local_TODO/monitor_timeOfDayMultiPiePlot.R

In MazamaScience/PWFSLSmoke: Utilities for Working with Air Quality Monitoring Data

# #' @keywords ws_monitor
# #' @export
# #' @import graphics
# #' @title Create 9 Daily Time of Day Pie Plots
# #' @param ws_monitor ws_monitor object
# #' @param monitorID id for a specific monitor in the ws_monitor object (optional if only one monitor
# #' in the ws_monitor object)
# #' @description Creates pie plots of 3-hour average PM2.5 concentrations over 9 consecutive days for a single monitor.
# #' 3-hour pie pieces are colored according to AQI category. Day/night is approximated using shading.
# #' @examples
# #' \dontrun{
# #' ws_monitor <- wrcc_load(20150801, 20150810)
# #' monitor <- ws_monitor$meta$monitorID[1]
# #' monitor_timeOfDayMultiPiePlot(ws_monitor, monitor)
# #' }
# 
# monitor_timeOfDayMultiPiePlot <- function(ws_monitor, monitorID=NULL) {
#   
#   # Allow single monitor objects to be used without specifying monitorID
#   if ( is.null(monitorID) && nrow(ws_monitor$meta) == 1 ) {
#     monitorID <- ws_monitor$meta$monitorID[1]
#   }
#   
#   # Plot Style
#   
#   aqiColors <- adjustcolor(AQI$colors, 0.5)
# 
#   cex_pie = 1.0
#   lwd_pie = 3
#   col_pieLines = 'white'
#   labelRadius = 0.80
#   col_nighttime = adjustcolor('black',0.2)
#   col_missing <- 'gray80'
#   
#   # Data Preparation ----------------------------------------------------------
#   
#   # NOTE:  Incomting insituTime is UTC
#   
#   # Insitu data requested 
#   pm25 <- ws_monitor$data[[monitorID]]
#   UTCTime <- ws_monitor$data$datetime 
#   index <- which(ws_monitor$meta$monitorID %in% monitorID)
#   
#   localTime <- lubridate::with_tz(UTCTime,ws_monitor$meta$timezone[index])
#   day <- lubridate::day(localTime)
#   hour <- lubridate::hour(localTime)
#   
#   dayChunk <- as.factor(.bincode(hour,breaks=seq(0,24,3),include.lowest=TRUE))
#   
#   # Create a new dataframe for local use
#   df <- data.frame(localTime,pm25,day,hour,dayChunk)
#   
#   # Set up to do a plot with the last four days 
#   lastDays <- rev(unique(day))[1:9]
#   lastDays <- c(rev(lastDays[1:3]),
#                 rev(lastDays[4:6]),
#                 rev(lastDays[7:9]))
#   
#   # I want the oldest plot in the lower left and the newest in the upper right
#   layout(matrix(c(7,8,9,4,5,6,1,2,3),nrow=3,byrow=TRUE))
#   
#   # Modify the margins
#   par(mar=c(3,3,3,3)+.1)
#   
#   # Simple "danger-pie" plot for each day
#   for (thisDay in lastDays) {
#     
#     dayDF <- df[df$day == thisDay,]
#     
#     dayDF %>% group_by(dayChunk) %>%
#       summarize(pm25=mean(pm25,na.rm=TRUE)) ->
#       dayChunkMean
#     
#     
#     # Plotting ------------------------------------------------------------------
#     
#     # Colors come from pm25Daily values
#     cols <- aqiColors[ .bincode(dayChunkMean$pm25, AQI$breaks_24, include.lowest=TRUE) ]
#     
#     # Handle incomplete days
#     if (length(cols) < 8) {
#       cols <- c(cols,rep(NA,(8-length(cols))))
#     }
#     
#     # Replace NA with gray
#     cols[is.na(cols)] <- col_missing
#     
#     x <- rep(1,length(levels(dayChunk)))
#     
#     pie(x,
#         clockwise=TRUE, init.angle=-90,
#         labels=NA,
#         col=cols, border=cols)
#     
#     title(paste0(strftime(dayDF$localTime[1],"%b %d")),line=0)
#     
#     abline(h=0,col=col_pieLines,lwd=lwd_pie)
#     abline(v=0,col=col_pieLines,lwd=lwd_pie)
#     abline(0,1,col=col_pieLines,lwd=lwd_pie)
#     abline(0,-1,col=col_pieLines,lwd=lwd_pie)
#     
#     text(0,-labelRadius*0.7,'Midnight',pos=1)
#     text(-labelRadius,-0,'6 am',pos=2)
#     text(0,labelRadius*0.7,'Noon',pos=3)
#     text(labelRadius,-0,'6 pm',pos=4)
#     
#     
#     ###rect(-labelRadius,-1,labelRadius,0, col=col_nighttime, border=col_nighttime)
#     rect(-1,-1,1,-0.05, col=col_nighttime, border=col_nighttime)
#     
#   }
#   
#   # Return to default settings
#   layout(1)
#   par(mar=c(5,4,4,2)+.1)
#   
# }
#