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# INBO CODING CLUB
13 December 2022
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 | ***
Raïsa Carmen |**
Lynn Pallemaerts |**
Ward Langeraert |*
Luc De Bruyn | *
Emma Cartuyvels |**
Dirk Maes |
Hans Van Calster | **
Pieter Huybrechts |**
Lynn Delgat |**
Matthieu Chastel |
## Challenge 1
### Damiano solution
### Ward solution
````
---
title: "Read and preprocess geese data"
author: "Ward Langeraert"
date: "`r Sys.Date()`"
output:
html_document:
df_print: paged
toc: true
toc_float: true
number_sections: true
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# CHALLENGE 1
Convert the code below to a Rmd document called `1_geese_read_data.Rmd` and make an html version of it.
title : "Read and preprocess geese data"
## Setup
Load libraries:
```{r, message=FALSE, warning=FALSE}
library(tidyverse) # to do datascience
library(geepack) # to do modelling
library(INBOtheme) # to apply INBO style to graphs
library(sf) # to work with geospatial vector data
library(leaflet) # to make dynamic maps
library(htmltools) # to make nice html labels for dynamic maps
```
## Introduction
In this document we will:
1. read geese data
2. explore data
3. preprocess data
## Read data
Read catches and counts of geese in Flanders:
```{r}
# credits to Hans for use here::here()
catch_fl <- read_csv(here::here("data/20221213/20221213_geese_counts.txt"),
na = "NA",
col_types = cols(
province = col_character(),
location = col_character(),
year = col_double(),
latinName = col_character(),
commonName = col_character(),
counts = col_double(),
catched = col_double(),
adult = col_double(),
pulli = col_double(),
not_catched = col_double()
))
```
Number of catch data:
```{r}
nrow(catch_fl)
```
Preview:
```{r}
head(catch_fl, n = 30)
```
## Explore data
### Taxonomic information
Species present:
```{r}
catch_fl %>% distinct(latinName, commonName)
```
### Geographic information
Data are geographically grouped by province and municipality (`location`):
```{r}
catch_fl %>% distinct(province, location)
```
### Temporal information
The data are temporally defined at year level:
```{r}
years <- catch_fl %>% distinct(year) %>% pull()
```
from:
```{r}
min(years)
```
to:
```{r}
max(years)
```
## Preprocess data
Data not linked to any `province` or `location` (`NAs`) will be removed.
Number of rows removed:
```{r, echo=FALSE}
catch_fl %>%
filter(is.na(province) | is.na(location)) %>%
nrow
```
Final dataset:
```{r, echo=FALSE}
catch_fl <- catch_fl %>% filter(!is.na(province) & !is.na(location))
catch_fl
```
````
### Hans solution
````
---
title: "Read and preprocess geese data"
author: "Hans Van Calster"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
number_sections: true
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = here::here())
# Load libraries:
library(tidyverse) # to do datascience
library(INBOtheme) # to apply INBO style to graphs
```
# Introduction
In this document we will:
1. read geese data
2. explore data
3. preprocess data
# Read data
Read catches and counts of geese in Flanders:
```{r read-geese-counts}
catch_fl <- read_csv("data/20221213/20221213_geese_counts.txt",
na = "NA",
col_types = cols(
province = col_character(),
location = col_character(),
year = col_double(),
latinName = col_character(),
commonName = col_character(),
counts = col_double(),
catched = col_double(),
adult = col_double(),
pulli = col_double(),
not_catched = col_double()
))
```
Number of catch data:
```{r}
nrow(catch_fl)
```
Preview:
```{r}
head(catch_fl, n = 30)
```
# Explore data
## Taxonomic information
Species present:
```{r taxa}
catch_fl %>% distinct(latinName, commonName)
```
## Geographic information
Data are geographically grouped by province and municipality (`location`):
```{r locations}
catch_fl %>% distinct(province, location)
```
## Temporal information
The data are temporally defined at year level:
```{r years}
years <- catch_fl %>% distinct(year) %>% pull()
```
from:
```{r echo=FALSE}
min(years)
```
to:
```{r echo=FALSE}
max(years)
```
## Preprocess data
Data not linked to any `province` or `location` (`NAs`) will be removed.
