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server.R
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server <- function(input, output) {
aWhereAPI::get_token(uid="1iiGfRRH9BewFNGw7tEOoRv13WbdgI7z", secret="8VSUKD8mrVUQ1gLd")
#register_google(key ='AIzaSyB5UWpjiUSyhi4fO7qKck81knTYSVAEdDI')
# Assign NULL to object values with fileinput NULL
values <- reactiveValues(fileInput = NULL)
# Clear map
observe({
input$clearMap1a
values$fileInput <- NULL
values$markers <- NULL
values$points <- NULL
leafletProxy("mymap1a") %>% clearMarkers()
})
# Input FileInput data
observe({
values$fileInput <- input$fileInput
})
# Create widget and show fileinput as a table
output$resetfileInput <- renderUI({
fileInput("fileInput", "(COORDENADAS X Y)", accept=c("text/csv",
"text/comma-separated-values,text/plain",
".csv") , width = "100%")
})
dataPoints <- reactive({
inFile = values$fileInput
if (!is.null(inFile)) {
values$points <- read.table(file = inFile$datapath, header = T, sep = ";")
}
})
output$fileInputPoints = DT::renderDataTable({
server = TRUE
data <- values$points
isolate({
datatable(
data,
class = "cell-border stripe",
selection = "none",
options = list(
pageLength = 10
)
)
})
})
# Create map with leaflet
#"https://{s}.tiles.mapbox.com/v3/jcheng.map-5ebohr46/{z}/{x}/{y}.png"
map1a = leaflet() %>%
addTiles() %>%
setView(lng = -4.04296, lat = 16.30796, zoom = 2) %>%
addControlGPS(options = gpsOptions(position = "topleft", activate = TRUE,
autoCenter = TRUE, maxZoom = 60,
setView = TRUE))
# Output variable "mymap1"
output$mymap1a <- renderLeaflet(map1a)
# Add GPS
observeEvent(input$mymap1a_gps_located, {
gps_points <- input$mymap1a_gps_located
gps_lat <- as.numeric(gps_points$coordinates$lat)
gps_lng <- as.numeric(gps_points$coordinates$lng)
values$markers <- rbind(data.frame(
Latitude = round(gps_lat, 4),
Longitude = round(gps_lng, 4)),
values$markers
)
})
# Add points markers
observe({
inFile = input$fileInput
if (!is.null(inFile)) {
df <- dataPoints()
if(!is.null(df)) {
for (i in 1:nrow(df)) {
leafletProxy("mymap1a") %>%
addMarkers(lng = df[i,3], lat = df[i,2], popup = paste(df[i,1], round(df[i,2], 4), round(df[i,3], 4), sep = ", "))
}
}
}
})
# Insert points markers with the mouse
observeEvent(input$mymap1a_click, {
click <- input$mymap1a_click
clat <- click$lat
clng <- click$lng
values$markers <- rbind(data.frame(
Latitude = round(clat, 4),
Longitude = round(clng, 4)),
values$markers
)
leafletProxy('mymap1a') %>%
addMarkers(lng = clng, lat = clat, popup = paste(round(clat, 4), round(clng, 4), sep = ", "))
})
# Create dataframe and table with ALL points markers
output$inputMarkers = DT::renderDataTable({
server = TRUE
data <- values$markers
isolate({
datatable(
data,
class = "cell-border stripe",
selection = "none",
options = list(
pageLength = 10
)
)
})
})
reactive ({
print(values$markers)
})
# --------------------------------------
output$var_names1 <- renderUI({
names_var_d1 <- read.csv("potato-res-data.csv", header = T, sep = ";", stringsAsFactors = F,
encoding = "UTF-8")
my_list <- as.list(names_var_d1$Resistance)
