Data Frame to Distance Matrix
Arguments
- df
(required, data frame) Data frame with numeric columns to transform into a distance matrix. Default: NULL
- name_column
(optional, column name) Column naming individual time series. Numeric names are converted to character with the prefix "X". Default: NULL
- distance
(optional, character vector) name or abbreviation of the distance method. Valid values are in the columns "names" and "abbreviation" of the dataset distances. Default: "euclidean".
Examples
#compute distance matrix
m <- distance_matrix(
df = cities_coordinates,
name_column = "name",
distance = "euclidean"
)
#get data used to compute the matrix
attributes(m)$df
#> x y elevation distance_to_ocean solar_radiation
#> 1 99.91 13.66 8 28.89156 19388.49
#> 2 -74.73 4.02 453 229.38473 16430.87
#> 3 31.38 29.74 93 97.69922 24238.00
#> 4 90.00 23.31 6 50.50440 20321.74
#> 5 107.18 10.45 0 0.00000 18590.33
#> 6 29.82 40.99 343 15.83333 19065.21
#> 7 106.55 -5.63 0 0.00000 18513.49
#> 8 67.39 24.92 98 14.19116 23848.15
#> 9 15.27 -4.02 396 334.34846 19496.57
#> 10 3.23 5.63 0 0.00000 20366.58
#> 11 -77.26 -12.05 0 0.00000 24928.20
#> 12 0.00 52.24 43 63.00904 12345.30
#> 13 -118.70 34.56 793 52.55949 23064.24
#> 14 120.83 15.27 13 48.76958 19034.83
#> 15 36.85 55.45 220 798.31944 12855.08
#> 16 2.45 49.03 91 141.24938 14119.42
#> 17 -42.82 -23.31 0 0.00000 21689.41
#> 18 -46.31 -23.31 715 55.59027 20473.77
#> 19 120.63 31.35 5 58.69530 19710.53
#> 20 139.23 36.17 77 74.83779 17437.20
#> annual_cloud_cover aridity_index annual_ndvi
#> 1 44.04416 0.86442246 0.5217820
#> 2 65.11307 1.98769951 0.5221947
#> 3 10.83446 0.01118099 0.1825605
#> 4 35.16821 1.48587675 0.4885728
#> 5 48.15423 1.40259164 0.4978680
#> 6 34.39246 0.92583137 0.5228935
#> 7 52.73448 2.03510977 0.4667296
#> 8 15.60892 0.12892254 0.1500496
#> 9 49.44775 1.17530376 0.5165942
#> 10 42.05610 1.48304883 0.3410932
#> 11 45.10634 0.35973152 0.1481188
#> 12 44.91607 1.01168651 0.5389599
#> 13 19.56887 0.29251825 0.3184072
#> 14 45.95042 2.22648105 0.5662389
#> 15 51.60477 1.14668544 0.4313264
#> 16 44.17474 0.96665528 0.5089241
#> 17 39.13979 1.29409965 0.5865306
#> 18 43.11042 1.43206262 0.6121682
#> 19 44.30517 1.28002703 0.3668733
#> 20 44.64218 1.89125024 0.4997027
#check matrix
m
#> Bangkok Bogotá Cairo Dhaka Ho Chi Minh City
#> Bangkok 0.0000 3002.803 4851.3593 933.6445 798.77820
#> Bogotá 3002.8031 0.000 7817.4868 3924.1721 2225.87723
#> Cairo 4851.3593 7817.487 0.0000 3918.0249 5649.94247
#> Dhaka 933.6445 3924.172 3918.0249 0.0000 1732.34079
#> Ho Chi Minh City 798.7782 2225.877 5649.9425 1732.3408 0.00000
#> Istanbul 471.8721 2647.772 5179.5407 1302.9147 592.04304
#> Jakarta 875.8025 2151.336 5726.8546 1809.3624 78.64539
#> Karachi 4460.8082 7430.443 400.4047 3527.9193 5259.02128
#> Kinshasa 512.8675 3069.396 4757.2967 959.2283 1048.10648
#> Lagos 983.3426 3969.165 3874.0706 111.7412 1779.30507
#> Lima 5542.6784 8512.530 713.6514 4609.9206 6340.59418
