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R: add alpha-value to png-image
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I have a two-dimensional density plot which I need to overlay onto a photograph. I can read-in the picture with the package rtiff:
install.packages("rtiff")
library(rtiff)
x <- readTiff('F01_screenshot.tiff')
plot(x)
The data for the density plot is a very large dataframe called eet2 (see reproducible data below); the code for the density plot is this:
library(ggplot2)
ggplot(eet2, aes(x=X, y= Y)) +
geom_point() + stat_density2d()
I can easily produce both plots separately but don't know how to overlay the density plot onto the photograph in such a way that the density plot is scaled down proportionately to fit the picture. Any help is much appreciated!
Reproducible data:
eet2 <- dput(eet1[1:1000, 2:3])
structure(list(X = c(0, 177.378, 27.289, 0, 852.719, 0, 852.813,
71.068, 0, 144.875, 0, 140.385, 21.598, 0, 170.325, 0, 136.746,
21.038, 0, 146.279, 0, 141.86, 11.822, 0, 146.078, 0, 137.681,
21.182, 0, 148.867, 0, 132.886, 20.444, 0, 146.129, 0, 80.251,
6.688, 0, 141.08, 0, 149.789, 23.044, 0, 74.097, 0, 141.182,
21.72, 0, 83.075, 0, 81.192, 6.766, 0, 849.784, 0, 153.96, 23.686,
0, 78.374, 0, 142.782, 21.967, 0, 72.929, 0, 142.922, 11.91,
0, 83.639, 0, 143.912, 22.14, 0, 134.809, 0, 142.826, 21.973,
0, 132.7, 0, 85.876, 7.156, 0, 138.935, 0, 80.951, 12.454, 0,
144.385, 0, 141.716, 21.802, 0, 76.768, 0, 74.406, 6.2, 0, 134.444,
0, 155.341, 23.899, 0, 189.336, 0, 224.517, 34.541, 0, 207.46,
0, 216.122, 18.01, 0, 204.552, 0, 208.524, 32.081, 0, 207.513,
0, 203.162, 31.256, 0, 197.578, 0, 204.362, 17.03, 0, 195.223,
0, 956.396, 147.138, 0, 201.969, 0, 224.989, 34.614, 0, 214.064,
0, 140.374, 11.698, 0, 140.235, 0, 958.501, 147.462, 0, 141.898,
0, 143.337, 22.052, 0, 955.901, 0, 954.965, 79.58, 0, 131.323,
0, 223.214, 34.341, 0, 952.203, 0, 143.102, 22.016, 0, 935.525,
0, 918.238, 76.52, 0, 123.337, 0, 127.93, 19.682, 0, 915.755,
0, 128.336, 19.744, 0, 913.158, 0, 120.076, 10.006, 0, 911.196,
0, 124.387, 19.136, 0, 911.631, 0, 911.389, 140.214, 0, 910.433,
0, 119.375, 9.948, 0, 115.898, 0, 910.073, 140.011, 0, 915.461,
0, 965.025, 148.465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1125.565,
0, 1074.389, 165.291, 0, 1061.611, 0, 1051.23, 161.728, 0, 1044.3,
0, 269.733, 22.478, 0, 245.869, 0, 206.934, 234.525, 245.609,
250.874, 251.576, 260.164, 262.974, 263.161, 269.25, 262.693,
261.818, 265.634, 266.479, 266.592, 266.39, 262.751, 262.508,
265.093, 262.666, 262.343, 1059.856, 1075.848, 1077.98, 1068.255,
1065.442, 1065.255, 1071.821, 1072.292, 1072.355, 1073.788, 1072.572,
1072.41, 1074.295, 1074.487, 1074.499, 1078.434, 1080.074, 1080.293,
1081.104, 1080.014, 1079.869, 1080.261, 1080.181, 1080.175, 1078.568,
1079.422, 1079.536, 1078.84, 1077.459, 1077.275, 1077.805, 1076.787,
1076.719, 1077.199, 1076.045, 1075.891, 1077.362, 1076.663, 1076.569,
1077.942, 1078.842, 1078.902, 1078.713, 1080.023, 1080.198, 1080.764,
1081.553, 1081.659, 1081.946, 1082.445, 1082.479, 1083.987, 1084.113,
1084.13, 1084.741, 1085.809, 1085.952, 1085.031, 1084.708, 1084.686,
1085.901, 1085.266, 1085.181, 1085.513, 1085.159, 1085.112, 1088.024,
1088.29, 1088.308, 1088.889, 1088.327, 1088.252, 1069.543, 1065.069,
1064.472, 1067.409, 1068.075, 1068.119, 1068.377, 1070.019, 1070.238,
1072.384, 1071.454, 1071.33, 1072.31, 1069.704, 1069.53, 1068.856,
1073.986, 1074.67, 1075.824, 1077.42, 1077.633, 1080.095, 1082.62,
1082.788, 1087.112, 1090.026, 1090.414, 1094.654, 1098.678, 1099.215,
1103.801, 1097.674, 1097.266, 1110.129, 1105.4, 1104.769, 1115.638,
1115.922, 1115.96, 1115.612, 1115.278, 1115.256, 1114.682, 1112.96,
1112.73, 1063.573, 948.249, 932.873, 844.512, 834.23, 833.545,
865.203, 867.402, 867.695, 838.499, 864.3, 867.74, 837.215, 864.187,
865.985, 0, 760.653, 862.073, 861.199, 860.757, 860.699, 0, 861.681,
71.807, 0, 851.287, 855.415, 855.965, 859.554, 864.523, 865.185,
868.109, 873.22, 873.561, 845.881, 882.877, 887.81, 893.391,
899.782, 900.634, 908.492, 56.781, 0, 0, 0, 0, 0, 0, 0, 319.09,
0, 0, 0, 0, 0, 0, 0, 0, 0, 696.905, 0, 704.008, 58.667, 0, 0,
0, 0, 677.276, 673.676, 673.195, 673.45, 671.209, 671.06, 672.474,
674.07, 674.283, 674.896, 656.33, 653.855, 678.654, 681.884,
682.099, 684.933, 715.507, 719.584, 735.659, 1290.244, 1364.188,
1362.632, 1366.322, 1366.568, 1371.973, 1364.1, 1363.051, 1361.383,
812.325, 739.117, 734.716, 1322.199, 1361.365, 730.422, 731.132,
731.227, 729.084, 724.164, 723.507, 726.199, 725.939, 725.922,
725.437, 1278.917, 1352.714, 726.791, 1281.431, 1355.383, 1366.558,
764.435, 724.294, 725.46, 724.544, 724.422, 724.178, 1288.932,
1364.232, 723.902, 680.495, 677.601, 724.172, 722.856, 722.681,
735.236, 743.706, 744.835, 554.779, 735.387, 747.427, 724.914,
720.25, 719.628, 721.122, 742.971, 745.884, 742.171, 737.947,
737.665, 729.207, 808.032, 818.542, 905.216, 106.496, 0, 1169.534,
0, 1195.852, 99.654, 0, 1236.396, 0, 0, 0, 0, 1190.473, 183.15,
0, 1143.686, 1132.754, 1132.025, 1123.856, 1103.6, 1100.9, 1083.716,
1079.191, 1078.588, 1083.785, 1070.395, 1069.503, 1066.885, 1065.121,
1064.885, 1064.401, 1065.285, 1065.403, 1075.009, 1076.036, 1076.105,
1086.308, 1075.821, 1074.423, 1073.282, 126.268, 0, 1092.281,
0, 1019.684, 1087.663, 286.389, 33.693, 0, 1117.064, 0, 1114.254,
171.424, 0, 1111.518, 0, 1110.347, 92.529, 0, 1077.293, 0, 1107.754,
170.424, 0, 1107.136, 0, 1076.571, 165.626, 0, 1067.475, 0, 1110.71,
92.559, 0, 274.852, 0, 280.654, 43.177, 0, 280.278, 271.773,
270.639, 0, 1060.434, 88.369, 0, 1054.964, 1047.876, 1046.931,
