INBO CODING CLUB

# INBO CODING CLUB 24 February 2025 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 | *** Dirk Maes | Jorre | Anja Leyman | *** Rhea Maesele | ** Hans Van Calster | Ward Langeraert |** Margot Vanhellemont | ** Oberon Geunens| Fleur Petersen | Raïsa Carmen | ** Pieter Verschelde | ** Falk Mielke | ** Bryce | Arno Thomaes | ** ## Challenge 1 ### Damiano's solution (example) Copy paste this section to show your solutions. ```r # dummy code print("This is how to insert code.") ``` ### Falk's attempt (This is the whole `.Rmd` file.) Based on the flexdashboard RMarkdown draft: ``` rmarkdown::draft("challenge1.Rmd", template = "flex_dashboard_bslib", package = "flexdashboard") ``` Code blocks copied from Damiano's `src` preparation. ````rmd --- title: "CrayWatch Dashboard" output: flexdashboard::flex_dashboard: vertical_layout: scroll theme: version: 4 fg: "#101010" bg: "#FDF7F7" primary: "#ED79F9" navbar-bg: "#3ADAC6" --- ```{r setup, include=FALSE} # 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 # Install thematic and un-comment for themed static plots (i.e., ggplot2) thematic::thematic_rmd() ``` ```{r load-data, include=FALSE} # Read data # WARNING: my local data is organized differently; adjust the path. cray_df <- readr::read_tsv( here::here("codingclub", "data", "20250224_craywatch_cleaned.txt"), na = "", guess_max = 10000 ) glimpse(cray_df) ``` Column {} ----------------------------------------------------------------------- ### Species Abundance ```{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") ``` ### Species Distribution ```{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") ``` ```` There are **quarto dashboards!** https://quarto.org/docs/dashboards ````qmd --- title: "CrayWatch Dashboard" format: dashboard: theme: united ---  ```{r setup} #| include: false # 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 # Install thematic and un-comment for themed static plots (i.e., ggplot2) # thematic::thematic_qmd() ``` ```{r load-data} #| include: false # Read data cray_df <- readr::read_tsv( here::here("codingclub", "data", "20250224_craywatch_cleaned.txt"), na = "", guess_max = 10000 ) glimpse(cray_df) ``` ## Row {height=60%} ```{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") ``` ## Row {height=40%} ```{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") ``` ```` ### Ward's attempt ````Rmd --- title: "Craywatch dashboard" output: flexdashboard::flex_dashboard: orientation: columns vertical_layout: fill editor_options: chunk_output_type: console --- ```{r setup, include=FALSE} # 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 ) ``` ### Number of observations ```{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 labs(title = "Number of observations per month and species", x = "", y = "Number of observations") + scale_x_date(breaks = n_obs_per_month_species$date, date_labels = "%b") + theme_minimal() ``` ### Observations of Crayfish species ```{r} cray_fl <- sf::st_as_sf(cray_df, coords = c("decimalLongitude", "decimalLatitude"), crs = 4326) # Create a palette that maps species to colors pal <- colorFactor("inferno", cray_fl$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") ``` ```` ## Challenge 2 ### Poging Anja ````rmd --- title: "Multiple pages" output: flexdashboard::flex_dashboard: theme: bg: "#101010" fg: "#FDF7F7" primary: "#ED79F9" base_font: google: Prompt code_font: google: JetBrains Mono orientation: rows vertical_layout: scroll --- ```{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) # to make interactive tables library(flexdashboard) # to make dashboards ``` ```{r load_data, include=FALSE} # include=FALSE: code wordt gerund maar niet getoond cray_df <- readr::read_tsv( here::here("data", "20250224", "20250224_craywatch_cleaned.txt"), na = "", guess_max = 10000 ) glimpse(cray_df) ``` ```{r } # 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 ``` SVZ ===================================== Row --- ###  ```{r gauge} # 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 gauge(percentage_craywatch, min = 0, max = 100, symbol = '%', gaugeSectors( success = c(80, 100), warning = c(40, 79), danger = c(0, 39) )) ``` ###  ```{r valuebox} valueBox(tot_obs, icon = "fa-camera") ``` Row -----------------------------------------------------------------------  ### Number of observations per dataset ```{r histo} n_obs_per_dataset <- cray_df %>% count(datasetName) ggplot(n_obs_per_dataset, aes(x = datasetName, y = n, fill = datasetName)) + geom_bar(stat = 'identity') + geom_text(aes(label = n), vjust = 0, hjust = 0) + scale_x_discrete(label = function(x) stringr::str_trunc(x, 30)) + # met dank aan Ward scale_y_continuous(limits = c(0, 1300)) + # Add title and labels # ggtitle("Number of observations per dataset") + labs(x = "", y = "Number of observations") + coord_flip() + theme(legend.position = "none") ``` Waarnemingen per maand ===================================== Row ------------------------------------- ### Number of observations ```{r histo} 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") ``` Row ----------------------------------------------------------------------- ### Map ```{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$species), stroke = FALSE, fillOpacity = 0.5, radius = 4) %>% addLegend(pal = pal, values = ~species, position = "bottomright") ``` ````qmd ```` ### Ward's Attempt ````Rmd --- title: "Craywatch dashboard" output: flexdashboard::flex_dashboard: orientation: columns vertical_layout: fill editor_options: chunk_output_type: console --- ```{r setup, include=FALSE} # Load libraries: library(tidyverse) # to do datascience library(here) # to work easily with paths library(sf) # to work with geospatial vector data library(leaflet) # to make dynamic maps library(DT) # to make interactive tables library(flexdashboard) # to make dashboards # Read data cray_df <- readr::read_tsv( here::here("data", "20250224", "20250224_craywatch_cleaned.txt"), na = "", guess_max = 10000 ) ``` Crayfish distribution ===================================== ### Number of observations ```{r} n_obs_per_month_species <- cray_df %>% count(year, month, species) %>% # combine year and month to a single date mutate(date = as_date(paste0(year, "-", month, "-01"))) %>% arrange(date, species) %>% relocate(date, species, n, everything()) ggplot(n_obs_per_month_species, aes(x = date, y = n, fill = species)) + geom_bar(stat = "identity") + # Use inferno colors for the species scale_fill_viridis_d(option = "inferno") + # Add title and labels labs(title = "Number of observations per month and species", x = "", y = "Number of observations") + scale_x_date(breaks = n_obs_per_month_species$date, date_labels = "%b") + theme_minimal() ``` ### Observations of Crayfish species ```{r} 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") ``` Citizen science {data-orientation=rows} ===================================== Row ------------------------------------- ### Citizen science data for detecting crayfish ```{r} dataset_name <- paste("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 gauge(percentage_craywatch, min = 0, max = 100, symbol = "%", gaugeSectors(success = c(80, 100), warning = c(40, 79), danger = c(0, 39)) ) ``` ### Articles per Day ```{r} valueBox(tot_obs, caption = "Number of observations linked to craywatch", icon = "ion-android-cloud-done") ``` Row ------------------------------------- ### Number of observations per dataset ```{r} cray_df %>% count(datasetName) %>% mutate(datasetName = reorder(datasetName, n)) %>% ggplot(aes(x = datasetName, y = n)) + geom_bar(stat = "identity", fill = "cornflowerblue") + geom_text(aes(label = n), vjust = 0, hjust = 0) + scale_x_discrete(label = function(x) stringr::str_trunc(x, 30)) + scale_y_continuous(limits = c(0, 1300)) + labs(x = "", y = "Number of observations") + theme_minimal() + coord_flip() ``` ```` ### Falk's Quarto Attempt ````Qmd --- title: "CrayWatch Dashboard" format: dashboard: theme: light: lux dark: solar logo: https://www.vlaanderen.be/inbo/images/INBO-logo.svg scrolling: false expandable: true nav-buttons: - icon: github href: https://coding-club.inbo.be ---  ```{r setup} #| include: false # 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 # Install thematic and un-comment for themed static plots (i.e., ggplot2) # thematic::thematic_qmd() # thematic does not cooperate with quarto yet https://github.com/quarto-dev/quarto-cli/discussions/9331 ``` ```{r load-data} #| include: false # Read data cray_df <- readr::read_tsv( here::here("codingclub", "data", "20250224_craywatch_cleaned.txt"), na = "", guess_max = 10000 ) glimpse(cray_df) ``` # Overview ## Row {height=25%} ```{r calculate-rates} #| include: false 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 ``` ### Column {.flow} ```{r} gauge(percentage_craywatch, min = 0, max = 100, symbol = '%', gaugeSectors( success = c(0,50), warning = c(50,80), danger = c(80,100), colors = c(success = "#bd4583", warning = "#bd4583", "danger" = "#bd4583") )) ``` ### Column {.flow}  ```{r} #| content: valuebox #| title: "total observations" list( icon = "camera", color = "#dea3c3", value = tot_obs ) ``` ## Row {.flow} ```{r} cray_df %>% count(datasetName) %>% mutate(datasetName = reorder(datasetName, n)) %>% ggplot(aes(x = datasetName, y = n)) + geom_bar(stat = "identity", fill = "#bd4583", color = "black") + geom_text(aes(label = n), vjust = 0, hjust = 0) + scale_x_discrete(label = function(x) stringr::str_trunc(x, 30)) + scale_y_continuous(limits = c(0, 1300)) + labs(x = "", y = "Number of observations") + theme_minimal() + coord_flip() ``` # Details ## Row {height=40%, .tabset} ```{r} #| title: Species Abundance 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") ``` ```{r} #| title: Species Distribution 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") ``` ```` ### Poging Rhea ````rmd --- title: "Craywatch dashboard" output: flexdashboard::flex_dashboard: orientation: rows vertical_layout: scroll --- ```{r setup, include=FALSE} # 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 ) ``` Citizen Science ===================================== Column {data-width=650} ----------------------------------------------------------------------- ### Impact citizen science ```{r Gauge chart} ## 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 gauge(percentage_craywatch, min = 0, max = 100, symbol = '%', gaugeSectors( success = c(75, 100), warning = c(35, 74), danger = c(0, 34) )) ``` ### Absolute number of observations linked to Craywatch (waarnemingen.be) ```{r value box} ## Gauge chart and value box on top #### #Tip: Use camera as icon # 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 valueBox(tot_obs, icon = "fa-camera") ``` Column {data-width=650} ----------------------------------------------------------------------- ### Number of observations per dataset ```{r} #histogram with the number of observations per dataset short_names <- c("Waarnemingen.be - Non-native animal occurrences in Flanders and the Brussels Capital Region, Belgium" = "Waarnemingen.be", "RATO - Daily operations commissioned by the province East Flanders, Belgium" = "RATO", "Invasive species - American bullfrog (Lithobates catesbeianus) in Flanders, Belgium (post 2018)" = "IS - American bullfrog", "iNaturalist research-grade observations" = "iNaturalist", "Monitoring of fishes and crustaceans by Province East Flanders in Flanders, Belgium" = "Monitoring Province EF") cray_df %>% group_by(datasetName) %>% summarise(tot_obs_dataset = n()) %>% mutate(short_name = short_names[datasetName]) %>% ggplot() + geom_bar(aes(x = short_name, y = tot_obs_dataset), stat = 'identity') + labs(title = "Aantal observaties per dataset", x = "Dataset", y = "Aantal observaties") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` Crayfishes in Flanders {data-orientation=rows} ===================================== Column {data-width=350} ----------------------------------------------------------------------- ### Plot per date (year/month) and species ```{r Chart 1} ## Chart 1 - top - Plot per date (year/month) and 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") ``` Column {data-width=350} ----------------------------------------------------------------------- ### Leaflet map ```{r Chart 2} ## 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") ``` ```` ## Challenge 3 ### Ward's attempt https://rpubs.com/wlangera/1275856 RPubs dashboard be deleted after coding club. yml header used: ``` --- title: "Craywatch dashboard" output: flexdashboard::flex_dashboard: theme: version: 4 bg: "#ffffff" fg: "#356196" primary: "#c04384" navbar-bg: "#000000" base_font: Cailibri heading_font: Sans Pro source_code: embed orientation: columns vertical_layout: fill --- ``` See screenshot example here: ![image](https://hackmd.io/_uploads/HyHA8CY9Jg.png)

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.