This function computes Euclidean distances (km) between all combinations of receivers.
dist_btw_receivers(moorings, f = NULL, return = c("data.frame", "matrix"))A dataframe that defines each unique receiver deployment. This should contain the columns: `receiver_id', a unique identifier of each receiver; `receiver_long' or `receiver_easting', the longitude or easting of that receiver; and `receiver_lat' or `receiver_northing', the latitude or northing that receiver (see dat_moorings).
(optional) A function to process distances. For example, round distances to the nearest km with f = function(x) round(x, digits = 0) or simply round.
A character that defines the class of the returned object ("data.frame" or "matrix").
The function returns a dataframe, with columns `r1', `r2' and `dist', or a matrix. Dataframe columns define the IDs of each combination of receivers and the associated distance between them, in km (or map units/1000). Note that the dataframe contains duplicate combinations of receivers (e.g., both r1 = 1 and r2 = 2 and r1 = 2 and r2 = 1). Alternatively, matrix rows and columns define receiver IDs and cell values give distances between each combination.
Distances are calculated via pointDistance. If moorings contains `receiver_long' and `receiver_lat', pointDistance is implemented with lonlat = TRUE and distances are in km; otherwise lonlat = FALSE and distances are in map units over 1000 (i.e., km if map units are metres).
To calculate distances between specific receiver pairs, call pointDistance directly.
#### Example (1): Implementation using lat/long coordinates
dat <- data.frame(
receiver_id = dat_moorings$receiver_id,
receiver_long = dat_moorings$receiver_long,
receiver_lat = dat_moorings$receiver_lat
)
# Compute distances
dist_btw_receivers_km <- dist_btw_receivers(dat)
head(dist_btw_receivers_km)
#> r1 r2 dist
#> 1 3 3 0.000000
#> 2 4 3 14.111344
#> 3 7 3 15.308019
#> 4 9 3 0.542825
#> 5 11 3 13.765477
#> 6 12 3 13.796536
#### Example (2): Implementation using planar coordinates
proj_wgs84 <- sp::CRS(SRS_string = "EPSG:4326")
proj_utm <- sp::CRS(SRS_string = "EPSG:32629")
xy <- sp::SpatialPoints(
dat[, c("receiver_long", "receiver_lat")],
proj_wgs84
)
xy <- sp::spTransform(xy, proj_utm)
xy <- sp::coordinates(xy)
dat <- data.frame(
receiver_id = dat_moorings$receiver_id,
receiver_easting = xy[, 1],
receiver_northing = xy[, 2]
)
head(dist_btw_receivers(dat))
#> r1 r2 dist
#> 1 3 3 0.0000000
#> 2 4 3 14.1131960
#> 3 7 3 15.3099894
#> 4 9 3 0.5428911
#> 5 11 3 13.7672055
#> 6 12 3 13.7983575
#### Example (3): Post-process distances via the f argument
dist_btw_receivers_km_round <- dist_btw_receivers(dat, f = round)
