.Rhistory

# Print the number of observations included
num_points <- nrow(cat1_observations)
print(paste("Number of points in cat1:", num_points))
# Save the plot
ggsave(species_plot_cat1, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/cat1_craywatch_map.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
View(cat1_waterlopen_in_vlaanderen)
#R - libraries
library(ggspatial)
library(sf)
library(tidyverse)
library(dplyr)
library(scales)
library(osmdata)
library(ggplot2)
library(tidyr)
library(lubridate)
# Read data
craywatch_data <- read.csv("~/GitHub/craywatch/R/data/observations/data_validation/craywatch_validation.csv")
map_data <- read.csv("~/GitHub/craywatch/assets/localities.csv")
# Ensure the 'date' column is in the correct Date format (assuming day-month-year format)
craywatch_data$date <- dmy(craywatch_data$date) # Convert to Date using dmy format
# Group by locID and process data
grouped_craywatch_data <- craywatch_data %>%
group_by(locID) %>%
summarize(
number_of_individuals = sum(number.of.individuals, na.rm = TRUE),
number_of_days = n(), # Rename count
start_date = min(date, na.rm = TRUE), # Get the first date
end_date = max(date, na.rm = TRUE), # Get the last date
species = list(
if (sum(number.of.individuals, na.rm = TRUE) > 0) unique(soort[number.of.individuals > 0]) else "absent"
), # Capture species or 'absent'
consecutive = {
# Check if the dates are consecutive for each locID
date_diff <- diff(sort(unique(date))) # Calculate the differences between sorted unique dates
all(date_diff == 1) # TRUE if all differences are 1, meaning consecutive days
},
vrijwillID = first(vrijwillID)  # Take the first 'vrijwillID' value for each group (assuming it is the same for each group)
) %>%
unnest(cols = c(species)) # Separate each species into its own row
# Select only the 'locID', 'Latitude', and 'Longitude' columns from localities
localities_selected <- map_data %>%
dplyr::select(locID, Latitude, Longitude)
# Merge with localities to add Latitude and Longitude based on locID
grouped_craywatch_data <- left_join(grouped_craywatch_data, localities_selected, by = "locID")
#preview new data
head(grouped_craywatch_data) # Show the first few rows of the data
# Inspect the structure of the dataset to check for lat/long columns
str(grouped_craywatch_data)
# Nagaan hoeveel mensen exact het protocol volgden
protocol_followers <- grouped_craywatch_data %>%
filter(number_of_days == 4 & consecutive == "TRUE") # locaties waar er exact 4 na elkaar dagen gecontroleerd is
n_distinct(protocol_followers$vrijwillID)
# Filter the data we want to use
craywatch_data_usable <- grouped_craywatch_data %>%
filter(!is.na(Longitude) & !is.na(Latitude)) %>%
filter(!(species == "absent" & number_of_days < 4))  # Exclude rows where species is "absent" and number_of_days < 4
craywatch_sf <- st_as_sf(craywatch_data_usable, coords = c("Longitude", "Latitude"), crs = 4326)
# Read shapefiles
vlaanderen <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/grenzenvlaanderen.shp")
hoofdrivieren <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/hoofdrivieren.shp")
kanalen <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/kanalen.shp")
gemeenten <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/gemeenten.shp")
# Zorg dat alle shapefiles dezelfde CRS hebben
vlaanderen <- st_transform(vlaanderen, st_crs(hoofdrivieren))
hoofdrivieren <- st_transform(hoofdrivieren, st_crs(vlaanderen))
kanalen <- st_transform(kanalen, st_crs(vlaanderen))
gemeenten <- st_transform(gemeenten, st_crs(vlaanderen))
# Clip de shapefiles tot de grenzen van Vlaanderen
hoofdrivieren_in_vlaanderen <- st_intersection(hoofdrivieren, vlaanderen)
kanalen_in_vlaanderen <- st_intersection(kanalen, vlaanderen)
gemeenten_in_vlaanderen <- st_intersection(gemeenten, vlaanderen)
sf_use_s2(FALSE)
# Make plot
base_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "lightgrey", size=0.2, colour= "black") +
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#4682B4")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#4682B4")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Make plot
gemeente_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "#98AF93", size=0.2, colour= "black") +
geom_sf(data = gemeenten_in_vlaanderen, size=0.1, colour="grey")+
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#4682B4")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#4682B4")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Define a color palette for species
species_colors <- c("faxonius limosus" = "#FFD700",
"procambarus clarkii" = "#FF0000", "procambarus virginalis" = "#FF00FF",
"faxonius virilis" = "#FFA500", "procambarus acutus" = "#000000", "absent" = "darkgrey")