1052.977, 1064.93, 1066.524, 1061.181, 1069.339, 1069.883, 275.888,
970.662, 1063.299, 268.308, 974.232, 1068.355, 1044.757, 1065.286,
1066.655, 1076.68, 1085.54, 1086.721, 1088.216, 1083.409, 1082.768,
1087.272, 1087.283, 1087.283, 1086.689, 1087.19, 1087.257, 1089.988,
1087.837, 1087.55, 272.953, 1040.007, 1091.144, 1076.712, 1089.387,
1091.077, 1103.104, -360.56, -555.715, 1072.081, 1073.131, 1073.201,
1073.04, 1089.257, 1091.419, 1072.888, 1073.111, 1073.141, 1104.182,
1075.399, 1073.48, 1103.711, 1076.133, 1072.456, 1104.247, 1105.181,
1105.305, 1072.744, 1104.02, 1106.105, 1074.673, 1102.759, 1106.503,
1076.082, 1104.401, 1108.177, 1078.025, 1109.772, 1111.888, 288.949,
292.278, 292.722, 1112.981, 1123.139, 1124.493, 1127.996, 1134.943,
1135.406, 1136.071, 1141.749, 1142.506, 1148.754, 1150.764, 1151.032,
1156.093, 1154.597, 1154.497, 1150.481, 1124.218, 1120.716, 1108.646,
1095.181, 1093.385, 1113.175, 1089.783, 1088.224, 1719.795, -286.164,
-553.625, 1112.49, 1099.064, 1097.274, 1106.601, 1105.705, 1105.645,
1100.75, 1110.143, 1111.395, 1114.044, 1108.662, 1107.944, 1115.615,
1114.492, 1114.417, 296.606, 1011.559, 1106.886, 213.15, 735.037,
804.622, 643.816, 590.868, 587.338, 588.443, 591.022, 591.366,
595.437, 598.575, 598.994, -70.23, 562.977, 605.191, 608.988,
14.755, -64.476, -86.326, 490.189, 567.057, 573.015, 572.841,
572.83, 576.607, 578.243, 578.461, 579.714, 1343.595, 1445.446,
585.277, 588.153, 588.345, 588.809, 588.843, 588.848, 592.898,
591.447, 591.254, 592.538, 589.623, 589.429, 590.233, 587.901,
587.59, 589.276, 603.526, 605.426, 691.889, 810.649, 818.566,
889.948, 866.466, 863.335, 164.367, 164.895, 164.966, 158.056,
851.73, 897.975, 164.51, 806.786, 892.423, 894.069, 892.465,
892.251, 153.408, 842.909, 888.876, 151.935, 802.889, 889.683,
154.817, 146.446, 145.329, 888.428, 193.529, 147.202, 885.088,
224.817, 136.78, 147.255, 802.581, 889.958, 888.64, 889.975,
890.064, 1575.971, 968.922, 887.982, 889.232, 860.991, 857.226,
872.007, 893.987, 895.452, 859.373, 892.292, 896.681, 859.84,
859.942, 859.955, 898.274, 860.41, 857.885, 879.17, 862.76, 860.572,
856.434, 854.683, 854.45, 851.949, 174.027, 128.832, 848.35,
847.791, 847.716, 847.847, 849.008, 849.163, 133.198, 806.744,
851.647, 146.291, 773.622, 857.267, 860.622, 834.937, 831.512,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 623.517, 0, 631.712, 97.187,
0, 631.526, 0, 641.912, 98.756, 0, 648.161, 0, 654.673, 54.556,
0, 659.733, 0, 603.775, 684.278, 696.548, 689.181, 688.198, 687.25,
682.216, 681.881, 711.089, 713.482, 713.802, 713.969, 715.124,
715.278, 711.369, 709.299, 709.162, 679.428, 675.016, 674.428,
675.948, 673.724, 673.427, 675.195, 678.8, 679.041, 681.486,
686.341, 686.988, 696.445, 694.867, 694.657, 703.31, 705.003,
705.116, 0, 711.882, 109.52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1269.549, 1438.822, 0, 720.784, 60.065, 0, 721.678, 0, 713.535,
109.775, 0, 709.244, 0, 681.534, 104.851, 0, 693.092, 0, 681.394,
56.783, 0, 679.538, 0, 680.866, 104.749, 0, 675.661, 0, 668.345,
102.822, 0, 0, 0, 0, 0, -69.364, -10.671, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 677.983, 0), Y = c(0, -560.914,
-86.294, 0, 12.57, 0, 10.336, 0.861, 0, -569.851, 0, -582.641,
-89.637, 0, -513.461, 0, -582.556, -89.624, 0, -567.846, 0, -574.869,
-47.906, 0, -567.25, 0, -571.902, -87.985, 0, -554.095, 0, -574.651,
-88.408, 0, -557.127, 0, -365.662, -30.472, 0, -565.123, 0, -564.378,
-86.827, 0, -380.282, 0, -587.338, -90.36, 0, -373.494, 0, -394.186,
-32.849, 0, -15.305, 0, -586.833, -90.282, 0, -391.571, 0, -603.611,
-92.863, 0, -392.659, 0, -595.738, -49.645, 0, -369.805, 0, -585.56,
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-30.227, 0, -590.569, 0, -365.806, -56.278, 0, -584.274, 0, -594.203,
-91.416, 0, -378.246, 0, -385.426, -32.119, 0, -585.678, 0, -569.332,
-87.589, 0, -572.327, 0, -566.971, -87.226, 0, -586.06, 0, -584.482,
-48.707, 0, -593.944, 0, -583.298, -89.738, 0, -580.776, 0, -590.48,
-90.843, 0, -600.616, 0, -598.535, -49.878, 0, -602.826, 0, -29.44,
-4.529, 0, -618.062, 0, -592.995, -91.23, 0, -609.041, 0, -411.878,
-34.323, 0, -413.262, 0, -52.608, -8.094, 0, -411.569, 0, -415.928,
-63.989, 0, -48.63, 0, -47.12, -3.927, 0, -415.299, 0, -599.916,
-92.295, 0, -44.314, 0, -411.139, -63.252, 0, -46.124, 0, -48.509,
-4.042, 0, -407.485, 0, -413.499, -63.615, 0, -55.391, 0, -415.922,
-63.988, 0, -57.45, 0, -422.827, -35.236, 0, -54.607, 0, -414.86,
-63.825, 0, -51.45, 0, -48.986, -7.536, 0, -45.942, 0, -413.839,
-34.487, 0, -411.075, 0, -38.956, -5.993, 0, -14.525, 0, 35.454,
5.455, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 279.172, 0, 315.145,
48.484, 0, 313.523, 0, 314.854, 48.439, 0, 314.166, 0, -89.369,
-7.447, 0, -125.57, 0, -138.883, -157.401, -162.487, -158.14,
-157.561, -171.662, -160.906, -160.189, -166.883, -171.154, -171.724,
-187.379, -175.901, -174.37, -188.734, -185.215, -184.98, -189.818,
-189.088, -188.991, 283.22, 302.83, 305.444, 270.066, 289.663,
290.969, 275.119, 276.953, 277.198, 260.347, 261.677, 261.855,
246.632, 250.916, 251.202, 238.808, 246.008, 246.968, 240.285,
253.356, 255.099, 251.122, 262.086, 262.817, 258.677, 268.433,
269.734, 264.812, 273.771, 274.966, 268.696, 276.112, 276.606,
268.26, 275.135, 276.051, 265.545, 272.265, 273.161, 264.077,
268.116, 268.385, 261.015, 266.217, 266.911, 259.724, 265.892,
266.714, 259.602, 265.93, 266.352, 258.209, 265.537, 266.514,
259.411, 265.687, 266.523, 259.761, 266.978, 267.459, 260.97,
268.01, 268.949, 262.4, 271.867, 273.13, 264.803, 273.569, 274.153,
