global.R
In NVE/FlomKart_ShinyApp: A list a functions to support the FlomKart Shiny app
# Preloading of NetCDF files and other pre-processing that should only happen once
# rm(list = ls()) # clean out workspace and set working directorY
# setwd('C:/Users/flbk/Documents/GitHub/FlomKart_ShinyApp')
# to consider appDir = getwd()
# Installing and loading required packages
packages <- c("shiny", "leaflet", "RNetCDF", "magrittr", "evd", "nsRFA", "formattable", "DT", "shinyBS", "plotly")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
install.packages(setdiff(packages, rownames(installed.packages())))
}
# apply(as.list(packages),library)
library(shiny) # TO run the app!
library(leaflet) # For the interactive map
library(RNetCDF) # To read the data in netCDF files
library(magrittr) # For piping functions
library(evd) # Functions for extreme value distributions
library(nsRFA) # For the GL distrib (,aybe pearson too)
library(formattable) # To add formatting to data tables
library(DT) # for the data tables
library(shinyBS) # for the interactive popover features
library(plotly) # for interactive graphs
# library(FlomKartShinyApp)
source('R/rawdata_plotting.R')
source('R/mapping.R')
source('R/main_plotting.R') # plots for the first "main plots" subtab
source('R/gof_plotting.R') # GOF plots
source('R/rperiods_plotting.R') # Plots that are a function of return periods (return levels, QS, BS, NT)
# dat <- read.csv("//nve/fil/h/HM/Interne Prosjekter/Flomkart/Model_fitting/Florian/Data/AMS_table_updated.csv", sep=";", as.is=TRUE)
# dat$date_DOGN <- as.POSIXlt(dat$date_DOGN)
# dummy_date <- dat$date_DOGN
# dat$day <- dummy_date$mday # day of month
# dat$month <- dummy_date$mon + 1 # month of year (zero-indexed)
# dat$year_test <- dummy_date$year+1800 # years since 1900
# for (i in seq(along = station$number) ) {
# Q_years[i, ] <- XXX
# }
# plot(dat$date_DOGN, dat$flom_DOGN)
#
# barplot(dat$flom_DOGN, dat$year)
nc <- open.nc("data/flood_database.nc", write = FALSE) # Put FALSE for read-only
gof_nc <- open.nc("data/gof.nc", write = FALSE) # Put FALSE for read-only
# Compare with gof_noXvalid.nc
# gof_nc <- open.nc("output/gof_noXvalid.nc", write = FALSE) # Put FALSE for read-only
Q <- var.get.nc(nc, "Q")
distr.name <- var.get.nc(nc, "distr.name")
method.name <- var.get.nc(nc, "method.name")
return.periods <- var.get.nc(gof_nc, "r.periods")
# For the map
station <- list()
station$name <- var.get.nc(nc, "station.name")
station$number <- var.get.nc(nc, "station.number")
# station$nve_nb <- var.get.nc(nc, "station.nve_nb")
# station$utmN <- var.get.nc(nc, "station.utmN") # DO WE NEED THOSE COORDINATES?
# station$utmE <- var.get.nc(nc, "station.utmE")
station$long <- var.get.nc(nc, "station.long")
station$lat <- var.get.nc(nc, "station.lat")
station$catchment.size <- var.get.nc(nc, "catchment.size")
station$catchment.min.height <- var.get.nc(nc, "catchment.min.height")
station$catchment.max.height <- var.get.nc(nc, "catchment.max.height")
# dates <- var.get.nc(nc, "dates")
years <- var.get.nc(nc, "years")
dim.random_runs <- var.get.nc(nc, "dim.random_runs")
sampling_years <<- var.get.nc(nc, "sampling_years")
sampling_years_full_record <- c(as.character(sampling_years), "FULL RECORD")
rperiods.bs <- c(2,5,10,15,20,30) # Those are return periods for BS ans NT. They should be saved in the NetCDF
