Related
I have part of this code which is the initial step of a rolling iteration
for (u in 1:5605){
for (j in 1:498){
work.on.col = j
work.dt = data[u:(u+365),work.on.col] # u:(u+365-1) considers the rolling window
}
}
So basically, this makes a rolling window from u == 1 to u == 5605 for each j. However, this is a step by step (or in this case: a day by day) rolling algorithm. What I want to change it to, is to do so
By 30 days: instead or rolling day by day, it should roll every 30 days (by this I meant here that the rolling window will REMOVE 30 days, then add 30 days to the window, thus rolling the 365 day-long window). OR
By identifying the month. For example, first iteration will start with daily data from January 2000 to January 2001. And the next iteration will start with February 2000 to February 2001, and so on (this approach is more preferable).
Data
> dput(data)
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4.4272, 4.4272, 4.4907, 4.3955, 4.5383, 4.4431, 4.8001, 4.8398,
5.0778, 5.4586, 5.3634, 5.3, 5.4586, 5.411, 5.2524, 5.2841, 5.1413,
5.173, 5.0937, 4.9985, 5.1095, 5.2682, 5.3952, 5.411, 5.6967,
5.4586, 5.4745, 5.3317, 5.3158, 5.3158, 5.1095, 5.1095, 4.8081,
4.7287, 4.7763, 4.7128, 4.5859, 4.5542, 4.6652, 4.451, 4.4907,
4.6335, 4.6335, 4.57, 4.7287, 4.4431, 4.3479, 4.3637, 4.57, 4.5859,
4.57, 4.4114, 4.3003, 4.4193, 4.4828, 4.5542, 4.5859, 4.578,
4.4812, 4.4812, 4.4653, 4.4335, 4.4335, 4.3064, 4.1316, 4.1316,
3.8217, 3.8297, 4.0045, 4.4812, 4.1634, 4.1475, 4.068, 3.8614,
4.0998, 4.0362, 3.8773, 3.8614, 3.8614, 3.925, 3.925, 3.9091,
4.211, 4.2269, 4.1634, 4.1634, 4.1475, 4.068, 4.1316, 4.1951,
4.1316, 4.0998, 4.3382, 4.7354, 4.5447, 4.5447, 4.4653, 4.5606,
4.7672, 4.5765, 4.4335, 4.4335, 4.4812, 4.3699, 4.2428, 4.2905,
4.3143, 4.1951, 4.2269, 4.1792, 4.0998, 4.1792, 4.2905, 4.2428,
4.5845, 4.8308, 4.7831, 4.6877, 4.4494, 4.9102, 4.8149, 4.7418,
4.7736, 4.694, 4.5508, 4.4235, 4.3042, 4.519, 4.6145, 4.5667,
4.694, 4.7895, 4.71, 4.71, 4.694, 4.6463, 4.5508, 4.3917, 4.4235,
4.4235, 4.3122, 4.2962, 4.1689, 4.3281, 4.4076, 4.3599, 4.2962,
4.2326, 4.3917, 4.4554, 4.4713, 4.694, 4.9645, 5.0998, 5.2191,
5.4101, 5.2828, 5.2828, 5.1396, 5.0759, 5.0282, 5.0282, 5.0282,
5.0282, 5.0282, 5.0282, 5.5215, 5.3623, 5.3942, 5.3464, 5.1555,
5.235, 5.1396, 5.0918, 4.9964, 4.9964, 4.9645, 5.0918, 5.2032,
5.3146, 5.1873, 5.0123, 4.9645, 4.9645, 4.8532, 4.7259, 4.7736,
4.7577, 4.7577, 4.9168, 4.9805, 5.1555, 5.3623, 5.3783, 5.2669,
