knitr::opts_chunk$set(echo = TRUE)
pacman::p_load(tidyverse, corrplot)
Load data
Average Patch Area
Merge by Event_ID
Calculate summary statistics
summary_stats <- landscape_data %>%
summarise(across(where(is.numeric), list(
mean = ~ mean(. , na.rm = TRUE),
median = ~ median(. , na.rm = TRUE),
sd = ~ sd(. , na.rm = TRUE),
min = ~ min(. , na.rm = TRUE),
max = ~ max(. , na.rm = TRUE)
)))
print(summary_stats)
## pdsi_mean pdsi_median pdsi_sd pdsi_min pdsi_max spei14d_mean
## 1 -0.3599167 0.2688552 2.681008 -5.899099 5.863457 0.2037359
## spei14d_median spei14d_sd spei14d_min spei14d_max spei180d_mean
## 1 0.3205397 0.9283891 -2.09 2.09 -0.125025
## spei180d_median spei180d_sd spei180d_min spei180d_max spei1y_mean
## 1 -0.1254383 0.8477341 -2.09 2.09 -0.1789367
## spei1y_median spei1y_sd spei1y_min spei1y_max spei270d_mean spei270d_median
## 1 -0.02575891 1.108422 -2.09 2.09 -0.1684722 -0.1183271
## spei270d_sd spei270d_min spei270d_max spei2y_mean spei2y_median spei2y_sd
## 1 0.9708147 -2.09 2.09 0.06857145 0.3255986 1.220175
## spei2y_min spei2y_max spei30d_mean spei30d_median spei30d_sd spei30d_min
## 1 -2.09 2.09 0.260008 0.1695088 0.9048348 -1.790708
## spei30d_max spei5y_mean spei5y_median spei5y_sd spei5y_min spei5y_max
## 1 2.09 -0.2197807 -0.1623561 1.237284 -2.09 2.09
## spei90d_mean spei90d_median spei90d_sd spei90d_min spei90d_max spi14d_mean
## 1 0.2075299 0.2692704 0.9079209 -2.09 2.09 0.2220055
## spi14d_median spi14d_sd spi14d_min spi14d_max spi180d_mean spi180d_median
## 1 0.32 0.9190697 -2.09 2.09 -0.002874279 -0.0511599
## spi180d_sd spi180d_min spi180d_max spi1y_mean spi1y_median spi1y_sd spi1y_min
## 1 0.8455267 -2.09 2.09 0.02964591 0.06660732 1.031893 -2.09
## spi1y_max spi270d_mean spi270d_median spi270d_sd spi270d_min spi270d_max
## 1 2.09 0.02564172 -0.008201311 0.9353455 -2.09 2.09
## spi2y_mean spi2y_median spi2y_sd spi2y_min spi2y_max spi30d_mean
## 1 0.4744977 0.7861455 1.308336 -2.09 2.09 0.3034413
## spi30d_median spi30d_sd spi30d_min spi30d_max spi5y_mean spi5y_median
## 1 0.2384823 0.9068001 -2.09 2.09 0.3804803 0.4821031
## spi5y_sd spi5y_min spi5y_max spi90d_mean spi90d_median spi90d_sd spi90d_min
## 1 1.321891 -2.09 2.09 0.2486588 0.24 0.9155388 -2.09
## spi90d_max pr_mea_10b_2a_mean pr_mea_10b_2a_median pr_mea_10b_2a_sd
## 1 2.09 4.732668 3.880046 3.467268
## pr_mea_10b_2a_min pr_mea_10b_2a_max pr_mea_30b_2a_mean pr_mea_30b_2a_median
## 1 0 21.31102 4.788989 4.332431
## pr_mea_30b_2a_sd pr_mea_30b_2a_min pr_mea_30b_2a_max pr_mea_3b_2a_mean
## 1 2.456749 0.5782809 14.1435 3.259559
## pr_mea_3b_2a_median pr_mea_3b_2a_sd pr_mea_3b_2a_min pr_mea_3b_2a_max
## 1 1.437276 4.43453 0 23.00936
## pr_mea_60b_2a_mean pr_mea_60b_2a_median pr_mea_60b_2a_sd pr_mea_60b_2a_min
## 1 4.579697 4.288874 1.852612 0.3249302
## pr_mea_60b_2a_max pr_min_10b_2a_mean pr_min_10b_2a_median pr_min_10b_2a_sd
## 1 10.65813 0 0 0
## pr_min_10b_2a_min pr_min_10b_2a_max pr_min_3b_2a_mean pr_min_3b_2a_median
## 1 0 0 0.0001098136 0
## pr_min_3b_2a_sd pr_min_3b_2a_min pr_min_3b_2a_max pr_max_10b_2a_mean
## 1 0.002547107 0 0.0590797 34.82171
## pr_max_10b_2a_median pr_max_10b_2a_sd pr_max_10b_2a_min pr_max_10b_2a_max
## 1 29.59049 26.76302 0 239.0739
## pr_max_3b_2a_mean pr_max_3b_2a_median pr_max_3b_2a_sd pr_max_3b_2a_min
## 1 15.13923 6.368025 20.09473 0
## pr_max_3b_2a_max rmax_mea_10b_2a_mean rmax_mea_10b_2a_median
## 1 119.9697 88.23523 88.80728
## rmax_mea_10b_2a_sd rmax_mea_10b_2a_min rmax_mea_10b_2a_max
## 1 6.12554 68.10906 100
## rmax_mea_30b_2a_mean rmax_mea_30b_2a_median rmax_mea_30b_2a_sd
## 1 88.58769 88.6224 5.220087
## rmax_mea_30b_2a_min rmax_mea_30b_2a_max rmax_mea_3b_2a_mean
## 1 70.07817 100 87.77636
## rmax_mea_3b_2a_median rmax_mea_3b_2a_sd rmax_mea_3b_2a_min rmax_mea_3b_2a_max
## 1 88.52968 7.236055 65.54641 100
## rmax_mea_60b_2a_mean rmax_mea_60b_2a_median rmax_mea_60b_2a_sd
## 1 88.90707 89.05737 4.757408
## rmax_mea_60b_2a_min rmax_mea_60b_2a_max rmax_min_10b_2a_mean
## 1 74.61429 100 69.96705
## rmax_min_10b_2a_median rmax_min_10b_2a_sd rmax_min_10b_2a_min
## 1 71.47769 12.34934 29.47213
## rmax_min_10b_2a_max rmax_min_3b_2a_mean rmax_min_3b_2a_median
## 1 100 74.88419 75.49963
## rmax_min_3b_2a_sd rmax_min_3b_2a_min rmax_min_3b_2a_max rmax_max_10b_2a_mean
## 1 12.26437 35.1689 100 99.70517
## rmax_max_10b_2a_median rmax_max_10b_2a_sd rmax_max_10b_2a_min
## 1 100 1.454214 85.56358
## rmax_max_10b_2a_max rmax_max_3b_2a_mean rmax_max_3b_2a_median
## 1 100 98.701 100
## rmax_max_3b_2a_sd rmax_max_3b_2a_min rmax_max_3b_2a_max rmin_mea_10b_2a_mean
## 1 3.791596 76.61992 100 42.28933
## rmin_mea_10b_2a_median rmin_mea_10b_2a_sd rmin_mea_10b_2a_min
## 1 42.20062 5.577738 27.42905
## rmin_mea_10b_2a_max rmin_mea_30b_2a_mean rmin_mea_30b_2a_median
## 1 56.95044 43.40041 43.56962
## rmin_mea_30b_2a_sd rmin_mea_30b_2a_min rmin_mea_30b_2a_max
## 1 4.509959 29.13995 55.1321
## rmin_mea_3b_2a_mean rmin_mea_3b_2a_median rmin_mea_3b_2a_sd
## 1 39.77063 39.53829 6.471502
## rmin_mea_3b_2a_min rmin_mea_3b_2a_max rmin_mea_60b_2a_mean
## 1 23.86665 58.7238 43.54926
## rmin_mea_60b_2a_median rmin_mea_60b_2a_sd rmin_mea_60b_2a_min
## 1 43.61111 4.270308 29.24137
## rmin_mea_60b_2a_max rmin_min_10b_2a_mean rmin_min_10b_2a_median
## 1 55.66244 23.74194 22.9643
## rmin_min_10b_2a_sd rmin_min_10b_2a_min rmin_min_10b_2a_max
## 1 5.937247 10.26206 51.68152
## rmin_min_3b_2a_mean rmin_min_3b_2a_median rmin_min_3b_2a_sd
## 1 26.59307 25.27299 7.206319
## rmin_min_3b_2a_min rmin_min_3b_2a_max sph_mea_10b_2a_mean
## 1 11.95675 52.28375 0.007653576
## sph_mea_10b_2a_median sph_mea_10b_2a_sd