0

To fill NA in one column by subtracting the sum of the other 2 columns from the total, I applied the trick in this thread Conditionally replace NA in one column by subtracting the sum of other columns from the total on non-integers and the resulting filled data frame missed some rows. For example, the NA in Weight_en_unknown row 1 should be 0, but from a manual calculation in R, 7.5859 - (5.1119 + 2.4740) = -8.881784e-16. In the end 33 out of 575 rows were missed.

Weight_xx <- grep("^Weight", names(dat ), value = TRUE)
i3 <- rowSums(dat[Weight_xx ], na.rm = TRUE) == dat$Total_wt_end
i3[is.na(i3)] <- FALSE
i4 <- is.na(dat[i3, Weight_xx])
dat[i3, Weight_xx][i4] <- 0

> dat[!complete.cases(dat), ]
# A tibble: 33 × 5
   Weight_end_female Weight_end_male Weight_end_unknown address  Total_wt_end
               <dbl>           <dbl>              <dbl> <chr>           <dbl>
 1            5.11            2.47              NA      14_E_4_1       7.59  
 2            0.0739         NA                  0.0097 14_W_2_5       0.0836
 3            0.385           0.104             NA      14_W_3_2       0.489 
 4            2.77            1.09              NA      14_W_7_1       3.86  
 5            4.55            0.534             NA      17_W_4_1       5.09  
 6            0.185           0.117             NA      17_W_6_3       0.303 
 7            0.159           0.0699            NA      22_E_1_3       0.229 
 8            0.641           0.104             NA      22_E_2_2       0.744 
 9            0.0507          0.0091            NA      22_E_3_5       0.0598
10            1.42            0.108             NA      22_E_4_2       1.53  



> dput(dat)
structure(list(Weight_end_female = c(0.9341, 4.4778, 0, 0.727, 
0.1419, 0, 1.9041, 0.0759, 0, 0.0804, 0.2421, 1.2034, 4.2455, 
0, 1.3487, 0, 1.9022, 0.1706, 0, 0.0853, 0, 0, 0, 0.0155, 0, 
0.29, 0, 0.3464, 0.6939, 5.938, 0.0902, 9.4023, 0.8246, 0.0615, 
0.2763, 5.1119, 0.3439, 1.3374, 0.361, 1.1428, 0, 0, 0, 0.0217, 
5.6778, 0.3683, 0.9401, 0.3557, 2.3618, 0.6486, 0.0739, 0.1663, 
0.385, 0, 3.4912, 0, 7.7997, 0.9618, 2.7659, 0, 0.3666, 0, 0, 
0, 0, 0, 0, 0.3587, 0, 0, 0, 0, 0, 0, 0, 0, 3.2885, 0, 0, 0, 
1.3013, 0, 0.7707, 0.0823, 0, 0.1122, 0.6648, 0, 1.1569, 0.1501, 
1.882, 0, 0, 0.0384, 0.25, 0.2973, 1.1543, 1.3869, 0.0634, 0.6232, 
5.5405, 4.5524, 0.756, 0.8857, 0, 3.5987, 0.1853, 0.9749, 0, 
0, 0, 0, 3.1977, 0, 0, 4.4359, 1.3732, 0, 0.0227, 3.7139, 0, 
0, 1.0018, 0, 3.6247, 0.0452, 0.1267, 6.6018, 0, 0, 0, 0, 0.1905, 
0, 0, 0, 0.266, 0.1973, 1.6018, 0, 0, 0, 0, 0, 0.1591, 0, 0.0084, 
0.0963, 0.6406, 0.0374, 0.008, 0.5122, 0.252, 0.0507, 1.8351, 
0.2426, 1.4203, 0.4968, 0.9882, 0, 0, 0.3348, 0, 0.1112, 0.1581, 
0, 1.1943, 0.6542, 0, 2.1416, 2.4568, 0, 2.5084, 0, 0, 0, 0.1588, 
0.8203, 0, 0.0872, 0, 0.4005, 0, 0, 0, 5.6089, 0, 0, 0.0731, 
1.3262, 0, 1.4659, 0, 0, 0.0425, 0.4735, 0.1307, 0.5991, 0.1074, 
0.0297, 0, 0.877, 0.0595, 0.089, NA, 0, 0, 0, 0, 0.1498, 0.0115, 
