I have a criteria table:
criteria <-data.frame(
dx = c(0.244897959,
6.112244898),
slim_frac = c(0L, 1L),
klim = c(1L, 40L),
frackhfrac = c(0L, 1L),
por = c(0.04, 0.16),
matrix_k_log = c(-11L, -2L),
srv_perm_log = c(-6L, 1L),
frac_perm_log = c(-3L, 4L),
pb_init = c(0L, 10000L),
bhp = c(200L, 2000L)
I want to find all matching rows from my main table and extract the "srv_volumn" column, so dx needs to be within (0.244898, 6.112245) and slim_factor is (0,1) till bhp is within (200, 2000). Do I need to apply a map function to go through all rows of the criteria table?
Here's part of my main database:
main <- tibble::tribble(
~matrix_k, ~matrix_k_log, ~well_dd, ~srv_perm, ~srv_perm_log, ~frac_perm, ~frac_perm_log, ~por, ~inter_frac_spac, ~fracs_per_mile_well, ~models_per_7500_well, ~mod_radius, ~dx, ~model_area, ~jlim, ~jlim2, ~iws, ~pb_init, ~klim, ~klim2, ~thickness, ~target_frac_len, ~flim, ~flength1, ~flength1_frac, ~flength2, ~flim2, ~eflim, ~eflim_1, ~eflim2, ~eflim2_2, ~frac_length_ratio, ~slim_frac, ~srv_width_along_well, ~slim, ~slim1_1, ~slim2, ~slim2_1, ~frackhfrac, ~f_height, ~fhlim, ~efhlim, ~efhlim2, ~fh_fl, ~well_cell, ~int_well_spac_frac, ~srv_area, ~srv_volume, ~rock_comp, ~srv_vol_frac,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 1.2970541, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 98L, 258.72, 0.98, 258.72, 98L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 25842.279, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 1.1791401, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 99L, 237.6, 0.99, 237.6, 99L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 23732.705, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 1.0808784, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 99L, 217.8, 0.99, 217.8, 99L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 21754.979, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 0.99773388, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 99L, 201.04615, 0.99, 201.04615, 99L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 20081.519, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 0.92646719, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 99L, 186.68572, 0.99, 186.68572, 99L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 18647.126, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 0.86470271, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 99L, 174.24, 0.99, 174.24, 99L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 17403.983, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
7.8e-08, -7.108125, 1000L, 0.026474569, -1.5771711, 0.57349005, -0.24147411, 0.11467016, 411.56523, 12.829072, 18.223114, 0.023894001, 4.1894411, 0.81065879, 52L, 101L, 166.4, 4869.1044, 23L, 24L, 115L, 256.57068, 99L, 163.35, 0.99, 163.35, 99L, 52L, 53L, 52L, 53L, 1L, 0.42605267, 87.674232, 22L, 23L, 79L, 78L, 0.86856627, 99.885121, 20L, 20L, 21L, 16316.235, 11L, 1, 2.7630667, 276.30667, 4e-06, 0.37387814,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 6.0353762, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 18L, 475.2, 0.18, 475.2, 18L, 18L, 19L, 18L, 19L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 36712.924, 9L, 0.34615385, 0.49134259, 39.307407, 4e-06, 0.11369702,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 3.0176881, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 36L, 475.2, 0.36, 475.2, 36L, 36L, 37L, 36L, 37L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 36712.924, 9L, 0.69230769, 0.98268517, 78.614814, 4e-06, 0.22739405,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 2.0117921, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 54L, 475.2, 0.54, 475.2, 54L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 36712.924, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 1.5088441, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 72L, 475.2, 0.72, 475.2, 72L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 36712.924, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 1.2070752, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 90L, 475.2, 0.9, 475.2, 90L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 36712.924, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 1.005896, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 435.6, 0.99, 435.6, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 33653.513, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.86219661, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 373.37143, 0.99, 373.37143, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 28845.869, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.75442203, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 326.7, 0.99, 326.7, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 25240.135, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.67059735, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 290.4, 0.99, 290.4, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 22435.676, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.60353762, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 261.36, 0.99, 261.36, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 20192.108, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.54867057, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 