names(my_list) <- names_var_d1$Cultivar
selectInput("res", "Select the potato variety:",
choices = my_list , selectize = FALSE)
})
# --------------------------------------
# function for ranges
in_range <- function(val,min,max) {
return(val >= min & val <= max);
}
# function for calculate blight units
# INPUTS: hrhours>90 , avg temp, variety type (resistance)
calc_bu <- function(hhr,htavg,vt) {
if(htavg > 27.5 & hhr == 24) {
return(0);
} else if(in_range(htavg,22.5,27.5)) {
if (vt == "s") {
if(hhr == 6) {
return(0);
}
else if(in_range(hhr,7.0,9.0)) {
return(1);
}
else if(in_range(hhr,10.0,12.0)) {
return(2);
}
else if(in_range(hhr,13.0,15.0)) {
return(3);
}
else if(in_range(hhr,16.0,18.0)) {
return(4);
}
else if(in_range(hhr,19.0,24.0)) {
return(5);
}else {
return(0);
}
}
else if (vt == "ms") {
if(hhr == 9) {
return(0);
}
else if(in_range(hhr,10.0,18.0)) {
return(1);
}
else if(in_range(hhr,19.0,24.0)) {
return(2);
} else{
return(0);
}
}
else if (vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 15) {
return(0);
}
else if(in_range(hhr,16.0,24.0)) {
return(1);
} else{
return(0);
}
}
else {
return(0)
}
}
else if(in_range(htavg,12.5,22.5)) {
if(vt == "s") {
if(hhr == 6) {
return(0);
}
else if(in_range(hhr,7.0,9.0)) {
return(5);
}
else if(in_range(hhr,10.0,12.0)) {
return(6);
}
else if(in_range(hhr,13.0,24.0)) {
return(7);
}
else{
return(0);
}
}
else if(vt == "ms") {
if(hhr == 6) {
return(0);
}
else if(hhr == 7) {
return(1);
}
else if(hhr == 8) {
return(2);
}
else if(hhr == 9) {
return(3);
}
else if(hhr == 10) {
return(4);
}
else if(in_range(hhr,11.0,12.0)) {
return(5);
}
else if(in_range(hhr,13.0,24.0)) {
return(6);
}
else{
return(0)
}
}
else if(vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 6) {
return(0);
}
else if(hhr == 7) {
return(1);
}
else if(hhr == 8) {
return(2);
}
else if(hhr == 9) {
return(3);
}
else if(in_range(hhr,10.0,12.0)) {
return(4);
}
else if(in_range(hhr,13.0,24.0)) {
return(5);
}
else {
return(0);
}
}
else{
return(0)
}
}
else if(in_range(htavg,7.5,12.5)) {
if(vt == "s") {
if(hhr == 6) {
return(0);
}
else if(hhr == 7) {
return(1);
}
else if(in_range(hhr,8.0,9.0)) {
return(2);
}
else if(hhr == 10) {
return(3);
}
else if(in_range(hhr,11.0,12.0)) {
return(4);
}
else if(in_range(hhr,13.0,15.0)) {
return(5);
}
else if(in_range(hhr,16.0,24.0)) {
return(6);
}
else{
return(0)
}
}
else if(vt == "ms") {
if(hhr == 6) {
return(0);
}
else if(in_range(hhr,7.0,9.0)) {
return(1);
}
else if(in_range(hhr,10.0,12.0)) {
return(2);
}
else if(in_range(hhr,13.0,15.0)) {
return(3);
}
else if(in_range(hhr,16.0,18.0)) {
return(4);
}
else if(in_range(hhr,19.0,24.0)) {
return(5);
}
else{
return(0);
}
}
else if(vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 9) {
return(0);
}
else if(in_range(hhr,10.0,12.0)) {
return(1);
}
else if(in_range(hhr,13.0,15.0)) {
return(2);
}
else if(in_range(hhr,16.0,24.0)) {
return(3);
}
else {
return(0);
}
}
else {
return(0);
}
}
else if(in_range(htavg,2.5,7.5)) {
if(vt == "s") {
if(hhr == 9) {
return(0);
}
else if(in_range(hhr,10.0,12.0)) {
return(1);