0, 0, 0, 0, 26.5733, 0.1444, 0, 9.7832, 0.1524, 0, 1.2069, 0.0189, 
26.0089, 0.0353, 0, 0, 0, 0.4029, 0.0136, 0, 29.1457, 0.1204, 
26.2199, 0, 0.0092, 2.0287, 0.4998, 2.9925, 1.5983, 9.058, 0.2788, 
0.5791, 0.0065, 5.5677, 0, 0.0253, 0.5598, 0.8555, 0.3725, 3.3771, 
1.1893, 0.0239, 0.0365, 0.0938, 0, 0.0801, 1.2488, 0, 0.0196, 
0.0222, 0.2011, 1.2699, 0.2747, 0.0488, 0, 0.0018, 0, 0.0142, 
2.1885, 0, 0.0211, 0.0771, 5.6654, 0.0173, 0.0208, 4.7468, 0.1836, 
0, 1.824, 0.0423, 0.78, 7.9215, 0, 0.1646, 1.0928, 1.7178, 0.0115, 
0.2133, 2.5588, 1.4984, 0.5538, 0.0999, 5.9924, 1.5784, 3.3951, 
0, 0.2214, 0.7336, 1.7269, 0, 0.4176, 0.2016, 0.1417, 0.0185, 
0.1624, 6.7412, 0.5473, 0, 0.1567, 0, 0, 0, 0, 0, 0.1461, 0.006, 
0, 0.1053, 0.3875, 0.0339, 0.9922, 0.0092, 0.0197, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0885, 0, 0, 0, 0, 0.1769, 
0, 0, 0, 0.0912, 0, 0, 0, 0, 0, 2.7463, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0.8368, 0, 0.0903, 0, 0.2761, 0, 0, 0.8624, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0.1352, 0, 0, 0.9063, 0.7307, 0, 0, 0, 0.0748, 
0, 0.3607, 1.2224, 3.2346, 1.3267, 7.0974, 0.045, 0.1171, 0.083, 
0.8299, 3.2228, 0, 0, 0.5914, 0.0506, 0, 3.9377, 0, 0.5789, 0, 
0.1366, 0.415, 0.2443, 0.0846, 0.0198, 1.0585, 0.6046, 0.0294, 
0.005, 0, 0.6716, 0.003, 0.5335, 0, 0.0827, 0.0431, 1.5098, 0, 
1.4005, 0.0146, 0.0869, 0, 0.4433, 0, 0.0262, 0.0239, 0, 0.6191, 
0, 0.4499, 1.4651, 0.0139, 0.0415, 0.4645, 0.1482, 0.0152, 0, 
0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0.0251, NA, 0.006, 0, 
0, 0.2464, 0, 0.0097, 0.509, 0, 0, 0, 0.0045, 0, 0, NA, 0.4779, 
0, 1.8211, 0, 0.0796, 0, 0, 0.2452, 0.2396, 0, 1.8793, 2.5558, 
0, 1.042, 0.3075, 0, 0, 0, 0.2841, 0, 0, 8.5818, 0, 0, 0, 0, 
0, 1.1441, 0, 0.0041, NA, 0, 0.1378, 0, 0.3031, 0, 0, 0.0235, 
0, 0, 0.0418, 0, 0, 0, 0, 0, 0.5047, 0.6552, 0.0918, 0, 0, 0.9424, 
1.4439, 0.3864, 0, 1.6599, 0.2582, 0.0226, 0, 0.0281, 0.1439, 
0, 0.1256, 0.9451, 0.2404, 0.0328, 3.824, 0.136, 1.7015, 0.4794, 
0.1796, 27.8623, 0, 0.1026, 1.4659, 0, 0.8452, 0, 5.1386, 0.2072, 
1.0817, 0.0074, 0, 1.9142, 0.662, 7.0692, 2.2351, 0, 0.5124, 
0.3283, 3.4974, 0.1733, 2.8548, 0.1207, 0.036, 0.172), Weight_end_male = c(0, 
1.8187, 0.1187, 0, 0.1607, 0.0192, 2.1327, 0, 0.0967, 0, 0, 0, 
1.3298, 4.4359, 0, 0.0349, 1.1449, 0, 0.2667, 0, 0.0197, 0.0036, 
0.0251, 0, 0, 0, 0, 0.0423, 0, 1.9956, 0, 0.8455, 0.9048, 0, 
0, 2.474, 0.0205, 0, 0, 0, 0, 0.0641, 0.0103, 0, 3.2211, 0.1309, 
0.9898, 0, 0, 0, NA, 0.0531, 0.1036, 0, 1.9829, 0.2434, 0, 1.0836, 
1.0935, 0.0033, 0, 0, 0, 0, 0.1557, 0, 0.0081, 0, 0, 0, 0, 0, 
0, 0, 0.4483, 0, 0.1936, 0, 0, 0, 0.0347, 0, 1.7255, 0.454, 0.4149, 
0.0734, 0, 0.2224, 0.6019, 0.2576, 0.6559, 0.0123, 0.0123, 0, 
0.2201, 0.0539, 2.3358, 0, 0, 0.8568, 0.3501, 0.5341, 0.1336, 
0.2025, 0.0583, 0, 0.1174, 0, 0.0551, 0.0136, 0.0511, 0, 0, 0.0408, 
0.0097, 1.6916, 5.8066, 0, 0, 2.0848, 0, 0.0263, 0.1147, 0.0118, 
