Clean Biological Occurrence Records

Clean using the following use cases (checkmarks indicate fxns exist - not necessarily complete):

  • [x] Impossible lat/long values: e.g., latitude 75
  • [x] Incomplete cases: one or the other of lat/long missing
  • [x] Unlikely lat/long values: e.g., points at 0,0
  • [x] Deduplication: try to identify duplicates, esp. when pulling data from multiple sources, e.g., can try to use occurrence IDs, if provided
  • [x] Date based cleaning
  • [x] Outside political boundary: User input to check for points in the wrong country, or points outside of a known country
  • [x] Taxonomic name based cleaning: via taxize (one method so far)
  • Political centroids: unlikely that occurrences fall exactly on these points, more likely a default position (Draft function started, but not exported, and commented out). see issue #6
  • Herbaria/Museums: many specimens may have location of the collection they are housed in, see issue #20
  • Habitat type filtering: e.g., fish should not be on land; marine fish should not be in fresh water
  • Check for contextually wrong values: That is, if 99 out of 100 lat/long coordinates are within the continental US, but 1 is in China, then perhaps something is wrong with that one point
  • Collector/recorder names: see issue #19

A note about examples: We think that using a piping workflow with %>% makes code easier to build up, and easier to understand. However, in some examples we provide examples without the pipe to demonstrate traditional usage.

Install

Stable CRAN version

Development version

devtools::install_github("ropensci/scrubr")
library("scrubr")

Coordinate based cleaning

data("sampledata1")

Remove impossible coordinates (using sample data included in the pkg)

# coord_impossible(dframe(sample_data_1)) # w/o pipe
dframe(sample_data_1) %>% coord_impossible()
#> # A tibble: 1,500 x 5
#>    name             longitude latitude date                       key
#>  * <chr>                <dbl>    <dbl> <dttm>                   <int>
#>  1 Ursus americanus     -79.7     38.4 2015-01-14 16:36:45 1065590124
#>  2 Ursus americanus     -82.4     35.7 2015-01-13 00:25:39 1065588899
#>  3 Ursus americanus     -99.1     23.7 2015-02-20 23:00:00 1098894889
#>  4 Ursus americanus     -72.8     43.9 2015-02-13 16:16:41 1065611122
#>  5 Ursus americanus     -72.3     43.9 2015-03-01 20:20:45 1088908315
#>  6 Ursus americanus    -109.      32.7 2015-03-29 17:06:54 1088932238
#>  7 Ursus americanus    -109.      32.7 2015-03-29 17:12:50 1088932273
#>  8 Ursus americanus    -124.      40.1 2015-03-28 23:00:00 1132403409
#>  9 Ursus americanus     -78.3     36.9 2015-03-20 21:11:24 1088923534
#> 10 Ursus americanus     -76.8     35.5 2015-04-05 23:00:00 1088954559
#> # … with 1,490 more rows

Remove incomplete coordinates

# coord_incomplete(dframe(sample_data_1)) # w/o pipe
dframe(sample_data_1) %>% coord_incomplete()
#> # A tibble: 1,306 x 5
#>    name             longitude latitude date                       key
#>  * <chr>                <dbl>    <dbl> <dttm>                   <int>
#>  1 Ursus americanus     -79.7     38.4 2015-01-14 16:36:45 1065590124
#>  2 Ursus americanus     -82.4     35.7 2015-01-13 00:25:39 1065588899
#>  3 Ursus americanus     -99.1     23.7 2015-02-20 23:00:00 1098894889
#>  4 Ursus americanus     -72.8     43.9 2015-02-13 16:16:41 1065611122
#>  5 Ursus americanus     -72.3     43.9 2015-03-01 20:20:45 1088908315
#>  6 Ursus americanus    -109.      32.7 2015-03-29 17:06:54 1088932238
#>  7 Ursus americanus    -109.      32.7 2015-03-29 17:12:50 1088932273
#>  8 Ursus americanus    -124.      40.1 2015-03-28 23:00:00 1132403409
#>  9 Ursus americanus     -78.3     36.9 2015-03-20 21:11:24 1088923534
#> 10 Ursus americanus     -76.8     35.5 2015-04-05 23:00:00 1088954559
#> # … with 1,296 more rows

Remove unlikely coordinates (e.g., those at 0,0)

# coord_unlikely(dframe(sample_data_1)) # w/o pipe
dframe(sample_data_1) %>% coord_unlikely()
#> # A tibble: 1,488 x 5
#>    name             longitude latitude date                       key
#>  * <chr>                <dbl>    <dbl> <dttm>                   <int>
#>  1 Ursus americanus     -79.7     38.4 2015-01-14 16:36:45 1065590124
#>  2 Ursus americanus     -82.4     35.7 2015-01-13 00:25:39 1065588899
#>  3 Ursus americanus     -99.1     23.7 2015-02-20 23:00:00 1098894889
#>  4 Ursus americanus     -72.8     43.9 2015-02-13 16:16:41 1065611122
#>  5 Ursus americanus     -72.3     43.9 2015-03-01 20:20:45 1088908315
#>  6 Ursus americanus    -109.      32.7 2015-03-29 17:06:54 1088932238
#>  7 Ursus americanus    -109.      32.7 2015-03-29 17:12:50 1088932273
#>  8 Ursus americanus    -124.      40.1 2015-03-28 23:00:00 1132403409
#>  9 Ursus americanus     -78.3     36.9 2015-03-20 21:11:24 1088923534
#> 10 Ursus americanus     -76.8     35.5 2015-04-05 23:00:00 1088954559
#> # … with 1,478 more rows

