R package developed by BLE LTER information managers and graduate students to download and collate the Beaufort Lagoon Ecosystems LTER's Core Program data from the Environmental Data Initiative (EDI) repository.
BLE LTER publishes its Core Program data across many data packages' "IDs" in the EDI repository. For example, different measurements, such as dissolved organic carbon (DOC) and concentrations of nutrients, done on the same container of water sampled from a Beaufort Sea lagoon, will be accessible from different places. While this serves our data publishing process well, it can be challenging for some of our data users who wants to see all the available data in one place. This package aims to provide a reproducible and trackable way to join the latest data according to time and location.
We encourage users to use the functionality in this package as a jump start to their analysis, while still keeping track of the original datasets and keep an eye out for updates.
We recommend users:
- Re-run the collation whenever there are updates to the underlying data packages, or whenever you start an analysis, to make sure you've got the latest data versions.
- Do not use stale versions. Check the dates on your files and re-run the collation frequently, ideally as the underlying EDI packages are updated. We have included two different means by which you can keep track of data versions:
- (1) When you choose the option to export/write to Excel or CSV files, the package will append the current date to the file name.
- (2) The dataset metadata summary included in the output contains information on which version of each underlying EDI data package was used in the output, its publication date, DOI, and URL to help you trace data provenance and locate the published sources.
- Check the collated data against the underlying data packages. By default, the outputs provide metadata that will help track down any particular number back to a published source on EDI. We try our best to preserve the numbers as-is from the originals, however in the case we have overlooked something, please refer to the published EDI source as authoritative instead of the collated data.
- Cite the underlying data packages in your publications and results. The outputs of this package are convenience products and should not be cited. The underlying EDI datasets, however, have DOIs and you can find suggested citations on the EDI website. Feel free to cite this R package if you found it helpful.
# install the package from GitHub
remotes::install_github("BLE-LTER/BLEdatatools")Restart the R session by selecting Session/Restart R in the RStudio Menu bar or pressing Ctrl+Shift+F10.
# load the package into the active R session
library(BLEdatatools)collate()This single function call queries EDI for the latest BLE LTER Core Program updates, downloads the relevant files, and finally collates the observations according to time and location to output two collated or "master" data sheets, one each of Core Program water and sediment data, plus dataset- and column-level metadata.
collate() by default fetches and collates all water and sediment datasets from the Core Program.
Quick reference list of Core Program dataset IDs
Quick reference list of Core Program dataset IDs:
2: water column dissolved organic carbon (DOC), total dissolved nitrogen
3: CTD/TCM mooring data, YSI data (only YSI data is collated by this R package)
4: water column d18O
11: water column particulate organic matter (POM) carbon and nitrogen content, d13C and d15N
12: sediment pigment concentrations
13: water column chlorophyll
14: water column and sediment nutrient concentrations
18: sediment carbon and nitrogen content, d13C and d15N
You can also call the which_ids() function, also included in this R package, in the R console to quickly reference this list.
If you want to skip certain datasets, specify them by customizing the ids optional argument. If ids is present, only dataset IDs listed in the argument will be collated.
collate(ids = c(2, 3, 4))collate() by defaults outputs a R list object. Change the "output" optional argument to "excel" or "csv" to also write to file a multi-tab Excel file or multiple CSV files respectively. The file(s) will be written to the current working directory by default, or a directory you specify in the optional argument path. Output file names will contain the current date, for tracking purposes.
collate(output = "excel", path = getwd())
# OR
collate(output = "csv", path = getwd())The nutrients (knb-lter-ble.14.1) started reporting two replicate measurements per sample in 2021. By default, collate() pivots these columns from long to a wider format to make the nutrients data more compatible with other Core Program datasets. Users have the option to change this behavior by changing the avg_rep argument to collate(). I've copied the documentation on avg_rep below.
avg_rep (logical): TRUE/FALSE on whether to average replicates. This really only affects the nutrients dataset (knb-lter-ble.14) because this is the only dataset in consideration still retaining replicates in the published version. If FALSE, any data with replicates will be pivoted to a wider format, with the rep number appended to the new column names. E.g., two rows (reps 1 and 2) of one column "ammonium_umol_N_L" become one row of two columns "ammonium_umol_N_L_rep1" and "ammonium_umol_N_L_rep2". If TRUE, numeric columns will be averaged (NAs are ignored) and character columns will be collapsed into one string (e.g. if two replicates from the same sample have the flags VALID and BD, this becomes "VALID BD"). Note that in the original nutrients data, 2018-2019 reps are always NA, because we did not report replicates for these years. "NA" reps become rep 1 for the purposes of this package. Defaults to FALSE.
collate(avg_rep = TRUE)collate() by default also outputs a summary of dataset- and column-level metadata to help you orient yourself in the dataset. Setting the optional function argument skip_metadata = TRUE will skip this step. I recommend including the metadata, as this is valuable information and will help you interpret the data and trace back what versions of each underlying EDI dataset contributed to the output. This argument is meant for when the function runs into errors during metadata summary; in those cases skipping may help you still use the functionality.
collate(skip_metadata = TRUE)Note that variable names in the metadata summaries will not correspond 100% to the order and names of the collated data columns. For example, "ammonium_umol_N_L" in metadata may become "ammonium_umol_N_L_rep1" and "ammonium_umol_N_L_rep2" (see above about the avg_rep argument). You should be able to easily deduct which column in the metadata corresponds to which column in the data. This is because the metadata summaries is lifted wholesale, as-is from the metadata versions on EDI, while the data columns may undergo additional processing (such as described above).
Intended for R console use only, which_ids() conveniently returns, as a R console message, a quick-and-dirty summary of the BLE Core Program IDs used in this R package's collate().
which_ids()Another way to quickly look up all BLE dataset IDs and their information is search_ble_packages(). Compared to which_ids, this function returns an in-memory data.frame about all BLE dataset IDs.
search_ble_packages()For customization options, please see this function's documentation.
We also include a convenience function, download_data(), to programmatically download the latest version of any BLE data package (not just the Core Prorgram water/sediment ones slated for collation using collate()). download_data() supports downloading one package at a time, either the entire package, or you can also cherry-pick one entity (file) from the data package to download.
download_data(identifier = 1,
entity_number = 1,
path = getwd(),
write = TRUE)In the above example: identifier indicates the EDI package with package ID knb-lter-ble.1.7, which is the latest version of that package as of this writing (2023-07-03). entity_number indicates the first data entity/file in this package. Leave this argument if you'd like to download all data entities. Specify path and write if you'd like to write to file.