266.979, 275.25, 276.353, 268.073, 274.926, 275.84, 269.625,
279.424, 280.077, 271.785, 281.741, 283.068, 276.083, 284.162,
285.239, 279.865, 291.741, 292.533, 291.52, 302.124, 303.538,
300.685, 313.141, 314.802, 313.594, 327.048, 327.945, 325.593,
339.796, 341.69, 341.58, 355.702, 357.585, 356.602, 381.738,
383.413, 370.869, 394.752, 397.936, 391.659, 409.376, 411.739,
415.584, 433.281, 434.461, 437.478, 454.368, 456.62, 445.173,
427.865, 425.558, 383.004, 370.373, 369.531, 382.343, 399.016,
401.239, 413.831, 422.157, 423.267, 432.015, 448.845, 449.967,
0, 425.956, 482.75, 487.1, 501.178, 503.055, 0, 508.065, 42.339,
0, 492.242, 494.125, 494.376, 484.667, 484.368, 484.328, 467.742,
464.267, 464.035, 468.697, 433.854, 429.208, 400.85, 393.727,
392.777, 376.735, 23.546, 0, 0, 0, 0, 0, 0, 0, 885.955, 0, 0,
0, 0, 0, 0, 0, 0, 0, 500.323, 0, 481.941, 40.162, 0, 0, 0, 0,
530.014, 516.432, 514.621, 494.618, 478.341, 477.256, 456.114,
458.922, 459.297, 456.514, 425.5, 421.365, 462.707, 472.408,
473.055, 467.768, 454.51, 452.742, 441.741, 201.307, 169.249,
167.631, 163.795, 163.539, 160.509, 161.775, 161.943, 161.298,
397.997, 429.557, 419.546, 177.979, 161.875, 418.022, 423.34,
424.049, 413.55, 419.645, 420.457, 407.472, 415.496, 416.031,
407.006, 148.509, 114.042, 409.442, 164.128, 131.419, 109.849,
407.293, 427.122, 418.844, 426.465, 427.481, 417.779, 161.317,
127.123, 409.12, 367.918, 365.171, 395.673, 396.298, 396.381,
388.306, 392.859, 393.466, 473.545, 400.213, 395.325, 366.771,
370.934, 371.489, 339.469, 387.501, 393.905, 386.022, 390.883,
391.207, 378.721, 394.352, 396.436, 384.581, 45.245, 0, 380.042,
0, 375.485, 31.29, 0, 335.187, 0, 0, 0, 0, 323.048, 49.7, 0,
359.169, 361.068, 361.195, 348.111, 362.176, 364.051, 383.932,
392.518, 393.663, 349.699, 392.668, 395.532, 391.36, 396.741,
397.458, 387.906, 391.383, 391.846, 371.849, 378.048, 378.461,
318.908, 357.694, 362.865, 342.973, 40.35, 0, 288.149, 0, 259.291,
276.577, -102.466, -12.055, 0, 241.977, 0, 234.288, 36.044, 0,
247.849, 0, 244.967, 20.414, 0, 290.17, 0, 258.996, 39.846, 0,
269.854, 0, 259.002, 39.847, 0, 297.18, 0, 266.569, 22.214, 0,
-87.029, 0, -89.617, -13.787, 0, -95.348, -79.043, -76.869, 0,
246.968, 20.581, 0, 272.257, 289.812, 292.152, 271.933, 287.652,
289.748, 276.443, 284.481, 285.017, -116.323, 230.711, 276.983,
-141.084, 229.411, 278.81, 314.961, 280.304, 277.994, 277.686,
282.62, 283.278, 287.306, 282.107, 281.414, 290.288, 291.936,
292.045, 288.296, 302.772, 304.702, 292.15, 302.242, 303.588,
-136.669, 270.271, 297.4, 274.154, 301.517, 305.165, 290.369,
-426.184, -521.724, 321.366, 329.046, 329.558, 321.7, 306.849,
304.869, 321.009, 327.981, 328.91, 290.339, 328.378, 330.914,
289.022, 325.172, 329.992, 288.447, 295.19, 296.089, 322.57,
298.091, 296.459, 321.394, 300.861, 298.123, 320.881, 300.679,
297.985, 319.966, 296.464, 294.897, -164.893, -160.079, -159.437,
278.924, 281.357, 281.682, 269.259, 274.752, 275.118, 267.165,
265.143, 264.873, 258.509, 264.614, 265.429, 256.744, 266.259,
266.893, 263.361, 301.473, 306.555, 262.416, 313.245, 320.022,
281.712, 320.535, 323.123, -194.846, -521.034, -564.526, 274.367,
278.267, 278.787, 264.133, 268.555, 268.85, 256.818, 266.194,
267.444, 260.084, 265.714, 266.464, 262.669, 272.063, 272.689,
-175.62, 225.568, 279.06, -159.431, 216.327, 266.428, 245.597,
254.043, 254.606, 244.851, 255.798, 257.258, 248.152, 256.508,
257.622, -319.948, 219.787, 255.769, 243.947, -235.689, -299.64,
-334.288, 164.071, 230.518, 224.188, 233.409, 234.024, 228.544,
236.363, 237.405, 235.462, -116.121, -162.999, 240.889, 251.796,
252.524, 246.8, 258.72, 260.309, 257.726, 267.828, 269.175, 265.087,
277.995, 278.856, 273.876, 287.09, 288.852, 280.858, 292.608,
294.174, 246.387, 213.675, 211.494, 201.469, 209.375, 210.429,
-217.38, -218.456, -218.6, -232.09, 196.612, 225.193, -248.699,
166.3, 221.633, 213.54, 219.095, 219.836, -261.438, 192.469,
222.729, -265.48, 164.234, 221.529, -254.99, -254.199, -254.094,
209.197, -218.172, -246.663, 207.52, -196.823, -250.735, -261.477,
163.4, 220.05, 210.838, 222.305, 223.069, -196.463, 176.753,
226.515, 217.024, 249.012, 253.277, 203.798, 226.753, 228.283,
241.225, 228.683, 227.011, 235.665, 243.606, 244.664, 212.621,
240.812, 242.692, 207.285, 207.473, 207.498, 194.745, 202.346,
203.359, 191.408, -221.759, -249.304, 186.022, 192.304, 193.141,
182.101, 191.751, 193.037, -260.225, 162.901, 191.109, -252.884,
141.317, 193.877, 186.754, 220.72, 225.249, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 304.742, 0, 316.744, 48.73, 0, 334.833, 0,
352.419, 54.218, 0, 368.749, 0, 378.178, 31.515, 0, 378.548,
0, 339.912, 385.233, 406.951, 373.432, 368.963, 411.64, 362.786,
359.529, 358.897, 353.738, 353.051, 336.054, 340.344, 340.915,
335.944, 355.461, 356.762, 345.061, 372.321, 375.955, 376.513,
404.13, 407.812, 416.338, 426.28, 426.943, 432.67, 429.483, 429.058,
426.368, 425.226, 425.074, 416.298, 411.677, 411.369, 0, 345.327,
53.127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 183.69, 208.182, 0,
261.561, 21.797, 0, 298.1, 0, 281.941, 43.375, 0, 314.226, 0,
304.413, 46.833, 0, 374.171, 0, 368.525, 30.71, 0, 359.118, 0,
367.905, 56.601, 0, 287.494, 0, 285.213, 43.879, 0, 0, 0, 0,
0, 735.244, 113.115, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 304.619, 0)), row.names = 31537:32536, class = "data.frame")
This should give you a way to get the image you want. However, I don't have your original TIFF, and therefore the alignment won't be correct here since I had to use a cut-and-pasted version of the png in your question.
