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server.R
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server.R
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## _____________________________
### File: server.R
##
## Date created: 2023-02-23
## Author: Pol Carbó Mestre
## Contact: [email protected]
##
## _____________________________
## Description:
## Server for the Interactive Planner app
## _____________________________
server <- function(input, output, session) {
# INTRO AND TOUR ----
observeEvent("", {
showModal(modalDialog(
htmltools::tags$iframe(src = "app_intro.html", width = '100%', height = 570, style = "border:none;"),
easyClose = TRUE,
footer = tagList(
actionButton(inputId = "no_tour", label = "Skip tour", style = "align: center"),
actionButton(inputId = "intro_tour", label = "Introduction Tour", icon = icon("fa-regular fa-compass"))
), style = "margin-top:-45px;")
)
})
observeEvent(input$intro_tour,{
removeModal()
})
observeEvent(input$no_tour,{
removeModal()
})
observeEvent(input$intro_tour ,{
conductor_nr$init()$start()
})
observeEvent(input$invoke_tour ,{
if (input$main_menu == "natural_resources_map"){
conductor_nr$init()$start()
} else if (input$main_menu == "environmental_stake_map"){
conductor_s$init()$start()
} else if (input$main_menu == "environmental_threats_map"){
conductor_t$init()$start()
}else if (input$main_menu == "equity_issues_map"){
conductor_d$init()$start()
}
})
# NATURAL RESOURCES AXIS (MAIN TAB) ----
# Defining Map Elements ----
## Select layers individually ----
observeEvent(input$checkbox_water, {
if (input$checkbox_water) {
updateSliderInput(session, "water_w", value = 100)
updateSliderInput(session, "soil_w", value = 0)
updateSliderInput(session, "biodiversity_w", value = 0)
updateSliderInput(session, "resil_w", value = 0)
}
})
observeEvent(input$checkbox_agri, {
if (input$checkbox_agri) {
updateSliderInput(session, "soil_w", value = 100)
updateSliderInput(session, "water_w", value = 0)
updateSliderInput(session, "biodiversity_w", value = 0)
updateSliderInput(session, "resil_w", value = 0)
}
})
observeEvent(input$checkbox_bio, {
if (input$checkbox_bio) {
updateSliderInput(session, "biodiversity_w", value = 100)
updateSliderInput(session, "soil_w", value = 0)
updateSliderInput(session, "water_w", value = 0)
updateSliderInput(session, "resil_w", value = 0)
}
})
observeEvent(input$checkbox_com, {
if (input$checkbox_com) {
updateSliderInput(session, "resil_w", value = 100)
updateSliderInput(session, "soil_w", value = 0)
updateSliderInput(session, "biodiversity_w", value = 0)
updateSliderInput(session, "water_w", value = 0)
}
})
## AHP weights ----
### Calculate aggregated preference values from weights ----
agg_pref_df <- reactive({
data.frame(resources = c("soil_fz","resil_fz","bio_fz","water_fz"),
weights= c(input$soil_w,
input$resil_w,
input$biodiversity_w,
input$water_w)) %>%
mutate(agg_pref = weights/sum(weights))
})
output$soil_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df()[1,3]),2)))
output$resilience_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df()[2,3]),2)))
output$biodiversity_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df()[3,3]),2)))
output$water_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df()[4,3]),2)))
### Set group weights ----
water_weights = reactiveValues(None=NULL,
All=88,
Government=78,
"Non-profit"=100,
"Farm/Ranch"=76,
Indigenous=58)
# If selected element changes, then update the slider
observeEvent(input$stakeholder_w, {
selected_weight = water_weights[[input$stakeholder_w]]