Number of rows removed:
```{r pre-process}
catch_fl %>%
filter(is.na(province) | is.na(location)) %>%
nrow
catch_fl <- catch_fl %>% filter(!is.na(province) & !is.na(location))
```
Final dataset:
```{r filtered-data}
catch_fl
```
````
### Pieter's solution
````
---
title: "Read and preprocess geese data"
author: "PieterH"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
df_print: paged
toc: true
toc_depth: 4
toc_float: true
number_sections: true
collapsed: false
smooth_scroll: true
---
set `html_document:` to `df_print: paged` for pagination in dataframe outputs
You can execute R code in the YAML header!
You can set general options for all chunks here, in this case we want to echo by default!
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Read and preprocess geese data
## Setup
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
```{r, message = FALSE}
library(tidyverse) # to do datascience
library(geepack) # to do modelling
library(INBOtheme) # to apply INBO style to graphs
library(sf) # to work with geospatial vector data
library(leaflet) # to make dynamic maps
library(htmltools) # to make nice html labels for dynamic maps
```
## Introduction
In this document we will:
1. read geese data
2. explore data
3. preprocess data
You can also embed plots, for example:
## Read data
Press `Ctrl+Alt+I` to insert a code chunk!
### Read catches and counts of geese in Flanders:
```{r}
catch_fl <- read_csv(here::here("./data/20221213/20221213_geese_counts.txt"),
na = "NA",
col_types = cols(
province = col_character(),
location = col_character(),
year = col_double(),
latinName = col_character(),
commonName = col_character(),
counts = col_double(),
catched = col_double(),
adult = col_double(),
pulli = col_double(),
not_catched = col_double()
))
```
### Number of catch data:
```{r}
nrow(catch_fl)
```
#### Preview:
```{r}
head(catch_fl, n = 30)
```
## Explore data
### Taxonomic information
#### Species present:
```{r}
catch_fl %>% distinct(latinName, commonName)
```
## Geographic information
Data are geographically grouped by province and municipality (`location`):
```{r}
catch_fl %>% distinct(province, location)
```
## Temporal information
The data are temporally defined at year level:
```{r}
years <- catch_fl %>% distinct(year) %>% pull()
```
from:
```{r, echo = FALSE}
min(years)
```
`echo = FALSE` results in the code not being shown, but it is evaluated to:
```{r}
max(years)
```
## Preprocess data
Data not linked to any `province` or `location` (`NAs`) will be removed.
Number of rows removed:
```{r, echo = FALSE}
catch_fl %>% filter(is.na(province) | is.na(location)) %>% nrow
```
### Final dataset:
```{r, echo = FALSE}
catch_fl <- catch_fl %>% filter(!is.na(province) & !is.na(location))
catch_fl
```
````
## Challenge 2
### Hans solution
````
---
title: "Data visualization"
author: "Hans Van Calster"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
number_sections: true
df_print: paged
code_folding: hide
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE,
tidy = "styler")
knitr::opts_knit$set(root.dir = here::here())
library(tidyverse) # to do datascience
library(geepack) # to do modelling
library(INBOtheme) # to apply INBO style to graphs
library(sf) # to work with geospatial vector data
library(leaflet) # to make dynamic maps
library(htmltools) # to make nice html labels for dynamic maps
```
In this section we will show how number of catches varies by year, province
and species. Both static plots and dynamic maps are generated.
# Read data
Read catches and counts of geese in Flanders:
```{r read-geese-counts}
catch_fl <- read_csv("data/20221213/20221213_geese_counts.txt",
na = "NA",
col_types = cols(
province = col_character(),
location = col_character(),
year = col_double(),
latinName = col_character(),
commonName = col_character(),
counts = col_double(),
catched = col_double(),
adult = col_double(),
pulli = col_double(),
not_catched = col_double()
))
```
Number of catch data:
```{r}
nrow(catch_fl)
```
Preview:
```{r}
head(catch_fl, n = 30)
```
# Explore data
## Taxonomic information
Species present:
```{r taxa}
catch_fl %>% distinct(latinName, commonName)
```
## Geographic information
Data are geographically grouped by province and municipality (`location`):
```{r locations}
catch_fl %>% distinct(province, location)
```
## Temporal information
The data are temporally defined at year level:
```{r years}
years <- catch_fl %>% distinct(year) %>% pull()
```
from:
```{r echo=FALSE}
min(years)
```
to:
```{r echo=FALSE}
max(years)
```
## Preprocess data
Data not linked to any `province` or `location` (`NAs`) will be removed.
