Please wait for the process to complete.
Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
||This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (email@example.com) for information on this resource.|
|Storage:||The size of this resource is 6.5 MB|
|Created:||Jun 03, 2016 at 9:31 p.m.|
|Last updated:|| Jun 11, 2016 at 2:04 p.m.
|Citation:||See how to cite this resource|
|+1 Votes:||Be the first one to this.|
|Comments:||No comments (yet)|
We present the procedure to obtain daily averages of ancillary water data (e.g., turbidity and chlorophyll) that may be used to support the interpretation of the daily metabolic rate estimates. The Ancillary_DarilyAvg.R script needs to be executed as many times as the number of available ancillary water parameters (twice for our case). For the case of the SJR restoration reach, the data can be accessed by doing a query on the CDEC website indicating the sensor number (28 for chlorophyll, 27 for turbidity), the interval of the data (event or hourly), and the starting and ending dates of the period of interest. A comma separated value file is produced after the request, and each of these files will be the input to this R script.
Section 1 of the script sets up the working directory; the ancillary water data input file is read in section 2 and particular configuration parameters are specified in section 3. According to these parameters, a new table (‘table1’) with only the columns of interest is created in section 4; section 5 converts into NA’s all the cell values that report missing data (“m”) or errors (“-9998” or “-9997”). The actual daily averages are calculated in section 6 and stored in the variable ‘table4’. Section 7 deals with the transformation of the date column to an actual date format in order to identify whether or not there are missing days within the time series. The output of this section (dates and intervals between dates) as well as the daily averages contained in ‘table4’, are merged into a new variable ‘table5’. Finally, section 8 prints the output of this script as a tab separated text file.
|This resource is referenced by||https://www.hydroshare.org/resource/885906ef97e542a8a33048ea486ddfdf/|
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/