head(dist_btw_receivers_km_round)
#> r1 r2 dist
#> 1 3 3 0
#> 2 4 3 14
#> 3 7 3 15
#> 4 9 3 1
#> 5 11 3 14
#> 6 12 3 14
# convert distances to m
dist_btw_receivers_m <- dist_btw_receivers(dat, f = function(x) x * 1000)
head(dist_btw_receivers_m)
#> r1 r2 dist
#> 1 3 3 0.0000
#> 2 4 3 14113.1960
#> 3 7 3 15309.9894
#> 4 9 3 542.8911
#> 5 11 3 13767.2055
#> 6 12 3 13798.3575
#### Example (4) Return distances in a matrix
# Get distances
dist_btw_receivers(dat, return = "matrix")
#> 3 4 7 9 11 12 14
#> 3 0.0000000 14.1131960 15.309989 0.5428911 13.767205 13.7983575 12.6143165
#> 4 14.1131960 0.0000000 1.717948 13.9034731 2.215489 0.5158684 6.3031421
#> 7 15.3099894 1.7179478 0.000000 15.0580751 1.873918 2.2331738 5.8899781
#> 9 0.5428911 13.9034731 15.058075 0.0000000 13.481255 13.6037023 12.2017218
#> 11 13.7672055 2.2154890 1.873918 13.4812546 0.000000 2.5660837 4.1767712
#> 12 13.7983575 0.5158684 2.233174 13.6037023 2.566084 0.0000000 6.5371715
#> 14 12.6143165 6.3031421 5.889978 12.2017218 4.176771 6.5371715 0.0000000
#> 18 8.5441944 5.5722752 6.864123 8.3492873 5.554672 5.2557499 6.4001268
#> 20 11.5955654 7.7771471 7.562382 11.1432486 5.769735 7.9414306 1.7771052
#> 21 2.5467428 14.9105530 16.286249 3.0823472 14.924666 14.5312253 14.3819801
#> 23 11.9185942 7.2878539 7.014934 11.4790917 5.241995 7.4729726 1.1972754
#> 24 8.8755381 5.2973710 6.682056 8.7076910 5.500435 4.9442922 6.7183667
#> 26 2.9396514 14.6181256 16.028093 3.4556398 14.712752 14.2267075 14.3350204
#> 27 4.2871757 18.2836191 19.392210 4.3998996 17.762543 17.9934339 16.0658454
#> 29 15.1933708 10.1459903 9.298340 14.7071607 7.930895 10.4655416 4.2037585
#> 30 1.7195617 14.9534529 16.263879 2.2620589 14.826364 14.5973982 14.0221915
#> 31 3.3966631 14.4509808 15.893473 3.8958907 14.624906 14.0478467 14.4018070
#> 33 1.0282054 14.5084817 15.779969 1.5705932 14.304541 14.1667948 13.3872340
#> 35 9.5241889 4.5906708 5.852923 9.3134393 4.562023 4.2986239 5.8062375
#> 36 3.1433774 12.6684020 14.089550 3.4695071 12.804383 12.2751456 12.6410027
#> 37 2.2446477 16.1831886 17.440281 2.6739513 15.941962 15.8444418 14.8577172
#> 38 8.0904421 6.0738849 7.220449 7.8505363 5.744775 5.8104488 5.9785146
#> 39 11.2689782 5.0910638 5.222467 10.9058152 3.348552 5.1924047 2.0209296
#> 40 14.4707081 0.5574851 1.161366 14.2457623 1.927402 1.0733398 6.0946835
#> 41 12.8648683 6.2954818 5.814749 12.4525123 4.141283 6.5472923 0.2508392
#> 43 1.9040241 14.9806280 16.305051 2.4469151 14.882729 14.6195669 14.1305212
#> 44 13.1823564 1.0990698 2.786249 12.9950847 2.752657 0.6425807 6.4783178
#> 45 2.7069880 14.2293917 15.629983 3.2010389 14.304739 13.8417960 13.9169172
#> 46 12.2513617 7.5334929 7.177464 11.8053208 5.444070 7.7391213 1.2892746
