Stable version from CRAN
-install.packages("dbhydroR")Otherwise, the dbhydroR can be installed by running the following command in the R console:
or development version from Github
-devtools::install_github("ropensci/dbhydroR")Once installed, the package can be loaded using the following command:
-library(dbhydroR)Once installed, the package can be loaded using the following command:
+
Water quality data
Water quality data can be retrieved using the get_wq function which takes four required arguments. The user must specify a station ID, a test name, and a date range. Station IDs can be located on the SFWMD Station Maps or the Google Earth kmz file. An abbreviated list of available test names can be found in the appendix to this document while a full listing can be found at the DBHYDRO metadata page. Dates must be specified in YYYY-MM-DD format (e.g. 2015-02-26). The following set of examples retrieve measurements between March 2011 and May 2012. They can be run from the R console by issuing the command:
example(get_wq)One variable at one station
+get_wq(station_id = "FLAB08", date_min = "2011-03-01",
+ date_max = "2012-05-01", test_name = "CHLOROPHYLLA-SALINE")One variable at multiple stations
+get_wq(station_id = c("FLAB08","FLAB09"), date_min = "2011-03-01",
+ date_max = "2012-05-01", test_name = "CHLOROPHYLLA-SALINE")One variable at a wildcard station
+get_wq(station_id = c("FLAB0%"), date_min = "2011-03-01",
+ date_max = "2012-05-01", test_name = "CHLOROPHYLLA-SALINE")Multiple variables at multiple stations
+get_wq(station_id = c("FLAB08","FLAB09"), date_min = "2011-03-01",
+ date_max = "2012-05-01", test_name = c("CHLOROPHYLLA-SALINE",
+ "SALINITY"))\end{itemize}
By default, get_wq returns a cleaned output. First, the cleaning function converts the raw output from native DBHYDRO long format (each piece of data on its own row) to wide format (each site x variable combination in its own column) using the reshape2 package (Wickham 2007). Next, the extra columns associated with QA flags, LIMS, and District receiving are removed. Finally, row entries associated with QA field blanks, which are used to check on potential sources of contamination, are removed. Setting the raw flag to TRUE will force to retain information on QA field blanks as well as the other QA fields. An example query that retains this information and the original long formatting is shown below.
raw_wq <- get_wq(station_id = "FLAB08", date_min = "2011-03-01",
- date_max = "2011-05-01", test_name = "CHLOROPHYLLA-SALINE",
- raw = TRUE)raw_wq <- get_wq(station_id = "FLAB08", date_min = "2011-03-01",
+ date_max = "2011-05-01", test_name = "CHLOROPHYLLA-SALINE",
+ raw = TRUE)This raw data can then be cleaned using the function:
-clean_wq(raw_wq)Hydrologic data
Hydrologic time series data can be retrieved using the get_hydro function. The first task to accomplish prior to running get_hydro is to identify one or more dbkeys which correspond to unique site x variable time-series. This can be done before-hand using the get_dbkey function, the SFWMD Station Maps or the DBHYDRO Browser. One useful strategy for finding desired dbkeys is to run the get_dbkey function interactively using progressively narrower search terms. For example, suppose we are interested in daily average wind data at Joe Bay but we have no alphanumeric dbkey. Initially we could run get_dbkey with the detail.level set to “summary”.
get_dbkey(stationid = "JBTS", category = "WEATHER", param = "WNDS",
- detail.level = "summary")Our search returns two results but only one of them has a daily average (DA) measurement frequency. We can verify the remaining attributes of our likely dbkey by setting the freq parameter to “DA” and the detail.level parameter to “full”.
get_dbkey(stationid = "JBTS", category = "WEATHER", param = "WNDS",
- freq = "DA", detail.level = "full")get_dbkey(stationid = "JBTS", category = "WEATHER", param = "WNDS",
+ freq = "DA", detail.level = "full")This exact dbkey can only be returned reliably by specifying all of the get_dbkey parameters applicable to the “WEATHER” category.
get_dbkey(stationid = "JBTS", category = "WEATHER", param = "WNDS",
- freq = "DA", stat = "MEAN", recorder = "CR10", agency = "WMD",
- detail.level = "dbkey")get_dbkey(stationid = "JBTS", category = "WEATHER", param = "WNDS",
+ freq = "DA", stat = "MEAN", recorder = "CR10", agency = "WMD",
+ detail.level = "dbkey")Now that we have our dbkey in hand, we can use is as input to get_hydro. In addition to a dbkey, we must specify a date range. Dates must be entered in YYYY-MM-DD format (e.g. 2015-02-26).
get_hydro(dbkey = "15081",
- date_min = "2013-01-01", date_max = "2013-02-02")Alternatively, we can specify a set of arguments in our call to get_hydro that will be passed to get_dbkey on-the-fly. Use caution when using this strategy as complex stationid/category/parameter combinations can easily cause errors or return unexpected results. It is good practice to pre-screen your parameter values using get_dbkey.
get_hydro(date_min = "2013-01-01", date_max = "2013-02-02",
- stationid = "JBTS", category = "WEATHER", param = "WNDS",
- freq = "DA", stat = "MEAN", recorder = "CR10", agency = "WMD")get_hydro(date_min = "2013-01-01", date_max = "2013-02-02",
+ stationid = "JBTS", category = "WEATHER", param = "WNDS",
+ freq = "DA", stat = "MEAN", recorder = "CR10", agency = "WMD")The contents of multiple data streams can be returned by specifying multiple dbkeys or entering on-the-fly get_dbkey queries that return multiple dbkeys.
get_hydro(dbkey = c("15081", "15069"), date_min = "2013-01-01",
- date_max = "2013-02-02")get_hydro(date_min = "2013-01-01", date_max = "2013-02-02",
- category = "WEATHER", stationid = c("JBTS", "MBTS"),
- param = "WNDS", freq = "DA", stat = "MEAN")get_hydro(date_min = "2013-01-01", date_max = "2013-02-02",
+ category = "WEATHER", stationid = c("JBTS", "MBTS"),
+ param = "WNDS", freq = "DA", stat = "MEAN")More get_hydro examples including queries of other category values (“SW”, “GW”, and “WQ”) can be viewed by issuing the following commands from the R console:
example(get_dbkey)
-example(get_hydro)By default, get_hydro returns a cleaned output. First, the cleaning function clean_hydro converts the raw output from native DBHYDRO long format (each piece of data on its own row) to wide format (each site x variable combination in its own column) using the reshape2 package (Wickham 2007). Next, some extra columns are removed that are associated with measurement location (longitude/latitude), frequency, and QA flags are removed. Setting the raw flag to TRUE will force get_hydro to retain the original formatting and metadata fields. An example query that retains this information and the original long formatting is shown below.
raw_data <- get_hydro(date_min = "2013-01-01", date_max = "2013-02-02",
- stationid = "JBTS", category = "WEATHER", param = "WNDS",
- freq = "DA", stat = "MEAN", recorder = "CR10", agency = "WMD", raw = TRUE)
-
-clean_hydro(raw_data)References
Wickham, Hadley. 2007. “Reshaping Data with the reshape Package.” Journal of Statistical Software 21 (12): 1–20. http://www.jstatsoft.org/v21/i12/.
+Wickham, Hadley. 2007. “Reshaping Data with the reshape Package.” Journal of Statistical Software 21 (12): 1–20. http://www.jstatsoft.org/v21/i12/.
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