237.6, 0.99, 237.6, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 18356.462, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.50294802, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 217.8, 0.99, 217.8, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 16826.757, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.4642597, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 201.04615, 0.99, 201.04615, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 15532.39, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.4310983, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 186.68572, 0.99, 186.68572, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 14422.935, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.40235841, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 174.24, 0.99, 174.24, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 13461.405, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
1.27e-09, -8.8955454, 1000L, 0.004173795, -2.3794689, 0.56748402, -0.24604636, 0.11778517, 191.50713, 27.570775, 39.163033, 0.01629904, 1.9439503, 0.37721101, 52L, 101L, 166.4, 4305.5723, 23L, 24L, 115L, 469.93566, 99L, 163.35, 0.99, 163.35, 99L, 52L, 53L, 52L, 53L, 1L, 0.47037139, 45.039737, 24L, 25L, 77L, 76L, 0.67180727, 77.257836, 16L, 16L, 17L, 12620.068, 9L, 1, 1.4194341, 113.55473, 4e-06, 0.32845807,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 17.928434, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 14L, 369.6, 0.14, 369.6, 14L, 14L, 15L, 14L, 15L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3962.0403, 2L, 0.26923077, 2.2709871, 34.064807, 4e-06, 0.02347828,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 8.964217, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 27L, 356.4, 0.27, 356.4, 27L, 27L, 28L, 27L, 28L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3820.5389, 2L, 0.51923077, 4.3797608, 65.696412, 4e-06, 0.045279539,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 5.9761446, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 41L, 360.8, 0.41, 360.8, 41L, 41L, 42L, 41L, 42L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3867.706, 2L, 0.78846154, 6.6507479, 99.761219, 4e-06, 0.068757819,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 4.4821085, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 54L, 356.4, 0.54, 356.4, 54L, 52L, 53L, 52L, 53L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3820.5389, 2L, 1, 8.4350949, 126.52642, 4e-06, 0.087205038,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 3.5856868, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 67L, 353.76, 0.67, 353.76, 67L, 52L, 53L, 52L, 53L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3792.2386, 2L, 1, 8.4350949, 126.52642, 4e-06, 0.087205038,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 2.9880723, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 81L, 356.4, 0.81, 356.4, 81L, 52L, 53L, 52L, 53L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3820.5389, 2L, 1, 8.4350949, 126.52642, 4e-06, 0.087205038,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 2.5612049, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 94L, 354.51429, 0.94, 354.51429, 94L, 52L, 53L, 52L, 53L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3800.3244, 2L, 1, 8.4350949, 126.52642, 4e-06, 0.087205038,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 2.2410542, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 99L, 326.7, 0.99, 326.7, 99L, 52L, 53L, 52L, 53L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3502.1606, 2L, 1, 8.4350949, 126.52642, 4e-06, 0.087205038,
7.29e-05, -4.136994, 1000L, 0.003622004, -2.4410511, 47.36649, 1.6754712, 0.060275867, 568.883, 9.2813461, 13.18373, 0.028091894, 5.7947245, 1.9920482, 52L, 101L, 166.4, 4650.3077, 33L, 34L, 165L, 353.11059, 99L, 290.4, 0.99, 290.4, 99L, 52L, 53L, 52L, 53L, 1L, 0.94097399, 267.65205, 48L, 49L, 53L, 52L, 0.064968519, 10.719806, 3L, 3L, 4L, 3113.0317, 2L, 1, 8.4350949, 126.52642, 4e-06, 0.087205038
)
CodePudding user response：
This is one of the most trick questions one can ask in the tidyverse-um, so I would first provide a general solution with the mpg dataset:
library(tidyverse)
mpg_criteria <-
tribble(~X, ~min, ~max,
"displ", 2.0, 5.4,
"year", 1999.0, 2008.0,
"cyl", 4.0, 8.0,
"cty", 11.0, 21.0,
"hwy", 16.3, 30.0,
)
mpg_criteria$X <- syms(mpg_criteria$X)
mpg_criteria_cond <- pmap(mpg_criteria, ~ partial(filter, ... =, between(!!..1, !!..2, !!..3)))
filter_fn <- compose(!!!mpg_criteria_cond)
mpg_filtered <- filter_fn(mpg)
so the solution for your question is something along
criteria$X <- syms(criteria$X)
criteria_cond <- pmap(criteria, ~ partial(filter, ... =, between(!!..1, !!..2, !!..3)))
filter_fn <- compose(!!!criteria_cond)
main_filtered <- filter_fn(main)
Edit
The question was edited meanwhile, so here is the "original" format of criteria:
criteria <- data.frame(
stringsAsFactors = FALSE,
X = c("dx","slim_frac","klim","frackhfrac","por","matrix_k_log","srv_perm_log","frac_perm_log","pb_init","bhp"),
min = c(0.244897959, 0, 1, 0, 0.04, -11, -6, -3, 0, 200),
max = c(6.112244898, 1, 40, 1, 0.16, -2, 1, 4, 10000, 2000)
)
CodePudding user response：
Here's an approach using dplyr and a reshaped-long version of the data.
