Calculate overlap ellipses regions in R - r
I have two datasets with ellipses centroids coordinates (X & Y), Areas, Major and Minor axes (and other variables). My aim is to calculate the overlap between the groups of ellipses and plot them.
The main question is regarding calculations of overlapping regions.
For plotting I would use draw.ellipse function but I'm open to other alternatives.
The two datasets are very similar, here's a subset of one of the two datasets.
dput(slide0[,2:5])
structure(list(Area = c(15453600L, 89L, 151L, 80L, 72L, 126L,
228L, 140L, 192L, 180L, 226L, 96L, 128L, 214L, 176L, 102L, 180L,
229L, 326L, 148L, 86L, 151L, 148L, 255L, 102L, 226L, 224L, 207L,
136L, 115L, 133L, 199L, 124L, 268L, 172L, 207L, 128L, 216L, 136L,
292L, 232L, 79L, 107L, 190L, 104L, 190L, 226L, 136L, 102L, 148L,
130L, 138L, 180L, 200L, 130L, 177L, 183L, 164L, 140L, 292L, 180L,
161L, 79L, 161L, 80L, 207L, 140L, 151L, 190L, 112L, 208L, 207L,
293L, 146L, 116L, 364L, 238L, 154L, 400L, 190L, 208L, 240L, 158L,
98L, 107L, 133L, 217L, 86L, 112L, 86L, 96L, 138L, 224L, 220L,
102L, 154L, 200L, 158L, 190L, 375L, 281L, 604L, 304L, 346L, 186L,
320L, 454L, 200L, 194L, 281L, 247L, 176L, 148L, 252L, 88L, 208L,
236L, 268L, 238L, 262L, 130L, 130L, 160L, 164L, 186L, 186L, 424L,
364L, 192L, 136L, 247L, 148L, 214L, 236L, 560L, 376L, 104L, 180L,
300L, 70L, 343L, 420L, 389L, 84L, 248L, 314L, 302L, 144L, 60L,
88L, 107L, 95L, 116L, 89L, 62L, 124L, 115L, 72L, 140L, 330L,
762L, 931L, 390L, 298L, 256L, 288L, 52L, 48L, 122L, 247L, 88L,
299L, 324L, 96L, 121L, 52L, 70L, 97L, 758L, 112L, 234L, 147L,
75L, 80L, 96L, 84L, 97L, 36L, 44L, 79L, 40L, 121L, 48L, 79L,
54L, 52L, 61L, 52L, 89L, 92L, 200L, 60L, 156L, 64L, 72L, 200L,
48L, 44L, 136L, 136L, 112L, 84L, 98L, 84L, 58L, 42L, 48L, 70L,
54L, 104L, 75L, 80L, 88L, 37L, 95L, 160L, 79L, 137L, 138L, 172L,
80L, 32L, 148L, 314L, 180L, 236L, 234L, 72L, 124L, 121L, 164L,
255L, 207L, 122L, 414L, 254L, 85L, 133L, 80L, 151L, 102L, 61L,
147L, 220L, 192L, 340L, 200L, 133L, 164L, 136L, 136L, 140L, 281L,
208L, 400L, 112L, 96L, 240L, 234L, 136L, 80L, 177L, 130L, 207L,
121L, 248L, 104L, 88L, 192L, 200L, 359L, 276L, 84L, 96L, 177L,
214L, 104L, 92L, 102L, 147L, 136L, 208L, 281L, 112L, 151L, 146L,
161L, 96L, 21L, 44L, 82L, 124L, 92L, 64L, 84L, 97L, 98L, 16L,
60L, 115L, 126L, 60L, 75L, 80L, 86L, 84L, 89L, 96L, 82L, 72L,
200L, 80L, 70L, 79L, 80L, 177L, 44L, 79L, 122L, 75L, 104L, 84L,
158L, 139L, 146L, 69L, 281L, 448L, 68L, 170L, 115L, 236L, 244L,
97L, 104L, 84L, 70L, 70L, 96L, 68L, 96L, 70L, 89L, 380L, 44L,
122L, 320L, 556L, 140L, 208L, 288L, 225L, 302L, 304L, 180L, 210L,
160L, 194L, 151L, 172L, 320L, 256L, 208L, 341L, 346L, 107L, 98L,
51L, 79L, 292L, 229L, 192L, 484L, 176L, 146L, 115L, 89L, 69L,
151L, 72L, 651L, 512L, 384L, 124L, 128L, 82L, 128L, 124L, 121L,
72L, 398L, 190L, 354L, 182L, 228L, 531L, 354L, 236L, 298L, 238L,
638L, 400L, 247L, 166L, 210L, 156L, 224L, 264L, 89L, 228L, 254L,
181L, 247L, 200L, 310L, 238L, 177L, 144L, 133L, 340L, 311L, 146L,
322L, 220L, 190L, 398L, 166L, 228L, 161L, 137L, 128L, 86L, 104L,
42L, 116L, 60L, 192L, 398L, 264L, 255L, 447L, 244L, 324L, 182L,
247L, 383L, 292L, 484L, 760L, 550L, 740L, 884L, 414L, 400L, 344L,
416L, 380L, 476L, 298L, 154L, 82L, 199L, 767L, 228L, 420L, 238L,
146L, 176L, 326L, 273L, 126L, 104L, 166L, 266L, 226L, 76L, 133L,
256L, 136L, 61L, 314L, 144L, 260L, 200L, 147L, 356L, 540L, 239L,
200L, 186L, 217L, 69L, 208L, 346L, 382L, 207L, 300L, 140L, 161L,
398L, 176L, 228L, 112L, 160L, 266L, 190L, 254L, 84L, 732L, 224L,
532L, 244L, 384L, 255L, 369L, 432L, 216L, 288L, 236L, 207L, 302L,
232L, 151L, 86L, 124L, 44L, 128L, 124L, 292L, 102L, 144L, 92L,
225L, 293L, 266L, 549L, 284L, 144L, 254L, 136L, 186L, 180L, 164L,
166L, 146L, 200L, 236L, 186L, 148L, 154L, 226L, 158L, 140L, 160L,
136L, 102L, 341L, 253L, 121L, 121L, 340L, 224L, 148L, 216L, 220L,
366L, 188L, 154L, 228L, 236L, 208L, 240L, 324L, 172L, 302L, 164L,
420L, 254L, 88L, 224L, 210L, 124L, 264L, 315L, 332L, 190L, 128L,
130L, 220L, 166L, 107L, 112L, 158L, 116L, 98L, 147L, 192L, 186L,
112L, 210L, 84L, 256L, 274L, 200L, 192L, 192L, 170L, 107L, 302L,
244L, 299L, 170L, 115L, 186L, 208L, 156L, 216L, 220L, 332L, 451L,
340L, 210L, 128L, 176L, 456L, 128L, 495L, 194L, 316L, 236L, 199L,
870L, 248L, 172L, 192L, 240L, 200L, 192L, 208L, 126L, 344L, 384L,
420L, 176L, 167L, 128L, 86L, 151L, 96L, 104L, 200L, 208L, 72L,
176L, 116L, 192L, 79L, 96L, 328L, 568L, 302L, 229L, 130L, 354L,
89L, 84L, 112L, 160L, 92L, 217L, 341L, 240L, 369L, 268L, 136L,
324L, 192L, 326L, 89L, 146L, 158L, 194L, 214L, 161L, 96L, 172L,
136L, 172L, 240L, 180L, 108L, 524L, 102L, 138L, 166L, 146L, 177L,
137L, 140L, 140L, 274L, 576L, 264L, 86L, 225L, 396L, 239L, 434L,
782L, 322L, 112L, 80L, 200L, 136L, 144L, 238L, 96L, 130L, 86L,
102L, 92L, 356L, 255L, 264L, 288L, 140L, 299L, 264L, 299L, 186L,
146L, 383L, 214L, 375L, 186L, 273L, 284L, 122L, 284L, 72L, 79L,
292L, 192L, 396L, 276L, 228L, 260L, 172L, 172L, 107L, 80L, 104L,
98L, 186L, 226L, 104L, 180L, 85L, 144L, 120L, 248L, 315L, 151L,
166L, 236L, 136L, 540L, 341L, 200L, 226L, 208L, 112L, 160L, 96L,
260L, 262L, 130L, 177L, 116L, 98L, 112L, 208L, 239L, 176L, 151L,
186L, 130L, 177L, 172L, 122L, 183L, 133L, 248L, 146L, 217L, 746L,
199L, 224L, 225L, 140L, 172L, 244L, 274L, 158L, 136L, 344L, 172L,
82L, 200L, 70L, 95L, 239L, 176L, 180L, 147L, 116L, 89L, 79L,
89L, 112L, 240L, 200L, 326L, 84L, 264L, 266L, 266L, 262L, 247L,
164L, 217L, 210L, 122L, 214L, 316L, 217L, 364L, 192L, 660L, 764L,
584L, 228L, 498L, 314L, 188L, 130L, 166L, 255L, 477L, 200L, 816L,
176L, 247L, 252L, 126L, 100L), Mean = c(175.038, 100.18, 99.781,
116.9, 108.375, 94.373, 105.987, 102.993, 90.74, 114.856, 99.412,
123.5, 99.008, 146.168, 88.818, 99.814, 87.244, 68.223, 86.868,
96.189, 133.523, 119.159, 137.311, 109.8, 102.98, 95.407, 112.612,
106.957, 147.382, 76.904, 101.774, 67.02, 118.984, 85.851, 77.355,
91.894, 91.773, 67.833, 103.397, 110.363, 124.513, 112.595, 123.794,
87.847, 88.058, 102.268, 105.354, 89.184, 141.618, 151.291, 135.746,
86.283, 87.389, 102.32, 106.592, 90.153, 100.055, 84.701, 93.264,
97.586, 99.317, 88.05, 94.038, 99.665, 89.763, 100.072, 82.714,
116.325, 102.879, 102.911, 90.317, 92.217, 99.594, 72.425, 91.336,
90.514, 96.197, 103.89, 106.513, 103.647, 98.216, 98.338, 122.513,
104.49, 114.467, 121.278, 109.327, 97.616, 93.223, 98.767, 132.302,
91.964, 86.58, 85.559, 98.5, 90.643, 87.21, 79.715, 90.995, 73.584,
96.153, 118.086, 112.441, 118.208, 115.199, 143.731, 132.555,
128.76, 79.83, 106.466, 83.142, 88.091, 94.243, 110.254, 118.489,
97.24, 74.771, 117, 89.706, 96.08, 93.846, 105.515, 102.225,
120.75, 97.828, 109.059, 102.245, 106.357, 97.891, 114.566, 92.344,
100.858, 130.467, 114.619, 133.125, 147.027, 95.673, 108.178,
104.57, 108.214, 123.924, 110.233, 115.861, 124.905, 106.996,
81.987, 93.914, 111.611, 118.7, 138.432, 150.972, 156.842, 163.371,
102.764, 141.629, 136.694, 163.287, 133.194, 104.5, 113.645,
149.705, 127.354, 91.405, 152.614, 148.102, 148.295, 151.462,
175.646, 148.139, 153.636, 159.909, 173.027, 131.5, 167.656,
176.066, 147.385, 108.643, 139.732, 105.799, 130.384, 131.346,
115.776, 110.573, 124.5, 126.167, 157.393, 107.66, 104.5, 113.75,
114.354, 97.825, 115.091, 104.521, 91.316, 103.056, 97.788, 118.361,
126.5, 114.629, 130.413, 120.07, 119.083, 139.359, 117.75, 99.583,
92.68, 128.667, 112.727, 105.206, 115.838, 112.946, 81.69, 139.439,
120.131, 152.517, 140.024, 165.854, 147.943, 137.944, 149.625,
154.4, 139.625, 135.58, 169.405, 97.253, 104.8, 109.215, 97.299,
117.406, 110.953, 107.138, 125.938, 72.791, 142.236, 121.117,
127.542, 118.175, 128.278, 132.556, 127.116, 132.098, 120.69,