10.9057, 0, 0.0318, 1.5607, 0.0064, 0, 0, 0, 0, 0, 0.0041, 0, 
0, 0, 1.0462, 0.0099, 0, 0, 0, 0, 0.0699, 0.7819, 0.0235, 0.6282, 
0.1035, 0, 0, 0.0988, 0, 0.0091, 0, 0, 0.1076, 0.0202, 0.8457, 
0.718, 0.1921, 0, 0.1429, 0, 0, 0.4542, 0.9874, 0, 0.0757, 1.8627, 
0.2989, 0.0658, 0, 0.5048, 0.1253, 0, 0, 0.3562, 0, 0, 0, 0.111, 
0, 0.012, 0.0652, 3.2237, 0, 0, 0, 2.3217, 0, 0, 0, 0, 0.0294, 
0, 0.0339, 0, 0.0113, 0, 0.0141, 0, 0, 0.0176, 0.0535, 0.0154, 
0, 0, 0, 0.0133, 0, 0.0117, 0, 0.0061, 0.0919, 25.7383, 0.1549, 
0, 2.5995, 0, 0, 0.5068, 0, 10.719, 0, 0, 0, 0.0575, 4.9279, 
0.007, 0.0082, 32.8155, 0, 30.9929, 0, 0, 0, 0, 1.6003, 3.1471, 
0.563, 0.3393, 1.7732, 0, 1.6109, 0.0254, 0, 0, 0.7911, 0.0859, 
2.0903, 0.1846, 0, 0, 0, 0.0261, 0, 0.2807, 0.177, 0, 0.0093, 
0, 0.6713, 0, 0, 0.0209, 0, 0, 0.1836, 0, 0, 0, 0, 5.1176, 0, 
0.0208, 0, 0.1277, 1.1622, 0.0453, 0, 1.2614, 2.2038, 0.0051, 
0.0204, 0.7304, 0.1325, 0, 0, 0.4, 0.3496, 0.1588, 0, 1.1168, 
0.8659, 2.2714, 0.0122, 0.289, 0.6211, 0.2312, 0.148, 0.2786, 
0.0114, 0, 0.0563, 0, 0, 0.0222, 0.0125, 0, 0.0034, 0.0096, 0, 
0.0222, 0, 0.0456, 0, 0, 0.0158, 0.2934, 0.015, 0.4364, 0, 0.0032, 
0.033, 0, 0, 0, 0.8197, 0, 0, 0.0177, 0, 0, 0.0058, 0.1358, 0, 
0.0388, 0.0789, 0.0051, 0, NA, 0.0242, 0.0124, 0.0141, 0.4745, 
0.1051, 0.0159, 0, 0, 0, 0, 0, 0, 0, 0, 0.6813, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, NA, 0, 3.4136, 0, 0.0053, 0, 0, 0, 0, 0, 0, 
0, 0.0065, 0, 0, 0, 0, 0.0458, 0.0048, 0.2024, 0, 0, 0.0425, 
0, 0.0374, 0.0328, 0, 0.2146, 0, 0.0726, 0, 0, 0.0308, 0, 0.4355, 
1.9152, 0.2174, 0.4393, 0.558, 0, 0.6064, 1.3828, 0.0817, 0.482, 
0.157, 0, 0.1489, 0, 0, 0, 1.4324, 0, 0.0488, 0, 0.0117, 0.2705, 
0, 0.0372, 0.0275, 0, 0, 0.4204, 0.0098, 0.6529, 0, 0.1342, 0.1303, 
0, 0.0653, 0, 0, 0.0707, 0, 0.0175, 0, 0.1186, 0, 0, 0.0822, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0225, 0.0137, 0.0018, 0.0041, 
0, 0, 0, 0.2903, 0.0039, 0, 0, 0.5051, 0, 0, 0, 0, 0, 0.6263, 
0.0014, 0, 0.0023, 0.5483, 0.0516, 0, 0.0524, 0, 0, 0, 0, 0.0604, 
0.6552, 0, 2.1549, 3.2201, 0, 0, 2.1619, 0, 0, 0.1909, 0.1007, 
0, 0.0104, 0, 0.0536, 0, 0, 0.0244, 0, 0.2158, 0.0099, 0.236, 
0.0229, 0, 0.0596, 0, 0.0907, 0, 0, 0.0179, 0, 0, 0.0504, 0.0016, 
0, 0.0798, 0, 0.0047, 0, 0, 0.189, 0.2007, 0.8079, 0.1651, 0.2022, 
0, 0.0308, 0.0628, 0, 0, 0.055, 0, 0, 2.0817, 0, 1.7368, 0.2654, 
0, 1.3337, 0.148, 1.0317, 0, 0.2838, 4.2966, 0.0583, 0.037, 0, 
0.0099, 0.5641, 0.0069, 0.0373, 0, 0, 0, 0.0539, 0, 0.0886, 0, 
0.1918, 0.0107, 0, 0, 0.6576, 0, 0.3784, 0.4101, 0, 0), Weight_end_unknown = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0.0089, 0.0128, 0.0116, 0, 0.1237, 0, 0, 0.221, 0, 0, 0, 
NA, 0, 0, 0, 0.3002, 0.014, 0, 0, 0, 0.0911, 0, 0, 0, 0, 0, 0.0097, 
0, NA, 0.0481, 0, 0, 0, 0, NA, 0.0012, 0, 0.0047, 0.0414, 0.0396, 
0, 0.0042, 0.0231, 0.1781, 0.0031, 0.12, 0.0055, 0.0151, 0.023, 
0.0016, 0, 0.0479, 0, 0.0206, 0.3794, 0.028, 0.1152, 0.0156, 