4.9964, 5.0123, 4.9709, 4.7479, 4.7718, 4.7638, 4.8275, 4.7319,
4.8435, 4.6523, 4.5885, 4.5407, 4.6523, 4.6045, 4.7638, 4.8275,
4.7957, 4.6523, 4.4292, 4.5089, 4.6523, 4.8116, 4.6602, 4.9311,
5.0187, 4.7797, 4.7797, 5.0665, 5.0506, 5.0187, 4.7001, 4.6523,
4.8275, 4.7319, 4.5248, 4.6523, 4.6841, 4.6682, 4.6523, 4.5089,
4.2938, 4.5089, 4.3416, 4.238, 4.238, 4.238, 4.4292, 4.4451,
4.9231, 5.1621, 5.2577, 5.2258, 5.2896, 5.4489, 5.2481, 5.2481,
5.1205, 5.0886, 5.2003, 5.3598, 5.4874, 5.5353, 5.5672, 5.4874,
5.3518, 5.2641, 5.3917, 5.3598, 5.296, 5.296, 5.1365, 5.0886,
5.0727, 5.1365, 5.1843, 4.8334, 4.9291, 4.9131, 4.9929, 5.3279,
5.615, 5.9341, 5.7905, 5.9181, 5.615, 5.5193, 5.5911, 5.5831,
5.4874, 5.6549, 5.7107, 5.631, 5.5911, 5.8703, 5.9022, 5.9181,
5.9181, 6.2371, 6.0936, 5.8064, 5.6948, 5.7666, 5.1684, 5.28,
5.4156, 5.5034, 5.296, 5.296, 5.3279, 5.296, 5.2641, 5.28, 5.304,
5.1365, 4.5941, 4.5782, 4.5542, 4.5303, 4.5144, 4.4984, 4.5064,
4.4745, 4.4346, 4.6324, 4.7442, 4.7442, 4.8081, 4.9199, 4.7762,
4.6164, 4.6324, 4.8161, 4.7921, 4.7762, 4.7362, 4.7362, 4.7362,
4.8081, 4.7362, 4.7442, 4.9119, 4.8241, 4.7762, 4.6971, 4.7287,
4.7762, 4.8078, 4.9659, 5.0925, 5.045, 4.9106, 5.0925, 5.0213,
5.1715, 5.3613, 5.1241, 5.2506, 5.2111, 5.4957, 5.2506, 5.4562,
5.4088, 5.1636, 4.8157, 5.0529, 4.9818, 4.9818, 5.1399, 5.1953,
5.1794, 5.2822, 5.2269, 5.219, 5.1874, 5.6381, 5.7251, 5.733,
5.7092, 5.646, 5.7883, 6.2944, 6.5949, 6.5474, 6.4447, 6.4288,
6.3102, 6.3482, 6.3798, 6.3086, 6.3561, 6.269, 6.4115, 6.4669,
6.4907, 6.5223, 6.3719, 6.5698, 6.5856, 6.5065, 6.3007, 6.0632,
6.3719, 6.3323, 6.2215, 6.2057, 6.2532, 5.8574, 6.1463, 6.182,
6.0793, 6.032, 6.1741, 6.1188, 6.0714, 6.0478, 6.032, 5.9451,
5.953, 5.7162, 5.7793, 6.1267, 6.1583, 5.3214, 5.4319, 5.3846,
5.424, 5.203, 5.3451, 5.274, 5.274, 5.3056, 5.4082, 5.2582, 5.3214,
5.2582, 5.1793, 5.3688, 5.2582, 5.1319, 4.9345, 4.4529, 4.5792,
4.3819, 4.4213, 4.374, 4.6582, 4.6977, 4.5871, 4.6819, 4.6819,
4.7687, 4.8477, 5.083, 5.2173, 5.2173, 5.1225, 5.1936, 5.3675,
5.2885, 5.3359, 5.2173, 5.1541, 5.0909, 4.917, 4.9644, 4.9644,
4.9644, 5.0118, 4.9407, 4.8695, 4.8379, 4.7272, 4.7272, 4.7984,
4.7589, 4.8695, 4.8537, 4.8458, 4.9644, 4.9881, 5.0592, 5.1383,
5.0988, 5.1067, 5.2173, 5.494, 5.494, 5.4782, 5.5415, 5.5494,
5.5336, 5.5454, 5.2806, 5.4071, 5.5098, 5.5889, 5.4545, 5.5177,
5.5494, 5.5256, 5.6284, 5.5177, 5.66, 5.664, 5.6126, 5.5494,
5.6205, 5.5177, 5.3913, 5.5573, 5.4703, 5.4545, 5.5731, 5.6205,
