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Commit f0b80be5 authored by Vincent DUPONT's avatar Vincent DUPONT
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Update Maping for tri_f

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modules/harmonized_loc.R
+ 187
− 126
View file @ f0b80be5
### harmonized_loc.R - Analyse par localisation
### Harmonized tab for Brazil/Colombia analysis
##################
### Préparation des données de base
### Data objects
##################
# Fonctions réactives pour les dates
date1 <- reactive({
as.Date(paste0(input$HARLOCdates[1],"-01-01"))
})
......@@ -13,148 +12,99 @@ date2 <- reactive({
as.Date(paste0(input$HARLOCdates[2],"-12-31"))
})
# Préparation des listes de zones pour la Colombie
# Colombia residential areas
HARLOC_residencial_area_co <- JSON_area_har %>%
filter(source == "CO") %>%
select(id, name) %>%
arrange(name)
HARLOC_residencial_area_list_co <- with(HARLOC_residencial_area_co, split(id, name))
# Création de la liste pour la sélection UI
HARLOC_residencial_area_list_co <- setNames(
as.list(HARLOC_residencial_area_co$id),
HARLOC_residencial_area_co$name
)
# Préparation des listes pour le Brésil
# Brazil residential areas
HARLOC_residencial_area_br <- JSON_area_har %>%
filter(source == "BR") %>%
select(id, name) %>%
arrange(name)
HARLOC_residencial_area_list_br <- with(HARLOC_residencial_area_br, split(id, name))
HARLOC_residencial_area_list_br <- setNames(
as.list(HARLOC_residencial_area_br$id),
HARLOC_residencial_area_br$name
)
print("Listes de sélection :")
print("Colombie :")
print(str(HARLOC_residencial_area_list_co))
print("Brésil :")
print(str(HARLOC_residencial_area_list_br))
# Préparation des centres de santé
# Pour la Colombie
# Colombia health centers
HARLOC_health_center_co <- JSON_healthcenter_har %>%
filter(source == "CO") %>%
select(id_center, name) %>%
arrange(name)
HARLOC_health_center_list_co <- with(HARLOC_health_center_co, split(id_center, name))
HARLOC_health_center_list_co <- setNames(
as.list(HARLOC_health_center_co$id_center),
HARLOC_health_center_co$name
)
# Pour le Brésil
# Brazil health centers
HARLOC_health_center_br <- JSON_healthcenter_har %>%
filter(source == "BR") %>%
select(id_center, name) %>%
arrange(name)
HARLOC_health_center_list_br <- with(HARLOC_health_center_br, split(id_center, name))
HARLOC_health_center_list_br <- setNames(
as.list(HARLOC_health_center_br$id_center),
HARLOC_health_center_br$name
)
##################
### Interface utilisateur réactive
##################
# Interface pour la sélection des localités brésiliennes
output$HARLOClocation_br <- renderUI({
# UI Outputs for location selection
output$HARLOClocation_co <- renderUI({
switch(input$HARLOCtype,
"residence_area" = {
choices <- HARLOC_residencial_area_list_br
selectInput(
"HARLOCselection_br",
tr("BR"),
choices = choices,
multiple = TRUE,
selectize = TRUE
)
},
"center" = {
choices <- HARLOC_health_center_list_br
selectInput(
"HARLOCselection_br",
tr("BR"),
choices = choices,
multiple = TRUE,
selectize = TRUE
)
},
"infection_place" = {
choices <- HARLOC_residencial_area_list_br
selectInput(
"HARLOCselection_br",
tr("BR"),
choices = choices,
multiple = TRUE,
selectize = TRUE
)
}
"residence_area" = selectInput("HARLOCselection_co", tr("CO"), HARLOC_residencial_area_list_co, multiple = TRUE),
"center" = selectInput("HARLOCselection_co", tr("CO"), HARLOC_health_center_list_co, multiple = TRUE),
"infection_place" = selectInput("HARLOCselection_co", tr("CO"), HARLOC_residencial_area_list_co, multiple = TRUE)