sph_mea_10b_2a_min sph_mea_10b_2a_max
## 1 0.006752732 0.00293852 0.001967333 0.01814355
## sph_mea_30b_2a_mean sph_mea_30b_2a_median sph_mea_30b_2a_sd
## 1 0.007440227 0.006574975 0.002801613
## sph_mea_30b_2a_min sph_mea_30b_2a_max sph_mea_3b_2a_mean sph_mea_3b_2a_median
## 1 0.002823028 0.01764952 0.007580031 0.006768285
## sph_mea_3b_2a_sd sph_mea_3b_2a_min sph_mea_3b_2a_max sph_mea_60b_2a_mean
## 1 0.003122416 0.001705448 0.01827759 0.007227465
## sph_mea_60b_2a_median sph_mea_60b_2a_sd sph_mea_60b_2a_min sph_mea_60b_2a_max
## 1 0.006531673 0.002736804 0.003229654 0.01726668
## sph_min_10b_2a_mean sph_min_10b_2a_median sph_min_10b_2a_sd
## 1 0.004301202 0.00332981 0.003054488
## sph_min_10b_2a_min sph_min_10b_2a_max sph_min_3b_2a_mean sph_min_3b_2a_median
## 1 0.0007070945 0.01705508 0.005250919 0.003911634
## sph_min_3b_2a_sd sph_min_3b_2a_min sph_min_3b_2a_max sph_max_10b_2a_mean
## 1 0.003445389 0.0007070945 0.01794008 0.01112127
## sph_max_10b_2a_median sph_max_10b_2a_sd sph_max_10b_2a_min sph_max_10b_2a_max
## 1 0.01081909 0.002640614 0.003247659 0.01995011
## sph_max_3b_2a_mean sph_max_3b_2a_median sph_max_3b_2a_sd sph_max_3b_2a_min
## 1 0.01014943 0.009906003 0.002862549 0.002847102
## sph_max_3b_2a_max tmmn_mea_10b_2a_mean tmmn_mea_10b_2a_median
## 1 0.01924896 281.6689 280.4419
## tmmn_mea_10b_2a_sd tmmn_mea_10b_2a_min tmmn_mea_10b_2a_max
## 1 5.56779 264.7726 296.1371
## tmmn_mea_30b_2a_mean tmmn_mea_30b_2a_median tmmn_mea_30b_2a_sd
## 1 281.0072 279.5928 5.355219
## tmmn_mea_30b_2a_min tmmn_mea_30b_2a_max tmmn_mea_3b_2a_mean
## 1 267.6162 295.7887 281.6726
## tmmn_mea_3b_2a_median tmmn_mea_3b_2a_sd tmmn_mea_3b_2a_min tmmn_mea_3b_2a_max
## 1 280.7001 6.127101 262.8201 296.3324
## tmmn_mea_60b_2a_mean tmmn_mea_60b_2a_median tmmn_mea_60b_2a_sd
## 1 280.3463 279.2794 5.318464
## tmmn_mea_60b_2a_min tmmn_mea_60b_2a_max tmmn_min_10b_2a_mean
## 1 268.6414 295.4697 275.0812
## tmmn_min_10b_2a_median tmmn_min_10b_2a_sd tmmn_min_10b_2a_min
## 1 273.2266 6.637331 258.3731
## tmmn_min_10b_2a_max tmmn_min_3b_2a_mean tmmn_min_3b_2a_median
## 1 294.8076 277.1857 275.3799
## tmmn_min_3b_2a_sd tmmn_min_3b_2a_min tmmn_min_3b_2a_max tmmn_max_10b_2a_mean
## 1 7.141209 258.5395 295.48 289.173
## tmmn_max_10b_2a_median tmmn_max_10b_2a_sd tmmn_max_10b_2a_min
## 1 289.369 4.329191 269.7141
## tmmn_max_10b_2a_max tmmn_max_3b_2a_mean tmmn_max_3b_2a_median
## 1 299.0814 287.1116 287.5672
## tmmn_max_3b_2a_sd tmmn_max_3b_2a_min tmmn_max_3b_2a_max tmmx_mea_10b_2a_mean
## 1 5.255876 266.7256 298.3554 294.9858
## tmmx_mea_10b_2a_median tmmx_mea_10b_2a_sd tmmx_mea_10b_2a_min
## 1 294.0294 5.278401 278.5314
## tmmx_mea_10b_2a_max tmmx_mea_30b_2a_mean tmmx_mea_30b_2a_median
## 1 308.8758 294.0947 292.8225
## tmmx_mea_30b_2a_sd tmmx_mea_30b_2a_min tmmx_mea_30b_2a_max
## 1 5.225253 279.9703 307.4257
## tmmx_mea_3b_2a_mean tmmx_mea_3b_2a_median tmmx_mea_3b_2a_sd
## 1 295.5929 294.9098 5.65091
## tmmx_mea_3b_2a_min tmmx_mea_3b_2a_max tmmx_mea_60b_2a_mean
## 1 276.3737 308.3988 293.4551
## tmmx_mea_60b_2a_median tmmx_mea_60b_2a_sd tmmx_mea_60b_2a_min
## 1 292.2049 5.174505 282.4822
## tmmx_mea_60b_2a_max tmmx_max_10b_2a_mean tmmx_max_10b_2a_median
## 1 308.1923 300.4984 300.3807
## tmmx_max_10b_2a_sd tmmx_max_10b_2a_min tmmx_max_10b_2a_max
## 1 3.829843 289.122 312.1503
## tmmx_max_3b_2a_mean tmmx_max_3b_2a_median tmmx_max_3b_2a_sd
## 1 299.6449 299.6801 4.327047
## tmmx_max_3b_2a_min tmmx_max_3b_2a_max vs_mea_10b_2a_mean vs_mea_10b_2a_median
## 1 285.2613 310.6641 4.079252 4.084951
## vs_mea_10b_2a_sd vs_mea_10b_2a_min vs_mea_10b_2a_max vs_mea_30b_2a_mean
## 1 0.7027316 2.265388 6.67108 4.120741
## vs_mea_30b_2a_median vs_mea_30b_2a_sd vs_mea_30b_2a_min vs_mea_30b_2a_max
## 1 4.114615 0.5716684 2.728416 6.112424
## vs_mea_3b_2a_mean vs_mea_3b_2a_median vs_mea_3b_2a_sd vs_mea_3b_2a_min
## 1 4.040674 4.013902 0.8934823 1.951263
## vs_mea_3b_2a_max vs_mea_60b_2a_mean vs_mea_60b_2a_median vs_mea_60b_2a_sd
## 1 7.387479 4.123269 4.090839 0.5169873
## vs_mea_60b_2a_min vs_mea_60b_2a_max vs_min_10b_2a_mean vs_min_10b_2a_median
## 1 2.923642 6.137538 2.118568 2.081612
## vs_min_10b_2a_sd vs_min_10b_2a_min vs_min_10b_2a_max vs_min_3b_2a_mean
## 1 0.5642987 0.8274577 4.46375 2.585341
## vs_min_3b_2a_median vs_min_3b_2a_sd vs_min_3b_2a_min vs_min_3b_2a_max
## 1 2.49724 0.7618914 0.9838564 5.969936
## vs_max_10b_2a_mean vs_max_10b_2a_median vs_max_10b_2a_sd vs_max_10b_2a_min
## 1 6.711807 6.685305 1.296436 3.4
## vs_max_10b_2a_max vs_max_3b_2a_mean vs_max_3b_2a_median vs_max_3b_2a_sd
## 1 10.69493 5.828259 5.759595 1.455649
## vs_max_3b_2a_min vs_max_3b_2a_max vpd_mea_10b_2a_mean vpd_mea_10b_2a_median
## 1 2.4 9.993147 0.7876164 0.726543
## vpd_mea_10b_2a_sd vpd_mea_10b_2a_min vpd_mea_10b_2a_max vpd_mea_30b_2a_mean
## 1 0.2667036 0.3322241 2.032805 0.735588
## vpd_mea_30b_2a_median vpd_mea_30b_2a_sd vpd_mea_30b_2a_min vpd_mea_30b_2a_max
## 1 0.6566733 0.2485392 0.3027342 1.762618
## vpd_mea_3b_2a_mean vpd_mea_3b_2a_median vpd_mea_3b_2a_sd vpd_mea_3b_2a_min
## 1 0.8492466 0.7900907 0.2980896 0.2893181
## vpd_mea_3b_2a_max vpd_mea_60b_2a_mean vpd_mea_60b_2a_median vpd_mea_60b_2a_sd
## 1 1.957924 0.7044305 0.6207395 0.2406388
## vpd_mea_60b_2a_min vpd_mea_60b_2a_max vpd_min_10b_2a_mean
## 1 0.3162898 1.679713 0.2485974
## vpd_min_10b_2a_median vpd_min_10b_2a_sd vpd_min_10b_2a_min vpd_min_10b_2a_max
## 1 0.1805203 0.2747079 0 1.184225
## vpd_min_3b_2a_mean vpd_min_3b_2a_median vpd_min_3b_2a_sd vpd_min_3b_2a_min
## 1 0.4543642 0.3928609 0.3404282 0
## vpd_min_3b_2a_max vpd_max_10b_2a_mean vpd_max_10b_2a_median vpd_max_10b_2a_sd