6.0711, 5.7549, 5.7391, 5.7154, 5.7928, 5.9036, 5.9511, 5.8482,
5.6662, 5.69, 5.7374, 5.7374, 5.9353, 5.9115, 6.1094, 6.1252,
6.1648, 6.331, 6.2123, 6.418, 6.2914, 6.4259, 6.3468, 6.1806,
6.4101, 6.3389, 6.3785, 6.3151, 6.2518, 6.236, 6.3072, 6.4101,
5.9828, 5.8245, 5.7374, 5.7295, 5.7454, 5.69, 5.7612, 5.6583,
5.5792, 5.4921, 5.4921, 5.2389, 5.2784, 5.3022, 5.0569, 4.9777,
5.1914, 5.0648, 5.1518, 5.1756, 5.1281, 5.5079, 5.318, 5.1677,
5.0648, 5.0173, 5.2864, 5.2547, 5.1835, 4.9936, 5.0806, 5.1281,
5.3259, 5.6187, 5.3734, 5.413, 5.3734, 5.269, 5.2452, 5.2452,
5.2056, 5.1659, 5.2769, 5.1739, 5.0946, 5.055, 4.9441, 4.9837,
5.0471, 5.0629, 5.1343, 5.0233, 5.0075, 4.9283, 4.8015, 4.8015,
4.7856, 4.5717, 4.3419, 4.0963, 4.0963, 4.0963, 4.0963, 4.0963,
4.0963, 4.0963, 4.0963, 4.0963, 4.0963, 4.0963, 4.0963, 4.0963,
4.0963, 4.0963, 4.0963, 4.0963, 4.0963, 4.0963, 3.407, 3.4466,
3.3278, 3.5021, 3.1693, 3.1455, 3.098), `5` = c(13.4319, 13.6184,
13.805, 13.8983, 13.8983, 13.805, 13.5252, 13.2453, 12.8722,
12.7789, 12.6857, 13.4319, 13.2453, 13.5252, 13.7117, 13.8983,
13.805, 13.8983, 13.805, 13.8983, 13.9915, 14.1781, 14.2154,
14.3089, 14.2154, 14.2154, 14.4024, 14.3089, 14.5895, 14.496,
14.3089, 14.3089, 14.3089, 14.4024, 14.496, 14.496, 14.2154,
14.2154, 14.4024, 14.4024, 14.496, 14.3089, 14.683, 14.4024,
14.496, 14.5895, 14.4024, 14.4024, 14.4024, 13.9348, 13.7478,
14.0284, 14.0284, 14.0284, 13.8413, 13.7478, 13.5607, 13.9348,
13.9348, 13.9348, 14.1219, 13.8413, 13.9348, 13.8413, 13.7478,
13.5607, 13.9348, 14.1219, 14.4024, 14.4024, 13.9348, 13.6543,
13.4672, 13.5607, 13.8413, 14.0284, 13.9348, 14.1219, 14.3089,
14.496, 14.2154, 14.3089, 14.3089, 14.3089, 14.1219, 14.3089,
14.1219, 14.3463, 14.1588, 14.065, 14.2526, 13.8775, 13.7837,
14.065, 14.1588, 14.1588, 14.065, 14.3463, 14.4401, 14.5339,
14.4401, 14.6276, 14.6276, 14.6276, 15.1902, 15.1902, 14.8152,
14.7214, 14.7214, 14.6276, 14.5339, 14.5339, 14.7214, 14.4401,
14.8152, 14.8152, 14.8152, 14.6276, 14.4401, 14.4401, 14.3463,
14.5339, 14.3463, 14.6276, 14.8152, 14.8152, 14.7214, 14.4401,
14.3463, 14.3463, 14.3463, 14.2526, 14.1588, 14.1588, 13.9713,
13.7837, 13.8775, 14.065, 13.9713, 13.3149, 13.0336, 12.9398,
12.5648, 12.8461, 12.6585, 12.5648, 12.6585, 12.6585, 12.6585,
12.8461, 12.7523, 13.1274, 13.3149, 13.3149, 13.5024, 13.5962,
13.5962, 13.915, 13.727, 13.6329, 13.5389, 13.5389, 13.6329,
13.821, 14.0184, 14.3146, 14.3146, 14.3146, 14.512, 14.3146,
14.4133, 14.512, 14.0184, 14.3146, 14.0184, 14.0184, 14.0184,
