bysoil3.rmd

---
title: "Pasture Potential"
runtime: shiny
output: html_document
---


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


```{r include=FALSE}
#### load libraries ####
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) 

#### 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 ####
file_name <- "DairyBaseForPowerBIsoils.csv"
data_all <- read_csv(file_name, na=c("","NaN","NA"), col_types=cols(Concatbest="c")) # force "Concatbest" to chr
n <- names(data_all)
data_all$Region <- as.factor(data_all$Region)
data_all$Season <- as.factor(data_all$Season)

# check duplicate columns for inconsistencies (not needed for this project)
# mismatch <- which(data_all$"DBID" != data_all$"DBID_1")
# mismatch <- which(data_all$"Dairy Company(s)" != data_all$"Dairy Company(s)_1") # INCONSISTENT!

# rename useful columns
n[n=="Pasture and Crop eaten t DM/ha"] <- "pasture_eaten"
n[n=="Region"] <- "region"
n[n=="Season"] <- "season"
n[n=="SupplyClean"] <- "supply_number"
n[n=="nzsc_order"] <- "soil"
names(data_all) <- n

# remove rows missing essential information
data_all <- data_all %>% 
  select(pasture_eaten, region, season, supply_number, long, lat, soil) %>%
  filter((pasture_eaten>0) && (long>0) && (lat<0)) %>%
  drop_na()

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

# 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 <- 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) # for sampcdf
nprobs <- length(probs)
windows <- c(60,40,20)
nmin <- 4L # minimum number of farms in a window for analysis
# cb9sron <- c('#88CCee', '#CC6677', '#DDCC77', '#117733', '#332288', '#AA4499', '#44AA99', '#999933', '#882255')
# nz <- map_data("nz") # nz coastline data

# 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))

```

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

# Where are You?

Please select which season of data to analyse, and click your location on the map. The contours show the availability of data.

```{r echo=FALSE, eval=TRUE}
#### shiny ui widgets to get inputs (reactive values) ####
# Note: Unlike Shiny Apps, Interactive R Markdown Documents are do not require ui and server, 
#       the whole document is treated as a server.
  
  # useful for testing
  my <- list(name='You Are Here', 
                       # long=172.833333, lat=-41.5, # Nelson
                       long=175.619105, lat=-40.386396, # Massey
                       # long=174.865530, lat=-41.259256, # Wellington
                       # long=175.352116, lat=-37.781841, # DairyNZ
                       distortion=1, 
                       east=NA, north=NA, 
                       seasons_here=list(NA), 
                       seasons_sel=list(NA),
                       soils_here=list(NA), 
                       soils_sel=list(NA),
                       data_sel=list(NA)
                       )

  # collect info about the current location and selections
  # Note: 'my' is not a reactive object, it's a list 
  my <- reactiveValues(name='You Are Here', 
                       # long=172.833333, lat=-41.5, # Nelson
                       long=175.619105, lat=-40.386396, # Massey
                       # long=174.865530, lat=-41.259256, # Wellington
                       # long=175.352116, lat=-37.781841, # DairyNZ
                       distortion=1, 
                       east=NA, north=NA, 
                       seasons_here=list(NA), 
                       seasons_sel=list(NA),
                       soils_here=list(NA), 
                       soils_sel=list(NA),
                       data_sel=list(NA)
                       )

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

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

  # whenever location changes, resubset data
  reactive({

    cat(file=stderr(), paste('my$long my$lat =', my$long, my$lat), "\n")

    isolate({
      
    # calculate aspect ratio near my farm
    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]

    # filter by distance #
    # we need to use rowwise()  because distm is not vectorised, I think, although rowwise() is deprecated
    # http://www.expressivecode.org/2014/12/17/mutating-using-functions-in-dplyr/
    data_sel <- data_all %>% 
      rowwise() %>% 
      mutate(dist = distm(c(my$long, my$lat), c(long, lat), fun=distHaversine), # this needs rowwise()
             dist = dist/1000) %>% # km
      filter(dist < max(windows))

    # what seasons and soils are available      
    i <- sort(unique(as.character(data_sel$season)))
    my$seasons_here <- seasons_all[match(i, seasons_all)]
    n <- unlist(map(my$seasons_here, function(u) sum(u==data_sel$season)))
    names(my$seasons_here) <- paste(names(my$seasons_here), ' (', n, ' Farms)', sep='')
    cat(file=stderr(), paste('my$seasons_here =', length(my$seasons_here)), "\n")
    cat(file=stderr(), paste(names(my$seasons_here)), "\n")

    i <- sort(unique(data_sel$soil))
    my$soils_here <- soils_all[match(i, soils_all)]
    n <- unlist(map(my$soils_here, function(u) sum(u==data_sel$soil)))
    names(my$soils_here) <- paste(names(my$soils_here), ' (', n, ' Farms)', sep='')
    cat(file=stderr(), paste('my$soils_here =', length(my$soils_here)), "\n")
    cat(file=stderr(), paste(names(my$soils_here)), "\n")

    # reset default seasons and soils selected
    my$seasons_sel <- tail(my$seasons_here, 1)
    my$soils_sel <- my$soils_here
    my$data_sel <- data_sel
    
    }) # end isolate

  })

  # # soil freq hist (will become redundant I think)
  # output$plot6 <- renderPlot({
  #   plot6 <- ggplot() +
  #     labs(y='Farms', x='Soil') +
  #     geom_bar(data=my$data_sel, mapping=aes(x=soil, fill=soil)) +
  #     guides(fill=FALSE) 
  #   plot6
  # }, height=150)

  # titlePanel("Where are You?")
  output$seasonSelector <- renderUI({
    cat(file=stderr(), paste('render season selector'), "\n")
    selectInput("season", h4("Which Season?"), my$seasons_here, selected=my$seasons_sel)
  })

  output$soilSelector <- renderUI({
    cat(file=stderr(), paste('render soil selector'), "\n")
    selectInput("soil", h4("Which Soils?"), my$soils_here, selected=my$soils_sel, 
                selectize=FALSE, multiple=TRUE)
  })
  
  # Make your 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$lat, 'layer1') %>%
          addCircles(my$long, my$lat, layerId=as.character(windows), radius=windows*1000, 
                     color=window_cols, weight=4, fill=NA) 
      })

    }) # end renderLeaflet
  
  #### define ui ####
  sidebarLayout(
    
    sidebarPanel(
      cat(file=stderr(), paste('render sidebar'), "\n"),
      uiOutput("seasonSelector"), # this control is created in the server
      uiOutput("soilSelector") # this control is created in the server
      # plotOutput("plot6", height=150)
      # actionButton("go", "Go!")
    ), # end sidebarPanel
    
    mainPanel(
      cat(file=stderr(), paste('render main panel'), "\n"),
      leafletOutput("map")
    ) # end mainPanel
    
  ) # end sidebarLayout

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

    click <- input$map_click
    my$long <- click$lng
    my$lat <- click$lat
  
    # mark map
    leafletProxy('map') %>%
      addMarkers(my$long, my$lat, 'layer1') %>%
      addCircles(my$long, my$lat, layerId=as.character(windows), radius=windows*1000, 
                 color=window_cols, weight=4, fill=NA) 
    
  })

  # reactive({
  #   my$seasons_sel <- input$season
  #   cat(file=stderr(), paste('my$seasons_sel ='), "\n")
  #   cat(file=stderr(), paste(my$seasons_sel), "\n")
  # })
  # 
  # reactive({
  #   my$soils_sel <- input$soil
  #   cat(file=stderr(), paste('my$soils_sel ='), "\n")
  #   cat(file=stderr(), paste(my$soils_sel), "\n")
  # })
  
  
```