#> 47 12.7982019 6.8895828 6.414011 12.3695947 4.742132 7.1334403 0.6090173
#> 48 0.7690849 14.2203232 15.478670 1.2975298 13.991965 13.8836922 13.0490347
#> 49 0.3065824 13.8679112 15.049961 0.3158502 13.495968 13.5586419 12.3124712
#> 50 7.2044442 6.9162041 8.123945 6.9873150 6.680230 6.6220023 6.7749247
#> 51 7.0484015 7.1834570 8.275751 6.7829129 6.723945 6.9309852 6.3811872
#> 52 12.5533132 7.2741439 6.852281 12.1142782 5.151727 7.4999671 0.9751503
#> 53 0.6743504 13.7016843 14.853815 0.2051285 13.276169 13.4031439 12.0053568
#> 54 11.9577584 7.7413732 7.447368 11.5062344 5.686416 7.9281017 1.5845003
#> 55 3.9842618 14.3738782 15.853354 4.4706648 14.642115 13.9571616 14.5946810
#> 56 3.2286348 14.4306359 15.862762 3.7290974 14.579379 14.0313218 14.3118579
#> 57 2.2978079 14.9207937 16.277994 2.8369614 14.893806 14.5480972 14.2752829
#> 18 20 21 23 24 26 27
#> 3 8.5441944 11.5955654 2.5467428 11.9185942 8.8755381 2.9396514 4.287176
#> 4 5.5722752 7.7771471 14.9105530 7.2878539 5.2973710 14.6181256 18.283619
#> 7 6.8641227 7.5623816 16.2862487 7.0149340 6.6820561 16.0280934 19.392210
#> 9 8.3492873 11.1432486 3.0823472 11.4790917 8.7076910 3.4556398 4.399900
#> 11 5.5546723 5.7697354 14.9246663 5.2419948 5.5004348 14.7127515 17.762543
#> 12 5.2557499 7.9414306 14.5312253 7.4729726 4.9442922 14.2267075 17.993434
#> 14 6.4001268 1.7771052 14.3819801 1.1972754 6.7183667 14.3350204 16.065845
#> 18 0.0000000 6.6343207 9.4252455 6.5176997 0.5715894 9.1828106 12.742700
#> 20 6.6343207 0.0000000 13.5652507 0.5800208 7.0648374 13.5861682 14.779500
#> 21 9.4252455 13.5652507 0.0000000 13.8229079 9.6226357 0.5941759 5.058317
#> 23 6.5176997 0.5800208 13.8229079 0.0000000 6.9154412 13.8210510 15.196923
#> 24 0.5715894 7.0648374 9.6226357 6.9154412 0.0000000 9.3493275 13.107267
#> 26 9.1828106 13.5861682 0.5941759 13.8210510 9.3493275 0.0000000 5.650887
#> 27 12.7427001 14.7794998 5.0583170 15.1969235 13.1072672 5.6508874 0.000000
#> 29 10.4295924 3.9336823 17.3115575 3.9244103 10.8069138 17.3760037 17.967781
#> 30 9.4072217 13.1086707 0.9505920 13.3982991 9.6570826 1.4959053 4.305071
#> 31 9.0767872 13.7144767 1.1293533 13.9289265 9.2118566 0.5389791 6.176484
#> 33 8.9433235 12.4433081 1.5324423 12.7422442 9.2226415 1.9765695 4.313521
#> 35 1.0121331 6.2883898 10.4373786 6.0899556 0.9912009 10.1933948 13.697791
#> 36 7.2594392 12.0265374 2.3160579 12.2135832 7.4079678 1.9521826 6.959990
#> 37 10.6149380 13.8171672 2.1234042 14.1506146 10.9006918 2.7168827 2.935873
#> 38 0.8820582 6.0122167 9.1876015 5.9553039 1.4536411 8.9966650 12.215710
#> 39 4.3990180 2.8356371 12.8242454 2.4707686 4.7001861 12.7227312 14.997975