13.9197, 14.0184, 14.0184, 14.2159, 14.2159, 14.1172, 14.4133,
14.3146, 14.4133, 14.3146, 14.512, 14.8082, 14.7095, 14.6108,
14.7095, 14.9069, 15.0056, 15.0056, 15.2031, 15.1044, 15.4992,
15.4992, 15.7954, 15.9929, 15.598, 15.598, 16.0916, 16.289, 17.1775,
17.0788, 16.6839, 16.5852, 16.9801, 17.4737, 17.375, 17.1775,
16.5852, 16.6839, 16.3877, 16.8813, 16.9801, 17.1775, 17.2762,
17.2762, 17.8686, 17.4144, 17.3155, 17.2166, 17.2166, 17.3155,
17.3155, 17.3155, 17.7113, 17.6123, 17.9092, 17.6123, 17.5134,
17.4144, 17.5134, 17.1176, 17.4144, 17.4144, 17.4144, 16.9197,
17.0187, 16.8208, 16.6229, 16.6229, 16.8208, 17.2166, 17.3155,
17.2166, 16.9197, 16.6229, 17.4144, 17.4144, 17.7113, 17.7113,
18.1071, 18.1071, 18.1071, 18.206, 18.0081, 17.9092, 17.4144,
17.4144, 17.4144, 17.4144, 17.2166, 17.6123, 17.5134, 17.4144,
17.6123, 17.6123, 18.206, 17.8102, 17.3155, 16.9197, 16.8208,
16.6229, 16.5239, 15.8313, 15.7324, 15.2376, 15.8313, 15.7324,
15.8313, 15.6334, 15.6334, 15.4355, 15.2772, 15.2772, 15.178,
15.178, 15.178, 15.178, 15.0788, 15.0788, 15.0788, 15.0788, 14.8804,
14.7812, 15.0788, 15.3764, 15.2772, 15.0788, 14.8804, 14.682,
14.682, 14.5828, 14.4836, 14.3844, 14.8804, 14.9796, 14.8804,
14.7812, 14.3844, 14.3844, 14.5828, 14.3844, 14.0868, 13.8884,
13.9876, 14.2852, 14.2852, 14.4836, 14.3844, 14.3844, 14.186,
14.0868, 13.7892, 13.9876, 13.69, 13.8884, 13.9876, 14.0868,
14.2852, 13.8884, 13.69, 13.7892, 13.9876, 13.9876, 14.2852,
14.2852, 14.4836, 14.4836, 14.5828, 14.5828, 14.186, 14.2852,
14.5828, 14.3844, 14.2852, 14.5828, 14.7812, 15.0193, 14.8204,
14.522, 14.522, 15.0193, 15.4172, 15.5166, 15.7155, 15.6161,
15.6161, 15.815, 15.7155, 16.0139, 16.0139, 15.9145, 15.815,
15.9145, 15.9145, 15.7155, 15.815, 15.6161, 16.4118, 16.5113,
16.6107, 17.4065, 16.6107, 16.4118, 16.2129, 16.0139, 16.5113,
17.4065, 17.2075, 17.5059, 17.6054, 17.7049, 18.1027, 17.7049,
18.8487, 18.6995, 18.4508, 18.6498, 18.9979, 18.9979, 18.5006,
18.6, 18.5006, 18.6498, 18.7492, 18.6, 18.3017, 18.6498, 18.053,
17.6054, 17.8043, 18.3017, 18.5006, 18.4011, 18.2519, 17.8043,
17.9038, 17.9038, 17.9038, 17.8541, 17.9535, 17.9038, 17.9436,
18.0433, 18.1429, 18.1928, 17.8937, 17.4451, 17.8464, 17.4277,
17.2184, 17.48, 17.7417, 17.5847, 18.3174, 18.4221, 18.2651,
18.3697, 18.1604, 18.1081, 18.1081, 18.4744, 18.4221, 18.3174,
18.1604, 18.5791, 18.8931, 18.9978, 18.9978, 19.6781, 19.3118,
19.7305, 19.3641, 19.6258, 19.9398, 19.6781, 19.8875, 20.1491,