Number of rows removed:
```{r pre-process}
catch_fl %>%
filter(is.na(province) | is.na(location)) %>%
nrow
catch_fl <- catch_fl %>% filter(!is.na(province) & !is.na(location))
```
Final dataset:
```{r filtered-data}
catch_fl
```
# Static plots
## Show catches {.tabset}
### per province
```{r}
catch_per_province <- catch_fl %>%
group_by(province) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched_total))
ggplot(catch_per_province,
aes(x = province, y = catched_total)) +
geom_bar(stat = 'identity') +
scale_x_discrete(breaks = 2009:2018)
```
### per year
```{r}
catch_per_year <- catch_fl %>%
group_by(year) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched_total))
ggplot(catch_per_year,
aes(x = year, y = catched_total)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
### per year and province
```{r}
catch_per_year_province <-
catch_fl %>%
group_by(year, province) %>%
summarize(catched = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched))
ggplot(catch_per_year_province,
aes(x = year, y = catched, fill = province)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
## Catch analysis at species level
### Species selection
Before we proceed to analyse the catches at species level, specify the species we are interested to by `commonName`:
```{r echo=TRUE}
species <- c(
"Brandgans",
"Canadese gans",
"Grauwe gans",
"Soepgans",
"Nijlgans"
)
```
### Catches per year and species
```{r}
catch_species <-
catch_fl %>%
filter(commonName %in% species) %>%
group_by(year, commonName) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
arrange(commonName)
ggplot(catch_species,
aes(x = year, y = catched_total, fill = commonName)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
### Data modelling
We apply a GEE (generalized estimating equations) model to data from 2010.
```{r}
model_per_species <-
map(
species,
function(s) {
dfs <- catch_fl %>%
filter(commonName == s & year >= 2010) %>%
arrange(location, year) %>%
mutate(year = as_factor(as.character(year)),
location = as_factor(location))
geeglm(counts ~ 0 + year,
family = poisson,
data = dfs,
waves = year,
id = location)
})
names(model_per_species) <- species
overview_model <- map(model_per_species, ~summary(.))
overview_gee <- map2_dfr(
overview_model,
names(overview_model), function(model, name) {
coefficients(model)[,1:2] %>%
rownames_to_column(var = "year") %>%
as_tibble() %>%
mutate(species = name,
year = str_sub(year, start = 5)) %>%
select(species, everything())
})
overview_gee <-
overview_gee %>%
mutate(
lwr = exp(Estimate - Std.err),
upr = exp(Estimate + Std.err),
Estimate = exp(Estimate)
)
```
```{r}
ggplot(overview_gee, aes(x = year, y = Estimate, ymin = lwr, ymax = upr)) +
geom_errorbar(colour = "cyan3") + geom_point(colour = "cyan4") +
facet_grid(.~species) +
xlab("year") + ylab("Estimated number of geese per location") +
theme_inbo(14) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 9)) +
labs(title = "All provinces")
```
# Dynamic maps
We make dynamic _leaflet_ maps of total number of catches per province.
```{r}
pr_fl <- st_read("./data/20221213/20221213_flemish_provinces.gpkg",
quiet = TRUE)
pr_fl <- pr_fl %>%
dplyr::left_join(catch_per_province,
by = c("TX_PROV_DESCR_NL" = "province"))
```
```{r}
bins <- seq(0, 14000, by = 2000)
pal <- colorBin("YlOrRd", domain = pr_fl$catched_total, bins = bins)
labels <- sprintf(
"<strong>%s</strong><br/>%g catches",
pr_fl$TX_PROV_DESCR_NL, pr_fl$catched_total
) %>% lapply(HTML)
map_catch_pr <- leaflet(pr_fl) %>%
addTiles() %>%
addPolygons(
fillColor = ~pal(catched_total),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))
map_catch_pr
```
````
### Pieter's solution
````
---
title: "Data visualization"
author: "PieterH"
date: "2022-12-13"
output:
html_document:
code_folding: hide
toc: true
toc_depth: 4
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache.globals = TRUE)
```
We are dependent on 1_geese_read_data:
```{r}
catch_fl <- data.table::fread("catch_fl.csv", encoding = "UTF-8")
```
Load libraries:
```{r class.source = 'fold-show', message = FALSE}
library(tidyverse) # to do datascience
library(geepack) # to do modelling
library(INBOtheme) # to apply INBO style to graphs
library(sf) # to work with geospatial vector data
library(leaflet) # to make dynamic maps
library(htmltools) # to make nice html labels for dynamic maps
```
# Data visualization
In this section we will show how number of catches varies by year, province and species. Both static plots and dynamic maps are generated.
