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app_beta.R
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# SPI Tool based on PRISM data
# 12/21/15
# SPI Explorer Tool v1.2 8/18/17
# added SPI selector 1-48 mos
# added multi-scale plot
# temps on site summary
# gantt chart, other minor labels
# v1.3 December 2017
# adding new grid map, SPEI...
# Version Control added! 7/23/18
# profile - run from command line
# library(profvis)
# profvis(runApp())
#####
# BETA version developed in Sept 2022
# updates with R 4.1.2 on hpz
# added error checks and lat/lon entry
#####
## NON-INTERACTIVE
library(weathermetrics)
library(ggmap)
library(RColorBrewer)
library(plotly)
library(RCurl)
library(jsonlite)
library(SPEI)
library(reshape)
library(shiny)
library(ggplot2)
library(leaflet)
library(zoo)
library(gridExtra)
library(gmodels)
# API key
source('APIkey.R', local=TRUE)
# initial point for marker
#latIn<-NULL
#lonIn<-NULL
# slider input for year selection
INTERVAL = 29
value = c(as.numeric(format(Sys.Date()-32, "%Y"))-INTERVAL, as.numeric(format(Sys.Date()-32, "%Y")))
# tags$link(rel = 'stylesheet', type = 'text/css', href = 'styles.css'),
# includeCSS("styles.css"),
## UI section
ui<-tagList(
tags$head(
tags$link(rel = "stylesheet", type = "text/css", href = "styles.css")
),
tags$head(HTML(
"<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src='https://www.googletagmanager.com/gtag/js?id=UA-108499551-1'></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-108499551-1');
</script>"
)),
navbarPage(strong("Standardized Precipitation Index Explorer Tool V2.0"),
tabPanel("About Tool",
sidebarLayout(
sidebarPanel(HTML('<body>
Links to other SPI resources:<br>
<ul>
<li><a href="http://www.wrcc.dri.edu/spi/explanation.html">Western
Regional Climate Center</a></li>
<li><a
href="http://www.wrcc.dri.edu/wwdt/index.php?folder=spi1">WestWide
Drought Tracker</a></li>
<li><a
href="http://iridl.ldeo.columbia.edu/maproom/Global/Precipitation/SPI.html">International
Research Institute for Climate and Society</a></li>
<li><a
href="https://www.drought.gov/drought/content/products-current-drought-and-monitoring-drought-indicators/standardized-precipitation-index">Drought.gov</a></li>
<li><a
href="http://drought.unl.edu/Planning/Monitoring/ComparisonofIndicesIntro/SPI.aspx">National
Drought Mitigation Center</a></li>
</ul>
</body>'
)
),
mainPanel(
h4("About the SPI Explorer Tool"),
HTML('<body>
The Standardized Precipitation Index (SPI) is a widely used drought
index that has several strenghts including the ability to calculate
precipitation anomalies at different timescales and the ability to
interpret SPI units (standard deviations) in probabilistic terms (<a
href="http://drought.unl.edu/monitoringtools/climatedivisionspi.aspx">click
here for more info on the SPI</a>). This tool was created to
explore SPI values at specific locations by using a gridded climate
dataset (<a href="http://www.prism.oregonstate.edu/">PRISM
Climate</a>) to estimate local precipitation time series. Data
are accessed through the <a
href="http://www.rcc-acis.org/docs_webservices.html">Applied
Climate Information Web Service</a> and analyzed and plotted
using several R based packages. This version of the tool includes the calculation
of the Standardized Precipitation-Evapotranspiration Index (SPEI). More info on the SPEI can be found
<a
href="http://spei.csic.es/home.html">
here.
</a>
<br>
<a
href="https://uaclimateextension.shinyapps.io/SPItoolV1/">
(Click here to access original version of the SPI Explorer Tool)
</a>
</body>
'),
hr(),
HTML('<table
style="width: 90%; height: 75px; text-align: left; margin-left: auto; margin-right: auto;"
border="0" cellpadding="2" cellspacing="2">
<tbody>
<tr>
<td style="text-align: center; width: 373px;"><a
href="http://cals.arizona.edu/climate"><img
style="border: 0px solid ; width: 121px; height: 50px;"
alt="cals"
src="cals.jpg"></a></td>
<td style="text-align: center; width: 405px;"><a
href="http://www.climas.arizona.edu"><img
style="border: 0px solid ; width: 62px; height: 58px;"
alt="climas"
src="climas.png"></a></td>
<td style="text-align: center; width: 442px;"><a
href="http://www.rcc-acis.org/"><img
style="border: 0px solid ; width: 195px; height: 25px;"
alt="acis"
src="acis_logo.png"></a></td>
</tr>
</tbody>
</table>'
),
HTML('<div style="text-align: center;">Contact Mike Crimmins (<a
href="mailto:crimmins@email.arizona.edu">crimmins@email.arizona.edu</a>)
with questions or comments. SPI Explorer Tool v2.0 06/17/22</div>'
)
)
)
),
tabPanel("Set location/time period",
sidebarLayout(
sidebarPanel(
h4("Set location and time period"),
p(HTML("1. <b>CLICK MAP</b> to select location (use +/- buttons to zoom, use cursor to pan)")),
p(HTML("<b>OR</b> enter in Lat/Lon location in decimal degrees and click 'Enter location'")),
# adding in lat/lon numeric input
numericInput("lat", "Latitude", NULL),
numericInput("lon", "Longitude", NULL),
actionButton("button", "Enter location"),
p(),
# text input for year selection
#numericInput("yearFirst","First year:",value = 1895, min=1895, max=(format(Sys.Date()-32, "%Y")), step=1, width = "100px"),
#numericInput("yearLast","Last year:",value = format(Sys.Date()-32, "%Y"), min=1896, max=format(Sys.Date()-32, "%Y"), step=1, width = "100px"),
p(HTML("2. <b>Use slider</b> to choose analysis period of interest (limited to at least 30 yr period)")),
# slider input for years
sliderInput("years", "Analysis Period",
min = 1895, max = as.numeric(format(Sys.Date()-32, "%Y")), value = value, sep=''),
p(HTML("3. <b>Choose</b> drought index (SPI or SPEI)")),
radioButtons("dindex", label =("Drought Index"),
choices = list("Standardized Precipitation Index" = 1, "Standardized Precipitation-Evapotranspiration Index" = 2),
selected = 1),
p(HTML("4. <b>Click download</b> (this may take a couple of seconds, look to upper right corner for progress message)")),
actionButton("refresh","Download data"),
hr(),
p(HTML("<b>Re-download the dataset if you make any changes to location, time period or selected drought index</b>")),
p("All statistics and figures on other pages are calculated based on the location and time period specified here. The time period selection forces a minimum length of 30 years to ensure enough observations to calculate meaningful drought indices and climate statistics.")