# Update the legend labels for species with italic formatting
species_labels <- c( "faxonius limosus" = expression(italic("Faxonius limosus")),
"procambarus clarkii" = expression(italic("Procambarus clarkii")),
"procambarus virginalis" = expression(italic("Procambarus virginalis")),
"faxonius virilis" = expression(italic("Faxonius virilis")),
"procambarus acutus" = expression(italic("Procambarus acutus")),
"absent" = expression(italic("Absence")))
# Update the legend labels for species in Dutch
species_labels_dutch <- c( "faxonius limosus" = expression("gevlekte Amerikaanse rivierkreeft"),
"procambarus clarkii" = expression("rode Amerikaanse rivierkreeft"),
"procambarus virginalis" = expression("marmerkreeft"),
"faxonius virilis" = expression("geknobbelde Amerikaanse rivierkreeft"),
"procambarus acutus" = expression("gestreepte Amerikaanse rivierkreeft"),
"absent" = expression("afwezigheid"))
# Create a color scale with updated labels
color_scale <- scale_color_manual(values = species_colors, labels = species_labels)
# Create a color scale with updated labels in Dutch
color_scale_dutch <- scale_color_manual(values = species_colors, labels = species_labels_dutch)
# Separate the data for 'crayfish.indet' (absence) and other species
crayfish_indet_sf <- craywatch_sf %>% filter(species == "absent")
other_species_sf <- craywatch_sf %>% filter(species != "absent")
# Plot 'crayfish.indet' (absence) points first, then other species
species_plot <- base_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_gemeente <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_dutch <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale_dutch  # Apply the color scale based on species
# Save the plot
ggsave(species_plot, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (gemeente)
ggsave(species_plot_gemeente, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_gemeenten.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (Dutch)
ggsave(species_plot_dutch, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_dutch.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Sla het ggplot-object op
dir.create("./data/output/SelectedMunic", showWarnings = FALSE, recursive = TRUE)
saveRDS(species_plot_gemeente, "./data/output/SelectedMunic/species_plot.rds")
# Print the number of points included after filtering
num_points <- nrow(craywatch_data_usable)
print(paste("Number of points included in the map:", num_points))
# Sla finale Craywatch data op als een CSV-bestand
write.csv(craywatch_data_usable, "~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv", row.names = FALSE)
library(colorspace)
original_color <- "#4682B4"
lighter_color <- lighten(original_color, amount = 0.3) # Adjust `amount` to control lightness
print(lighter_color)
# Make plot
base_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "lightgrey", size=0.2, colour= "black") +
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#4682B4")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#4682B4")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Make plot
gemeente_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "#98AF93", size=0.2, colour= "black") +
geom_sf(data = gemeenten_in_vlaanderen, size=0.1, colour="grey")+
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#72A7DA")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#72A7DA")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Define a color palette for species
species_colors <- c("faxonius limosus" = "#FFD700",
"procambarus clarkii" = "#FF0000", "procambarus virginalis" = "#FF00FF",
"faxonius virilis" = "#FFA500", "procambarus acutus" = "#000000", "absent" = "darkgrey")
# Update the legend labels for species with italic formatting
species_labels <- c( "faxonius limosus" = expression(italic("Faxonius limosus")),
"procambarus clarkii" = expression(italic("Procambarus clarkii")),
"procambarus virginalis" = expression(italic("Procambarus virginalis")),
"faxonius virilis" = expression(italic("Faxonius virilis")),
"procambarus acutus" = expression(italic("Procambarus acutus")),
"absent" = expression(italic("Absence")))
# Update the legend labels for species in Dutch
species_labels_dutch <- c( "faxonius limosus" = expression("gevlekte Amerikaanse rivierkreeft"),
"procambarus clarkii" = expression("rode Amerikaanse rivierkreeft"),
"procambarus virginalis" = expression("marmerkreeft"),
"faxonius virilis" = expression("geknobbelde Amerikaanse rivierkreeft"),
"procambarus acutus" = expression("gestreepte Amerikaanse rivierkreeft"),
"absent" = expression("afwezigheid"))
# Create a color scale with updated labels
color_scale <- scale_color_manual(values = species_colors, labels = species_labels)