## Static plots
### Show catches {.tabset}
#### per province
```{r}
catch_per_province <- catch_fl %>%
group_by(province) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched_total))
ggplot(catch_per_province,
aes(x = province, y = catched_total)) +
geom_bar(stat = 'identity') +
scale_x_discrete(breaks = 2009:2018)
```
#### per year
```{r}
catch_per_year <- catch_fl %>%
group_by(year) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched_total))
ggplot(catch_per_year,
aes(x = year, y = catched_total)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
#### per year and province
```{r}
catch_per_year_province <-
catch_fl %>%
group_by(year, province) %>%
summarize(catched = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched))
ggplot(catch_per_year_province,
aes(x = year, y = catched, fill = province)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
### Catch analysis at species level
#### Species selection
Before we proceed to analyse the catches at species level, specify the species we are interested to by `commonName`:
SHOW THIS CHUNK CODE!
```{r class.source = 'fold-show'}
species <- c(
"Brandgans",
"Canadese gans",
"Grauwe gans",
"Soepgans",
"Nijlgans"
)
```
#### Catches per year and species
```{r message = FALSE}
catch_species <-
catch_fl %>%
filter(commonName %in% species) %>%
group_by(year, commonName) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
arrange(commonName)
ggplot(catch_species,
aes(x = year, y = catched_total, fill = commonName)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
#### Data modelling
We apply a GEE (generalized estimating equations) model to data from 2010.
```{r}
model_per_species <-
map(
species,
function(s) {
dfs <- catch_fl %>%
filter(commonName == s & year >= 2010) %>%
arrange(location, year) %>%
mutate(year = as_factor(as.character(year)),
location = as_factor(location))
geeglm(counts ~ 0 + year,
family = poisson,
data = dfs,
waves = year,
id = location)
})
names(model_per_species) <- species
overview_model <- map(model_per_species, ~summary(.))
overview_gee <- map2_dfr(
overview_model,
names(overview_model), function(model, name) {
coefficients(model)[,1:2] %>%
rownames_to_column(var = "year") %>%
as_tibble() %>%
mutate(species = name,
year = str_sub(year, start = 5)) %>%
select(species, everything())
})
overview_gee <-
overview_gee %>%
mutate(
lwr = exp(Estimate - Std.err),
upr = exp(Estimate + Std.err),
Estimate = exp(Estimate)
)
ggplot(overview_gee, aes(x = year, y = Estimate, ymin = lwr, ymax = upr)) +
geom_errorbar(colour = "cyan3") + geom_point(colour = "cyan4") +
facet_grid(.~species) +
xlab("year") + ylab("Estimated number of geese per location") +
theme_inbo(14) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 9)) +
labs(title = "All provinces")
```
## Dynamic maps
We make dynamic _leaflet_ maps of total number of catches per province.
```{r}
pr_fl <- st_read(here::here("./data/20221213/20221213_flemish_provinces.gpkg"))
pr_fl <- pr_fl %>%
dplyr::left_join(catch_per_province,
by = c("TX_PROV_DESCR_NL" = "province"))
bins <- seq(0, 14000, by = 2000)
pal <- colorBin("YlOrRd", domain = pr_fl$catched_total, bins = bins)
labels <- sprintf(
"<strong>%s</strong><br/>%g catches",
pr_fl$TX_PROV_DESCR_NL, pr_fl$catched_total
) %>% lapply(HTML)
map_catch_pr <- leaflet(pr_fl) %>%
addTiles() %>%
addPolygons(
fillColor = ~pal(catched_total),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))
map_catch_pr
```
````
### Solution Ward
````
---
title: "Data visualization"
author: "Ward Langeraert"
date: "2022-12-13"
output:
html_document:
df_print: paged
toc: true
toc_float:
collapsed: FALSE
number_sections: true
code_folding: show
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# CHALLENGE 2
Title: Data visualization
In this section we will show how number of catches varies by year, province and species. Both static plots and dynamic maps are generated.
