---
title: "Coding club 24/02"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: rows
    vertical_layout: scroll
    theme:
      version: 4
      primary: "#C04384"
---

```{r set_theme_inbo, echo = FALSE, eval = FALSE}
theme_set(theme_inbo(base_size = 12))
switch_colour(inbo_steun_blauw)
```

```{r setup, include=FALSE}
library(flexdashboard)
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) 
library(shiny)
library(INBOtheme)
```

```{r}
cray_df <- readr::read_tsv(
  here::here("data", "20250224_craywatch_cleaned.txt"),
  na = "",
  guess_max = 10000
)
```

Overzicht
=====================================  

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

### Craywatch data
```{r}
dataset_name <- "Waarnemingen.be - Non-native animal occurrences in Flanders and the Brussels Capital Region, Belgium"
n_obs_craywatch <- cray_df %>%
  dplyr::filter(datasetName == dataset_name) %>%
  nrow()
tot_obs <- nrow(cray_df)
percentage_craywatch <- n_obs_craywatch / tot_obs * 100

gauge(n_obs_craywatch, min = 0, max = tot_obs )
```

### Total number of Craywatch observations
```{r}
valueBox(n_obs_craywatch, icon = "fa-solid fa-camera")
```

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Aantal waarnemingen per dataset
```{r fig.width=15, fig.height=7}
cray_df |> 
  group_by(datasetName) |> 
  summarise(n_obs = n()) |> 
  arrange(n_obs) -> n_obs_per_dataset

ggplot(n_obs_per_dataset, aes(x = n_obs, y = reorder(datasetName, n_obs))) +
  geom_bar(stat = "identity", color = "#C04384", fill = "#C04384") +
  labs(x = "Aantal waarnemingen", y = NULL) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45)
  )
```

### Aantal waarnemingen per provincie
```{r fig.width=15, fig.height=7}
cray_df |> 
  dplyr::filter(stateProvince != "NA") |> 
  group_by(stateProvince) |> 
  summarise(n_obs = n()) |> 
  arrange(n_obs) -> n_obs_per_province

ggplot(n_obs_per_province, aes(x = n_obs, y = reorder(stateProvince, n_obs))) +
  geom_bar(stat = "identity", color = "#C04384", fill = "#C04384") +
  labs(x = "Aantal waarnemingen", y = NULL) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45)
  )
```

Visualisatie
=====================================  
    
### Histogram (Species per date (year/month))
```{r plot-histogram}
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")
```
    
### Leaflet map
```{r 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
=====================================  
    
Row
-------------------------------------

```{r}
DT::datatable(cray_df, options = list(
  pageLength = 100
))
```