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irdmalaria_painel_trif
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ESPACE-DEV
lmi-sentinela
irdmalaria_painel_trif
Commits
f0b80be5
Commit
f0b80be5
authored
2 months ago
by
Vincent DUPONT
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Update Maping for tri_f
parent
65599ad7
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2 changed files
modules/harmonized_loc.R
+187
-126
187 additions, 126 deletions
modules/harmonized_loc.R
modules/map.R
+114
-178
114 additions, 178 deletions
modules/map.R
with
301 additions
and
304 deletions
modules/harmonized_loc.R
+
187
−
126
View file @
f0b80be5
###
h
armonized
_loc.R - Analyse par localisation
###
H
armonized
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 list
es
de
zones pour la Colombie
#
Colombia r
es
i
de
ntial 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
This diff is collapsed.
Click to expand it.
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
=
3.93
8
,
zoom
=
9
)
%>%
setView
(
lng
=
-
69.9456
,
lat
=
-4.207
8
,
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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