8.7 Missing Values for the Chicago ridership data

Chicago ridership raw entries

only_rides <- 
     raw_entries %>% 
     select(-date)

only_rides %>% 
     glimpse()

## Rows: 5,733
## Columns: 146
## $ s_40010 <dbl> 0.290, 1.240, 1.412, 1.388, 1.465, 0.613, 0.403, 1.463, 1.505,…
## $ s_40020 <dbl> 0.633, 2.950, 3.107, 3.259, 3.357, 1.569, 0.887, 3.222, 3.281,…
## $ s_40030 <dbl> 0.483, 1.230, 1.394, 1.370, 1.453, 0.839, 0.589, 1.500, 1.547,…
## $ s_40040 <dbl> 0.374, 7.737, 8.051, 8.027, 7.653, 0.844, 0.464, 8.371, 8.351,…
## $ s_40050 <dbl> 0.804, 3.199, 3.476, 3.540, 3.684, 2.467, 1.367, 3.544, 3.612,…
## $ s_40060 <dbl> 1.165, 4.046, 4.153, 4.362, 4.400, 2.231, 1.565, 4.599, 4.725,…
## $ s_40070 <dbl> 0.649, 5.777, 6.482, 6.766, 6.308, 1.798, 1.055, 7.341, 7.537,…
## $ s_40080 <dbl> 1.116, 3.854, 4.147, 4.202, 4.404, 2.545, 1.823, 4.527, 4.514,…
## $ s_40090 <dbl> 0.411, 1.823, 1.905, 2.008, 2.088, 1.024, 0.561, 2.095, 2.185,…
## $ s_40100 <dbl> 1.698, 3.807, 4.047, 4.107, 4.381, 3.021, 2.187, 4.411, 4.399,…
## $ s_40120 <dbl> 0.318, 1.654, 1.777, 1.878, 1.825, 0.650, 0.380, 2.009, 2.088,…
## $ s_40130 <dbl> 0.364, 0.913, 1.071, 0.994, 1.068, 0.599, 0.409, 1.174, 1.066,…
## $ s_40140 <dbl> 0.000, 2.107, 2.363, 2.379, 2.334, 0.000, 0.000, 2.522, 2.572,…
## $ s_40150 <dbl> 0.000, 0.398, 0.397, 0.456, 0.450, 0.004, 0.000, 0.461, 0.483,…
## $ s_40160 <dbl> 0.096, 3.508, 3.809, 3.820, 3.547, 0.310, 0.154, 3.780, 3.902,…
## $ s_40170 <dbl> 0.246, 1.142, 1.235, 1.343, 1.216, 0.558, 0.339, 1.460, 1.541,…
## $ s_40180 <dbl> 0.157, 1.156, 1.282, 1.357, 1.298, 0.358, 0.205, 1.421, 1.429,…
## $ s_40190 <dbl> 1.131, 2.661, 2.843, 3.023, 3.184, 1.977, 1.515, 3.420, 3.499,…
## $ s_40200 <dbl> 0.834, 5.194, 5.472, 5.503, 5.529, 1.885, 0.923, 5.616, 5.629,…
## $ s_40210 <dbl> 0.000, 0.566, 0.613, 0.629, 0.627, 0.001, 0.000, 0.666, 0.643,…
## $ s_40220 <dbl> 0.318, 0.702, 0.812, 0.774, 0.860, 0.654, 0.414, 0.945, 0.947,…
## $ s_40230 <dbl> 0.788, 4.110, 4.436, 4.541, 4.461, 1.520, 1.028, 4.885, 4.947,…
## $ s_40240 <dbl> 2.470, 6.075, 6.694, 7.097, 7.437, 4.715, 3.316, 7.496, 7.586,…
## $ s_40250 <dbl> 0.448, 1.074, 1.084, 1.106, 1.232, 0.868, 0.586, 1.280, 1.276,…
## $ s_40260 <dbl> 2.059, 7.286, 7.781, 7.808, 7.954, 3.662, 2.483, 8.082, 8.063,…
## $ s_40270 <dbl> 0.279, 1.104, 1.139, 1.203, 1.187, 0.688, 0.352, 1.208, 1.246,…
## $ s_40280 <dbl> 0.700, 1.835, 1.763, 2.076, 2.099, 1.208, 0.856, 2.240, 2.335,…
## $ s_40290 <dbl> 0.540, 1.523, 1.592, 1.698, 1.720, 0.862, 0.632, 1.810, 1.825,…
## $ s_40300 <dbl> 0.199, 0.456, 0.514, 0.474, 0.551, 0.279, 0.242, 0.573, 0.596,…
## $ s_40310 <dbl> 0.460, 2.627, 2.846, 3.086, 3.073, 0.994, 0.614, 3.319, 3.344,…
## $ s_40320 <dbl> 0.854, 3.650, 4.029, 4.158, 4.139, 1.933, 1.290, 4.370, 4.476,…
## $ s_40330 <dbl> 2.542, 7.790, 8.301, 8.543, 8.871, 5.234, 3.823, 8.221, 8.358,…
## $ s_40340 <dbl> 1.046, 2.891, 3.114, 3.176, 3.295, 1.979, 1.502, 3.298, 3.385,…
## $ s_40350 <dbl> 0.273, 1.775, 1.945, 2.049, 2.145, 0.729, 0.485, 4.663, 4.676,…
## $ s_40360 <dbl> 0.417, 2.409, 