This vignette demonstrates using dwctaxon on “real life” data found in the wild. Our goal is to import the data and validate it.
First, load the packages used for this vignette.
library(dwctaxon)
library(readr)
library(tibble)
library(dplyr)We will use the Database of Vascular Plants of Canada (VASCAN), which is available as a Darwin Core Archive.
The data can be obtained manually by going to the VASCAN website, downloading the Darwin Core Archive, and unzipping it1.
Alternatively, it can be downloaded and unzipped with R. First, we set up some temporary folders for downloading and specify the URL:
# - Specify temporary folder for downloading data
temp_dir <- tempdir()
# - Set name of zip file
temp_zip <- paste0(temp_dir, "/dwca-vascan.zip")
# - Set name of unzipped folder
temp_unzip <- paste0(temp_dir, "/dwca-vascan")vascan_url <- "https://data.canadensys.net/ipt/archive.do?r=vascan&v=37.12"Next, download and unzip the zip file.
# Download data
download.file(url = vascan_url, destfile = temp_zip, mode = "wb")
# Unzip
unzip(temp_zip, exdir = temp_unzip)
# Check the contents of the unzipped data (the Darwin Core Archive)
list.files(temp_unzip)
#> [1] "description.txt" "distribution.txt" "eml.xml" "meta.xml" "resourcerelationship.txt"
#> [6] "taxon.txt" "vernacularname.txt"Finally, load the taxonomic data (taxon.txt) into R. It
is a tab-separated text file, so we use readr::read_tsv()
to load it.
vascan <- read_tsv(paste0(temp_unzip, "/taxon.txt"))
#> Warning: One or more parsing issues, call `problems()` on your data frame for details, e.g.:
#> dat <- vroom(...)
#> problems(dat)
#> Rows: 32770 Columns: 24
#> ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr (20): nameAccordingToID, scientificName, acceptedNameUsage, parentNameUsage, nameAccordingTo, higherClassification, class, order, fa...
#> dbl (4): id, taxonID, acceptedNameUsageID, parentNameUsageID
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Take a peak at the data
vascan
#> # A tibble: 32,770 × 24
#> id taxonID acceptedNameUsageID parentNameUsageID nameAccordingToID scientificName acceptedNameUsage parentNameUsage nameAccordingTo
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 73 73 73 NA http://dx.doi.org/1… Equisetopsida… Equisetopsida C.… <NA> Chase, M.W. & …
#> 2 26 26 26 73 http://dx.doi.org/1… Equisetidae W… Equisetidae Warm… Equisetopsida … Chase, M.W. & …
#> 3 25 25 25 26 http://www.jstor.or… Equisetales d… Equisetales de C… Equisetidae Wa… Smith, A.R., K…
#> 4 128 128 128 25 http://www.jstor.or… Equisetaceae … Equisetaceae Mic… Equisetales de… Smith, A.R., K…
#> 5 1142 1142 1142 128 http://www.efloras.… Equisetum Lin… Equisetum Linnae… Equisetaceae M… FNA Editorial …
#> 6 2004 2004 2004 1142 http://www.efloras.… Equisetum sub… Equisetum subg. … Equisetum Linn… FNA Editorial …
#> 7 5467 5467 5467 2004 http://www.efloras.… Equisetum flu… Equisetum fluvia… Equisetum subg… FNA Editorial …
#> 8 5466 5466 5466 2004 http://www.efloras.… Equisetum arv… Equisetum arvens… Equisetum subg… FNA Editorial …
#> 9 5472 5472 5472 2004 http://www.efloras.… Equisetum pra… Equisetum praten… Equisetum subg… FNA Editorial …
#> 10 5471 5471 5471 2004 http://www.efloras.… Equisetum pal… Equisetum palust… Equisetum subg… FNA Editorial …
#> # ℹ 32,760 more rows
#> # ℹ 15 more variables: higherClassification <chr>, class <chr>, order <chr>, family <chr>, genus <chr>, subgenus <chr>,
#> # specificEpithet <chr>, infraspecificEpithet <chr>, taxonRank <chr>, scientificNameAuthorship <chr>, taxonomicStatus <chr>,
#> # modified <chr>, license <chr>, bibliographicCitation <chr>, references <chr>The dataset includes 32770 rows (taxa) and 24 columns.
Let’s see if the dataset passes validation with dwctaxon.