),
mainPanel(
leafletOutput("MyMap",width = "700px", height = "700px"),
verbatimTextOutput("latSel"),
verbatimTextOutput("lonSel")
)
)
),
tabPanel("Site Climate Summary",
sidebarLayout(
sidebarPanel(h4("Climate Summary"),
hr(),
plotOutput("mapPRISM"),
h5("Red Dot - Selected Point, Blue Rectangle - PRISM Grid Cell", align="center"),
hr(),
tags$b("Site Description"),
p(textOutput("elev")),
p(textOutput("annavg")),
p(textOutput("annavgTemp")),
hr(),
p("The annual total precipitation for the selected location and time period are depicted in this
figure as a simple way to visualize longer-term climate variability including wet periods and droughts.
The data table below the figure can be re-sorted to find driest and wettest years. Click on
the arrows next to the column names to change the sort order for that column."),
hr(),
downloadButton("downloadAnnData", "Download Data Table"),
hr()
),
mainPanel(
plotOutput("annPlot"),
plotOutput("annPlotTemp"),
dataTableOutput('annTable')
)
)
),
tabPanel("SPI Timescale Comparison",
sidebarLayout(
sidebarPanel(h4("Comparing different SPI timescales"),
p("The monthly SPI values for the specified location and time period are depicted in the bar plots on the right.
The top figure is the 1-month SPI which has no moving window used in the calculation.
The middle (3-month SPI) and bottom (12-month SPI) figures use moving windows of 3 and 12 months
respectively to quantify precipitation anomalies at these longer timescales. For example,
the 3 month SPI would be sensitive to seasonally varying amounts of precipitation helping
to detect short-term changes in drought conditions that may occur over a summer season while
the 12-month SPI would smooth out seasonal changes and better capture variability at the annual scale."),
p("The heatmap below shows all SPI values for timescales from 1-48 months for the specified location and time period.
Use the cursor to hover over the plot to find the monthly SPI value for any timescale and date. Note how longer
timescale SPI values vary much slower over time with longer wet spells and drought events. Shorter timescales
vary much more rapidly over time and can be a good indicator of short-term drought conditions on the order
of months to seasons. This multiscale-SPI plot can also depict when there is a disconnect between short and long
term drought conditions. Sometimes drought conditions at longer SPI timescales can persist even when short
term conditions have improved or vice versa. By looking at all SPI timescales for a given date, you can diagnose
whether short or long term drought conditions are present or a combination of both.")
),
mainPanel(
plotOutput("spiPlots"),
hr(),
plotlyOutput("plotMultiSPI")
)
)
),
tabPanel("SPI-Precip Comparison",
sidebarLayout(
sidebarPanel(
# radioButtons("precipSum","Choose SPI timescale",
# c("1 month"="1","3 month"="2","12 month"="3"), selected = ),
sliderInput("precipSum", "Choose timescale (1-48 mos)", min=1,max=48,value=3,step=1),
sliderInput("mo1sum",
"Choose end month",
min = 1,
max = 12,
value = 7,
step=1),
hr(),
downloadButton("downloadMoData", "Download Data Table"),
hr(),
tags$b("SPI/SPEI Normality Tests"),
p(htmlOutput("swstat")),
p(htmlOutput("swpval")),
p(htmlOutput("spiMedian")),
p("Arid locations can often have many months with very low or zero precipitation. In these cases the transformation
applied to SPI or SPEI data will struggle to properly normalize the historical time series.
If all three of these normality tests are red, then the current month/time period selection is not normal and the standardized values
should be interpreted with caution (Wu et al. 2007).")