updateSliderInput(session, "water_w", value = selected_weight)
})
agri_weights = reactiveValues(None=NULL,
All=53,
Government=32,
"Non-profit"=96,
"Farm/Ranch"=100,
Indigenous=44)
observeEvent(input$stakeholder_w, {
selected_weight = agri_weights[[input$stakeholder_w]]
updateSliderInput(session, "soil_w", value = selected_weight)
})
bio_weights = reactiveValues(None=NULL,
All= 100,
Government=100,
"Non-profit"=100,
"Farm/Ranch"=20,
Indigenous=75)
observeEvent(input$stakeholder_w, {
selected_weight = bio_weights[[input$stakeholder_w]]
updateSliderInput(session, "biodiversity_w", value = selected_weight)
})
com_weights = reactiveValues(None=NULL,
All=72,
Government=59,
"Non-profit"=74,
"Farm/Ranch"=22,
Indigenous=100)
observeEvent(input$stakeholder_w, {
selected_weight = com_weights[[input$stakeholder_w]]
updateSliderInput(session, "resil_w", value = selected_weight)
})
### Update selection to "None" ----
observe({
if(input$resil_w == com_weights[["Government"]] &
input$biodiversity_w == bio_weights[["Government"]] &
input$soil_w == agri_weights[["Government"]] &
input$water_w == water_weights[["Government"]]){
} else if(input$resil_w == com_weights[["All"]] &
input$biodiversity_w == bio_weights[["All"]] &
input$soil_w == agri_weights[["All"]] &
input$water_w == water_weights[["All"]]) {
} else if(input$resil_w == com_weights[["Non-profit"]] &
input$biodiversity_w == bio_weights[["Non-profit"]] &
input$soil_w == agri_weights[["Non-profit"]] &
input$water_w == water_weights[["Non-profit"]]) {
} else if(input$resil_w == com_weights[["Farm/Ranch"]] &
input$biodiversity_w == bio_weights[["Farm/Ranch"]] &
input$soil_w == agri_weights[["Farm/Ranch"]] &
input$water_w == water_weights[["Farm/Ranch"]]) {
} else if(input$resil_w == com_weights[["Indigenous"]] &
input$biodiversity_w == bio_weights[["Indigenous"]] &
input$soil_w == agri_weights[["Indigenous"]] &
input$water_w == water_weights[["Indigenous"]]) {
} else {
updateSelectInput(session,"stakeholder_w",selected="None")
}
})
## Apply selected weights ----
weights_reactive <- reactive({
### Using Aggregated Preference values ----
resources_axis_r %>%
mutate("soil_fz" = soil_fz * agg_pref_df()[1,3]) %>%
mutate("resil_fz" = resil_fz * agg_pref_df()[2,3]) %>%
mutate("bio_fz" = bio_fz * agg_pref_df()[3,3]) %>%
mutate("water_fz" = water_fz * agg_pref_df()[4,3]) %>%
mutate(score = soil_fz + resil_fz + bio_fz + water_fz) %>%
mutate(norm_score = range_norm_manual(score))
#### Applying weights directly ----
#### Process for Raster data
# resources_axis_r %>%
# mutate("soil_fz" = soil_fz * input$soil_w) %>%
# mutate("resil_fz" = resil_fz * input$resil_fz) %>%
# mutate("bio_fz" = bio_fz * input$biodiversity_w) %>%
# mutate("water_fz" = water_fz * input$water_w) %>%
# mutate(score = soil_fz + resil_fz + bio_fz + water_fz) %>%
# mutate(norm_score = range_norm_manual(score))
#### Process for Vector data
# resources_axis_df %>%
# mutate_at(vars(soil_fz), function(x) input$soil_w * x) %>%
# mutate_at(vars(resil_fz), function(x) input$resil_fz * x) %>%
# mutate_at(vars(bio_fz), function(x) input$biodiversity_w * x) %>%
# mutate_at(vars(water_fz), function(x) input$water_w * x) %>%
# mutate(score = soil_fz+resil_fz+bio_fz+water_fz) %>%
# mutate(norm_score = range_norm(score)[,1],.after = objectid) %>%
# select(c(objectid,norm_score))
}) # end weights_reactive
# Leaflet Map Display ----
## Create palette ----
pallete_reactive <- reactive({
#### Process for Raster data
colorNumeric(palette= "Greens",
domain = weights_reactive()["norm_score"][[1]],
na.color = "transparent",
reverse = TRUE)