## 1 1.796383 1.223061 1.194304 0.3224995
## vpd_max_10b_2a_min vpd_max_10b_2a_max vpd_max_3b_2a_mean vpd_max_3b_2a_median
## 1 0.669692 2.751387 1.159002 1.137252
## vpd_max_3b_2a_sd vpd_max_3b_2a_min vpd_max_3b_2a_max fm100_mea_10b_2a_mean
## 1 0.3264871 0.4677894 2.288866 16.2603
## fm100_mea_10b_2a_median fm100_mea_10b_2a_sd fm100_mea_10b_2a_min
## 1 16.35414 1.970553 9.769252
## fm100_mea_10b_2a_max fm100_mea_30b_2a_mean fm100_mea_30b_2a_median
## 1 21.22487 16.68759 16.63842
## fm100_mea_30b_2a_sd fm100_mea_30b_2a_min fm100_mea_30b_2a_max
## 1 1.578056 11.15595 20.23679
## fm100_mea_3b_2a_mean fm100_mea_3b_2a_median fm100_mea_3b_2a_sd
## 1 15.57212 15.58793 2.19139
## fm100_mea_3b_2a_min fm100_mea_3b_2a_max fm100_mea_60b_2a_mean
## 1 8.620042 21.70424 16.89758
## fm100_mea_60b_2a_median fm100_mea_60b_2a_sd fm100_mea_60b_2a_min
## 1 16.88815 1.40697 12.73033
## fm100_mea_60b_2a_max fm100_min_10b_2a_mean fm100_min_10b_2a_median
## 1 19.88094 12.84751 12.83039
## fm100_min_10b_2a_sd fm100_min_10b_2a_min fm100_min_10b_2a_max
## 1 2.159424 7.379346 18.88703
## fm100_min_3b_2a_mean fm100_min_3b_2a_median fm100_min_3b_2a_sd
## 1 13.58032 13.51758 2.399672
## fm100_min_3b_2a_min fm100_min_3b_2a_max fm100_max_10b_2a_mean
## 1 7.379346 19.54961 20.03896
## fm100_max_10b_2a_median fm100_max_10b_2a_sd fm100_max_10b_2a_min
## 1 19.83793 2.80031 12.03396
## fm100_max_10b_2a_max fm100_max_3b_2a_mean fm100_max_3b_2a_median
## 1 29.81515 17.98424 18.07479
## fm100_max_3b_2a_sd fm100_max_3b_2a_min fm100_max_3b_2a_max
## 1 2.531316 9.832138 25.52204
## fm1000_mea_10b_2a_mean fm1000_mea_10b_2a_median fm1000_mea_10b_2a_sd
## 1 19.28703 19.22302 2.082588
## fm1000_mea_10b_2a_min fm1000_mea_10b_2a_max fm1000_mea_30b_2a_mean
## 1 12.79748 25.85296 19.36555
## fm1000_mea_30b_2a_median fm1000_mea_30b_2a_sd fm1000_mea_30b_2a_min
## 1 19.29167 1.897126 13.81275
## fm1000_mea_30b_2a_max fm1000_mea_3b_2a_mean fm1000_mea_3b_2a_median
## 1 25.21617 19.05514 19.08296
## fm1000_mea_3b_2a_sd fm1000_mea_3b_2a_min fm1000_mea_3b_2a_max
## 1 2.072723 12.07045 25.32849
## fm1000_mea_60b_2a_mean fm1000_mea_60b_2a_median fm1000_mea_60b_2a_sd
## 1 19.42119 19.39396 1.736419
## fm1000_mea_60b_2a_min fm1000_mea_60b_2a_max fm1000_min_10b_2a_mean
## 1 14.82581 24.31303 18.12215
## fm1000_min_10b_2a_median fm1000_min_10b_2a_sd fm1000_min_10b_2a_min
## 1 18.11747 1.909311 11.72913
## fm1000_min_10b_2a_max fm1000_min_3b_2a_mean fm1000_min_3b_2a_median
## 1 23.68843 18.43912 18.48773
## fm1000_min_3b_2a_sd fm1000_min_3b_2a_min fm1000_min_3b_2a_max
## 1 2.009027 11.72913 24.42028
## fm1000_max_10b_2a_mean fm1000_max_10b_2a_median fm1000_max_10b_2a_sd
## 1 20.36082 20.16348 2.272142
## fm1000_max_10b_2a_min fm1000_max_10b_2a_max fm1000_max_3b_2a_mean
## 1 13.84999 27.93917 19.70381
## fm1000_max_3b_2a_median fm1000_max_3b_2a_sd fm1000_max_3b_2a_min
## 1 19.71212 2.200708 12.65498
## fm1000_max_3b_2a_max mean_hydro_dist_m_mean mean_hydro_dist_m_median
## 1 25.94421 232.4729 164.3632
## mean_hydro_dist_m_sd mean_hydro_dist_m_min mean_hydro_dist_m_max
## 1 363.5123 0 4072.445
## NDVI_pre_15d_mean NDVI_pre_15d_median NDVI_pre_15d_sd NDVI_pre_15d_min
## 1 0.4302939 0.5355763 0.3003415 -0.4611636
## NDVI_pre_15d_max NDVI_pre_30d_mean NDVI_pre_30d_median NDVI_pre_30d_sd
## 1 0.8426029 0.4200856 0.4895688 0.2632214
## NDVI_pre_30d_min NDVI_pre_30d_max NDVI_pre_90d_mean NDVI_pre_90d_median
## 1 -0.499292 0.8372057 0.4120643 0.4219792
## NDVI_pre_90d_sd NDVI_pre_90d_min NDVI_pre_90d_max NDVI_pre_365d_mean
## 1 0.1464083 -0.1465647 0.7117205 0.4442853
## NDVI_pre_365d_median NDVI_pre_365d_sd NDVI_pre_365d_min NDVI_pre_365d_max
## 1 0.4429133 0.07769519 0.2265425 0.6507905
## prop_afg_mean prop_afg_median prop_afg_sd prop_afg_min prop_afg_max
## 1 1.180895 1.062644 0.7335937 0.06613699 5.192193
## prop_pfg_mean prop_pfg_median prop_pfg_sd prop_pfg_min prop_pfg_max
## 1 10.5362 6.782803 9.693333 0.8723725 48.4635
## prop_ltr_mean prop_ltr_median prop_ltr_sd prop_ltr_min prop_ltr_max
## 1 3.191337 2.731527 1.561306 1.195251 12.98313
## prop_shr_mean prop_shr_median prop_shr_sd prop_shr_min prop_shr_max
## 1 2.862949 2.41904 1.961696 0.09857416 10.84572
## prop_tre_mean prop_tre_median prop_tre_sd prop_tre_min prop_tre_max
## 1 73.71314 79.15552 17.08212 18.01823 95.61133
## prop_bgr_mean prop_bgr_median prop_bgr_sd prop_bgr_min prop_bgr_max
## 1 0.6841915 0.370909 0.9497217 0.01537329 8.326838
## road_dens_km_km2_mean road_dens_km_km2_median road_dens_km_km2_sd
## 1 1.314771 1.221218 0.7009972
## road_dens_km_km2_min road_dens_km_km2_max nearest_road_m_mean
## 1 0 4.410981 24.10246
## nearest_road_m_median nearest_road_m_sd nearest_road_m_min nearest_road_m_max
## 1 0 298.9739 0 6032.637
## awhc_median_mean awhc_median_median awhc_median_sd awhc_median_min
## 1 23.52599 24.5106 6.7101 8.889027
## awhc_median_max awhc_p25_mean awhc_p25_median awhc_p25_sd awhc_p25_min
## 1 41.77842 21.92767 22.55109 6.478802 7.372674
## awhc_p25_max awhc_p50_mean awhc_p50_median awhc_p50_sd awhc_p50_min
## 1 41.34819 23.52599 24.5106 6.7101 8.889027
## awhc_p50_max awhc_p75_mean awhc_p75_median awhc_p75_sd awhc_p75_min
## 1 41.77842 25.34057 26.61167 7.367642 9.311114
## awhc_p75_max soil_order_1_mean soil_order_1_median soil_order_1_sd
## 1 50.64624 7.973978 10 3.247585
## soil_order_1_min soil_order_1_max soil_order_2_mean soil_order_2_median
## 1 1 11 6.048701 6
## soil_order_2_sd soil_order_2_min soil_order_2_max soil_order_3_mean
## 1 3.517416 1 11 7.054422
## soil_order_3_median soil_order_3_sd soil_order_3_min soil_order_3_max
## 1 7 2.849615 1 