#> London 7044.1747 4110.465 11892.9601 7977.0999 6246.55188
#> Los Angeles 3765.1973 6644.806 1375.6334 2860.8727 4549.71451
#> Manila 354.8898 2654.350 5204.9225 1287.3749 447.59407
#> Moscow 6582.4207 3630.359 11405.2691 7507.3434 5795.32530
#> Paris 5271.9463 2343.079 10118.7859 6204.2464 4475.46015
#> Rio De Janeiro 2305.8325 5283.231 2553.9423 1375.8370 3102.90314
#> São Paulo 1304.2781 4055.366 3816.7948 739.3494 2021.48531
#> Shanghai 324.5763 3320.454 4529.4882 612.1505 1122.03524
#> Tokyo 1953.5768 1106.891 6801.7925 2885.9786 1158.84860
#> Istanbul Jakarta Karachi Kinshasa Lagos Lima
#> Bangkok 471.8721 875.80246 4460.8082 512.8675 983.3426 5542.6784
#> Bogotá 2647.7722 2151.33580 7430.4429 3069.3958 3969.1651 8512.5305
#> Cairo 5179.5407 5726.85463 400.4047 4757.2967 3874.0706 713.6514
#> Dhaka 1302.9147 1809.36238 3527.9193 959.2283 111.7412 4609.9206
#> Ho Chi Minh City 592.0430 78.64539 5259.0213 1048.1065 1779.3051 6340.5942
#> Istanbul 0.0000 656.27191 4789.4227 541.1059 1346.6568 5874.2644
#> Jakarta 656.2719 0.00000 5335.9396 1115.0759 1856.0344 6417.3536
#> Karachi 4789.4227 5335.93960 0.0000 4374.0447 3483.7215 1095.2076
#> Kinshasa 541.1059 1115.07590 4374.0447 0.0000 1012.8279 5457.0964
#> Lagos 1346.6568 1856.03444 3483.7215 1012.8279 0.0000 4562.3668
#> Lima 5874.2644 6417.35359 1095.2076 5457.0964 4562.3668 0.0000
#> London 6726.8453 6169.85163 11503.3456 7165.3470 8021.7741 12583.5287
#> Los Angeles 4027.2112 4625.40951 1064.7754 3603.5594 2815.1725 2027.4373
#> Manila 346.3919 524.43244 4814.5973 673.0344 1337.9269 5896.9808
#> Moscow 6260.4836 5719.43065 11021.8169 6660.2999 7557.2510 12102.2138
#> Paris 4953.8830 4398.85935 9729.8486 5389.5288 6249.5696 10810.5529
#> Rio De Janeiro 2648.3514 3179.50974 2164.4833 2254.1108 1323.9503 3239.0002
#> São Paulo 1460.8330 2093.03889 3432.9009 1067.0558 727.3920 4511.9081
#> Shanghai 735.5049 1199.17939 4139.3434 535.7489 669.5615 5221.9289
#> Tokyo 1654.3068 1082.95723 6411.7430 2104.0634 2934.6553 7495.0482
#> London Los Angeles Manila Moscow Paris
#> Bangkok 7044.1747 3765.197 354.8898 6582.4207 5271.946
#> Bogotá 4110.4645 6644.806 2654.3495 3630.3590 2343.079
#> Cairo 11892.9601 1375.633 5204.9225 11405.2691 10118.786
#> Dhaka 7977.0999 2860.873 1287.3749 7507.3434 6204.246
#> Ho Chi Minh City 6246.5519 4549.715 447.5941 5795.3253 4475.460
#> Istanbul 6726.8453 4027.211 346.3919 6260.4836 4953.883
#> Jakarta 6169.8516 4625.410 524.4324 5719.4307 4398.859
#> Karachi 11503.3456 1064.775 4814.5973 11021.8169 9729.849
#> Kinshasa 7165.3470 3603.559 673.0344 6660.2999 5389.529
#> Lagos 8021.7741 2815.173 1337.9269 7557.2510 6249.570
#> Lima 12583.5287 2027.437 5896.9808 12102.2138 10810.553
#> London 0.0000 10745.847 6690.7990 912.8495 1776.493
#> Los Angeles 10745.8473 0.000 4111.3290 10253.6384 8973.626
#> Manila 6690.7990 4111.329 0.0000 6229.1779 4918.437
#> Moscow 912.8495 10253.638 6229.1779 0.0000 1431.160