)
})
# Interface pour la sélection des localités colombiennes
output$HARLOClocation_co <- renderUI({
output$HARLOClocation_br <- renderUI({
switch(input$HARLOCtype,
"residence_area" = {
choices <- HARLOC_residencial_area_list_co
selectInput(
"HARLOCselection_co",
tr("CO"),
choices = choices,
multiple = TRUE,
selectize = TRUE
)
},
"center" = {
choices <- HARLOC_health_center_list_co
selectInput(
"HARLOCselection_co",
tr("CO"),
choices = choices,
multiple = TRUE,
selectize = TRUE
)
},
"infection_place" = {
choices <- HARLOC_residencial_area_list_co
selectInput(
"HARLOCselection_co",
tr("CO"),
choices = choices,
multiple = TRUE,
selectize = TRUE
)
}
"residence_area" = selectInput("HARLOCselection_br", tr("BR"), HARLOC_residencial_area_list_br, multiple = TRUE, selected = c("126")),
"center" = selectInput("HARLOCselection_br", tr("BR"), HARLOC_health_center_list_br, multiple = TRUE),
"infection_place" = selectInput("HARLOCselection_br", tr("BR"), HARLOC_residencial_area_list_br, multiple = TRUE)
)
})
##################
### Filtrage des données
##################
# Données pour la Colombie
# Filter for Colombia data
HARLOC_sub_co <- reactive({
req(input$HARLOCtype)
req(input$HARLOCselection_co)
df <- consultation_har_filter(JSON_cons_har,
source = "CO",
new_attack = input$HARLOCnew_attack,
diagn = input$HARLOCdiagn,
sex = input$HARLOCsex,
minAge = input$HARLOCage[1],
maxAge = input$HARLOCage[2]
) %>%
filter(consultation_date >= date1() & consultation_date <= date2())
if(input$HARLOCtype == "center"){
df <- df %>% filter(id_center %in% input$HARLOCselection_co)
} else if(input$HARLOCtype == "residence_area"){
df <- df %>% filter(residence_place %in% input$HARLOCselection_co)
} else if(input$HARLOCtype == "infection_place"){
df <- df %>% filter(infection_place %in% input$HARLOCselection_co)
}
return(df)
})
# Filter for Brazil data
HARLOC_sub_br <- reactive({
df <- consultation_har_filter(JSON_cons_har,
source = "BR",
new_attack = input$HARLOCnew_attack,
diagn = input$HARLOCdiagn,
sex = input$HARLOCsex,
minAge = input$HARLOCage[1],
maxAge = input$HARLOCage[2]
) %>%
filter(consultation_date >= date1() & consultation_date <= date2())
# Ajout de messages de diagnostic
print("Filtrage des données colombiennes")
print(paste("Type sélectionné:", input$HARLOCtype))
print(paste("Sélections:", paste(input$HARLOCselection_co, collapse=", ")))
if(input$HARLOCtype == "center"){
df <- df %>% filter(id_center %in% input$HARLOCselection_br)
} else if(input$HARLOCtype == "residence_area"){
df <- df %>% filter(residence_place %in% input$HARLOCselection_br)
} else if(input$HARLOCtype == "infection_place"){
df <- df %>% filter(infection_place %in% input$HARLOCselection_br)
}
return(df)
})
# Information quality filters
HARLOC_sub_infqual_co <- reactive({
df <- consultation_har_filter(JSON_cons_har,
source = "CO",
new_attack = input$HARLOCnew_attack,
......@@ -163,21 +113,132 @@ HARLOC_sub_co <- reactive({
minAge = input$HARLOCage[1],
maxAge = input$HARLOCage[2]
) %>%
filter(consultation_date >= date1() &
consultation_date <= date2())
filter(consultation_date >= date1() & consultation_date <= date2()) %>%
is.na() %>%
colMeans() * 100
# Filtrage selon le type de localisation
df <- switch(input$HARLOCtype,
"center" = df %>% filter(id_center %in% input$HARLOCselection_co),
"residence_area" = df %>% filter(residence_place %in% input$HARLOCselection_co),
"infection_place" = df %>% filter(infection_place %in% input$HARLOCselection_co)
)
