pasture15.rmd

---
title: "Pasture And Crop Potential"
runtime: shiny
output: 
  prettydoc::html_pretty:
    theme: architect
---

<!-- Simon Woodward, DairyNZ, 2018 -->
<!-- now use joined.rds data, including elevation nitrogen etc -->
<!-- # Note: Unlike Shiny Apps, Interactive R Markdown Documents are do not require ui and server,  -->
<!-- #       the whole document is treated as a server. -->
<!-- # You can't guarantee what order the reactive elements will execute I think -->


```{r setup, include=FALSE} 
knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(leaflet) # interactive map
library(ggmap) 
library(cowplot)
library(geosphere)
library(proj4) # warning masked by rgdal
library(KernSmooth) # for contouring
library(sp) # spatial polygons
library(quantreg)
#library(broom) # used to convert spatial data to tibble
library(rgdal) 
library(scales)
library(ggthemes)
# library(shinyjs)

#### these objects are available to all sessions ####
# this is good place to put global data or utility functions
# you can change them (for every session) but you have to use the <<- operator

# read multiyear data and tidy up 
data_all <- readRDS("joined.rds") %>%
  mutate(pasture_eaten=as.numeric(pasture_and_crop_eaten_t_dm_ha),
         region=as.factor(region),
         season=as.factor(season),
         slope=slope.x,
         aspect=aspect.x,
         nitrogen_applied=as.numeric(nitrogen_applied_for_year_l2),
         topo=topo_position_index.y,
         soil=as.character(nzsc_order.x))  
data_all <- data_all %>%
  select(farm_number, pasture_eaten, region, season, long, lat, soil, elev, 
         slope, aspect, topo, nitrogen_applied) 
temp <- nrow(data_all)
data_all <- data_all %>% drop_na() # drops a lot of rows!
cat(file=stderr(), paste("Rows dropped with missing data =", temp-nrow(data_all), "\n"))
cat(file=stderr(), paste("Rows remaining =", nrow(data_all), "\n"))

# add elevation factor
data_all <- data_all %>%
  mutate(elev_fact=factor(x=case_when(elev<=50 ~ "Low (0-50m)", 
                                      elev<=200 ~ "Middle (50-200m)", 
                                     TRUE ~ "High (200m+)"),
                          levels=c("Low (0-50m)", "Middle (50-200m)", "High (200m+)")))

summary(data_all)

# create season and soil list
season_all <- as.list(sort(unique(as.character(data_all$season))))
names(season_all) <- season_all
soil_all <- as.list(sort(unique(data_all$soil)))
names(soil_all) <- soil_all
names(soil_all)[soil_all=="L"] <- "Allophanic"
names(soil_all)[soil_all=="A"] <- "Anthropic"
names(soil_all)[soil_all=="B"] <- "Brown"
names(soil_all)[soil_all=="Z"] <- "Podzol"
names(soil_all)[soil_all=="M"] <- "Pumice"
names(soil_all)[soil_all=="W"] <- "Raw"
names(soil_all)[soil_all=="G"] <- "Gley"
names(soil_all)[soil_all=="N"] <- "Granular"
names(soil_all)[soil_all=="E"] <- "Melanic"
names(soil_all)[soil_all=="R"] <- "Recent"
names(soil_all)[soil_all=="S"] <- "Semiarid"
names(soil_all)[soil_all=="U"] <- "Ultic"
names(soil_all)[soil_all=="O"] <- "Organic"
names(soil_all)[soil_all=="X"] <- "Oxidic"
names(soil_all)[soil_all=="P"] <- "Pallic"
elev_all <- levels(data_all$elev_fact)
names(elev_all) <- elev_all

# define nitrogen steps
nitrogen_range <- seq(0L, 250L, 50L)
nitrogen_here <- as.list(c("Don't Adjust", paste(nitrogen_range, "kgN/ha/y")))
nitrogen_default <- nitrogen_here[[4]]