}
else if(in_range(hhr,13.0,15.0)) {
return(2);
}
else if(in_range(hhr,16.0,18.0)) {
return(3);
}
else if(in_range(hhr,19.0,24.0)) {
return(4);
}
else{
return(0);
}
}
else if(vt == "ms") {
if(hhr == 12) {
return(0);
}
else if(in_range(hhr,13.0,24.0)) {
return(1);
}
else{
return(0);
}
}
else if(vt == "mr" | vt == "r" | vt == "hr") {
if(hhr == 18) {
return(0);
}
else if(in_range(hhr,19.0,24.0)) {
return(1);
}
else{
return(0)
}
}
else{
return(0)
}
}
else if(htavg < 2.5 & hhr == 24) {
return(0)
}
else {
return(0)
}
}
# function for calculate fungicide units
# INPUTS: rain mm, dsa - days since fungicide aplication
calc_fu <- function(rain,dsa) {
if(rain < 1 & rain > 0) {
return(1);
} else {
if(dsa == 1) {
if(in_range(rain,1.0,1.4)) {
return(4);
}
else if(in_range(rain,1.5,3.4)) {
return(5);
}
else if(in_range(rain,3.5,6.0)) {
return(6);
}
else if(rain > 6) {
return(7);
}
else{
return(0);
}
}
else if(dsa == 2) {
if(in_range(rain,1.0,1.4)) {
return(3);
}
else if(in_range(rain,1.5,4.4)) {
return(4);
}
else if(in_range(rain,4.5,8.0)) {
return(5);
}
else if(rain > 8) {
return(6);
}
else{
return(0);
}
}
else if(dsa == 3) {
if(in_range(rain,1.0,2.4)) {
return(3);
}
else if(in_range(rain,2.4,5.0)) {
return(4);
}
else if(rain > 5) {
return(5);
}
else{
return(0);
}
}
else if(in_range(dsa,4.0,5.0)) {
if(in_range(rain,1.0,2.4)) {
return(3);
}
else if(in_range(rain,2.5,8)) {
return(4);
}
else if(rain > 8) {
return(5);
}
else{
return(0);
}
}
else if(in_range(dsa,6.0,9.0)) {
if(in_range(rain,1.0,4.0)) {
return(3);
}
else if(rain > 4) {
return(4);
}
else{
return(0);
}
}
else if (in_range(dsa,10.0,14.0)) {
if(in_range(rain,1.0,1.4)) {
return(2);
}
else if(in_range(rain,1.5,8.0)) {
return(3);
}
else if(rain > 8) {
return(4);
}
else{
return(0);
}
}
else if (dsa > 14) {
if(in_range(rain,1.0,8.0)) {
return(2);
}
else if(rain > 8) {
return(3);
}
else{
return(0);
}
}
else {
return(0);
}
}
}
# function for decision rules
# INPUTS: abu, afu
check_bu_cutoff <- function(abu,vt) {
if(vt == "s" & abu >= 30) {
return(TRUE);
} else if(vt == "ms" & abu >= 35) {
return(TRUE);
} else if(vt == "mr" & abu >= 40) {
return(TRUE);
} else if(vt == "r" & abu >= 45) {
return(TRUE);
} else if(vt == "hr" & abu >= 50){
return(TRUE);
} else {
return(FALSE);
}
}
check_fu_cutoff <- function(afu,vt) {
if(vt == "s" & afu >= 15) {
return(TRUE);
} else if(vt == "ms" & afu >= 20) {
return(TRUE);
} else if(vt == "mr" & afu >= 25) {
return(TRUE);
} else if(vt == "r" & afu >= 30) {
return(TRUE);
} else if(vt == "hr" & afu >= 35){
return(TRUE);
} else {
return(FALSE);
}
}
# function for calculate humidity relative hourly
# INPUTS: meterological data, coordinates (lat y long)
calculate_hhr <- function(climdata, lon, lat) {
#########################
date_df <- as.Date(climdata[[1]], format = "%Y-%m-%d")
tn <- as.numeric(climdata[[2]])
tx <- as.numeric(climdata[[3]])
tavg <- (tn+tx)/2
hrn <- as.numeric(climdata[[4]])
hrx <- as.numeric(climdata[[5]])
hravg <- (hrn+hrx)/2
tdew <- tavg - ( (100 - hravg) / 5)
rain <- as.numeric(climdata[[6]])