It is usually a good idea to just run dct_validate()
with default settings the first time. If it passes, you can move on.
dct_validate(vascan)
#> Error: check_sci_name failed
#> scientificName detected with duplicated value
#> Bad scientificName: Scilla esculenta Ker Gawler, Arnica monocephala Rydberg, Arnica pedunculata Rydberg, Trifolium tridentatum Lindley, Oenothera angustissima R.R. Gates, Spiraea discolor Pursh, Viola discurrens Greene, Cogswellia simplex (Nuttall ex S. Watson) M.E. Jones, Scilla esculenta Ker Gawler, Ginseng trifolium (Linnaeus) Alph. Wood, Arnica pedunculata Rydberg, Arnica monocephala Rydberg, Swida racemosa (Lamarck) Moldenke, Spiraea discolor Pursh, Swida racemosa (Lamarck) Moldenke, Trifolium tridentatum Lindley, Oenothera angustissima R.R. Gates, Aralia triphylla Poiret, Panax lanceolatus Rafinesque, Panax pusillus Sims, Aralia triphylla Poiret, Ginseng trifolium (Linnaeus) Alph. Wood, Panax lanceolatus Rafinesque, Panax pusillus Sims, Cogswellia simplex (Nuttall ex S. Watson) M.E. Jones, Viola discurrens GreeneLooks like we’ve got problems…
To dig into these in more detail, let’s run
dct_validate() again, but this time output a summary of
errors.
validation_res <- dct_validate(vascan, on_fail = "summary")
#> Warning: scientificName detected with duplicated value
#> Warning: Invalid column names detected: id
validation_res
#> # A tibble: 27 × 4
#> taxonID scientificName error check
#> <dbl> <chr> <glue> <chr>
#> 1 NA <NA> Invalid column names detected: id check_col_names
#> 2 10170 Scilla esculenta Ker Gawler scientificName detected with duplicated value check_sci_name
#> 3 10664 Arnica monocephala Rydberg scientificName detected with duplicated value check_sci_name
#> 4 10665 Arnica pedunculata Rydberg scientificName detected with duplicated value check_sci_name
#> 5 16398 Trifolium tridentatum Lindley scientificName detected with duplicated value check_sci_name
#> 6 17569 Oenothera angustissima R.R. Gates scientificName detected with duplicated value check_sci_name
#> 7 20099 Spiraea discolor Pursh scientificName detected with duplicated value check_sci_name
#> 8 21522 Viola discurrens Greene scientificName detected with duplicated value check_sci_name
#> 9 21946 Cogswellia simplex (Nuttall ex S. Watson) M.E. Jones scientificName detected with duplicated value check_sci_name
#> 10 24660 Scilla esculenta Ker Gawler scientificName detected with duplicated value check_sci_name
#> # ℹ 17 more rowsThe summary lists one taxonID per row. Let’s count these
to get a higher-level view of what’s going on.
validation_res %>%
count(check, error)
#> # A tibble: 2 × 3
#> check error n
#> <chr> <glue> <int>
#> 1 check_col_names Invalid column names detected: id 1
#> 2 check_sci_name scientificName detected with duplicated value 26We see there are 2 kinds of errors. There is 1 column with an invalid
name (id) and 26 rows with duplicated scientific names.
Let’s take a closer look at some of those duplicated names.
dup_names <-
validation_res %>%
filter(grepl("scientificName detected with duplicated value", error)) %>%
arrange(scientificName)
dup_names
#> # A tibble: 26 × 4
#> taxonID scientificName error check
#> <dbl> <chr> <glue> <chr>
#> 1 29934 Aralia triphylla Poiret scientificName detected with duplicated value check_sci_name
#> 2 31463 Aralia triphylla Poiret scientificName detected with duplicated value check_sci_name
#> 3 10664 Arnica monocephala Rydberg scientificName detected with duplicated value check_sci_name
#> 4 25705 Arnica monocephala Rydberg scientificName detected with duplicated value check_sci_name
#> 5 10665 Arnica pedunculata Rydberg scientificName detected with duplicated value check_sci_name
#> 6 25704 Arnica pedunculata Rydberg scientificName detected with duplicated value check_sci_name
#> 7 21946 Cogswellia simplex (Nuttall ex S. Watson) M.E. Jones scientificName detected with duplicated value check_sci_name
#> 8 32013 Cogswellia simplex (Nuttall ex S. Watson) M.E. Jones scientificName detected with duplicated value check_sci_name
#> 9 25350 Ginseng trifolium (Linnaeus) Alph. Wood scientificName detected with duplicated value check_sci_name
#> 10 31464 Ginseng trifolium (Linnaeus) Alph. Wood scientificName detected with duplicated value check_sci_name
#> # ℹ 16 more rowsWe can join back to the original data to investigate these names.