),
mainPanel(
plotOutput("monthSPIplot"),
plotOutput("monthPlot"),
plotOutput("histoPlot"),
plotOutput("ecdfPlot"),
hr(),
h4("Precipitation Statistics"),
textOutput("avgPrecip"),
tags$head(tags$style("#avgPrecip{color:black;font-size: 16px;font-style: bold;}")),
textOutput("minPrecip"),
tags$head(tags$style("#minPrecip{color:black;font-size: 16px;font-style: bold;}")),
textOutput("maxPrecip"),
tags$head(tags$style("#maxPrecip{color:black;font-size: 16px;font-style: bold;}")),
h4("Precipitation terciles - use to interpret seasonal climate outlooks"),
textOutput("belowPrecip"),
tags$head(tags$style("#belowPrecip{color:#663300;font-size: 16px;font-style: bold;}")),
textOutput("medianPrecip"),
tags$head(tags$style("#medianPrecip{color:black;font-size: 16px;font-style: bold;}")),
textOutput("abovePrecip"),
tags$head(tags$style("#abovePrecip{color:#006600;font-size: 16px;font-style: bold;}")),
hr(),
dataTableOutput('moTable')
)
)
),
tabPanel("Drought Category Transitions",
sidebarLayout(
sidebarPanel(
# radioButtons("SPIState1","Period 1 SPI timescale:",
# c("1 month"="8","3 month"="9","12 month"="10"), selected = ),
sliderInput("SPIState1", "Period 1 timescale (1-12 mos):", min=1,max=12,value=3,step=1),
sliderInput("mo1",
"Period 1 - End Month:",
min = 1,
max = 12,
value = 7,
step=1),
# radioButtons("SPIState2","Period 2 SPI timescale:",
# c("1 month"="8","3 month"="9","12 month"="10")),
sliderInput("SPIState2", "Period 2 timescale (1-12 mos):", min=1,max=12,value=3,step=1),
sliderInput("mo2",
"Period 2 - End Month:",
min = 1,
max = 12,
value = 9,
step=1),
hr(),
p("This tool examines the probability of moving from one SPI based drought
category to another based on the historical precipitation record at this
location. The chart is read as the row indicating the drought category for
the initial month and the column as 'forecast' month. For example, if you were
interested in how often a very wet July was followed by an overall very wet summer (July-Aug-Sep)
precipitation total, you would set Month 1 to 1-month SPI and July and Month 2 to 3-month SPI and September.
Reading across the first row (month 1-very wet) to the last column (month 2-very wet) indicates the probability
of this outcome based on historical occurrences. A longer period of record yields more stable results.")
),
mainPanel(
#plotOutput("transPlot"),
plotlyOutput("transPlot", width = "100%", height = "1000px"),
#plotOutput("gantt"),
hr(),
p(""),
fluidRow(
column(12, align="center",
tableOutput('spiBoundsTable')
)
)
)
)
)
)
)
## Server Section
server <- function(input, output, session) {
latIn<-reactiveVal(-999)
lonIn<-reactiveVal(-999)
# coords <- reactiveValues(latIn = NULL,
# lonIn = NULL)
#####
# original map click get lat/lon
# add in leaflet map, overlay PRISM avg precip map or DEM grid...
output$MyMap <- renderLeaflet({
m <- leaflet() %>% setView(lng = -109.054530, lat = 36.992438, zoom = 6)
m %>% addProviderTiles("Esri.WorldTopoMap")
#%>% addMarkers(lonIn, latIn)
})
observeEvent(input$MyMap_click, {
leafletProxy("MyMap")%>% clearMarkers() %>%
addMarkers(input$MyMap_click$lng, input$MyMap_click$lat)
# update text boxes based on map click
updateNumericInput(session, "lat", value = input$MyMap_click$lat)
updateNumericInput(session, "lon", value = input$MyMap_click$lng)
# get location
latIn(input$MyMap_click$lat)
lonIn(input$MyMap_click$lng)
output$latSel<-renderText({paste("Latitude: ",latIn())})
output$lonSel<-renderText({paste("Longitude: ",lonIn())})
})
# look for numeric location input
observeEvent(input$button, {
leafletProxy("MyMap")%>% clearMarkers() %>%
addMarkers(input$lon, input$lat) %>%
setView(lng = isolate(input$lon),
lat = isolate(input$lat),
zoom = 6)
# get location
latIn(input$lat)
lonIn(input$lon)
output$latSel<-renderText({paste("Latitude: ",latIn())})
output$lonSel<-renderText({paste("Longitude: ",lonIn())})
})
#####
#####
# new drag marker to get lat/lon
# output$MyMap = renderLeaflet({
# m <- leaflet() %>% setView(lng = -109.054530, lat = 36.992438, zoom = 6)
# m %>% addProviderTiles("Esri.WorldTopoMap") %>%
# addMarkers(lat = latIn,lng = lonIn, options = markerOptions(draggable = TRUE))
# })
#
# observe({
# #print(input$MyMap_marker_dragend)
# latIn<-input$MyMap_marker_dragend$lat
# lonIn<-input$MyMap_marker_dragend$lng
# output$latSel<-renderText({paste("Latitude: ",latIn)})
# output$lonSel<-renderText({paste("Longitude: ",lonIn)})
# })
#####
#####
# slider input for years that limits to at least 30yr period
observeEvent(input$years,{
newvalue = input$years