2.635, 2.672, 2.795, 0.964, 0.545, 2.691, 2.704,…
## $ s_40370 <dbl> 1.039, 7.757, 8.257, 8.303, 8.482, 2.143, 1.399, 8.397, 8.452,…
## $ s_40380 <dbl> 1.080, 13.263, 14.416, 15.118, 14.980, 2.267, 1.405, 15.561, 1…
## $ s_40390 <dbl> 0.660, 2.971, 3.241, 3.260, 3.395, 1.199, 0.752, 3.597, 3.672,…
## $ s_40400 <dbl> 0.072, 0.389, 0.459, 0.482, 0.534, 0.336, 0.180, 0.549, 0.553,…
## $ s_40420 <dbl> 0.000, 0.754, 0.791, 0.820, 0.892, 0.003, 0.000, 0.845, 0.836,…
## $ s_40430 <dbl> 0.546, 2.329, 2.409, 2.398, 2.389, 0.804, 0.676, 2.403, 2.402,…
## $ s_40440 <dbl> 0.000, 0.588, 0.677, 0.728, 0.723, 0.001, 0.000, 0.805, 0.821,…
## $ s_40450 <dbl> 3.948, 11.692, 12.824, 13.091, 13.263, 7.284, 5.134, 14.409, 1…
## $ s_40460 <dbl> 0.185, 5.350, 5.766, 5.824, 5.554, 0.775, 0.231, 6.481, 6.477,…
## $ s_40470 <dbl> 0.286, 1.142, 1.388, 1.574, 1.510, 0.765, 0.393, 2.528, 2.532,…
## $ s_40480 <dbl> 0.405, 1.019, 1.145, 1.222, 1.157, 0.709, 0.486, 1.321, 1.366,…
## $ s_40490 <dbl> 0.182, 1.003, 1.073, 1.106, 1.129, 0.533, 0.313, 1.132, 1.134,…
## $ s_40500 <dbl> 1.181, 6.507, 6.783, 6.906, 6.802, 2.582, 1.498, 6.909, 7.123,…
## $ s_40510 <dbl> 0.248, 0.617, 0.657, 0.697, 0.770, 0.443, 0.295, 0.695, 0.696,…
## $ s_40520 <dbl> 0.126, 0.546, 0.641, 0.720, 0.683, 0.416, 0.223, 0.708, 0.736,…
## $ s_40530 <dbl> 0.670, 3.858, 4.124, 4.151, 4.288, 1.825, 0.925, 4.380, 4.441,…
## $ s_40540 <dbl> 1.449, 3.519, 4.211, 4.132, 4.144, 2.780, 1.913, 4.603, 4.519,…
## $ s_40550 <dbl> 0.731, 3.444, 3.519, 3.591, 3.624, 1.582, 1.200, 3.744, 3.853,…
## $ s_40560 <dbl> 1.255, 8.712, 9.608, 9.735, 9.586, 3.411, 1.909, 12.197, 12.00…
## $ s_40570 <dbl> 0.666, 2.508, 2.617, 2.723, 2.661, 1.355, 0.884, 2.887, 2.863,…
## $ s_40580 <dbl> 0.000, 1.404, 1.466, 1.530, 1.554, 0.004, 0.000, 1.546, 1.495,…
## $ s_40590 <dbl> 0.870, 3.624, 3.793, 3.944, 4.020, 1.735, 1.229, 4.098, 4.180,…
## $ s_40600 <dbl> 0.000, 0.218, 0.269, 0.275, 0.253, 0.001, 0.000, 0.332, 0.337,…
## $ s_40610 <dbl> 0.141, 1.036, 1.222, 1.217, 1.201, 0.372, 0.195, 1.235, 1.303,…
## $ s_40630 <dbl> 2.314, 5.657, 6.012, 6.239, 6.405, 4.631, 3.355, 6.425, 6.630,…
## $ s_40640 <dbl> 0.368, 3.294, 3.523, 3.530, 3.767, 1.093, 0.342, 3.604, 3.832,…
## $ s_40650 <dbl> 1.156, 3.093, 3.263, 3.345, 3.491, 2.621, 1.802, 3.572, 3.605,…
## $ s_40660 <dbl> 0.355, 2.537, 2.831, 2.938, 2.846, 1.090, 0.499, 3.517, 3.566,…
## $ s_40670 <dbl> 0.621, 2.504, 2.601, 2.837, 2.740, 1.270, 0.812, 2.848, 2.959,…
## $ s_40680 <dbl> 0.700, 5.750, 6.149, 6.461, 6.311, 1.979, 0.943, 7.055, 7.619,…
## $ s_40690 <dbl> 0.177, 0.561, 0.628, 0.617, 0.679, 0.420, 0.269, 0.622, 0.612,…
## $ s_40700 <dbl> 0.346, 1.091, 1.128, 1.189, 1.223, 0.657, 0.378, 1.312, 1.416,…
## $ s_40710 <dbl> 0.384, 3.485, 3.797, 3.826, 3.806, 1.225, 0.554, 4.172, 4.248,…
## $ s_40720 <dbl> 0.391, 1.216, 1.316, 1.358, 1.387, 0.675, 0.474, 1.433, 1.399,…
## $ s_40730 <dbl> 0.259, 6.788, 7.321, 7.350, 6.983, 0.662, 0.307, 7.560, 7.576,…
## $ s_40740 <dbl> 0.000, 0.418, 0.457, 0.478, 0.505, 0.002, 0.000, 0.533, 0.505,…
## $ s_40750 <dbl> 0.469, 2.299, 2.443, 2.579, 2.542, 1.012, 0.625, 2.655, 2.760,…
## $ s_40760 <dbl> 1.059, 2.717, 2.878, 2.875, 3.028, 1.932, 1.529, 3.057, 3.047,…
## $ s_40770 <dbl> 0.874, 2.212, 2.441, 2.558, 2.599, 1.874, 1.364, 2.663, 3.093,…
## $ s_40780 <dbl> 0.000, 0.337, 0.419, 0.411, 0.419, 0.002, 0.000, 0.471, 0.471,…
## $ s_40790 <dbl> 0.342, 4.971, 5.431, 5.604, 5.541, 0.948, 0.608, 5.672, 6.013,…
## $ s_40800 <dbl> 0.431, 1.844, 1.954, 2.047, 2.092, 0.936, 0.554, 2.146, 2.257,…
## $ s_40810 <dbl> 0.479, 1.372, 1.628, 1.638, 1.698, 0.965, 0.689, 2.013, 1.826,…
## $ s_40820 <dbl> 0.808, 4.433, 4.769, 5.003, 5.336, 1.929, 1.190, 5.048, 5.338,…
## $ s_40830 <dbl> 0.000, 0.813, 0.881, 0.884, 0.925, 0.002, 0.000, 0.981, 0.979,…
## $ s_40840 <dbl> 0.202, 0.745, 0.842, 0.827, 0.843, 0.431, 0.233, 0.887, 0.875,…
## $ s_40850 <dbl> 0.156, 1.878, 2.095, 2.223, 1.961, 0.598, 0.239, 2.614, 2.563,…
## $ s_40870 <dbl> 0.196, 0.965, 0.986, 1.026, 1.056, 0.425, 0.225, 1.084, 1.123,…
## $ s_40880 <dbl> 0.953, 2.291, 2.476, 2.599, 2.721, 1.790, 1.326, 2.962, 3.104,…
## $ s_40890 <dbl> 6.383, 9.301, 8.584, 8.280, 8.109, 6.109, 6.438, 7.854, 7.518,…
## $ s_40900 <dbl> 2.068, 5.437, 5.814, 5.876, 5.955, 3.828, 2.501, 6.150, 6.324,…
## $ s_40910 <dbl> 1.366, 3.047, 3.350, 3.371, 3.456, 2.437, 1.815, 3.594, 3.768,…
## $ s_40920 <dbl> 0.474, 1.001, 1.060, 1.058, 1.209, 0.845, 0.641, 1.195, 1.232,…
## $ s_40930 <dbl> 2.030, 7.727, 7.448, 7.257, 7.265, 2.536, 1.806, 7.653, 7.692,…
## $ s_40940 <dbl> 0.230, 0.674, 0.681, 0.659, 0.724, 0.439, 0.282, 0.688, 0.682,…
## $ s_40960 <dbl> 0.469, 3.563, 3.922, 3.204, 4.036, 1.183, 0.621, 5.496, 5.345,…
## $ s_40970 <dbl> 0.413, 0.836, 0.949, 0.918, 0.933, 0.730, 0.487, 0.989, 0.986,…
## $ s_40980 <dbl> 0.173, 0.667, 0.727, 0.773, 0.768, 0.379, 0.239, 0.793, 0.756,…
## $ s_40990 <dbl> 2.366, 5.732, 5.977, 6.206, 6.436, 4.497, 3.041, 6.889, 6.899,…
## $ s_41000 <dbl> 1.273, 2.036, 2.114, 2.168, 2.421, 2.167, 1.556, 2.244, 2.331,…
## $ s_41010 <dbl> 0.209, 1.107, 1.186, 1.166, 1.227, 0.523, 0.324, 1.220, 1.302,…
## $ s_41020 <dbl> 0.987, 4.199, 4.366, 4.405, 4.649, 2.204, 1.521, 4.669, 4.828,…
## $ s_41030 <dbl> 0.000, 2.011, 2.217, 2.302, 2.246, 0.008, 0.000, 2.481, 2.436,…
## $ s_41040 <dbl> 0.000, 0.335, 0.380, 0.366, 0.399, 0.000, 0.000, 0.452, 0.463,…
## $ s_41050 <dbl> 0.176, 0.941, 0.993, 1.113, 1.122, 0.406, 0.227, 1.052, 1.094,…
## $ s_41060 <dbl> 0.242, 0.988, 1.091, 1.145, 1.190, 0.543, 0.304, 1.319, 1.314,…
## $ s_41070 <dbl> 0.357, 0.850, 0.943, 0.967, 1.008, 0.607, 0.385, 1.065, 1.204,…
## $ s_41080 <dbl> 0.427, 1.116, 1.216, 1.165, 1.349, 0.754, 0.448, 1.259, 1.224,…
## $ s_41090 <dbl> 0.979, 6.848, 6.922, 7.227, 7.271, 2.410, 1.408, 6.877, 7.537,…
## $ s_41120 <dbl> 0.448, 1.195, 1.357, 1.389, 1.478, 0.765, 0.460, 1.661, 1.701,…
## $ s_41130 <dbl> 0.306, 1.774, 2.029, 2.074, 2.129, 0.689, 0.352, 2.479, 2.556,…
## $ s_41140 <dbl> 0.144, 0.580, 0.627, 0.705, 0.695, 0.403, 0.243, 0.718, 0.690,…
## $ s_41150 <dbl> 0.460, 2.335, 2.588, 2.710, 2.682, 1.142, 0.599, 3.013, 3.020,…