140.57, 114.943, 142.087, 136.551, 153.835, 157.098, 159.688,
91.841, 119, 122.82, 135.707, 125.764, 115.781, 112.762, 139.22,
126.759, 125.378, 116.353, 122.706, 123.086, 90.249, 101.139,
128.018, 71.812, 83.365, 101.421, 108.568, 113.36, 120.787, 136.712,
136.908, 118.826, 96.091, 65.258, 80.337, 81.307, 99.708, 79.43,
110.613, 133.293, 119.548, 120.531, 145.672, 152.551, 112.212,
99.511, 174.431, 124.639, 96.213, 96.519, 73.313, 95.089, 107.768,
83.267, 61.609, 75.427, 71.286, 71.432, 46.537, 45.476, 43.022,
62.453, 57.119, 70.371, 51.357, 61.375, 83.333, 82.426, 79.96,
116.067, 105.707, 87.325, 112.895, 109.095, 117.281, 111.854,
148.683, 133.125, 104.845, 127.963, 137.671, 138.911, 140.825,
81.808, 96.727, 105.228, 97.221, 111.427, 132.558, 128.643, 81.949,
80.36, 92.705, 118.899, 144.306, 74.203, 143.779, 101.706, 97.2,
112.246, 128.369, 138.031, 120.625, 89.94, 106.357, 128.086,
123.375, 131.397, 150.083, 132.829, 145.618, 91.547, 116.659,
106.016, 112.1, 130.752, 116.714, 109.788, 123.153, 88.089, 87.811,
76.714, 105.028, 116.762, 110.763, 86.912, 124.126, 113.262,
100.559, 82.238, 100.933, 84.739, 88.28, 103.925, 123.255, 131.902,
101.19, 91.973, 133.035, 145.464, 138.7, 114.074, 141.062, 149.383,
153.36, 154.899, 157.742, 150.806, 136.886, 148.42, 161.966,
128.024, 154.977, 156.037, 183.664, 134.484, 143.471, 174.431,
112.766, 84.021, 109.633, 102.055, 122.228, 119.169, 126.669,
92.72, 84.557, 109.622, 115.52, 98.305, 100.016, 86.705, 88.524,
93.91, 121.433, 115.186, 138.775, 112.171, 139.945, 111.459,
137.648, 148.56, 151.7, 147.475, 130.412, 150.611, 144.699, 124.879,
127.666, 133.582, 128.419, 113.841, 143.732, 129.874, 147.084,
125.908, 98.857, 128.482, 135.695, 98.767, 94.26, 96.024, 68.207,
96.417, 122.438, 122.296, 105.178, 113.808, 123.345, 120.631,
77.151, 102.319, 118.283, 111.146, 103.658, 117.19, 119.658,
131.902, 141.754, 136.67, 130.594, 123.112, 99.02, 128.873, 143.095,
92.819, 113.151, 127.487, 113.512, 121.085, 147.4, 142.526, 151.355,
134.403, 146.5, 138.858, 155.043, 132.473, 126.667, 68.433, 70.066,
147.763, 146.615, 128.263, 130.767, 121.531, 125.728, 159.721,
132.185, 106.326, 162.808, 150.12, 133, 153.806, 138.598, 138.126,
120.59, 121.269, 139.493, 152.304, 71.971, 82.636, 98.034, 82.251,
102.697, 122.014, 97.708, 75.286, 52.676, 98.066, 94.866, 95.919,
82.12, 109.942, 91.606, 86.238, 125.225, 105.902, 119.013, 126.057,
124.857, 109.714, 120.282, 149.981, 81.801, 70.83, 142.661, 149.28,
139.454, 137.897, 119.04, 140.558, 148.685, 140.614, 164.547,
149.968, 136.695, 153.167, 171.521, 148.609, 151.662, 140.758,
139.857, 138.801, 116.912, 97.34, 61.571, 80.522, 88.731, 98.472,
74.622, 69.777, 82.164, 103.745, 99.347, 93.855, 102.203, 94.188,
98.527, 68.627, 63.579, 70.838, 87.632, 102.745, 92.405, 88.791,
70.331, 92.744, 106.656, 79.491, 61.176, 78.644, 98.818, 87.208,
98.686, 91.403, 75.044, 94.814, 87.394, 102.771, 90.769, 80.698,
58.295, 94.543, 81.545, 82.067, 95.034, 85.513, 89.924, 98.911,
68.598, 95.787, 71.672, 76.026, 79.773, 87.8, 71.2, 95.482, 87.57,
119.214, 77.196, 75.767, 62.02, 76.571, 89.089, 79.957, 75.821,
80.648, 85.476, 93.469, 78.106, 82.495, 83.729, 95.271, 85.641,
89.673, 86.904, 74.656, 105.274, 91.341, 106.678, 118.634, 100.529,
62.436, 72.042, 110.141, 78.265, 66.938, 87.006, 73.267, 94.289,
87.381, 97.522, 83.391, 147.455, 76.407, 94.759, 112.042, 104.347,
122.056, 104.762, 129.169, 125.432, 120.088, 84.685, 94.495,
92.923, 128.421, 120.352, 118.516, 106.805, 92.767, 98.958, 109.758,
121.721, 103.106, 123.208, 134.923, 69.045, 78.149, 60.458, 161.341,
174.319, 140.344, 157.43, 145.615, 66.009, 105.366, 112.884,
81.873, 109.462, 124.463, 106.618, 92.798, 111.902, 109.031,
110.728, 122.747, 146.079, 95.329, 71.645, 71.396, 105.551, 125.577,
108.667, 96.525, 129.236, 112.384, 108.867, 124.959, 115.832,
84.398, 109.375, 86.628, 106.676, 118.657, 113.338, 120.794,
127.88, 133.464, 98.284, 114.703, 92.94, 102.952, 107.424, 99.102,
81.107, 110.479, 137.737, 163.38, 151.75, 152.721, 133.849, 139.288,
117.071, 150.302, 142.243, 131.826, 127.75, 114.162, 118.68,
118.971, 102.493, 130.689, 159.573, 169.638, 178.302, 166.951,
156.924, 101.371, 108.549, 91.515, 93.934, 110.864, 111.93, 131.61,
109.916, 105.618, 116.5, 101.277, 114.187, 119.904, 105.36, 127.161,
154.102, 60.025, 49.68, 136.403, 138.291, 61.87, 94.677, 95.404,
98.725, 98.675, 93.288, 88.75, 78.517, 67.692, 90.513, 97.99,
88.561, 90.403, 80.934, 81.885, 97.8, 86.376, 85.306, 75.233,
111.649, 118.692, 103.755, 91.964, 98.381, 117.338, 164.294,
86.323, 58.865, 66.819, 116.067, 121.83, 127.481, 134.677, 112.538,
96.477, 98.662, 96.006, 81.853, 97.816, 106.143, 89.558, 114.297,
139.307, 134.272, 130.72, 130.2, 88.085, 108.413, 142.852, 133.355,
121.06, 89.391, 95.952, 96.659, 133.213, 125, 100.683, 92.884,
106.593, 107.779, 99.766, 103.653, 62.968, 116.287, 126.256,
135.006, 128.476, 132.255, 124.1, 149.011, 127.481, 136.932,
144.322, 113.102, 129.422, 122.393, 115.367, 111.584, 144.991,
116.725, 147.935, 135.571, 137.036, 171.311, 129.237, 128.368,
144.16, 115.514, 123.665, 129.138, 145.752, 118.607, 129.668,
113.674, 117.143, 108.503, 96.714, 137.52, 107.203, 111.435,
90.057, 119.367, 110.799, 101.931, 113.069, 120.825, 119.478,
106.346, 118.9, 161.491, 102.136, 93.389, 102.111, 96.429, 120.19
), Min = c(25L, 85L, 88L, 103L, 99L, 77L, 70L, 70L, 68L, 95L,
75L, 109L, 77L, 117L, 69L, 87L, 65L, 47L, 47L, 72L, 112L, 95L,
117L, 77L, 85L, 68L, 94L, 83L, 130L, 58L, 83L, 49L, 92L, 53L,
57L, 72L, 72L, 49L, 83L, 86L, 102L, 93L, 112L, 76L, 69L, 89L,
77L, 69L, 128L, 131L, 117L, 65L, 67L, 72L, 86L, 67L, 78L, 64L,
73L, 78L, 82L, 56L, 77L, 79L, 71L, 81L, 57L, 92L, 71L, 87L, 70L,
73L, 72L, 58L, 71L, 57L, 68L, 90L, 72L, 79L, 77L, 64L, 96L, 90L,
100L, 101L, 88L, 77L, 70L, 85L, 106L, 72L, 63L, 66L, 76L, 72L,
71L, 57L, 67L, 46L, 56L, 78L, 82L, 80L, 87L, 111L, 100L, 107L,
54L, 84L, 51L, 62L, 74L, 85L, 88L, 68L, 50L, 80L, 58L, 66L, 74L,
84L, 83L, 105L, 80L, 86L, 62L, 57L, 68L, 89L, 56L, 78L, 90L,
85L, 103L, 116L, 80L, 85L, 85L, 94L, 84L, 88L, 78L, 102L, 85L,
64L, 58L, 90L, 100L, 112L, 126L, 127L, 134L, 83L, 120L, 117L,
131L, 119L, 80L, 78L, 109L, 84L, 73L, 116L, 116L, 122L, 130L,
149L, 110L, 136L, 130L, 126L, 104L, 134L, 151L, 128L, 95L, 114L,
84L, 108L, 92L, 90L, 90L, 107L, 98L, 124L, 84L, 88L, 103L, 89L,
87L, 87L, 90L, 74L, 85L, 85L, 99L, 109L, 99L, 107L, 95L, 102L,
108L, 98L, 83L, 61L, 110L, 98L, 79L, 93L, 94L, 62L, 111L, 96L,
126L, 119L, 154L, 123L, 123L, 122L, 129L, 107L, 113L, 138L, 76L,
84L, 90L, 80L, 93L, 93L, 90L, 109L, 55L, 105L, 94L, 99L, 95L,
107L, 103L, 107L, 107L, 75L, 99L, 95L, 113L, 112L, 130L, 122L,
135L, 65L, 79L, 104L, 87L, 94L, 71L, 85L, 113L, 106L, 103L, 80L,
85L, 97L, 58L, 76L, 98L, 50L, 65L, 72L, 81L, 88L, 106L, 107L,
99L, 85L, 77L, 46L, 60L, 61L, 80L, 59L, 78L, 83L, 96L, 100L,
109L, 119L, 85L, 77L, 131L, 77L, 67L, 65L, 50L, 72L, 74L, 56L,
45L, 62L, 64L, 65L, 39L, 39L, 38L, 53L, 50L, 57L, 43L, 56L, 66L,
62L, 64L, 91L, 90L, 73L, 93L, 82L, 89L, 81L, 121L, 104L, 69L,
107L, 118L, 111L, 120L, 65L, 78L, 87L, 77L, 96L, 98L, 108L, 64L,
69L, 75L, 107L, 98L, 42L, 132L, 75L, 62L, 62L, 101L, 110L, 106L,
76L, 96L, 97L, 92L, 94L, 123L, 105L, 122L, 62L, 87L, 87L, 82L,
94L, 101L, 90L, 97L, 68L, 44L, 56L, 82L, 74L, 86L, 66L, 105L,
87L, 67L, 59L, 70L, 58L, 54L, 77L, 104L, 98L, 85L, 65L, 96L,
89L, 87L, 95L, 101L, 123L, 117L, 136L, 106L, 128L, 90L, 117L,
104L, 91L, 132L, 132L, 140L, 100L, 108L, 129L, 61L, 64L, 67L,
80L, 91L, 80L, 75L, 63L, 61L, 85L, 67L, 67L, 73L, 69L, 59L, 70L,
105L, 88L, 114L, 72L, 112L, 79L, 104L, 129L, 112L, 120L, 95L,
119L, 122L, 92L, 104L, 113L, 103L, 82L, 106L, 98L, 125L, 83L,
