global_old.R

In NVE/Flood_forecasting: Title: A list a functions to support the Flomvarsling Shiny app

packages <- c("curl", "shiny", "tidyverse", "sp", "plotly", "leaflet", "DT")
sapply(packages, library, character.only = TRUE, lib.loc = "/home/flbk/R")
# sp: For the point.in.polygon function


## My modules: either load package or source modules from this directory
source('map_modules.R')
source('table_modules.R')
source('plot_modules.R')
source('plotting_functions.R')
source('mapping_functions.R')

# Source all files from the RCura version of leaflet DOESNT WORK
# file.sources = list.files("./leaflet-plugins", pattern="*.R", full.names=TRUE)
# for (f in file.sources) {source(f) }

hbv_catchments <- readLines("data/hbv_catchments.json") %>% paste(collapse = "\n")

# Load the Rdata files that were prepared with the NVEDATA package.
# This creates the global variable

load("HBV_2014.RData")
load("HBV_2016.RData")
load("DDD.RData")
load("flomtabell.RData")
load("HBV_past_year.RData")
load("meta_data.rda")

station_numbers <- as.character(unique(HBV_2014$regine.main))  # All of the HBV_2016 and DD stations are in HBV_2014
station_names <- as.character(unique(HBV_2014$station.name))  # May not be optimal (if 2 stations had same name), but it works
station_nbname <- as.character(unique(HBV_2014$nbname))

## Metadata organized as below is needed for the maps.
# Maybe we can streamline with the rest later
station_indices <- which(meta_data$regine_main %in% station_numbers)  # 1 station in HBV_2014 is not in the metadata
stations <- lapply(meta_data, function(x) {x[station_indices]})
# stations$nbname_SPECIALCHAR <- paste(stations$regine_main, "-", stations$name, sep ="")
stations$nbname <- paste(stations$regine_main, "-", station_names[match(stations$regine_main, station_numbers)], sep ="")

# Calculation of a stations$flood_warning indicator for the forecast period
# I want to have it under the "stations" list for the moment as this list is used by the map functions
## WARNING: this first implementation has potential bugs and requires decisions on which variables o use!

HBV_2014_SimCorr <- dplyr::filter(HBV_2014, Type == "Runoff" & Variable == "SimCorr")
HBV_2014_SimCorr_maxed <- group_by(HBV_2014_SimCorr, nbname, regine.main) %>% dplyr::summarise(maxed = max(na.omit(Values)))

flom_obs1Y <- dplyr::filter(flomtabell, Type == "Obs" & Variable == "mean") 

index_HBV <- match(stations$regine_main, HBV_2014_SimCorr_maxed$regine.main)
index_flomtabell <- match(HBV_2014_SimCorr_maxed$regine.main, flom_obs1Y$regine.main)

stations$flood_warning <- HBV_2014_SimCorr_maxed$maxed[index_HBV] / flom_obs1Y$Values[index_flomtabell[index_HBV]]


HBV_2014_SimH90 <- dplyr::filter(HBV_2014, Type == "Runoff" & Variable == "SimH90")
HBV_2014_SimL90 <- dplyr::filter(HBV_2014, Type == "Runoff" & Variable == "SimL90")
HBV_2014_diff <- HBV_2014_SimH90
HBV_2014_diff$Values <- HBV_2014_SimH90$Values - HBV_2014_SimL90$Values

HBV_2014_diff_maxed <- group_by(HBV_2014_diff, nbname, regine.main) %>% dplyr::summarise(maxed = max(Values, na.rm = TRUE))
stations$uncertainty <- HBV_2014_diff_maxed$maxed[index_HBV] / flom_obs1Y$Values[index_flomtabell[index_HBV]]