# calculate nitrogen adjustment
#  could fail if there is insufficient data to do regression
# temp <- data_all %>%
#   group_by(season, elev_fact, soil) %>%
#   do(nitrogen_slope=lm(pasture_eaten ~ nitrogen_applied + 1, data=.)$coefficients["nitrogen_applied"],
#      nitrogen_count=length(.$nitrogen_applied)
#      )
# temp$nitrogen_slope <- unlist(temp$nitrogen_slope)
# ggplot(data=temp) +
#   geom_histogram(mapping=aes(nitrogen_slope*1000)) +
#   scale_x_continuous(limits=c(-30,50))
# temp$nitrogen_count <- unlist(temp$nitrogen_count)
# temp$nitrogen_slope <- median(temp$nitrogen_slope, na.rm=TRUE) # median about 0.010 anyway?
# temp$nitrogen_slope <- 0.010 # after all that, use standard value
data_all <- data_all %>%
  # left_join(temp, by=c("season", "elev_fact", "soil")) %>%
  mutate(
    nitrogen_slope = 0.010,
    pasture_eaten_raw = pasture_eaten,
    pasture_eaten_min = pasture_eaten + (min(nitrogen_range) - nitrogen_applied) * nitrogen_slope,
    pasture_eaten_max = pasture_eaten + (max(nitrogen_range) - nitrogen_applied) * nitrogen_slope
    )

# create data point map/contours for plotting on leaflet
# https://gis.stackexchange.com/questions/168886/r-how-to-build-heatmap-with-the-leaflet-package
data_pts <- unique(data_all[c("long", "lat")])
kde <- bkde2D(data.matrix(data_pts), bandwidth=c(0.1, 0.1), gridsize=c(100,100))
CL <- contourLines(kde$x1 , kde$x2 , kde$fhat) # contour lines (list)
LEVS <- as.factor(sapply(CL, `[[`, "level")) # contour levels (vector)
NLEV <- length(levels(LEVS)) # number of levels 
pgons <- lapply(1:length(CL), function(i) # convert to polygons (ID=i is actually the level)
    Polygons(list(Polygon(cbind(CL[[i]]$x, CL[[i]]$y))), ID=i))
spgons = SpatialPolygons(pgons)
spgon_cols <- topo.colors(NLEV, NULL)[LEVS]

# convert spatialPolygons to data frame for use in ggplot
#spgonsdf <- broom::tidy(spgons, region=ID)

# calculate NZTM2000 coordinates for farm locations
proj4string <- "+proj=tmerc +lat_0=0.0 +lon_0=173.0 +k=0.9996 +x_0=1600000.0 +y_0=10000000.0 +datum=WGS84 +units=m"
nzgd <- data.matrix(data_all[,c("long", "lat")])
nztm <- proj4::project(xy=nzgd, proj=proj4string)
temp <- proj4::project(xy=nzgd, proj=proj4string, inverse=TRUE)
data_all$east <- nztm[,1]
data_all$north <- nztm[,2]

# define some constants 
trim <- 0.0 # rqss fails near tails if insufficient data
probs <- seq(trim, 1-trim, 0.02) # probabilities for sampcdf
nprobs <- length(probs)
windows <- c(60,40,20)
nmin <- 4L # minimum number of farms in a window for analysis

# gets a list of default ggplot colours
gg_colour_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}
window_cols <- gg_colour_hue(length(windows))

# default_loc must be somewhere with data!!!
# default_loc <- list(long=172.833333, lat=-41.5) # Nelson
default_loc <- list(long=175.619105, lat=-40.386396) # Massey
# default_loc <- list(long=174.865530, lat=-41.259256) # Wellington
# default_loc <- list(long=175.352116, lat=-37.781841) # DairyNZ
# default_loc <- list(long=167.893066, lat=-43.036775) # In the ocean!

# normal list with same elements, useful for testing
my <- list(name="You Are Here",
                     long=default_loc$long,
                     lat=default_loc$lat,
                     distortion=1,
                     east=NA, north=NA,
                     adjust_here=nitrogen_here,
                     adjust_default=nitrogen_default,
                     adjust=nitrogen_default,
                     season_here=list(NA),
                     season=list(NA),
                     soil_here=list(NA),
                     soil=list(NA),
                     elev_here=list(NA),
                     elev=list(NA),
                     data=list(NA),
                     breaks=list(NA),
                     recalc=0L
                     )

```

Pasture and crops are fundamental components of profitable dairy systems. In general, the more pasture and crops you can grow and feed to your animals, the more profitable your system will be. This tool allows you to compare your pasture and crops produced and consumed with other farms in your region. This can indicate whether there is potential for you to increase pasture and crop production and intake, and hence profitability.