df <- replace(df, is.nan(df), 100)
return(df)
})
HARLOC_sub_infqual_br <- reactive({
df <- consultation_har_filter(JSON_cons_har,
source = "BR",
new_attack = input$HARLOCnew_attack,
diagn = input$HARLOCdiagn,
sex = input$HARLOCsex,
minAge = input$HARLOCage[1],
maxAge = input$HARLOCage[2]
) %>%
filter(consultation_date >= date1() & consultation_date <= date2()) %>%
is.na() %>%
colMeans() * 100
print(paste("Nombre de cas après filtrage:", nrow(df)))
df <- replace(df, is.nan(df), 100)
return(df)
})
# Données pour le Brésil (même structure)
HARLOC_sub_br <- reactive({
# Code similaire pour le Brésil
# Day of week tabulation
HARLOC_co_tab_day_week <- reactive({
if(nrow(HARLOC_sub_co()) > 0){
as.data.frame(incidence(HARLOC_sub_co()$consultation_date, interval = 1))
}
})
HARLOC_br_tab_day_week <- reactive({
if(nrow(HARLOC_sub_br()) > 0){
as.data.frame(incidence(HARLOC_sub_br()$consultation_date, interval = 1))
}
})
##################
### Plots
##################
# Incidence plot
output$HARLOC_plot_incidence <- renderPlotly({
# Variables
HARLOCtype <- input$HARLOCtype
HARLOCagg <- as.numeric(input$HARLOCagg)
# Main plot configuration
plot_incidence <- plot_ly() %>%
layout(xaxis = list(title = tr("date")), yaxis = list(title = tr("cases"))) %>%
layout(legend = list(x = 0, y = -.25, orientation = 'h'), showlegend=TRUE)
if(HARLOCtype == "center"){
if(HARLOCagg == 30){ # Monthly aggregation (example for one aggregation type)
# Colombia
for(loc in unique(HARLOC_sub_co()$id_center)){
df <- HARLOC_sub_co() %>% filter(id_center == loc)
tab <- df %>%
group_by(dates = floor_date(consultation_date, "month")) %>%
summarize(counts=n()) %>%
complete(dates = seq.Date(date1(), date2(), by = "month"), fill = list(counts = 0))
loc_name <- HARLOC_health_center_co[which(HARLOC_health_center_co$id_center == loc), "name"]
plot_incidence <- add_trace(plot_incidence, data = tab, x = ~dates, y = ~counts,
type = 'scatter', mode = 'lines', name = paste0(loc_name, " (CO)"))
}
# Brazil
for(loc in unique(HARLOC_sub_br()$id_center)){
df <- HARLOC_sub_br() %>% filter(id_center == loc)
tab <- df %>%
group_by(dates = floor_date(consultation_date, "month")) %>%
summarize(counts=n()) %>%
complete(dates = seq.Date(date1(), date2(), by = "month"), fill = list(counts = 0))
loc_name <- HARLOC_health_center_br[which(HARLOC_health_center_br$id_center == loc), "name"]
plot_incidence <- add_trace(plot_incidence, data = tab, x = ~dates, y = ~counts,
type = 'scatter', mode = 'lines', name = paste0(loc_name, " (BR)"))
}
}
# Add similar blocks for other aggregation periods (1, 6, 90, 366)
}
# Add similar blocks for residence_area and infection_place
return(plot_incidence)
})
# Information completeness
output$HARLOC_information_completness <- renderUI({
tabBox(
width = 12,
height = "180px",
title = tr("missing_information"),
tabPanel(
title = tr("infection_place"),
valueBox(paste0(round(HARLOC_sub_infqual_co()["infection_place"],2),"%"), tr("CO"), color = "purple", width = 6),
valueBox(paste0(round(HARLOC_sub_infqual_br()["infection_place"],2),"%"), tr("BR"), color = "purple", width = 6)
),
tabPanel(
title = tr("residence_area"),
valueBox(paste0(round(HARLOC_sub_infqual_co()["residence_place"],2),"%"), tr("CO"), color = "purple", width = 6),
valueBox(paste0(round(HARLOC_sub_infqual_br()["residence_place"],2),"%"), tr("BR"), color = "purple", width = 6)
),
tabPanel(
title = tr("center"),
valueBox(paste0(round(HARLOC_sub_infqual_co()["id_center"],2),"%"), tr("CO"), color = "purple", width = 6),