0.0759, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
NA, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0.0026, 0, 0, 0.0019, 0.1637, 
0, 0.0031, 0, 0, 8e-04, 0, 0, 0.0173, 0, 0.0037, 0.0183, 0, 0, 
0.0982, 0.0022, 0.0168, 0.003, 0.0085, 0, 0.0015, 0, 0, 0, 0, 
0.0022, 0.0026, 0.159, 0.0068, NA, 0, 0, 0, NA, 0, 0.0016, 0, 
0, NA, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0.0075, 0.0022, 0, 0.0073, 0, 0.0082, 0, 0.0111, 0, 
0.0173, 0.017, 0.0345, 0.0022, 0, 0.0466, 0.012, 0, 0.0037, 0.0025, 
0, 0.4219, NA, 0, 0.011, 0.009, 0.2127, 0, 0.0166, 0.013, 0.1795, 
0.022, 0.0069, 0.0018, 0.0012, 0.0033, 0, 0.004, 0.0017, 0.004, 
0, 0.2153, 0.0253, 0.0217, 0, 0, 0.0033, NA, 0.0118, 1.1115, 
0, 0.002, 0.1191, 0, 0, 0, 0, 0.7239, 0.0088, 1.3903, 0.0133, 
0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0026, 0.0324, 0, 0.0036, 
0, 0.0035, 0, 0, 0.059, 0, 0, 0.1208, 0, 0, 0.0385, NA, 0, 0.0042, 
0, 0, 0, 0, 0, 0.0115, NA, 0, 0.0144, 0, NA, 0.1932, 0, 0.0789, 
0, 0, 0, 0, 0, 0, 0, 0.2297, 0, 0, 0, 0, 0, 0.008, 0, 0.0189, 
0.0752, 0, 0.0034, 0.027, 0, 0.0119, 0.0616, 0, 0.0361, 0.1545, 
0.0289, 0.0588, 0.0656, 0.0483, 0.0082, 0.0015, 0.0357, 0.0314, 
0.0028, 0.012, 0.0065, 0.0024, 0.0179, 0, 0.0375, 0.008, 0.1082, 
0.0678, 0, 0, 0.0343, 0, 0.0121, 0.001, 0.0033, 0, 0.0036, 0.0247, 
0.0022, 0.0051, 0.0022, 0, 0.2103, 0.0015, 0.1195, 0.0031, 0.0094, 
0.0166, 0.001, 0.0054, 0.0201, 0, 0.0069, 0.025, 0.0016, 0, 0.0058, 
0.0662, 0, 0.003, 0.0069, 0.0073, 0.011, 0.0065, 0.0031, 0, 0.0066, 
0.0055, 0.0015, 0, 0, 0.0017, 0.4328, 0, 0.0034, 0.0674, 0.0035, 
0.0254, 0, 0, NA, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0277, 0, 0, 
NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 
0.0181, 0.0054, 0.0144, 0.0272, 0.0026, 0.0259, 0.0061, 0.0463, 
0.0239, 0.0136, 0.0174, 0.0016, 0.0117, 0.0019, 0.0048, 0.3353, 
NA, 0.0028, 0.0483, 0, 0.01, 0, 0, 0.0071, 0.1082, 0, 0, 0.0052, 
0, 0.0579, 0, 0.0414, 0.0051, 0.0017, 0, 0.0062, 0.0014, 0.0329, 
0, 7e-04, 0, 0, 0.0161, 0, 0.5749, 0.018, 0.0158, 0, 0.2629, 
0.0036, 0, 0, 0.0066, 0.0048, 0.0029, 0, 0.0018, 0.1201, 0.0069, 
0.0408, 0.0126, 0.007, 0.1246, 0.0176, 0.0463, 0.0095, 0.2019, 
0, 0.0133, 0.0089, 0.0691, 0.0185, 0.0015, 0.0049, 0.0312, 0.0125, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 
0, NA, 0.1008, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0), address = c("11_E_1_1", "11_E_2_1", 
"11_E_2_3", "11_E_3_1", "11_E_3_2", "11_E_3_3", "11_E_4_1", "11_E_4_3", 
"11_E_4_5", "11_E_5_4", "11_E_5_1", "11_E_8_1", "11_W_1_1", "11_W_2_1", 
"11_W_4_1", "11_W_5_4", "11_W_5_1", "11_W_5_2", "11_W_8_1", "11_W_8_2", 
"12_E_1_1", "12_E_3_3", "12_E_4_2", "12_E_5_3", "12_E_8_2", "12_E_8_1", 
"12_W_2_2", "14_E_1_3", "14_E_1_2", "14_E_1_1", "14_E_1_5", "14_E_2_1", 
"14_E_2_2", "14_E_2_5", "14_E_2_3", "14_E_4_1", "14_E_4_3", "14_E_5_1", 
"14_E_5_2", "14_E_6_1", "14_E_6_3", "14_E_7_1", "14_E_7_3", "14_E_7_4", 
"14_E_8_1", "14_E_8_3", "14_E_8_2", "14_W_1_4", "14_W_1_1", "14_W_2_2", 