#### Process for Vector data
# colorNumeric("Greens", weights_reactive()$norm_score) # pallete for leaflet
}) # end pallete_reactive
### Process for Vector data
# map_reactive <- reactive({
#
# left_join(resources_axis_sf, weights_reactive(), by = 'objectid')
#
# }) # end map_reactive
## Create Map ----
output$map <- renderLeaflet({
### Process for Raster data
map <- leaflet(options = leafletOptions(minZoom = 9)) %>% addTiles() %>%
addGeoRaster(weights_reactive()["norm_score"],
opacity = input$alpha,
colorOptions =leafem:::colorOptions(
palette = "Greens",
breaks = seq(min(weights_reactive()["norm_score"][[1]], na.rm = TRUE),
max(weights_reactive()["norm_score"][[1]], na.rm = TRUE),
100),
na.color = "transparent"
),
resolution=10000) %>%
addProviderTiles(providers$Esri.WorldTopoMap) %>%
addLegend(pal = pallete_reactive(),
values = weights_reactive()["norm_score"][[1]],
position = "bottomright",
opacity = input$alpha,
labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE))) %>%
fitBounds(lng1=as.numeric(bb(weights_reactive())[1]),
lat1=as.numeric(bb(weights_reactive())[2]),
lng2=as.numeric(bb(weights_reactive())[3]),
lat2=as.numeric(bb(weights_reactive())[4])) %>%
setMaxBounds(lng1=as.numeric(bb(weights_reactive())[1])-1,
lat1=as.numeric(bb(weights_reactive())[2])-0.4,
lng2=as.numeric(bb(weights_reactive())[3])+1,
lat2=as.numeric(bb(weights_reactive())[4])+0.4) %>%
addDrawToolbar(targetGroup = "draw",
polylineOptions = FALSE,
circleOptions = FALSE,
markerOptions = FALSE,
circleMarkerOptions = FALSE,
editOptions = editToolbarOptions(
edit = TRUE, remove = FALSE, selectedPathOptions = NULL,
allowIntersection = FALSE
),
position = "topright",
singleFeature = TRUE)
## Process for Raster data
# map <- leaflet(map_reactive()) %>%
# setView(lng = -120.3, lat = 34.53, zoom =10 ) %>%
# addPolygons(stroke = FALSE, fillOpacity = input$alpha, smoothFactor = 0.5,
# color = "grey",
# fillColor = ~pallete_reactive()(norm_score)) %>%
# addProviderTiles(providers$Esri.WorldTopoMap) %>%
# addLegend("bottomright", pal = pallete_reactive(), values = ~map_reactive()$norm_score,
# title = "Score",
# opacity = 1)
})
# Extracting data from the map ----
## Extract drawn polygon ----
observeEvent(input$drawing, {
ddf <- data.frame() # The drawing dataframe
ind <- which(ddf == "Feature") # Index for drawing df to break up the df to redraw the shapes.
ind <- as.array(ind)
for (i in 1:nrow(ind)) {
if(i != nrow(ind)) thisShape <- ddf[ind[i]:ind[i+1]]
else thisShape <- ddf[ind[i]:ncol(ddf)]
if(thisShape[3] == "polygon") {
tf <- array(startsWith(names(thisShape),"features.geometry.coordinates"))
w <- 1
pnts <- array()
for (i in 1:nrow(tf)) {
if(tf[i] == TRUE) {
pnts[w] <- thisShape[i]
w <- w+1
}
}
n <- 1
m <- 1
plng <- array()
plat <- array()
pnts <- as.array(pnts)
for (j in 1:nrow(pnts)) {
if(j %% 2 == 1) {
plng[n] <- pnts[j]
n <- n+1
}
else if(j %% 2 == 0) {
plat[m] <- pnts[j]
m <- m+1
}
}
as.vector(plng, mode = "any")
as.vector(plat, mode = "any")
PG <- data.frame(matrix(unlist(plng)))
PGsub <- data.frame(matrix(unlist(plat)))
PG <- cbind(PG, PGsub)
colnames(PG) <- c("lng","lat")
PG1 <- reactiveVal(PG)
proxy <- leafletProxy("map", data = PG1())
proxy %>% addPolygons(lng = ~lng, lat = ~lat, group = "draw")
output$statsBut <- renderUI({
actionButton("removeShapes", h5(strong("Generate Stats")))
})
}
else if(thisShape[3] == "rectangle"){
rlng1 <- as.numeric(thisShape[5])
rlat1 <- as.numeric(thisShape[6])
rlng2 <- as.numeric(thisShape[9])
rlat2 <- as.numeric(thisShape[10])
proxy <- leafletProxy("map")
proxy %>% addRectangles(lng1 = rlng1, lat1 = rlat1, lng2 = rlng2, lat2 = rlat2,