Anyway, the method I would use is:
Convert the image to a raster
Convert the raster to a grid::rasterGrob
Plot the rasterGrob as your first layer in ggplot using annotation_custom
Plot your other layers as normal.
Here's an example:
library(ggplot2)
library(rtiff)
library(grid)
x <- readTiff('F01_screenshot.tiff')
pic <- as.raster(array(c(x#red, x#green, x#blue), c(x#size, 3)))
picgrob <- rasterGrob(pic)
ggplot(eet2, aes(x=X, y= Y)) +
annotation_custom(picgrob) +
geom_point() +
stat_density2d() +
coord_equal()
You may need to scale your Y axis data to make it match the aspect ratio of the picture.
As an example, if we assume max(eet2$Y) is the top edge of the image, and min(eet2$Y) the bottom edge, and also assume that min(eet2$X) is the left edge and max(eet2$X) the right edge (as you have suggested is the case in your comments), we can marry the picture to the data like this:
pic_ratio <- dim(pic)[2]/dim(pic)[1]
data_ratio <- diff(range(eet2$X)) / diff(range(eet2$Y))
eet2$Y <- eet2$Y * data_ratio / pic_ratio
ggplot(eet2, aes(x=X, y= Y)) +
annotation_custom(picgrob) +
geom_point() +
stat_density2d() +
coord_equal(xlim = range(eet2$X), ylim = range(eet2$Y))
If this alignment is not correct, then we need extra calibration information not present in the data (i.e. what value of eet2$Y should represent the top and bottom of the image, and what value of eet2$X represents the left and right edges.
Related
I am using James Stegen et al's code here to calculate an abundance-weighted raup-crick value for my 16S dataset.
I load in my phyloseq object then extract the otu_table. I then use the otu_table as the spXsite argument in the function raup_crick_abundance(). My otu_table is available as a dput() below the text.
physeq<-qza_to_phyloseq(
features="~/Documents/qiime2-analyses/CRD/fresh_run/table.qza",
tree="~/Documents/qiime2-analyses/CRD/fresh_run/rooted-tree.qza",
"~/Documents/qiime2-analyses/CRD/fresh_run/taxonomy.qza",
metadata = "crd-metadata.txt")
otu_table <- (otu_table(physeq))
raup_crick_abundance = function(spXsite=otu_table, plot_names_in_col1=TRUE,
reps=9999, set_all_species_equal=FALSE,
as.distance.matrix=TRUE, report_similarity=FALSE){
Where the remaining code is verbatim from the github link above.
I am having a hard time understanding the error I have been receiving:
Error in sample.int(x, size, replace, prob) :
incorrect number of probabilities
Called from: sample.int(x, size, replace, prob)
Browse[1]> traceback()
1: raup_crick_abundance()
I had thought perhaps the function was looking for an equivalent number of columns and rows, but that was not the case. I searched the function sample() and think it's hoping to select values from my argument otu_table but can't find the expected range?
The sample() causing problems is on line 48 of the github where I believe the function is weighing the otu occurrences and entering the number of occurrences into a column.
##two empty null communities of size gamma:
com1<-rep(0,gamma)
com2<-rep(0,gamma)
##add observed number of species to com1, weighting by species occurrence frequencies:
com1[sample(1:gamma, sum(spXsite.inc[null.one,]), replace=FALSE, prob=occur)]<-1
com1.samp.sp = sample(which(com1>0),(sum(spXsite[null.one,])-sum(com1)),replace=TRUE,prob=abundance[which(com1>0)]);
com1.samp.sp = cbind(com1.samp.sp,1); # head(com1.samp.sp);
com1.sp.counts = as.data.frame(tapply(com1.samp.sp[,2],com1.samp.sp[,1],FUN=sum)); colnames(com1.sp.counts) = 'counts'; # head(com1.sp.counts);
com1.sp.counts$sp = as.numeric(rownames(com1.sp.counts)); # head(com1.sp.counts);
com1[com1.sp.counts$sp] = com1[com1.sp.counts$sp] + com1.sp.counts$counts; # com1;
#sum(com1) - sum(spXsite[null.one,]); ## this should be zero if everything work properly
rm('com1.samp.sp','com1.sp.counts');
Any thoughts are greatly appreciated. Thank you in advance!
> dput(otu_table)
new("otu_table", .Data = structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 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, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 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, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 3, 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, 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, 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,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 4), .Dim = c(6L, 48L), .Dimnames = list(
c("e54924083c02bd088c69537d02406eb8", "3112899fc7a2adb7f74a081a82c7cde4",
"db5d745b02355b6fed513c4953b62326", "2faf2046aa9ab2f6598df79cd52e9c7b",
"bec8db81cc8ec453136c82ede8327a9f", "601e47b8adcbd21d159f74421710e1b5"
), c("sample-10", "sample-11", "sample-12", "sample-14",
"sample-16", "sample-18", "sample-19", "sample-20", "sample-21",
"sample-22", "sample-23", "sample-24", "sample-25", "sample-26",
"sample-27", "sample-28", "sample-29", "sample-30", "sample-31",
"sample-32", "sample-33", "sample-34", "sample-35", "sample-36",
"sample-37", "sample-40", "sample-41", "sample-43", "sample-44",
"sample-45", "sample-46", "sample-50", "sample-55", "sample-56",
"sample-57", "sample-58", "sample-59", "sample-61", "sample-62",
"sample-63", "sample-64", "sample-65", "sample-67", "sample-68",
"sample-69", "sample-70", "sample-71", "sample-8"))), taxa_are_rows = TRUE)
>
I am trying to reuse some old code which I have used to make two separate plots in past, and would like to pout it together into one now.
However I have few problems
color_var <- vector(mode = "double",length = length(OP_2016$risk))
color_var[color_var== '0']<- NA
color_var[OP_2016$risk>=1 & OP_2016$risk<12] <- "yellow"
color_var[OP_2016$risk>=12] <- "red"
ggplot(OP_2016)+
geom_col(aes(x = short_date, y = risk, color = color_var , group = 1), size= 0.9) +
scale_y_continuous(limits = c(0, 100),name = "Accumulated EBHours")+
scale_color_identity("Risk Level", breaks= levels(as.factor(color_var))[c(1,2)],
labels = c("High >12 EBH","Medium 0-12EBH"),
guide = "legend"
)+
geom_line(aes(x = short_date, y= 12), linetype= "dotted", size = 0.8, colour = "red")+
# scale_color_manual("Varieties", values =c( "British Queen"= "orchid1"))+
geom_line(data = dis_fun_df, aes(x= date, y = rating, colour = "green"))
Problems:
Bars and and boxes in the legend are not filled,
I can not add manual color for geom_line and add it to the legend, that I have added from other plot.