"14_W_2_5", "14_W_3_3", "14_W_3_2", "14_W_3_4", "14_W_3_1", "14_W_5_4", 
"14_W_5_1", "14_W_6_1", "14_W_7_1", "16_E_1_2", "16_E_1_1", "16_E_2_3", 
"16_E_2_2", "16_E_2_1", "16_E_3_1", "16_E_4_3", "16_E_4_2", "16_E_4_1", 
"16_E_5_3", "16_E_5_2", "16_E_6_3", "16_E_6_2", "16_E_6_1", "16_W_1_2", 
"16_W_1_1", "16_W_2_2", "16_W_2_1", "16_W_3_2", "16_W_3_1", "16_W_4_2", 
"16_W_4_1", "16_W_5_2", "16_W_5_1", "16_W_7_1", "16_W_8_1", "17_E_1_3", 
"17_E_1_2", "17_E_3_3", "17_E_3_2", "17_E_4_3", "17_E_4_2", "17_E_4_5", 
"17_E_4_1", "17_E_5_2", "17_E_8_2", "17_W_2_3", "17_W_2_4", "17_W_2_1", 
"17_W_2_5", "17_W_3_3", "17_W_3_2", "17_W_4_1", "17_W_4_2", "17_W_5_3", 
"17_W_5_4", "17_W_5_1", "17_W_6_3", "17_W_6_1", "17_W_6_2", "17_W_6_5", 
"19_E_1_2", "19_E_1_4", "19_E_1_1", "19_E_3_2", "19_E_3_4", "19_E_3_1", 
"19_E_4_1", "19_E_4_3", "19_E_5_2", "19_E_5_1", "19_E_5_3", "19_E_6_2", 
"19_E_6_1", "19_E_7_2", "19_E_7_1", "19_E_7_3", "19_E_8_2", "19_E_8_1", 
"19_W_1_2", "19_W_1_1", "19_W_3_1", "19_W_5_2", "19_W_6_2", "19_W_6_3", 
"19_W_7_3", "19_W_8_2", "21_E_4_1", "21_E_5_1", "21_E_7_1", "21_W_2_3", 
"21_W_2_4", "21_W_2_2", "21_W_6_3", "21_W_7_3", "22_E_1_3", "22_E_1_2", 
"22_E_1_5", "22_E_2_3", "22_E_2_2", "22_E_2_4", "22_E_2_5", "22_E_3_3", 
"22_E_3_4", "22_E_3_5", "22_E_3_1", "22_E_4_3", "22_E_4_2", "22_W_2_3", 
"22_W_2_2", "22_W_3_3", "22_W_3_2", "23_E_4_4", "23_W_1_1", "23_W_1_2", 
"23_W_2_1", "23_W_3_1", "23_W_4_1", "23_W_4_2", "23_W_5_4", "23_W_5_1", 
"23_W_5_2", "23_W_5_3", "23_W_6_1", "23_W_7_1", "23_W_8_1", "25_E_1_4", 
"25_E_1_2", "25_E_1_1", "25_E_2_4", "25_E_2_1", "25_E_2_3", "25_E_3_1", 
"25_E_3_3", "25_E_4_4", "25_E_4_2", "25_E_4_1", "25_E_4_3", "25_E_5_4", 
"25_E_5_2", "25_E_5_1", "25_E_5_3", "25_E_6_1", "25_E_6_3", "25_E_8_4", 
"25_E_8_2", "25_E_8_1", "25_W_1_2", "25_W_1_1", "25_W_1_3", "25_W_4_4", 
"25_W_4_2", "25_W_4_1", "25_W_4_3", "25_W_5_2", "25_W_5_1", "25_W_5_3", 
"25_W_8_4", "27_E_1_3", "27_E_1_4", "27_E_2_2", "27_E_3_2", "27_E_4_2", 
"27_E_7_3", "27_E_7_2", "27_E_8_1", "27_W_1_1", "27_W_1_2", "27_W_2_3", 
"27_W_2_1", "27_W_2_2", "27_W_3_4", "27_W_3_1", "27_W_3_2", "27_W_4_1", 
"27_W_4_2", "27_W_5_3", "27_W_5_1", "27_W_5_2", "27_W_6_1", "27_W_6_2", 
"27_W_7_4", "27_W_7_1", "27_W_7_2", "27_W_8_1", "27_W_8_2", "29_E_1_4", 
"29_E_2_1", "29_E_4_5", "29_E_5_3", "29_E_5_2", "29_E_5_1", "29_E_7_3", 
"29_E_7_2", "29_E_7_5", "29_E_7_1", "29_W_4_3", "29_W_4_2", "29_W_4_4", 
"29_W_4_1", "29_W_5_3", "29_W_5_1", "29_W_5_2", "29_W_5_5", "29_W_5_4", 
"29_W_6_3", "29_W_6_5", "29_W_7_3", "29_W_7_1", "29_W_7_2", "29_W_7_5", 
"29_W_7_4", "29_W_8_3", "29_W_8_1", "29_W_8_2", "29_W_8_4", "31_E_1_2", 
"31_E_3_4", "31_E_5_3", "31_W_2_2", "31_W_2_1", "31_W_2_3", "31_W_3_4", 
"32_E_1_3", "32_E_1_1", "32_E_2_3", "32_E_2_2", "32_E_2_1", "32_E_3_3", 
"32_E_3_2", "32_E_3_1", "32_E_4_3", "32_E_4_2", "32_E_4_1", "32_E_4_5", 
"32_E_5_3", "32_E_5_2", "32_E_5_1", "32_E_6_6", "32_E_6_4", "32_E_6_1", 