if(value[1] != newvalue[1] && newvalue[2] !=as.numeric(format(Sys.Date()-32, "%Y")) && newvalue[2] - newvalue[1] <= INTERVAL)
updateSliderInput(session, "years", value = c(newvalue[1], newvalue[1] + INTERVAL))
if(value[2] != newvalue[2] && newvalue[1] !=1895 && newvalue[2] - newvalue[1] <= INTERVAL)
updateSliderInput(session, "years", value = c(newvalue[2] - INTERVAL, newvalue[2]))
if(value[2]==as.numeric(format(Sys.Date()-32, "%Y")) && newvalue[2] - newvalue[1] <= INTERVAL)
updateSliderInput(session, "years", value = c(newvalue[2] - INTERVAL, newvalue[2]))
if(value[1]==1895 && newvalue[2] - newvalue[1] <= INTERVAL)
updateSliderInput(session, "years", value = c(newvalue[1],newvalue[1]+INTERVAL))
value <<- newvalue
})
#####
# download and process data
observeEvent(input$refresh, {
######
# error catch on no map click with download button
if(is.null(input$MyMap_click) && is.na(input$lat)){
locCheck<-0
}else {
locCheck<-1
}
#if(is.null(input$MyMap_click) && is.na(input$lat))
if(locCheck==0)
showModal(modalDialog(
title = "Click on map to add location first, then click Download Data",
easyClose = TRUE,
footer = NULL
))
shiny::validate(need(locCheck!=0, message = "Click map or enter coordinates to add location"))
#shiny::validate(need(is.null(input$MyMap_click) != is.na(input$lat), message = "Click map or enter coordinates to add location"))
# shiny::validate(need(length(locCheck)!=0, message = "Click map or enter coordinates to add location"))
#shiny::validate(need(!is.null(input$MyMap_click), message = "Click map to add location"))
#req(input$MyMap_click)
#####
withProgress(message = 'Downloading data set', style="old",
detail = 'Please wait...',{
# map location
lat<-latIn()
lon<-lonIn()
# map click lat/lon
#lat=input$MyMap_click$lat # input from map
#lon=input$MyMap_click$lng # input from map
# drag marker lat/lon
#lat=input$MyMap_marker_dragend$lat # input from map
#lon=input$MyMap_marker_dragend$lng # input from map
# text year input
#firstYR=as.numeric(input$yearFirst)
#lastYR=as.numeric(input$yearLast)
# sliderinput years
firstYR=as.numeric(value[1])
lastYR=as.numeric(value[2])
# download data
jsonQuery=paste0('{"loc":"',lon,',',lat,'","sdate":"',firstYR,'01","edate":"',lastYR,'12","grid":"21",
"elems":[{"name":"mly_maxt","units":"degreeF"},{"name":"mly_mint","units":"degreeF"},
{"name":"mly_avgt","units":"degreeF"},{"name":"mly_pcpn","units":"inch"}]}')
out<-postForm("http://data.rcc-acis.org/GridData",
.opts = list(postfields = jsonQuery,
httpheader = c('Content-Type' = 'application/json', Accept = 'application/json')))
out<-fromJSON(out)
# meta output
jsonQuery=paste0('{"loc":"',lon,',',lat,'","grid":"21","elems":"4","meta":"ll,elev","date":"2010-01-01"}')
metaOut<-postForm("http://data.rcc-acis.org/GridData",
.opts = list(postfields = jsonQuery,
httpheader = c('Content-Type' = 'application/json', Accept = 'application/json')))
metaOut<-fromJSON(metaOut)
output$elev<-renderText({paste("Avg Elevation of Grid Cell (ft): ",as.character(metaOut$meta$elev))})
# get data from list
data<-data.frame(out$data)
colnames(data)<-c("date","t_max","t_min","t_mean","precip")
data$date<-as.Date(paste(data$date,"-01",sep=""))
# set -999 to NA
data[data == -999] <- NA
# convert columns to numeric
unfactorize<-c("t_max","t_min","t_mean","precip")
data[,unfactorize]<-lapply(unfactorize, function(x) as.numeric(as.character(data[,x])))
# get months and years
data$month<-as.numeric(format(data$date, "%m"))
data$year<-as.numeric(format(data$date, "%Y"))
# Switch between SPI/SPEI
if (as.numeric(input$dindex)==1){
## Loop thru full SPI set
for(i in 1:48){
tempSPI <- spi(data$precip,i, na.rm = TRUE)
data[[paste('spi',i,sep="")]] <-tempSPI$fitted
}
indexName="Standardized Precipitation Index"
indexNameShort="SPI"
}
else{
# # SPEI switch?
# PET <- thornthwaite(fahrenheit.to.celsius(data$t_mean,round=2), lat, na.rm = TRUE)
PET <- hargreaves(fahrenheit.to.celsius(data$t_min,round=2),fahrenheit.to.celsius(data$t_max,round=2),Ra=NA, lat, na.rm = TRUE)
for(i in 1:48){
tempSPI <- spei(inches_to_metric(data$precip,unit="mm",round=2)-PET,i, na.rm = TRUE)
data[[paste('spi',i,sep="")]] <-tempSPI$fitted
}
indexName="Standardized Precipitation-Evapotranspiration Index"
indexNameShort="SPEI"
}
# # calculate SPI
# spi1<-spi(data$precip,1, na.rm = TRUE)
# data$spi1<-spi1$fitted
# spi3<-spi(data$precip,3, na.rm = TRUE)
# data$spi3<-spi3$fitted
# spi12<-spi(data$precip,12, na.rm = TRUE)
# data$spi12<-spi12$fitted
# # calculate raw precipitation totals, % of avg
# data$precip1sum<-data$precip
# temp<-rollapply(data$precip,3, sum)
# data$precip3sum= c(rep(NA, nrow(data) - length(temp)),temp)
# temp<-rollapply(data$precip,12, sum)
# data$precip12sum= c(rep(NA, nrow(data) - length(temp)),temp)
## Loop thru full raw precip totals
data$precip1sum<-data$precip