## How can the following be replaced by apply (# station$length_rec <- apply(na.omit(Q), length...)?)
# REPLACE BY var.get.nc(nc, "record.length")
station$length_rec <- var.get.nc(nc, "record.length")
# station$length_rec <- as.vector(rep(NA,length(station$number)))
# for (st in seq(along = station$number)) {
# station$length_rec[st] <- length(as.vector(na.omit(Q[st, ])))
# }
#### WILL PROBLABLY NOT BE NEEDED ANYMORE
# keep <- which(!is.na(station$lat + station$long))
# ## Lets keep only the stations that have more than 30 years of data
# keep <- intersect(keep, which(station$length_rec > 29))
#
station$index <- seq(1:length(station$number)) # it is a bit stupid but I think I had indexes before as I was not using every station
# station$length_rec <- station$length_rec[keep]
# station$name <- station$name[keep]
# station$number <- station$number[keep]
# # station$nve_nb <- station$nve_nb[keep]
# station$long <- station$long[keep]
# station$lat <- station$lat[keep]
# # station$utmN <- station$utmN[keep]
# # station$utmE <- station$utmE[keep]
# station$catchment.size <- station$catchment.size[keep]
# station$catchment.min.height <- station$catchment.min.height[keep]
# station$catchment.max.height <- station$catchment.max.height[keep]
#### WILL PROBLABLY NOT BE NEEDED ANYMORE END
## PARAMETRIZE 14 = dim.length_rec <- length(sampling_years) + 1
# adding the shape param for gev (it is the 3rd parameter)
gev.shape.estimate <- var.get.nc(nc, "param.estimate",
start = c(1, 3, 1, 3, 14, 1), # from each station, distr number 3,
# method number 1 to 4, parameter number 3, full length o record, random run number 1
count = c(length(station$number), 1, 4, 1, 1, 1) )
# Create here the data frame that will be use in DT tables in the code
stations.summary.df <- data.frame("Station name" = station$number, # need to fix this NAME PB
"Length of record" = station$length_rec,
"Shape param GEV_Lmom" = gev.shape.estimate[ , 2],
"Catchment area" = station$catchment.size,
"Min elevation" = station$catchment.min.height,
"Max elevation" = station$catchment.max.height
) # taken out "NVE number" = station$nve_nb,
## Supporting functions for Shiny App: functions that pre-process data for the plotting, mapping or table functions
gof_summary <- function(gof, station) {
# print(gof)
# print(station)
gof.table <- data.frame(gum = rep(NA,4), gam = rep(NA,4), gev = rep(NA,4), glo = rep(NA,4), pe3 = rep(NA,4))
# gof.table <- matrix(NA,4,5)
min.gof <- 100
max.gof <- 0
min.index <- c(NA, NA)
max.index <- c(NA, NA)
row.names(gof.table) <- method.name
for (m in seq(along = method.name)) {
for (d in seq(along = distr.name)) {
gof.table[m,d] <- var.get.nc(gof_nc, gof, start = c(station, d, m, 14, 1),
count = c(1, 1, 1, 1, 1)) # in dataframe [row, column]
# print("print(gof.table[m,d])")
# print(gof.table[m,d])
if (!is.na(gof.table[m,d])) {
if (gof.table[m,d] < min.gof) {
min.gof <- gof.table[m,d]
min.index <- c(m, d)
}
if (gof.table[m,d] > max.gof) {
max.gof <- gof.table[m,d]
max.index <- c(m, d)
}
}
gof.table[m,d] <- round(gof.table[m,d], 4)
}
}
min.gof <- round(min.gof, 4)
max.gof <- round(max.gof, 4)
results <- list(min_gof = min.gof, min_index = min.index, max_gof = max.gof, max_index = max.index, gof_table = gof.table)
# print("results")
# print(results)
invisible(results)
}
gof_summary_rperiods <- function(gof, station, r.period) {
if (gof == "BS" || gof == "QS") {
r.period.index <- which(rperiods.bs == r.period)
} else {r.period.index <- which(return.periods == r.period)}
print("rperiods.bs")
print(rperiods.bs)
print("return.periods")
print(return.periods)
print("r.period")
print(r.period)
print("r.period.index")
print(r.period.index)
gof.table <- data.frame(gum = rep(NA,4), gam = rep(NA,4), gev = rep(NA,4), glo = rep(NA,4), pe3 = rep(NA,4))
row.names(gof.table) <- method.name
for (m in seq(along = method.name)) {
for (d in seq(along = distr.name)) {
if (gof == "r.levels") {
print("r.period.index")
print(r.period.index)
r.period.index <- as.numeric(r.period.index)
print(c(station, d, m, 14, 1, r.period.index))
gof.table[m,d] <- round(var.get.nc(gof_nc, gof, start = c(station, d, m, 14, 1, r.period.index),
count = c(1, 1, 1, 1, 1, 1)), 0) # in dataframe [row, column]
} else {
print("r.period.index")
print(r.period.index)
r.period.index <- as.numeric(r.period.index)
print(c(station, d, m, 14, 1, r.period.index))
gof.table[m,d] <- round(var.get.nc(gof_nc, gof, start = c(station, d, m, 14, 1, r.period.index),