Apologies, data set to reproduce the plot is a bit big.
dis_fun_df <- structure(list(date = structure(c(15534, 15540, 15548, 15555,
15562, 15573, 15580), class = "Date"), rating = c(10.2, 30, 61.6666666666667,
81.6666666666667, 95.8333333333333, 99.1666666666667, 100)), row.names = c(NA,
-7L), class = c("tbl_df", "tbl", "data.frame"))
OP_2016 <- structure(list(date = structure(c(1342224000, 1342227600, 1342231200,
1342234800, 1342238400, 1342242000, 1342245600, 1342249200, 1342252800,
1342256400, 1342260000, 1342263600, 1342267200, 1342270800, 1342274400,
1342278000, 1342281600, 1342285200, 1342288800, 1342292400, 1342296000,
1342299600, 1342303200, 1342306800, 1342310400, 1342314000, 1342317600,
1342321200, 1342324800, 1342328400, 1342332000, 1342335600, 1342339200,
1342342800, 1342346400, 1342350000, 1342353600, 1342357200, 1342360800,
1342364400, 1342368000, 1342371600, 1342375200, 1342378800, 1342382400,
1342386000, 1342389600, 1342393200, 1342396800, 1342400400, 1342404000,
1342407600, 1342411200, 1342414800, 1342418400, 1342422000, 1342425600,
1342429200, 1342432800, 1342436400, 1342440000, 1342443600, 1342447200,
1342450800, 1342454400, 1342458000, 1342461600, 1342465200, 1342468800,
1342472400, 1342476000, 1342479600, 1342483200, 1342486800, 1342490400,
1342494000, 1342497600, 1342501200, 1342504800, 1342508400, 1342512000,
1342515600, 1342519200, 1342522800, 1342526400, 1342530000, 1342533600,
1342537200, 1342540800, 1342544400, 1342548000, 1342551600, 1342555200,
1342558800, 1342562400, 1342566000, 1342569600, 1342573200, 1342576800,
1342580400, 1342584000, 1342587600, 1342591200, 1342594800, 1342598400,
1342602000, 1342605600, 1342609200, 1342612800, 1342616400, 1342620000,
1342623600, 1342627200, 1342630800, 1342634400, 1342638000, 1342641600,
1342645200, 1342648800, 1342652400, 1342656000, 1342659600, 1342663200,
1342666800, 1342670400, 1342674000, 1342677600, 1342681200, 1342684800,
1342688400, 1342692000, 1342695600, 1342699200, 1342702800, 1342706400,
1342710000, 1342713600, 1342717200, 1342720800, 1342724400, 1342728000,
1342731600, 1342735200, 1342738800, 1342742400, 1342746000, 1342749600,
1342753200, 1342756800, 1342760400, 1342764000, 1342767600, 1342771200,
1342774800, 1342778400, 1342782000, 1342785600, 1342789200, 1342792800,
1342796400, 1342800000, 1342803600, 1342807200, 1342810800, 1342814400,
1342818000, 1342821600, 1342825200, 1342828800, 1342832400, 1342836000,
1342839600, 1342843200, 1342846800, 1342850400, 1342854000, 1342857600,
1342861200, 1342864800, 1342868400, 1342872000, 1342875600, 1342879200,
1342882800, 1342886400, 1342890000, 1342893600, 1342897200, 1342900800,
1342904400, 1342908000, 1342911600, 1342915200, 1342918800, 1342922400,
1342926000, 1342929600, 1342933200, 1342936800, 1342940400, 1342944000,
1342947600, 1342951200, 1342954800, 1342958400, 1342962000, 1342965600,
1342969200, 1342972800, 1342976400, 1342980000, 1342983600, 1342987200,
1342990800, 1342994400, 1342998000, 1343001600, 1343005200, 1343008800,
1343012400, 1343016000, 1343019600, 1343023200, 1343026800, 1343030400,
1343034000, 1343037600, 1343041200, 1343044800, 1343048400, 1343052000,
1343055600, 1343059200, 1343062800, 1343066400, 1343070000, 1343073600,
1343077200, 1343080800, 1343084400, 1343088000, 1343091600, 1343095200,
1343098800, 1343102400, 1343106000, 1343109600, 1343113200, 1343116800,
1343120400, 1343124000, 1343127600, 1343131200, 1343134800, 1343138400,
1343142000, 1343145600, 1343149200, 1343152800, 1343156400, 1343160000,
1343163600, 1343167200, 1343170800, 1343174400, 1343178000, 1343181600,
1343185200, 1343188800, 1343192400, 1343196000, 1343199600, 1343203200,
1343206800, 1343210400, 1343214000, 1343217600, 1343221200, 1343224800,
1343228400, 1343232000, 1343235600, 1343239200, 1343242800, 1343246400,
1343250000, 1343253600, 1343257200, 1343260800, 1343264400, 1343268000,
1343271600, 1343275200, 1343278800, 1343282400, 1343286000, 1343289600,
1343293200, 1343296800, 1343300400, 1343304000, 1343307600, 1343311200,
1343314800, 1343318400, 1343322000, 1343325600, 1343329200, 1343332800,
1343336400, 1343340000, 1343343600, 1343347200, 1343350800, 1343354400,
1343358000, 1343361600, 1343365200, 1343368800, 1343372400, 1343376000,
1343379600, 1343383200, 1343386800, 1343390400, 1343394000, 1343397600,
1343401200, 1343404800, 1343408400, 1343412000, 1343415600, 1343419200,
1343422800, 1343426400, 1343430000, 1343433600, 1343437200, 1343440800,
1343444400, 1343448000, 1343451600, 1343455200, 1343458800, 1343462400,
1343466000, 1343469600, 1343473200, 1343476800, 1343480400, 1343484000,
1343487600, 1343491200, 1343494800, 1343498400, 1343502000, 1343505600,
1343509200, 1343512800, 1343516400, 1343520000, 1343523600, 1343527200,
1343530800, 1343534400, 1343538000, 1343541600, 1343545200, 1343548800,
1343552400, 1343556000, 1343559600, 1343563200, 1343566800, 1343570400,
1343574000, 1343577600, 1343581200, 1343584800, 1343588400, 1343592000,
1343595600, 1343599200, 1343602800, 1343606400, 1343610000, 1343613600,
1343617200, 1343620800, 1343624400, 1343628000, 1343631600, 1343635200,
1343638800, 1343642400, 1343646000, 1343649600, 1343653200, 1343656800,
1343660400, 1343664000, 1343667600, 1343671200, 1343674800, 1343678400,
1343682000, 1343685600, 1343689200, 1343692800, 1343696400, 1343700000,
1343703600, 1343707200, 1343710800, 1343714400, 1343718000, 1343721600,
1343725200, 1343728800, 1343732400, 1343736000, 1343739600, 1343743200,
1343746800, 1343750400, 1343754000, 1343757600, 1343761200, 1343764800,
1343768400, 1343772000, 1343775600, 1343779200, 1343782800, 1343786400,
1343790000, 1343793600, 1343797200, 1343800800, 1343804400, 1343808000,
1343811600, 1343815200, 1343818800, 1343822400, 1343826000, 1343829600,