11
## soil_order_4_mean soil_order_4_median soil_order_4_sd soil_order_4_min
## 1 6.634615 7 2.679105 1
## soil_order_4_max soil_order_5_mean soil_order_5_median soil_order_5_sd
## 1 10 6.285714 7 2.399634
## soil_order_5_min soil_order_5_max soil_order_6_mean soil_order_6_median
## 1 1 10 4.75 4
## soil_order_6_sd soil_order_6_min soil_order_6_max soil_prop_1_mean
## 1 4.5 1 10 0.852411
## soil_prop_1_median soil_prop_1_sd soil_prop_1_min soil_prop_1_max
## 1 0.9706775 0.1867124 0.2794779 1
## soil_prop_2_mean soil_prop_2_median soil_prop_2_sd soil_prop_2_min
## 1 0.2069069 0.2031078 0.1403831 0.000338922
## soil_prop_2_max soil_prop_3_mean soil_prop_3_median soil_prop_3_sd
## 1 0.4931074 0.08290437 0.07710038 0.06716546
## soil_prop_3_min soil_prop_3_max soil_prop_4_mean soil_prop_4_median
## 1 4.36e-05 0.2731617 0.05687486 0.04071773
## soil_prop_4_sd soil_prop_4_min soil_prop_4_max soil_prop_5_mean
## 1 0.04984649 6.19e-05 0.1937328 0.03374479
## soil_prop_5_median soil_prop_5_sd soil_prop_5_min soil_prop_5_max
## 1 0.01749306 0.0363652 6.27e-05 0.1109726
## soil_prop_6_mean soil_prop_6_median soil_prop_6_sd soil_prop_6_min
## 1 0.0146803 0.004581416 0.02324717 0.000334924
## soil_prop_6_max mean_TRI_mean mean_TRI_median mean_TRI_sd mean_TRI_min
## 1 0.04922343 1.718281 1.43975 0.7440539 0.711785
## mean_TRI_max mean_aspect_mean mean_aspect_median mean_aspect_sd
## 1 5.18048 163.7689 164.1521 13.16548
## mean_aspect_min mean_aspect_max mean_elevation_mean mean_elevation_median
## 1 53.5444 204.6582 80.51354 66.6482
## mean_elevation_sd mean_elevation_min mean_elevation_max mean_slope_mean
## 1 75.98927 6.546088 403.9803 4.115614
## mean_slope_median mean_slope_sd mean_slope_min mean_slope_max
## 1 3.481571 1.771083 1.60971 12.89983
Soil Data Analysis
Remove columns
Correlation
soil_numeric_data <- soil[sapply(soil, is.numeric)]
soil_cor_mat <- cor(soil_numeric_data, use = "pairwise.complete.obs")
corrplot(soil_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
soil_high_corr <- which(abs(soil_cor_mat) > threshold, arr.ind = TRUE)
soil_high_corr_pairs <- soil_high_corr[soil_high_corr[,1] < soil_high_corr[,2], ]
soil_flagged <- data.frame(
Var1 = rownames(soil_cor_mat)[soil_high_corr_pairs[,1]],
Var2 = colnames(soil_cor_mat)[soil_high_corr_pairs[,2]],
Correlation = soil_cor_mat[soil_high_corr_pairs]
)
# Sort by absolute correlation
soil_flagged <- soil_flagged[order(abs(soil_flagged$Correlation), decreasing = TRUE), ]
soil_flagged
## [1] Var1 Var2 Correlation
## <0 rows> (or 0-length row.names)
Topographic Data Analysis
Remove columns
topo <- topo %>%
select(-c(mean_slope))
Correlation
topo_numeric_data <- topo[sapply(topo, is.numeric)]
topo_cor_mat <- cor(topo_numeric_data, use = "pairwise.complete.obs")
corrplot(topo_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
topo_high_corr <- which(abs(topo_cor_mat) > threshold, arr.ind = TRUE)
topo_high_corr_pairs <- topo_high_corr[topo_high_corr[,1] < topo_high_corr[,2], ]
topo_flagged <- data.frame(
Var1 = rownames(topo_cor_mat)[topo_high_corr_pairs[,1]],
Var2 = colnames(topo_cor_mat)[topo_high_corr_pairs[,2]],
Correlation = topo_cor_mat[topo_high_corr_pairs]
)
# Sort by absolute correlation
topo_flagged <- topo_flagged[order(abs(topo_flagged$Correlation), decreasing = TRUE), ]
topo_flagged
## [1] Var1 Var2 Correlation
## <0 rows> (or 0-length row.names)
Roads Data Analysis
Correlation
roads_numeric_data <- roads[sapply(roads, is.numeric)]
roads_cor_mat <- cor(roads_numeric_data, use = "pairwise.complete.obs")
corrplot(roads_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
roads_high_corr <- which(abs(roads_cor_mat) > threshold, arr.ind = TRUE)
roads_high_corr_pairs <- roads_high_corr[roads_high_corr[,1] < roads_high_corr[,2], ]
roads_flagged <- data.frame(
Var1 = rownames(roads_cor_mat)[roads_high_corr_pairs[,1]],
Var2 = colnames(roads_cor_mat)[roads_high_corr_pairs[,2]],
Correlation = roads_cor_mat[roads_high_corr_pairs]
)
# Sort by absolute correlation
roads_flagged <- roads_flagged[order(abs(roads_flagged$Correlation), decreasing = TRUE), ]
roads_flagged
## [1] Var1 Var2 Correlation
## <0 rows> (or 0-length row.names)
Weather Data Analysis
Correlation
weather_numeric_data <- weather[sapply(weather, is.numeric)]
weather_cor_mat <- cor(weather_numeric_data, use = "pairwise.complete.obs")
## Warning in cor(weather_numeric_data, use = "pairwise.complete.obs"): the
## standard deviation is zero
corrplot(weather_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
weather_high_corr <- which(abs(weather_cor_mat) > threshold, arr.ind = TRUE)
weather_high_corr_pairs <- weather_high_corr[weather_high_corr[,1] < weather_high_corr[,2], ]
weather_flagged <- data.frame(
Var1 = rownames(weather_cor_mat)[weather_high_corr_pairs[,1]],
Var2 = colnames(weather_cor_mat)[weather_high_corr_pairs[,2]],
Correlation = weather_cor_mat[weather_high_corr_pairs]
)
# Sort by absolute correlation
weather_flagged <- weather_flagged[order(abs(weather_flagged$Correlation), decreasing = TRUE), ]
weather_flagged