count = c(1, 1, 1, 1, 1, 1)), 2) # in dataframe [row, column]
}
}
}
invisible(gof.table)
}
# Function to calculate the indexes of a group of common stations
bestgof.indexing <- function(gof, minmax) {
bestgof.indexes <- matrix(NA,length(station$index),2)
# Caution: station$index is required when reading the NetCDF files as we need to get back to the whole set of stations (445)
for (st in seq(along = station$index)) {
temp.list <- gof_summary(gof, station$index[st])
if (minmax == "min") {
bestgof.indexes[st,] <- temp.list$min_index
}
if (minmax == "max") {
bestgof.indexes[st,] <- temp.list$max_index
}
}
invisible(bestgof.indexes)
}
# This function extracts from the matrix created above, the station indexes corresponding to the user's choices
station_group_indexes <- function(gof, distr, method, minmax) {
group.indexes <- rep(NA,length(station$number))
bestgof.indexes <- bestgof.indexing(gof, minmax)
if (distr == "ALL" && method != "ALL") {
m <- which(method.name == method)
group.indexes <- intersect(seq(1,length(station$number)), which(bestgof.indexes[,1] == m))
}
if (distr != "ALL" && method == "ALL") {
d <- which(distr.name == distr)
group.indexes <- intersect(seq(1,length(station$number)), which(bestgof.indexes[,2] == d))
}
if (distr == "ALL" && method == "ALL") {
group.indexes <- seq(1,length(station$number))
}
if (distr != "ALL" && method != "ALL") {
d <- which(distr.name == distr)
m <- which(method.name == method)
group.indexes <- intersect(which(bestgof.indexes[,2] == d), which(bestgof.indexes[,1] == m))
}
invisible(group.indexes)
}
# This function extracts the station indexes corresponding to the user's choices for the first tab
station_group_indexes_first_tab <- function(min_years, max_years, min_height, max_height) {
group.indexes <- rep(NA,length(station$number))
group.indexes <- which(station$length_rec > min_years)
group.indexes <- intersect(group.indexes, which(station$length_rec < max_years))
group.indexes <- intersect(group.indexes, which(station$catchment.min.height > min_height))
group.indexes <- intersect(group.indexes, which(station$catchment.max.height < max_height))
}
# Function for updating the df to put in the interactive table
group.dfmaker <- function(group.indexes) {
group.df <- data.frame("Station number" = station$number[group.indexes],
"Length of record" = station$length_rec[group.indexes],
"Catchment area" = station$catchment.size[group.indexes],
"Min elevation" = station$catchment.min.height[group.indexes],
"Max elevation" = station$catchment.max.height[group.indexes])
print("group.indexes")
print(group.indexes)
print("group.df")
print(group.df)
invisible(group.df)
}
stations2average <- function(min_years, max_years) {
stations2ave <- c()
print("entered the station2average function")
print(min_years)
print(max_years)
print(class(max_years))
min_years <- as.numeric(min_years)
max_years <- as.numeric(max_years)
print(class(max_years))
for (st in seq(along = station$index)) {
if (station$length_rec[st] >= min_years) {
if (station$length_rec[st] <= max_years) {
stations2ave <- c(stations2ave, st)
# print("prout1")
# print(st)
}
}
}
print(stations2ave)
invisible(stations2ave)
}
##
# st_selection <- which(norm_area < 0.05)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
#
# st_selection <- which(norm_area > 0.05)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
##
# median_perc_lake <- median(na.omit(flood_metadata$perc_lake))
# st_selection <- which(flood_metadata$perc_lake < median_perc_lake)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
#
# st_selection <- which(flood_metadata$perc_lake > median_perc_lake)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
NVE/FlomKart_ShinyApp documentation built on May 7, 2019, 6:03 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Preloading of NetCDF files and other pre-processing that should only happen once
# rm(list = ls()) # clean out workspace and set working directorY
# setwd('C:/Users/flbk/Documents/GitHub/FlomKart_ShinyApp')
# to consider appDir = getwd()
# Installing and loading required packages
packages <- c("shiny", "leaflet", "RNetCDF", "magrittr", "evd", "nsRFA", "formattable", "DT", "shinyBS", "plotly")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
install.packages(setdiff(packages, rownames(installed.packages())))
}
# apply(as.list(packages),library)
library(shiny) # TO run the app!