# Calculate hourly for sunrise and sunset
crds <- matrix(c(lon[[1]], lat[[1]]), ncol = 2)
sunrise <- sunriset(crds, as.POSIXct(date_df),
proj4string=CRS("+proj=longlat +datum=WGS84"),
direction=c("sunrise"), POSIXct.out= F)
sunset <- sunriset(crds, as.POSIXct(date_df),
proj4string=CRS("+proj=longlat +datum=WGS84"),
direction=c("sunset"), POSIXct.out= F)
df_format_hhr <- data.frame(date_df[-c(length(date_df),1)], tn[-c(length(tn),1)], tn[-c(1:2)],
tx[-c(length(tx),1)], tx[-c(length(tx), length(tx) - 1)],
tdew[-c(length(tdew),1)],
sunrise[-c(length(sunrise),1)], sunset[-c(length(sunset),1)],
sunset[-c(length(sunset),length(sunset) - 1)])
names(df_format_hhr) <- c("date", "tmin", "tminnext",
"tmax", "tmaxold", "tdew",
"sunrise", "sunset", "sunset old")
#########################
# Tn temp min, Tx temp max, To sunset , Tp temp min next
# Hn sunrise, Hx h temp max, Ho h sunset , Hp h temp min next
# date <- df_in_hhr[[1]]
#####
model_temp_hr <- function(df_in_hhr){
Tn <- as.numeric(df_in_hhr[[2]])
Tp <- as.numeric(df_in_hhr[[3]])
Tx <- as.numeric(df_in_hhr[[4]])
Hn <- as.numeric(df_in_hhr[[7]]) * 24
Ho <- as.numeric(df_in_hhr[[8]]) * 24
tdew <- as.numeric(df_in_hhr[[6]])
#####
Tp_old <- Tn
Hp_old <- Hn + 24
Tx_old <- as.numeric(df_in_hhr[[5]])
Ho_old <- as.numeric(df_in_hhr[[9]]) * 24
To_old <- Tx_old - 0.39*(Tx_old - Tp_old)
# Parameters for model
To <- Tx - 0.39*(Tx - Tp)
Hp <- Hn + 24
Hx <- Ho - 4
alpha <- Tx - Tn
r <- Tx - To
beta1 <- (Tp - To) / sqrt(Hp - Ho)
beta2 <- (Tp_old - To_old) / sqrt(Hp_old - Ho_old)
t <- 1:24
T_model <- 0
for(i in 1:24) {
if(t[i] > Hn & t[i] <= Hx){
T_model[i] <- Tn + alpha * ( ((t[i]-Hn) / (Hx-Hn)) * (pi/2) )
}
else if(t[i] > Hx & t[i] <= Ho){
T_model[i] <- To + r * sin( (pi/2) + ( ((t[i]-Hx)/4) * (pi/2) ) )
}
else if( t[i] > Ho & t[i] <= 24 ){
T_model[i] <- To + beta1 * sqrt( (t[i] - Ho) )
}
else if (t[i] >= 1 & t[i] <= Hn){
T_model[i] <- To_old + beta2 * sqrt( (t[i]+24) - Ho_old)
}
else {
T_model[i] <- "Error"
}
}
# Buck formula for es and e (kPa)
es <- 0.61121 * exp( ( (18.678 - (T_model/234.5)) * T_model) / (257.14 + T_model))
e <- 0.61121 * exp( ( (18.678 - (tdew/234.5)) * tdew) / (257.14 + tdew))
# hr limit > 90
hr <- (e/es) * 100
hours_hr_limit <- length(hr[hr>90])
return(hours_hr_limit)
}
df_hrlim_hours <- apply(df_format_hhr, 1 , function(x) model_temp_hr(x))
df_output_hr <- data.frame(date_df[-c(length(date_df),1)], df_hrlim_hours,
tavg[-c(length(tavg),1)], rain[-c(length(rain),1)])
names(df_output_hr) <- c("date", "hr90_hour", "tavg_C", "rain_mm")
return(df_output_hr)
}
# function for calculate humidity relative hourly - AwhereAPI
# INPUTS: day initial, day final, coordinates (lat y long), hr limit
climDataAPI_input_model <- function(day0, dayn, lat, long, hrlimite, calculate_hhr) {
dayinitial = day0;
dayfinal = dayn;
sysdate = Sys.Date();
print(day0)
print(dayn)
if((dayinitial < sysdate) == TRUE && (dayfinal < sysdate) == TRUE) {
DataAPI <- aWhereAPI::daily_observed_latlng(latitude = lat,
longitude = long,
day_start = as.character(dayinitial),
day_end = as.character(dayfinal))
