#' Plot PAWMAP Water Quality Data for each station within a selected watershed.
#'
#' @param indf The data frame containing the variable to be plotted
#' @param wat The watershed for which station data will be plotted
#' @param analyte_field The field containing the name of the analyte
#' @param result The field storing the numeric value
#' @param analyte_units The field containing the measurement units
#' @param storm The field indicating whether it is a storm or seasonal sample
#' @param storm.value The value indicating it is a storm sample
#' @param geo.mean Should the geometric mean be used instead of the arithmetic?
#' @return A ggplot dot plot of the variable at each station within a watershed
#' @examples
#' library(ggplot2)
#' stations <- unique(stationInfo$site_identifier)
#' num_stations <- length(stations)
#' d <- data.frame(site_identifier=stations, janus_analyte_name='copper',
#' numeric_result=rlnorm(num_stations), storm_affected='No',
#' cycle=replicate(num_stations, sample(c(1,2), 1)))
#' d <- rbind(d, data.frame(site_identifier=stations, janus_analyte_name='copper',
#' numeric_result=2*rlnorm(num_stations), storm_affected='Yes',
#' cycle=replicate(num_stations, sample(c(1,2), 1))))
#' d <- mergeStatInfo(d)
#' p <- plotWQ_InWat(d, 'Johnson Creek')
#' p + ggtitle('Copper - Generated Data for Example\n')
#' @importFrom plyr ddply
#' @import ggplot2
#' @export
plotWQ_InWat <- function(indf, wat, analyte_field='janus_analyte_name',
result='numeric_result', analyte_units='analyte_units',
storm='storm_affected', storm.value='Yes', geo.mean=FALSE) {
# Subset data to single analyte & watershed, using either metric_code or metric_name
indf <- indf[indf[['watershed']] == wat, ]
analyte <- unique(indf[[analyte_field]])
if (length(analyte) > 1) {
stop(stop("Multiple analytes are present in data frame. Reconfigure data."))
}
# get metric code for variable
m.tmp <- as.character(met.cod$metric_code[match(analyte, met.cod[['metric_name']])])
# Create labels
poll.lab <- met.cod$label[match(analyte, met.cod[['metric_name']])]
units <- as.character(unique(indf[[analyte_units]]))
breaks <- as.vector(c(1, 2, 5) %o% 10^(-5:5)) #make scale non-scientific format
# Summarize seasonal data. Reshape data; add station info; format
seas.df <- indf[indf[[storm]] != storm.value, ]
# Get mean and range by station. If E. coli, use geometric mean.
if (geo.mean) {
seas.sum <- ddply(seas.df, c('loc.lbl', 'cycle'), function(x) {
data.frame(smean=exp(mean(log(x[, result]), na.rm=T)),
smin=min(x[, result], na.rm=T),
smax=max(x[, result], na.rm=T))
})
leg.lbl <- c('Seasonal\nRange', 'Seasonal\nGeometric\nMean', 'Storm\nSample')
} else {
seas.sum <- ddply(seas.df, c('loc.lbl', 'cycle'), function(x){
data.frame(smean=mean(x[, result], na.rm=T),
smin=min(x[, result], na.rm=T),
smax=max(x[, result], na.rm=T))
})
leg.lbl <- c('Seasonal\nRange', 'Seasonal\nMean', 'Storm\nSample')
}
# Join back storm data
storm.df <- indf[indf[[storm]]==storm.value, c('loc.lbl', 'cycle', result)]
names(storm.df)[names(storm.df) == result] <- 'storm'
outdf <- merge(seas.sum, storm.df)
outdf$cycle <- factor(outdf$cycle)
# Sort data by mean of seasonal data
outdf <- transform(outdf, loc.lbl=reorder(loc.lbl, smean) )
# Set up plot
p <- suppressWarnings(ggplot(data = outdf, aes(smean, loc.lbl, color=cycle)) + #coord_flip() +
geom_errorbarh(aes(y=loc.lbl, xmin=smin, xmax=smax,
shape='range', linetype='range'), size=1.2,
height = 0, position=position_dodgev(height=0.7)) +
geom_point(aes(x=smean, shape='smean', linetype='smean'), size=4,
position=position_dodgev(height=0.7)) +
geom_point(aes(x=storm, shape='storm', linetype='storm'), size=4,
position=position_dodgev(height=0.7)) +
ylab('') + xlab(paste('\n', poll.lab, ' (', units, ')\n', sep="")) + theme_bw() +
theme(plot.margin = unit(c(.75, 0, 0, 0), "lines"),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 18, face='bold'),
legend.text = element_text(size = 14),
legend.key.size = unit(1.3, "cm"),
legend.title = element_text(size=16, face = "bold", hjust=0)) +
guides(shape = guide_legend(override.aes = list(shape = c(NA, 19, 17)))) +
scale_linetype_manual(name = "Results", labels=leg.lbl, values=c(1,0,0)) +
scale_shape_manual(name = "Results", labels=leg.lbl, values=c(19, 19, 17)))
# log transform for all but DO
if (analyte != 'dissolved oxygen') {
p <- p + scale_x_log10(breaks = breaks, labels = breaks)
}
# Add standard lines where available
if (m.tmp %in% std.lns$metric_code) {
r.lin <- std.lns[match(m.tmp, std.lns$metric_code), ]$red.line
if (m.tmp == 'do') {
p <- p + geom_vline(xintercept=8, linetype='solid', color='red', size=1.5) +
geom_vline(xintercept=11, linetype='dashed', color='red', size=1.5)
} else {
p <- p + geom_vline(xintercept=r.lin, color='red', size=1.5)
}}
if (m.tmp == 'ecoli') {
p <- p + geom_vline(xintercept=126, color='red', size=1.5, linetype=2)
}
return(p)
}
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