library(leaflet) # For the interactive map
library(RNetCDF) # To read the data in netCDF files
library(magrittr) # For piping functions
library(evd) # Functions for extreme value distributions
library(nsRFA) # For the GL distrib (,aybe pearson too)
library(formattable) # To add formatting to data tables
library(DT) # for the data tables
library(shinyBS) # for the interactive popover features
library(plotly) # for interactive graphs
# library(FlomKartShinyApp)
source('R/rawdata_plotting.R')
source('R/mapping.R')
source('R/main_plotting.R') # plots for the first "main plots" subtab
source('R/gof_plotting.R') # GOF plots
source('R/rperiods_plotting.R') # Plots that are a function of return periods (return levels, QS, BS, NT)
# dat <- read.csv("//nve/fil/h/HM/Interne Prosjekter/Flomkart/Model_fitting/Florian/Data/AMS_table_updated.csv", sep=";", as.is=TRUE)
# dat$date_DOGN <- as.POSIXlt(dat$date_DOGN)
# dummy_date <- dat$date_DOGN
# dat$day <- dummy_date$mday # day of month
# dat$month <- dummy_date$mon + 1 # month of year (zero-indexed)
# dat$year_test <- dummy_date$year+1800 # years since 1900
# for (i in seq(along = station$number) ) {
# Q_years[i, ] <- XXX
# }
# plot(dat$date_DOGN, dat$flom_DOGN)
#
# barplot(dat$flom_DOGN, dat$year)
nc <- open.nc("data/flood_database.nc", write = FALSE) # Put FALSE for read-only
gof_nc <- open.nc("data/gof.nc", write = FALSE) # Put FALSE for read-only
# Compare with gof_noXvalid.nc
# gof_nc <- open.nc("output/gof_noXvalid.nc", write = FALSE) # Put FALSE for read-only
Q <- var.get.nc(nc, "Q")
distr.name <- var.get.nc(nc, "distr.name")
method.name <- var.get.nc(nc, "method.name")
return.periods <- var.get.nc(gof_nc, "r.periods")
# For the map
station <- list()
station$name <- var.get.nc(nc, "station.name")
station$number <- var.get.nc(nc, "station.number")
# station$nve_nb <- var.get.nc(nc, "station.nve_nb")
# station$utmN <- var.get.nc(nc, "station.utmN") # DO WE NEED THOSE COORDINATES?
# station$utmE <- var.get.nc(nc, "station.utmE")
station$long <- var.get.nc(nc, "station.long")
station$lat <- var.get.nc(nc, "station.lat")
station$catchment.size <- var.get.nc(nc, "catchment.size")
station$catchment.min.height <- var.get.nc(nc, "catchment.min.height")
station$catchment.max.height <- var.get.nc(nc, "catchment.max.height")
# dates <- var.get.nc(nc, "dates")
years <- var.get.nc(nc, "years")
dim.random_runs <- var.get.nc(nc, "dim.random_runs")
sampling_years <<- var.get.nc(nc, "sampling_years")
sampling_years_full_record <- c(as.character(sampling_years), "FULL RECORD")
rperiods.bs <- c(2,5,10,15,20,30) # Those are return periods for BS ans NT. They should be saved in the NetCDF