#### Where are You?

Please click your location on the map, and select which season, soil(s) and elevation(s) of data to include. The map contours show the availability of data. You can also adjust the pasture and crop production data to match your nitrogen application rate.

```{r input_pane, echo=FALSE, eval=TRUE}

#### initialise session ####

# useful trigger object to allow manual control (not used yet)
# https://www.r-bloggers.com/dynamically-generated-shiny-ui/
# makeReactiveTrigger <- function() {
#    rv <- reactiveValues(a = 0)
#    list(
#      depend = function() {
#        rv$a
#        invisible()
#      },
#      trigger = function() {
#        rv$a <- isolate(rv$a + 1)
#      }
#    )
# }
# usage
# my_trigger <- makeReactiveTrigger() # create trigger object
# my_trigger$depends() # put this where you want a dependency
# my_trigger$trigger() # put this where you want to trigger the dependency

# collect info about the current location and selections
# these variables are available in the reactive conext
# "my" is not itself a reactive object, it's a list of reactive objects
my <- reactiveValues(name="You Are Here", 
                     long=default_loc$long,
                     lat=default_loc$lat,
                     distortion=1, 
                     east=NA, north=NA, 
                     adjust_here=nitrogen_here,
                     adjust_default=nitrogen_default,
                     adjust=nitrogen_default,
                     season_here=list(NA), 
                     season_default=list(NA), 
                     season=list(NA),
                     soil_here=list(NA), 
                     soil_default=list(NA), 
                     soil=list(NA),
                     elev_here=list(NA), 
                     elev_default=list(NA), 
                     elev=list(NA),
                     data=list(NA),
                     breaks=list(NA),
                     recalc=0L
                     )

# set leaflet info
v <- reactiveValues(zoom=5, minzoom=5, maxzoom=15, long=NA, lat=NA) 

# initialise map centre
isolate({
  cat(file=stderr(), paste("initialise location"), "\n")
  v$long <- my$long
  v$lat <- my$lat
})

#### ui elements ### 

# titlePanel("Where are You?")

output$season_selector <- renderUI({
  cat(file=stderr(), paste("render season selector"), "\n")
  selectInput("season", strong("Season?"), my$season_here, selected=my$season_default)
})

output$soil_selector <- renderUI({
  cat(file=stderr(), paste("render soil selector"), "\n")
  selectInput("soil", strong("Soils?"), my$soil_here, selected=my$soil_default, 
              selectize=FALSE, multiple=TRUE)
})

output$elev_selector <- renderUI({
  cat(file=stderr(), paste("render elev selector"), "\n")
  selectInput("elev", strong("Elevation?"), my$elev_here, selected=my$elev_default,
              selectize=FALSE, multiple=TRUE)
})

output$nitrogen_checkbox <- renderUI({
  cat(file=stderr(), paste("render adjust checkbox"), "\n")
  selectInput("adjust", strong("Nitrogen?"), my$adjust_here, selected=my$adjust_default)
})

#### make initial map ####

# https://stackoverflow.com/questions/34348737/r-leaflet-how-to-click-on-map-and-add-a-circle
output$map <- renderLeaflet({
  cat(file=stderr(), paste("render leaflet"), "\n")
  