63L, 100L, 107L, 83L, 63L, 73L, 53L, 73L, 98L, 91L, 84L, 88L,
81L, 86L, 54L, 80L, 84L, 82L, 53L, 81L, 68L, 85L, 88L, 84L, 87L,
80L, 80L, 92L, 99L, 58L, 80L, 89L, 88L, 89L, 94L, 100L, 113L,
110L, 103L, 113L, 122L, 114L, 95L, 59L, 56L, 114L, 117L, 110L,
112L, 92L, 103L, 131L, 100L, 85L, 118L, 116L, 109L, 129L, 102L,
121L, 98L, 92L, 108L, 132L, 50L, 48L, 64L, 52L, 58L, 86L, 71L,
49L, 42L, 67L, 80L, 68L, 55L, 69L, 70L, 62L, 96L, 81L, 75L, 79L,
86L, 77L, 77L, 90L, 59L, 47L, 90L, 116L, 102L, 97L, 101L, 112L,
114L, 131L, 132L, 115L, 99L, 102L, 136L, 114L, 126L, 106L, 108L,
93L, 88L, 72L, 46L, 66L, 56L, 75L, 54L, 49L, 63L, 73L, 77L, 79L,
84L, 74L, 68L, 51L, 51L, 52L, 57L, 88L, 71L, 61L, 55L, 75L, 75L,
57L, 47L, 59L, 74L, 70L, 68L, 69L, 52L, 69L, 65L, 77L, 52L, 65L,
37L, 66L, 60L, 57L, 79L, 66L, 62L, 72L, 42L, 66L, 44L, 52L, 63L,
72L, 44L, 78L, 72L, 91L, 59L, 59L, 53L, 51L, 58L, 59L, 59L, 59L,
70L, 64L, 54L, 71L, 58L, 71L, 67L, 76L, 54L, 53L, 81L, 61L, 90L,
85L, 63L, 40L, 50L, 78L, 48L, 43L, 51L, 50L, 67L, 68L, 57L, 57L,
57L, 50L, 71L, 81L, 82L, 88L, 81L, 96L, 100L, 78L, 57L, 71L,
72L, 104L, 89L, 88L, 52L, 59L, 76L, 86L, 111L, 78L, 102L, 105L,
51L, 59L, 53L, 125L, 140L, 106L, 129L, 116L, 40L, 54L, 73L, 52L,
82L, 84L, 75L, 73L, 95L, 89L, 100L, 104L, 117L, 79L, 48L, 43L,
89L, 78L, 69L, 62L, 108L, 83L, 85L, 97L, 88L, 59L, 88L, 66L,
88L, 97L, 95L, 103L, 110L, 101L, 85L, 94L, 73L, 71L, 79L, 76L,
52L, 88L, 90L, 101L, 117L, 122L, 117L, 104L, 89L, 108L, 94L,
102L, 113L, 78L, 88L, 94L, 85L, 105L, 136L, 131L, 143L, 131L,
127L, 52L, 76L, 58L, 68L, 88L, 79L, 89L, 82L, 76L, 99L, 58L,
89L, 89L, 68L, 84L, 95L, 45L, 43L, 107L, 122L, 40L, 75L, 74L,
73L, 82L, 69L, 67L, 65L, 51L, 75L, 73L, 73L, 76L, 56L, 68L, 81L,
65L, 70L, 55L, 84L, 90L, 89L, 65L, 76L, 82L, 96L, 58L, 38L, 47L,
94L, 100L, 106L, 114L, 87L, 70L, 70L, 83L, 61L, 84L, 89L, 72L,
87L, 111L, 113L, 98L, 102L, 67L, 93L, 128L, 102L, 89L, 57L, 75L,
67L, 89L, 93L, 77L, 59L, 91L, 89L, 76L, 70L, 50L, 85L, 100L,
102L, 100L, 95L, 99L, 114L, 114L, 116L, 109L, 95L, 105L, 104L,
100L, 91L, 116L, 92L, 114L, 114L, 114L, 134L, 100L, 106L, 112L,
87L, 99L, 104L, 112L, 97L, 105L, 85L, 92L, 85L, 75L, 98L, 59L,
80L, 66L, 83L, 79L, 80L, 86L, 97L, 91L, 75L, 95L, 109L, 72L,
71L, 78L, 79L, 105L), Max = c(255L, 124L, 117L, 140L, 123L, 124L,
167L, 157L, 149L, 152L, 145L, 137L, 129L, 180L, 157L, 117L, 129L,
110L, 248L, 157L, 162L, 161L, 173L, 158L, 129L, 140L, 157L, 144L,
189L, 122L, 133L, 98L, 178L, 159L, 131L, 127L, 140L, 110L, 139L,
159L, 174L, 153L, 148L, 110L, 114L, 141L, 157L, 142L, 162L, 187L,
179L, 122L, 133L, 161L, 160L, 136L, 133L, 114L, 120L, 137L, 126L,
142L, 114L, 128L, 116L, 137L, 123L, 147L, 150L, 131L, 142L, 126L,
144L, 103L, 125L, 185L, 173L, 133L, 157L, 152L, 141L, 178L, 153L,
130L, 142L, 154L, 146L, 143L, 154L, 118L, 202L, 131L, 131L, 125L,
134L, 127L, 115L, 119L, 120L, 193L, 203L, 209L, 173L, 250L, 184L,
183L, 204L, 174L, 217L, 180L, 187L, 149L, 144L, 216L, 192L, 161L,
132L, 229L, 152L, 163L, 125L, 155L, 127L, 153L, 129L, 152L, 172L,
223L, 163L, 166L, 202L, 148L, 212L, 162L, 175L, 197L, 124L, 156L,
148L, 126L, 230L, 146L, 192L, 167L, 152L, 129L, 163L, 189L, 148L,
194L, 201L, 195L, 195L, 152L, 188L, 186L, 242L, 162L, 224L, 186L,
255L, 195L, 161L, 227L, 210L, 210L, 198L, 214L, 248L, 180L, 238L,
245L, 200L, 205L, 252L, 179L, 139L, 189L, 197L, 177L, 252L, 202L,
150L, 163L, 201L, 204L, 151L, 133L, 133L, 154L, 125L, 178L, 129L,
124L, 152L, 117L, 144L, 166L, 149L, 154L, 173L, 152L, 201L, 161L,
134L, 235L, 175L, 138L, 183L, 152L, 141L, 111L, 172L, 171L, 190L,
173L, 182L, 202L, 159L, 199L, 234L, 169L, 166L, 218L, 154L, 146L,
142L, 153L, 180L, 156L, 137L, 149L, 108L, 217L, 159L, 171L, 169L,
181L, 186L, 154L, 193L, 222L, 209L, 144L, 198L, 185L, 226L, 243L,
197L, 138L, 189L, 153L, 255L, 162L, 221L, 170L, 184L, 178L, 180L,
197L, 185L, 194L, 182L, 151L, 197L, 158L, 115L, 143L, 167L, 176L,
164L, 193L, 196L, 198L, 141L, 117L, 140L, 124L, 138L, 144L, 217L,
225L, 159L, 153L, 200L, 241L, 166L, 136L, 254L, 186L, 148L, 190L,
173L, 145L, 183L, 155L, 119L, 112L, 81L, 86L, 59L, 68L, 50L,
80L, 74L, 95L, 71L, 67L, 108L, 126L, 130L, 141L, 145L, 123L,
177L, 168L, 195L, 193L, 204L, 179L, 209L, 190L, 169L, 201L, 171L,
143L, 130L, 133L, 142L, 139L, 210L, 161L, 118L, 104L, 137L, 134L,
253L, 185L, 165L, 161L, 180L, 212L, 194L, 186L, 156L, 122L, 126L,
218L, 204L, 255L, 204L, 169L, 207L, 161L, 157L, 143L, 170L, 196L,
154L, 140L, 174L, 158L, 192L, 138L, 142L, 210L, 171L, 133L, 166L,
148L, 158L, 127L, 196L, 160L, 178L, 140L, 153L, 186L, 122L, 150L,
188L, 242L, 252L, 156L, 183L, 190L, 222L, 194L, 248L, 183L, 237L,
193L, 255L, 171L, 234L, 205L, 255L, 192L, 196L, 245L, 217L, 131L,
157L, 192L, 181L, 201L, 228L, 146L, 155L, 217L, 217L, 156L, 169L,
151L, 138L, 155L, 158L, 174L, 199L, 193L, 185L, 181L, 195L, 179L,
218L, 208L, 177L, 240L, 210L, 210L, 167L, 171L, 192L, 196L, 205L,
203L, 183L, 191L, 224L, 177L, 209L, 128L, 195L, 130L, 87L, 142L,
175L, 202L, 144L, 221L, 243L, 190L, 144L, 134L, 167L, 173L, 254L,
189L, 225L, 232L, 245L, 234L, 209L, 187L, 139L, 214L, 237L, 162L,
163L, 218L, 171L, 184L, 241L, 220L, 242L, 175L, 231L, 190L, 194L,
169L, 172L, 93L, 135L, 225L, 203L, 167L, 169L, 167L, 163L, 219L,
184L, 183L, 242L, 193L, 160L, 201L, 245L, 157L, 179L, 186L, 203L,
193L, 135L, 178L, 181L, 142L, 255L, 168L, 137L, 128L, 94L, 143L,
117L, 184L, 138L, 175L, 150L, 128L, 186L, 177L, 226L, 229L, 206L,
171L, 225L, 240L, 157L, 130L, 224L, 203L, 209L, 240L, 152L, 247L,
205L, 161L, 235L, 188L, 183L, 218L, 249L, 206L, 185L, 235L, 204L,
227L, 203L, 200L, 90L, 115L, 174L, 159L, 159L, 123L, 141L, 154L,
137L, 129L, 135L, 122L, 207L, 121L, 112L, 152L, 159L, 135L, 134L,
181L, 117L, 149L, 166L, 172L, 87L, 111L, 128L, 137L, 152L, 117L,
125L, 145L, 145L, 152L, 190L, 106L, 97L, 154L, 150L, 123L, 133L,
121L, 178L, 156L, 127L, 159L, 125L, 112L, 118L, 129L, 146L, 121L,
109L, 168L, 108L, 110L, 82L, 147L, 158L, 111L, 111L, 124L, 124L,
155L, 154L, 113L, 143L, 153L, 124L, 115L, 135L, 124L, 134L, 139L,
141L, 169L, 193L, 115L, 127L, 167L, 173L, 134L, 176L, 136L, 141L,
145L, 193L, 140L, 255L, 141L, 132L, 155L, 150L, 195L, 145L, 175L,
174L, 244L, 156L, 136L, 140L, 179L, 193L, 176L, 255L, 171L, 141L,
160L, 142L, 154L, 166L, 174L, 110L, 123L, 78L, 223L, 233L, 223L,
216L, 189L, 148L, 198L, 185L, 148L, 186L, 174L, 205L, 137L, 141L,
186L, 129L, 156L, 186L, 133L, 137L, 146L, 167L, 231L, 197L, 191L,
162L, 184L, 143L, 190L, 153L, 161L, 158L, 134L, 148L, 151L, 145L,
166L, 170L, 164L, 123L, 154L, 133L, 157L, 138L, 159L, 172L, 166L,
236L, 255L, 193L, 215L, 180L, 191L, 165L, 209L, 241L, 188L, 151L,
167L, 174L, 162L, 116L, 185L, 213L, 228L, 250L, 236L, 184L, 222L,
164L, 177L, 127L, 190L, 209L, 255L, 163L, 173L, 151L, 221L, 167L,
190L, 190L, 207L, 214L, 114L, 74L, 199L, 185L, 162L, 125L, 135L,
161L, 132L, 122L, 140L, 123L, 125L, 133L, 127L, 116L, 119L, 164L,
119L, 123L, 123L, 110L, 119L, 188L, 173L, 132L, 136L, 142L, 202L,
250L, 133L, 117L, 97L, 146L, 159L, 172L, 184L, 153L, 134L, 255L,
127L, 130L, 134L, 133L, 143L, 189L, 171L, 181L, 184L, 183L, 132L,
147L, 163L, 199L, 188L, 164L, 130L, 155L, 192L, 182L, 131L, 168L,
133L, 146L, 147L, 192L, 97L, 159L, 160L, 189L, 148L, 167L, 140L,
204L, 144L, 150L, 194L, 142L, 171L, 148L, 151L, 167L, 207L, 176L,
203L, 167L, 193L, 254L, 181L, 188L, 213L, 178L, 194L, 198L, 173L,
145L, 162L, 151L, 153L, 179L, 140L, 220L, 176L, 230L, 124L, 211L,
189L, 136L, 166L, 182L, 165L, 151L, 176L, 223L, 150L, 154L, 129L,
145L, 164L)), .Names = c("Area", "Mean", "Min", "Max"), class = "data.frame", row.names = c(NA,
-866L))
Related
Is There a Way to Make A More Granular Heatmap with ggmap in R?
