hornb
commented
9 years ago not sure how a ph of 22 or-1.5 would be there, as our system doesn't allow that---so maybe in some data manipulation? We have some auto validation steps.
For our data a BLANK means no data exists and 0 means a measurement was taken and value was below method's detection limit. Sometimes some programs will put 0's in blanks, dbase used to do that so we used -9's to represent no data present vs 0 value. The primary purpose of doing our watershed reports is to find outliers that we do let go through when we validate "batches" of data and do not get to see that batch in context with previous data from that site...--We are moving around the state finishing these WSR for the first time, just finishing CO and SP basins (huge basins), so all data corrections have not been completed on actual database. Once we get around the state one time, we can "add" new data and find outliers quicker if you will. This has been a decade goal of mine, slowly but finally happening. I do these WSR w/ a 6 month temp and an intern, and my available time, so it will take us about another 2 years likely to get through the entire state one time and on an update rotating basis. Bottom line, is the outlier problem is being dealt w/, just slowly. If in this process some are identified before we get to them, we can mark them for that analyses.
Remember too, that some analytes came on later in the program. Ca/ Mg we started when we switched for AA to ICP around 2000, so previous to that will be no CA values.
On Sat, May 9, 2015 at 11:00 PM, mjpdenver notifications@github.com wrote:
Hi,
I have made a few time series plots for the more commonly measured analytes at the more popular sites. For reasons discussed earlier - I also am only looking at data collected after 1999.
I made a simple shiny plot and deployed it at shinyapp.io. There left panel has a drop down menu to choose and analyte.
(https://mjpdenver.shinyapps.io/shinyPlot/)
Looking through the plots, I see what I assume are bad (or really interesting) results. For example, for Calcium, at the first site we see a very high value. For pH, there is a value of 22 and one of ~1.5.
Flipping through the analytes, I so notice that prior to 2005, zero value are more often measured. Again, looking at Calcium - is it possible that river samples could have no measurable calcium? My aqueous chemistry is a bit rusty, but isn't some calcium in nearly all river water.
We could - but I don't think this is the place - try and identify outliers. But that would be an project unto itself and shouldn't be done by statisticians who don't have knowledge of river water chemistry. Moreover, this seems to be more an issue to address with the Colorado Data Sharing Network.
Any ideas?
— Reply to this email directly or view it on GitHub https://github.com/CoWy-ASA/RiverWatch/issues/9.
Barb HornStatewide Water Quality Specialist Water Unit
P 970.382.6667 | F 970.247.4785 151 East 16th Avenue, Durango, CO 81301 barb.horn@state.co.us | cwp.state.co.us
"Humankind, be both"
mjpdenver
Hi,
I have made a few time series plots for the more commonly measured analytes at the more popular sites. For reasons discussed earlier - I also am only looking at data collected after 1999.
I made a simple shiny plot and deployed it at shinyapp.io. There left panel has a drop down menu to choose and analyte.
(https://mjpdenver.shinyapps.io/shinyPlot/) NOTE: THE FIRST TIME YOU OPEN THE PAGE YOU MAY HAVE TO WAIT 5 or 10 SECONDS for the DATA to Load.
Looking through the plots, I see what I assume are bad (or really interesting) results. For example, for Calcium, at the first site we see a very high value. For pH, there is a value of 22 and one of ~1.5.
Flipping through the analytes, I so notice that prior to 2005, zero value are more often measured. Again, looking at Calcium - is it possible that river samples could have no measurable calcium? My aqueous chemistry is a bit rusty, but isn't some calcium in nearly all river water.
We could - but I don't think this is the place - try and identify outliers. But that would be an project unto itself and shouldn't be done by statisticians who don't have knowledge of river water chemistry. Moreover, this seems to be more an issue to address with the Colorado Data Sharing Network.
Any ideas?