group = "draw")
}
}
})
## Extract statistics ----
### All study area Summary and plot tabs ----
observe({
#### Using Generate Stats button ----
# if(!input$printShapes) {
# # Output to be displayed if button has been clicked
# extracted_df <- cbind(soil_fz= as.numeric(resources_axis_r$soil_fz),
# resil_fz= as.numeric(resources_axis_r$resil_fz),
# bio_fz= as.numeric(resources_axis_r$bio_fz),
# water_fz= as.numeric(resources_axis_r$water_fz),
# agg_val= as.numeric(resources_axis_r$agg_val)) %>%
# as.data.frame() %>%
# na.omit()
#
# mean_extracted_values <- apply(extracted_df,2,mean,na.rm=T)
#
# summary_data <- data.frame(Resource=c("Soil","Resilience","Biodiversity","Water resources","Aggregated score"),
# Score= mean_extracted_values)
# rownames(summary_data)<-NULL
#
# ### Table --------------------
# output$mytable = render_gt({
# dplyr::tibble(img=c( "www/img/soil_icon.png",
# "www/img/resilience_icon.png",
# "www/img/bio_icon.png",
# "www/img/water_icon.png"),
# summary_data %>%
# filter(!Resource %in% "Aggregated score")) %>%
# arrange(desc(Score)) %>%
# gt() %>%
# fmt_number(columns = Score,decimals = 3) %>%
# cols_label(img = "") %>%
# gt_img_rows(columns = img, height = 25, img_source = "local") %>%
# tab_caption("Average scores without weighting:") %>%
# tab_options(table.background.color = "transparent",
# table.font.size = 17,
# data_row.padding = px(2),
# table.width = 300) %>%
# tab_style(
# style = list(
# cell_text(weight = "bold")
# ),
# locations = cells_column_labels()
# )
# })
#
# ### Gauge --------------------
# output$gauge = renderGauge({
# gauge(mean(as.numeric(weights_reactive()$norm_score), na.rm=T),
# min = 0,
# max = 1,
# abbreviateDecimals=2,
# label ="Aggregated score",
# sectors = gaugeSectors(success = c(0.6, 1),
# warning = c(0.35, 0.6),
# danger = c(0, 0.35),
# colors = c("#3e8536","#83c47c","#bad9b6"))
# )
# })
#
# ### Boxplot --------------------
# output$boxplot <- renderPlot({
# extracted_df %>%
# rename(Soil = soil_fz, Resilience = resil_fz, Biodiversity = bio_fz, Water = water_fz) %>%
# pivot_longer(cols=c(Soil, Resilience, Biodiversity, Water)) %>%
# group_by(name) %>%
# mutate(mean_value = mean(value)) %>%
# ungroup() %>%
# arrange(mean_value) %>%
# mutate(name = factor(name, levels = unique(name))) %>%
# ggplot(aes(x=name, y=value, fill=name)) +
# geom_boxplot(color="black", alpha=0.9, lwd=0.3, outlier.size=0.7,
# outlier.stroke=0, outlier.alpha=0.5, outlier.color="black") +
# scale_fill_manual(values=brewer.pal(n=4, name="Greens")) +
# theme_minimal() +
# labs(x="", y="", title="Resources data distribution (without weighting):") +
# theme(plot.title = element_text(hjust = 6, vjust = -2,size=15,
# color="#808080", margin = margin(0,0,15,0)),
# axis.text.y = element_text( size=12, face="bold"),
# axis.text.x = element_text(angle=45, vjust=0.7, hjust=0.7,
# size=10, face="bold"),
# panel.grid.major.y = element_blank(),
# panel.grid.minor.y = element_blank(),
# panel.grid.major.x = element_line(color = "gray87"),
# panel.grid.minor.x = element_blank(),
# panel.background = element_blank(),
# legend.position="none") +
# coord_flip()
# },bg="transparent",height = 230, width = 400 )
#
#
# ### Radar graph --------------------
# output$radar_graph <- renderPlotly({
#
# selected <- as.data.frame(df)
#
# fig <- plot_ly(
# type = 'scatterpolar',
# r = as.numeric(mean_extracted_values[1:4]),
# theta = c("Soil", "Resilience", "Biodiversity", "Water"),
# fill = 'toself',
# marker = list(color = 'rgba(23, 135, 53, 0.9)', size = 5),
# fillcolor = list(color = 'rgba(27, 181, 68, 0.5)')
# )
#
# fig <- fig %>%
# layout(
# polar = list(radialaxis = list(
# visible = T,
# range = c(0,1))
# ),
# plot_bgcolor = "rgba(0, 0, 0, 0)",
# paper_bgcolor = "rgba(0, 0, 0, 0)",
# fig_bgcolor = "rgba(0, 0, 0, 0)",
# showlegend = F
# )
# fig
# })
#
#
# } else if (input$printShapes) {
## Selected area Summary and plot tabs ----
observeEvent(input$map_draw_all_features, {
