---
title: "Craywatch project"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: rows
    theme:
      primary: "#C04384"
editor_options:
  chunk_output_type : console
---

```{r}
# Load libraries:
library(tidyverse)      # to do datascience
library(here)           # to work easily with paths
library(sf)             # to work with geospatial vector data
library(leaflet)        # to make dynamic maps
library(DT)             # to make interactive tables
library(flexdashboard)  # to make dashboards


# Read data
cray_df <- readr::read_tsv(
  here::here("data", "20250224", "20250224_craywatch_cleaned.txt"),
  na = "",
  guess_max = 10000
)

## Gauge chart and value box  on top ####

# Number of observations linked to craywatch (via waarnemingen.be)
dataset_name <- "Waarnemingen.be - Non-native animal occurrences in Flanders and the Brussels Capital Region, Belgium"
n_obs_craywatch <- cray_df %>%
  filter(datasetName == dataset_name) %>%
  nrow()
tot_obs <- nrow(cray_df)
percentage_craywatch <- n_obs_craywatch / tot_obs * 100
```
    
    

Dashboard
=====================================  

Row 
-------------------------------------

### Observations from Craywatch
    
```{r}
gauge(percentage_craywatch, min = 0, max = 100, symbol = '%')
```
   

### Total number of observations

```{r}
valueBox(tot_obs, icon = "ion-android-camera")
```   
 
Row 
-------------------------------------
   
### Chart 3
    
```{r}
n_obs_per_month_species <-
  cray_df %>%
  count(year, month, species) %>%
  # combine year and month to a single date
  mutate(date = as.Date(paste0(year, "-", month, "-01"))) %>%
  arrange(date, species) %>%
  relocate(date,species, n, everything())
ggplot(n_obs_per_month_species,
       aes(x = date, y = n, fill = species)) +
  geom_bar(stat = 'identity') +
  # Use inferno colors for the species
  scale_fill_viridis_d(option = "inferno") +
  # Add title and labels
  ggtitle("Number of observations per month and species") +
  xlab("Date") + ylab("Number of observations")
```

    


Observations on the map {data-orientation=rows}
=====================================     
   


    
### Crayfish histogram
    
```{r}
## Chart 1 - top - Plot per date (year/month) and species ####n_obs_per_month_species <-
n_obs_per_month_species <-
  cray_df %>%
  count(year, month, species) %>%
  # combine year and month to a single date
  mutate(date = as.Date(paste0(year, "-", month, "-01"))) %>%
  arrange(date, species) %>%
  relocate(date,species, n, everything())
ggplot(n_obs_per_month_species,
       aes(x = date, y = n, fill = species)) +
  geom_bar(stat = 'identity') +
  # Use inferno colors for the species
  scale_fill_viridis_d(option = "inferno") +
  # Add title and labels
  ggtitle("Number of observations per month and species") +
  xlab("Date") + ylab("Number of observations")

```
    
### Map of observations in Flanders

```{r}

## Chart 2 - bottom - Leaflet map ####
cray_fl <- sf::st_as_sf(cray_df,
                        coords = c("decimalLongitude", "decimalLatitude"),
                        crs = 4326)

# Create a palette that maps species to colors
pal <- colorFactor("inferno", cray_fl$species)
leaflet(cray_fl) %>%
  addTiles() %>%
  addCircleMarkers(popup = ~paste0(cray_fl$eventDate, ": ", cray_fl$species),
                   color = pal(cray_fl$species),
                   stroke = FALSE,
                   fillOpacity = 0.5,
                   radius = 4) %>%
  addLegend(pal = pal, values = ~species,
            position = "bottomright")


```


Data table
=====================================     
### Cars

```{r}
DT::datatable(cray_df, options = list(
  bPaginate = FALSE
))
```