# Create a color scale with updated labels in Dutch
color_scale_dutch <- scale_color_manual(values = species_colors, labels = species_labels_dutch)
# Separate the data for 'crayfish.indet' (absence) and other species
crayfish_indet_sf <- craywatch_sf %>% filter(species == "absent")
other_species_sf <- craywatch_sf %>% filter(species != "absent")
# Plot 'crayfish.indet' (absence) points first, then other species
species_plot <- base_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_gemeente <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_dutch <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale_dutch  # Apply the color scale based on species
# Save the plot
ggsave(species_plot, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (gemeente)
ggsave(species_plot_gemeente, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_gemeenten.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (Dutch)
ggsave(species_plot_dutch, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_dutch.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Sla het ggplot-object op
dir.create("./data/output/SelectedMunic", showWarnings = FALSE, recursive = TRUE)
saveRDS(species_plot_gemeente, "./data/output/SelectedMunic/species_plot.rds")
# Print the number of points included after filtering
num_points <- nrow(craywatch_data_usable)
print(paste("Number of points included in the map:", num_points))
# Sla finale Craywatch data op als een CSV-bestand
write.csv(craywatch_data_usable, "~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv", row.names = FALSE)
library(colorspace)
original_color <- "#4682B4"
lighter_color <- lighten(original_color, amount = 0.5) # Adjust `amount` to control lightness
print(lighter_color)
original_color <- "#4682B4"
lighter_color <- lighten(original_color, amount = 0.2) # Adjust `amount` to control lightness
print(lighter_color)
original_color <- "#4682B4"
lighter_color <- lighten(original_color, amount = 0.25) # Adjust `amount` to control lightness
print(lighter_color)
# Make plot
gemeente_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "#98AF93", size=0.2, colour= "black") +
geom_sf(data = gemeenten_in_vlaanderen, size=0.1, colour="grey")+
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#6BA1D3")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#6BA1D3")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Define a color palette for species
species_colors <- c("faxonius limosus" = "#FFD700",
"procambarus clarkii" = "#FF0000", "procambarus virginalis" = "#FF00FF",
"faxonius virilis" = "#FFA500", "procambarus acutus" = "#000000", "absent" = "darkgrey")
# Update the legend labels for species with italic formatting
species_labels <- c( "faxonius limosus" = expression(italic("Faxonius limosus")),
"procambarus clarkii" = expression(italic("Procambarus clarkii")),
"procambarus virginalis" = expression(italic("Procambarus virginalis")),
"faxonius virilis" = expression(italic("Faxonius virilis")),
"procambarus acutus" = expression(italic("Procambarus acutus")),
"absent" = expression(italic("Absence")))
# Update the legend labels for species in Dutch
species_labels_dutch <- c( "faxonius limosus" = expression("gevlekte Amerikaanse rivierkreeft"),
"procambarus clarkii" = expression("rode Amerikaanse rivierkreeft"),
"procambarus virginalis" = expression("marmerkreeft"),
"faxonius virilis" = expression("geknobbelde Amerikaanse rivierkreeft"),
"procambarus acutus" = expression("gestreepte Amerikaanse rivierkreeft"),
"absent" = expression("afwezigheid"))
# Create a color scale with updated labels
color_scale <- scale_color_manual(values = species_colors, labels = species_labels)
# Create a color scale with updated labels in Dutch
color_scale_dutch <- scale_color_manual(values = species_colors, labels = species_labels_dutch)
# Separate the data for 'crayfish.indet' (absence) and other species
crayfish_indet_sf <- craywatch_sf %>% filter(species == "absent")
other_species_sf <- craywatch_sf %>% filter(species != "absent")
# Plot 'crayfish.indet' (absence) points first, then other species
species_plot <- base_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_gemeente <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_dutch <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale_dutch  # Apply the color scale based on species
# Save the plot
ggsave(species_plot, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (gemeente)
ggsave(species_plot_gemeente, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_gemeenten.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (Dutch)
ggsave(species_plot_dutch, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_dutch.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Sla het ggplot-object op
dir.create("./data/output/SelectedMunic", showWarnings = FALSE, recursive = TRUE)