valueBox(paste0(round(HARLOC_sub_infqual_br()["id_center"],2),"%"), tr("BR"), color = "purple", width = 6)
)
)
})
# Calendar heatmaps
output$HARLOC_plot_day_week_br <- renderPlot({
if(length(HARLOC_br_tab_day_week() > 0)){
calendarHeat(dates = HARLOC_br_tab_day_week()$dates,
values = HARLOC_br_tab_day_week()$counts,
color="g2r")
} else {
plot.new()
}
})
output$HARLOC_plot_day_week_co <- renderPlot({
if(length(HARLOC_co_tab_day_week() > 0)){
calendarHeat(dates = HARLOC_co_tab_day_week()$dates,
values = HARLOC_co_tab_day_week()$counts,
color="g2r")
} else {
plot.new()
}
})
\ No newline at end of file
modules/map.R
+ 114
− 178
View file @ f0b80be5
### Map tab
avg.formula =
"function (cluster) {
var markers = cluster.getAllChildMarkers();
var sum = 0;
var count = 0;
var avg = 0;
var mFormat = ' marker-cluster-';
for (var i = 0; i < markers.length; i++) {
if(markers[i].options.weight != undefined){
sum += markers[i].options.weight;
count += 1;
}
}
//avg = Math.round(sum/count);
avg = sum;
if(avg<500) {mFormat+='small'} else if (avg>10000){mFormat+='large'}else{mFormat+='medium'};
return L.divIcon({ html: '<div><span>' + avg + '</span></div>', className: 'marker-cluster'+mFormat, iconSize: L.point(40, 40) });
}"
### limites Guyane - Oiapoque
f_lim_guyam <- "data/shp/guy-oiapoque_final.shp"
lim_guyam <- sf::st_read(f_lim_guyam, quiet = TRUE) %>%
st_transform(crs=4326)
### OSM data 2022
f_hosp <- "data/osm/hospital_guyamapa.osm"
f_clin <- "data/osm/clinic_guyamapa.osm"
query_update <- FALSE
if (query_update) {
bbox_guyam <- "(-1.24,-54.88,5.78,-49.34)"
q_hosp_guyam <- paste0(
"( nwr[amenity=hospital]", bbox_guyam, "; ); out center;")
q_clin_guyam <- paste0(
"( nwr[amenity=clinic]", bbox_guyam, "; ); out center;")
result_hosp <- osmdata_xml(q_hosp_guyam, filename=f_hosp)
result_clin <- osmdata_xml(q_clin_guyam, filename=f_clin)
}
# read osm points
hospital <- sf::st_read(f_hosp, layer = 'points', quiet = TRUE)
clinica <- sf::st_read(f_clin, layer = 'points', quiet = TRUE)
hospital_icon <- makeIcon(
iconUrl = 'data/svg/circle-h-solid.svg', iconWidth = 20, iconHeight = 20)
clinica_icon <- makeIcon(
iconUrl='data/svg/house-medical-solid.svg', iconWidth = 20, iconHeight = 20)
### data wms cayenne 2022
url_wms <- paste0(
"https://cartogy.cayenne.ird.fr/index.php/lizmap/service/",
"?repository=risquepalu&project=progysat_carto_risque_lizmap&SERVICE=WMS"
# Définition des limites de la zone d'étude
# Coordonnées approximatives de la zone tri-frontalière
tri_frontier_coords <- matrix(c(
-70.5, -3.9, # Nord-Ouest
-69.5, -3.9, # Nord-Est
-69.5, -4.5, # Sud-Est
-70.5, -4.5, # Sud-Ouest
-70.5, -3.9 # Fermer le polygone
), ncol = 2, byrow = TRUE)
# Création d'un objet sf simple pour les limites
lim_guyam <- st_sf(
geometry = st_sfc(st_polygon(list(tri_frontier_coords))),
crs = 4326
)
### data GBIF
url_anopheles_darlingi <- paste0(
"https://api.gbif.org/v2/map/occurrence/density/{z}/{x}/{y}@1x.png?",
"style=classic.poly&bin=hex&hexPerTile=17&taxonKey=1650149&srs=EPSG:3857")
url_anopheles_marajoara <- paste0(
"https://api.gbif.org/v2/map/occurrence/density/{z}/{x}/{y}@1x.png?",
"style=classic.poly&bin=hex&hexPerTile=17&taxonKey=1650776&srs=EPSG:3857")
output$MAPmissing <- renderText("")
# Fonction réactive pour préparer les données de la carte
df_map <- reactive({
map_dates <- req(input$MAPdates)
map_type <- req(input$MAPtype)
map_diagn <- req(input$MAPdiagn)
# print("**df_map**")
# print(map_dates)
# print(map_type)
date1 <- as.Date(paste0(map_dates[1],"-01-01"))