## $ s_41160 <dbl> 0.217, 1.681, 1.733, 1.791, 1.634, 0.395, 0.282, 1.809, 1.901,…
## $ s_41170 <dbl> 1.457, 3.748, 3.977, 4.185, 4.533, 3.341, 1.972, 4.590, 4.511,…
## $ s_41180 <dbl> 0.402, 1.173, 1.403, 1.379, 1.461, 0.883, 0.560, 1.449, 1.417,…
## $ s_41190 <dbl> 0.590, 1.338, 1.345, 1.390, 1.510, 1.072, 0.782, 1.454, 1.496,…
## $ s_41200 <dbl> 0.993, 2.405, 2.569, 2.641, 2.638, 1.805, 1.280, 2.763, 2.847,…
## $ s_41210 <dbl> 0.270, 2.194, 2.449, 2.548, 2.466, 0.759, 0.416, 2.606, 2.639,…
## $ s_41220 <dbl> 1.763, 7.054, 7.519, 7.919, 8.074, 4.535, 3.114, 10.856, 10.85…
## $ s_41230 <dbl> 0.787, 1.902, 1.965, 2.130, 2.182, 1.613, 1.252, 2.370, 2.322,…
## $ s_41240 <dbl> 0.388, 1.998, 2.095, 2.117, 2.106, 0.846, 0.516, 2.500, 2.570,…
## $ s_41250 <dbl> 0.180, 0.842, 0.865, 0.886, 0.847, 0.468, 0.249, 0.963, 0.959,…
## $ s_41260 <dbl> 0.399, 1.621, 1.871, 1.951, 1.985, 0.855, 0.553, 1.983, 2.048,…
## $ s_41270 <dbl> 0.211, 0.640, 0.713, 0.695, 0.724, 0.411, 0.258, 0.821, 0.791,…
## $ s_41280 <dbl> 1.302, 5.812, 6.171, 6.385, 6.334, 2.692, 1.856, 6.595, 6.750,…
## $ s_41290 <dbl> 0.869, 3.335, 3.480, 3.519, 3.730, 2.101, 1.200, 4.059, 4.268,…
## $ s_41300 <dbl> 1.403, 3.508, 3.781, 3.963, 3.967, 2.733, 1.884, 4.306, 4.337,…
## $ s_41310 <dbl> 0.323, 1.795, 2.004, 1.925, 2.067, 0.966, 0.543, 2.028, 2.042,…
## $ s_41320 <dbl> 2.872, 7.703, 8.253, 8.897, 9.639, 6.733, 4.514, 8.922, 9.026,…
## $ s_41330 <dbl> 0.383, 1.637, 1.786, 1.859, 1.819, 0.668, 0.485, 1.836, 1.915,…
## $ s_41340 <dbl> 0.142, 1.866, 2.023, 1.991, 1.888, 0.337, 0.184, 2.254, 2.369,…
## $ s_41350 <dbl> 0.170, 1.272, 1.333, 1.429, 1.375, 0.467, 0.296, 1.419, 1.405,…
## $ s_41360 <dbl> 0.213, 0.619, 0.660, 0.694, 0.805, 0.385, 0.252, 0.756, 0.781,…
## $ s_41380 <dbl> 1.407, 3.983, 4.063, 4.113, 4.189, 2.609, 2.003, 4.444, 4.414,…
## $ s_41400 <dbl> 1.363, 4.775, 5.243, 5.270, 5.184, 3.068, 2.112, 5.809, 5.920,…
## $ s_41410 <dbl> 0.252, 1.706, 1.918, 1.962, 1.997, 0.633, 0.429, 2.071, 2.163,…
## $ s_41420 <dbl> 1.227, 3.937, 4.329, 4.607, 4.666, 2.710, 1.935, 4.785, 4.906,…
## $ s_41430 <dbl> 1.659, 4.577, 4.782, 5.111, 5.266, 3.010, 2.044, 5.464, 5.408,…
## $ s_41440 <dbl> 0.225, 1.512, 1.699, 1.717, 1.736, 0.613, 0.307, 1.868, 1.889,…
## $ s_41450 <dbl> 4.395, 11.058, 11.680, 11.883, 12.771, 8.718, 5.822, 11.698, 1…
## $ s_41460 <dbl> 0.327, 2.040, 2.124, 2.246, 2.362, 0.962, 0.541, 2.402, 2.417,…
## $ s_41480 <dbl> 0.715, 3.194, 3.272, 3.398, 3.346, 1.656, 1.006, 3.609, 3.528,…
## $ s_41490 <dbl> 0.502, 2.390, 2.495, 2.531, 2.202, 1.214, 0.822, 2.903, 2.932,…
## $ s_41500 <dbl> 0.338, 1.710, 1.888, 1.905, 2.049, 0.877, 0.531, 2.001, 2.008,…
## $ s_41510 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ s_41580 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ s_41660 <dbl> 2.942, 12.087, 12.622, 12.936, 13.043, 5.444, 3.579, 13.170, 1…
## $ s_41670 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ s_41680 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ s_41690 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