1343833200, 1343836800, 1343840400, 1343844000, 1343847600, 1343851200,
1343854800, 1343858400, 1343862000, 1343865600, 1343869200, 1343872800,
1343876400, 1343880000, 1343883600, 1343887200, 1343890800, 1343894400,
1343898000, 1343901600, 1343905200, 1343908800, 1343912400, 1343916000,
1343919600, 1343923200, 1343926800, 1343930400, 1343934000, 1343937600,
1343941200, 1343944800, 1343948400, 1343952000, 1343955600, 1343959200,
1343962800, 1343966400, 1343970000, 1343973600, 1343977200, 1343980800,
1343984400, 1343988000, 1343991600, 1343995200, 1343998800, 1344002400,
1344006000, 1344009600, 1344013200, 1344016800, 1344020400, 1344024000,
1344027600, 1344031200, 1344034800, 1344038400, 1344042000, 1344045600,
1344049200, 1344052800, 1344056400, 1344060000, 1344063600, 1344067200,
1344070800, 1344074400, 1344078000, 1344081600, 1344085200, 1344088800,
1344092400, 1344096000, 1344099600, 1344103200, 1344106800, 1344110400,
1344114000, 1344117600, 1344121200, 1344124800, 1344128400, 1344132000,
1344135600, 1344139200, 1344142800, 1344146400, 1344150000, 1344153600,
1344157200, 1344160800, 1344164400, 1344168000, 1344171600, 1344175200,
1344178800, 1344182400, 1344186000, 1344189600, 1344193200, 1344196800,
1344200400, 1344204000, 1344207600, 1344211200, 1344214800, 1344218400,
1344222000, 1344225600, 1344229200, 1344232800, 1344236400, 1344240000,
1344243600, 1344247200, 1344250800, 1344254400, 1344258000, 1344261600,
1344265200, 1344268800, 1344272400, 1344276000, 1344279600, 1344283200,
1344286800, 1344290400, 1344294000, 1344297600, 1344301200, 1344304800,
1344308400, 1344312000, 1344315600, 1344319200, 1344322800, 1344326400,
1344330000, 1344333600, 1344337200, 1344340800, 1344344400, 1344348000,
1344351600, 1344355200, 1344358800, 1344362400, 1344366000, 1344369600,
1344373200, 1344376800, 1344380400, 1344384000, 1344387600, 1344391200,
1344394800, 1344398400, 1344402000, 1344405600, 1344409200, 1344412800,
1344416400, 1344420000, 1344423600, 1344427200, 1344430800, 1344434400,
1344438000, 1344441600, 1344445200, 1344448800, 1344452400, 1344456000,
1344459600, 1344463200, 1344466800, 1344470400, 1344474000, 1344477600,
1344481200, 1344484800, 1344488400, 1344492000, 1344495600, 1344499200,
1344502800, 1344506400, 1344510000, 1344513600, 1344517200, 1344520800,
1344524400, 1344528000, 1344531600, 1344535200, 1344538800, 1344542400,
1344546000, 1344549600, 1344553200, 1344556800, 1344560400, 1344564000,
1344567600, 1344571200, 1344574800, 1344578400, 1344582000, 1344585600,
1344589200, 1344592800, 1344596400, 1344600000, 1344603600, 1344607200,
1344610800, 1344614400, 1344618000, 1344621600, 1344625200, 1344628800,
1344632400, 1344636000, 1344639600, 1344643200, 1344646800, 1344650400,
1344654000, 1344657600, 1344661200, 1344664800, 1344668400, 1344672000,
1344675600, 1344679200, 1344682800, 1344686400, 1344690000, 1344693600,
1344697200, 1344700800, 1344704400, 1344708000, 1344711600, 1344715200,
1344718800, 1344722400, 1344726000, 1344729600, 1344733200, 1344736800,
1344740400, 1344744000, 1344747600, 1344751200, 1344754800, 1344758400,
1344762000, 1344765600, 1344769200, 1344772800, 1344776400, 1344780000,
1344783600, 1344787200, 1344790800, 1344794400, 1344798000, 1344801600,
1344805200, 1344808800, 1344812400, 1344816000, 1344819600, 1344823200,
1344826800, 1344830400, 1344834000, 1344837600, 1344841200, 1344844800,
1344848400, 1344852000, 1344855600, 1344859200, 1344862800, 1344866400,
1344870000, 1344873600, 1344877200, 1344880800, 1344884400, 1344888000,
1344891600, 1344895200, 1344898800, 1344902400, 1344906000, 1344909600,
1344913200, 1344916800, 1344920400, 1344924000, 1344927600, 1344931200,
1344934800, 1344938400, 1344942000, 1344945600, 1344949200, 1344952800,
1344956400, 1344960000, 1344963600, 1344967200, 1344970800, 1344974400,
1344978000, 1344981600, 1344985200, 1344988800, 1344992400, 1344996000,
1344999600, 1345003200, 1345006800, 1345010400, 1345014000, 1345017600,
1345021200, 1345024800, 1345028400, 1345032000, 1345035600, 1345039200,
1345042800, 1345046400, 1345050000, 1345053600, 1345057200, 1345060800,
1345064400, 1345068000, 1345071600, 1345075200, 1345078800, 1345082400,
1345086000, 1345089600, 1345093200, 1345096800, 1345100400, 1345104000,
1345107600, 1345111200, 1345114800, 1345118400, 1345122000, 1345125600,
1345129200, 1345132800, 1345136400, 1345140000, 1345143600, 1345147200,
1345150800, 1345154400, 1345158000, 1345161600, 1345165200, 1345168800,
1345172400, 1345176000, 1345179600, 1345183200, 1345186800, 1345190400,
1345194000, 1345197600, 1345201200, 1345204800, 1345208400, 1345212000,
1345215600, 1345219200, 1345222800, 1345226400, 1345230000, 1345233600,
1345237200, 1345240800, 1345244400, 1345248000, 1345251600, 1345255200,
1345258800, 1345262400, 1345266000, 1345269600, 1345273200, 1345276800,
1345280400, 1345284000, 1345287600, 1345291200, 1345294800, 1345298400,
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I think this might do the trick, using fill instead of colour
OP_2016$date <- as.Date(OP_2016$date)
color_var <- vector(mode = "double",length = length(OP_2016$risk))
color_var[color_var== '0']<- NA
color_var[OP_2016$risk>=1 & OP_2016$risk<12] <- "yellow"
color_var[OP_2016$risk>=12] <- "red"
ggplot(OP_2016)+
geom_col(aes(x = date, y = risk, group = 1,fill=color_var), size= 0.9) +
scale_y_continuous(limits = c(0, 100),name = "Accumulated EBHours")+
scale_fill_identity("Risk Level", breaks= levels(as.factor(color_var))[c(1,2)],
labels = c("High >12 EBH","Medium 0-12EBH"),
guide = "legend"
)+
geom_line(aes(x = date, y= 12), linetype= "dotted", size = 0.8, colour = "red")+
geom_line(data = dis_fun_df, aes(x= date, y = rating),colour = "green")
To my knowledge ggplot does not support multiple scales of the same type, but others would know better than I.