When I ran the code below on the sample data, I didn't find any out of range. So I made an altered version of the data set where row 1 and 2 were intentionally assigned values out of range from the criteria.
library(tidyverse)
main_errors <- main %>%
mutate(orig_row = row_number(), .before = 1) %>%
mutate(dx = if_else(orig_row == 1, 0, dx),
slim_frac = if_else(orig_row == 2, 1.5, slim_frac))
Here, I reshape long using tidyr::pivot_longer, so every value in the original wide table gets its own row, with the column name in the "X" column. Then I join to the criteria on the X variable that they have in common. Then, I filter to just keep rows where a criteria was specified (otherwise it will be NA), and where the value is either too low or two high. The out_of_criteria table will show the specific rows we want to exclude, and the variables that were out of range.
out_of_criteria <- main_errors %>%
pivot_longer(-orig_row, names_to = "X") %>%
mutate(X = fct_inorder(X)) %>% # Optional, to preserve original column order
left_join(criteria) %>%
filter(!is.na(min), value < min | value > max)
out_of_criteria
## A tibble: 2 × 5
# orig_row X value min max
# <int> <chr> <dbl> <dbl> <dbl>
#1 1 dx 0 0.245 6.11
#2 2 slim_frac 1.5 0 1
Finally, we can "anti-join" to remove the rows with errors from the main table. Note that rows 1 and 2 are now excluded.
main_errors %>%
anti_join(out_of_criteria, by = "orig_row")
# A tibble: 30 × 51
orig_row matrix_k matrix_k_log well_dd srv_perm srv_perm_log frac_perm frac_perm_log por inter_frac_spac fracs_per_mile_…
<int> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
2 4 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
3 5 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
4 6 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
5 7 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
6 8 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
7 9 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
8 10 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
9 11 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
10 12 6.8e-10 -9.17 1000 0.0285 -1.54 0.408 -0.390 0.0516 321. 16.5
# … with 20 more rows, and 40 more variables: models_per_7500_well <dbl>, mod_radius <dbl>, dx <dbl>, model_area <dbl>,
# jlim <int>, jlim2 <int>, iws <dbl>, pb_init <dbl>, klim <int>, klim2 <int>, thickness <int>, target_frac_len <dbl>,
# flim <int>, flength1 <dbl>, flength1_frac <dbl>, flength2 <dbl>, flim2 <int>, eflim <int>, eflim_1 <int>, eflim2 <int>,
# eflim2_2 <int>, frac_length_ratio <int>, slim_frac <dbl>, srv_width_along_well <dbl>, slim <int>, slim1_1 <int>, slim2 <int>,
# slim2_1 <int>, frackhfrac <dbl>, f_height <dbl>, fhlim <int>, efhlim <int>, efhlim2 <int>, fh_fl <dbl>, well_cell <int>,
# int_well_spac_frac <dbl>, srv_area <dbl>, srv_volume <dbl>, rock_comp <dbl>, srv_vol_frac <dbl>