date2 <- as.Date(paste0(map_dates[2],"-12-31"))
if(map_type == "residence_area"){
df <- consultation_har_filter(
JSON_cons_har, new_attack="Any",
df <- consultation_har_filter(
JSON_cons_har,
new_attack = "Any",
diagn = map_diagn
) %>%
filter(consultation_date >= date1 & consultation_date <= date2) %>%
......@@ -89,42 +37,68 @@ df_map <- reactive({
inner_join(coords, by = c("residence_place" = "id")) %>%
mutate(id = residence_place)
missing_total <- JSON_cons_har %>%
filter(consultation_date >= date1 & consultation_date <= date2) %>%
nrow()
# missing_per <- round((sum(df$count)/missing_total)*100, 2)
# missing_n <- sum(df$count)
} else if(map_type == "infection_place"){
df <- consultation_har_filter(
JSON_cons_har, new_attack="Any",
df <- consultation_har_filter(
JSON_cons_har,
new_attack = "Any",
diagn = map_diagn
) %>%
filter(consultation_date >= date1 & consultation_date <= date2) %>%
select(infection_place, source) %>%
inner_join(coords, by = c("infection_place" = "id")) %>%
mutate(id = infection_place)
}
return(df)
})
# Rendu de la carte
# Fonction réactive pour préparer les données de la carte
df_map <- reactive({
map_dates <- req(input$MAPdates)
map_type <- req(input$MAPtype)
map_diagn <- req(input$MAPdiagn)
date1 <- as.Date(paste0(map_dates[1],"-01-01"))
date2 <- as.Date(paste0(map_dates[2],"-12-31"))
if(map_type == "residence_area"){
df <- consultation_har_filter(
JSON_cons_har,
new_attack = "Any",
diagn = map_diagn
) %>%
filter(consultation_date >= date1 & consultation_date <= date2) %>%
select(residence_place, source) %>%
inner_join(coords, by = c("residence_place" = "id")) %>%
mutate(id = residence_place)
missing_total <- JSON_cons_har %>%
} else if(map_type == "infection_place"){
df <- consultation_har_filter(
JSON_cons_har,
new_attack = "Any",
diagn = map_diagn
) %>%
filter(consultation_date >= date1 & consultation_date <= date2) %>%
nrow()
select(infection_place, source) %>%
inner_join(coords, by = c("infection_place" = "id")) %>%
mutate(id = infection_place)
}
return(df)
})
# Rendu de la carte
output$map <- renderLeaflet({
req(df_map()) # S'assurer que les données sont disponibles
# reinit map on language change !
lang <- req(input$language)
print(lang)
dados_mapa <- isolate(df_map())
pal <- colorFactor(c("red","blue"), dados_mapa$source)
dados_mapa <- df_map()
pal <- colorFactor(c("red", "blue"), dados_mapa$source)
leaflet() %>%
addTiles(options = providerTileOptions(maxZoom = 12), group = "OSM Tiles") %>%
setView(lng = -51.548, lat = 3.938, zoom = 9) %>%
setView(lng = -69.9456, lat = -4.2078, zoom = 8) %>%
addMapPane("pane_lim", zIndex = 395) %>%
addMapPane("pane_an_darlingi", zIndex = 400) %>%
addMapPane("pane_clinic", zIndex = 410) %>%
......@@ -139,118 +113,80 @@ output$map <- renderLeaflet({
fillOpacity = 0.05,
options = pathOptions(pane = "pane_lim")
) %>%
addTiles(
urlTemplate = url_anopheles_darlingi,
attribution = "GBIF",
layerId = "GBIF_Anopheles_Darlingi",
group = "GBIF_An_Darlingi",
options = tileOptions(pane = "pane_an_darlingi")
# Villes principales de la tri-frontière
addMarkers(
lng = -69.9456, lat = -4.2078,
label = "Leticia (CO)",
group = "Cities"
) %>%
addMarkers(
data = clinica, group = "Clinics", icon = clinica_icon,
popup = ~name,
options = markerOptions(opacity=0.8, pane = "pane_clinic")) %>%
lng = -69.9388, lat = -4.2333,
label = "Tabatinga (BR)",
group = "Cities"
) %>%
addMarkers(
data = hospital, group = "Hospitals", icon = hospital_icon,
popup = ~name,
options = markerOptions(pane = "pane_hospital")) %>%