I'm trying to make a heatmap of locations using latitude and longitude. My map works but the points in an area are being grouped together too much. What I want is something like what I have but with the heat traced much more granularly so that you can see smaller differences between places. This is the relevant code: bbox <- c(left = -79.1, bottom = 38, right = -71.8, top = 41.5) coords.map <- get_stamenmap(bbox, zoom = 9, maptype = "toner-lite") coords.map <- ggmap(coords.map, extent="device", legend="none") coords.map <- coords.map + stat_density2d(data=recruits2, aes(x=MailingLong, y=MailingLat, fill=..level.., alpha=..level..), bins = 20, geom = "polygon") coords.map <- coords.map + scale_fill_gradientn(colours=rev(brewer.pal(20, "Spectral"))) coords.map <- coords.map + theme_bw() coords.map This is what I'm getting Edit: Here is the first 200 rows of recruits from dput structure(list(MailingLong = c(-77.024196799, -77.003914977, -76.800101376, -77.116797729, -77.018188811, -77.120614114, -76.975844627, -77.147034967, -77.089368856, -76.97864858, -76.844030329, -76.9639802739999, -77.257533391, -76.822314419, -80.0753704429999, -76.551298185, -76.767346001, -78.7422733669999, -77.2918568269999, -74.252185664, -77.035277127, -76.957338864, -77.403195991, -77.6319986669999, -73.944649177, -76.5599475129999, -73.571443862, -76.989599958, -76.8866457089999, -76.8900467889999, -77.027862935, -76.763716223, -74.035259712, -77.075101833, -76.9065139969999, -77.009444624, -76.991265512, -76.998362508, -77.0138546809999, -76.92147388, -76.937957008, -73.897316132, -77.2021361069999, -76.9865863379999, -77.057626187, -77.2698420199999, -73.962312531, -77.061793597, -74.118483634, -73.8812691919999, -84.1753095879999, -76.849177244, -76.9494415629999, -77.011200646, -77.12310071, -76.8884595649999, -77.0061180499999, -77.017453829, -76.946342907, -76.980566735, -76.923624123, -77.010469798, -77.02638151, -76.918852741, -76.9224746959999, -87.802292575, -76.813914677, -73.9355411179999, -77.0086792399999, -76.9349190729999, -77.020158344, -76.9487103719999, -73.846362546, -77.015177632, -80.255301241, -77.428318142, -77.0007635819999, -76.929540264, -76.947718204, -76.93487089, -77.019630846, -77.018265258, -73.893035738, -76.887356744, -74.746807223, -76.825205743, -76.8953029259999, -76.962229153, -77.0435782929999, -73.844867129, -76.926558429, -76.8879371259999, -73.8547164669999, -76.920915296, -81.556675863, -77.088045767, -76.8971086939999, -73.2218500929999, -77.283520263, -77.0097035049999, -73.995325534, -82.7629230599999, -76.952207819, -77.012592416, -77.0260711729999, -76.942692268, -76.981824075, -73.261707374, -77.0867976709999, -76.904238182, -77.172019128, -76.9965300979999, -77.0255964739999, -76.984859179, -76.9812736649999, -77.081601215, -76.759961416, -74.6169999639999, -77.043506263, -77.202603773, -76.949909772, -76.9530278129999, -76.7527797119999, -75.053749344, -76.746774219, -71.778088926, -77.02110899, -76.854547506, -77.084631879, -76.997437527, -76.901572162, -73.870879122, -76.878514023, -74.1344631809999, -74.318660444, -82.7075784359999, -74.7182968279999, -76.797062055, -77.5707813399999, -77.6124343819999, -75.5027960319999, -76.987749881, -76.9821815619999, -75.828326588, -79.783059322, -76.9499001519999, -80.259390617, -77.117644909, -77.2343587779999, -73.8982356609999, -77.472350213, -76.9425510419999, -76.977082436, -76.9939519549999, -77.1868244199999, -74.7314534239999, -76.965619788, -77.007779215, -76.967062614, -76.951343771, -77.396949305, -76.999679182, -76.9883032339999, -77.0871543499999, -76.901727819, -77.106636225, -75.01563442, -84.127288743, -84.4811913569999, -88.267507704, -77.011828453, -74.081075439, -72.669365125, -73.007336905, -76.923843066, -77.019205179, -76.9821401839999, -73.927507514, -77.0365238109999, -76.695715095, -76.871854318, -76.831811042, -75.341965451, -74.0187008359999, -76.851878874, -72.8766466989999, -76.9806527699999, -77.327771618, -73.9872406649999, -73.9230185489999, -74.127273734, -74.050468364, -77.044012991, -74.068816409, -73.950062444, -76.874810759, -76.976120023, -77.0778578119999, -77.410060892, -104.726662525), MailingLat = c(38.9681180370001, 38.728353848, 38.795798179, 38.8259334890001, 38.7331564900001, 38.8947512510001, 38.5234046750001, 38.8229569550001, 38.85372949, 38.9289913380001, 39.2386466790001, 38.929587806, 38.7748015860001, 39.4697715190001, 40.4490489200001, 39.126109242, 38.808320123, 35.80390168, 38.605807435, 40.1699337740001, 38.6193497140001, 38.922676057, 38.6439925450001, 39.9533144690001, 40.7054857840001, 39.345963984, 41.1666994920001, 38.81984234, 38.9428468840001, 38.733140373, 38.982152528, 38.824874175, 40.1742035030001, 38.78925345, 39.0010581130001, 38.9664260480001, 38.8579487740001, 38.946905756, 38.8784059170001, 38.902325202, 38.911513062, 40.9063276460001, 38.8638673040001, 38.9097773670001, 38.9240989080001, 38.8797625300001, 40.5770966100001, 38.792493279, 40.6751541570001, 42.8175953150001, 33.5073237140001, 38.9272529080001, 38.8836017150001, 38.8243908630001, 38.8446904240001, 38.8658511960001, 38.917771981, 38.938885574, 38.8719206270001, 38.885876071, 38.8924471080001, 38.833980207, 38.9562671210001, 38.8918037330001, 38.876033218, 41.914514481, 39.3841564950001, 40.8475071350001, 38.8235965260001, 38.9344933350001, 39.0245538930001, 38.639340094, 42.666483283, 38.7850074940001, 36.093245149, 38.210712964, 38.8477400190001, 38.888638916, 38.867858267, 38.8906912090001, 38.929587746, 38.9521219760001, 40.8675224940001, 38.8787024690001, 41.140377415, 38.804541885, 38.852253413, 38.8221755930001, 38.9213773750001, 40.8910776750001, 38.88197389, 39.0817835830001, 40.8247535940001, 38.8854398610001, 30.1117637080001, 39.087920518, 38.940159973, 41.2659053740001, 38.801129729, 38.896368373, 40.8391054510001, 33.720799115, 38.8651627890001, 38.826764516, 38.9238514780001, 38.9046515320001, 38.925578864, 44.0306323230001, 39.035399668, 38.5953835010001, 39.119000919, 38.9563723100001, 38.976737762, 38.960012873, 38.934212899, 38.961016375, 38.6100870970001, 40.2422624710001, 38.9248050900001, 38.85129355, 38.90282683, 38.8914932600001, 38.576783327, 40.0710674210001, 38.62669212, 42.292809602, 38.956420038, 38.845185488, 38.859804126, 38.904392242, 38.9032118310001, 40.6809593840001, 38.887417929, 40.6499415820001, 40.9926879540001, 28.0700605050001, 40.2071700650001, 38.761238259, 38.7582776680001, 38.8111178980001, 39.956535079, 38.8784065950001, 38.903570121, 44.0488523160001, 40.4418602050001, 38.9028348410001, 26.1337374180001, 38.8889284410001, 39.185845467, 40.9479277930001, 38.769957467, 38.869303099, 38.901602515, 38.8247793570001, 38.770411516, 40.1243989540001, 38.935076042, 38.9514988700001, 38.87461904, 38.8908902330001, 38.457439761, 38.8236920620001, 38.883861181, 38.938825771, 38.854090721, 38.715420469, 40.8443550060001, 41.4013022350001, 34.0559358790001, 40.0861231000001, 38.8252308690001, 40.729698834, 42.1328508010001, 40.9432147800001, 38.8837031510001, 38.845665739, 38.84924341, 41.0833210810001, 38.911186511, 39.0930236410001, 39.0773085930001, 38.990928769, 40.633318704, 40.7794771000001, 38.946823571, 41.665512818, 38.929088312, 38.861215551, 40.588697102, 40.830763007, 40.664786738, 41.044012874, 39.077050066, 40.714393369, 40.5922452140001, 38.8516571170001, 38.85133934, 39.059085399, 38.431202824, 38.7833722510001)), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 60L, 61L, 62L, 64L, 65L, 66L, 67L, 68L, 69L, 71L, 72L, 73L, 74L, 75L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 92L, 93L, 94L, 95L, 96L, 97L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 138L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 189L, 190L, 191L, 192L, 193L, 195L, 197L, 198L, 200L, 201L, 202L, 203L, 204L, 205L, 207L, 208L, 209L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L), class = "data.frame")