#> 40 5.9498253 7.6404103 15.3330973 7.1299387 5.7105335 15.0530556 18.612736
#> 41 6.5918782 1.9512598 14.6286311 1.3736142 6.8979845 14.5795987 16.314584
#> 43 9.4441668 13.2353242 0.7548355 13.5189459 9.6832788 1.3133578 4.428784
#> 44 4.6460224 7.7807680 13.8926678 7.3419109 4.3174804 13.5859672 17.388521
#> 45 8.7798392 13.1740229 0.7670158 13.4062712 8.9539771 0.4189289 5.744716
#> 46 6.9261733 0.7015680 14.1840752 0.4117529 7.3200967 14.1902813 15.473132
#> 47 6.9041463 1.5085177 14.6408855 0.9735764 7.2446345 14.6162630 16.140995
#> 48 8.6514109 12.1032117 1.7856984 12.4022581 8.9408582 2.1714875 4.470249
#> 49 8.3030154 11.2894320 2.7882141 11.6134124 8.6448837 3.1475690 4.483335
#> 50 1.3942939 6.6379968 8.2476938 6.6430539 1.8618772 8.0550068 11.371998
#> 51 1.8845501 6.0960933 8.3040385 6.1463774 2.4219760 8.1611042 11.125992
#> 52 6.9699872 1.0898436 14.4489966 0.6352526 7.3396367 14.4420664 15.824035
#> 53 8.1492723 10.9557113 3.1695348 11.2882094 8.5103522 3.5153974 4.596943
#> 54 6.8626245 0.3638950 13.9200456 0.4552630 7.2771183 13.9371404 15.140777
#> 55 9.0875228 13.9749019 1.7400480 14.1671548 9.1845057 1.1576856 6.761111
#> 56 9.0356434 13.6084948 0.9855479 13.8281547 9.1807226 0.3913964 6.041726
#> 57 9.4140802 13.4304238 0.2768433 13.6973548 9.6269808 0.8471860 4.829450
#> 29 30 31 33 35 36 37
#> 3 15.193371 1.7195617 3.3966631 1.0282054 9.5241889 3.143377 2.244648
#> 4 10.145990 14.9534529 14.4509808 14.5084817 4.5906708 12.668402 16.183189
#> 7 9.298340 16.2638786 15.8934728 15.7799692 5.8529228 14.089550 17.440281
#> 9 14.707161 2.2620589 3.8958907 1.5705932 9.3134393 3.469507 2.673951
#> 11 7.930895 14.8263639 14.6249061 14.3045405 4.5620228 12.804383 15.941962
#> 12 10.465542 14.5973982 14.0478467 14.1667948 4.2986239 12.275146 15.844442
#> 14 4.203759 14.0221915 14.4018070 13.3872340 5.8062375 12.641003 14.857717
#> 18 10.429592 9.4072217 9.0767872 8.9433235 1.0121331 7.259439 10.614938
#> 20 3.933682 13.1086707 13.7144767 12.4433081 6.2883898 12.026537 13.817167
#> 21 17.311558 0.9505920 1.1293533 1.5324423 10.4373786 2.316058 2.123404
#> 23 3.924410 13.3982991 13.9289265 12.7422442 6.0899556 12.213583 14.150615
#> 24 10.806914 9.6570826 9.2118566 9.2226415 0.9912009 7.407968 10.900692
#> 26 17.376004 1.4959053 0.5389791 1.9765695 10.1933948 1.952183 2.716883
#> 27 17.967781 4.3050710 6.1764841 4.3135212 13.6977907 6.959990 2.935873
#> 29 0.000000 16.7848390 17.5376097 16.1003293 9.9417673 15.891320 17.363549
#> 30 16.784839 0.0000000 2.0300421 0.7032578 10.4146972 2.709543 1.421747
#> 31 17.537610 2.0300421 0.0000000 2.4718836 10.0827789 1.822556 3.246255
#> 33 16.100329 0.7032578 2.4718836 0.0000000 9.9440344 2.669906 1.680246