Do all three

dframe(sample_data_1) %>%
  coord_impossible() %>%
  coord_incomplete() %>%
  coord_unlikely()
#> # A tibble: 1,294 x 5
#>    name             longitude latitude date                       key
#>  * <chr>                <dbl>    <dbl> <dttm>                   <int>
#>  1 Ursus americanus     -79.7     38.4 2015-01-14 16:36:45 1065590124
#>  2 Ursus americanus     -82.4     35.7 2015-01-13 00:25:39 1065588899
#>  3 Ursus americanus     -99.1     23.7 2015-02-20 23:00:00 1098894889
#>  4 Ursus americanus     -72.8     43.9 2015-02-13 16:16:41 1065611122
#>  5 Ursus americanus     -72.3     43.9 2015-03-01 20:20:45 1088908315
#>  6 Ursus americanus    -109.      32.7 2015-03-29 17:06:54 1088932238
#>  7 Ursus americanus    -109.      32.7 2015-03-29 17:12:50 1088932273
#>  8 Ursus americanus    -124.      40.1 2015-03-28 23:00:00 1132403409
#>  9 Ursus americanus     -78.3     36.9 2015-03-20 21:11:24 1088923534
#> 10 Ursus americanus     -76.8     35.5 2015-04-05 23:00:00 1088954559
#> # … with 1,284 more rows

Don’t drop bad data

dframe(sample_data_1) %>% coord_incomplete(drop = TRUE) %>% NROW
#> [1] 1306
dframe(sample_data_1) %>% coord_incomplete(drop = FALSE) %>% NROW
#> [1] 1500

Deduplicate

smalldf <- sample_data_1[1:20, ]
# create a duplicate record
smalldf <- rbind(smalldf, smalldf[10,])
row.names(smalldf) <- NULL
# make it slightly different
smalldf[21, "key"] <- 1088954555
NROW(smalldf)
#> [1] 21
dp <- dframe(smalldf) %>% dedup()
NROW(dp)
#> [1] 20
attr(dp, "dups")
#> # A tibble: 1 x 5
#>   name             longitude latitude date                       key
#>   <chr>                <dbl>    <dbl> <dttm>                   <dbl>
#> 1 Ursus americanus     -76.8     35.5 2015-04-05 23:00:00 1088954555

Dates

Standardize/convert dates

df <- sample_data_1
# date_standardize(dframe(df), "%d%b%Y") # w/o pipe
dframe(df) %>% date_standardize("%d%b%Y")
#> # A tibble: 1,500 x 5
#>    name             longitude latitude date             key
#>    <chr>                <dbl>    <dbl> <chr>          <int>
#>  1 Ursus americanus     -79.7     38.4 14Jan2015 1065590124
#>  2 Ursus americanus     -82.4     35.7 13Jan2015 1065588899
#>  3 Ursus americanus     -99.1     23.7 20Feb2015 1098894889
#>  4 Ursus americanus     -72.8     43.9 13Feb2015 1065611122
#>  5 Ursus americanus     -72.3     43.9 01Mar2015 1088908315
#>  6 Ursus americanus    -109.      32.7 29Mar2015 1088932238
#>  7 Ursus americanus    -109.      32.7 29Mar2015 1088932273
#>  8 Ursus americanus    -124.      40.1 28Mar2015 1132403409
#>  9 Ursus americanus     -78.3     36.9 20Mar2015 1088923534
#> 10 Ursus americanus     -76.8     35.5 05Apr2015 1088954559
#> # … with 1,490 more rows

Drop records without dates

NROW(df)
#> [1] 1500
NROW(dframe(df) %>% date_missing())
#> [1] 1498

Create date field from other fields

dframe(sample_data_2) %>% date_create(year, month, day)
#> # A tibble: 1,500 x 8
#>    name             longitude latitude        key year  month day   date      
#>    <chr>                <dbl>    <dbl>      <int> <chr> <chr> <chr> <chr>     
#>  1 Ursus americanus     -79.7     38.4 1065590124 2015  01    14    2015-01-14
#>  2 Ursus americanus     -82.4     35.7 1065588899 2015  01    13    2015-01-13
#>  3 Ursus americanus     -99.1     23.7 1098894889 2015  02    20    2015-02-20
#>  4 Ursus americanus     -72.8     43.9 1065611122 2015  02    13    2015-02-13
#>  5 Ursus americanus     -72.3     43.9 1088908315 2015  03    01    2015-03-01
#>  6 Ursus americanus    -109.      32.7 1088932238 2015  03    29    2015-03-29
#>  7 Ursus americanus    -109.      32.7 1088932273 2015  03    29    2015-03-29
#>  8 Ursus americanus    -124.      40.1 1132403409 2015  03    28    2015-03-28
#>  9 Ursus americanus     -78.3     36.9 1088923534 2015  03    20    2015-03-20
#> 10 Ursus americanus     -76.8     35.5 1088954559 2015  04    05    2015-04-05
#> # … with 1,490 more rows

Ecoregion

Filter by FAO areas

wkt <- 'POLYGON((72.2 38.5,-173.6 38.5,-173.6 -41.5,72.2 -41.5,72.2 38.5))'
manta_ray <- rgbif::name_backbone("Mobula alfredi")$usageKey
res <- rgbif::occ_data(manta_ray, geometry = wkt, limit=300, hasCoordinate = TRUE)
dat <- sf::st_as_sf(res$data, coords = c("decimalLongitude", "decimalLatitude"))
dat <- sf::st_set_crs(dat, 4326)
mapview::mapview(dat)
tmp <- eco_region(dframe(res$data), dataset = "fao", region = "OCEAN:Indian")
tmp <- tmp[!is.na(tmp$decimalLongitude), ]
tmp2 <- sf::st_as_sf(tmp, coords = c("decimalLongitude", "decimalLatitude"))
tmp2 <- sf::st_set_crs(tmp2, 4326)
mapview::mapview(tmp2)

Meta