climData <- DataAPI[, c(3,5,4,9,8,6)]
names(climData) <- c("date","tmin","tmax","hrmin","hrmax","pp")
# input-runsimcast: date, hr>90, temp avg, rain
input_runsimcast <- calculate_hhr(climData, long, lat)
return(input_runsimcast);
}
else if((dayinitial < sysdate) == TRUE && (dayfinal >= sysdate) == TRUE) {
DataAPI1 <- aWhereAPI::daily_observed_latlng(latitude = lat,
longitude = long,
day_start = as.character(dayinitial),
day_end = as.character(sysdate-1))
climData1 <- DataAPI1[, c(3,5,4,9,8,6)]
names(climData1) <- c("date","tmin","tmax","hrmin","hrmax","pp")
input_runsimcast1 <- calculate_hhr(climData1, long, lat)
###################
DataAPI2 <- aWhereAPI::forecasts_latlng(latitude = lat,
longitude = long,
day_start = as.character(sysdate),
day_end = as.character(dayfinal),
block_size = 1)
climData_hour <- DataAPI2[, c(3,7,15,11)]
hourinitial <- paste(sysdate,"00:00")
climData_hour$date_h <- seq(from = as.POSIXct(hourinitial, tz="UTC"),
by="hour",
length.out = nrow(climData_hour))
climData_hour$date_d <- format(climData_hour$date_h,"%Y-%m-%d")
temp_avg2 <- aggregate(temperatures.value ~ date_d, climData_hour, mean)
hr_hours2 <- aggregate(relativeHumidity.average ~ date_d, climData_hour,
FUN = function(RH) {
return(length(RH[RH>as.numeric(hrlimite)]))
})
precip2 <- aggregate(precipitation.amount ~ date_d, climData_hour, sum)
input_runsimcast2 <- data.frame(hr_hours2,temp_avg2[1:length(temp_avg2)-1,2],
precip2[1:length(precip2)-1,2])
names(input_runsimcast2) <- c("date", "hr90_hour", "tavg_C", "rain_mm")
input_runsimcast <- rbind(input_runsimcast1, input_runsimcast2)
return(input_runsimcast);
}
else if ((dayinitial >= sysdate) == TRUE && (dayfinal >= sysdate) == TRUE) {
DataAPI2 <- aWhereAPI::forecasts_latlng(latitude = lat,
longitude = long,
day_start = as.character(dayinitial),
day_end = as.character(dayfinal),
block_size = 1)
climData_hour <- DataAPI2[, c(3,7,15,11)]
hourinitial <- paste(sysdate,"00:00")
climData_hour$date_h <- seq(from = as.POSIXct(hourinitial, tz="UTC"),
by="hour",
length.out = nrow(climData_hour))
climData_hour$date_d <- format(climData_hour$date_h,"%Y-%m-%d")
temp_avg2 <- aggregate(temperatures.value ~ date_d, climData_hour, mean)
hr_hours2 <- aggregate(relativeHumidity.average ~ date_d, climData_hour,
FUN = function(RH) {
return(length(RH[RH>as.numeric(hrlimite)]))
})
precip2 <- aggregate(precipitation.amount ~ date_d, climData_hour, sum)
input_runsimcast <- data.frame(hr_hours2,temp_avg2[1:length(temp_avg2)-1,2],
precip2[1:length(precip2)-1,2])
names(input_runsimcast) <- c("date", "hr90_hour", "tavg_C", "rain_mm")
return(input_runsimcast);
}
}
# function for SIMCAST model Fry et. al 1983
simcast_model <- function(df_in_simcast, vt){
vt <- as.character(vt[[1]])
lines <- list()
for(i in 1:nrow(df_in_simcast)){
lines[[i]] = df_in_simcast[i,]
}
#########
firstApplication <- TRUE
returnValue <- 1
bu <- 0
fu <- 0
bua <- 0
fua <- 0
afu <- 0
app <- 0
i <- 0
last_bua <- 0
last_fua <- 0
app_ctr <- 0
days_since_app <- 0
min_day <- 5
abu <- 0
tabu <- 0
tafu <- 0
#begin <- 0
end <- length(lines)
###############