20.2538, 19.9398, 20.0445, 20.0445, 19.9921, 19.9921, 19.9921,
20.1491, 20.2538, 20.0968, 20.0445, 19.9921, 19.9921, 19.9398,
19.5734, 19.7305, 19.5211, 19.5734, 19.7305, 19.9398, 19.7305,
19.4688, 17.794, 19.2071, 18.5267, 18.4221, 18.6314, 19.0501,
18.9454, 19.1966, 18.9344, 18.7246, 18.5672, 18.0952, 18.0952,
18.0952, 18.0427, 18.3574, 18.2001, 18.4623, 18.3574, 18.3574,
18.0952, 17.7805, 17.6231, 17.6231, 17.9378, 18.0427, 17.7805,
17.5707, 17.6231, 17.9378, 17.8329, 17.4658, 17.256, 17.4133,
17.3084, 18.0427, 18.1476, 18.2525, 18.2001, 18.0952, 18.0427,
17.4658, 17.4133, 17.4133, 17.6231, 17.7805, 17.9378, 18.2001,
18.2001, 18.2001, 18.2525, 17.8329, 18.9344, 18.7246, 18.4099,
18.7246, 18.6197, 18.5148, 18.4623, 18.4099, 18.4099, 18.4623,
18.4099, 18.5148, 18.5148, 18.5148, 18.2001, 17.9903, 18.2001,
17.6756, 18.0952, 18.305, 18.4518, 18.3993, 18.557, 18.925, 18.8724,
18.9775, 19.0827, 18.8724, 18.8724, 18.7673, 18.9775, 18.925,
18.925, 18.6096, 18.7147, 18.8724, 18.8724, 18.8724, 19.0301,
19.1878, 19.4507, 19.0827, 19.4507, 19.3981, 19.6084, 19.6609,
19.8187, 19.7661, 19.0301, 18.5044, 18.6096, 18.6621, 18.4518,
18.5044, 18.7147, 18.3993, 18.189, 18.189, 18.189, 18.4518, 18.6096,
18.9775, 18.8198, 18.3467, 18.189, 18.0839, 18.2416, 18.2416,
18.0839, 18.2416, 18.5044, 18.557, 18.6096, 18.2941, 18.5044,
18.1364, 18.1364, 18.189, 17.9787, 18.189, 18.1364, 18.0839,
17.9262, 17.8736, 17.7684, 17.7579, 17.6525, 17.6525, 17.4945,
17.4418, 17.1783, 17.0202, 16.8621, 16.704, 16.704, 16.8621,
16.8094, 16.4406, 16.4406, 16.2825, 15.8609, 16.3352, 15.9136,
16.4406, 16.3352, 16.1771, 15.7029, 17.7052, 16.8621, 16.8094,
16.8094, 17.0729, 16.9148, 16.3879, 16.546, 17.4418, 17.231,
17.231, 16.8094, 16.9675, 17.1783, 16.8621, 16.6514, 16.3879,
16.3352, 16.5987, 16.3879, 16.3879, 16.6514, 16.019, 15.9136,
15.334, 15.334, 15.0178, 15.1759, 15.0178, 14.9124, 14.8598,
14.7017, 14.5963, 14.7017, 14.4382, 14.5436, 13.964, 14.122,
14.4382, 14.7544, 14.4382, 14.3328, 13.9113, 14.1115, 13.9529,
14.0058, 15.01, 15.01, 15.01, 14.7457, 14.3758, 14.2172, 14.5343,
14.4286, 14.8171, 14.6506, 14.4841, 14.3731, 14.0401, 13.4852,
13.8737, 13.3187, 13.9292, 13.8182, 13.4852, 13.3187)), row.names = c(NA,
-700L), class = c("tbl_df", "tbl", "data.frame"))
Below r1 is the mean of the last 365 rows. (Note that that is what the question asked for but there are not 365 days of data in a year since it seems weekends are missing so this is not the mean of the last year.)