Solve minimization question in R using dynamic program
I am currently trying to find a solution to a transportation problem. I have a table with the travel times from 30 customers to each other. The goal is to use dynamic program to go from this table with direct durations to a table with shortest duration possible (via possible subtours between pairs of customers) I have no clue how to solve this. The question states "Let 𝐷𝑖,𝑗,𝑘 be the shortest (in duration) path between customer 𝑖 and 𝑗, using only nodes 0 to 𝑘 as intermediaries." Is there someone who can help me with this? Thank you in advance. :-) to use only nodes 0 to k as intermediates. structure(list(ID = 1:30, X1 = c(0L, 98L, 132L, 245L, 17L, 69L, 139L, 112L, 207L, 35L, 249L, 43L, 215L, 62L, 152L, 237L, 59L, 119L, 45L, 59L, 23L, 12L, 16L, 66L, 177L, 31L, 118L, 165L, 117L, 193L), X2 = c(37L, 0L, 74L, 176L, 91L, 111L, 143L, 208L, 202L, 40L, 172L, 101L, 163L, 51L, 220L, 180L, 48L, 98L, 45L, 62L, 91L, 9L, 50L, 145L, 198L, 48L, 3L, 163L, 159L, 176L), X3 = c(118L, 10L, 0L, 138L, 108L, 115L, 221L, 274L, 231L, 89L, 197L, 36L, 175L, 93L, 174L, 184L, 13L, 109L, 109L, 22L, 172L, 91L, 12L, 149L, 140L, 108L, 8L, 133L, 168L, 139L), X4 = c(252L, 137L, 112L, 0L, 248L, 276L, 373L, 350L, 353L, 137L, 31L, 186L, 49L, 170L, 325L, 81L, 117L, 184L, 175L, 185L, 279L, 182L, 120L, 205L, 130L, 132L, 133L, 226L, 158L, 67L), X5 = c(40L, 93L, 118L, 188L, 0L, 38L, 147L, 106L, 208L, 46L, 228L, 26L, 208L, 54L, 163L, 212L, 81L, 49L, 45L, 84L, 12L, 82L, 114L, 81L, 173L, 67L, 52L, 63L, 146L, 156L), X6 = c(54L, 117L, 143L, 296L, 74L, 0L, 78L, 97L, 218L, 52L, 239L, 47L, 267L, 111L, 216L, 269L, 167L, 91L, 105L, 99L, 16L, 117L, 95L, 15L, 264L, 136L, 148L, 100L, 145L, 242L), X7 = c(143L, 145L, 208L, 302L, 130L, 86L, 0L, 84L, 308L, 151L, 348L, 102L, 305L, 139L, 254L, 272L, 156L, 182L, 87L, 214L, 110L, 82L, 145L, 59L, 283L, 98L, 171L, 192L, 211L, 228L), X8 = c(142L, 195L, 238L, 336L, 161L, 80L, 2L, 0L, 289L, 135L, 342L, 134L, 307L, 169L, 197L, 347L, 261L, 143L, 128L, 243L, 52L, 128L, 169L, 97L, 318L, 214L, 229L, 225L, 287L, 288L), X9 = c(230L, 238L, 217L, 264L, 229L, 154L, 299L, 269L, 0L, 210L, 335L, 204L, 342L, 260L, 25L, 258L, 264L, 76L, 170L, 222L, 126L, 193L, 272L, 203L, 218L, 186L, 214L, 120L, 240L, 207L), X10 = c(2L, 30L, 34L, 186L, 75L, 96L, 161L, 179L, 244L, 0L, 199L, 17L, 181L, 59L, 240L, 178L, 101L, 117L, 1L, 48L, 79L, 72L, 24L, 60L, 236L, 5L, 32L, 151L, 203L, 133L), X11 = c(243L, 191L, 114L, 38L, 164L, 211L, 295L, 370L, 350L, 211L, 0L, 174L, 14L, 166L, 278L, 164L, 165L, 223L, 252L, 129L, 201L, 196L, 201L, 271L, 98L, 199L, 209L, 219L, 168L, 62L), X12 = c(22L, 14L, 64L, 164L, 68L, 113L, 117L, 115L, 208L, 8L, 239L, 0L, 204L, 36L, 230L, 217L, 37L, 62L, 41L, 30L, 64L, 25L, 22L, 60L, 150L, 30L, 90L, 109L, 92L, 178L), X13 = c(224L, 168L, 120L, 23L, 213L, 310L, 324L, 377L, 366L, 212L, 54L, 178L, 0L, 207L, 316L, 105L, 117L, 266L, 235L, 106L, 231L, 186L, 147L, 223L, 109L, 182L, 211L, 183L, 130L, 132L), X14 = c(66L, 22L, 77L, 204L, 23L, 143L, 93L, 171L, 259L, 37L, 182L, 68L, 218L, 0L, 216L, 186L, 56L, 139L, 65L, 44L, 118L, 64L, 19L, 112L, 183L, 14L, 40L, 142L, 96L, 131L), X15 = c(167L, 236L, 263L, 275L, 187L, 189L, 212L, 282L, 9L, 181L, 309L, 231L, 340L, 238L, 0L, 207L, 216L, 97L, 204L, 237L, 120L, 160L, 217L, 188L, 258L, 203L, 183L, 106L, 174L, 267L), X16 = c(173L, 196L, 161L, 152L, 168L, 268L, 322L, 280L, 265L, 230L, 133L, 160L, 102L, 209L, 193L, 0L, 162L, 168L, 228L, 208L, 283L, 159L, 195L, 270L, 14L, 233L, 176L, 172L, 48L, 38L), X17 = c(70L, 44L, 13L, 184L, 90L, 186L, 194L, 267L, 257L, 72L, 179L, 38L, 107L, 88L, 177L, 172L, 0L, 93L, 71L, 14L, 167L, 117L, 37L, 146L, 116L, 35L, 62L, 83L, 93L, 129L), X18 = c(115L, 128L, 153L, 196L, 78L, 109L, 147L, 185L, 103L, 138L, 218L, 123L, 200L, 85L, 91L, 178L, 86L, 0L, 35L, 125L, 67L, 68L, 142L, 42L, 202L, 84L, 81L, 91L, 87L, 210L), X19 = c(36L, 104L, 71L, 166L, 16L, 99L, 88L, 133L, 200L, 35L, 246L, 2L, 263L, 89L, 144L, 229L, 47L, 103L, 0L, 31L, 114L, 32L, 44L, 106L, 219L, 43L, 92L, 119L, 150L, 103L), X20 = c(74L, 66L, 22L, 101L, 76L, 101L, 194L, 211L, 170L, 21L, 179L, 105L, 197L, 36L, 180L, 204L, 48L, 71L, 59L, 0L, 178L, 32L, 58L, 125L, 180L, 46L, 16L, 77L, 130L, 75L), X21 = c(85L, 158L, 144L, 210L, 29L, 16L, 44L, 65L, 141L, 88L, 258L, 70L, 279L, 124L, 168L, 190L, 145L, 45L, 40L, 110L, 0L, 129L, 157L, 52L, 177L, 117L, 175L, 153L, 133L, 190L), X22 = c(25L, 44L, 124L, 236L, 62L, 39L, 108L, 149L, 175L, 18L, 177L, 16L, 259L, 44L, 163L, 179L, 74L, 110L, 28L, 104L, 110L, 0L, 97L, 123L, 216L, 5L, 112L, 170L, 153L, 196L), X23 = c(26L, 2L, 39L, 205L, 102L, 74L, 205L, 203L, 257L, 49L, 194L, 96L, 207L, 5L, 174L, 160L, 37L, 159L, 55L, 55L, 91L, 1L, 0L, 153L, 216L, 52L, 11L, 192L, 116L, 149L), X24 = c(12L, 127L, 115L, 288L, 90L, 60L, 73L, 79L, 194L, 87L, 203L, 81L, 257L, 108L, 188L, 263L, 185L, 90L, 25L, 177L, 51L, 85L, 93L, 0L, 234L, 114L, 111L, 72L, 131L, 153L), X25 = c(194L, 120L, 191L, 143L, 212L, 253L, 281L, 318L, 291L, 160L, 108L, 174L, 94L, 188L, 203L, 43L, 127L, 153L, 204L, 162L, 170L, 237L, 194L, 251L, 0L, 237L, 122L, 149L, 75L, 104L), X26 = c(10L, 58L, 22L, 171L, 101L, 131L, 144L, 154L, 177L, 5L, 222L, 48L, 220L, 59L, 192L, 193L, 9L, 134L, 23L, 80L, 75L, 19L, 19L, 132L, 197L, 0L, 57L, 146L, 163L, 154L), X27 = c(104L, 58L, 15L, 175L, 46L, 178L, 206L, 185L, 199L, 44L, 191L, 115L, 211L, 33L, 200L, 148L, 25L, 104L, 47L, 8L, 154L, 35L, 41L, 106L, 159L, 14L, 0L, 171L, 88L, 87L), X28 = c(80L, 137L, 93L, 225L, 76L, 138L, 245L, 195L, 102L, 130L, 197L, 112L, 169L, 163L, 68L, 83L, 108L, 50L, 125L, 108L, 139L, 106L, 131L, 111L, 136L, 179L, 150L, 0L, 103L, 151L), X29 = c(170L, 113L, 89L, 120L, 138L, 172L, 224L, 263L, 233L, 155L, 170L, 132L, 194L, 133L, 160L, 99L, 106L, 86L, 154L, 90L, 132L, 158L, 142L, 209L, 55L, 127L, 118L, 93L, 0L, 67L), X30 = c(197L, 170L, 155L, 66L, 178L, 193L, 226L, 303L, 213L, 161L, 104L, 94L, 140L, 138L, 241L, 29L, 102L, 131L, 108L, 129L, 182L, 167L, 167L, 198L, 83L, 154L, 89L, 106L, 87L, 0L)), class = "data.frame", row.names = c(NA, -30L))
Following up on my comment to use the igraph package, below is a way to accomplish what is described.
