Removes or flags records within a certain radius around country capitals. Poorly geo-referenced occurrence records in biological databases are often erroneously geo-referenced to capitals.
Usage
cc_cap(
x,
lon = "decimallongitude",
lat = "decimallatitude",
species = "species",
buffer = 10000,
geod = TRUE,
ref = NULL,
verify = FALSE,
value = "clean",
verbose = TRUE
)Arguments
- x
data.frame. Containing geographical coordinates and species names.
- lon
character string. The column with the longitude coordinates. Default = “decimallongitude”.
- lat
character string. The column with the latitude coordinates. Default = “decimallatitude”.
- species
character string. The column with the species identity. Only required if verify = TRUE.
- buffer
The buffer around each capital coordinate (the centre of the city), where records should be flagged as problematic. Units depend on geod. Default = 10 kilometres.
- geod
logical. If TRUE the radius around each capital is calculated based on a sphere, buffer is in meters and independent of latitude. If FALSE the radius is calculated assuming planar coordinates and varies slightly with latitude, in this case buffer is in degrees. Default = TRUE. See https://seethedatablog.wordpress.com/ for detail and credits.
- ref
SpatialPointsDataFrame. Providing the geographic gazetteer. Can be any SpatialPointsDataFrame, but the structure must be identical to
countryref. Default =countryref.- verify
logical. If TRUE records are only flagged if they are the only record in a given species flagged close to a given reference. If FALSE, the distance is the only criterion
- value
character string. Defining the output value. See value.
- verbose
logical. If TRUE reports the name of the test and the number of records flagged.
Value
Depending on the ‘value’ argument, either a data.frame
containing the records considered correct by the test (“clean”) or a logical vector (“flagged”), with TRUE = test passed and FALSE = test failed/potentially problematic . Default = “clean”.
Note
See https://ropensci.github.io/CoordinateCleaner/ for more details and tutorials.
Examples
x <- data.frame(species = letters[1:10],
decimallongitude = runif(100, -180, 180),
decimallatitude = runif(100, -90,90))
cc_cap(x)
#> Testing country capitals
#> Removed 0 records.
#> species decimallongitude decimallatitude
#> 1 a 130.598152 -53.5625313
#> 2 b -7.029613 -88.8441041
#> 3 c 40.306636 45.7979496
#> 4 d 177.087661 58.2112314
#> 5 e 27.017149 -48.1496593
#> 6 f 177.036960 46.0878979
#> 7 g -83.278309 -43.1236190
#> 8 h 69.613065 -75.0306081
#> 9 i 141.876266 -82.3772927
#> 10 j -116.733658 68.3997894
#> 11 a 14.705996 -10.7627747
#> 12 b -132.192692 38.5557331
#> 13 c -59.234198 67.2892694
#> 14 d -100.775744 -89.7308427
#> 15 e 153.880183 -38.6406224
#> 16 f 74.760758 42.9598550
#> 17 g -81.448348 63.4115978
#> 18 h 20.011729 87.8391584
#> 19 i -75.741761 -34.2478881
#> 20 j 112.990750 52.8829687
#> 21 a 81.805461 -85.9516089
#> 22 b -35.157623 -56.2446269
#> 23 c 132.744031 42.3945971
#> 24 d -39.920522 -22.9097037
#> 25 e 70.551068 51.5402860
#> 26 f -84.833681 56.6617511
#> 27 g 96.907936 -82.6038318
#> 28 h -41.592077 18.6247396
#> 29 i -77.489299 -39.1263494
#> 30 j -119.905618 -46.2926296
#> 31 a -105.523912 -9.9072769
#> 32 b -127.148990 20.2487969
#> 33 c 48.240420 11.9546451
#> 34 d 10.846654 28.4632870
#> 35 e -114.783553 55.5180408
#> 36 f -39.226539 14.6253024
#> 37 g 85.701585 34.0953174
#> 38 h -134.589907 -34.3421685
#> 39 i 146.033785 -10.9893034
#> 40 j -135.149861 20.9706037
#> 41 a 166.378801 -27.3879876
#> 42 b 102.545615 6.3709695
#> 43 c 41.600750 65.2677749
#> 44 d -165.439328 78.5859378
#> 45 e 153.470107 13.0890271
#> 46 f 41.270826 -26.6374363
#> 47 g 98.618769 -12.4844048
#> 48 h -129.137593 -7.7895008
#> 49 i 99.311564 -19.0026846
#> 50 j 153.704068 -41.9324982
#> 51 a -76.067514 -6.1317321
#> 52 b 171.851992 -28.9861717
#> 53 c 35.709439 16.8744371
#> 54 d 46.164498 -68.1182139
#> 55 e -68.084173 -89.4538394
#> 56 f -96.774031 -7.6440439
#> 57 g 151.508670 56.2716765
#> 58 h 161.745642 15.0282230
#> 59 i 176.523619 59.6024311
#> 60 j 36.383901 87.2565670
#> 61 a -25.803044 69.5313806
#> 62 b 34.127613 -50.1576536
#> 63 c -1.641263 -56.9921824
#> 64 d 89.603812 63.4444321
#> 65 e 113.985291 -0.0722344
#> 66 f 47.942680 85.6516235
#> 67 g -75.358567 74.3506676
#> 68 h -23.693901 31.7516960
#> 69 i -160.451776 63.7323145
#> 70 j -45.138146 56.4882394
#> 71 a -36.519212 42.1955911
#> 72 b 2.981604 58.7121455
#> 73 c 166.913033 -8.5266048
#> 74 d -129.995662 -27.7428102
#> 75 e -103.438977 -80.7760740
#> 76 f 18.328308 -70.9442356
#> 77 g -169.136250 29.1842748
#> 78 h -70.857180 36.8828364
#> 79 i 175.437445 25.2791784
#> 80 j -69.264349 41.5779763
#> 81 a -147.490866 0.4426015
#> 82 b 80.075221 25.2258545
#> 83 c 115.176848 7.4257117
#> 84 d -150.493627 -46.6059027
#> 85 e -121.698279 -75.0056638
#> 86 f -11.504510 0.1902900
#> 87 g 123.118028 -26.8328663
#> 88 h 93.461048 -25.9695779
#> 89 i 143.833574 62.6536190
#> 90 j 58.527603 77.0283763
#> 91 a 120.786684 -18.5805093
#> 92 b -24.436508 6.0610409
#> 93 c 161.950213 -1.5611306
#> 94 d -36.867876 40.1193254
#> 95 e 109.242497 10.6642354
#> 96 f 164.430161 -84.6113214
#> 97 g -37.966638 73.9046077
#> 98 h -159.265873 -2.7712655
#> 99 i -149.539700 15.7999622
#> 100 j -53.513992 84.0743730
cc_cap(x, value = "flagged")
#> Testing country capitals
#> Flagged 0 records.
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 97 98 99 100
#> TRUE TRUE TRUE TRUE