shapedf <- data.frame()
reactive(shapedf)
shapedf <- input$map_draw_all_features
sh <- as.data.frame(shapedf)
### Alert not drawn shape if Generate stats button is used
# num_features <- length(input$map_draw_all_features$features)
#
# if(is.null(unlist(input$map_draw_all_features$features[num_features]))){
# shinyalert("Draw a shape",
# "Draw a polygon to extract statistics",
# type = "info",
# size="xs",
# animation=F,
# closeOnClickOutside = TRUE)
# }
#
#
# else {
### Clip raster ----
polygon <- sh %>%
dplyr::select(starts_with("features.geometry.coordinates")) %>%
as.numeric() %>%
matrix(ncol=2, byrow=TRUE) %>%
list() %>%
st_polygon() %>%
st_sfc(crs="EPSG:4326")
polygon_sp <- as(polygon, Class = "Spatial")
### Alert for invalid shape ----
if (gIsValid(polygon_sp) == FALSE){
shinyalert(html = TRUE,
"Shape edges can not cross!",
"Draw again the polygon to extract statistics",
type = "warning",
size="xs",
animation=F,
closeOnClickOutside = TRUE)
remove(shapedf)
}
### Extract data ----
else {
extracted_area <- st_crop(resources_axis_r, polygon) # Original spatial data
extracted_area_aggr <- st_crop(weights_reactive(), polygon) # Aggregated spatial data
extracted_df <- cbind(soil_fz= as.numeric(extracted_area$soil_fz),
resil_fz= as.numeric(extracted_area$resil_fz),
bio_fz= as.numeric(extracted_area$bio_fz),
water_fz= as.numeric(extracted_area$water_fz),
aggregated_val= as.numeric(extracted_area_aggr$norm_score)) %>%
as.data.frame() %>%
na.omit()
mean_extracted_values <- apply(extracted_df,2,mean,na.rm=T)
summary_data <- data.frame(Resource=c("Soil","Resilience","Biodiversity","Water resources","Aggregated score"),
Score=mean_extracted_values)
rownames(summary_data)<-NULL
#### Table ----
output$mytable = render_gt({
dplyr::tibble(img=c(here("www","img","soil_icon.png"),
here("www","img","resilience_icon.png"),
here("www","img","bio_icon.png"),
here("www","img","water_icon.png")),
summary_data %>%
filter(!Resource %in% "Aggregated score")) %>%
arrange(desc(Score)) %>%
gt() %>%
fmt_number(columns = Score,decimals = 3) %>%
cols_label(img = "") %>%
gt_img_rows(columns = img, height = 25, img_source = "local") %>%
tab_caption("Average scores without weighting:") %>%
tab_options(table.background.color = "transparent",
table.font.size = 17,
data_row.padding = px(2),
table.width = 300) %>%
tab_style(style = list(cell_text(weight = "bold")),
locations = cells_column_labels())
},height = 210)
#### Gauge ----
output$gauge = renderGauge({
gauge(as.numeric(mean_extracted_values[5]),
min = 0,
max = 1,
abbreviateDecimals=2,
label ="Aggregated score",
sectors = gaugeSectors(success = c(0.6, 1),
warning = c(0.35, 0.6),
danger = c(0, 0.35),
colors = c("#3e8536","#83c47c","#bad9b6"))
)
})
#### Radar graph ----
output$radar_graph <- renderPlotly({
selected <- as.data.frame(df)
fig <- plot_ly(
type = 'scatterpolar',
r = as.numeric(mean_extracted_values[1:4]),
theta = c("Soil", "Resilience", "Biodiversity", "Water"),
fill = 'toself',
marker = list(color = 'rgba(23, 135, 53, 0.9)', size = 5),
fillcolor = list(color = 'rgba(27, 181, 68, 0.5)')
)
fig <- fig %>%
layout(
polar = list(radialaxis = list(
visible = T,
range = c(0,1))
),
plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)",
fig_bgcolor = "rgba(0, 0, 0, 0)",
showlegend = F
)
fig
})
#### Boxplot ----
output$boxplot <- renderPlot({
extracted_df %>%
rename(Soil = soil_fz, Resilience = resil_fz, Biodiversity = bio_fz, Water = water_fz) %>%
pivot_longer(cols=c(Soil, Resilience, Biodiversity, Water)) %>%
group_by(name) %>%
mutate(mean_value = mean(value)) %>%
ungroup() %>%
arrange(mean_value) %>%
mutate(name = factor(name, levels = unique(name))) %>%
ggplot(aes(x=name, y=value, fill=name)) +
geom_boxplot(color="black", alpha=0.9, lwd=0.3, outlier.size=0.7,
outlier.stroke=0, outlier.alpha=0.5, outlier.color="black") +
scale_fill_manual(values=brewer.pal(n=4, name="Greens")) +
theme_minimal() +
labs(x="", y="", title="Resources data distribution (without weighting):") +
theme(plot.title = element_text(hjust = 6, vjust = -2,size=15,
color="#808080", margin = margin(0,0,15,0)),
axis.text.y = element_text( size=12, face="bold"),
axis.text.x = element_text(angle=45, vjust=0.7, hjust=0.7,
size=10, face="bold"),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(color = "gray87"),
panel.grid.minor.x = element_blank(),
panel.background = element_blank(),
legend.position="none") +
coord_flip()
},bg="transparent",height = 230, width = 400 )
}
# }
})
})
## Return to the whole area if the map is refreshed ----
observeEvent(c(input$removeShapes, input$soil_w,input$water_w,input$biodiversity_w,input$resil_w), {
extracted_df <- cbind(soil_fz= as.numeric(resources_axis_r$soil_fz),
resil_fz= as.numeric(resources_axis_r$resil_fz),
bio_fz= as.numeric(resources_axis_r$bio_fz),
water_fz= as.numeric(resources_axis_r$water_fz),
agg_val= as.numeric(resources_axis_r$agg_val)) %>%
as.data.frame() %>%
na.omit()
mean_extracted_values <- apply(extracted_df,2,mean,na.rm=T)
summary_data <- data.frame(Resource=c("Soil","Resilience","Biodiversity","Water resources","Aggregated score"),
Score= mean_extracted_values)
rownames(summary_data)<-NULL
### Table ----
output$mytable = render_gt({
dplyr::tibble(img=c(here("www","img","soil_icon.png"),
here("www","img","resilience_icon.png"),
here("www","img","bio_icon.png"),
here("www","img","water_icon.png")),
summary_data %>%
filter(!Resource %in% "Aggregated score")) %>%
arrange(desc(Score)) %>%
gt() %>%
fmt_number(columns = Score,decimals = 3) %>%
cols_label(img = "") %>%
gt_img_rows(columns = img, height = 25, img_source = "local") %>%
tab_caption("Average scores without weighting:") %>%
tab_options(table.background.color = "transparent",
table.font.size = 17,
data_row.padding = px(2),
table.width = 300) %>%
tab_style(
style = list(
cell_text(weight = "bold")
),
locations = cells_column_labels()
)
})
### Gauge ----
output$gauge = renderGauge({
gauge(mean(as.numeric(weights_reactive()$norm_score), na.rm=T),
min = 0,
max = 1,
abbreviateDecimals=2,
label ="Aggregated score",
sectors = gaugeSectors(success = c(0.6, 1),
warning = c(0.35, 0.6),
danger = c(0, 0.35),
colors = c("#3e8536","#83c47c","#bad9b6"))
)
})
### Boxplot ----
output$boxplot <- renderPlot({
extracted_df %>%
rename(Soil = soil_fz, Resilience = resil_fz, Biodiversity = bio_fz, Water = water_fz) %>%
pivot_longer(cols=c(Soil, Resilience, Biodiversity, Water)) %>%
group_by(name) %>%
mutate(mean_value = mean(value)) %>%
ungroup() %>%
arrange(mean_value) %>%
mutate(name = factor(name, levels = unique(name))) %>%
ggplot(aes(x=name, y=value, fill=name)) +
geom_boxplot(color="black", alpha=0.9, lwd=0.3, outlier.size=0.7,
outlier.stroke=0, outlier.alpha=0.5, outlier.color="black") +
scale_fill_manual(values=brewer.pal(n=4, name="Greens")) +
