# INBO CODING CLUB
26 March 2020

Welcome!

## 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*)

### Deelnemers

No yellow sticky notes today. Just put a "|" if you are present and add a "*" each time you solve a challenge.
E.g. Damiano Oldoni (he is present and 1st challenge solved) | *

Patrik Oosterlynck |***
An Leyssen |**
Raïsa Carmen |
Els De bie |
Yasmine Verzelen |
Peter Van Gossum
Wouter Van Landuyt
Katrijn Alaerts
Sander Devisscher
Martijn Bollen |**
Jim Casaer
Leen Govaere |
An Vanden Broeck |
Erika Van den Bergh|
Ruben Elsen
Wim Mertens|
Dries Adriaens
Nicolas Noé
Maxime Sweetlove
Arno Thomaes |
Stien Heremans
Tanja Milotic
joost vanoverbeke
Pieterjan Verhelst |
Luc De Bruyn | **
Anja Leyman|**
Toon Spanhove
Loïc van Doorn |
Maarten Stevens
Mathias Wackenier |***
Emma Cartuyvels |***
Els Lommelen |**
Anneleen Rutten |*
Anton van de Putte ||
Hans Van Calster |

### Challenge 1

Emma:

butterfly_df %>% head(n = 10) %>% View
spatial_butterfly_df <- st_as_sf(butterfly_df, coords = c(6:7), crs = 4326)
spatial_atalanta_df <- spatial_butterfly_df %>% 
  filter(species == "Atalanta")
grids <- st_read("data/20200326_utm10_bel.gpkg")
# Oplossing crs
grids <- st_transform(grids, 4326)
municipalities <- st_read("data/20200326_Belgian_municipalities.geojson")

st_crs(spatial_butterfly_df)
st_crs(grids)
st_crs(municipalities)

Anneleen:

butterfly_df <- read_csv("data/20200326_butterflies.txt", na = "")

crs_wgs <- CRS("+init=epsg:4326")

lonlat <- cbind(butterfly_df$decimal_longitude,
                butterfly_df$decimal_latitude)

spatial_butterfly_df <- SpatialPointsDataFrame(lonlat,
                       data = butterfly_df,
                       proj4string = crs_wgs)

spatial_atalanta_df <- spatial_butterfly_df %>%
  subset(species == "Atalanta")

Belgium_grid <- st_read ("data/20200326_utm10_bel.gpkg")
gemeentes_grid <- st_read ("data/20200326_Belgian_municipalities.geojson")

st_crs(spatial_butterfly_df)
st_crs(Belgium_grid)
st_crs(gemeentes_grid)  
  

Luc

met %>%

st_read("20200326 Spatial/20200326_utm10_bel.gpkg") %>% 
  st_set_crs(3035) -> BelgRefGrid

Damiano

spatial_butterfly_df <- st_as_sf(butterfly_df,
                                coords = c("decimal_longitude",
                                           "decimal_latitude"),
                                crs = 4326)

spatial_atalanta_df <- 
  spatial_butterfly_df %>%
  filter(species == "Atalanta")

utm10_belgium <- st_read("data/20200326_utm10_bel.gpkg")

st_crs(utm10_belgium)

st_crs(utm10_belgium) <- 3035

st_crs(utm10_belgium)

bel_mun <- st_read("data/20200326_Belgian_municipalities.geoj

### Challenge 2

Luc

st_read("20200326 Spatial/20200326_utm10_bel.gpkg") %>% 
  st_transform(4326) -> BelgRefGrid
  
spatial_atalanta_df %>% 
  st_intersection(BelgMunicipal) %>%
  st_set_geometry(NULL) %>% # om tabelletje te maken zonder                               # coordinaten
  group_by(NameDut) %>% 
  summarise(Aantal = n())

Emma:

grids <- st_transform(grids, 3035)
spatial_atalanta_df <- st_transform(spatial_atalanta_df, 3035)
municipalities <- st_transform(municipalities, 3035)

perUTM <- st_intersection(spatial_atalanta_df, grids) %>% 
  group_by(CELLCODE) %>% 
  summarise(Aantal_obs = n())

perGem <- st_intersection(spatial_atalanta_df, municipalities) %>% 
  group_by(NameDut) %>% 
  summarise(Aantal_obs = n())

Damiano: Note that intersecting geometries using longitude-latitude should be avoided. See explanation about warning got from sf:
https://r-spatial.github.io/sf/articles/sf6.html#although-coordinates-are-longitudelatitude-xxx-assumes-that-they-are-planar

Wim:
##### 1
utm10_sf <- st_transform(utm10_sf,crs = 4326)

st_intersection(atal_sf, utm10_sf)

##### 2
st_intersection(atal_sf, utm10_sf) %>%
group_by(CELLCODE) %>%
count()

##### 3
Gem_sf %>%
select(NameDut) %>%
st_intersection( x = atal_sf)

# 4
Gem_sf %>% select(NameDut) %>%
st_intersection( x = atal_sf) %>%
group_by(NameDut) %>%
count()

### Challenge 3
Emma:

st_layers("data/20200326_protected_areas.gpkg")

hab_flanders <- st_read("data/20200326_protected_areas.gpkg", layer = "ps_hbtrl")
hab_flanders <- st_transform(hab_flanders, 31370)

hab_flanders <- st_transform(hab_flanders, 3035)
st_intersection(hab_flanders, municipalities) %>% 
  filter(NAAM == "Kalmthoutse Heide")

st_intersection(hab_flanders, municipalities) %>% 
  filter(NAAM == "Voerstreek")

ELs:
hab_flanders <- st_read("./data/ps_hbtrl.shp")
#no EPSG code
st_crs(hab_flanders) <- 31370

municipalities_31370 <- municipalities %>%
st_transform(31370)

hab_flanders_intersect <- hab_flanders %>%
st_intersection(municipalities_31370)

hab_KALH <- hab_flanders_intersect %>%
filter(NAAM == "Kalmthoutse Heide")

#--> essen en kalmthout

hab_VOER <- hab_flanders_intersect %>%
filter(NAAM == "Voerstreek")

#--> Dalhem, Fourons, Plombières

Patrik:

protect_area_fl <- st_layers("20200326_protected_areas.gpkg")
hab_flanders <- st_read("20200326_protected_areas.gpkg", "ps_hbtrl")

st_crs(hab_flanders)
hab_flanders <- st_transform(hab_flanders, 31370)
muni <- st_transform(muni, 31370)

intersect_muni_hab_flanders <- st_intersection(hab_flanders, muni)

filter <- intersect_muni_hab_flanders %>%
  filter(NAAM == "Kalmthoutse Heide" | NAAM =="Voerstreek")

### BONUS
Emma:

ps_hbtrl <- st_read("data/20190226_ps_hbtrl/ps_hbtrl.shp")
st_crs(ps_hbtrl)
ps_hbtrl <- st_transform(ps_hbtrl, 3035)

centroids <- st_centroid(municipalities)

distances <- st_distance(spatial_atalanta_df, centroids)

### Useful extra resource

When to reproject:
https://geocompr.robinlovelace.net/reproj-geo-data.html#when-to-reproject