    isolate({ # prevent redraw if arguments change

      leaflet(spgons, options=leafletOptions(minZoom=v$minzoom, maxZoom=v$maxzoom)) %>%
        setView(v$long, v$lat, zoom=v$zoom)  %>%
        addTiles() %>% # default map
        addPolygons(data=spgons, color=spgon_cols, weight=0, options=pathOptions(clickable=FALSE)) %>%
        addMarkers(my$long, my$lat, "layer1", options=pathOptions(clickable=FALSE)) %>%
        addCircles(my$long, my$lat, layerId=as.character(windows), radius=windows*1000, 
                   color=window_cols, weight=4, fill=NA, options=pathOptions(clickable=FALSE))
      
    })

  }) # end renderLeaflet

#### ui layout ####

css <- "
.selectize-input { 
font-size: 14px; line-height: 14px;
} 
.selectize-dropdown { 
font-size: 14px; line-height: 14px; 
}"

shinyUI(fluidPage(

  tags$style(type='text/css', css),

  sidebarLayout(
    
    sidebarPanel(
      cat(file=stderr(), paste("render sidebar"), "\n"),
      width=5, # 12ths of the panel
      uiOutput("season_selector"), 
      uiOutput("soil_selector"), 
      uiOutput("elev_selector"),
      uiOutput("nitrogen_checkbox") 
    ), # end sidebarPanel
    
    mainPanel(
      cat(file=stderr(), paste("render main panel"), "\n"),
      width=7, # 12ths of the panel
      leafletOutput("map", width="100%", height=480) # can manipulate size here
    ), # end mainPanel
    
    position="right"
    
  ) # end sidebarLayout

)) # end fluidPage

#### react to mouse clicks ####

# see also https://rstudio.github.io/leaflet/shiny.html
observeEvent(input$map_click, {
  
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("observed map_click"), "\n")    

  click <- input$map_click
  my$long <- click$lng
  my$lat <- click$lat

  # mark map
  leafletProxy("map", deferUntilFlush=FALSE) %>%
    addMarkers(my$long, my$lat, "layer1", options=pathOptions(clickable=FALSE)) %>%
    addCircles(my$long, my$lat, layerId=as.character(windows), radius=windows*1000, 
               color=window_cols, weight=4, fill=NA, options=pathOptions(clickable=FALSE)) 
  
}) # end observe mouse click

#### react to change of location ####

observeEvent(c(my$long, my$lat), {

  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("change of location =", my$long, my$lat), "\n")
    
  # calculate aspect ratio near my farm (not used)
  # nzgd <- data.matrix(tibble(long=c(my$long, my$long, my$long-0.5, my$long+0.5),
  #                            lat=c(my$lat-0.5, my$lat+0.5, my$lat, my$lat)))
  # nztm <- proj4::project(xy=nzgd, proj=proj4string)
  # my$distortion <- (max(nztm[,2])-min(nztm[,2]))/(max(nztm[,1])-min(nztm[,1]))
  
  # location for map centre
  nzgd <- data.matrix(c(my$long, my$lat))
  nztm <- proj4::project(xy=nzgd, proj=proj4string)
  my$east <- nztm[,1]
  my$north <- nztm[,2]

  # calculate distance #
  # we need to use rowwise()  because distm is not vectorised, I think, although rowwise() is deprecated
  # ungroup() removes the effect of rowwise()
  # http://www.expressivecode.org/2014/12/17/mutating-using-functions-in-dplyr/
  data <- data_all %>% 
    rowwise() %>% 
    mutate(dist = distm(c(my$long, my$lat), c(long, lat), fun=distHaversine), # this needs rowwise()
           dist = dist/1000) %>%
    ungroup()
  
  # filter data
  temp <- data %>% 
    filter(dist < max(windows)) # km
  