library(igraph)
m <- data.frame(X1 = c(0L, 98L, 132L, 245L, 17L, 69L, 139L, 112L, 207L, 35L, 249L, 43L, 215L, 62L, 152L, 237L, 59L, 119L, 45L, 59L, 23L, 12L, 16L, 66L, 177L, 31L, 118L, 165L, 117L, 193L),
X2 = c(37L, 0L, 74L, 176L, 91L, 111L, 143L, 208L, 202L, 40L, 172L, 101L, 163L, 51L, 220L, 180L, 48L, 98L, 45L, 62L, 91L, 9L, 50L, 145L, 198L, 48L, 3L, 163L, 159L, 176L),
X3 = c(118L, 10L, 0L, 138L, 108L, 115L, 221L, 274L, 231L, 89L, 197L, 36L, 175L, 93L, 174L, 184L, 13L, 109L, 109L, 22L, 172L, 91L, 12L, 149L, 140L, 108L, 8L, 133L, 168L, 139L),
X4 = c(252L, 137L, 112L, 0L, 248L, 276L, 373L, 350L, 353L, 137L, 31L, 186L, 49L, 170L, 325L, 81L, 117L, 184L, 175L, 185L, 279L, 182L, 120L, 205L, 130L, 132L, 133L, 226L, 158L, 67L),
X5 = c(40L, 93L, 118L, 188L, 0L, 38L, 147L, 106L, 208L, 46L, 228L, 26L, 208L, 54L, 163L, 212L, 81L, 49L, 45L, 84L, 12L, 82L, 114L, 81L, 173L, 67L, 52L, 63L, 146L, 156L),
X6 = c(54L, 117L, 143L, 296L, 74L, 0L, 78L, 97L, 218L, 52L, 239L, 47L, 267L, 111L, 216L, 269L, 167L, 91L, 105L, 99L, 16L, 117L, 95L, 15L, 264L, 136L, 148L, 100L, 145L, 242L),
X7 = c(143L, 145L, 208L, 302L, 130L, 86L, 0L, 84L, 308L, 151L, 348L, 102L, 305L, 139L, 254L, 272L, 156L, 182L, 87L, 214L, 110L, 82L, 145L, 59L, 283L, 98L, 171L, 192L, 211L, 228L),
X8 = c(142L, 195L, 238L, 336L, 161L, 80L, 2L, 0L, 289L, 135L, 342L, 134L, 307L, 169L, 197L, 347L, 261L, 143L, 128L, 243L, 52L, 128L, 169L, 97L, 318L, 214L, 229L, 225L, 287L, 288L),
X9 = c(230L, 238L, 217L, 264L, 229L, 154L, 299L, 269L, 0L, 210L, 335L, 204L, 342L, 260L, 25L, 258L, 264L, 76L, 170L, 222L, 126L, 193L, 272L, 203L, 218L, 186L, 214L, 120L, 240L, 207L),
X10 = c(2L, 30L, 34L, 186L, 75L, 96L, 161L, 179L, 244L, 0L, 199L, 17L, 181L, 59L, 240L, 178L, 101L, 117L, 1L, 48L, 79L, 72L, 24L, 60L, 236L, 5L, 32L, 151L, 203L, 133L),
X11 = c(243L, 191L, 114L, 38L, 164L, 211L, 295L, 370L, 350L, 211L, 0L, 174L, 14L, 166L, 278L, 164L, 165L, 223L, 252L, 129L, 201L, 196L, 201L, 271L, 98L, 199L, 209L, 219L, 168L, 62L),
X12 = c(22L, 14L, 64L, 164L, 68L, 113L, 117L, 115L, 208L, 8L, 239L, 0L, 204L, 36L, 230L, 217L, 37L, 62L, 41L, 30L, 64L, 25L, 22L, 60L, 150L, 30L, 90L, 109L, 92L, 178L),
X13 = c(224L, 168L, 120L, 23L, 213L, 310L, 324L, 377L, 366L, 212L, 54L, 178L, 0L, 207L, 316L, 105L, 117L, 266L, 235L, 106L, 231L, 186L, 147L, 223L, 109L, 182L, 211L, 183L, 130L, 132L),
X14 = c(66L, 22L, 77L, 204L, 23L, 143L, 93L, 171L, 259L, 37L, 182L, 68L, 218L, 0L, 216L, 186L, 56L, 139L, 65L, 44L, 118L, 64L, 19L, 112L, 183L, 14L, 40L, 142L, 96L, 131L),
X15 = c(167L, 236L, 263L, 275L, 187L, 189L, 212L, 282L, 9L, 181L, 309L, 231L, 340L, 238L, 0L, 207L, 216L, 97L, 204L, 237L, 120L, 160L, 217L, 188L, 258L, 203L, 183L, 106L, 174L, 267L),
X16 = c(173L, 196L, 161L, 152L, 168L, 268L, 322L, 280L, 265L, 230L, 133L, 160L, 102L, 209L, 193L, 0L, 162L, 168L, 228L, 208L, 283L, 159L, 195L, 270L, 14L, 233L, 176L, 172L, 48L, 38L),
X17 = c(70L, 44L, 13L, 184L, 90L, 186L, 194L, 267L, 257L, 72L, 179L, 38L, 107L, 88L, 177L, 172L, 0L, 93L, 71L, 14L, 167L, 117L, 37L, 146L, 116L, 35L, 62L, 83L, 93L, 129L),
X18 = c(115L, 128L, 153L, 196L, 78L, 109L, 147L, 185L, 103L, 138L, 218L, 123L, 200L, 85L, 91L, 178L, 86L, 0L, 35L, 125L, 67L, 68L, 142L, 42L, 202L, 84L, 81L, 91L, 87L, 210L),
X19 = c(36L, 104L, 71L, 166L, 16L, 99L, 88L, 133L, 200L, 35L, 246L, 2L, 263L, 89L, 144L, 229L, 47L, 103L, 0L, 31L, 114L, 32L, 44L, 106L, 219L, 43L, 92L, 119L, 150L, 103L),
X20 = c(74L, 66L, 22L, 101L, 76L, 101L, 194L, 211L, 170L, 21L, 179L, 105L, 197L, 36L, 180L, 204L, 48L, 71L, 59L, 0L, 178L, 32L, 58L, 125L, 180L, 46L, 16L, 77L, 130L, 75L),
X21 = c(85L, 158L, 144L, 210L, 29L, 16L, 44L, 65L, 141L, 88L, 258L, 70L, 279L, 124L, 168L, 190L, 145L, 45L, 40L, 110L, 0L, 129L, 157L, 52L, 177L, 117L, 175L, 153L, 133L, 190L),
X22 = c(25L, 44L, 124L, 236L, 62L, 39L, 108L, 149L, 175L, 18L, 177L, 16L, 259L, 44L, 163L, 179L, 74L, 110L, 28L, 104L, 110L, 0L, 97L, 123L, 216L, 5L, 112L, 170L, 153L, 196L),
X23 = c(26L, 2L, 39L, 205L, 102L, 74L, 205L, 203L, 257L, 49L, 194L, 96L, 207L, 5L, 174L, 160L, 37L, 159L, 55L, 55L, 91L, 1L, 0L, 153L, 216L, 52L, 11L, 192L, 116L, 149L),
X24 = c(12L, 127L, 115L, 288L, 90L, 60L, 73L, 79L, 194L, 87L, 203L, 81L, 257L, 108L, 188L, 263L, 185L, 90L, 25L, 177L, 51L, 85L, 93L, 0L, 234L, 114L, 111L, 72L, 131L, 153L),
X25 = c(194L, 120L, 191L, 143L, 212L, 253L, 281L, 318L, 291L, 160L, 108L, 174L, 94L, 188L, 203L, 43L, 127L, 153L, 204L, 162L, 170L, 237L, 194L, 251L, 0L, 237L, 122L, 149L, 75L, 104L),
X26 = c(10L, 58L, 22L, 171L, 101L, 131L, 144L, 154L, 177L, 5L, 222L, 48L, 220L, 59L, 192L, 193L, 9L, 134L, 23L, 80L, 75L, 19L, 19L, 132L, 197L, 0L, 57L, 146L, 163L, 154L),
X27 = c(104L, 58L, 15L, 175L, 46L, 178L, 206L, 185L, 199L, 44L, 191L, 115L, 211L, 33L, 200L, 148L, 25L, 104L, 47L, 8L, 154L, 35L, 41L, 106L, 159L, 14L, 0L, 171L, 88L, 87L),
X28 = c(80L, 137L, 93L, 225L, 76L, 138L, 245L, 195L, 102L, 130L, 197L, 112L, 169L, 163L, 68L, 83L, 108L, 50L, 125L, 108L, 139L, 106L, 131L, 111L, 136L, 179L, 150L, 0L, 103L, 151L),
X29 = c(170L, 113L, 89L, 120L, 138L, 172L, 224L, 263L, 233L, 155L, 170L, 132L, 194L, 133L, 160L, 99L, 106L, 86L, 154L, 90L, 132L, 158L, 142L, 209L, 55L, 127L, 118L, 93L, 0L, 67L),
X30 = c(197L, 170L, 155L, 66L, 178L, 193L, 226L, 303L, 213L, 161L, 104L, 94L, 140L, 138L, 241L, 29L, 102L, 131L, 108L, 129L, 182L, 167L, 167L, 198L, 83L, 154L, 89L, 106L, 87L, 0L))
system.time({
g <- graph_from_adjacency_matrix(as.matrix(m), "directed", TRUE, FALSE)
mOpt <- shortest.paths(g, V(g), V(g), mode = "out")
})
#> user system elapsed
#> 0 0 0
mOpt[1:10, 1:10]
#> X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
#> X1 0 21 25 133 36 27 71 73 123 2
#> X2 18 0 10 122 40 45 89 91 127 17
#> X3 36 18 0 112 58 63 107 109 145 27
#> X4 130 112 117 0 152 157 201 203 239 129
#> X5 17 36 40 148 0 44 88 81 127 17
#> X6 39 48 52 160 28 0 86 68 142 41
#> X7 67 88 92 200 56 60 0 2 170 69
#> X8 88 109 113 221 77 81 84 0 191 90
#> X9 157 165 170 250 140 157 208 193 0 154
#> X10 22 19 23 131 34 49 92 94 121 0
mOptPaths <- lapply(V(g), function(from) all_shortest_paths(g, from = from, mode = "out"))
# optimal path from 1 to 2
(path_1_2 <- as.integer(mOptPaths[[1]]$res[[2]]))
#> [1] 1 10 26 22 2
# distances for path from 1 to 2
m[matrix(c(head(path_1_2, -1), path_1_2[-1]), ncol = 2)]
#> [1] 2 5 5 9
Box-plot R calculating outliers
> summary(mydata) Min. 1st Qu. Median Mean 3rd Qu. Max. 0 93 107 110 125 197 > range=1.5*(125-93) > upper_whisker=125+range > lower_whisker=93-range > upper_whisker [1] 173 > lower_whisker [1] 45 > boxplot(mydata)$stats [,1] [1,] 56 #Lower whisker by boxplot [2,] 93 [3,] 107 [4,] 125 [5,] 173 I tried looking up the formula for calculating after and before what values are the points to be considered outliers It was Above =>3rd Qu +(3rd Qu - 1st Qu)*1.5 Below =>1st Qu -(3rd Qu - 1st Qu)*1.5 For some reason they don't seem to match with the stats returned by boxplot function in R I have a feeling it's something silly here Are they calculated differently? Or am I reading the wrong answer from boxplot? Edit: I've used https://www.kaggle.com/uciml/pima-indians-diabetes-database and ran mydata=raw$Glucose[raw$Outcome==0] EDIT2: I suppose if #max(min(x), Q1 - (IQR(x)*1.5)) #lower whisker is returning min(x), there shouldn't be any outliers and min(mydata) is 0 Edit 3: Clearer view of Quantile quantile(mydata) 0% 25% 50% 75% 100% 0 93 107 125 197 Edit 4: Added vector as requested c(85L, 89L, 116L, 115L, 110L, 139L, 103L, 126L, 99L, 97L, 145L, 117L, 109L, 88L, 92L, 122L, 103L, 138L, 180L, 133L, 106L, 159L, 146L, 71L, 105L, 103L, 101L, 88L, 150L, 73L, 100L, 146L, 105L, 84L, 44L, 141L, 99L, 109L, 95L, 146L, 139L, 129L, 79L, 0L, 62L, 95L, 112L, 113L, 74L, 83L, 101L, 110L, 106L, 100L, 107L, 80L, 123L, 81L, 142L, 144L, 92L, 71L, 93L, 151L, 125L, 81L, 85L, 126L, 96L, 144L, 83L, 89L, 76L, 78L, 97L, 99L, 111L, 107L, 132L, 120L, 118L, 84L, 96L, 125L, 