Skim only_rides

skimr::skim(only_rides)

(#tab:onle_rides_skim)Data summary

Name	only_rides
Number of rows	5733
Number of columns	146
_______________________
Column type frequency:
numeric	146
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
s_40010	0	1.00	1.52	0.57	0.19	0.96	1.66	1.99	2.73	▂▅▅▇▂
s_40020	0	1.00	3.08	0.98	0.07	2.34	3.38	3.81	5.98	▂▅▇▇▁
s_40030	0	1.00	1.45	0.46	0.40	1.11	1.47	1.81	2.59	▂▅▇▆▁
s_40040	0	1.00	5.59	2.92	0.00	1.85	7.02	7.79	9.82	▅▁▁▇▅
s_40050	0	1.00	3.33	0.81	0.00	2.90	3.63	3.87	4.88	▁▂▂▇▃
s_40060	0	1.00	4.09	1.26	0.71	2.91	4.47	4.94	6.42	▁▃▂▇▃
s_40070	0	1.00	6.03	2.61	0.00	3.33	7.03	7.89	18.41	▅▆▇▁▁
s_40080	0	1.00	4.38	1.16	0.62	3.62	4.63	5.21	10.59	▂▆▇▁▁
s_40090	0	1.00	1.74	0.80	0.00	1.12	1.92	2.33	3.41	▃▅▇▇▂
s_40100	0	1.00	3.80	0.94	0.00	3.21	3.99	4.44	5.80	▁▁▃▇▂
s_40120	0	1.00	2.21	0.87	0.20	1.41	2.44	2.88	4.86	▃▂▇▃▁
s_40130	0	1.00	0.96	0.30	0.00	0.72	1.05	1.15	3.01	▂▇▂▁▁
s_40140	0	1.00	1.80	1.00	0.00	1.00	2.21	2.52	4.08	▅▂▇▆▁
s_40150	0	1.00	0.76	0.41	0.00	0.44	0.75	1.14	1.75	▅▇▆▇▁
s_40160	0	1.00	2.24	1.27	0.00	0.52	2.95	3.17	5.01	▆▁▅▇▁
s_40170	0	1.00	1.79	0.84	0.00	1.19	1.68	2.46	8.35	▇▇▁▁▁
s_40180	0	1.00	1.32	0.56	0.08	0.73	1.51	1.77	2.51	▃▂▃▇▁
s_40190	30	0.99	3.93	1.85	0.00	2.81	3.69	4.76	13.83	▃▇▂▁▁
s_40200	0	1.00	5.73	2.39	0.00	3.93	6.01	7.33	13.51	▂▃▇▁▁
s_40210	0	1.00	0.91	0.48	0.00	0.57	0.88	1.32	1.95	▅▇▆▇▂
s_40220	0	1.00	1.19	0.42	0.00	0.90	1.10	1.55	2.58	▁▇▇▅▁
s_40230	0	1.00	4.05	1.44	0.00	2.85	4.59	5.03	9.47	▂▂▇▁▁
s_40240	30	0.99	6.55	1.84	0.00	5.26	7.30	7.85	11.82	▁▂▃▇▁
s_40250	0	1.00	1.62	0.53	0.45	1.28	1.61	2.01	3.03	▃▅▇▅▁
s_40260	0	1.00	7.63	2.60	0.00	5.69	8.24	9.61	14.15	▁▃▆▇▁
s_40270	0	1.00	1.03	0.28	0.00	0.85	1.16	1.23	1.92	▁▃▃▇▁
s_40280	0	1.00	1.99	0.56	0.01	1.49	2.21	2.40	3.89	▁▆▇▇▁
s_40290	0	1.00	1.44	0.51	0.00	1.00	1.54	1.75	3.96	▂▇▇▁▁
s_40300	0	1.00	0.69	0.28	0.00	0.47	0.68	0.91	2.38	▃▇▂▁▁
s_40310	0	1.00	2.87	1.12	0.28	1.77	3.27	3.60	5.74	▃▃▇▅▁
s_40320	0	1.00	4.33	1.62	0.00	3.00	4.56	5.46	9.36	▂▃▇▃▁
s_40330	0	1.00	9.37	2.49	0.00	8.21	9.32	10.76	24.73	▁▇▅▁▁
s_40340	0	1.00	2.98	0.70	0.00	2.55	3.25	3.47	4.29	▁▁▃▇▆
s_40350	0	1.00	4.04	2.34	0.26	1.87	3.59	6.22	9.64	▇▇▃▇▁
s_40360	0	1.00	2.33	1.02	0.00	1.56	2.67	3.07	4.56	▂▃▅▇▁
s_40370	0	1.00	7.47	3.26	0.00	4.58	8.32	9.71	16.11	▃▂▇▅▁