#> 35 9.941767 10.4146972 10.0827789 9.9440344 0.0000000 8.267202 11.612774
#> 36 15.891320 2.7095430 1.8225556 2.6699059 8.2672016 0.000000 4.130797
#> 37 17.363549 1.4217469 3.2462551 1.6802459 11.6127738 4.130797 0.000000
#> 38 9.879638 9.0840532 8.9417191 8.5744293 1.5365369 7.120138 10.226060
#> 39 6.180323 12.5538927 12.7424016 11.9554799 3.7858384 10.946645 13.505982
#> 40 9.824117 15.3523494 14.8978024 14.8930648 4.9516918 13.106402 16.563636
#> 41 4.092524 14.2711807 14.6441626 13.6368298 5.9751782 12.881003 15.108330
#> 43 16.925416 0.2011203 1.8505701 0.8789086 10.4533741 2.640074 1.514974
#> 44 10.511112 13.9650314 13.4057523 13.5394528 3.7069575 11.634237 15.217920
#> 45 16.969997 1.4710768 0.6969503 1.8134643 9.7910313 1.579640 2.815450
#> 46 3.512852 13.7466371 14.3047315 13.0864122 6.4871019 12.596928 14.478550
#> 47 3.616977 14.2470410 14.7027135 13.6001825 6.3506815 12.961258 15.036451
#> 48 15.762471 1.0293418 2.6362800 0.3401205 9.6491402 2.598061 1.963660
#> 49 14.887480 1.9970330 3.5828216 1.2965528 9.2775164 3.175786 2.545294
#> 50 10.552456 8.1560280 8.0021918 7.6573751 2.3263660 6.181224 9.316399
#> 51 10.026748 8.1298756 8.1553625 7.5881421 2.6630612 6.350732 9.218546
#> 52 3.467953 14.0297916 14.5445259 13.3749368 6.4762760 12.821446 14.785696
#> 53 14.531985 2.3824941 3.9383013 1.6824974 9.1119312 3.413680 2.862558
#> 54 3.634449 13.4677830 14.0615544 12.8034047 6.4737130 12.367432 14.180120
#> 55 17.828601 2.6517570 0.6220397 3.0832014 10.0841571 1.955337 3.840910
#> 56 17.423824 1.8699919 0.1687610 2.3034980 10.0433981 1.776227 3.108141
#> 57 17.157707 0.6737893 1.3861601 1.2751459 10.4258187 2.402373 1.894413
#> 38 39 40 41 43 44 45
#> 3 8.0904421 11.268978 14.4707081 12.8648683 1.9040241 13.1823564 2.7069880
#> 4 6.0738849 5.091064 0.5574851 6.2954818 14.9806280 1.0990698 14.2293917
#> 7 7.2204494 5.222467 1.1613657 5.8147493 16.3050509 2.7862494 15.6299827
#> 9 7.8505363 10.905815 14.2457623 12.4525123 2.4469151 12.9950847 3.2010389
#> 11 5.7447752 3.348552 1.9274016 4.1412832 14.8827293 2.7526573 14.3047395
#> 12 5.8104488 5.192405 1.0733398 6.5472923 14.6195669 0.6425807 13.8417960
#> 14 5.9785146 2.020930 6.0946835 0.2508392 14.1305212 6.4783178 13.9169172
#> 18 0.8820582 4.399018 5.9498253 6.5918782 9.4441668 4.6460224 8.7798392
#> 20 6.0122167 2.835637 7.6404103 1.9512598 13.2353242 7.7807680 13.1740229
#> 21 9.1876015 12.824245 15.3330973 14.6286311 0.7548355 13.8926678 0.7670158
#> 23 5.9553039 2.470769 7.1299387 1.3736142 13.5189459 7.3419109 13.4062712
#> 24 1.4536411 4.700186 5.7105335 6.8979845 9.6832788 4.3174804 8.9539771
#> 26 8.9966650 12.722731 15.0530556 14.5795987 1.3133578 13.5859672 0.4189289
