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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.

# 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.

---
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

---
title: "CrayWatch Dashboard"
format: 
  dashboard:
    theme: united
---

<!-- 
themes: https://quarto.org/docs/dashboards/theming.html
-->

```{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

---
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

---
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 
---

### 

<!-- 1. A gauge on top to show in percentage the number of observations linked to craywatch (= obs from waarnemingen.be dataset). In this way, we show how impactful citizen science is for detecting these species. Tip: check the gauges section in the flexdashboard documentation. -->

```{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)
))

```

### 

<!-- A value box with the absolute number of observations linked to craywatch (waarnemingen.be). Use a camera as icon. Tip: check the value boxes section in the flexdashboard documentation. And if you find a way to combine the gauge and the value box next to each other, please, shout it out loudly 📢 -->

```{r valuebox}
valueBox(tot_obs, icon = "fa-camera")
```


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

<!-- At the bottom, a histogram with the number of observations per dataset. -->

### 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

---
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

---
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

---

<!-- 
themes: https://quarto.org/docs/dashboards/theming.html
-->

```{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}

<!-- https://quarto.org/docs/dashboards/data-display.html -->

```{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

---
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

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