| CDECquery {sharpshootR} | R Documentation |
Get water-related data (California only) from the CDEC website.
CDECquery(id, sensor, interval = "D", start, end)
id |
station ID (e.g. 'spw'), see details |
sensor |
the sensor ID (e.g. 45), see details |
interval |
character, 'D' for daily, 'H' for hourly, 'M' for monthly |
start |
starting date, in the format 'YYYY-MM-DD' |
end |
ending date, in the format 'YYYY-MM-DD' |
Station IDs can be found here: http://cdec.water.ca.gov/staInfo.html
Sensor IDs can be found using this URL: http://cdec.water.ca.gov/cgi-progs/queryCSV?station_id=, followed by the station ID.
Resevoir capacities can be found here: http://cdec.water.ca.gov/misc/resinfo.html
A new interactive map of CDEC stations can be found here: http://cdec.water.ca.gov/cdecstation/
a data.frame object with the following fields: 'datetime', 'year', 'month', 'value'.
D.E. Beaudette
http://cdec.water.ca.gov/queryCSV.html
## Not run:
##D library(latticeExtra)
##D library(plyr)
##D library(e1071)
##D
##D # get daily resevoir storage (ac. ft) from Pinecrest, New Melones and Lyons resevoirs
##D pinecrest <- CDECquery(id='swb', sensor=15, interval='D', start='2012-09-01', end='2015-01-01')
##D new.melones <- CDECquery(id='nml', sensor=15, interval='D', start='2012-09-01', end='2015-01-01')
##D lyons <- CDECquery(id='lys', sensor=15, interval='D', start='2012-09-01', end='2015-01-01')
##D
##D # compute storage capacity
##D pinecrest$capacity <- pinecrest$value / 18312 * 100
##D new.melones$capacity <- new.melones$value / 2400000 * 100
##D lyons$capacity <- lyons$value / 6228 * 100
##D
##D # combine
##D g <- make.groups(new.melones, lyons, pinecrest)
##D
##D # resonable date scale
##D r <- range(g$datetime)
##D s.r <- seq(from=r[1], to=r[2], by='1 month')
##D
##D # better colors
##D tps <- list(superpose.line=list(lwd=2, col=brewer.pal(n=3, name='Set1')))
##D
##D # plot
##D xyplot(capacity ~ datetime, groups=which, data=g, type='l',
##D xlab='', ylab='Capacity (##D
##D scales=list(x=list(at=s.r, labels=format(s.r, "##D
##D auto.key=list(columns=3, lines=TRUE, points=FALSE),
##D par.settings=tps,
##D panel=function(...) {
##D panel.abline(h=seq(0, 100, by=10), col='grey')
##D panel.abline(v=s.r, col='grey')
##D panel.xyplot(...)
##D })
##D
##D
##D ##
##D # New Melones monthly data, retrieve as far back in time as possible
##D new.melones.monthly <- CDECquery(id='nml', sensor=15, interval='M',
##D start='1900-01-01', end='2015-01-01')
##D
##D # convert to pct. capacity
##D new.melones.monthly$capacity <- new.melones.monthly$value / 2400000 * 100
##D
##D
##D # make a nice color ramp function
##D cols <- colorRampPalette(brewer.pal(9, 'Spectral'),
##D space='Lab', interpolate='spline')
##D
##D # plot, each pixel is colored by the total precip by year/month
##D levelplot(capacity ~ year * month, data=new.melones.monthly, col.regions=cols, xlab='',
##D ylab='', scales=list(x=list(tick.number=20)), main='New Melones Capacity (##D
##D
##D
##D ##
##D # get daily precip totals from Stan Powerhouse
##D x <- CDECquery(id='spw', sensor=45, interval='D', start='1900-01-01', end='2015-01-01')
##D
##D # compute total precip by year/month
##D a <- ddply(x, c('year', 'month'), summarize, s=sum(value, na.rm=TRUE))
##D
##D # convert monthly precipitation values into Z-scores by month
##D a.scaled <- ddply(a, 'month', summarize, year=year, scaled.ppt=scale(s))
##D
##D # make a nice color ramp function, scaled by the skewness of the underlying distribution
##D cols <- colorRampPalette(brewer.pal(9, 'Spectral'),
##D space='Lab', interpolate='spline', bias=skewness(a.scaled$scaled.ppt, na.rm=TRUE))
##D
##D # plot, each pixel is colored by the total precip by year/month
##D levelplot(scaled.ppt ~ year * month, data=a.scaled, col.regions=cols, xlab='',
##D ylab='', scales=list(x=list(tick.number=10)),
##D main='Monthly Total Precipitation (as z-score) SPW')
##D
##D
##D ##
##D # get pre-aggregated monthly data from Sonora RS
##D x <- CDECquery(id='sor', sensor=2, interval='M', start='1900-01-01', end='2015-01-01')
##D
##D # make a nice color ramp function, scaled by the skewness of the underlying distribution
##D cols <- colorRampPalette(brewer.pal(9, 'Spectral'), space='Lab',
##D interpolate='spline', bias=skewness(x$value, na.rm=TRUE))
##D
##D # plot
##D levelplot(value ~ year * month, data=x, col.regions=cols, xlab='',
##D ylab='', scales=list(x=list(tick.number=20)), main='Monthly Total Precipitation (inches) SOR')
##D
##D
##D
##D
##D
## End(Not run)