#> 27 12.2157099 14.997975 18.6127356 16.3145838 4.4287844 17.3885212 5.7447163
#> 29 9.8796383 6.180323 9.8241166 4.0925235 16.9254162 10.5111115 16.9699971
#> 30 9.0840532 12.553893 15.3523494 14.2711807 0.2011203 13.9650314 1.4710768
#> 31 8.9417191 12.742402 14.8978024 14.6441626 1.8505701 13.4057523 0.6969503
#> 33 8.5744293 11.955480 14.8930648 13.6368298 0.8789086 13.5394528 1.8134643
#> 35 1.5365369 3.785838 4.9516918 5.9751782 10.4533741 3.7069575 9.7910313
#> 36 7.1201383 10.946645 13.1064025 12.8810027 2.6400742 11.6342373 1.5796396
#> 37 10.2260596 13.505982 16.5636361 15.1083296 1.5149736 15.2179201 2.8154499
#> 38 0.0000000 4.045125 6.3977465 6.1888126 9.1384154 5.2318757 8.5844977
#> 39 4.0451247 0.000000 5.0445940 2.1981565 12.6441190 4.9772072 12.3039767
#> 40 6.3977465 5.044594 0.0000000 6.0662961 15.3845979 1.6339604 14.6606891
#> 41 6.1888126 2.198157 6.0662961 0.0000000 14.3791296 6.5130004 14.1613955
#> 43 9.1384154 12.644119 15.3845979 14.3791296 0.0000000 13.9856650 1.3217280
#> 44 5.2318757 4.977207 1.6339604 6.5130004 13.9856650 0.0000000 13.2016915
#> 45 8.5844977 12.303977 14.6606891 14.1613955 1.3217280 13.2016915 0.0000000
#> 46 6.3669712 2.827465 7.3509968 1.4024796 13.8698358 7.6334018 13.7763107
#> 47 6.4384778 2.570601 6.6662619 0.6021222 14.3620266 7.0836007 14.1994540
#> 48 8.2676077 11.623625 14.5999794 13.2986976 1.1915451 13.2583384 1.9525515
#> 49 7.8329277 10.976505 14.2199538 12.5630720 2.1754080 12.9453429 2.8873841
#> 50 0.9416665 4.898327 7.2662898 6.9950501 8.2070347 6.0220304 7.6428421
#> 51 1.1269773 4.601098 7.4913654 6.6112916 8.1977942 6.3568247 7.7438610
#> 52 6.4530201 2.740853 7.0697301 1.0424705 14.1495871 7.4228912 14.0267357
#> 53 7.6472156 10.703238 14.0429119 12.2561204 2.5613772 12.7952271 3.2415055
#> 54 6.2674672 2.908752 7.5794275 1.7256446 13.5937224 7.7964032 13.5244679
#> 55 9.0126567 12.886215 14.8343750 14.8342197 2.4709765 13.3145928 1.2793841
#> 56 8.8849309 12.665066 14.8736246 14.5548406 1.6934175 13.3895816 0.5335230
#> 57 9.1513335 12.742597 15.3366129 14.5227027 0.4783430 13.9110553 0.9306502
#> 46 47 48 49 50 51 52
#> 3 12.2513617 12.7982019 0.7690849 0.3065824 7.2044442 7.048402 12.5533132
#> 4 7.5334929 6.8895828 14.2203232 13.8679112 6.9162041 7.183457 7.2741439
#> 7 7.1774636 6.4140106 15.4786701 15.0499610 8.1239447 8.275751 6.8522809
#> 9 11.8053208 12.3695947 1.2975298 0.3158502 6.9873150 6.782913 12.1142782
#> 11 5.4440703 4.7421318 13.9919654 13.4959679 6.6802300 6.723945 5.1517273
#> 12 7.7391213 7.1334403 13.8836922 13.5586419 6.6220023 6.930985 7.4999671
#> 14 1.2892746 0.6090173 13.0490347 12.3124712 6.7749247 6.381187 0.9751503