saveRDS(species_plot_gemeente, "./data/output/SelectedMunic/species_plot.rds")
# Print the number of points included after filtering
num_points <- nrow(craywatch_data_usable)
print(paste("Number of points included in the map:", num_points))
# Sla finale Craywatch data op als een CSV-bestand
write.csv(craywatch_data_usable, "~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv", row.names = FALSE)
library(scales)
original_color <- "#4682B4"
lighter_color <- scales::muted(original_color, l = 90) # Adjust `l` for lightness
print(lighter_color)
original_color <- "#4682B4"
lighter_color <- scales::muted(original_color, l = 30) # Adjust `l` for lightness
print(lighter_color)
# Make plot
base_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "lightgrey", size=0.2, colour= "black") +
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#6BA1D3")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#6BA1D3")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Make plot
gemeente_plot <- ggplot() +
geom_sf(data = vlaanderen, fill= "#98AF93", size=0.2, colour= "black") +
geom_sf(data = gemeenten_in_vlaanderen, size=0.1, colour="grey")+
geom_sf(data = hoofdrivieren_in_vlaanderen, size=0.1, colour="#6BA1D3")+
geom_sf(data = kanalen_in_vlaanderen, size=0.1, colour="#6BA1D3")+
theme_void() +
theme(legend.title = element_blank(),
legend.text=element_text(size=8, face="italic"),
legend.key.size = unit(0.2, "cm"),
legend.position = "bottom",
plot.title= element_text(face = "italic")) +
coord_sf()
# Define a color palette for species
species_colors <- c("faxonius limosus" = "#FFD700",
"procambarus clarkii" = "#FF0000", "procambarus virginalis" = "#FF00FF",
"faxonius virilis" = "#FFA500", "procambarus acutus" = "#000000", "absent" = "darkgrey")
# Update the legend labels for species with italic formatting
species_labels <- c( "faxonius limosus" = expression(italic("Faxonius limosus")),
"procambarus clarkii" = expression(italic("Procambarus clarkii")),
"procambarus virginalis" = expression(italic("Procambarus virginalis")),
"faxonius virilis" = expression(italic("Faxonius virilis")),
"procambarus acutus" = expression(italic("Procambarus acutus")),
"absent" = expression(italic("Absence")))
# Update the legend labels for species in Dutch
species_labels_dutch <- c( "faxonius limosus" = expression("gevlekte Amerikaanse rivierkreeft"),
"procambarus clarkii" = expression("rode Amerikaanse rivierkreeft"),
"procambarus virginalis" = expression("marmerkreeft"),
"faxonius virilis" = expression("geknobbelde Amerikaanse rivierkreeft"),
"procambarus acutus" = expression("gestreepte Amerikaanse rivierkreeft"),
"absent" = expression("afwezigheid"))
# Create a color scale with updated labels
color_scale <- scale_color_manual(values = species_colors, labels = species_labels)
# Create a color scale with updated labels in Dutch
color_scale_dutch <- scale_color_manual(values = species_colors, labels = species_labels_dutch)
# Separate the data for 'crayfish.indet' (absence) and other species
crayfish_indet_sf <- craywatch_sf %>% filter(species == "absent")
other_species_sf <- craywatch_sf %>% filter(species != "absent")
# Plot 'crayfish.indet' (absence) points first, then other species
species_plot <- base_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_gemeente <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale  # Apply the color scale based on species
# Plot craywatch map with municipalities
species_plot_dutch <- gemeente_plot +
geom_sf(data = crayfish_indet_sf, aes(color = species), size = 1) +  # lightgrey (absence) points
geom_sf(data = other_species_sf, aes(color = species), size = 1) +   # other species points
color_scale_dutch  # Apply the color scale based on species
# Save the plot
ggsave(species_plot, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (gemeente)
ggsave(species_plot_gemeente, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_gemeenten.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Save the plot (Dutch)
ggsave(species_plot_dutch, file = "~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_dutch.png",
width = 15, height = 6.4, units = "cm", dpi = 200)
# Sla het ggplot-object op
dir.create("./data/output/SelectedMunic", showWarnings = FALSE, recursive = TRUE)
saveRDS(species_plot_gemeente, "./data/output/SelectedMunic/species_plot.rds")
# Print the number of points included after filtering
num_points <- nrow(craywatch_data_usable)
print(paste("Number of points included in the map:", num_points))
# Sla finale Craywatch data op als een CSV-bestand
write.csv(craywatch_data_usable, "~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv", row.names = FALSE)