## Var1 Var2 Correlation
## 373 fm1000_mea_3b_2a fm1000_min_3b_2a 0.9894293
## 391 fm1000_mea_3b_2a fm1000_max_3b_2a 0.9875930
## 379 fm1000_mea_10b_2a fm1000_max_10b_2a 0.9829320
## 363 fm1000_mea_10b_2a fm1000_min_10b_2a 0.9719438
## 78 sph_mea_60b_2a tmmn_mea_60b_2a 0.9690158
## 58 sph_mea_30b_2a tmmn_mea_30b_2a 0.9664825
## 192 tmmn_mea_60b_2a tmmx_mea_60b_2a 0.9662954
## 156 tmmn_mea_30b_2a tmmx_mea_30b_2a 0.9646534
## 389 fm1000_mea_10b_2a fm1000_max_3b_2a 0.9641997
## 352 fm1000_mea_10b_2a fm1000_mea_3b_2a 0.9635573
## 394 fm1000_min_3b_2a fm1000_max_3b_2a 0.9633409
## 196 tmmx_mea_30b_2a tmmx_mea_60b_2a 0.9603508
## 306 vpd_mea_30b_2a vpd_mea_60b_2a 0.9600977
## 23 sph_mea_30b_2a sph_mea_60b_2a 0.9597833
## 107 tmmn_mea_3b_2a tmmn_min_3b_2a 0.9596759
## 84 tmmn_mea_30b_2a tmmn_mea_60b_2a 0.9585122
## 19 sph_mea_10b_2a sph_mea_30b_2a 0.9569295
## 49 sph_mea_10b_2a tmmn_mea_10b_2a 0.9569245
## 33 sph_mea_3b_2a sph_min_3b_2a 0.9561220
## 375 fm1000_min_10b_2a fm1000_min_3b_2a 0.9557129
## 371 fm1000_mea_10b_2a fm1000_min_3b_2a 0.9533064
## 139 tmmn_mea_10b_2a tmmx_mea_10b_2a 0.9530429
## 357 fm1000_mea_30b_2a fm1000_mea_60b_2a 0.9525601
## 4 pr_mea_3b_2a pr_max_3b_2a 0.9513430
## 365 fm1000_mea_3b_2a fm1000_min_10b_2a 0.9501264
## 68 sph_mea_3b_2a tmmn_mea_3b_2a 0.9474363
## 76 sph_mea_30b_2a tmmn_mea_60b_2a 0.9468704
## 395 fm1000_max_10b_2a fm1000_max_3b_2a 0.9467734
## 233 tmmx_mea_3b_2a tmmx_max_3b_2a 0.9458941
## 173 tmmn_mea_3b_2a tmmx_mea_3b_2a 0.9451515
## 65 tmmn_mea_10b_2a tmmn_mea_30b_2a 0.9448676
## 20 sph_mea_10b_2a sph_mea_3b_2a 0.9444910
## 163 tmmx_mea_10b_2a tmmx_mea_30b_2a 0.9439311
## 94 tmmn_mea_10b_2a tmmn_min_10b_2a 0.9432278
## 214 tmmx_mea_10b_2a tmmx_max_10b_2a 0.9407082
## 86 sph_mea_10b_2a tmmn_min_10b_2a 0.9398296
## 26 sph_mea_10b_2a sph_min_10b_2a 0.9396314
## 235 tmmx_max_10b_2a tmmx_max_3b_2a 0.9393962
## 184 sph_mea_60b_2a tmmx_mea_60b_2a 0.9388149
## 57 sph_mea_10b_2a tmmn_mea_30b_2a 0.9382470
## 90 sph_min_10b_2a tmmn_min_10b_2a 0.9374985
## 381 fm1000_mea_3b_2a fm1000_max_10b_2a 0.9352116
## 102 sph_min_3b_2a tmmn_min_3b_2a 0.9340989
## 393 fm1000_min_10b_2a fm1000_max_3b_2a 0.9339807
## 73 tmmn_mea_10b_2a tmmn_mea_3b_2a 0.9335877
## 179 tmmx_mea_10b_2a tmmx_mea_3b_2a 0.9326106
## 383 fm1000_min_10b_2a fm1000_max_10b_2a 0.9317723
## 330 vpd_max_10b_2a vpd_max_3b_2a 0.9317461
## 190 tmmn_mea_30b_2a tmmx_mea_60b_2a 0.9316269
## 148 sph_mea_30b_2a tmmx_mea_30b_2a 0.9309797
## 360 fm100_mea_30b_2a fm1000_min_10b_2a 0.9300733
## 100 sph_mea_3b_2a tmmn_min_3b_2a 0.9291383
## 36 sph_mea_10b_2a sph_max_10b_2a 0.9252910
## 158 tmmn_mea_60b_2a tmmx_mea_30b_2a 0.9239583
## 302 tmmx_mea_60b_2a vpd_mea_60b_2a 0.9238437
## 239 vs_mea_30b_2a vs_mea_60b_2a 0.9234509
## 44 sph_mea_3b_2a sph_max_3b_2a 0.9227199
## 270 tmmx_mea_30b_2a vpd_mea_30b_2a 0.9212339
## 275 vpd_mea_10b_2a vpd_mea_30b_2a 0.9211954
## 182 sph_mea_30b_2a tmmx_mea_60b_2a 0.9207762
## 384 fm1000_min_3b_2a fm1000_max_10b_2a 0.9202933
## 51 sph_mea_3b_2a tmmn_mea_10b_2a 0.9199737
## 300 tmmx_mea_30b_2a vpd_mea_60b_2a 0.9194764
## 342 fm100_mea_30b_2a fm1000_mea_10b_2a 0.9191197
## 176 tmmn_min_3b_2a tmmx_mea_3b_2a 0.9173452
## 155 tmmn_mea_10b_2a tmmx_mea_30b_2a 0.9172704
## 63 sph_max_10b_2a tmmn_mea_30b_2a 0.9162646
## 329 vpd_mea_3b_2a vpd_max_3b_2a 0.9159700
## 37 sph_mea_30b_2a sph_max_10b_2a 0.9158938
## 347 fm1000_mea_10b_2a fm1000_mea_30b_2a 0.9156861
## 105 tmmn_mea_10b_2a tmmn_min_3b_2a 0.9156805
## 35 sph_min_10b_2a sph_min_3b_2a 0.9152757
## 368 fm100_mea_30b_2a fm1000_min_3b_2a 0.9131906
## 60 sph_mea_60b_2a tmmn_mea_30b_2a 0.9118933
## 9 rmax_mea_30b_2a rmax_mea_60b_2a 0.9111825
## 355 fm100_mea_60b_2a fm1000_mea_60b_2a 0.9107708
## 333 fm100_mea_30b_2a fm100_mea_60b_2a 0.9093508
## 109 tmmn_min_10b_2a tmmn_min_3b_2a 0.9089397
## 88 sph_mea_3b_2a tmmn_min_10b_2a 0.9086415
## 147 sph_mea_10b_2a tmmx_mea_30b_2a 0.9080406
## 349 fm100_mea_30b_2a fm1000_mea_3b_2a 0.9077918
## 31 sph_mea_10b_2a sph_min_3b_2a 0.9077118
## 131 sph_mea_10b_2a tmmx_mea_10b_2a 0.9073875
## 364 fm1000_mea_30b_2a fm1000_min_10b_2a 0.9071070
## 231 tmmx_mea_10b_2a tmmx_max_3b_2a 0.9066937
## 95 tmmn_mea_30b_2a tmmn_min_10b_2a 0.9062478
## 216 tmmx_mea_3b_2a tmmx_max_10b_2a 0.9052978
## 55 sph_max_10b_2a tmmn_mea_10b_2a 0.9051315
## 140 tmmn_mea_30b_2a tmmx_mea_10b_2a 0.9051152
## 284 vpd_mea_10b_2a vpd_mea_3b_2a 0.9049749
## 380 fm1000_mea_30b_2a fm1000_max_10b_2a 0.9049159
## 346 fm100_mea_60b_2a fm1000_mea_30b_2a 0.9049024
## 269 tmmx_mea_10b_2a vpd_mea_30b_2a 0.9031032
## 143 tmmn_min_10b_2a tmmx_mea_10b_2a 0.9019048
## 28 sph_mea_3b_2a sph_min_10b_2a 0.9017672
## 250 tmmx_mea_10b_2a vpd_mea_10b_2a 0.9002833
## 27 sph_mea_30b_2a sph_min_10b_2a 0.8999904
## 141 tmmn_mea_3b_2a tmmx_mea_10b_2a 0.8975320
## 50 sph_mea_30b_2a tmmn_mea_10b_2a 0.8951168
## 70 sph_min_3b_2a tmmn_mea_3b_2a 0.8938383
## 206 tmmn_mea_10b_2a tmmx_max_10b_2a 0.8926984
## 386 fm100_mea_30b_2a fm1000_max_3b_2a 0.8916617
## 335 fm100_mea_3b_2a fm100_min_3b_2a 0.8914984
## 166 sph_mea_3b_2a tmmx_mea_3b_2a 0.8901480
## 171 tmmn_mea_10b_2a tmmx_mea_3b_2a 0.8890554
## 96 tmmn_mea_3b_2a tmmn_min_10b_2a 0.8886765
## 48 sph_max_10b_2a sph_max_3b_2a 0.8886375
## 281 tmmx_mea_3b_2a vpd_mea_3b_2a 0.8881590
## 87 sph_mea_30b_2a tmmn_min_10b_2a 0.8880322
## 227 tmmn_mea_3b_2a tmmx_max_3b_2a 0.8873400
## 144 tmmn_min_3b_2a tmmx_mea_10b_2a 0.8866730
## 91 sph_min_3b_2a tmmn_min_10b_2a 0.8866610
## 72 sph_max_3b_2a tmmn_mea_3b_2a 0.8852183
## 320 vpd_mea_10b_2a vpd_max_10b_2a 0.8842729
## 215 tmmx_mea_30b_2a tmmx_max_10b_2a 0.8841178
## 124 sph_max_3b_2a tmmn_max_3b_2a 0.8829050
## 21 sph_mea_30b_2a sph_mea_3b_2a 0.8818872
## 6 rmax_mea_10b_2a rmax_mea_3b_2a 0.8818556
## 17 rmin_mea_30b_2a rmin_mea_60b_2a 0.8818304
## 150 sph_mea_60b_2a tmmx_mea_30b_2a 0.8818134
## 42 sph_mea_10b_2a sph_max_3b_2a 0.8815223
## 345 fm100_mea_30b_2a fm1000_mea_30b_2a 0.8793358
## 5 rmax_mea_10b_2a rmax_mea_30b_2a 0.8791767
## 376 fm100_mea_30b_2a fm1000_max_10b_2a 0.8784834
## 127 tmmn_mea_3b_2a tmmn_max_3b_2a 0.8771035
## 133 sph_mea_3b_2a tmmx_mea_10b_2a 0.8770338
## 56 sph_max_3b_2a tmmn_mea_10b_2a 0.8769701
## 