100L, 93L, 129L, 105L, 128L, 106L, 108L, 154L, 102L, 57L, 106L, 147L, 90L, 136L, 114L, 153L, 99L, 109L, 88L, 151L, 102L, 114L, 100L, 148L, 120L, 110L, 111L, 87L, 79L, 75L, 85L, 143L, 87L, 119L, 0L, 73L, 141L, 111L, 123L, 85L, 105L, 113L, 138L, 108L, 99L, 103L, 111L, 96L, 81L, 147L, 179L, 125L, 119L, 142L, 100L, 87L, 101L, 197L, 117L, 79L, 122L, 74L, 104L, 91L, 91L, 146L, 122L, 165L, 124L, 111L, 106L, 129L, 90L, 86L, 111L, 114L, 193L, 191L, 95L, 142L, 96L, 128L, 102L, 108L, 122L, 71L, 106L, 100L, 104L, 114L, 108L, 129L, 133L, 136L, 155L, 96L, 108L, 78L, 161L, 151L, 126L, 112L, 77L, 150L, 120L, 137L, 80L, 106L, 113L, 112L, 99L, 115L, 129L, 112L, 157L, 179L, 105L, 118L, 87L, 106L, 95L, 165L, 117L, 130L, 95L, 0L, 122L, 95L, 126L, 139L, 116L, 99L, 92L, 137L, 61L, 90L, 90L, 88L, 158L, 103L, 147L, 99L, 101L, 81L, 118L, 84L, 105L, 122L, 98L, 87L, 93L, 107L, 105L, 109L, 90L, 125L, 119L, 100L, 100L, 131L, 116L, 127L, 96L, 82L, 137L, 72L, 123L, 101L, 102L, 112L, 143L, 143L, 97L, 83L, 119L, 94L, 102L, 115L, 94L, 135L, 99L, 89L, 80L, 139L, 90L, 140L, 147L, 97L, 107L, 83L, 117L, 100L, 95L, 120L, 82L, 91L, 119L, 100L, 135L, 86L, 134L, 120L, 71L, 74L, 88L, 115L, 124L, 74L, 97L, 154L, 144L, 137L, 119L, 136L, 114L, 137L, 114L, 126L, 132L, 123L, 85L, 84L, 139L, 173L, 99L, 194L, 83L, 89L, 99L, 80L, 166L, 110L, 81L, 154L, 117L, 84L, 94L, 96L, 75L, 130L, 84L, 120L, 139L, 91L, 91L, 99L, 125L, 76L, 129L, 68L, 124L, 114L, 125L, 87L, 97L, 116L, 117L, 111L, 122L, 107L, 86L, 91L, 77L, 105L, 57L, 127L, 84L, 88L, 131L, 164L, 189L, 116L, 84L, 114L, 88L, 84L, 124L, 97L, 110L, 103L, 85L, 87L, 99L, 91L, 95L, 99L, 92L, 154L, 78L, 130L, 111L, 98L, 143L, 119L, 108L, 133L, 109L, 121L, 100L, 93L, 103L, 73L, 112L, 82L, 123L, 67L, 89L, 109L, 108L, 96L, 124L, 124L, 92L, 152L, 111L, 106L, 105L, 106L, 117L, 68L, 112L, 92L, 183L, 94L, 108L, 90L, 125L, 132L, 128L, 94L, 102L, 111L, 128L, 92L, 104L, 94L, 100L, 102L, 128L, 90L, 103L, 157L, 107L, 91L, 117L, 123L, 120L, 106L, 101L, 120L, 127L, 162L, 112L, 98L, 154L, 165L, 99L, 68L, 123L, 91L, 93L, 101L, 56L, 95L, 136L, 129L, 130L, 107L, 140L, 107L, 121L, 90L, 99L, 127L, 118L, 122L, 129L, 110L, 80L, 127L, 158L, 126L, 134L, 102L, 94L, 108L, 83L, 114L, 117L, 111L, 112L, 116L, 141L, 175L, 92L, 106L, 105L, 95L, 126L, 65L, 99L, 102L, 109L, 153L, 100L, 81L, 121L, 108L, 137L, 106L, 88L, 89L, 101L, 122L, 121L, 93L)
Your calculation was almost right, R uses this: #max(min(x), Q1 - (IQR(x)*1.5)) #lower whisker #min(max(x), Q3 + (IQR(x)*1.5)) #upper whisker That's why, it picks the max/min between the min(x)/max(x), and the standard formula. Here an example: my_data <- mtcars$mpg bp <- boxplot(my_data) bp$stats # [1,] 10.40 # lower whisker # [2,] 15.35 # [3,] 19.20 # == median(my_data) # [4,] 22.80 # [5,] 33.90 # upper whisker max(min(my_data,na.rm=T), as.numeric(quantile(my_data, 0.25)) - (IQR(my_data)*1.5)) #[1] 10.4 #lower whisker min(max(my_data,na.rm=T), as.numeric(quantile(my_data, 0.75)) + (IQR(my_data)*1.5)) #[1] 33.9 # upper whisker
I think there are few things to clarified. The first thing is that you should always provide a reproducible example for helping people to help you. An outlier is defined as a data point that is located outside the whiskers of the boxplot (e.g: outside 1.5 times the interquartile range above the upper quartile and bellow the lower quartile). The correct way to figure out how this work is simulating some Student's T data under a pre-specified a random number generator state. set.seed(1) mydata <- rt(100, df = 3) boxplot(mydata) summary(mydata) Then we can calculate the interquartile range and the lower and upper bounds for outliers according to the rule in the text above t <- as.vector(summary(mydata)) iqr.range <- t[5]-t[2] upper_outliers <- t[5]+iqr.range*1.5 lower_outliers <- t[2]-iqr.range*1.5 Let's check the data which are defined as outliers, while the boxplot whiskers are the data points immediately before/after the lower/upper boundaries. mydata[mydata<lower_outliers] [1] -3.527006 -2.959327 -2.754192 mydata[mydata>upper_outliers] [1] 3.080302 3.527205
Making a line graph in R with multiple treatments?
sorry for the multiple questions about R. I'm new and still learning! So I am currently trying to make a multiple-line line graph with my data. I have 3 treatment groups with 4 individuals each. I am planning on factoring those into 3 groups for R. First, I want to make sure my data is set up in such a way in excel that i could make this graph. Second, how would I go about doing this? Is ggplot the best tool or is there another package that could be utilized?
I would like to have my X-axis as the dates (these are 10.15.2015for eg.), my Y-axis as the weights, and my 3 treatment groups, Lean, AdLib, and HF, as the data lines. As I said above, I used datum$Group= factor(Datum$Group) to group the Pig individuals into their 3 treatment groups.
I have looked at other questions on here but it did not seem like they were what I wanted.
Here is my data:
dput(datum)
structure(list(X10.5.15 = c(56L, 54L, 61L, 39L, 52L, 66L, 48L,
49L, 59L, 55L, 37L, 59L), X10.26.15 = c(76L, 70L, 72L, 61L, 79L,
93L, 72L, 72L, 84L, 71L, 50L, 85L), X11.3.15 = c(82L, 76L, 88L,
67L, 90L, 102L, 83L, 83L, 100L, 96L, 56L, 100L), X11.10.15 = c(87L,
84L, 93L, 71L, 99L, 110L, 93L, 93L, 109L, 107L, 65L, 112L), X11.17.15 = c(93L,
90L, 100L, 77L, 106L, 116L, 101L, 100L, 121L, 122L, 71L, 119L
), X11.24.15 = c(102L, 99L, 109L, 86L, 113L, 124L, 107L, 108L,
128L, 128L, 80L, 122L), X12.3.15 = c(114L, 113L, 123L, 100L,
118L, 132L, 122L, 118L, 143L, 142L, 91L, 137L), X12.10.15 = c(117L,
117L, 125L, 106L, 134L, 141L, 130L, 126L, 152L, 151L, 98L, 148L
), X12.17.15 = c(125L, 122L, 134L, 112L, 150L, 154L, 135L, 134L,
162L, 162L, 106L, 160L), X12.22.15 = c(128L, 127L, 135L, 114L,
156L, 161L, 141L, 140L, 166L, 176L, 109L, 166L), X12.29.15 = c(135L,
130L, 142L, 119L, 155L, 164L, 149L, 149L, 174L, 195L, 121L, 176L
), X1.5.16 = c(138L, 135L, 150L, 129L, 167L, 172L, 163L, 154L,
185L, 205L, 128L, 182L), X1.12.16 = c(154L, 157L, 166L, 146L,
180L, 188L, 173L, 163L, 200L, 208L, 140L, 188L), X1.19.16 = c(148L,
151L, 165L, 141L, 180L, 182L, 171L, 176L, 211L, 219L, 149L, 197L
), X1.26.16 = c(154L, 151L, 171L, 148L, 192L, 196L, 181L, 179L,
212L, 230L, 156L, 205L), X2.2.16 = c(162L, 162L, 179L, 154L,
200L, 200L, 191L, 184L, 228L, 228L, 162L, 225L), X2.9.16 = c(172L,
169L, 187L, 164L, 203L, 202L, 188L, 194L, 237L, 253L, 168L, 234L
), X2.16.16 = c(173L, 167L, 192L, 162L, 211L, 215L, 199L, 202L,
233L, 258L, 173L, 238L), X2.23.16 = c(185L, 174L, 202L, 172L,
220L, 218L, 208L, 204L, 253L, 254L, 185L, 239L), X2.29.16 = c(183L,
169L, 202L, 166L, 216L, 220L, 204L, 206L, 256L, 269L, 187L, 252L
), Pig = c(102L, 105L, 108L, 204L, 101L, 104L, 106L, 602L, 103L,
107L, 205L, 603L), Group = structure(c(3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L), .Label = c("AdLib", "HF", "Lean"), class = "factor")), .Names = c("X10.5.15",
"X10.26.15", "X11.3.15", "X11.10.15", "X11.17.15", "X11.24.15",
"X12.3.15", "X12.10.15", "X12.17.15", "X12.22.15", "X12.29.15",
"X1.5.16", "X1.12.16", "X1.19.16", "X1.26.16", "X2.2.16", "X2.9.16",
"X2.16.16", "X2.23.16", "X2.29.16", "Pig", "Group"), row.names = c(NA,
-12L), class = "data.frame")
Thanks for your help in advance!
library(ggplot2)
library(reshape2)
#Remove the 'X' from the dates
names(datum) <- sub("^X", "", names(datum))
We should reshape the data to long format. The idea is to have one column for each type of data.
datum_mlt <- melt(datum, id=c("Group", "Pig"), variable.name="dates")
head(datum_mlt)
# Group Pig dates value
# 1 Lean 102 10.5.15 56
# 2 Lean 105 10.5.15 54
# 3 Lean 108 10.5.15 61
# 4 Lean 204 10.5.15 39
# 5 AdLib 101 10.5.15 52
# 6 AdLib 104 10.5.15 66
As you can see there is a column for values, dates, ids, and treatment groups. This makes it easier to organize the information for plotting.