for(i in 2:48){
tempSum<-rollapply(data$precip,i, sum)
data[[paste('precip',i,'sum',sep="")]] <-c(rep(NA, nrow(data) - length(tempSum)),tempSum)
}
# choose SPI windows, create groups
breaks<-c(-999,-1,0,1,999) # 4 breaks
lblText<-c("1"="very dry (<-1)","2"="dry (-1 to 0)","3"="wet (0 to 1)","4"="very wet(>1)")
lims<-c(1:4) # lims for plot
# trim data without precip data = NA
dataTrim<-data
if (lastYR==as.numeric(format(Sys.Date(), "%Y"))){
data<-data[-(min(which(is.na(data$precip))):nrow(data)), ] # breaks Drought Category tool past current month
# trim again to previous full year
dataTrim<-data[-(which(data$year==as.numeric(format(Sys.Date(), "%Y")))),]
}else{
}
})
#####
# error catch if all are NA, choose another location
if(nrow(data)==0)
showModal(modalDialog(
title = "No data available -- Please select another location within continental U.S.",
easyClose = TRUE,
footer = NULL
))
shiny::validate(need(nrow(data)>0, message = "No data available, please choose another location"))
#####
### END INTERACTIVE
# ---- Plot PRISM Grid map
# ggmap of PRISM grid
#lat=input$MyMap_click$lat # input from map
#lon=input$MyMap_click$lng # input from map
#lat=latIn() # input from map
#lon=lonIn() # input from map
latlon<-as.data.frame(cbind(lat,lon))
lonP = metaOut$meta$lon
latP = metaOut$meta$lat
res=0.04166667
myLocation <- c(lon = lonP, lat = latP)
myMap <- get_map(location=myLocation,
source="google", maptype="terrain", zoom = 13)
p<-ggmap(myMap)+
annotate('rect', xmin=lonP-(res/2), ymin=latP-(res/2), xmax=lonP+(res/2), ymax=latP+(res/2), col="blue", size=1.5,fill=NA)+
labs(x = 'Longitude', y = 'Latitude') +
geom_point(aes(x = lon, y = lat),data = latlon, color="darkred", size=2)+
ggtitle("PRISM Data Grid Cell")
output$mapPRISM<-renderPlot(p)
# ----
# annual precip time series and table & temperature
# ann temp
tempAvgAnn<-data[,c(6,4)]
tempAvg<-rollmean(tempAvgAnn$t_mean,12)
tempAvgAnn$annTemp <-c(rep(NA, nrow(tempAvgAnn) - length(tempAvg)),tempAvg)
annTemp<-tempAvgAnn[ which(tempAvgAnn$month==12), ]
# combined
annData<- data[ which(data$month==12), ]
annData<- annData[,c("year","precip12sum")]
annData$annAvgTemp<- annTemp$annTemp
output$annavg<-renderText({paste("Avg Annual Precip (in): ",round(mean(annData[,c("precip12sum")], na.rm = TRUE),1))})
output$annavgTemp<-renderText({paste("Avg Annual Temp (F): ",round(mean(annData$annAvgTemp, na.rm = TRUE),1))})
output$annPlot<-renderPlot({
ggplot(annData,aes(x=year, y=annData[,c("precip12sum")]))+
geom_bar(stat='identity', fill='springgreen4', color='grey')+
labs(y="precip (in.)", title='Annual Total Precipitation')+
geom_hline(aes(yintercept=(mean(annData[,c("precip12sum")], na.rm = TRUE))))+
geom_text(aes(firstYR,(mean(annData[,c("precip12sum")], na.rm = TRUE)),label ='average', vjust = -1)) +
theme_bw()+
theme(axis.text = element_text(size =14))+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))
})
output$annPlotTemp<-renderPlot({
ggplot(annData,aes(x=year, y=annData$annAvgTemp))+
geom_bar(stat='identity', fill='coral3', color='grey')+
labs(y="deg F", title='Annual Average Temperature')+
geom_hline(aes(yintercept=(mean(annData$annAvgTemp, na.rm = TRUE))))+
geom_text(aes(firstYR,(mean(annData$annAvgTemp, na.rm = TRUE)),label ='average', vjust = -1))+
coord_cartesian(ylim=c(min(annData$annAvgTemp, na.rm = TRUE)-1, max(annData$annAvgTemp, na.rm = TRUE)+1))+
theme_bw()+
theme(axis.text = element_text(size =14))+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))
})
annDataTemp<-annData
annDataTemp$annAvgTemp<-round(annDataTemp$annAvgTemp,1)
colnames(annDataTemp)[2] <- "Total Precip (in)"
colnames(annDataTemp)[3] <- "Avg Temp (F)"
output$annTable<-renderDataTable({annDataTemp})
## --- Add Download Annual Data
# Downloadable csv of selected dataset ----
output$downloadAnnData <- downloadHandler(
filename = function() {
paste0("AnnualClimateData_",lat,"_",lon, ".csv", sep = "")
},
content = function(file) {
write.csv(annDataTemp, file, row.names = FALSE)
}
)
## SPI monthly time series
tempSPI<-melt.data.frame(data, id.vars="date", variable.name = "year", measure.vars =c("spi1", "spi3","spi12"))
tempSPI$pos<-tempSPI$value >=0
levels(tempSPI$variable) <- c(paste0("1-month ",indexNameShort), paste0("3-month ",indexNameShort), paste0("12-month ",indexNameShort))