## How can the following be replaced by apply (# station$length_rec <- apply(na.omit(Q), length...)?)
# REPLACE BY var.get.nc(nc, "record.length")
station$length_rec <- var.get.nc(nc, "record.length")
# station$length_rec <- as.vector(rep(NA,length(station$number)))
# for (st in seq(along = station$number)) {
# station$length_rec[st] <- length(as.vector(na.omit(Q[st, ])))
# }
#### WILL PROBLABLY NOT BE NEEDED ANYMORE
# keep <- which(!is.na(station$lat + station$long))
# ## Lets keep only the stations that have more than 30 years of data
# keep <- intersect(keep, which(station$length_rec > 29))
#
station$index <- seq(1:length(station$number)) # it is a bit stupid but I think I had indexes before as I was not using every station
# station$length_rec <- station$length_rec[keep]
# station$name <- station$name[keep]
# station$number <- station$number[keep]
# # station$nve_nb <- station$nve_nb[keep]
# station$long <- station$long[keep]
# station$lat <- station$lat[keep]
# # station$utmN <- station$utmN[keep]
# # station$utmE <- station$utmE[keep]
# station$catchment.size <- station$catchment.size[keep]
# station$catchment.min.height <- station$catchment.min.height[keep]
# station$catchment.max.height <- station$catchment.max.height[keep]
#### WILL PROBLABLY NOT BE NEEDED ANYMORE END
## PARAMETRIZE 14 = dim.length_rec <- length(sampling_years) + 1
# adding the shape param for gev (it is the 3rd parameter)
gev.shape.estimate <- var.get.nc(nc, "param.estimate",
start = c(1, 3, 1, 3, 14, 1), # from each station, distr number 3,
# method number 1 to 4, parameter number 3, full length o record, random run number 1
count = c(length(station$number), 1, 4, 1, 1, 1) )
# Create here the data frame that will be use in DT tables in the code
stations.summary.df <- data.frame("Station name" = station$number, # need to fix this NAME PB
"Length of record" = station$length_rec,
"Shape param GEV_Lmom" = gev.shape.estimate[ , 2],
"Catchment area" = station$catchment.size,
"Min elevation" = station$catchment.min.height,
"Max elevation" = station$catchment.max.height
) # taken out "NVE number" = station$nve_nb,
## Supporting functions for Shiny App: functions that pre-process data for the plotting, mapping or table functions
gof_summary <- function(gof, station) {
# print(gof)
# print(station)
gof.table <- data.frame(gum = rep(NA,4), gam = rep(NA,4), gev = rep(NA,4), glo = rep(NA,4), pe3 = rep(NA,4))
# gof.table <- matrix(NA,4,5)
min.gof <- 100
max.gof <- 0
min.index <- c(NA, NA)
max.index <- c(NA, NA)
row.names(gof.table) <- method.name
for (m in seq(along = method.name)) {
for (d in seq(along = distr.name)) {
gof.table[m,d] <- var.get.nc(gof_nc, gof, start = c(station, d, m, 14, 1),
count = c(1, 1, 1, 1, 1)) # in dataframe [row, column]
# print("print(gof.table[m,d])")
# print(gof.table[m,d])
if (!is.na(gof.table[m,d])) {
if (gof.table[m,d] < min.gof) {
min.gof <- gof.table[m,d]
min.index <- c(m, d)
}
if (gof.table[m,d] > max.gof) {
max.gof <- gof.table[m,d]
max.index <- c(m, d)
}
}
gof.table[m,d] <- round(gof.table[m,d], 4)
}
}
min.gof <- round(min.gof, 4)
max.gof <- round(max.gof, 4)
results <- list(min_gof = min.gof, min_index = min.index, max_gof = max.gof, max_index = max.index, gof_table = gof.table)
# print("results")
# print(results)
invisible(results)
}
gof_summary_rperiods <- function(gof, station, r.period) {
if (gof == "BS" || gof == "QS") {
r.period.index <- which(rperiods.bs == r.period)
} else {r.period.index <- which(return.periods == r.period)}
print("rperiods.bs")
print(rperiods.bs)
print("return.periods")
print(return.periods)
print("r.period")
print(r.period)
print("r.period.index")
print(r.period.index)
gof.table <- data.frame(gum = rep(NA,4), gam = rep(NA,4), gev = rep(NA,4), glo = rep(NA,4), pe3 = rep(NA,4))
row.names(gof.table) <- method.name
for (m in seq(along = method.name)) {
for (d in seq(along = distr.name)) {
if (gof == "r.levels") {
print("r.period.index")
print(r.period.index)
r.period.index <- as.numeric(r.period.index)
print(c(station, d, m, 14, 1, r.period.index))