  # what do to if insufficient data?
  if (nrow(temp)>=nmin){
    data <- temp
  } else {
    data <- data[1:max(2,nmin-1),]
  }

  # calculate width of histogram for region
  my$breaks <- seq(  floor(min(data$pasture_eaten_raw, data$pasture_eaten_min, data$pasture_eaten_max)), 
                   ceiling(max(data$pasture_eaten_raw, data$pasture_eaten_min, data$pasture_eaten_max)), 
                   1)
  
  # store data for region
  my$data <- data 
 
  # what season are available here   
  i <- sort(unique(as.character(data$season)))
  my$season_here <- season_all[match(i, season_all)]
  n <- unlist(map(my$season_here, function(u) sum(u==data$season)))
  names(my$season_here) <- paste(names(my$season_here), " (", n, " Farms)", sep="")
  cat(file=stderr(), paste("my$season_here =", length(my$season_here)), "\n")
  cat(file=stderr(), paste(names(my$season_here)), "\n")

  # reset selections
  my$season_default <- tail(my$season_here, 1)
  my$season <- my$season_default
  my$soil <- list(NA)
  my$elev <- list(NA)
  # don't change my$adjust
  
}) # end reaction to location changing

#### react to change of season ####

observeEvent(input$season, {
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("observed input$season =", input$season), "\n")
  req(input$season, input$season!="NA")
  if (my$season != input$season) {
    my$season <- input$season
    my$soil <- list(NA)
    my$elev <- list(NA)
  }
})

observeEvent(my$season, {

  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("change of my$season =", my$season), "\n")
  req(my$season, my$season!="NA")

  # make soil list
  data <- my$data %>%
    filter(season == my$season)
  i <- sort(unique(data$soil))
  my$soil_here <- soil_all[match(i, soil_all)]
  n <- unlist(map(my$soil_here, function(u) sum(u==data$soil)))
  names(my$soil_here) <- paste(names(my$soil_here), " (", n, " Farms)", sep="")
  cat(file=stderr(), paste("my$soil_here =", length(my$soil_here)), "\n")
  cat(file=stderr(), paste(names(my$soil_here)), "\n")

  # reset selections
  my$soil_default <- my$soil_here
  my$soil <- my$soil_default 
  my$elev <- list(NA)
  # don't change my$adjust

}) # end reaction to season changing

#### react to change of soil ####

observeEvent(input$soil, {
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("observed input$soil ="), paste(input$soil), "\n")
  req(input$soil, input$soil!="NA")
  if (any(my$soil != input$soil)) {
    my$soil <- input$soil
    my$elev <- list(NA)
  }
})

observeEvent(my$soil, {
  
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("change of my$soil = "), paste(my$soil), "\n")
  req(my$soil, my$soil!="NA")
  
  # make elev list
  data <- my$data %>%
    filter(season == my$season) %>%
    filter(soil %in% my$soil)
  # data <- data_all[1:10,] # subset for testing
  i <- unique(data$elev_fact)
  my$elev_here <- elev_all[which(elev_all %in% i)] # retains sorting
  n <- unlist(map(my$elev_here, function(u) sum(u==data$elev_fact)))
  names(my$elev_here) <- paste(my$elev_here, " (", n, " Farms)", sep="")
  cat(file=stderr(), paste("my$elev_here =", length(my$elev_here)), "\n")
  cat(file=stderr(), paste(names(my$elev_here)), "\n")

  # reset selections
  my$elev_default <- my$elev_here
  my$elev <- my$elev_default 
  # don't change my$adjust
  
}) # end reaction to soil changing

#### react to change of elevation ####

observeEvent(input$elev,  {
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("observed input$elev ="), paste(input$elev), "\n")
  req(input$elev, input$elev!="NA")
  if (any(my$elev != input$elev)) {
    my$elev <-  input$elev
  }
})

observeEvent(my$elev,  {

  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("change of my$elev ="), paste(my$elev), "\n")
  req(my$elev, my$elev!="NA")

  # trigger recalc
  my$recalc <- my$recalc + 1L
  
})

#### react to change of adjust ####

observeEvent(input$adjust, {
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("observed input$adjust =", input$adjust), "\n")
  req(my$adjust, input$adjust)
  if (my$adjust != input$adjust) {
    my$adjust <- input$adjust
  }
})

observeEvent(my$adjust, {
  
  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("change of my$adjust =", my$adjust), "\n")