There are ten thousand ways to do this depending on how you want the data to look. You did not specify, so here is one example. We can clean up the axes and make everything look better if the format is correct:
p <- ggplot(datum_mlt, aes(x=dates, y=value, colour=Group, group=Pig))
p + geom_line()
Edit
Before grouping individuals, I would first remove the 'Pig' column, it looks like it helps, but it doesn't.
datum2 <- datum[names(datum) != "Pig"]
library(dplyr)
datum2 %<>% group_by(Group) %>% summarise_all(mean)
d_melt <- melt(datum2, id="Group")
We plot the data. And try to make it look a little nicer.
p <- ggplot(d_melt, aes(x=variable, y=value, colour=Group, group=Group))
p <- p + geom_line()
p <- p + scale_x_discrete(name="Date", breaks=unique(d_melt$variable)[c(T,F,F)])
p + ggtitle("Grouped Weights Over Time") + theme_minimal()
Label points in a circle plot R
Is there a way to show the numbers that correspond to a point on a circle? I read up on the text(xy) function but it is for scatter plots which this is not. The scripts is as follows and the image attached shows what the result is. I would like to identify the point in the plots. Any help rendered is appreciated! Thanks.
library (circular)
df<- read.csv("Direction.csv", header = TRUE)
df1 <- df [ which(df$Month==1 & df$Day>0 & df$Day <32) ,]
df2 <- df1[c(-1,-2,-3)]
df3<- lapply(df2, function(df2) circular(df2, units='degrees', template='geographics'))
dens<- lapply(df3, density.circular, bw =5)
par(mfrow=c(5,4), oma=c(2,1.3,2,2), mar=c(1.5,2,2,1), tcl=-0.2, mgp=c(0,1,0))
titles <- c("1000mb", "925mb", "850mb", "700mb", "600mb", "500mb", "400mb", "300mb",
"250mb", "200mb", "150mb", "100mb","70mb", "50mb", "30mb", "20mb", "10mb")
for(i in 1:17){
plot(mean(df3[[1]]), main = titles[1],)
print(mean(df3[[1]]))
print(var(df3[[1]]))
print(summary(df3[[1]]))
}
dput(df3[1])
structure(list(X1000mb = structure(c(86L, 130L, 75L, 59L, 56L,
69L, 139L, 358L, 98L, 175L, 322L, 17L, 336L, 46L, 137L, 1L, 2L,
102L, 225L, 121L, 179L, 291L, 325L, 317L, 321L, 349L, 28L, 38L,
36L, 117L, 144L, 73L, 121L, 135L, 131L, 127L, 139L, 167L, 298L,
213L, 37L, 33L, 71L, 120L, 156L, 14L, 51L, 92L, 168L, 332L, 24L,
71L, 128L, 98L, 104L, 86L, 155L, 5L, 281L, 342L, 356L, 346L,
210L, 186L, 199L, 133L, 191L, 282L, 139L, 168L, 158L, 154L, 117L,
149L, 162L, 157L, 192L, 175L, 197L, 171L, 184L, 305L, 70L, 169L,
207L, 8L, 72L, 134L, 160L, 135L, 154L, 149L, 161L, 182L, 259L,
173L, 205L, 331L, 112L, 26L, 129L, 137L, 120L, 136L, 156L, 327L,
332L, 349L, 16L, 28L, 42L, 352L, 94L, 149L, 153L, 183L, 183L,
196L, 170L, 164L, 212L, 169L, 180L, 206L, 81L, 135L, 145L, 148L,
172L, 174L, 160L, 188L, 193L, 197L, 247L, 68L, 181L, 177L, 219L,
204L, 86L, 333L, 354L, 132L, 0L, 35L, 27L, 38L, 77L, 123L, 174L,
172L, 191L, 312L, 307L, 29L, 161L, 62L, 104L, 240L, 300L, 292L,
194L, 202L, 274L, 349L, 26L, 198L, 294L, 185L, 178L, 324L, 28L,
36L, 93L, 115L, 280L, 24L, 353L, 348L, 68L, 24L, 357L, 17L, 47L,
45L, 238L, 333L, 342L, 111L, 233L, 183L, 193L, 212L, 188L, 164L,
142L, 158L, 179L, 300L, 336L, 297L, 346L, 17L, 149L, 115L, 8L,
358L, 341L, 22L, 142L, 283L, 349L, 273L, 271L, 224L, 313L, 62L,
100L, 137L, 158L, 235L, 155L, 184L, 132L, 153L, 206L, 182L, 187L,
238L, 275L, 292L, 1L, 36L, 148L, 334L, 30L, 58L, 356L, 6L, 345L,
91L, 157L, 332L, 327L, 11L, 170L, 169L, 120L, 158L, 160L, 177L,
168L, 300L, 295L, 7L, 75L, 172L, 328L, 3L, 63L, 348L, 34L, 185L,
347L, 66L, 105L, 130L, 151L, 83L, 120L, 154L, 172L, 152L, 174L,
174L, 159L, 147L, 173L, 212L, 327L, 55L, 203L, 192L, 95L, 139L,
200L, 227L, 209L, 262L, 129L, 151L, 200L, 133L, 190L, 112L, 85L,
184L, 185L, 186L, 256L, 28L, 157L, 54L, 55L, 88L, 315L, 27L,
53L, 126L, 179L, 161L, 163L, 168L, 280L, 336L, 89L, 175L, 253L,
357L, 250L, 36L, 62L, 103L, 1L, 5L, 55L, 97L, 114L, 143L, 156L,
156L, 178L, 183L, 191L, 285L, 4L, 16L, 69L, 340L, 63L, 131L,
128L, 137L, 137L, 253L, 213L, 165L, 166L, 166L, 171L, 193L, 186L,
180L, 194L, 255L, 294L, 60L, 175L, 123L, 136L, 147L, 144L, 146L,
135L, 157L, 228L, 177L, 165L, 168L, 176L, 182L, 352L, 23L, 260L,
298L, 283L, 152L, 151L, 180L, 170L, 2L, 60L, 121L, 110L, 153L,
174L, 204L, 312L, 153L, 250L, 223L, 244L, 345L, 225L, 233L, 289L,
212L, 190L, 285L, 226L, 136L, 111L, 179L, 200L, 274L, 2L, 351L,
10L, 12L, 13L, 340L, 336L, 331L, 258L, 36L, 95L, 117L, 149L,
151L, 155L, 135L, 187L, 191L, 195L, 15L, 103L, 161L, 194L, 186L,
167L, 90L, 174L, 205L, 173L, 208L, 197L, 217L, 246L, 151L, 161L,
119L, 128L, 159L, 232L, 198L, 227L, 175L, 213L, 220L, 226L, 171L,
244L, 203L, 167L, 185L, 156L, 182L, 157L, 154L, 144L, 146L, 174L,
196L, 141L, 348L, 22L, 63L, 125L, 163L, 32L, 331L, 19L, 72L,
85L, 186L, 297L, 353L, 32L, 242L, 240L, 191L, 200L, 192L, 208L,
256L, 193L, 243L, 3L, 18L, 293L, 357L, 233L, 169L, 160L, 189L,
310L, 305L, 288L, 201L, 334L, 56L, 274L, 269L, 303L, 237L, 224L,
230L, 170L, 192L, 135L, 194L, 132L, 122L, 149L, 171L, 199L, 217L,
133L, 172L, 195L, 329L, 11L, 48L, 120L, 158L, 198L, 23L, 109L,
154L, 145L, 86L, 41L, 156L, 186L, 222L, 150L, 163L, 19L, 278L,
325L, 352L, 5L, 72L, 136L, 123L, 149L, 154L, 132L, 155L, 233L,
187L, 168L, 9L, 41L, 262L, 4L, 40L, 154L, 157L, 233L, 97L, 162L,
171L, 171L, 181L, 355L, 35L, 103L, 214L, 355L, 335L, 345L, 13L,
331L, 347L, 323L, 294L, 234L, 295L, 190L, 151L, 182L, 231L, 268L,
286L, 20L, 11L, 144L, 181L, 149L, 160L, 180L, 343L, 65L, 130L,
108L, 166L, 164L, 182L, 160L, 174L, 101L, 27L, 62L, 110L, 76L,
25L, 150L, 173L, 169L, 183L, 181L, 189L, 167L, 232L, 345L, 154L,
216L, 195L, 212L, 242L, 289L, 252L, 111L, 148L, 161L, 159L, 153L,
162L, 139L, 158L, 150L, 164L, 198L, 14L, 141L, 156L, 288L, 355L,
36L, 73L, 208L, 215L, 323L, 135L, 188L, 289L, 232L, 227L, 317L,
222L, 192L, 76L, 40L, 172L, 157L, 142L, 216L, 223L, 163L, 237L,
344L, 30L, 126L, 143L, 162L, 162L, 104L, 103L, 123L, 110L, 140L,
146L, 149L, 139L, 161L, 194L, 187L, 283L, 13L, 16L, 185L, 177L,
200L, 155L, 152L, 169L, 238L, 282L, 161L, 185L, 224L, 198L, 159L,
208L, 309L, 179L, 182L, 244L, 290L, 217L, 236L, 20L, 61L, 130L,
162L, 262L, 245L, 206L, 225L, 193L, 331L, 34L, 133L, 216L, 277L,
343L, 300L, 342L, 15L, 50L, 307L, 314L, 5L, 24L, 19L, 86L, 120L,
356L, 34L, 19L, 346L, 359L, 25L, 45L, 97L, 151L, 67L, 100L, 23L,
66L, 9L, 223L, 121L, 164L, 175L, 174L, 217L, 227L, 241L, 184L,
265L, 196L, 215L, 178L, 326L, 102L, 339L, 21L, 43L, 19L, 65L,
289L, 288L, 94L, 97L, 132L, 123L, 141L, 141L, 282L, 220L, 281L,
202L, 252L, 225L, 350L, 77L, 199L, 274L, 209L, 229L, 5L, 67L,
19L, 28L, 56L, 89L, 71L, 68L, 126L, 120L, 124L, 112L, 83L, 171L,
25L, 306L, 305L, 338L, 3L, 319L, 12L, 70L, 19L, 185L, 199L, 88L,
140L, 176L, 207L, 149L, 155L, 162L, 152L, 164L, 178L, 201L, 214L,
169L, 175L, 180L, 168L, 183L, 163L, 186L, 257L, 223L, 166L, 157L,
133L, 24L, 115L, 162L, 173L, 245L, 147L, 105L, 81L, 75L, 75L,
47L, 27L, 15L, 347L, 21L, 116L, 160L, 178L, 193L, 51L, 232L,
295L, 358L, 311L, 16L, 17L, 7L, 47L, 345L, 4L, 36L, 118L, 209L,
173L, 231L, 8L, 90L, 156L, 237L, 163L, 343L, 350L, 354L, 36L,
62L, 45L, 43L, 95L, 113L, 164L, 317L, 315L, 168L, 188L, 190L,
168L, 227L, 185L, 142L, 249L, 200L, 228L, 7L, 50L, 95L, 265L,
10L, 75L, 63L, 151L, 124L, 146L, 35L, 303L, 331L, 218L, 303L,
312L, 341L, 33L, 36L, 9L, 74L, 85L, 105L, 99L, 101L, 91L, 130L,
152L, 14L, 211L, 271L, 319L, 315L, 309L, 358L, 31L), circularp = structure(list(
type = "angles", units = "degrees", template = "geographics",
modulo = "asis", zero = 1.5707963267949, rotation = "clock"), .Names = c("type",
"units", "template", "modulo", "zero", "rotation")), class = c("circular",
"integer"))), .Names = "X1000mb")
It doesn't matter that it's not strictly a "scatterplot" . Now that you've set up an array of subplots, you can cycle thru them again, but this time using text() to place data at the desired location within each subplot. Roughly, for (i in 1:17 ) text(x_loc[i],y_loc[i], some_text_vector[i]) Where you've "preloaded" the text strings and locations.