75 sph_mea_10b_2a tmmn_mea_60b_2a 0.8764173
## 98 sph_mea_10b_2a tmmn_min_3b_2a 0.8756823
## 159 tmmn_min_10b_2a tmmx_mea_30b_2a 0.8755021
## 59 sph_mea_3b_2a tmmn_mea_30b_2a 0.8746892
## 66 sph_mea_10b_2a tmmn_mea_3b_2a 0.8733965
## 22 sph_mea_10b_2a sph_mea_60b_2a 0.8729098
## 180 tmmx_mea_30b_2a tmmx_mea_3b_2a 0.8705283
## 299 tmmx_mea_10b_2a vpd_mea_60b_2a 0.8705133
## 153 sph_max_10b_2a tmmx_mea_30b_2a 0.8702798
## 113 sph_max_10b_2a tmmn_max_10b_2a 0.8678517
## 81 sph_max_10b_2a tmmn_mea_60b_2a 0.8673197
## 195 tmmx_mea_10b_2a tmmx_mea_60b_2a 0.8672687
## 54 sph_min_3b_2a tmmn_mea_10b_2a 0.8666482
## 53 sph_min_10b_2a tmmn_mea_10b_2a 0.8652713
## 272 tmmx_mea_60b_2a vpd_mea_30b_2a 0.8643248
## 273 tmmx_max_10b_2a vpd_mea_30b_2a 0.8643160
## 322 vpd_mea_3b_2a vpd_max_10b_2a 0.8643149
## 181 sph_mea_10b_2a tmmx_mea_60b_2a 0.8639424
## 92 sph_max_10b_2a tmmn_min_10b_2a 0.8627743
## 83 tmmn_mea_10b_2a tmmn_mea_60b_2a 0.8601025
## 3 pr_mea_10b_2a pr_max_10b_2a 0.8595644
## 208 tmmn_mea_3b_2a tmmx_max_10b_2a 0.8594083
## 38 sph_mea_3b_2a sph_max_10b_2a 0.8588708
## 132 sph_mea_30b_2a tmmx_mea_10b_2a 0.8573646
## 74 tmmn_mea_30b_2a tmmn_mea_3b_2a 0.8568347
## 390 fm1000_mea_30b_2a fm1000_max_3b_2a 0.8557827
## 61 sph_min_10b_2a tmmn_mea_30b_2a 0.8554889
## 115 tmmn_mea_10b_2a tmmn_max_10b_2a 0.8553204
## 254 tmmx_max_10b_2a vpd_mea_10b_2a 0.8538452
## 93 sph_max_3b_2a tmmn_min_10b_2a 0.8536833
## 149 sph_mea_3b_2a tmmx_mea_30b_2a 0.8536109
## 225 tmmn_mea_10b_2a tmmx_max_3b_2a 0.8534962
## 198 sph_mea_10b_2a tmmx_max_10b_2a 0.8533260
## 175 tmmn_min_10b_2a tmmx_mea_3b_2a 0.8519583
## 361 fm100_mea_60b_2a fm1000_min_10b_2a 0.8517614
## 353 fm1000_mea_30b_2a fm1000_mea_3b_2a 0.8517165
## 14 rmax_mea_3b_2a rmax_min_3b_2a 0.8511814
## 294 tmmn_mea_30b_2a vpd_mea_60b_2a 0.8509620
## 252 tmmx_mea_3b_2a vpd_mea_10b_2a 0.8507761
## 39 sph_mea_60b_2a sph_max_10b_2a 0.8505092
## 106 tmmn_mea_30b_2a tmmn_min_3b_2a 0.8469643
## 340 fm100_mea_3b_2a fm100_max_3b_2a 0.8469356
## 283 tmmx_max_3b_2a vpd_mea_3b_2a 0.8466466
## 32 sph_mea_30b_2a sph_min_3b_2a 0.8465549
## 137 sph_max_10b_2a tmmx_mea_10b_2a 0.8464548
## 64 sph_max_3b_2a tmmn_mea_30b_2a 0.8462062
## 189 tmmn_mea_10b_2a tmmx_mea_60b_2a 0.8460951
## 229 tmmn_min_3b_2a tmmx_max_3b_2a 0.8457355
## 168 sph_min_3b_2a tmmx_mea_3b_2a 0.8454285
## 296 tmmn_mea_60b_2a vpd_mea_60b_2a 0.8454101
## 271 tmmx_mea_3b_2a vpd_mea_30b_2a 0.8447972
## 305 vpd_mea_10b_2a vpd_mea_60b_2a 0.8440298
## 327 vpd_mea_10b_2a vpd_max_3b_2a 0.8428709
## 220 sph_mea_3b_2a tmmx_max_3b_2a 0.8427024
## 372 fm1000_mea_30b_2a fm1000_min_3b_2a 0.8425714
## 264 tmmn_mea_30b_2a vpd_mea_30b_2a 0.8413219
## 200 sph_mea_3b_2a tmmx_max_10b_2a 0.8405840
## 343 fm100_mea_60b_2a fm1000_mea_10b_2a 0.8405249
## 157 tmmn_mea_3b_2a tmmx_mea_30b_2a 0.8404856
## 356 fm1000_mea_10b_2a fm1000_mea_60b_2a 0.8392754
## 43 sph_mea_30b_2a sph_max_3b_2a 0.8377781
## 236 vs_mea_10b_2a vs_mea_30b_2a 0.8355896
## 210 tmmn_min_10b_2a tmmx_max_10b_2a 0.8347312
## 388 fm100_max_10b_2a fm1000_max_3b_2a 0.8347246
## 160 tmmn_min_3b_2a tmmx_mea_30b_2a 0.8344214
## 164 sph_mea_10b_2a tmmx_mea_3b_2a 0.8344079
## 285 vpd_mea_30b_2a vpd_mea_3b_2a 0.8341865
## 207 tmmn_mea_30b_2a tmmx_max_10b_2a 0.8339247
## 187 sph_max_10b_2a tmmx_mea_60b_2a 0.8337180
## 382 fm1000_mea_60b_2a fm1000_max_10b_2a 0.8336899
## 243 vs_mea_3b_2a vs_max_3b_2a 0.8336697
## 255 tmmx_max_3b_2a vpd_mea_10b_2a 0.8333428
## 211 tmmn_min_3b_2a tmmx_max_10b_2a 0.8329770
## 101 sph_min_10b_2a tmmn_min_3b_2a 0.8329694
## 232 tmmx_mea_30b_2a tmmx_max_3b_2a 0.8328023
## 263 tmmn_mea_10b_2a vpd_mea_30b_2a 0.8326027
## 366 fm1000_mea_60b_2a fm1000_min_10b_2a 0.8301086
## 337 fm100_mea_10b_2a fm100_max_10b_2a 0.8295130
## 136 sph_min_3b_2a tmmx_mea_10b_2a 0.8288415
## 172 tmmn_mea_30b_2a tmmx_mea_3b_2a 0.8280160
## 62 sph_min_3b_2a tmmn_mea_30b_2a 0.8277873
## 97 tmmn_mea_60b_2a tmmn_min_10b_2a 0.8275733
## 104 sph_max_3b_2a tmmn_min_3b_2a 0.8251112
## 318 tmmx_max_10b_2a vpd_max_10b_2a 0.8245280
## 2 pr_mea_30b_2a pr_mea_60b_2a 0.8241044
## 303 tmmx_max_10b_2a vpd_mea_60b_2a 0.8239619
## 142 tmmn_mea_60b_2a tmmx_mea_10b_2a 0.8239193
## 274 tmmx_max_3b_2a vpd_mea_30b_2a 0.8226579
## 130 tmmn_max_10b_2a tmmn_max_3b_2a 0.8214656
## 40 sph_min_10b_2a sph_max_10b_2a 0.8210164
## 351 fm100_max_10b_2a fm1000_mea_3b_2a 0.8194656
## 151 sph_min_10b_2a tmmx_mea_30b_2a 0.8193626
## 138 sph_max_3b_2a tmmx_mea_10b_2a 0.8190688
## 170 sph_max_3b_2a tmmx_mea_3b_2a 0.8186731
## 326 tmmx_max_3b_2a vpd_max_3b_2a 0.8182077
## 287 sph_mea_30b_2a vpd_mea_60b_2a 0.8176210
## 336 fm100_min_10b_2a fm100_min_3b_2a 0.8174036
## 367 fm100_mea_10b_2a fm1000_min_3b_2a 0.8167881
## 279 tmmx_mea_10b_2a vpd_mea_3b_2a 0.8161704
## 135 sph_min_10b_2a tmmx_mea_10b_2a 0.8161292
## 71 sph_max_10b_2a tmmn_mea_3b_2a 0.8160966
## 251 tmmx_mea_30b_2a vpd_mea_10b_2a 0.8160650
## 15 rmax_min_10b_2a rmax_min_3b_2a 0.8160204
## 348 fm100_mea_10b_2a fm1000_mea_3b_2a 0.8152304
## 350 fm100_mea_60b_2a fm1000_mea_3b_2a 0.8145604
## 46 sph_min_10b_2a sph_max_3b_2a 0.8143213
## 47 sph_min_3b_2a sph_max_3b_2a 0.8137141
## 29 sph_mea_60b_2a sph_min_10b_2a 0.8123372
## 204 sph_max_10b_2a tmmx_max_10b_2a 0.8114789
## 293 tmmn_mea_10b_2a vpd_mea_60b_2a 0.8114770
## 228 tmmn_min_10b_2a tmmx_max_3b_2a 0.8114444
## 193 tmmn_min_10b_2a tmmx_mea_60b_2a 0.8111608
## 319 tmmx_max_3b_2a vpd_max_10b_2a 0.8109923
## 378 fm100_max_10b_2a fm1000_max_10b_2a 0.8104962
## 152 sph_min_3b_2a tmmx_mea_30b_2a 0.8104490
## 224 sph_max_3b_2a tmmx_max_3b_2a 0.8104002
## 377 fm100_mea_60b_2a fm1000_max_10b_2a 0.8101348
## 