On Oct 5, 2009, at 10:45 PM, stephen sefick wrote:
Sorry, I want the cumsum of precipitation by gauge name.
that can then be used with the appropriate datetime stamp to pot a
cumulative rainfall plot.
I dunno. Here is what I get when I extract the data-gathering part of
that extended function.
> str(both)
'data.frame': 3973 obs. of 8 variables:
$ gauge : int 2102908 2102908 2102908 2102908 2102908
2102908 2102908 2102908 2102908 2102908 ...
$ agency : Factor w/ 1 level "USGS": 1 1 1 1 1 1 1 1 1 1 ...
$ date : Factor w/ 8 levels "2009-09-28","2009-09-29",..: 1
1 1 1 1 1 1 1 1 1 ...
$ time : Factor w/ 96 levels "00:00","00:15",..: 1 2 3 4 5 6
7 8 9 10 ...
$ gauge_height : num 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89
0.88 ...
$ discharge : num 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.1 ...
$ precipitation: num 0 0 0 0 0 0 0 0 0 0 ...
$ gauge_name : Factor w/ 6 levels "CANOOCHEE RIVER NEAR CLAXTON,
GA",..: 3 3 3 3 3 3 3 3 3 3 ...
> df$c_sum_precip <- ave(DF$precipitation, DF$guage_name, cumsum)
Error in as.vector(x, mode) : invalid 'mode' argument
> describe(DF$precipitation) # describe from Hmisc package
DF$precipitation
n missing unique Mean .05 .10 .25 .
50 .75 .90
2255 1718 15 0.001610 0 0 0
0 0 0
.95
0
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.14 0.21
0.22 0.37 0.46
Frequency 2146 60 16 10 6 3 4 3 1 1 1
1 1 1 1
% 95 3 1 0 0 0 0 0 0 0 0
0 0 0 0
>-------------
Seems that a cumsum on a variable that has >60% missing values is
going to present certain problems. I'm also not sure how this approach
will carry across the time variable which at the moment appears to be
a factor rather than any of the date or datetime classes.
--
David.
On Mon, Oct 5, 2009 at 9:03 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
On Oct 5, 2009, at 9:39 PM, stephen sefick wrote:
Look at the bottom of the message for my question
#here is a little function that I wrote
USGS <- function(input="discharge", days=7){
library(chron)
library(gsubfn)
#021973269 is the Waynesboro Gauge on the Savannah River Proper
(SRS)
#02102908 is the Flat Creek Gauge (ftbrfcms)
#02133500 is the Drowning Creek (ftbrbmcm)
#02341800 is the Upatoi Creek Near Columbus (ftbn)
#02342500 is the Uchee Creek Near Fort Mitchell (ftbn)
#02203000 is the Canoochee River Near Claxton (ftst)
a <- "http://waterdata.usgs.gov/nwis/uv?format=rdb&period=";
b <-
"&site_no=021973269,02102908,02133500,02341800,02342500,02203000"
z <- paste(a, days, b, sep="")
L <- readLines(z)
#trimmed long comment that broke function
L.USGS <- grep("^USGS", L, value = TRUE)
DF <- read.table(textConnection(L.USGS), fill = TRUE)
colnames(DF) <- c("agency", "gauge", "date", "time", "gauge_height",
"discharge", "precipitation")
pat <- "^# +USGS +([0-9]+) +(.*)"
L.DD <- grep(pat, L, value = TRUE)
library(gsubfn)
DD <- strapply(L.DD, pat, c, simplify = rbind)
DDdf <- data.frame(gauge = as.numeric(DD[,1]), gauge_name = DD[,2])
both <- merge(DF, DDdf, by = "gauge", all.x = TRUE)
dts <- as.character(both[,"date"])
tms <- as.character(both[,"time"])
date_time <- as.chron(paste(dts, tms), "%Y-%m-%d %H:%M")
DF <- data.frame(date_time, both)
library(ggplot2)
#discharge
if(input=="discharge"){
qplot(as.POSIXct(date_time), discharge, data=DF,
geom="line")+facet_wrap(~gauge_name,
scales="free_y")+coord_trans(y="log10")
}else{
#precipitation
qplot(as.POSIXct(date_time),
precipitation, data=subset(DF, precipitation!="NA"),
geom="line")+facet_wrap(~gauge_name, scales="free_y")
}
}
USGS("precip")
I would like to have the cumsum based on the facet gauge_name - in
other words a cummulative rainfall amount for each gauge_name
You want "the cumsum" of <something> but you have wrapped so much
up in that
function (inlcuding library calls???) that I cannot see what that
<something> would be. Not all of us read ggplot calls. The
canonical route
to getting cumsums by a factor is with ave. Something like:
DF$cum_x <- ave(DF$x, DF$fac, cumsum)
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
--
Stephen Sefick
Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods. We are mammals, and have not exhausted the
annoying little problems of being mammals.
-K. Mullis
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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