theme_minimal() +
labs(x="", y="", title="Resources data distribution (without weighting):") +
theme(plot.title = element_text(hjust = 6, vjust = -2,size=15,
color="#808080", margin = margin(0,0,15,0)),
axis.text.y = element_text( size=12, face="bold"),
axis.text.x = element_text(angle=45, vjust=0.7, hjust=0.7,
size=10, face="bold"),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(color = "gray87"),
panel.grid.minor.x = element_blank(),
panel.background = element_blank(),
legend.position="none") +
coord_flip()
},bg="transparent",height = 230, width = 400 )
### Radar graph ----
output$radar_graph <- renderPlotly({
selected <- as.data.frame(df)
fig <- plot_ly(
type = 'scatterpolar',
r = as.numeric(mean_extracted_values[1:4]),
theta = c("Soil", "Resilience", "Biodiversity", "Water"),
fill = 'toself',
marker = list(color = 'rgba(23, 135, 53, 0.9)', size = 5),
fillcolor = list(color = 'rgba(27, 181, 68, 0.5)')
)
fig <- fig %>%
layout(
polar = list(radialaxis = list(
visible = T,
range = c(0,1))
),
plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)",
fig_bgcolor = "rgba(0, 0, 0, 0)",
showlegend = F
)
fig
})
})
## Edit tab's note about data displayed ----
observeEvent(c(input$alpha, input$soil_w,input$water_w,input$biodiversity_w,input$resil_w), {
output$data_displayed_note_summary <- renderText({
"Data for the entire region"
})
output$data_displayed_note_plot <- renderText({
"Data for the entire region"
})
})
observeEvent(input$map_draw_all_features, {
output$data_displayed_note_summary <- renderText({
"Data for the selected area"
})
output$data_displayed_note_plot <- renderText({
"Data for the selected area"
})
})
## Select summary tab when stats are generated if About tab is open ----
observeEvent(input$map_draw_all_features, {
if (input$tabs == "about") {
# Add a new tab panel to the tabset
updateTabsetPanel(session, "tabs",
selected = paste0("summary", input$controller)
)
}
})
## Reset map to remove shapes ----
# Uses the update of the alpha slider to force refresh (there must be a better solution)
observeEvent(input$removeShapes,{
reset_value = input$alpha
updateSliderInput(session, "alpha", value = reset_value+0.01)
})
# >-----
# STAKEHODLERS PRIORITIES ----
### Selecting group preferences ----
observeEvent(c(input$group_1, input$group_2), {
if(input$group_1 == "My") {
resources_axis_1 <- weights_reactive()
}
else {
group1 <- ahp_weights %>%
filter(group %in% paste0(input$group_1))
resources_axis_1 <- resources_axis_r %>%
mutate("soil_fz" = soil_fz * as.numeric(group1[3,3])) %>%
mutate("resil_fz" = resil_fz * as.numeric(group1[4,3])) %>%
mutate("bio_fz" = bio_fz * as.numeric(group1[1,3])) %>%
mutate("water_fz" = water_fz * as.numeric(group1[2,3])) %>%
mutate(score = soil_fz + resil_fz + bio_fz + water_fz) %>%
mutate(norm_score = range_norm_manual(score))
}
if(input$group_2 == "My") {
resources_axis_2 <- weights_reactive()
}
else {
group2 <- ahp_weights %>%
filter(group %in% paste0(input$group_2))
resources_axis_2 <- resources_axis_r %>%
mutate("soil_fz" = soil_fz * as.numeric(group2[3,3])) %>%
mutate("resil_fz" = resil_fz * as.numeric(group2[4,3])) %>%
mutate("bio_fz" = bio_fz * as.numeric(group2[1,3])) %>%
mutate("water_fz" = water_fz * as.numeric(group2[2,3])) %>%
mutate(score = soil_fz + resil_fz + bio_fz + water_fz) %>%
mutate(norm_score = range_norm_manual(score))
}
if (input$group_1 == input$group_2) {
shinyalert(html = TRUE,
"Select another group",
"Same group comparison leads to no detectable differences",
type = "warning",
size="xs",
animation=F,
closeOnClickOutside = TRUE)
