search for: rivermile

...ata from a twenty four month data set that I would like to apply this too. These data are subsets of the same stations throught time, but differing ones were included on different sampling dates. I would like to subset these data and then put them together as a big matrix with the by column being RiverMile. What is the easiest way to proceed as this is a process that will be done on 96+ constituents? feb06 <- structure(list(RiverMile = c(202L, 190L, 185L), X2.1.06 = c(2850000, NA, NA)), .Names = c("RiverMile", "X2.1.06"), row.names = c(29L, 31L, 32L), class = "data.frame...

merge can only merge two objects at a time- I would like to merge more than two objects at a time. s.d <- structure(list(RiverMile = c(202L, 198L, 190L, 185L, 179L, 148L, 119L, 61L)), .Names = "RiverMile", row.names = c(NA, -8L), class = "data.frame") #s.d is all of the river miles that can occur in all of the data frames that I want to put together feb06 <- structure(list(RiverMile = c(202L, 190L, 185...

I would like to reformat this data frame into something that I can produce some descriptive statistics. I have been playing around with the reshape package and maybe this is not the best way to proceed. I would like to use RiverMile and constituent as the grouping variables to get the summary statistics: 198a 198b mean mean sd sd ... ... etc. for all of these. I have tried reshape and aggregate and I am sure that I am missing something... below is a naive attempt at making a data frame with the columns in...

...1.537773727, 1.537773727, 3.249103284, 3.249103284, 2.470317011, 0.768531626, 2.633107621, 3.113199095, 0.773824094, 3.208461305, 0.680150068, 0.680150068, 0.026385752, 0.369310858, 8.049276658, 7.487378383, 0.950072035, 0.950072035, 0.763580377, 0.333244629, 5.475999014, 9.235631398, 2.587682905), RiverMile = c(148L, 179L, 185L, 202L, 179L, 185L, 190L, 119L, 148L, 179L, 185L, 202L, 215L, 119L, 202L, 198L, 215L, 148L, 198L, 190L, 202L, 215L, 198L, 198L, 215L, 148L, 148L, 202L, 202L, 215L, 119L, 179L, 185L, 190L, 198L, 61L, 61L, 119L, 148L, 202L, 202L, 61L, 61L, 119L, 148L, 185L, 202L, 215L )), .Names =...

...0.31, 0.07, 0.16, 0.54, 0.36, 0.13, 0.07), TIN.TP = c(3.117647059, 2.736842105, 2.25, 22, 3, 2.428571429, 6.428571429, NA, NA, 2.384615385, 3, NA, 3.25, NA, 3.166666667, 9, 23, 3.5, 11, 3.916666667, 3.4, 3.444444444, 8.75, 2.733333333, 3.583333333, 2.75, 17, NA, 2.933333333, 2.533333333, 2.5, 11), RiverMile = c(148L, 179L, 185L, 202L, 179L, 185L, 190L, 119L, 148L, 179L, 185L, 202L, 119L, 202L, 148L, 190L, 202L, 148L, 202L, 119L, 179L, 185L, 190L, 61L, 119L, 148L, 202L, 202L, 119L, 148L, 185L, 202L)), .Names = c("GPP", "TSS", "Iron", "TIN.TP", "RiverMile&quo...

...1.537773727, 1.537773727, 3.249103284, 3.249103284, 2.470317011, 0.768531626, 2.633107621, 3.113199095, 0.773824094, 3.208461305, 0.680150068, 0.680150068, 0.026385752, 0.369310858, 8.049276658, 7.487378383, 0.950072035, 0.950072035, 0.763580377, 0.333244629, 5.475999014, 9.235631398, 2.587682905), RiverMile = c(148L, 179L, 185L, 202L, 179L, 185L, 190L, 119L, 148L, 179L, 185L, 202L, 215L, 119L, 202L, 198L, 215L, 148L, 198L, 190L, 202L, 215L, 198L, 198L, 215L, 148L, 148L, 202L, 202L, 215L, 119L, 179L, 185L, 190L, 198L, 61L, 61L, 119L, 148L, 202L, 202L, 61L, 61L, 119L, 148L, 185L, 202L, 215L )), .Names =...

...were value great than an order of magnitude from the actual value. When I put in the fill argument everything is okay. I don't provide a reproducible example because the data set is to large to post to the list, but if you would like to see it then I can provide it. #this works cast(x, Date+RiverMile+location~Order, sum, fill=0) #this doesn't cast(x, Date+RiverMile+location~Order, sum) R version 2.8.0 (2008-10-20) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United S...

...t;HCTOC", "SCDAmm", "SCDN", "SCDNN", "SCDO", "SCDOC", "SCFlow", "SCNit", "SCON", "SCOPhos", "SCOrgP", "SCPhos", "SCTKN", "SCTOC"), class = "factor"), RiverMile = structure(c(8L, 8L), .Label = c("119", "148", "179", "185", "190", "198", "202", "215", "61", "BC", "HC", "SC"), class = "factor"), Constituent = structure(5:6...

...), .Label = c("2006/04", "2006/05", "2006/07", "2006/10", "2006/12", "2007/02", "2007/04", "2007/06", "2007/08", "2007/10", "2007/12", "2008/01"), class = "factor"), RiverMile = c(61L, 119L, 148L, 179L, 185L, 187L, 190L, 196L, 198L, 202L, 215L, 61L, 119L, 148L, 179L, 185L, 187L, 190L, 196L, 198L), Site = structure(c(9L, 1L, 2L, 3L, 4L, 10L, 5L, 11L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 10L, 5L, 11L, 6L ), .Label = c("119", "148", "179&...

Hadley, I don't know if I am doing something wrong or if it is ggplot please see the two graphs at the bottom of the page (code). melt.nut <- (structure(list(RiverMile = c(119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 179L, 179L, 179L, 179L, 179L, 179L, 179L, 185L, 185L, 185L, 185L, 185L, 185L, 185L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 1...

I would like to be able to reverse the xlim on qplot this is the code that I am using qplot(a[,"River.Mile"], a[,26] ,ylab=colnames(a)[26], xlab="RiverMile", xlim=rev(c(60, 216)))+geom_smooth()+scale_x_continuous(breaks=c(215,202,198,190,185,179,148,119,61), labels=c("215","202","198","190","185","179","148","119","61")) -- Stephen Sefick Research Scientis...

I would like to Summarize values that are repeated measures at a certain river mile with box plot i.e. The data matrix looks like this 123 124 125 #fiver mile 0.5 0.6 0.7 0.4 0.5 0.6 ... ... ... #values I would like to make a boxplot with the river mile naming the different box plot. How do you suppress the X123? Stephen -- Let's not

Does anyone know of a package to make a map from GIS data, and/or would it be easier in one of the free GIS programs. I would like to make a map of the savannah river area with our sampling locations. thanks stephen -- 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