125 tmmn_mea_10b_2a tmmn_max_3b_2a 0.8101142
## 116 tmmn_mea_30b_2a tmmn_max_10b_2a 0.8098734
## 122 sph_mea_3b_2a tmmn_max_3b_2a 0.8090359
## 344 fm100_max_10b_2a fm1000_mea_10b_2a 0.8081441
## 369 fm100_mea_60b_2a fm1000_min_3b_2a 0.8076377
## 154 sph_max_3b_2a tmmx_mea_30b_2a 0.8067012
## 286 sph_mea_10b_2a vpd_mea_60b_2a 0.8050192
## 324 tmmx_mea_3b_2a vpd_max_3b_2a 0.8050185
## 314 vpd_mea_3b_2a vpd_min_3b_2a 0.8049575
## 218 sph_mea_10b_2a tmmx_max_3b_2a 0.8044754
## 387 fm100_mea_60b_2a fm1000_max_3b_2a 0.8041111
## 217 tmmx_mea_60b_2a tmmx_max_10b_2a 0.8036604
## 69 sph_min_10b_2a tmmn_mea_3b_2a 0.8036209
## 282 tmmx_max_10b_2a vpd_mea_3b_2a 0.8034870
## 178 tmmn_max_3b_2a tmmx_mea_3b_2a 0.8034373
## 99 sph_mea_30b_2a tmmn_min_3b_2a 0.8022643
## 77 sph_mea_3b_2a tmmn_mea_60b_2a 0.8019133
## 79 sph_min_10b_2a tmmn_mea_60b_2a 0.8009040
## 354 fm100_mea_30b_2a fm1000_mea_60b_2a 0.8008961
## 267 tmmn_min_10b_2a vpd_mea_30b_2a 0.8008744
## 52 sph_mea_60b_2a tmmn_mea_10b_2a 0.8005852
## 205 sph_max_3b_2a tmmx_max_10b_2a 0.8003790
## 321 vpd_mea_30b_2a vpd_max_10b_2a 0.8000642
## 301 tmmx_mea_3b_2a vpd_mea_60b_2a 0.7995666
## 245 tmmn_mea_10b_2a vpd_mea_10b_2a 0.7990491
## 237 vs_mea_10b_2a vs_mea_3b_2a 0.7990366
## 18 rmin_min_10b_2a rmin_min_3b_2a 0.7988210
## 370 fm100_max_10b_2a fm1000_min_3b_2a 0.7968714
## 183 sph_mea_3b_2a tmmx_mea_60b_2a 0.7966613
## 114 sph_max_3b_2a tmmn_max_10b_2a 0.7964401
## 67 sph_mea_30b_2a tmmn_mea_3b_2a 0.7963096
## 10 rmax_mea_10b_2a rmax_min_10b_2a 0.7955371
## 289 sph_mea_60b_2a vpd_mea_60b_2a 0.7946201
## 199 sph_mea_30b_2a tmmx_max_10b_2a 0.7932728
## 297 tmmn_min_10b_2a vpd_mea_60b_2a 0.7924448
## 89 sph_mea_60b_2a tmmn_min_10b_2a 0.7915948
## 103 sph_max_10b_2a tmmn_min_3b_2a 0.7908986
## 385 fm100_mea_10b_2a fm1000_max_3b_2a 0.7900129
## 265 tmmn_mea_3b_2a vpd_mea_30b_2a 0.7884929
## 256 sph_mea_10b_2a vpd_mea_30b_2a 0.7878817
## 268 tmmn_min_3b_2a vpd_mea_30b_2a 0.7873127
## 317 tmmx_mea_3b_2a vpd_max_10b_2a 0.7864357
## 185 sph_min_10b_2a tmmx_mea_60b_2a 0.7856143
## 110 sph_mea_10b_2a tmmn_max_10b_2a 0.7853490
## 197 tmmx_mea_3b_2a tmmx_mea_60b_2a 0.7852296
## 24 sph_mea_3b_2a sph_mea_60b_2a 0.7849454
## 266 tmmn_mea_60b_2a vpd_mea_30b_2a 0.7847490
## 241 vs_mea_10b_2a vs_max_10b_2a 0.7843498
## 230 tmmn_max_3b_2a tmmx_max_3b_2a 0.7842433
## 226 tmmn_mea_30b_2a tmmx_max_3b_2a 0.7828473
## 203 sph_min_3b_2a tmmx_max_10b_2a 0.7817465
## 247 tmmn_mea_3b_2a vpd_mea_10b_2a 0.7816098
## 316 tmmx_mea_10b_2a vpd_max_10b_2a 0.7810091
## 41 sph_min_3b_2a sph_max_10b_2a 0.7792104
## 117 tmmn_mea_3b_2a tmmn_max_10b_2a 0.7788492
## 82 sph_max_3b_2a tmmn_mea_60b_2a 0.7783472
## 222 sph_min_3b_2a tmmx_max_3b_2a 0.7782739
## 392 fm1000_mea_60b_2a fm1000_max_3b_2a 0.7782317
## 257 sph_mea_30b_2a vpd_mea_30b_2a 0.7769749
## 277 tmmn_mea_3b_2a vpd_mea_3b_2a 0.7768379
## 249 tmmn_min_3b_2a vpd_mea_10b_2a 0.7761715
## 8 rmax_mea_10b_2a rmax_mea_60b_2a 0.7760584
## 304 tmmx_max_3b_2a vpd_mea_60b_2a 0.7754155
## 165 sph_mea_30b_2a tmmx_mea_3b_2a 0.7744047
## 134 sph_mea_60b_2a tmmx_mea_10b_2a 0.7735775
## 358 fm1000_mea_3b_2a fm1000_mea_60b_2a 0.7731031
## 145 tmmn_max_10b_2a tmmx_mea_10b_2a 0.7728859
## 7 rmax_mea_30b_2a rmax_mea_3b_2a 0.7727621
## 325 tmmx_max_10b_2a vpd_max_3b_2a 0.7719813
## 278 tmmn_min_3b_2a vpd_mea_3b_2a 0.7702945
## 288 sph_mea_3b_2a vpd_mea_60b_2a 0.7700474
## 169 sph_max_10b_2a tmmx_mea_3b_2a 0.7676273
## 238 vs_mea_10b_2a vs_mea_60b_2a 0.7672088
## 258 sph_mea_3b_2a vpd_mea_30b_2a 0.7659762
## 331 fm100_mea_10b_2a fm100_mea_30b_2a 0.7636262
## 85 tmmn_mea_3b_2a tmmn_mea_60b_2a 0.7630096
## 129 tmmn_min_3b_2a tmmn_max_3b_2a 0.7624753
## 240 vs_mea_3b_2a vs_min_3b_2a 0.7624698
## 167 sph_min_10b_2a tmmx_mea_3b_2a 0.7610778
## 290 sph_min_10b_2a vpd_mea_60b_2a 0.7606277
## 374 fm1000_mea_60b_2a fm1000_min_3b_2a 0.7602377
## 298 tmmn_min_3b_2a vpd_mea_60b_2a 0.7566183
## 191 tmmn_mea_3b_2a tmmx_mea_60b_2a 0.7565302
## 186 sph_min_3b_2a tmmx_mea_60b_2a 0.7564029
## 202 sph_min_10b_2a tmmx_max_10b_2a 0.7551722
## 80 sph_min_3b_2a tmmn_mea_60b_2a 0.7545809
## 362 fm100_max_10b_2a fm1000_min_10b_2a 0.7544689
## 13 rmax_mea_10b_2a rmax_min_3b_2a 0.7542812
## 328 vpd_mea_30b_2a vpd_max_3b_2a 0.7538760
## 248 tmmn_min_10b_2a vpd_mea_10b_2a 0.7537701
## 291 sph_min_3b_2a vpd_mea_60b_2a 0.7534378
## 332 fm100_mea_10b_2a fm100_mea_3b_2a 0.7525933
## 108 tmmn_mea_60b_2a tmmn_min_3b_2a 0.7519949
## 45 sph_mea_60b_2a sph_max_3b_2a 0.7517904
## 188 sph_max_3b_2a tmmx_mea_60b_2a 0.7514673
## 194 tmmn_min_3b_2a tmmx_mea_60b_2a 0.7513228
## 295 tmmn_mea_3b_2a vpd_mea_60b_2a 0.7509624
## 209 tmmn_mea_60b_2a tmmx_max_10b_2a 0.7496166
## 126 tmmn_mea_30b_2a tmmn_max_3b_2a 0.7487218
## 161 tmmn_max_10b_2a tmmx_mea_30b_2a 0.7485015
## 339 fm100_mea_10b_2a fm100_max_3b_2a 0.7480004
## 123 sph_max_10b_2a tmmn_max_3b_2a 0.7473826
## 119 tmmn_min_10b_2a tmmn_max_10b_2a 0.7466314
## 146 tmmn_max_3b_2a tmmx_mea_10b_2a 0.7455049
## 223 sph_max_10b_2a tmmx_max_3b_2a 0.7454624
## 212 tmmn_max_10b_2a tmmx_max_10b_2a 0.7454285
## 234 tmmx_mea_60b_2a tmmx_max_3b_2a 0.7454020
## 261 sph_min_3b_2a vpd_mea_30b_2a 0.7449614
## 307 vpd_mea_3b_2a vpd_mea_60b_2a 0.7443110
## 359 fm100_mea_10b_2a fm1000_min_10b_2a 0.7439155
## 34 sph_mea_60b_2a sph_min_3b_2a 0.7430221
## 111 sph_mea_30b_2a tmmn_max_10b_2a 0.7424322
## 16 rmin_mea_10b_2a rmin_mea_30b_2a 0.7422024
## 280 tmmx_mea_30b_2a vpd_mea_3b_2a 0.7403596
## 128 tmmn_min_10b_2a tmmn_max_3b_2a 0.7403052
## 174 tmmn_mea_60b_2a tmmx_mea_3b_2a 0.7401437
## 246 tmmn_mea_30b_2a vpd_mea_10b_2a 0.7398622
## 242 vs_mea_30b_2a vs_max_10b_2a 