  req(my$adjust)

  # trigger recalc
  my$recalc <- my$recalc + 1L
  
})

```

<!-- # Your Neighbourhood -->

```{r output_calculations, eval=TRUE, echo=FALSE, warning=TRUE}

#### react to change of inputs ####

calc <- eventReactive(my$recalc,  {

  cat(file=stderr(), "\n")
  cat(file=stderr(), paste("analyse for location =", my$long, my$lat), "\n")
  cat(file=stderr(), paste("analyse for season =", my$season), "\n")
  cat(file=stderr(), paste("analyse for soil ="), paste(my$soil), "\n")
  cat(file=stderr(), paste("analyse for elev ="), paste(my$elev), "\n")
  cat(file=stderr(), paste("analyse for adjust ="), my$adjust, "\n")
  req(my$season!="NA", my$soil!="NA", my$elev!="NA", my$adjust!="NA")
  req(my$long, my$lat, my$season, my$soil, my$elev, my$adjust)

  dont_adjust <- nitrogen_here[[1]]
  nitrogen_level <- as.numeric(word(my$adjust,1))
  data <-  my$data %>% 
    filter(season == my$season) %>% 
    filter(soil %in% my$soil) %>%
    filter(elev_fact %in% my$elev) %>%
    mutate(pasture_eaten = 
             case_when(
               my$adjust==dont_adjust ~ pasture_eaten_raw,
               TRUE ~ pasture_eaten_raw + (nitrogen_level - nitrogen_applied) * nitrogen_slope
               )
           )

  if (my$adjust==dont_adjust){
    my$name <- paste("You (", season_all[my$season[[1]]], ")", sep="")
  } else {
    my$name <- paste("You (", season_all[my$season[[1]]], ")",
                     " (Nitrogen Adjustment = ", sprintf("%.1f", data$nitrogen_slope[1]*1000), ")", 
                     sep="")
  }
  
  cat(file=stderr(), paste("nrow(data) = ", nrow(data)), "\n")

  # circle function
  circle_fun <- function(centre=c(0,0), r=1, npoints=100){
    tt <- seq(0, 2*pi, length.out=npoints)
    xx <- centre[1] + r * cos(tt)
    yy <- centre[2] + r * sin(tt)
    return(tibble(x=xx, y=yy))
  }    
    
  # prepare empty data frames for loop
  farms <- tibble(x=numeric(), y=numeric(), east=numeric(), north=numeric(), long=numeric(), lat=numeric(),
                  pasture=numeric(), dist=numeric(), window=numeric(), radius=factor())
  sampcdf <- tibble(probs=numeric(), quants=numeric(), radius=factor())
  samppdf <- tibble(pasture=numeric(), window=numeric(), radius=factor(),
                    q=numeric(), qr=numeric(), qrlower=numeric(), qrupper=numeric())
  circles <- tibble(east=numeric(), north=numeric(), radius=factor())
  
  # loop through decreasing window sizes 
  for (window in windows) {

    # select data within window
    data_window <- data %>% filter(dist < window)
    n <- nrow(data_window)
    code <- paste(window," km\n(", format(n, width=1), " Farms)", sep="")
    cat(file=stderr(), paste("window = ", window, " km"), "\n")

    # calculate circle
    circle <- circle_fun(centre=c(my$east, my$north), r=window*1000, npoints=100)
    nztm <- data.matrix(circle[,c("x", "y")])
    nzgd <- proj4::project(xy=nztm, proj=proj4string, inverse=TRUE)
    circle$long <- nzgd[,1]
    circle$lat <- nzgd[,2]

    # save selected farms for plot
    if (n >= 1) {
      farms <- rbind(farms, tibble(east=data_window$east, north=data_window$north,
                                 long=data_window$long, lat=data_window$lat,
                                 pasture=data_window$pasture_eaten,
                                 dist=data_window$dist, window=window, radius=as.factor(code)))
    }
    
    circles <- rbind(circles, tibble(long=circle$long, lat=circle$lat, radius=as.factor(code)))

    # save sample quantiles if enough data to be sensible
    if (n >= nmin) {
      
      # calculate quantile
      qr1 <- rq(formula=pasture_eaten ~ 1, tau=0.9, data=data_window) # linear quantile regression
      se_method <- "boot" # how condience intervals are calculated, some methods more robust
      yqr1<- predict(qr1, tibble(east=my$east, north=my$north), interval="confidence", level=0.95, se=se_method)
      q90 <- quantile(data_window$pasture_eaten, 0.9, type=1) # also calc simple q90
  
      cat(file=stderr(), paste("yqr1 =", yqr1), "\n")
      # cat(file=stderr(), paste("q90 =", q90), "\n") # should be the same

      quants <- quantile(data_window$pasture_eaten, probs=probs, type=8) # see documentation for type=?