UPDATE:
For anyone looking to actually use multiple scales for the same type of geom the {ggnewscale} package should provide the functionality that you are looking for:
https://github.com/eliocamp/ggnewscale
I use this loop to connect to Elasticsearch, retrieve some data and add it into my data frame based on a common identifier:
library(elastic); library(jsonlite)
for (i in first_chats_eu$associate) {
firstchats_eu_body <- paste0(
'{"size":1,"query":{"bool":{"must":[{"term":{"associate":"',
i
,'"}},{"term":{"type":"conversation-archived"}}]}},"sort":{"time":"asc"}}'
)
firstchats_eu_connection <- Search(
index = "my_index",
type = "my_type",
body = firstchats_eu_body
)
first_chats_eu$firstChat[first_chats_eu$associate == i] <- fromJSON(toJSON(firstchats_eu_connection$hits$hits), flatten = TRUE)[1]$`_source.time`
}
The problem is that it always produces the following message:
Error in `$<-.data.frame`(`*tmp*`, "firstChat", value = list("2017-06-08T00:17:01.118Z", :
replacement has 445 rows, data has 446
I have looked at other answers on SO but the solutions appear to be specific to each case, and thus not applicable to mine.
What could be causing this?
If it is of any use, this loop sometimes partially succeeds, in that it returns the first 34 results and then fails for all other rows after that point.
Data frame:
structure(list(associate = c("Pm0jYumSjx", "PbjmBqnIdY", "Vq3VblKpZn",
"JUCx0MOygt", "9IxVPXPGvQ", "FUUobAsS2Z", "uflgsKMNze", "VzeedmQuiv",
"BWYbDAIPkr", "PNCzrO3TRA", "zwemlUrxQE", "y19AiLUxLN", "4hhURCoC8H",
"Ak13YJF63y", "aoQI4Ncrt4", "syaulepdzW", "ISdTwUISIG", "wncQ5pXq88",
"MYbsg1E2EQ", "OhkAz5aren", "A2wqC9F1WR", "ZDVmRyHkUY", "n5kKRmTMzj",
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
446L), class = "data.frame")
This is a small portion of a vey big data
df<- structure(list(A = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.68906, 0, 0, 0, 0, 0, 0, 0, 0, 0.13597, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), B = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.40001, 0, 0, 0, 0, 0.69718, 0, 0, 0, 0, 0, 0, 0,
0, 0.090752, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), C = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.84068, 0, 0, 0, 0.34713, 0, 0, 0, 0, 0.65201,
0, 0, 0.25725, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.86419, 0, 0, 0, 0.3845,
0, 0, 0, 0, 0.67091, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), E = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1083, 0.8324,
0, 0, 0, 0.38499, 0, 0, 0, 0, 0.69064, 0, 0, 0.14596, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), F = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 1.0954, 0.74426, 0, 0, 0, 0.37715, 0, 0, 0, 0, 0.68884,
0, 0, 0.20826, 0, 0.38782, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), G = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0985, 0.66651, 0, 0,
0, 0, 0, 0, 0, 0, 0.68861, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1812,
0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("A", "B", "C", "D", "E",
"F", "G"), class = "data.frame", row.names = c(NA, -39L))
What I want is to show the values in a more stressed way when there are a lot of zeros in a data
How I plot it is like this
eucl_dist=dist(df,method = 'euclidean')
hie_clust=hclust(eucl_dist,method = 'complete')
my_palette <- colorRampPalette(c( "green", "yellow", "red"))(n = 1000)
heatmap.2(mydata, scale = c("none"), Colv=F, Rowv=as.dendrogram(hie_clust),
xlab = "X", ylab = "Y", key=TRUE, keysize=1.1, trace="none",
density.info=c("none"), margins=c(4, 4), col=my_palette, dendrogram="row")
But as you see, in this small example, the zero dominate my plot and when it is very large then it is impossible to see anything. also I cannot change the position of the values
You are asking a lot of questions here, I'll try to answer those I see.
Zero dominates plot
Zeros dominate you data but, what do the zeros mean? Without some insight into what the zeros actually mean its hard to prescribe one best way to deal with it.
Colormap
The colorful colormap that you chose is not the best way to describe quantitative data. I would suggest a simple white to blue (or color of your choice) so that your zeros are shown as white and get hidden with the nonzero data emphasized. Example (only changing my_palette <- colorRampPalette(c("white", "cornflowerblue"))(n = 1000)):
Changing the position of the values
I'm not certain what you mean here but the layout is fixed by the dendrogram you defined.
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Closed 6 years ago.
Improve this question
I would like to put one line plot on each page of pdf file. All the data for plotting is stored in single data frame. Each row should be plotted.
That's how the data looks like:
structure(list(`10` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `34` = c(0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0), `59` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 2831286.1, 0, 0, 0, 0, 0, 0, 10643218.2,
0, 0, 0, 0, 0, 0), `110` = c(0, 0, 0, 0, 1778743.3, 0, 0, 0,
1465966.7, 0, 0, 0, 0, 0, 0, 0, 0, 3111700, 0, 0, 1955337.5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5584784.4, 5584784.4,
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0, 0, 0, 0, 0, 7847143.8, 0, 0, 0, 0, 0, 0), `134` = c(0, 0,
0, 0, 1121869.4, 0, 0, 0, 1439430.6, 0, 0, 0, 0, 0, 0, 0, 0,
2854250, 0, 0, 0, 0, 0, 0, 914890, 0, 0, 847880, 0, 0, 0, 0,
0, 0, 0, 8191800, 0, 0, 0, 0, 0, 0, 1830904.5, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1650150, 0, 0, 837130, 0, 0, 0, 4925095.1, 0,