#> 18 6.9261733 6.9041463 8.6514109 8.3030154 1.3942939 1.884550 6.9699872
#> 20 0.7015680 1.5085177 12.1032117 11.2894320 6.6379968 6.096093 1.0898436
#> 21 14.1840752 14.6408855 1.7856984 2.7882141 8.2476938 8.304039 14.4489966
#> 23 0.4117529 0.9735764 12.4022581 11.6134124 6.6430539 6.146377 0.6352526
#> 24 7.3200967 7.2446345 8.9408582 8.6448837 1.8618772 2.421976 7.3396367
#> 26 14.1902813 14.6162630 2.1714875 3.1475690 8.0550068 8.161104 14.4420664
#> 27 15.4731320 16.1409945 4.4702486 4.4833348 11.3719983 11.125992 15.8240349
#> 29 3.5128522 3.6169771 15.7624708 14.8874798 10.5524557 10.026748 3.4679526
#> 30 13.7466371 14.2470410 1.0293418 1.9970330 8.1560280 8.129876 14.0297916
#> 31 14.3047315 14.7027135 2.6362800 3.5828216 8.0021918 8.155363 14.5445259
#> 33 13.0864122 13.6001825 0.3401205 1.2965528 7.6573751 7.588142 13.3749368
#> 35 6.4871019 6.3506815 9.6491402 9.2775164 2.3263660 2.663061 6.4762760
#> 36 12.5969278 12.9612584 2.5980607 3.1757857 6.1812239 6.350732 12.8214457
#> 37 14.4785497 15.0364506 1.9636603 2.5452941 9.3163986 9.218546 14.7856956
#> 38 6.3669712 6.4384778 8.2676077 7.8329277 0.9416665 1.126977 6.4530201
#> 39 2.8274648 2.5706015 11.6236255 10.9765045 4.8983268 4.601098 2.7408526
#> 40 7.3509968 6.6662619 14.5999794 14.2199538 7.2662898 7.491365 7.0697301
#> 41 1.4024796 0.6021222 13.2986976 12.5630720 6.9950501 6.611292 1.0424705
#> 43 13.8698358 14.3620266 1.1915451 2.1754080 8.2070347 8.197794 14.1495871
#> 44 7.6334018 7.0836007 13.2583384 12.9453429 6.0220304 6.356825 7.4228912
#> 45 13.7763107 14.1994540 1.9525515 2.8873841 7.6428421 7.743861 14.0267357
#> 46 0.0000000 0.8681827 12.7463099 11.9456474 7.0535285 6.553029 0.4085893
#> 47 0.8681827 0.0000000 13.2607473 12.4941961 7.2014549 6.768356 0.4701178
#> 48 12.7463099 13.2607473 0.0000000 1.0034010 7.3547834 7.272071 13.0349961
#> 49 11.9456474 12.4941961 1.0034010 0.0000000 6.9544696 6.781925 12.2481934
#> 50 7.0535285 7.2014549 7.3547834 6.9544696 0.0000000 0.719671 7.1760869
#> 51 6.5530289 6.7683561 7.2720714 6.7819253 0.7196710 0.000000 6.7053339
#> 52 0.4085893 0.4701178 13.0349961 12.2481934 7.1760869 6.705334 0.0000000
#> 53 11.6161106 12.1763021 1.3869487 0.3859969 6.7856734 6.578489 11.9233241
#> 54 0.3650836 1.2225547 12.4632943 11.6516761 6.9195508 6.393966 0.7722191
#> 55 14.5494294 14.9165833 3.2319269 4.1556545 8.0802194 8.285953 14.7759663
#> 56 14.2023303 14.6075989 2.4677657 3.4163422 7.9443758 8.083897 14.4452283
#> 57 14.0549788 14.5244867 1.5455108 2.5486308 8.2137135 8.246297 14.3252357
#> 53 54 55 56 57
#> 3 0.6743504 11.9577584 3.9842618 3.2286348 2.2978079
#> 4 13.7016843 7.7413732 14.3738782 14.4306359 14.9207937