clearGroup("Dados") %>%
lng = -69.9611, lat = -4.2172,
label = "Santa Rosa (PE)",
group = "Cities"
) %>%
# Données des cas
addCircleMarkers(
data = dados_mapa,
lat = ~y_coordinate,
lng = ~x_coordinate,
color = ~pal(source),
stroke = FALSE, fillOpacity = 0.5,
stroke = FALSE,
fillOpacity = 0.5,
clusterOptions = markerClusterOptions(spiderfyOnMaxZoom = FALSE),
options = pathOptions(pane = "pane_cases"),
group = "Dados") %>%
hideGroup(
c("Clinics", "Hospitals", "GBIF_An_Darlingi"))
group = "Dados",
label = ~paste0(name, " (", source, ")")
) %>%
addLegend(
"bottomright",
pal = pal,
values = dados_mapa$source,
title = "Source",
opacity = 0.7
)
})
# Interactive populate
# Observer pour mettre à jour la carte quand les filtres changent
observe({
print("MAP observeEvent")
df <- df_map()
print(nrow(df))
# Map
pal <- colorFactor(c("red","blue"), df$source)
leafletProxy("map", data = df) %>%
dados_mapa <- df_map()
pal <- colorFactor(c("red", "blue"), dados_mapa$source)
leafletProxy("map", data = dados_mapa) %>%
clearGroup("Dados") %>%
# clearShapes() %>%
# clearMarkers() %>%
# clearMarkerClusters() %>%
addCircleMarkers(
data = df,
lat = ~y_coordinate,
lng = ~x_coordinate,
color = ~pal(source),
stroke = FALSE, fillOpacity = 0.5,
stroke = FALSE,
fillOpacity = 0.5,
clusterOptions = markerClusterOptions(spiderfyOnMaxZoom = FALSE),
group = "Dados"
options = pathOptions(pane = "pane_cases"),
group = "Dados",
label = ~paste0(name, " (", source, ")")
)
})
# Observer pour mettre à jour la carte quand les filtres changent
observe({
val <- input$MAPosm
mapobj <- leafletProxy("map")
if ("Hospitals" %in% val) {
mapobj %>% showGroup("Hospitals")
} else {
mapobj %>% hideGroup("Hospitals")
}
if ("Clinics" %in% val) {
mapobj %>% showGroup("Clinics")
} else {
mapobj %>% hideGroup("Clinics")
}
})
observe({
val <- input$MAPgbif
if ("Anopheles darlingi" %in% val) {
leafletProxy("map") %>% showGroup("GBIF_An_Darlingi")
} else {
leafletProxy("map") %>% hideGroup("GBIF_An_Darlingi")
}
})
# Interactive WMS Layers
observeEvent(input$MAPrasters, {
layer_wms <- req(input$MAPrasters)
print(paste("MAPrasters", ":", layer_wms))
proxy_map <- leafletProxy("map", data = df)
if (layer_wms=="NO_SEL") {
proxy_map %>% clearGroup("WMS_Raster")
} else {
proxy_map %>%
clearGroup("WMS_Raster") %>%
addWMSTiles(
url_wms,
layers = layer_wms,
options = WMSTileOptions(
format = "image/png", opacity=0.8, transparent = TRUE),
attribution = "UMR Espace-Dev",
group = "WMS_Raster"
)
}
})
output$MAPlegend <- renderUI({
layer_wms <- req(input$MAPrasters)
if (layer_wms=="NO_SEL") {
html_output <- ""
} else {
html_output <- img(src=paste0(
"https://cartogy.cayenne.ird.fr/index.php/lizmap/service/?",
"repository=risquepalu&project=progysat_carto_risque_lizmap&",
"SERVICE=WMS&VERSION=1.3.0&REQUEST=GetLegendGraphic&",
"LAYER=", layer_wms, "&",
"FORMAT=image/png&STYLE=padrão&SLD_VERSION=1.1.0&",
"ITEMFONTSIZE=9&SYMBOLSPACE=1&ICONLABELSPACE=2&DPI=96&LAYERSPACE=0&",
"LAYERFONTBOLD=FALSE&LAYERTITLE=FALSE"))
}
html_output
dados_mapa <- df_map()
pal <- colorFactor(c("red", "blue"), dados_mapa$source)
leafletProxy("map", data = dados_mapa) %>%
clearGroup("Dados") %>%
addCircleMarkers(
lat = ~y_coordinate,
lng = ~x_coordinate,
color = ~pal(source),
stroke = FALSE,
fillOpacity = 0.5,
clusterOptions = markerClusterOptions(spiderfyOnMaxZoom = FALSE),
options = pathOptions(pane = "pane_cases"),
group = "Dados",
label = ~paste0(name, " (", source, ")")
)
})
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