output$spiPlots<-renderPlot({
ggplot(tempSPI, aes(x=date,y=value, fill=pos))+
geom_bar(stat = "identity", position = "identity")+
scale_fill_manual(values = c("#8c510a","#01665e"), guide=FALSE)+
facet_wrap(~ variable, ncol = 1)+
labs(x='month/year',y='SPI', title=paste0(indexName,' - 1/3/12 month'))+
theme_bw()
})
# multiscale SPI ggplot
tempSPIall<-melt.data.frame(data, id.vars="date", variable.name = "year", measure.vars =c(8:55))
tempSPIall$variable<-as.numeric(gsub("\\D", "", tempSPIall$variable))
output$plotMultiSPI <- renderPlotly({
plot_ly(tempSPIall, x = ~date, y = ~variable, z = ~value, colors=brewer.pal(11,'BrBG'), type = "heatmap", zmin=-3, zmax=3) %>%
layout(title = paste0("Multi-scale ", indexNameShort, " Plot"),
xaxis=list(title="Month-Year"),
yaxis=list(title="Scale(mos)")
)
})
# Explore SPI/month sum
# subset month -- outside of PRISM refresh
observe({
dataSubset <- data[ which(data$month==input$mo1sum), ] # set to input
# further reduce dataframe based on 1,3,12 month selection
monthSelect<-as.integer(input$precipSum) # "1 month"="1","3 month"="2","12 month"="3"
# # ggmap of PRISM grid
# lat=input$MyMap_click$lat # input from map
# lon=input$MyMap_click$lng # input from map
# latlon<-as.data.frame(cbind(lat,lon))
#
# lonP = metaOut$meta$lon
# latP = metaOut$meta$lat
# res=0.04166667
# myLocation <- c(lon = lonP, lat = latP)
# myMap <- get_map(location=myLocation,
# source="google", maptype="terrain", zoom = 13)
#
# p<-ggmap(myMap)+
# annotate('rect', xmin=lonP-(res/2), ymin=latP-(res/2), xmax=lonP+(res/2), ymax=latP+(res/2), col="blue", size=1.5,fill=NA)+
# labs(x = 'Longitude', y = 'Latitude') +
# geom_point(aes(x = lon, y = lat),data = latlon, color="darkred", size=2)+
# ggtitle("PRISM Data Grid Cell")
# output$mapPRISM<-renderPlot(p)
# # trying to control for ggmap mem usage
# rm(myMap)
# gc()
# #----
# plot of precip time series
output$monthPlot<-renderPlot({
ggplot(dataSubset,aes(x=year, y=dataSubset[,55+monthSelect]))+
geom_bar(stat='identity', fill='springgreen4', color='grey')+
labs(y="precip (in.)", title=paste0('Precipitation, ',monthSelect,"-month total ending in ",month.name[input$mo1sum]))+
geom_hline(aes(yintercept=(mean(dataSubset[,55+monthSelect], na.rm = TRUE))))+
#geom_text(aes(firstYR,(mean(dataSubset[,55+monthSelect], na.rm = TRUE)),label ='average', vjust = -1))+
annotate("text", x=firstYR, y=mean(dataSubset[,55+monthSelect], na.rm = TRUE), label="average", color = "black")+
theme(text = element_text(size = 20))+
theme_bw()+
theme(axis.text = element_text(size =14))+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))
})
# plot of month SPI
tempMO<-melt.data.frame(dataSubset, id.vars = "date", variable.name ="year", measure.vars = (7+monthSelect))
tempMO$pos<-tempMO$value >=0
# normality tests - following Wu et al. 2007 https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/joc.1371
shapiroSPI<-shapiro.test(tempMO$value)
medianSPI<-median(tempMO$value)
#
output$monthSPIplot<-renderPlot({
ggplot(tempMO, aes(x=date,y=value, fill=pos))+
geom_bar(stat = "identity", position = "identity")+
scale_fill_manual(values = c("orange4","chartreuse4"), guide=FALSE)+
labs(y=paste0(indexNameShort,"-",monthSelect), title=paste0(indexName,', ',monthSelect,"-month timescale ending in ",month.name[input$mo1sum]))+
theme(text = element_text(size = 20))+
theme_bw()+
theme(axis.text = element_text(size =14))+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))
})
# write out normality tests
output$swstat <- renderText({
if(shapiroSPI$statistic < 0.96){
return(paste("<span style=\"color:red\">Shapiro-Wilks Statistic:",round(shapiroSPI$statistic,2),"</span>"))
}else{
return(paste("<span style=\"color:green\">Shapiro-Wilks Statistic:",round(shapiroSPI$statistic,2),"</span>"))
}
})
output$swpval <- renderText({
if(shapiroSPI$p.value < 0.10){
return(paste("<span style=\"color:red\">Shapiro-Wilks p-val:",round(shapiroSPI$p.value,2),"</span>"))
}else{
return(paste("<span style=\"color:green\">Shapiro-Wilks p-val:",round(shapiroSPI$p.value,2),"</span>"))
}
})
output$spiMedian <- renderText({
if(medianSPI > 0.05){
return(paste("<span style=\"color:red\">Median SPI/SPEI:",round(medianSPI,2),"</span>"))
}else{
return(paste("<span style=\"color:green\">Median SPI/SPEI:",round(medianSPI,2),"</span>"))
}
})
#---
# plot of distribution
output$histoPlot<-renderPlot({
ggplot(dataSubset,aes(x=dataSubset[,55+monthSelect]))+
geom_histogram(fill='grey',color='black')+
geom_vline(xintercept=quantile(dataSubset[,55+monthSelect],0.33, na.rm=TRUE),color="chocolate4", size=1.5)+