"32_E_6_3", "32_E_6_2", "32_E_7_4", "32_E_7_3", "32_E_7_2", "32_E_7_1", 
"32_E_8_4", "32_E_8_3", "32_E_8_2", "32_E_8_1", "32_W_2_1", "32_W_3_3", 
"32_W_3_2", "32_W_3_1", "32_W_4_3", "32_W_4_2", "32_W_4_1", "32_W_5_3", 
"32_W_5_2", "32_W_5_1", "32_W_6_3", "32_W_6_1", "32_W_6_5", "32_W_7_3", 
"32_W_7_4", "32_W_7_1", "32_W_7_5", "32_W_8_6", "32_W_8_3", "32_W_8_2", 
"32_W_8_4", "32_W_8_1", "32_W_8_5", "33_E_1_3", "33_E_1_2", "33_E_1_1", 
"33_E_2_4", "33_E_2_2", "33_E_2_1", "33_E_2_3", "33_E_3_6", "33_E_3_1", 
"33_E_4_2", "33_E_4_3", "33_E_5_2", "33_E_6_2", "33_E_7_4", "33_E_7_2", 
"33_E_7_1", "33_E_7_3", "33_E_8_4", "33_E_8_2", "33_E_8_3", "33_W_1_2", 
"33_W_1_1", "33_W_2_2", "33_W_2_1", "33_W_2_3", "33_W_3_6", "33_W_3_4", 
"33_W_3_1", "33_W_3_3", "33_W_6_1", "33_W_7_4", "33_W_8_3", "35_E_1_4", 
"35_E_1_1", "35_E_2_2", "35_E_2_4", "35_E_2_1", "35_E_2_5", "35_E_2_3", 
"35_E_3_2", "35_E_3_4", "35_E_3_3", "35_E_4_2", "35_E_4_1", "35_E_4_3", 
"35_E_5_2", "35_E_5_4", "35_E_5_1", "35_E_5_3", "35_E_6_2", "35_E_6_1", 
"35_E_6_3", "35_E_7_2", "35_E_7_3", "35_E_8_2", "35_E_8_3", "35_W_1_4", 
"35_W_1_2", "35_W_2_2", "35_W_3_2", "35_W_3_3", "35_W_5_1", "35_W_5_2", 
"35_W_5_3", "35_W_6_1", "35_W_6_2", "35_W_6_3", "35_W_7_2", "35_W_7_3", 
"35_W_8_2", "38_E_1_4", "38_E_1_1", "38_E_2_1", "38_E_5_1", "38_E_7_1", 
"38_E_8_1", "38_E_8_2", "38_E_8_3", "38_W_2_4", "38_W_2_1", "38_W_3_1", 
"38_W_3_2", "38_W_3_3", "38_W_4_1", "38_W_5_1", "38_W_6_1", "38_W_7_1", 
"38_W_8_4", "38_W_8_1", "38_W_8_2", "39_E_1_2", "39_E_6_3", "39_E_6_2", 
"39_E_6_1", "39_E_6_5", "39_E_7_1", "39_E_8_1", "39_W_1_2", "39_W_1_5", 
"39_W_2_4", "39_W_2_1", "39_W_2_5", "39_W_3_4", "39_W_3_2", "39_W_4_2", 
"41_E_1_3", "41_E_1_2", "41_E_1_4", "41_E_2_2", "41_E_2_4", "41_E_3_2", 
"41_E_3_4", "41_E_4_2", "41_E_5_4", "41_E_6_4", "41_E_7_5", "41_E_8_4", 
"41_E_8_2", "41_W_2_2", "41_W_4_2", "41_W_5_3", "41_W_5_4", "41_W_5_2", 
"41_W_6_3", "41_W_6_5", "41_W_8_5", "42_E_1_2", "42_E_2_2", "42_E_3_2", 
"42_E_3_3", "42_E_4_2", "42_E_5_2", "42_E_5_3", "42_E_6_2", "42_E_7_2", 
"42_E_7_3", "42_E_8_2", "42_W_1_2", "42_W_1_3", "42_W_2_6", "42_W_2_2", 
"42_W_2_1", "42_W_2_3", "42_W_3_4", "42_W_3_2", "42_W_3_1", "42_W_3_3", 
"42_W_4_2", "42_W_4_1", "42_W_4_3", "42_W_5_2", "42_W_5_1", "42_W_5_3", 
"42_W_6_4", "42_W_7_3", "42_W_7_2", "42_W_7_1", "42_W_8_2", "42_W_8_1", 
"43_W_4_3", "45_E_1_3", "45_E_1_2", "45_E_2_4", "45_E_3_2", "45_E_4_2", 
"45_E_5_3", "45_E_5_1", "45_E_5_2", "45_E_6_6", "45_E_6_1", "45_E_6_2", 
"45_E_7_3", "45_E_7_4", "45_E_7_1", "45_E_7_2", "45_E_8_3", "45_E_8_4", 
"45_E_8_1", "45_E_8_2", "45_W_1_3", "45_W_1_4", "45_W_1_2", "45_W_1_5", 
"45_W_2_2", "45_W_2_3", "45_W_3_2", "45_W_3_3", "45_W_3_5", "45_W_4_2", 
"45_W_4_3", "45_W_5_2", "45_W_5_4", "45_W_5_3", "45_W_6_2", "45_W_6_4", 
"45_W_6_3", "45_W_7_2", "45_W_7_3", "45_W_7_5", "45_W_8_2", "45_W_8_4", 
"45_W_8_3", "48_E_1_2", "48_E_2_1", "48_E_3_4", "48_E_3_1", "48_E_3_2", 