s_40380	0	1.00	13.61	6.57	0.60	6.17	15.89	18.93	26.06	▅▂▃▇▁
s_40390	0	1.00	3.12	1.13	0.33	1.90	3.69	3.94	7.35	▂▂▇▁▁
s_40400	0	1.00	0.60	0.22	0.00	0.46	0.63	0.75	1.69	▃▇▇▁▁
s_40420	0	1.00	0.92	0.39	0.00	0.71	0.96	1.21	2.32	▂▆▇▂▁
s_40430	0	1.00	2.44	1.04	0.00	1.43	2.68	3.15	4.56	▃▅▆▇▃
s_40440	0	1.00	0.93	0.50	0.00	0.58	0.91	1.32	9.70	▇▁▁▁▁
s_40450	30	0.99	10.87	3.65	0.00	7.67	12.18	13.77	18.90	▁▃▂▇▁
s_40460	0	1.00	4.68	2.54	0.00	1.78	5.60	6.45	12.10	▅▁▇▂▁
s_40470	0	1.00	1.83	0.78	0.14	1.10	1.99	2.51	5.81	▆▇▇▁▁
s_40480	0	1.00	1.21	0.34	0.35	0.95	1.31	1.47	2.28	▂▃▇▃▁
s_40490	0	1.00	1.56	0.72	0.00	1.12	1.50	2.06	8.83	▇▅▁▁▁
s_40500	2780	0.52	4.85	3.81	0.00	0.00	5.95	7.98	12.44	▇▃▃▆▂
s_40510	0	1.00	1.44	2.01	0.19	0.82	1.20	1.44	17.30	▇▁▁▁▁
s_40520	0	1.00	0.66	0.23	0.00	0.49	0.70	0.83	1.48	▂▃▇▂▁
s_40530	0	1.00	3.96	1.67	0.00	2.62	4.46	5.24	8.97	▂▃▇▃▁
s_40540	0	1.00	4.89	1.50	1.10	3.70	5.14	5.93	11.57	▃▇▇▁▁
s_40550	0	1.00	3.41	1.13	0.00	2.26	3.84	4.25	5.48	▁▃▂▇▃
s_40560	0	1.00	8.97	3.84	0.00	5.22	10.09	11.90	23.94	▃▃▇▁▁
s_40570	0	1.00	3.25	1.28	0.00	2.45	3.22	4.16	6.29	▂▃▇▅▂
s_40580	0	1.00	1.52	0.67	0.00	1.10	1.67	2.03	3.88	▂▅▇▁▁
s_40590	0	1.00	4.55	1.68	0.00	3.82	4.56	5.61	16.88	▂▇▁▁▁
s_40600	0	1.00	0.33	0.16	0.00	0.22	0.32	0.44	0.70	▃▇▇▆▂
s_40610	0	1.00	1.07	0.45	0.01	0.56	1.31	1.39	2.35	▃▂▇▃▁
s_40630	0	1.00	6.51	1.46	0.00	5.88	6.85	7.45	12.06	▁▂▇▅▁
s_40640	0	1.00	4.08	2.28	0.00	2.20	4.37	5.88	12.52	▆▇▇▁▁
s_40650	0	1.00	4.33	1.23	0.00	3.67	4.17	5.20	7.92	▁▂▇▃▁
s_40660	0	1.00	3.14	1.23	0.01	2.14	3.53	4.04	5.25	▂▃▂▇▃
s_40670	0	1.00	3.58	1.48	0.02	2.43	3.72	4.56	16.36	▆▇▁▁▁
s_40680	0	1.00	6.23	2.65	0.00	3.86	6.98	7.89	21.62	▃▇▂▁▁
s_40690	0	1.00	0.71	0.17	0.00	0.62	0.74	0.82	1.87	▁▇▇▁▁
s_40700	0	1.00	1.17	0.35	0.31	0.89	1.28	1.42	4.15	▃▇▁▁▁
s_40710	0	1.00	4.32	2.05	0.03	2.64	4.24	5.96	11.31	▅▇▆▃▁
s_40720	0	1.00	1.14	0.37	0.22	0.81	1.25	1.38	2.59	▂▃▇▁▁
s_40730	0	1.00	5.26	2.94	0.00	1.31	6.78	7.36	11.09	▅▁▂▇▁
s_40740	0	1.00	0.76	0.36	0.00	0.54	0.75	1.08	2.04	▂▇▇▂▁
s_40750	0	1.00	2.32	0.88	0.00	1.39	2.72	2.95	5.06	▂▃▇▃▁
s_40760	0	1.00	3.28	0.84	0.00	2.83	3.38	3.79	6.16	▁▂▇▅▁
s_40770	0	1.00	2.77	0.73	0.00	2.37	2.86	3.27	5.32	▁▃▇▅▁
s_40780	0	1.00	0.74	0.43	0.00	0.42	0.73	1.11	1.64	▆▇▆▇▃
s_40790	0	1.00	4.85	2.37	0.00	2.20	5.69	6.33	12.30	▅▁▇▂▁
s_40800	0	1.00	2.80	1.04	0.00	2.19	2.85	3.66	8.92	▃▇▃▁▁