}
### Calculating group differences ----
resources_comp <- reactive({resources_axis_1-resources_axis_2})
### Displaying differences on a map ----
value <- reactive({
max(abs(max(resources_comp()["norm_score"][[1]], na.rm = T)),
abs(min(resources_comp()["norm_score"][[1]], na.rm = T)))
})
color_pal <- reactive({
colorNumeric(
palette = "RdBu",
domain = c(-1*value(), 0, value()),
na.color = "transparent")
})
output$map_stake <- renderLeaflet({
leaflet(options = leafletOptions(minZoom = 9)) %>% addTiles() %>%
addGeoRaster(resources_comp()["norm_score"],
opacity = input$alpha_stake,
colorOptions =leafem:::colorOptions(
palette = "RdBu",
domain = c(-1*value(), 0, value()),
breaks = seq(min(resources_comp()["norm_score"][[1]], na.rm = TRUE),
max(resources_comp()["norm_score"][[1]], na.rm = TRUE),
length.out=100),
na.color = "transparent"
),
resolution=10000) %>%
addProviderTiles(providers$Esri.WorldTopoMap) %>%
addLegend(pal = color_pal(),
values = resources_comp()["norm_score"][[1]],
position = "bottomright",
opacity= input$alpha_stake
)
})
})
# >-----
# ENVIRONMENTAL THREATS AXIS (MAIN TAB) ----
# Defining Map Elements ----
## Select layers individually ----
observeEvent(input$checkbox_climate, {
if (input$checkbox_climate) {
updateSliderInput(session, "cli_w", value = 100)
updateSliderInput(session, "drgh_w", value = 0)
updateSliderInput(session, "fld_w", value = 0)
updateSliderInput(session, "wf_w", value = 0)
}
})
observeEvent(input$checkbox_droughts, {
if (input$checkbox_droughts) {
updateSliderInput(session, "drgh_w", value = 100)
updateSliderInput(session, "cli_w", value = 0)
updateSliderInput(session, "fld_w", value = 0)
updateSliderInput(session, "wf_w", value = 0)
}
})
observeEvent(input$checkbox_floods, {
if (input$checkbox_floods) {
updateSliderInput(session, "fld_w", value = 100)
updateSliderInput(session, "drgh_w", value = 0)
updateSliderInput(session, "cli_w", value = 0)
updateSliderInput(session, "wf_w", value = 0)
}
})
observeEvent(input$checkbox_wildfires, {
if (input$checkbox_wildfires) {
updateSliderInput(session, "wf_w", value = 100)
updateSliderInput(session, "drgh_w", value = 0)
updateSliderInput(session, "cli_w", value = 0)
updateSliderInput(session, "fld_w", value = 0)
}
})
## AHP weights ----
### Calculate aggregated preference values from weights ----
agg_pref_df_threats <- reactive({
data.frame(resources = c("cli_exp_fz","drgh_m_fz","fld_m_fz","wf_m_fz"),
weights= c(input$cli_w,
input$drgh_w,
input$fld_w,
input$wf_w)) %>%
mutate(agg_pref = weights/sum(weights))
})
output$cli_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df_threats()[1,3]),2)))
output$drgh_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df_threats()[2,3]),2)))
output$fld_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df_threats()[3,3]),2)))
output$wf_agg_pref <- renderText(paste(round(as.numeric(agg_pref_df_threats()[4,3]),2)))
## Select climate model ----
drgh_model_selected <- reactive({
if (input$model_drgh == "(Ensemble)") {
c("drgh_h_fz","drgh_w_fz")
} else if (input$model_drgh == "(MIROC-esm)") {
c("drgh_m_fz","drgh_w_fz")
}
else if (input$model_drgh == "(CCSM4)") {
c("drgh_m_fz","drgh_h_fz")
}
})
fld_model_selected <- reactive({
if (input$model_fld == "(Ensemble)") {
c("fld_h_fz","fld_w_fz")
} else if (input$model_fld == "(MIROC-esm)") {
c("fld_m_fz","fld_w_fz")
}
else if (input$model_fld == "(CCSM4)") {
c("fld_m_fz","fld_h_fz")
}
})
wf_model_selected <- reactive({
if (input$model_wf == "(Ensemble)") {
c("wf_cn_fz","wf_mi_fz")
} else if (input$model_wf == "(MIROC5)") {
c("wf_m_fz","wf_cn_fz")
}