      sampcdf <- rbind(sampcdf, tibble(probs=probs, quants=quants, radius=as.factor(code)))
      samppdf <- rbind(samppdf, tibble(pasture=data_window$pasture_eaten, window=window,
                                       radius=as.factor(code),
                                       q=q90, qr=yqr1[1], qrlower=yqr1[2], qrupper=yqr1[3]))
      
    } # if n >= nmin

    # add a blank line
    samppdf <- rbind(samppdf, tibble(pasture=NA, window=window,
                                 radius=as.factor(code),
                                 q=NA, qr=NA, qrlower=NA, qrupper=NA))

  } # next window size

  # biggest circle
  circle <- circle_fun(centre=c(my$east, my$north), r=max(windows)*1000, npoints=100)
  nztm <- data.matrix(circle[,c("x", "y")])
  nzgd <- proj4::project(xy=nztm, proj=proj4string, inverse=TRUE)
  circle$long <- nzgd[,1]
  circle$lat <- nzgd[,2]

  # return results as function for testing
  #calc <- function() list(data=data, circles=circles, circle=circle, farms=farms, sampcdf=sampcdf, samppdf=samppdf)
  
  # return results in a list
  return(list(data=data, circles=circles, circle=circle, farms=farms, 
              sampcdf=sampcdf, samppdf=samppdf))
  
}) # end reaction to elev chaning, calculation of calc <- list(results)

```

This chart shows the distribution of pasture and crops produced and consumed within various distances of your location. The 90th percentile and its uncertainty band is also shown. Graphs require a minimum of `r nmin` farms.

```{r output_plot, eval=TRUE, echo=FALSE}

#### create histograms ####

output$stacked_histogram <- renderPlot({

  samppdf <- calc()$samppdf # get data for histogram when calc() changes

  isolate({
    
    cat(file=stderr(), paste("render stacked histograms"), "\n")
    
    title_string <- paste("Pasture And Crop Eaten Near", my$name)

    # create empty plot
    stacked_histogram <- ggplot() +
      labs(title=title_string, y="Number of Farms\n", 
           x="Pasture And Crop Eaten (tDM "*ha^-1~y^-1*")", colour="Radius (km)") +
      # theme_cowplot() +
      #theme_stata(base_size=16, scheme="s2color") +
      theme_economist(base_size=12, horizontal=FALSE) +
      theme(axis.text.x=element_text(size=16, colour="grey35"),
            axis.text.y=element_text(size=16, colour="grey35"),
            strip.text=element_text(size=16, face="bold", colour="grey35"),
            plot.title=element_text(hjust=0.5, colour="grey35"),
            axis.title=element_text(size=16, face="bold", colour="grey35"),
            axis.title.x=element_text(size=18, face="bold", colour="grey35")
            ) +
      #scale_y_continuous(breaks=c()) + # remove y-scale when too many facets
      panel_border(colour="grey35") +  
      theme(legend.position="none")
    
    # add histograms to empty plot
    if (nrow(drop_na(samppdf))>0) { 
      
      breaks <- my$breaks  
      
      # cat(file=stderr(), paste("xlim =", min(breaks), max(breaks)), "\n")
      
      stacked_histogram <- stacked_histogram +
        geom_rect(data=samppdf, mapping=aes(xmin=qrlower, xmax=qrupper, ymin=0, ymax=Inf), fill="lightcyan") +
        geom_histogram(data=samppdf, mapping=aes(x=pasture, colour=radius), fill=NA, size=1.1, binwidth=1) +
        geom_vline(data=samppdf, mapping=aes(xintercept=qr), size=1.5, colour="lightcyan4", alpha=0.2) +
        geom_vline(data=samppdf, mapping=aes(xintercept=q), size=1.5, colour="blue4") +
        geom_text(data=samppdf, mapping=aes(x=q, y=4, label="90th"), 
                  colour="blue4", size=6, hjust=0, nudge_x=0.2) +
        facet_grid(radius ~ ., as.table=FALSE) + # as.table=FALSE reverses the order
        theme(strip.background=element_blank(), strip.text.y=element_text(angle=0)) +
        scale_x_continuous(breaks=breaks) +
        # scale_y_continuous(breaks=pretty_breaks(n=4)) +
        scale_y_continuous(breaks=NULL) +
        coord_cartesian(xlim=c(min(breaks),max(breaks)))
  
    } # end add histograms to empty plot
    
  }) # end isolate
  
  stacked_histogram

}) # end renderPlot
  
# show histogram
shinyUI(fluidPage(

    fluidRow(
      
      plotOutput("stacked_histogram")
      
    ) # end fluidRow
  
)) # end fluidPage

```

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