r2 is the mean of the last 365 rows every 30 days. Again I doubt that that is what you wanted even though that is what was asked for.
r3 is the mean of the last 12 months. Presumably this is what you actually need.
Each of these is a zoo object. It is probably best to leave them as zoo objects so you can use the rest of that package easily and not convert them to data frames but if you do want to convert then just use fortify.zoo(r1), etc.
library(zoo)
z <- zoo(data[-1], data[[1]])
r1 <- rollapplyr(z, 365, mean, fill = NA)
r2 <- rollapplyr(z, 365, mean, by = 30, fill = NA)
z.ym <- aggregate(z, as.yearmon, sum)
z.n <- aggregate(z, as.yearmon, length)
r3 <- rollsumr(z.ym, 12, fill = NA) / rollsumr(z.n, 12, fill = NA)
How about this (notice I switched the order of the loops)
If you are iterating over a number:
for (j in 1:498){
for (u in seq(0,5605-30,30))
{
work.on.col = j
work.dt = data[u:u+30,work.on.col] #u:u+30 iterates i.e: 0:30, 30:60,...etc. It only keeps a 30-day rolling window.
}
}
If you are iterating over a date (this would only work if you had your rownames as dates):
for (j in 1:498){
for (u in sapply(seq.Date(as.Date(min(data$Dates)), as.Date(max(data$Dates)),
"day")," [[",30))
{
work.on.col = j
work.dt = data[u:u+30,work.on.col]
}
}
I am trying to learn forecast using fpp2 package on bitcoin data. The bitcoin_ts excel just has two columns, one the date column another the closing price for each date.
library(fpp2)
bitcoin_ts <- read_excel("bitcoin_ts.xlsx")
head(bitcoin_ts)
myts <- ts(bitcoin_ts[,2], start = c(2013, 2), frequency = 366)
##autoplot(myts, facets = FALSE)
ggseasonplot(myts)
As beginning of the data is 2013 and from the month of April, which is the second quarter, I specified start as 2013 and 2 and frequency I gave as number of days in a year.
I get a graph as attached the lower scale (season), my requirement is to show months but I guess due to the all the dates being included in the data it is getting superimposed. How can I fix this to achieve only months over there?
I would like to see something similar to this chart (of course the data upon which this chart was plotted had only year and month (ex: Mar-81)
Unable to attach the entire 1655 observations as the this textbox is set to a limit of 30K characters.
However I attaching the link to the dataset as well.