s_40810	0	1.00	2.28	1.11	0.32	1.17	2.31	3.11	5.09	▇▅▇▅▂
s_40820	0	1.00	4.51	1.66	0.13	3.39	4.82	5.53	15.68	▃▇▁▁▁
s_40830	0	1.00	1.22	0.56	0.00	0.93	1.16	1.67	3.59	▂▇▆▁▁
s_40840	0	1.00	0.66	0.23	0.00	0.44	0.77	0.82	1.16	▁▃▁▇▁
s_40850	0	1.00	2.87	1.40	0.00	1.88	2.73	4.02	12.90	▇▇▁▁▁
s_40870	0	1.00	1.04	0.45	0.00	0.67	1.11	1.42	1.83	▂▅▆▇▆
s_40880	0	1.00	2.51	0.69	0.00	1.98	2.73	2.99	5.39	▁▃▇▁▁
s_40890	0	1.00	8.93	1.88	0.00	7.70	8.80	10.01	18.55	▁▂▇▁▁
s_40900	0	1.00	5.41	1.29	1.38	4.44	5.91	6.28	8.58	▁▂▂▇▁
s_40910	30	0.99	2.93	0.82	0.00	2.37	3.14	3.53	9.55	▁▇▁▁▁
s_40920	0	1.00	1.40	0.39	0.44	1.18	1.35	1.73	2.40	▂▅▇▅▁
s_40930	0	1.00	7.32	2.40	1.19	5.03	8.20	9.05	14.97	▂▂▇▂▁
s_40940	0	1.00	0.70	0.33	0.00	0.48	0.69	0.83	2.90	▅▇▁▁▁
s_40960	0	1.00	4.08	1.70	0.29	2.23	4.89	5.40	9.31	▃▂▇▁▁
s_40970	0	1.00	1.08	0.30	0.25	0.86	1.07	1.31	1.83	▁▅▇▆▂
s_40980	0	1.00	0.83	0.29	0.03	0.60	0.87	1.05	1.58	▁▅▇▇▁
s_40990	30	0.99	5.12	1.45	0.00	4.19	5.60	6.14	8.74	▁▂▃▇▁
s_41000	30	0.99	3.48	1.00	0.00	2.95	3.51	4.08	11.70	▁▇▁▁▁
s_41010	0	1.00	1.26	0.53	0.00	0.79	1.38	1.68	2.20	▂▃▃▇▃
s_41020	0	1.00	4.87	1.74	0.76	3.60	4.90	6.10	13.15	▃▇▅▁▁
s_41030	0	1.00	2.32	1.27	0.00	0.88	2.87	3.34	4.51	▅▁▂▇▂
s_41040	0	1.00	0.64	0.37	0.00	0.37	0.62	0.95	2.44	▇▇▅▁▁
s_41050	0	1.00	0.90	0.35	0.00	0.75	0.93	1.08	2.35	▃▇▇▂▁
s_41060	0	1.00	1.25	0.42	0.15	0.86	1.39	1.57	2.28	▂▃▅▇▁
s_41070	0	1.00	1.17	0.38	0.32	0.86	1.23	1.44	2.28	▃▃▇▃▁
s_41080	0	1.00	1.13	0.37	0.00	0.88	1.20	1.35	2.80	▁▅▇▁▁
s_41090	0	1.00	7.17	3.15	0.00	4.68	7.24	9.74	17.70	▃▆▇▂▁
s_41120	0	1.00	1.88	0.87	0.14	1.31	1.92	2.27	8.53	▆▇▁▁▁
s_41130	0	1.00	2.10	0.84	0.16	1.25	2.42	2.69	6.76	▅▇▂▁▁
s_41140	0	1.00	0.57	0.18	0.08	0.43	0.61	0.69	1.34	▂▅▇▁▁
s_41150	0	1.00	2.52	0.91	0.26	1.61	2.91	3.19	4.93	▃▃▇▇▁
s_41160	0	1.00	2.63	1.51	0.10	1.38	2.29	4.24	5.44	▆▇▂▇▅
s_41170	30	0.99	3.45	0.98	0.00	2.90	3.77	4.13	5.99	▁▂▃▇▁
s_41180	0	1.00	1.56	0.63	0.00	1.23	1.60	1.90	5.46	▂▇▁▁▁
s_41190	0	1.00	1.33	0.31	0.00	1.17	1.40	1.53	2.15	▁▁▃▇▁
s_41200	0	1.00	2.46	0.60	0.00	2.12	2.57	2.79	4.70	▁▂▇▃▁
s_41210	0	1.00	2.04	1.11	0.00	1.03	2.43	2.81	10.48	▅▇▁▁▁
s_41220	0	1.00	10.23	3.39	0.70	7.99	10.42	12.60	18.06	▁▅▇▆▂
s_41230	30	0.99	2.63	0.78	0.00	2.10	2.87	3.20	7.39	▂▇▇▁▁
s_41240	0	1.00	2.17	0.83	0.00	1.35	2.48	2.77	3.85	▁▃▂▇▁
