crab_df_2015,
                              coords = c("decimalLongitude","decimalLatitude"),
                              crs=4326)

m <- leaflet() %>% 
  setView(4.2813167,51.1373384,13) %>%
  addTiles() %>% 
  addCircleMarkers(data = crabs2015_spatial,
                   color = 'green',
                   radius = 5,
                   stroke = FALSE, 
                   fillOpacity = 0.5,
                   popup = crabs2015_spatial$eventDate,
                   popupOptions = popupOptions(maxWidth = 1000, 
                                               closeOnClick = TRUE)) %>%
  addWMSTiles("http://geo.vliz.be/geoserver/wms",
              layers = "Scheldemonitor:gebiedsindeling_om",
              options = WMSTileOptions(format = "image/png", transparent = TRUE))
m

### Ivy (zonder WMS)

crab_df_2015 <- read_csv("./data/20190321_crab_occurrences_2015.csv") %>%
  st_as_sf(coords = c("decimalLongitude", "decimalLatitude"))

leaflet(crab_df_2015) %>%
  addTiles() %>%
  addCircles() %>%
  addMarkers(label = ~as.character(eventDate)) %>%
  setView(lng = 4.2813167, lat = 51.1373384, zoom = 12)

### floris

crab_df_2015_xy <-
    crab_df_2015 %>%
    rename(X = decimalLongitude,
           Y = decimalLatitude)


leaflet() %>%
    addTiles() %>%
    addPolygons(data = prov_crab,
                weight = 1,
                smoothFactor = 0.2,
                fillOpacity = 0.5,
                color = ~pal(n)) %>%
    addMarkers(data = crab_df_2015_xy,  ~X, ~Y) %>%
    addPopups(data = crab_df_2015_xy,  ~X, ~Y, popup = ~identifiedBy)