INBO CODING CLUB

# INBO CODING CLUB 26 January 2026 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*) ## Yellow sticky notes No yellow sticky notes online. Put your name + " | " and add a "*" each time you solve a challenge (see below). ## Participants Name | Challenges --- | --- Damiano Oldoni | *** Nele Mullens | Pieter Huybrechts | Hanne Van Gompel | Anne-Lie Van Praet | Jolien Buyse | Suzanna Lettens |** Lawrence Whatley | Adriaan | ** ## Challenge 1 ### Damiano's solution (example) Copy paste this section to show your solutions. ```r # dummy code print("This is how to insert code.") ``` ### Pieter ``` ``` ### Raïsa ```r #Challenge 1.1 occs <- st_as_sf(occs_df, coords = c("decimalLongitude", "decimalLatitude"), crs = 4326) #Challenge 1.2 sf::st_layers("data/20260126/20260126_protected_areas_be.gpkg") #1st layer is multipolygon with known crs, other two have unknown geometry_type and crs. #Challenge 1.3 pa <- st_read("data/20260126/20260126_protected_areas_be.gpkg", layer = "NaturaSite_polygon") #Challenge 1.4 pa_bioregion <- st_read("data/20260126/20260126_protected_areas_be.gpkg", layer = "BIOREGION") pa_habitats <- st_read("data/20260126/20260126_protected_areas_be.gpkg", layer = "HABITATS") #they are read as simple data frames, not spatial data frames #Challenge 1.5 municipalities <- st_read(municipalities_folder) #Challenge 1.6 #unzipping the data put the files in another subfolder. # I manually moved them out of the subfolder for this to work. # Alternatively, we could have updated the aae_grid_folder: # eea_grid <- st_read(paste0(eea_grid_folder, "/eea_v_3035_10_km_eea-ref-grid-be_p_2013_v02_r00")) eea_grid <- st_read(eea_grid_folder) #Challenge 1.7 st_crs(eea_grid) #ETRS89 / ETRS-LAEA st_crs(pa) #ETRS89-extended / LAEA Europe st_crs(occs) #WGS 84 st_crs(municipalities) #WGS 84 #Challenge 1.8 pa_a <- pa %>% dplyr::filter(SITETYPE == "A") ``` ### Lawrence ```r #1 occs <- st_as_sf(occs_df, coords = c("decimalLongitude", "decimalLatitude"), crs = 4326) occs mapview(occs) #2 protected <- "data/20260126/20260126_protected_areas_be.gpkg" st_layers(protected) #3 pa <- st_read(protected, layer = "NaturaSite_polygon") pa mapview(pa) #4 pa_bioregion <- st_read(protected, layer = "BIOREGION") pa_habitats <- st_read(protected, layer = "HABITATS") #5 municipalities <- st_read(municipalities_folder) municipalities mapview(municipalities) #6 eea_grid <- st_read("data/20260126/20260126_eea_grid_be/eea_v_3035_10_km_eea-ref-grid-be_p_2013_v02_r00/be_10km.shp") eea_grid mapview(eea_grid) #7 st_crs(occs) #WGS84 st_crs(pa) #ETRS89 st_crs(municipalities) #WGS84 st_crs(eea_grid) #ETRS89 #8 pa_a <- subset(pa, SITETYPE == "A") pa_a mapview(pa_a) ``` ### Jolien ``` r occs <- st_as_sf(occs_df, coords = c("decimalLongitude","decimalLatitude"), crs = 4326) st_layers("data/20260126/20260126_protected_areas_be.gpkg.curltmp", ) pa <- st_read("data/20260126/20260126_protected_areas_be.gpkg.curltmp", "NaturaSite_polygon") pa_bioregion <- st_read("data/20260126/20260126_protected_areas_be.gpkg.curltmp", "BIOREGION") pa_habitats <- st_read("data/20260126/20260126_protected_areas_be.gpkg.curltmp", "HABITATS") municipalities <- read_sf("data/20260126/20260126_municipalities_belgium/georef-belgium-municipality-millesime.shp") eea_grid <- read_sf("data/20260126/20260126_eea_grid_be/eea_v_3035_10_km_eea-ref-grid-be_p_2013_v02_r00/be_10km.shp") st_crs(eea_grid) # ETRS89 / ETRS-LAEA st_crs(municipalities) # WGS 84 st_crs(pa) # ETRS89-extended / LAEA Europe st_crs(occs) # WGS 84 pa_a <- pa %>% filter(SITETYPE == "A") ``` ### Nele ``` r #1.1 #use decimalLongitude and decimalLatitude to specify coordinates #start from occs_df occs <- st_as_sf(occs_df, coords = c("decimalLongitude", "decimalLatitude"), crs = 4326) #1.2 prot_ar <- st_layers("data/20260126/20260126_protected_areas_be.gpkg") prot_ar$name prot_ar$geomtype #1.3 pa <- st_read(dsn = "data/20260126/20260126_protected_areas_be.gpkg", layer = "NaturaSite_polygon") #1.4 pa_bioregion <- st_read(dsn = "data/20260126/20260126_protected_areas_be.gpkg", layer = "BIOREGION") pa_habitats <- st_read(dsn = "data/20260126/20260126_protected_areas_be.gpkg", layer = "HABITATS") #1.5 municipalities <- st_read(municipalities_folder) #1.6 eea_grid <- st_read(eea_grid_folder) #1.7 st_crs(occs) # epsg 4326 st_crs(pa) # epsg 3035 st_crs(municipalities) # epsg 4326 st_crs(eea_grid) # epsg 3035 #1.8 pa_a <- pa %>% dplyr::filter(SITETYPE == "A") ``` ## Challenge 2 ### Raïsa ```r #Challenge 2.1 occs_3035 <- st_transform(occs, crs = 3035) #Challenge 2.2 st_write(pa, layer = "NaturaSite", "data/20260126/pa_occs_3035.gpkg") st_write(occs_3035, layer = "racoon_occs", "data/20260126/pa_occs_3035.gpkg") #check the layers st_layers("data/20260126/pa_occs_3035.gpkg") #Challenge 2.3 municipalities_3035 <- st_transform(municipalities, crs = 3035) #Challenge 2.4 municipalities_3035_centroids <- st_centroid(municipalities_3035) #quick visual check mapview(municipalities_3035) #polygons mapview(municipalities_3035_centroids) #points ``` ### Nele ```r #2.1 occs_3035 <- st_transform(occs, 3035) st_crs(occs_3035) #2.2 # layers: NaturaSite: protected aread from pa, and raccoon_occs: occs_3035 st_write(pa, layer = "NaturaSite", "data/20260126/pa_occs_3035.gpkg") st_write(occs_3035, layer = "raccoon_occs", "data/20260126/pa_occs_3035.gpkg") st_layers("data/20260126/pa_occs_3035.gpkg") #2.3 municipalities_3035 <- st_transform(municipalities, 3035) st_crs(municipalities) st_crs(municipalities_3035) #2.4 municipalities_3035_centroids <- st_centroid(municipalities_3035) ``` ### Adriaan occs_3035 <- st_transform(occs, 3035) st_write(pa, dsn = 'pa_occs_3035.gpkg', layer = 'NaturaSite') st_write(occs_3035, dsn = 'pa_occs_3035.gpkg', layer = 'raccoon_occs') municipalities_3035 <- st_transform(municipalities, 3035) municipalities_3035_centroids <- st_centroid(municipalities_3035) mapview(municipalities_3035_centroids) ### Jolien ````r occs_3035 <- st_transform(occs, 3035) st_crs(occs_3035) st_write(pa, "pa_occs_3035.gpkg", layer = "NaturaSite") st_write(occs_3035, "pa_occs_3035.gpkg", layer = "raccoon_occs", append = TRUE) municipalities_3035 <- st_transform(municipalities, 3035) st_crs(municipalities_3035) municipalities_3035_centroids <- st_centroid(municipalities_3035) ```` ### Lawrence ```r #1 occs_3035 <- st_transform(occs, crs = 3035) occs_3035 #2 st_write(pa, layer = "NaturaSite", "data/20260126/pa_occs_3035.gpkg") st_write(occs_3035, layer = "raccoon_occs", "data/20260126/pa_occs_3035.gpkg") st_layers("data/20260126/pa_occs_3035.gpkg") #3 municipalities_3035 <- st_transform(municipalities, crs = 3035) municipalities_3035 #4 municipalities_3035_centroids <- st_centroid(municipalities_3035) mapview(municipalities_3035_centroids) ``` ## Challenge 3 ```r #Challenge 3.1 cube_sf <- cube %>% left_join(eea_grid %>% dplyr::select(CELLCODE)) %>% st_as_sf() #Challenge 3.2 # A list with which pa intersect with each of the municipalities intersections <- st_intersects(municipalities_3035, pa) #add number of intersecting pa to municipalities municipalities_3035 <- municipalities_3035 %>% mutate(n_pa = sapply(intersections, FUN = function(x) {length(x)})) #Challenge 3.3 no_pa_municipalities_3035 <- municipalities_3035 %>% dplyr::filter(n_pa == 0) nrow(no_pa_municipalities_3035) #there are 114 municipalities without pa #Challenge 3.4 no_pa_municipalities_3035 %>% mutate(area = st_area(no_pa_municipalities_3035)) %>% arrange(-area) %>% head(10) #Challenge 3.5 intersections <- st_intersects(municipalities_3035, occs_3035) #I'm unsure whether we need the number of occurences or the number of individuals... #I'm going with the number of individuals since it would otherwise be similar to 3.2 :-) #Note that I'm ignoring occurences where "individualCount" is not filled in (it's NA in 129 rows) municipalities_3035 <- municipalities_3035 %>% mutate(n_occs = sapply(intersections, FUN = function(x) { if (length(x) > 0) { sum(occs_3035[x, "individualCount"] %>% st_drop_geometry(), na.rm = T)} else { 0 }}) ) #Challenge 3.6 municipalities_3035 %>% arrange(-n_occs) %>% head(10) #Challenge 3.7 #Note that there are overlapping pa which could lead to double counting of the area. #We use st_cast(st_union()) to simplify the pa first intersecting_pa <- st_intersection(municipalities_3035, st_cast(st_union(pa),"POLYGON")) area_pa <- intersecting_pa %>% mutate(area = st_area(intersecting_pa)) %>% st_drop_geometry() %>% group_by(mun_code) %>% summarize(protected_area_m2 = sum(area)) municipalities_3035 <- municipalities_3035 %>% left_join(area_pa) mapview(municipalities_3035, zcol = "protected_area_m2") #Challenge 3.8 municipalities_3035 <- municipalities_3035 %>% mutate(area = st_area(municipalities_3035), protected_percentage = units::drop_units(protected_area_m2 / area)) mapview(municipalities_3035, zcol = "protected_percentage") #still, the percentage is sometimes higher than 100% for Hechtel-Eksel:-( municipalities_3035 %>% dplyr::filter(!is.na(protected_percentage) & protected_percentage > 1) %>% arrange(-protected_percentage) ```

Syntax	Example	Reference
# Header	Header	基本排版
- Unordered List	Unordered List
1. Ordered List	Ordered List
- [ ] Todo List	Todo List
> Blockquote	Blockquote
Bold font	Bold font
Italics font	Italics font
~~Strikethrough~~	~~Strikethrough~~
19^th^	19^th
H~2~O	H₂O
++Inserted text++	Inserted text
==Marked text==	Marked text
[link text](https:// "title")	Link
![image alt](https:// "title")	Image
`Code`	`Code`	在筆記中貼入程式碼
```javascript var i = 0; ```	`var i = 0;`
:smile:		Emoji list
{%youtube youtube_id %}	Externals
$L^aT_eX$	L^aT_eX
:::info This is a alert area. :::	This is a alert area.