s_41250	0	1.00	0.75	0.37	0.00	0.44	0.84	0.91	5.50	▇▁▁▁▁
s_41260	0	1.00	1.67	0.55	0.02	1.15	1.93	2.08	3.24	▁▅▅▇▁
s_41270	0	1.00	0.83	0.30	0.00	0.58	0.88	1.04	1.94	▂▃▇▁▁
s_41280	0	1.00	5.39	1.83	0.26	3.44	6.24	6.73	11.33	▂▃▇▂▁
s_41290	0	1.00	3.25	1.11	0.00	2.44	3.66	4.08	5.62	▁▂▂▇▁
s_41300	0	1.00	4.58	1.22	0.88	3.82	4.73	5.46	8.15	▁▃▇▅▁
s_41310	0	1.00	1.94	0.88	0.00	1.26	2.12	2.55	4.53	▂▃▇▃▁
s_41320	0	1.00	10.41	2.50	1.24	9.15	10.39	12.23	36.32	▂▇▁▁▁
s_41330	0	1.00	1.71	0.67	0.00	1.06	1.91	2.14	3.09	▁▃▂▇▂
s_41340	0	1.00	2.13	1.00	0.00	1.16	2.37	2.93	5.08	▃▂▇▃▁
s_41350	0	1.00	1.29	0.44	0.02	0.90	1.45	1.62	2.67	▂▅▇▅▁
s_41360	0	1.00	0.86	0.29	0.19	0.61	0.89	1.11	1.50	▃▃▆▇▁
s_41380	0	1.00	4.06	0.99	0.00	3.30	4.40	4.77	6.59	▁▂▂▇▁
s_41400	0	1.00	8.29	2.69	1.24	6.29	8.11	10.51	21.73	▂▇▆▁▁
s_41410	0	1.00	2.74	1.20	0.00	1.86	2.69	3.70	5.09	▂▃▇▅▅
s_41420	0	1.00	7.27	3.59	0.13	5.17	6.11	7.83	21.33	▂▇▂▁▁
s_41430	30	0.99	4.21	1.28	0.00	3.34	4.71	5.15	9.67	▁▃▇▁▁
s_41440	0	1.00	1.71	0.86	0.00	0.97	1.86	2.36	4.38	▃▃▇▂▁
s_41450	0	1.00	12.91	3.01	0.00	11.68	13.44	14.83	25.28	▁▂▇▂▁
s_41460	0	1.00	2.06	0.98	0.00	1.24	2.28	2.88	6.14	▅▇▇▁▁
s_41480	0	1.00	3.19	0.96	0.39	2.61	3.35	3.94	7.25	▂▃▇▁▁
s_41490	0	1.00	3.09	1.24	0.00	2.14	3.03	3.99	7.89	▂▇▆▂▁
s_41500	0	1.00	1.90	0.85	0.00	1.23	2.07	2.58	4.04	▂▃▇▆▁
s_41510	4138	0.28	1.89	0.73	0.00	1.29	1.98	2.44	4.28	▂▆▇▃▁
s_41580	5702	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.03	▇▁▁▁▁
s_41660	0	1.00	13.20	5.50	0.00	8.78	13.61	17.28	28.46	▂▅▇▅▁
s_41670	151	0.97	0.70	0.25	0.00	0.53	0.71	0.91	1.84	▂▇▇▁▁
s_41680	4108	0.28	0.67	0.36	0.00	0.38	0.82	0.96	1.26	▃▅▂▇▃
s_41690	5113	0.11	1.12	0.47	0.00	0.86	1.19	1.39	3.55	▂▇▂▁▁

Select stations with missing values only

only_rides %>% 
     select_if(~any(is.na(.))) %>% 
     plot_missing()

MCAR test

mcar_test(only_rides)

## Warning in norm::prelim.norm(data): NAs introduced by coercion to integer range

## # A tibble: 1 × 4
##   statistic    df p.value missing.patterns
##       <dbl> <dbl>   <dbl>            <int>
## 1    18032.   842       0                7

Plot pattern of missingness using an upset plot

gg_miss_upset(only_rides, nsets = 10)

Stations 40500 (Washington), 41680 (Oakton-Skokie), 41510 (Morgan), 41690 (Cermak-McCormick Place) and 41580 (Homan) missing values are highly related.