0, 0, 0, 0, 0), `165` = c(0, 0, 0, 0, 1432775, 0, 0, 0, 1394186.1,
0, 1120183.3, 0, 0, 0, 0, 0, 0, 2262421.7, 0, 0, 0, 615660, 0,
0, 1292795.8, 0, 0, 712622.5, 0, 0, 0, 0, 0, 0, 0, 2683469.4,
0, 0, 0, 0, 0, 0, 2318485.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1561800,
0, 0, 0, 0, 0, 0, 4382993.7, 0, 0, 763460, 0, 0, 0), `199` = c(0,
0, 0, 0, 1314220, 0, 0, 0, 1439718.8, 0, 1929266.7, 0, 0, 0,
1101800, 0, 0, 2759366.7, 0, 0, 0, 1291728.6, 0, 0, 2489775.6,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2858345.8, 0, 0, 0, 1819542.1,
0, 0, 1497640.3, 0, 0, 0, 1300250, 0, 0, 0, 0, 0, 0, 1566875,
0, 0, 0, 0, 0, 0, 4625895.6, 0, 0, 1308158.3, 0, 0, 0), `234` = c(1257250,
0, 0, 0, 0, 0, 0, 0, 1276080, 0, 1848500, 0, 0, 0, 1529350, 0,
0, 2155275, 0, 0, 0, 2023041.9, 0, 0, 1966447.7, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1184200, 1184200, 0, 0, 1652350, 0, 0, 2018581.7,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1835225, 0, 0, 0, 0, 0, 0, 4639414.7,
0, 0, 720715, 0, 0, 0), `257` = c(0, 0, 0, 0, 0, 669442.5, 0,
0, 1253026.7, 0, 960410, 960410, 0, 0, 1258267.5, 0, 0, 1707392.5,
0, 0, 0, 563280, 0, 0, 2403237.9, 0, 0, 0, 1044100, 0, 2075700,
0, 0, 0, 0, 0, 5718450, 0, 0, 1704550, 0, 0, 1350286.9, 0, 0,
0, 0, 2011700, 0, 0, 0, 0, 0, 1739500, 0, 0, 0, 0, 0, 0, 4612520.8,
4612520.8, 0, 0, 0, 0, 0), `362` = c(0, 1593500, 0, 0, 0, 1610625.3,
0, 0, 1234902.5, 0, 0, 1481036.8, 0, 0, 1583647.5, 0, 0, 1752089.2,
0, 0, 0, 0, 0, 0, 2410809.2, 0, 0, 0, 654940, 0, 0, 0, 0, 0,
0, 0, 7014905.6, 0, 0, 0, 0, 0, 1165672.1, 0, 0, 0, 0, 0, 0,
0, 1029910, 0, 0, 2153087.5, 0, 0, 0, 422920, 0, 0, 0, 7495855.9,
0, 0, 0, 0, 0), `433` = c(0, 0, 0, 0, 0, 1340283.9, 0, 0, 1268996.9,
0, 0, 1416683.3, 0, 0, 1047862.5, 0, 0, 1819653.8, 0, 0, 0, 0,
0, 0, 2227565.7, 0, 0, 0, 763765, 0, 0, 1595430, 0, 0, 0, 0,
4894549, 0, 0, 0, 0, 0, 1061375.4, 0, 0, 0, 0, 0, 2251950, 0,
1042130, 0, 0, 2055300, 0, 0, 0, 696278.3, 0, 0, 0, 5353797.8,
0, 0, 0, 0, 0), `506` = 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, 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, 0, 0, 0, 2020300,
2020300, 0, 0, 0, 0, 0, 0, 7681526, 0, 0, 0, 0, 0), `581` = c(0,
0, 1749237.5, 0, 0, 0, 2421665.8, 0, 0, 1773262.5, 0, 0, 2251004.3,
0, 0, 2570175, 0, 0, 3379756.9, 0, 0, 0, 2054455.6, 0, 0, 2518270.8,
0, 0, 0, 0, 0, 0, 2917968.2, 0, 0, 0, 0, 7004350, 0, 0, 1451600,
0, 0, 1394411, 0, 0, 0, 0, 0, 2507858.3, 0, 2377012.5, 0, 0,
3719165.4, 0, 0, 0, 1472870.3, 0, 0, 9666916.1, 0, 0, 1730300,
0, 0), `652` = c(0, 0, 476910, 476910, 0, 0, 1149078.8, 1149078.8,
0, 1082468.7, 0, 0, 882769.7, 0, 0, 1370449.4, 1370449.4, 0,
1529049, 1529049, 0, 0, 943632.2, 0, 0, 916587.8, 0, 0, 0, 988261.1,
0, 0, 1778007.1, 1778007.1, 0, 0, 0, 3087304.8, 3087304.8, 0,
782860, 782860, 0, 510158.5, 510158.5, 0, 0, 0, 0, 1503750, 0,
1100677.5, 1100677.5, 0, 1669260, 1669260, 0, 0, 770733.2, 0,
0, 4939242.8, 4939242.8, 0, 643564.4, 643564.4, 0), `733` = c(0,
0, 0, 1095060, 0, 0, 0, 1674089.3, 0, 1252101.3, 0, 0, 1259111,
0, 0, 0, 2429293.3, 0, 0, 2326928.3, 0, 0, 1259216.5, 0, 0, 1238837.5,
0, 0, 0, 1224858.3, 0, 0, 0, 2952529.9, 0, 0, 0, 0, 4626414.7,
0, 0, 1121440, 0, 0, 1025386.2, 0, 0, 0, 0, 1917900, 0, 0, 2197533.3,
0, 0, 2840155.5, 0, 0, 1054285.7, 0, 0, 0, 7516814.2, 0, 0, 1329434.4,
0), `818` = c(0, 0, 0, 720551.1, 0, 0, 0, 714662.7, 0, 617012.9,
0, 0, 549850.8, 0, 0, 0, 1197460, 0, 0, 771979.2, 0, 0, 585847.5,
585847.5, 0, 875475.4, 0, 0, 0, 576774, 0, 0, 0, 1147389.8, 0,
0, 0, 0, 2292421.7, 0, 0, 755258.3, 0, 0, 0, 0, 0, 0, 0, 858930,
0, 0, 1242668.3, 0, 0, 1580088.3, 0, 0, 641938.6, 641938.6, 0,
0, 3838660.4, 0, 0, 733140.8, 733140.8), `896` = c(0, 0, 0, 590480,
0, 0, 0, 817087.6, 0, 569869.5, 0, 0, 650822.5, 650822.5, 0,
0, 1624052.5, 0, 0, 682570.8, 0, 0, 0, 1538800, 0, 690488.6,
690488.6, 0, 0, 797923.9, 0, 0, 0, 1204889.3, 0, 0, 0, 0, 2184432.2,
0, 0, 676654.7, 0, 0, 0, 210680, 0, 0, 0, 791152.5, 0, 0, 1599855.8,
0, 0, 1358543.8, 0, 0, 0, 931288, 0, 0, 4683895.2, 0, 0, 0, 1202806
), `972` = c(0, 0, 0, 799116.4, 0, 0, 0, 759169.9, 0, 408845,
0, 0, 0, 948980, 0, 0, 968766.7, 0, 0, 675349.7, 0, 0, 0, 0,
0, 0, 1811117.6, 0, 0, 609098.5, 0, 0, 0, 1073749.1, 0, 0, 0,
0, 2392258.9, 0, 0, 743580, 0, 0, 0, 1020485, 0, 0, 0, 446596.7,
0, 0, 1178583, 0, 0, 1438261.7, 0, 0, 0, 1133057.9, 0, 0, 4445814.7,
0, 0, 0, 1057776.9), `1039` = c(0, 0, 0, 447255.3, 0, 0, 0, 609409.1,
0, 304340, 0, 0, 0, 0, 0, 0, 694232.8, 0, 0, 473015.3, 0, 0,
0, 0, 0, 0, 419524.9, 0, 0, 447760.6, 0, 0, 0, 932513.5, 0, 0,
0, 0, 1251960.5, 0, 0, 276560, 0, 0, 0, 259640, 0, 0, 0, 354995,
0, 0, 1570222.5, 0, 0, 1021822, 0, 0, 0, 811614, 0, 0, 2941698.2,
0, 0, 0, 1199942.5), Gene = 1:67), .Names = c("10", "34", "59",
"84", "110", "134", "165", "199", "234", "257", "362", "433",
"506", "581", "652", "733", "818", "896", "972", "1039", "Gene"
), row.names = c(NA, 67L), class = "data.frame")
I have tried something like that so far...:
for(i in 1:nrow(Tra_decon)){
Tra_decon_melt <- melt(Tra_decon[i,], id = "Gene")
pdf("Test_plot.pdf", onefile = TRUE)
ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
dev.off()
}
A solution without a for loop (and so faster) is this one:
plots <- lapply(1:nrow(Tra_decon), function(i){
Tra_decon_melt <- melt(Tra_decon[i,], id = "Gene")
ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
})
pdf("Test_plot.pdf", onefile = TRUE)
plots
dev.off()
This works for me:
pdf("Test_plot.pdf", onefile = TRUE)
for(i in 1:nrow(Tra_decon)){
Tra_decon_melt <- melt(Tra_decon[i,], id.vars = "Gene")
plot<-list()
plot[[i]]<-ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(plot[[i]])
}
dev.off()