# read data
final_craywatch_data <- read.csv("~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv")
#read shapefiles
waterlopen <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/provincies.shp")
colnames(provincies)
# Laad benodigde libraries
library(sf)
library(dplyr)
# Laad datasets
final_craywatch_data <- read.csv("~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv")
provincies <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/provincies.shp")
colna
colnames(provincies)
# Laad benodigde libraries
library(sf)
library(dplyr)
# Laad datasets
final_craywatch_data <- read.csv("~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv")
provincies <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/provincies.shp")
colnames(provincies)
# Controleer en zorg dat CRS gelijk is
provincies <- st_transform(provincies, crs = st_crs(provincies))
craywatch_sf <- st_as_sf(final_craywatch_data, coords = c("Longitude", "Latitude"), crs = st_crs(provincies))
# Ruimtelijke join: koppel craywatch-punten aan provincies
punten_in_provincies <- st_join(craywatch_sf, provincies, join = st_within)
# Tel punten per provincie
punten_per_provincie <- punten_in_provincies %>%
group_by(PROVNAAM) %>%
summarise(aantal_punten = n())
# Bekijk resultaten
print(punten_per_provincie)
# Controleer en zorg dat CRS gelijk is
provincies <- st_transform(provincies, crs = st_crs(provincies))
craywatch_sf <- st_as_sf(final_craywatch_data, coords = c("Longitude", "Latitude"), crs = st_crs(provincies))
craywatch_sf <- st_transform(craywatch_sf, crs = st_crs(provincies))
# Laad datasets
final_craywatch_data <- read.csv("~/GitHub/craywatch/R/data/output/final_craywatch_data_2024.csv")
provincies <- st_read("~/GitHub/craywatch/R/data/input/shapefiles/provincies.shp")
colnames(provincies)
# Controleer en zorg dat CRS gelijk is
provincies <- st_transform(provincies, crs = st_crs(provincies))
craywatch_sf <- st_as_sf(final_craywatch_data, coords = c("Longitude", "Latitude"), crs = 4326)
craywatch_sf <- st_transform(craywatch_sf, crs = st_crs(provincies))
# Ruimtelijke join: koppel craywatch-punten aan provincies
punten_in_provincies <- st_join(craywatch_sf, provincies, join = st_within)
# Tel punten per provincie
punten_per_provincie <- punten_in_provincies %>%
group_by(PROVNAAM) %>%
summarise(aantal_punten = n())
# Bekijk resultaten
print(punten_per_provincie)
library(sf)
# Maak een sf-object met het punt
point <- st_sfc(st_point(c(248665, 207795)), crs = 31370) # Stel CRS in als Belgian Lambert 72
# Transformeer het punt naar WGS 84 (EPSG:4326)
point_wgs84 <- st_transform(point, crs = 4326)
# Bekijk de coördinaten
coordinates <- st_coordinates(point_wgs84)
print(coordinates)
load("~/GitHub/craywatch/R/data/output/processed_data.RData")
# Suppress messages and warnings and prevent output display
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
# Load the processed data
load("../data/output/processed_data.RData")
# Suppress messages and warnings and prevent output display
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
knitr::include_graphics("https://imgur.com/7g3eJVw.jpg")
---
output:
html_document:
theme: null
knitr::include_graphics(~/GitHub/craywatch/assets/images/happy_holidays_crayfish.webp)
knitr::include_graphics("~/GitHub/craywatch/assets/images/happy_holidays_crayfish.webp")
library(knitr)
data <- data.frame(
Kolom1 = c("1200 kreeftenvallen", "3000 meter touw", ">100 kg hondenvoer als lokaas", "2500 aaszakjes", "1600 Craywatch labels")
)
kable(data)
library(knitr)
data <- data.frame(
Materiaal = c("1200 kreeftenvallen", "3000 meter touw", ">100 kg hondenvoer als lokaas", "2500 aaszakjes", "1600 Craywatch labels")
)
kable(data)
knitr::include_graphics("~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_dutch.png")
knitr::include_graphics("~/GitHub/craywatch/assets/images/virilis_nieuwsbrief.jpg")
knitr::include_graphics(image_links$banner_bottom)
knitr::include_graphics("~/GitHub/craywatch/assets/images/craywatch_banner.jpg")
# Suppress messages and warnings and prevent output display
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
# Load the processed data
load("~/GitHub/craywatch/R/data/output/processed_data.RData")
# Suppress messages and warnings and prevent output display
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
# Load the processed data
load("~/GitHub/craywatch/R/data/output/processed_data.RData")
knitr::include_graphics(image_links$banner_bottom)
knitr::include_graphics(image_links$banner_bottom)
setwd("~/GitHub/craywatch/assets/images")
setwd("~/GitHub/craywatch")
knitr::include_graphics("~/GitHub/craywatch/assets/images/happy_holidays_crayfish.webp")
knitr::include_graphics("~/GitHub/craywatch/R/data/output/craywatch_maps/validated_craywatch_map_dutch.png")