geom_vline(xintercept=quantile(dataSubset[,55+monthSelect],0.50, na.rm=TRUE), size=1.5)+
geom_vline(xintercept=quantile(dataSubset[,55+monthSelect],0.66, na.rm=TRUE), color="chartreuse4", size=1.5)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='precip(in.)', y='count',title=paste0('Frequency of Precipitation Values, ',monthSelect,"-month total ending in ",month.name[input$mo1sum]))+
theme(text = element_text(size = 20))+
theme_bw()+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))
})
# plot of ecdf
output$ecdfPlot<-renderPlot({
ggplot(dataSubset,aes(x=dataSubset[,55+monthSelect]))+
stat_ecdf()+
geom_vline(xintercept=quantile(dataSubset[,55+monthSelect],0.33, na.rm=TRUE),color="chocolate4", size=1.5)+
geom_vline(xintercept=quantile(dataSubset[,55+monthSelect],0.50, na.rm=TRUE), size=1.5)+
geom_vline(xintercept=quantile(dataSubset[,55+monthSelect],0.66, na.rm=TRUE), color="chartreuse4", size=1.5)+
labs(x='precip(in.)', y='cumulative probability',title=paste0('Cumulative Probability of Precipitation Values, ',monthSelect,"-month total ending in ",month.name[input$mo1sum]))+
theme(text = element_text(size = 20))+
theme_bw()+
theme(axis.text = element_text(size =14))+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))
})
# write out percentiles
output$belowPrecip<-renderText({paste("Below median (33%tile, inches): ",round(quantile(dataSubset[,55+monthSelect],0.33, na.rm=TRUE),1))})
output$medianPrecip<-renderText({paste("Median (50%tile, inches): ",round(quantile(dataSubset[,55+monthSelect],0.50, na.rm=TRUE),1))})
output$abovePrecip<-renderText({paste("Above median (66%tile, inches): ",round(quantile(dataSubset[,55+monthSelect],0.66, na.rm=TRUE),1))})
output$avgPrecip<-renderText({paste("Average (inches): ",round(mean(dataSubset[,55+monthSelect], na.rm = TRUE),2))})
output$maxPrecip<-renderText({paste("Max (inches): ",round(max(dataSubset[,55+monthSelect], na.rm = TRUE),2))})
output$minPrecip<-renderText({paste("Min (inches): ",round(min(dataSubset[,55+monthSelect], na.rm = TRUE),2))})
# output data table
tempMonthTable<-cbind(dataSubset[,7],dataSubset[,55+monthSelect],round(dataSubset[,7+monthSelect],2),
round(dataSubset[,55+monthSelect]/mean(dataSubset[,55+monthSelect], na.rm = TRUE)*100,0))
colnames(tempMonthTable)<-c("year","Precip (in)",indexNameShort,"% of Avg Precip")
tempMonthTable<-as.data.frame(tempMonthTable)
output$moTable<-renderDataTable({tempMonthTable})
## --- Add Download Monthly Data
# Downloadable csv of selected dataset ----
output$downloadMoData <- downloadHandler(
filename = function() {
paste0(month.abb[input$mo1sum],"_",monthSelect,"mo_","ClimateData_",lat,"_",lon, ".csv", sep = "")
},
content = function(file) {
write.csv(tempMonthTable, file, row.names = FALSE)
}
)
})
# Transition Prob Plot -- replaced with dataTrim
output$transPlot<- renderPlotly({
state1spi<-as.integer(input$SPIState1) # 8=spi1, 9=spi3, 10=spi12
state2spi<-as.integer(input$SPIState2) # 8=spi1, 9=spi3, 10=spi12
state1<-cut(as.numeric(dataTrim[,state1spi+7]),breaks, labels=c(1:4)) # SET SPI state 1, labels
state2<-cut(as.numeric(dataTrim[,state2spi+7]),breaks, labels=c(1:4)) # SET SPI state 2, labels
# choose months to compare
#spiStates<-cbind.data.frame(data$month, data$year, state1, state2)
spiStates<-cbind.data.frame(dataTrim$month, dataTrim$year, state1, state2, dataTrim[,state1spi+7], dataTrim[,state2spi+7], dataTrim[,state1spi+55],dataTrim[,state2spi+55])
colnames(spiStates)[5] <- "spi1"
colnames(spiStates)[6] <- "spi2"
colnames(spiStates)[7] <- "precip1"
colnames(spiStates)[8] <- "precip2"
# select months
mo1=input$mo1
mo2=input$mo2
state1<-spiStates[which(dataTrim$month==mo1),] # SET month 1
state1<-state1["state1"]
state2<-spiStates[which(dataTrim$month==mo2),] # SET month 2
state2<-state2["state2"]
# date label logic for labels
if (mo1 > mo2){
startYr<-"2016-01-01"
}
else{
startYr<-"2017-01-01"
}
p1en<-as.Date(format(as.yearmon(startYr)+((mo1)/12), "%Y-%m-%d"))-1
p1st<-as.Date(format(as.yearmon(startYr)+((mo1)/12)-((state1spi)/12), "%Y-%m-%d"))
p2en<-as.Date(format(as.yearmon("2017-01-01")+((mo2)/12), "%Y-%m-%d"))-1
p2st<-as.Date(format(as.yearmon("2017-01-01")+((mo2)/12)-((state2spi)/12), "%Y-%m-%d"))
# dynamic labels
ylabText<-paste0("Period 1: ", indexNameShort,"-",state1spi," (", format(p1st,"%b"),"-", format(p1en,"%b"),")")
xlabText<-paste0("Period 2: ", indexNameShort,"-",state2spi," (", format(p2st,"%b"),"-", format(p2en,"%b"),")")