#> Paris 1776.4932 8973.626 4918.4374 1431.1597 0.000
#> Rio De Janeiro 9344.8208 1590.995 2660.3881 8873.7696 7572.336
#> São Paulo 8156.6858 2593.405 1610.2336 7671.6606 6386.090
#> Shanghai 7366.3480 3453.435 676.0224 6899.1448 5593.663
#> Tokyo 5093.9558 5678.366 1599.3600 4642.2671 3321.321
#> Rio De Janeiro São Paulo Shanghai Tokyo
#> Bangkok 2305.832 1304.2781 324.5763 1953.577
#> Bogotá 5283.231 4055.3659 3320.4538 1106.891
#> Cairo 2553.942 3816.7948 4529.4882 6801.793
#> Dhaka 1375.837 739.3494 612.1505 2885.979
#> Ho Chi Minh City 3102.903 2021.4853 1122.0352 1158.849
#> Istanbul 2648.351 1460.8330 735.5049 1654.307
#> Jakarta 3179.510 2093.0389 1199.1794 1082.957
#> Karachi 2164.483 3432.9009 4139.3434 6411.743
#> Kinshasa 2254.111 1067.0558 535.7489 2104.063
#> Lagos 1323.950 727.3920 669.5615 2934.655
#> Lima 3239.000 4511.9081 5221.9289 7495.048
#> London 9344.821 8156.6858 7366.3480 5093.956
#> Los Angeles 1590.995 2593.4048 3453.4354 5678.366
#> Manila 2660.388 1610.2336 676.0224 1599.360
#> Moscow 8873.770 7671.6606 6899.1448 4642.267
#> Paris 7572.336 6386.0903 5593.6630 3321.321
#> Rio De Janeiro 0.000 1411.4209 1987.2439 4257.873
#> São Paulo 1411.421 0.0000 1057.1195 3109.042
#> Shanghai 1987.244 1057.1195 0.0000 2274.610
#> Tokyo 4257.873 3109.0415 2274.6097 0.000
#> attr(,"type")
#> [1] "distance"
#> attr(,"distance")
#> [1] "euclidean"
#> attr(,"df")
#> x y elevation distance_to_ocean solar_radiation
#> 1 99.91 13.66 8 28.89156 19388.49
#> 2 -74.73 4.02 453 229.38473 16430.87
#> 3 31.38 29.74 93 97.69922 24238.00
#> 4 90.00 23.31 6 50.50440 20321.74
#> 5 107.18 10.45 0 0.00000 18590.33
#> 6 29.82 40.99 343 15.83333 19065.21
#> 7 106.55 -5.63 0 0.00000 18513.49
#> 8 67.39 24.92 98 14.19116 23848.15
#> 9 15.27 -4.02 396 334.34846 19496.57
#> 10 3.23 5.63 0 0.00000 20366.58
#> 11 -77.26 -12.05 0 0.00000 24928.20
#> 12 0.00 52.24 43 63.00904 12345.30
#> 13 -118.70 34.56 793 52.55949 23064.24
#> 14 120.83 15.27 13 48.76958 19034.83
#> 15 36.85 55.45 220 798.31944 12855.08
#> 16 2.45 49.03 91 141.24938 14119.42
#> 17 -42.82 -23.31 0 0.00000 21689.41
#> 18 -46.31 -23.31 715 55.59027 20473.77
#> 19 120.63 31.35 5 58.69530 19710.53
#> 20 139.23 36.17 77 74.83779 17437.20
#> annual_cloud_cover aridity_index annual_ndvi
#> 1 44.04416 0.86442246 0.5217820
#> 2 65.11307 1.98769951 0.5221947
#> 3 10.83446 0.01118099 0.1825605
#> 4 35.16821 1.48587675 0.4885728
#> 5 48.15423 1.40259164 0.4978680
#> 6 34.39246 0.92583137 0.5228935
#> 7 52.73448 2.03510977 0.4667296
#> 8 15.60892 0.12892254 0.1500496
#> 9 49.44775 1.17530376 0.5165942
#> 10 42.05610 1.48304883 0.3410932
#> 11 45.10634 0.35973152 0.1481188
#> 12 44.91607 1.01168651 0.5389599
#> 13 19.56887 0.29251825 0.3184072
#> 14 45.95042 2.22648105 0.5662389
#> 15 51.60477 1.14668544 0.4313264
#> 16 44.17474 0.96665528 0.5089241
#> 17 39.13979 1.29409965 0.5865306
#> 18 43.11042 1.43206262 0.6121682
#> 19 44.30517 1.28002703 0.3668733
#> 20 44.64218 1.89125024 0.4997027