<!-- # Your Neighbourhood -->

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

calc <- reactive({

  req(input$season) # this prevents it running before input$season is defined
  req(input$soil) # this prevents it running before input$soil is defined

  cat(file=stderr(), paste('analyse'), "\n")
  
  cat(file=stderr(), paste('input$season ='), "\n")
  cat(file=stderr(), paste(input$season), "\n")
  cat(file=stderr(), paste('input$soil ='), "\n")
  cat(file=stderr(), paste(input$soil), "\n")

  my$name <- paste('You (', seasons_all[input$season], ')', sep='')

  data_sel <-  my$data_sel %>% 
    filter(season == input$season) %>% 
    filter(soil %in% input$soil)
  
  cat(file=stderr(), paste('nrow(data_sel) = ', nrow(data_sel)), "\n")

  req(nrow(data_sel)>0)
  
  # 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))
  }    
    
  # 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_sel %>% filter(dist < window)
    n <- nrow(data_window)
    code <- paste(window,' km (',n,' Farms)', sep='')

    # 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")

      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

  } # next window size
    
#   # little function to return a quantile
#   qfn <- function(x){
# #    q <- quantile(x, 0.9, na.rm=TRUE)
#     q <- quantile(x, 0.9)
#     return(q)
#   }
# 
#   # add quantiles to data
#   samppdf <- samppdf %>%
#     group_by(radius) %>%
#     mutate(q=qfn(pasture))

  # 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
  return(list(data_sel=data_sel, circles=circles, circle=circle, farms=farms, sampcdf=sampcdf, samppdf=samppdf))
  
})

```



```{r eval=FALSE, echo=FALSE}
# for testing
  renderTable({ head(calc()$data_sel) })
  renderTable({ reactiveValuesToList(my) }) # my is not a data table
  renderTable({ head(calc()$circles) })
  renderTable({ head(calc()$farms) })
  renderTable({ head(calc()$sampcdf) })
  renderTable({ head(calc()$samppdf) })

```

This is the distribution of pasture produced and consumed within various distances of your location. The 90th percentile is also shown.

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

# stacked histograms
output$plot7 <- renderPlot({

  samppdf <- calc()$samppdf

  breaks <- seq(floor(min(samppdf$pasture))-1, ceiling(max(samppdf$pasture))+1, 1)

  cat(file=stderr(), paste('render stacked histograms'), "\n")
  cat(file=stderr(), paste('xlim =', min(breaks), max(breaks)), "\n")
  
  title_string <- paste('Pasture Eaten near', my$name)
  
  plot7 <- ggplot() +
    labs(title=title_string, y='Number of Farms', x='Pasture Eaten (tDM '*ha^-1~y^-1*')', colour='Radius (km)') +
    theme_cowplot() +
  #  scale_y_continuous(breaks=c()) + # remove y-scale when too many facets
    panel_border(colour='black') +  
    theme(legend.position='none')
  
  if (nrow(samppdf)>0) { 
  plot7 <- plot7 +
    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='black') +
    geom_text(data=samppdf, mapping=aes(x=q, y=4, label='90th'), hjust=0, nudge_x=0.1) +
    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) +
    coord_cartesian(xlim=c(min(breaks),max(breaks)))

  }
  
  plot7

}) # end renderPlot
  
fluidPage({
    fluidRow(
      plotOutput("plot7")
    ) # end fluidRow
})

```