This entire Repository has been cloned from amandovi/NEP-QA-WQ to USEPA/NEP-Water-Quality as of January 17, 2025. If you do not have access to that repository, please email Steve Pacella (pacella.stephen@epa.gov) or Andrew Mandovi (mandovi.andrew@epa.gov).

The purpose of this repository is to store shared R code for working with data from National Estuary Program (NEP) monitoring sites to:

I. QA/QC-ing raw data for analysis for the following NEPs (4): Barnegat Bay, Casco Bay, Delaware Inland Bays, and Pensacola Bay

IIa. Creating a new version of the data_list (qa_data_list) which contains all QA flags performed in step I, and harmonizing all QA flags for all variables into a common 'flags' column.

IIb. Filtering qa_data_list based on flagging performed either internally or by the NEPs' respective staff. This filtered version of qa_data_list is named 'pass_data_list'.

III. Performing final revisions on datasets (in NEP_revisions.R script) prior to publication of data to data.gov. These finalized datasets are renamed:

• qa_data_list_revision --> nep_unfiltered_data : The original data with all QA flagging
• pass_data_list_revision --> nep_filtered_data : the data filtered based on finalized QA metrics

IV. (IN FUTURE) Performing analysis on the data

V. (IN FUTURE) Creating visualizations of data for communication and publication of results

I. QA/QC-ing raw data for analysis, steps:

1. This requires the following files to be downloaded to your local machine:

• qaqc_NEP_main.R
• qaqc_run_all.R
• qaqc_NEP_Barnegat.R
• qaqc_NEP_Casco.R
• qaqc_NEP_Delaware.R
• qaqc_NEP_Pensacola.R
• (and any corresponding additional NEP files to QA, but as of 6/23/25, these are the only 4)

2. Then, the user must ensure that the thresholds for each NEP file are correct, .
3. Once R is opened, the user should open the 'qaqc_run_all.R' script and customize row 39:
   • this is where the user sets their working directory to the local filepath where the R scripts were downloaded (NOT where the data is on the O:drive)
4. Finally, the user can execute all of the scripts by either:
   • Running the following line of code in the R console: source('qaqc_run_all.R')
   • Or running the full qaqc_run_all.R script by pressing ctrl-shift-enter
5. The user will be prompted on save preferences (local and O:drive) for the output dataframe .Rdata

II. Creating qa_data_list and pass_data_list:

1. Run qaqc_NEP_filter_pass.R script (must also be downloaded onto machine)
2. This will create the R objects qa_data_list and pass_data_list which must be saved for future use

Naming conventions: .Rdata files:

• data_list: the raw data, organized into a list of data frames, each data frame corresponding to an NEP (e.g. data_list$Barnegat for Barnegat Bay's data)
• qa_data_list: data_list post-QA/QC flagging and having a harmonized "flags"
• pass_data_list: qa_data_list filtered for passing "flags" (i.e. where flags == 1)

The PREFIX of a R script file dictates the file's category:

• qaqc_: scripts containing R code for performing QA/QC and filtering of data
• calc_: scripts containing R code for running analysis on the data. This includes performing carbonate calculations, calculating uncertainties, or other statistical calculations.
• plot_: scripts containing R code for plotting visualizations of data
• test_: scripts written for testing, practice, or reference material