#> 7 14.8538151 7.4473679 15.8533544 15.8627616 16.2779937
#> 9 0.2051285 11.5062344 4.4706648 3.7290974 2.8369614
#> 11 13.2761692 5.6864160 14.6421149 14.5793786 14.8938063
#> 12 13.4031439 7.9281017 13.9571616 14.0313218 14.5480972
#> 14 12.0053568 1.5845003 14.5946810 14.3118579 14.2752829
#> 18 8.1492723 6.8626245 9.0875228 9.0356434 9.4140802
#> 20 10.9557113 0.3638950 13.9749019 13.6084948 13.4304238
#> 21 3.1695348 13.9200456 1.7400480 0.9855479 0.2768433
#> 23 11.2882094 0.4552630 14.1671548 13.8281547 13.6973548
#> 24 8.5103522 7.2771183 9.1845057 9.1807226 9.6269808
#> 26 3.5153974 13.9371404 1.1576856 0.3913964 0.8471860
#> 27 4.5969426 15.1407769 6.7611112 6.0417265 4.8294497
#> 29 14.5319853 3.6344487 17.8286012 17.4238240 17.1577071
#> 30 2.3824941 13.4677830 2.6517570 1.8699919 0.6737893
#> 31 3.9383013 14.0615544 0.6220397 0.1687610 1.3861601
#> 33 1.6824974 12.8034047 3.0832014 2.3034980 1.2751459
#> 35 9.1119312 6.4737130 10.0841571 10.0433981 10.4258187
#> 36 3.4136798 12.3674321 1.9553372 1.7762271 2.4023729
#> 37 2.8625581 14.1801201 3.8409099 3.1081406 1.8944131
#> 38 7.6472156 6.2674672 9.0126567 8.8849309 9.1513335
#> 39 10.7032376 2.9087521 12.8862148 12.6650655 12.7425970
#> 40 14.0429119 7.5794275 14.8343750 14.8736246 15.3366129
#> 41 12.2561204 1.7256446 14.8342197 14.5548406 14.5227027
#> 43 2.5613772 13.5937224 2.4709765 1.6934175 0.4783430
#> 44 12.7952271 7.7964032 13.3145928 13.3895816 13.9110553
#> 45 3.2415055 13.5244679 1.2793841 0.5335230 0.9306502
#> 46 11.6161106 0.3650836 14.5494294 14.2023303 14.0549788
#> 47 12.1763021 1.2225547 14.9165833 14.6075989 14.5244867
#> 48 1.3869487 12.4632943 3.2319269 2.4677657 1.5455108
#> 49 0.3859969 11.6516761 4.1556545 3.4163422 2.5486308
#> 50 6.7856734 6.9195508 8.0802194 7.9443758 8.2137135
#> 51 6.5784892 6.3939664 8.2859533 8.0838967 8.2462966
#> 52 11.9233241 0.7722191 14.7759663 14.4452283 14.3252357
#> 53 0.0000000 11.3184975 4.5009575 3.7730800 2.9324444
#> 54 11.3184975 0.0000000 14.3172818 13.9565980 13.7865952
#> 55 4.5009575 14.3172818 0.0000000 0.7858942 2.0030226
#> 56 3.7730800 13.9565980 0.7858942 0.0000000 1.2345829
#> 57 2.9324444 13.7865952 2.0030226 1.2345829 0.0000000
# Compare sample to dataframe
dist_btw_receivers(dat)[1:5, ]
#> r1 r2 dist
#> 1 3 3 0.0000000
#> 2 4 3 14.1131960
#> 3 7 3 15.3099894
#> 4 9 3 0.5428911
#> 5 11 3 13.7672055
dist_btw_receivers(dat, return = "matrix")[1:5, 1:5]
#> 3 4 7 9 11
#> 3 0.0000000 14.113196 15.309989 0.5428911 13.767205
#> 4 14.1131960 0.000000 1.717948 13.9034731 2.215489
#> 7 15.3099894 1.717948 0.000000 15.0580751 1.873918
#> 9 0.5428911 13.903473 15.058075 0.0000000 13.481255
#> 11 13.7672055 2.215489 1.873918 13.4812546 0.000000