0.7394763
## 292 sph_max_10b_2a vpd_mea_60b_2a 0.7382368
## 213 tmmn_max_3b_2a tmmx_max_10b_2a 0.7365864
## 219 sph_mea_30b_2a tmmx_max_3b_2a 0.7363737
## 112 sph_mea_3b_2a tmmn_max_10b_2a 0.7353093
## 323 tmmx_mea_10b_2a vpd_max_3b_2a 0.7349118
## 118 tmmn_mea_60b_2a tmmn_max_10b_2a 0.7341556
## 334 fm100_mea_3b_2a fm100_min_10b_2a 0.7340848
## 30 rmin_min_3b_2a sph_min_3b_2a 0.7328930
## 341 fm100_mea_10b_2a fm1000_mea_10b_2a 0.7328532
## 260 sph_min_10b_2a vpd_mea_30b_2a 0.7328024
## 121 sph_mea_10b_2a tmmn_max_3b_2a 0.7322526
## 338 fm100_mea_30b_2a fm100_max_10b_2a 0.7315318
## 308 vpd_mea_10b_2a vpd_min_10b_2a 0.7303579
## 253 tmmx_mea_60b_2a vpd_mea_10b_2a 0.7302923
## 11 rmax_mea_30b_2a rmax_min_10b_2a 0.7258649
## 221 sph_min_10b_2a tmmx_max_3b_2a 0.7257265
## 276 tmmn_mea_10b_2a vpd_mea_3b_2a 0.7232915
## 262 sph_max_10b_2a vpd_mea_30b_2a 0.7219168
## 25 rmin_min_10b_2a sph_min_10b_2a 0.7167212
## 259 sph_mea_60b_2a vpd_mea_30b_2a 0.7166580
## 177 tmmn_max_10b_2a tmmx_mea_3b_2a 0.7119584
## 311 tmmx_mea_3b_2a vpd_min_3b_2a 0.7109865
## 309 vpd_mea_30b_2a vpd_min_10b_2a 0.7087527
## 312 vpd_mea_10b_2a vpd_min_3b_2a 0.7086487
## 120 tmmn_min_3b_2a tmmn_max_10b_2a 0.7085530
## 1 pr_mea_10b_2a pr_mea_30b_2a 0.7080461
## 201 sph_mea_60b_2a tmmx_max_10b_2a 0.7076918
## 12 rmax_mea_3b_2a rmax_min_10b_2a 0.7073449
## 315 tmmn_mea_3b_2a vpd_max_10b_2a 0.7071848
## 162 tmmn_max_3b_2a tmmx_mea_30b_2a 0.7061560
## 244 sph_mea_3b_2a vpd_mea_10b_2a 0.7038500
## 313 vpd_mea_30b_2a vpd_min_3b_2a 0.7009173
## 310 vpd_mea_60b_2a vpd_min_10b_2a 0.7004817
Drought Data Analysis
Correlation
drought_numeric_data <- drought[sapply(drought, is.numeric)]
drought_cor_mat <- cor(drought_numeric_data, use = "pairwise.complete.obs")
corrplot(drought_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
drought_high_corr <- which(abs(drought_cor_mat) > threshold, arr.ind = TRUE)
drought_high_corr_pairs <- drought_high_corr[drought_high_corr[,1] < drought_high_corr[,2], ]
drought_flagged <- data.frame(
Var1 = rownames(drought_cor_mat)[drought_high_corr_pairs[,1]],
Var2 = colnames(drought_cor_mat)[drought_high_corr_pairs[,2]],
Correlation = drought_cor_mat[drought_high_corr_pairs]
)
# Sort by absolute correlation
drought_flagged <- drought_flagged[order(abs(drought_flagged$Correlation), decreasing = TRUE), ]
drought_flagged
## Var1 Var2 Correlation
## 44 spei90d spi90d 0.9772039
## 35 spei2y spi2y 0.9759757
## 21 spei1y spi1y 0.9672280
## 38 spei30d spi30d 0.9564792
## 14 spei14d spi14d 0.9506904
## 16 spei180d spi180d 0.9504171
## 41 spei5y spi5y 0.9496125
## 28 spei270d spi270d 0.9424413
## 22 spei270d spi1y 0.9189033
## 19 pdsi spi1y 0.9181572
## 2 pdsi spei1y 0.9150541
## 6 spei1y spei270d 0.9106080
## 4 pdsi spei270d 0.8917144
## 31 spi1y spi270d 0.8814242
## 25 pdsi spi270d 0.8399088
## 7 pdsi spei2y 0.8393836
## 32 pdsi spi2y 0.8346379
## 42 spi2y spi5y 0.8326091
## 40 spei2y spi5y 0.8220077
## 11 spei2y spei5y 0.8190439
## 27 spei1y spi270d 0.8125203
## 36 spei5y spi2y 0.8088976
## 5 spei180d spei270d 0.8045463
## 26 spei180d spi270d 0.8043149
## 30 spi180d spi270d 0.8037294
## 1 pdsi spei180d 0.8025228
## 45 spi180d spi90d 0.7968319
## 12 spei180d spei90d 0.7959434
## 18 spei90d spi180d 0.7951290
## 20 spei180d spi1y 0.7891033
## 37 spi1y spi2y 0.7620473
## 23 spei2y spi1y 0.7604021
## 43 spei180d spi90d 0.7562671
## 3 spei180d spei1y 0.7547638
## 8 spei1y spei2y 0.7470112
## 33 spei1y spi2y 0.7434680
## 39 pdsi spi5y 0.7387218
## 29 spei90d spi270d 0.7349395
## 17 spei270d spi180d 0.7347740
## 15 pdsi spi180d 0.7329061
## 24 spi180d spi1y 0.7284065
## 46 spi270d spi90d 0.7243975
## 13 spei270d spei90d 0.7146572
## 9 spei270d spei2y 0.7085060
## 10 pdsi spei5y 0.7007206
## 34 spei270d spi2y 0.7005646
Rap Data Analysis
Correlation
rap_numeric_data <- rap[sapply(rap, is.numeric)]
rap_cor_mat <- cor(rap_numeric_data, use = "pairwise.complete.obs")
corrplot(rap_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
rap_high_corr <- which(abs(rap_cor_mat) > threshold, arr.ind = TRUE)
rap_high_corr_pairs <- rap_high_corr[rap_high_corr[,1] < rap_high_corr[,2], ]
rap_flagged <- data.frame(
Var1 = rownames(rap_cor_mat)[rap_high_corr_pairs[,1]],
Var2 = colnames(rap_cor_mat)[rap_high_corr_pairs[,2]],
Correlation = rap_cor_mat[rap_high_corr_pairs]
)
# Sort by absolute correlation
rap_flagged <- rap_flagged[order(abs(rap_flagged$Correlation), decreasing = TRUE), ]
rap_flagged
## Var1 Var2 Correlation
## 3 prop_pfg prop_tre -0.8798649
## 6 prop_ltr prop_bgr 0.8669045
## 7 prop_tre prop_bgr -0.7701901
## 1 prop_afg prop_ltr 0.7518690
## 4 prop_ltr prop_tre -0.7402682
## 2 prop_afg prop_tre -0.7324994
## 5 prop_afg prop_bgr 0.7003461
Hydro Dist Data Analysis
Correlation
hydroDist_numeric_data <- hydroDist[sapply(hydroDist, is.numeric)]
hydroDist_cor_mat <- cor(hydroDist_numeric_data, use = "pairwise.complete.obs")
corrplot(hydroDist_cor_mat, method = "color", tl.cex = 0.6)
threshold <- 0.70
hydroDist_high_corr <- which(abs(hydroDist_cor_mat) > threshold, arr.ind = TRUE)
hydroDist_high_corr_pairs <- hydroDist_high_corr[hydroDist_high_corr[,1] < hydroDist_high_corr[,2], ]
hydroDist_flagged <- data.frame(
Var1 = rownames(hydroDist_cor_mat)[hydroDist_high_corr_pairs[,1]],
Var2 = colnames(hydroDist_cor_mat)[hydroDist_high_corr_pairs[,2]],
Correlation = hydroDist_cor_mat[hydroDist_high_corr_pairs]
)
# Sort by absolute correlation
hydroDist_flagged <- hydroDist_flagged[order(abs(hydroDist_flagged$Correlation), decreasing = TRUE), ]
hydroDist_flagged
## [1] Var1 Var2 Correlation
## <0 rows> (or 0-length row.names)