# gantt chart for SPI periods ----
task1 <- c('Period 1', format(p1st,"%Y-%m-%d"),format(p1en,"%Y-%m-%d"))
task2 <- c('Period 2', format(p2st,"%Y-%m-%d"),format(p2en,"%Y-%m-%d"))
#task2 <- c('Period 2', paste0('2017-',mo2-(state2spi)+1,'-01') ,paste0('2017-',mo2+1,'-01'))
df <- as.data.frame(rbind(task1, task2))
names(df) <- c('task', 'start', 'end')
df$task <- factor(df$task, levels = c("Period 2","Period 1"))
df$start <- as.Date(df$start)
df$end <- as.Date(df$end)
df_melted <- melt(df, measure.vars = c('start', 'end'))
# starting date to begin plot
start_date <- p1st-30
# ggplot(df_melted, aes(value, task)) +
# geom_line(size = 10) +
# labs(x = '', y = '', title = 'SPI Timescales') +
# theme_bw(base_size = 20) +
# theme(plot.title = element_text(hjust = 0.5),
# panel.grid.major.x = element_line(colour="black", linetype = "dashed"),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.x = element_text(angle = 0)) +
# scale_x_date(date_labels = "%b", limits = c(start_date, NA), date_breaks = '1 month')
# ---- end gantt chart
# new code - write out SPI bounds in inches
state1temp<-spiStates[which(dataTrim$month==mo1),] # SET month 1
state2temp<-spiStates[which(dataTrim$month==mo2),] # SET month 2
spiBounds<-data.frame()
temp<-state1temp[which.min(abs(state1temp$spi1-1)), ]
spiBounds[1,1]<-temp[1,7]
temp<-state1temp[which.min(abs(state1temp$spi1-0)), ]
spiBounds[2,1]<-temp[1,7]
temp<-state1temp[which.min(abs(state1temp$spi1-(-1))), ]
spiBounds[3,1]<-temp[1,7]
temp<-state2temp[which.min(abs(state2temp$spi2-1)), ]
spiBounds[1,2]<-temp[1,8]
temp<-state2temp[which.min(abs(state2temp$spi2-0)), ]
spiBounds[2,2]<-temp[1,8]
temp<-state2temp[which.min(abs(state2temp$spi2-(-1))), ]
spiBounds[3,2]<-temp[1,8]
# format output table
spiBoundsOut<-data.frame()
spiBoundsOut[1:4,1]<-c("very dry (<-1)","dry (-1 to 0)","wet (0 to 1)","very wet(>1)")
spiBoundsOut[1,2]<-paste("< ",as.character(spiBounds[3,1])," in.")
spiBoundsOut[2,2]<-paste(as.character(spiBounds[3,1])," to ",as.character(spiBounds[2,1])," in.")
spiBoundsOut[3,2]<-paste(as.character(spiBounds[2,1])," to ",as.character(spiBounds[1,1])," in.")
spiBoundsOut[4,2]<-paste("> ",as.character(spiBounds[1,1])," in.")
spiBoundsOut[1,3]<-paste("< ",as.character(spiBounds[3,2])," in.")
spiBoundsOut[2,3]<-paste(as.character(spiBounds[3,2])," to ",as.character(spiBounds[2,2])," in.")
spiBoundsOut[3,3]<-paste(as.character(spiBounds[2,2])," to ",as.character(spiBounds[1,2])," in.")
spiBoundsOut[4,3]<-paste("> ",as.character(spiBounds[1,2])," in.")
colnames(spiBoundsOut)<-c("Category","Period 1","Period 2")
output$spiBoundsTable<-renderTable({spiBoundsOut})
## NEW BUG FIX deal with use case mo1>mo2...not quite working
if (mo1 > mo2){
state2<-as.data.frame(state2[2:nrow(state2),])
state2[nrow(state2)+1,]<-NA
colnames(state2)<-"state2"
}
##
# combine and convert to factors, cross tabulate
states<-cbind.data.frame(state1,state2)
#---- getting chi square info
statesTest<-cbind.data.frame(state1,state2)
crossStats<-CrossTable(statesTest$state1,statesTest$state2, expected = TRUE)
#----
states<-table(states) # counts
stateCts<-states
states<-prop.table(states,1) # row proportions 1, col=2
states<-states*100
# data table for reference?
# dynamic title
droughtTitle<-paste0("Drought Category Transition Probabilities: ",min(spiStates$`dataTrim$year`),"-",max(spiStates$`dataTrim$year`),
" (Chi Sq. p val=", round(crossStats$chisq$p.value, 2), ")")
# plot table
plotStates<-as.data.frame(melt(states))
plotStatesCts<-as.data.frame(melt(stateCts))
#plotStates$textlabel<-paste0(round(plotStates$value,1)," (n=",plotStatesCts$value,")")
# calculate anomalies
plotStates$anom <- ifelse(plotStates$state2 ==2 | plotStates$state2 ==3, plotStates$value-34, plotStates$value-16)
# up/down arrows
plotStates$arrow<-NA
plotStates$arrow <- ifelse(plotStates$anom <0, paste0('⬇'), paste0('⬆'))
plotStates$textlabel<-paste0(round(plotStates$value,1)," (n=",plotStatesCts$value,") ",plotStates$arrow)
p1<-ggplot(data=plotStates)+
geom_tile(aes(x=state2,y=state1,fill=anom),color = "black")+
geom_text(aes(x=state2,y=state1,fill=anom, label = textlabel), size=4)+
scale_fill_gradient2(low="lightyellow", mid="white",high="lightblue", midpoint = 0,
guide = guide_legend(title="prob anom(%)"))+
scale_x_discrete(labels=lblText, limits=lims)+
scale_y_discrete(labels=lblText, limits=lims)+
labs(x = xlabText, y=ylabText, title=(droughtTitle))+
theme(axis.text = element_text(size =14))+
theme(axis.title = element_text(size=14))+
theme(axis.text.x = element_text(size=14))+
theme(axis.text.y = element_text(size=14))+