## Setup
Load libraries:
```{r, message=FALSE, warning=FALSE}
library(tidyverse) # to do datascience
library(geepack) # to do modelling
library(INBOtheme) # to apply INBO style to graphs
library(sf) # to work with geospatial vector data
library(leaflet) # to make dynamic maps
library(htmltools) # to make nice html labels for dynamic maps
```
## Read data
Read catches and counts of geese in Flanders:
```{r}
catch_fl <- read.csv(here::here("data/20221213/20221213_geese_counts_processed.txt"))
```
## Static plots {.tabset .tabset-fade .tabset-pills}
Show catches (make a tabbed section out of it)
### per province (1st tab)
```{r}
catch_per_province <- catch_fl %>%
group_by(province) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched_total))
ggplot(catch_per_province, aes(x = province, y = catched_total)) +
geom_bar(stat = 'identity') +
scale_x_discrete(breaks = 2009:2018)
```
### per year (2nd tab)
```{r}
catch_per_year <- catch_fl %>%
group_by(year) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched_total))
ggplot(catch_per_year, aes(x = year, y = catched_total)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
### per year and province (3rd tab)
```{r}
catch_per_year_province <-
catch_fl %>%
group_by(year, province) %>%
summarize(catched = sum(catched, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(catched))
ggplot(catch_per_year_province, aes(x = year, y = catched, fill = province)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
## Catch analysis at species level
### Species selection
Before we proceed to analyse the catches at species level, specify the species we are interested to by `commonName`:
```{r}
species <- c(
"Brandgans",
"Canadese gans",
"Grauwe gans",
"Soepgans",
"Nijlgans"
)
```
### Catches per year and species
```{r}
catch_species <-
catch_fl %>%
filter(commonName %in% species) %>%
group_by(year, commonName) %>%
summarize(catched_total = sum(catched, na.rm = TRUE)) %>%
arrange(commonName)
ggplot(catch_species, aes(x = year, y = catched_total, fill = commonName)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = 2009:2018)
```
### Data modelling
We apply a GEE (generalized estimating equations) model to data from 2010.
```{r, class.source = 'fold-hide'}
model_per_species <-
map(
species,
function(s) {
dfs <- catch_fl %>%
filter(commonName == s & year >= 2010) %>%
arrange(location, year) %>%
mutate(year = as_factor(as.character(year)),
location = as_factor(location))
geeglm(counts ~ 0 + year,
family = poisson,
data = dfs,
waves = year,
id = location)
})
names(model_per_species) <- species
overview_model <- map(model_per_species, ~summary(.))
overview_gee <- map2_dfr(
overview_model,
names(overview_model), function(model, name) {
coefficients(model)[,1:2] %>%
rownames_to_column(var = "year") %>%
as_tibble() %>%
mutate(species = name,
year = str_sub(year, start = 5)) %>%
select(species, everything())
})
overview_gee <-
overview_gee %>%
mutate(
lwr = exp(Estimate - Std.err),
upr = exp(Estimate + Std.err),
Estimate = exp(Estimate))
ggplot(overview_gee, aes(x = year, y = Estimate, ymin = lwr, ymax = upr)) +
geom_errorbar(colour = "cyan3") + geom_point(colour = "cyan4") +
facet_grid(.~species) +
xlab("year") + ylab("Estimated number of geese per location") +
theme_inbo(14) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 9)) +
labs(title = "All provinces")
```
## Dynamic maps
We make dynamic _leaflet_ maps of total number of catches per province.
```{r, class.source = 'fold-hide'}
pr_fl <- st_read(here::here("data/20221213/20221213_flemish_provinces.gpkg"))
pr_fl <- pr_fl %>%
dplyr::left_join(catch_per_province,
by = c("TX_PROV_DESCR_NL" = "province"))
bins <- seq(0, 14000, by = 2000)
pal <- colorBin("YlOrRd", domain = pr_fl$catched_total, bins = bins)
labels <- sprintf(
"<strong>%s</strong><br/>%g catches",
pr_fl$TX_PROV_DESCR_NL, pr_fl$catched_total
) %>%
lapply(HTML)
map_catch_pr <- leaflet(pr_fl) %>%
addTiles() %>%
addPolygons(
fillColor = ~pal(catched_total),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))
map_catch_pr
```
````
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