gof.table[m,d] <- round(var.get.nc(gof_nc, gof, start = c(station, d, m, 14, 1, r.period.index),
count = c(1, 1, 1, 1, 1, 1)), 0) # in dataframe [row, column]
} else {
print("r.period.index")
print(r.period.index)
r.period.index <- as.numeric(r.period.index)
print(c(station, d, m, 14, 1, r.period.index))
gof.table[m,d] <- round(var.get.nc(gof_nc, gof, start = c(station, d, m, 14, 1, r.period.index),
count = c(1, 1, 1, 1, 1, 1)), 2) # in dataframe [row, column]
}
}
}
invisible(gof.table)
}
# Function to calculate the indexes of a group of common stations
bestgof.indexing <- function(gof, minmax) {
bestgof.indexes <- matrix(NA,length(station$index),2)
# Caution: station$index is required when reading the NetCDF files as we need to get back to the whole set of stations (445)
for (st in seq(along = station$index)) {
temp.list <- gof_summary(gof, station$index[st])
if (minmax == "min") {
bestgof.indexes[st,] <- temp.list$min_index
}
if (minmax == "max") {
bestgof.indexes[st,] <- temp.list$max_index
}
}
invisible(bestgof.indexes)
}
# This function extracts from the matrix created above, the station indexes corresponding to the user's choices
station_group_indexes <- function(gof, distr, method, minmax) {
group.indexes <- rep(NA,length(station$number))
bestgof.indexes <- bestgof.indexing(gof, minmax)
if (distr == "ALL" && method != "ALL") {
m <- which(method.name == method)
group.indexes <- intersect(seq(1,length(station$number)), which(bestgof.indexes[,1] == m))
}
if (distr != "ALL" && method == "ALL") {
d <- which(distr.name == distr)
group.indexes <- intersect(seq(1,length(station$number)), which(bestgof.indexes[,2] == d))
}
if (distr == "ALL" && method == "ALL") {
group.indexes <- seq(1,length(station$number))
}
if (distr != "ALL" && method != "ALL") {
d <- which(distr.name == distr)
m <- which(method.name == method)
group.indexes <- intersect(which(bestgof.indexes[,2] == d), which(bestgof.indexes[,1] == m))
}
invisible(group.indexes)
}
# This function extracts the station indexes corresponding to the user's choices for the first tab
station_group_indexes_first_tab <- function(min_years, max_years, min_height, max_height) {
group.indexes <- rep(NA,length(station$number))
group.indexes <- which(station$length_rec > min_years)
group.indexes <- intersect(group.indexes, which(station$length_rec < max_years))
group.indexes <- intersect(group.indexes, which(station$catchment.min.height > min_height))
group.indexes <- intersect(group.indexes, which(station$catchment.max.height < max_height))
}
# Function for updating the df to put in the interactive table
group.dfmaker <- function(group.indexes) {
group.df <- data.frame("Station number" = station$number[group.indexes],
"Length of record" = station$length_rec[group.indexes],
"Catchment area" = station$catchment.size[group.indexes],
"Min elevation" = station$catchment.min.height[group.indexes],
"Max elevation" = station$catchment.max.height[group.indexes])
print("group.indexes")
print(group.indexes)
print("group.df")
print(group.df)
invisible(group.df)
}
stations2average <- function(min_years, max_years) {
stations2ave <- c()
print("entered the station2average function")
print(min_years)
print(max_years)
print(class(max_years))
min_years <- as.numeric(min_years)
max_years <- as.numeric(max_years)
print(class(max_years))
for (st in seq(along = station$index)) {
if (station$length_rec[st] >= min_years) {
if (station$length_rec[st] <= max_years) {
stations2ave <- c(stations2ave, st)
# print("prout1")
# print(st)
}
}
}
print(stations2ave)
invisible(stations2ave)
}
##
# st_selection <- which(norm_area < 0.05)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
#
# st_selection <- which(norm_area > 0.05)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
##
# median_perc_lake <- median(na.omit(flood_metadata$perc_lake))
# st_selection <- which(flood_metadata$perc_lake < median_perc_lake)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
#
# st_selection <- which(flood_metadata$perc_lake > median_perc_lake)
# plot4server_rlevels_coeffvar_ave("QS", 10,30,150, st_selection)
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