inner_join(
select(dup_names, taxonID),
vascan,
by = "taxonID"
) %>%
# Just look at the first 6 columns
select(1:6)
#> # A tibble: 26 × 6
#> taxonID id acceptedNameUsageID parentNameUsageID nameAccordingToID scientificName
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 29934 29934 2695 NA <NA> Aralia triphylla P…
#> 2 31463 31463 2695 NA <NA> Aralia triphylla P…
#> 3 10664 10664 2857 NA http://www.efloras.org/volume_page.aspx?volume_id=1021&flora_id=1 Arnica monocephala…
#> 4 25705 25705 2857 NA http://www.botanicus.org/item/31753003488092 Arnica monocephala…
#> 5 10665 10665 2857 NA http://www.efloras.org/volume_page.aspx?volume_id=1021&flora_id=1 Arnica pedunculata…
#> 6 25704 25704 2857 NA http://www.botanicus.org/item/31753003488092 Arnica pedunculata…
#> 7 21946 21946 2613 NA <NA> Cogswellia simplex…
#> 8 32013 32013 2613 NA http://www.tropicos.org Cogswellia simplex…
#> 9 25350 25350 2695 NA <NA> Ginseng trifolium …
#> 10 31464 31464 2695 NA <NA> Ginseng trifolium …
#> # ℹ 16 more rowsWe see that in some cases, multiple entries for the exact same
scientific name (for example, Arnica monocephala Rydberg)
differ only in the value for nameAccordingToID.
So this seems like something the database manager should fix.
Let’s see what is in the id column.
vascan %>%
select(id)
#> # A tibble: 32,770 × 1
#> id
#> <dbl>
#> 1 73
#> 2 26
#> 3 25
#> 4 128
#> 5 1142
#> 6 2004
#> 7 5467
#> 8 5466
#> 9 5472
#> 10 5471
#> # ℹ 32,760 more rows
n_distinct(vascan$id)
#> [1] 32770id contains numbers that are all unique. In other words,
these appear to be unique key values to each row in the dataset (as one
would expect from the name id).
It is probably the case that this dataset has a good reason for using
the id column, even though it is not a standard DwC
column.
Let’s see if we can get this dataset to pass validation.
First, let’s remove the duplicated names. This is something that should be done with more thought, but here let’s just keep the first name of each pair.
vascan_fixed <-
vascan %>%
filter(!duplicated(scientificName))Next, we will run validation again, but this time allow
id as an extra column.
dct_validate(
vascan_fixed,
extra_cols = "id"
)
#> # A tibble: 32,757 × 24
#> id taxonID acceptedNameUsageID parentNameUsageID nameAccordingToID scientificName acceptedNameUsage parentNameUsage nameAccordingTo
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 73 73 73 NA http://dx.doi.org/1… Equisetopsida… Equisetopsida C.… <NA> Chase, M.W. & …
#> 2 26 26 26 73 http://dx.doi.org/1… Equisetidae W… Equisetidae Warm… Equisetopsida … Chase, M.W. & …
#> 3 25 25 25 26 http://www.jstor.or… Equisetales d… Equisetales de C… Equisetidae Wa… Smith, A.R., K…
#> 4 128 128 128 25 http://www.jstor.or… Equisetaceae … Equisetaceae Mic… Equisetales de… Smith, A.R., K…
#> 5 1142 1142 1142 128 http://www.efloras.… Equisetum Lin… Equisetum Linnae… Equisetaceae M… FNA Editorial …
#> 6 2004 2004 2004 1142 http://www.efloras.… Equisetum sub… Equisetum subg. … Equisetum Linn… FNA Editorial …
#> 7 5467 5467 5467 2004 http://www.efloras.… Equisetum flu… Equisetum fluvia… Equisetum subg… FNA Editorial …
#> 8 5466 5466 5466 2004 http://www.efloras.… Equisetum arv… Equisetum arvens… Equisetum subg… FNA Editorial …
#> 9 5472 5472 5472 2004 http://www.efloras.… Equisetum pra… Equisetum praten… Equisetum subg… FNA Editorial …
#> 10 5471 5471 5471 2004 http://www.efloras.… Equisetum pal… Equisetum palust… Equisetum subg… FNA Editorial …
#> # ℹ 32,747 more rows
#> # ℹ 15 more variables: higherClassification <chr>, class <chr>, order <chr>, family <chr>, genus <chr>, subgenus <chr>,
#> # specificEpithet <chr>, infraspecificEpithet <chr>, taxonRank <chr>, scientificNameAuthorship <chr>, taxonomicStatus <chr>,
#> # modified <chr>, license <chr>, bibliographicCitation <chr>, references <chr>It passes, so we have now confirmed that the only steps needed to
obtain correctly formatted DwC data are to de-duplicate the species
names and account for the id column.
This vignette shows how dwctaxon can be used on DwC data to find possible problems in a taxonomic dataset. We were able to identify several rows with duplicated scientific names and one column that does not follow DwC standards. Other than that, it passes validation, giving us confidence that this dataset can be used for downstream taxonomic analyses.
If you download the data manually, it may be a different version than the one used here, v37.12↩︎