"48_W_1_1", "48_W_2_1", "48_W_2_2", "48_W_2_3", "48_W_4_2", "48_W_6_1", 
"48_W_6_3", "48_W_7_1", "49_E_1_3", "49_E_1_5", "49_E_1_1", "49_E_2_3", 
"49_E_2_1", "49_E_3_3", "49_E_3_2", "49_E_3_1", "49_E_4_3", "49_E_4_1", 
"49_E_5_3", "49_E_5_2", "49_E_5_1", "49_E_6_3", "49_E_6_2", "49_E_6_1", 
"49_E_6_4", "49_E_7_3", "49_E_7_5", "49_E_7_1", "49_E_8_2", "49_E_8_1", 
"49_E_8_4", "49_W_1_5", "49_W_1_1", "49_W_2_3", "49_W_3_1", "49_W_4_3", 
"49_W_4_5", "49_W_4_1", "49_W_6_3", "49_W_6_1", "49_W_7_3", "49_W_7_1", 
"49_W_8_3", "49_W_8_2", "49_W_8_1"), Total_wt_end = c(0.9341, 
6.2965, 0.1187, 0.727, 0.3026, 0.0192, 4.0368, 0.0759, 0.0967, 
0.0804, 0.2421, 1.2034, 5.5753, 4.4359, 1.3487, 0.0349, 3.0471, 
0.1706, 0.2667, 0.0853, 0.0197, 0.0036, 0.0251, 0.0155, 0.0089, 
0.3028, 0.0116, 0.3887, 0.8176, 7.9336, 0.0902, 10.4688, 1.7294, 
0.0615, 0.2763, 7.5859, 0.3644, 1.3374, 0.361, 1.443, 0.014, 
0.0641, 0.0103, 0.0217, 8.99, 0.4992, 1.9299, 0.3557, 2.3618, 
0.6486, 0.0836, 0.2194, 0.4886, 0.0481, 5.4741, 0.2434, 7.7997, 
2.0454, 3.8594, 0.0045, 0.3666, 0.0047, 0.0414, 0.0396, 0.1557, 
0.0042, 0.0312, 0.5368, 0.0031, 0.12, 0.0055, 0.0151, 0.023, 
0.0016, 0.4483, 0.0479, 3.4821, 0.0206, 0.3794, 0.028, 1.4512, 
0.0156, 2.5721, 0.5363, 0.4149, 0.1856, 0.6648, 0.2224, 1.7588, 
0.4077, 2.5379, 0.0123, 0.0123, 0.0384, 0.4701, 0.3512, 3.4901, 
1.3869, 0.0634, 1.48, 5.8906, 5.0865, 0.8896, 1.0882, 0.0583, 
3.5987, 0.3027, 0.9749, 0.0551, 0.0136, 0.0511, 0.0026, 3.1977, 
0.0408, 0.0116, 6.2912, 7.1798, 0.0031, 0.0227, 5.7987, 8e-04, 
0.0263, 1.1165, 0.0291, 14.5304, 0.0489, 0.1768, 8.1625, 0.0064, 
0.0982, 0.0022, 0.0168, 0.1935, 0.0085, 0.0041, 0.0015, 0.266, 
0.1973, 2.648, 0.0099, 0.0022, 0.0026, 0.159, 0.0068, 0.229, 
0.7819, 0.0319, 0.7245, 0.7441, 0.0374, 0.0096, 0.611, 0.252, 
0.0598, 1.8351, 0.2426, 1.5279, 0.517, 1.8339, 0.718, 0.1921, 
0.3348, 0.1429, 0.1112, 0.1581, 0.4542, 2.1817, 0.6542, 0.0757, 
4.0043, 2.7557, 0.0658, 2.5084, 0.5048, 0.1253, 0.0075, 0.161, 
1.1765, 0.0073, 0.0872, 0.0082, 0.5115, 0.0111, 0.012, 0.0825, 
8.8496, 0.0345, 0.0022, 0.0731, 3.6945, 0.012, 1.4659, 0.0037, 
0.0025, 0.0719, 0.8954, 0.1646, 0.5991, 0.1297, 0.0387, 0.2268, 
0.877, 0.0761, 0.1196, 0.233, 0.0374, 0.0069, 0.0018, 0.0012, 
0.1664, 0.0115, 0.0157, 0.0017, 0.0101, 0.0919, 52.5269, 0.3246, 
0.0217, 12.3827, 0.1524, 0.0033, 1.7137, 0.0307, 37.8394, 0.0353, 
0.002, 0.1191, 0.0575, 5.3308, 0.0206, 0.0082, 62.6851, 0.1292, 
58.6031, 0.0133, 0.0092, 2.0287, 0.4998, 4.5928, 4.7454, 9.621, 
0.6181, 2.3523, 0.0065, 7.1786, 0.0254, 0.0253, 0.5598, 1.6466, 
0.4584, 5.4674, 1.3739, 0.0239, 0.0365, 0.0938, 0.0261, 0.0801, 
1.5295, 0.177, 0.0196, 0.0315, 0.2011, 1.9412, 0.2747, 0.0488, 
0.0209, 0.0018, 0.0026, 0.2302, 2.1885, 0.0036, 0.0211, 0.0806, 
10.783, 0.0173, 0.1006, 4.7468, 0.3113, 1.283, 1.8693, 0.0423, 
2.0799, 10.1253, 0.0051, 0.1892, 1.8232, 1.8503, 0.0115, 0.2133, 
2.9588, 1.8595, 0.7126, 0.0999, 7.1236, 2.4443, 5.6665, 0.2054, 
0.5104, 1.4336, 1.9581, 0.148, 0.6962, 0.213, 0.1417, 0.0748, 
0.1624, 6.9709, 0.5695, 0.0125, 0.1567, 0.0034, 0.0096, 0.008, 
0.0222, 0.0189, 0.2669, 0.006, 0.0034, 0.1481, 0.6809, 0.0608, 
1.4902, 0.0092, 0.059, 0.1875, 0.0289, 0.0588, 0.0656, 0.868, 
0.0082, 0.0015, 0.0534, 0.0314, 0.0028, 0.0178, 0.1423, 0.0024, 
0.0567, 0.0789, 0.0426, 0.008, 0.2068, 0.092, 0.0124, 0.0141, 
0.5088, 0.282, 0.028, 0.001, 0.0033, 0.0912, 0.0036, 0.0247, 
0.0022, 0.0051, 0.0022, 3.4276, 0.2103, 0.0015, 0.1195, 0.0031, 
0.0094, 0.0166, 0.001, 0.0054, 0.0201, 0.8368, 0.0069, 0.1153, 
0.0016, 3.6897, 0.0058, 0.0715, 0.8624, 0.003, 0.0069, 0.0073, 
0.011, 0.0065, 0.0031, 0.0065, 0.0066, 0.0055, 0.0015, 0.1352, 
0.0458, 0.0065, 1.5415, 0.7307, 0.0034, 0.1099, 0.0035, 0.1376, 
0.0328, 0.3607, 1.437, 3.2346, 1.3993, 7.0974, 0.045, 0.1479, 
0.083, 1.2654, 5.138, 0.2174, 0.4393, 1.1494, 0.0506, 0.6064, 
5.3205, 0.0817, 1.0609, 0.157, 0.1366, 0.5639, 0.2443, 0.0846, 
0.0198, 2.4909, 0.6046, 0.0782, 0.005, 0.0117, 0.9421, 0.003, 
0.5707, 0.0552, 0.0827, 0.0431, 1.9302, 0.0098, 2.0534, 0.0146, 
0.2211, 0.1303, 0.4433, 0.0653, 0.0262, 0.0239, 0.0707, 0.6191, 
0.0175, 0.4499, 1.5837, 0.0139, 0.0415, 0.5467, 0.1482, 0.0152, 
0.0181, 0.0054, 0.0144, 0.0272, 0.0026, 0.0259, 0.0061, 0.0463, 
0.0464, 0.0273, 0.0192, 0.0057, 0.0117, 0.0019, 0.0299, 0.6256, 
0.0099, 0.0028, 0.0483, 0.7515, 0.01, 0.0097, 0.509, 0.0071, 
0.1082, 0.6263, 0.0059, 0.0052, 0.0023, 0.6062, 0.5295, 0.0414, 
1.8786, 0.0017, 0.0796, 0.0062, 0.0014, 0.3385, 0.8948, 7e-04, 
4.0342, 5.7759, 0.0161, 1.042, 3.0443, 0.018, 0.0158, 0.1909, 
0.6477, 0.0036, 0.0104, 8.5818, 0.0602, 0.0048, 0.0029, 0.0244, 
0.0018, 1.48, 0.0168, 0.2809, 0.0355, 0.007, 0.322, 0.0176, 0.4401, 
0.0095, 0.2019, 0.0414, 0.0133, 0.0089, 0.1613, 0.0201, 0.0015, 
0.0847, 0.0312, 0.0172, 0.5047, 0.6552, 0.2808, 0.2007, 0.8079, 
1.1075, 1.6461, 0.3864, 0.0308, 1.7227, 0.2582, 0.0226, 0.055, 
0.0281, 0.1439, 2.0817, 0.1256, 2.6819, 0.5058, 0.0328, 5.1577, 
0.284, 2.834, 0.4794, 0.4634, 32.1589, 0.0583, 0.1396, 1.4659, 
0.0099, 1.4093, 0.0069, 5.1759, 0.2072, 1.0817, 0.0074, 0.0539, 
1.9142, 0.7506, 7.0692, 2.4269, 0.0107, 0.5124, 0.3283, 4.155, 
0.1733, 3.2332, 0.5308, 0.036, 0.172)), row.names = c(NA, -575L
), class = c("tbl_df", "tbl", "data.frame"))
hnguyen
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    As these are floating point, the `==` could give inconsistent results due to precision – akrun Feb 19 '22 at 19:20
  • 1
    i.e. when creating the 'i3', it can be compared with rounded vvalues i.e. `i3 <- round(rowSums(dat[Weight_xx ], na.rm = TRUE), 3) == round(dat$Total_wt_end, 3)` which results in the last expression giving 3 rows `nrow(dat[!complete.cases(dat), ])# [1] 3` – akrun Feb 19 '22 at 19:25
  • Thank you for the hint. I rounded the original data set (all numeric columns rounded to 4 decimal places), and it helped a little bit. Now 25 rows are left unfilled. – hnguyen Feb 20 '22 at 01:17

0 Answers0