Complete Dataset
Below find the sample of first 500,
> dput(head(bitcoin_ts,500))
structure(list(Date = structure(c(1367107200, 1367193600, 1367280000,
1367366400, 1367452800, 1367539200, 1367625600, 1367712000, 1367798400,
1367884800, 1367971200, 1368057600, 1368144000, 1368230400, 1368316800,
1368403200, 1368489600, 1368576000, 1368662400, 1368748800, 1368835200,
1368921600, 1369008000, 1369094400, 1369180800, 1369267200, 1369353600,
1369440000, 1369526400, 1369612800, 1369699200, 1369785600, 1369872000,
1369958400, 1370044800, 1370131200, 1370217600, 1370304000, 1370390400,
1370476800, 1370563200, 1370649600, 1370736000, 1370822400, 1370908800,
1370995200, 1371081600, 1371168000, 1371254400, 1371340800, 1371427200,
1371513600, 1371600000, 1371686400, 1371772800, 1371859200, 1371945600,
1372032000, 1372118400, 1372204800, 1372291200, 1372377600, 1372464000,
1372550400, 1372636800, 1372723200, 1372809600, 1372896000, 1372982400,
1373068800, 1373155200, 1373241600, 1373328000, 1373414400, 1373500800,
1373587200, 1373673600, 1373760000, 1373846400, 1373932800, 1374019200,
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"Close"), row.names = c(NA, -500L), class = c("tbl_df", "tbl",
"data.frame"))
I'm looking for a possibility to generate a constrained spline in order to approximate a shape (in my case, a footprint outline). As raw data, I have a table with several hundred xy-coordinate pairs, which have been collected from the boundary of the footprint. The spline should only approximate the data points (the spline does not need to pass the data points). I want to be able to smooth the spline to certain degrees. Also, I need to be able to constrain the spline: Defining several critical data points which the spline has to pass.
The R package "cobs" (COnstrained B-Splines, https://cran.r-project.org/web/packages/cobs/index.html) comes very close to providing a solution, offering parameters to constrain the spline as wanted. However, this package does not spline through an ordered sequence of data points, which of course is crucial when you want the spline to follow the boundary of a shape. I tried to spline x and y coordinates separately, but after recombining them two distinct shapes appear in the plot, so this does not seem to work (Or I got something wrong?). Is anybody aware of a solution?
Update: working example (dinosaur footprint outline)
data.txt:
structure(list(V1 = c(124.9, 86.44, 97.22, 81.34, 49.09, 57.18,
-77.6, -191.95, -284.67, -383.18, -379.27, -492.85, -547.72,
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-859.61, -745.06, -656.5, -682.01, -637.25, -601.71, -539.09,
-394.79, -219.17, -170.17, -201.48, -122.52, -43.56, 127.97,
344.42, 539.09, 686.11, 987.63, 1253.31, 1283.15, 1536.32, 1741.14,
1832.35, 1700.3, 1787.43, 1911.31, 2017.49, 2097.81, 2135.93,
2093.73, 2066.96, 2063.78, 2022.94, 1978.69, 1919.44, 1904.03,
1895.37, 1854.22, 1810.23, 1771.09, 1741.48, 1642.45, 1553.96,
1472.96, 1396.04, 1141.65, 1085.82, 1055.02, 1358.24, 1325.94,
1031.91, 1287.14, 1265.36, 931.15, 872.12, 811.48, 755.65, 738.32,
697.41, 682.49, 647.35, 628.25, 620.09, 629.62, 675.22, 709.25,
718.78, 717.42, 551.09, 535.21, 540.98, 534.73, 546.76, 811.96,
823.03, 822.07, 607.4, 626.18, 637.73, 659.87, 756.13, 753.72,
735.91, 720.99, 676.71, 576.6, 508.26, 339.8, 179.53, 121.16,
45.6, 12.93, -9.87, -12.59, 16, 27.91, 37.78, 49.35, 8.51, 2.72,
-1.02, 59.22, 58.2, 51.73, 54.45, 0.96, 10.59, 138.62, 149.69,
144.87, 142.26, 146.34, 125.24, 124.9, 86.44, 97.22, 81.34, 49.09,
57.18, -77.6, -191.95, -284.67, -383.18, -379.27, -492.85, -547.72,
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-394.79, -219.17, -170.17, -201.48, -122.52, -43.56, 127.97,
344.42, 539.09, 686.11, 987.63, 1253.31, 1283.15, 1536.32, 1741.14,
1832.35, 1700.3, 1787.43, 1911.31, 2017.49, 2097.81, 2135.93,
2093.73, 2066.96, 2063.78, 2022.94, 1978.69, 1919.44, 1904.03,
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1472.96, 1396.04, 1141.65, 1085.82, 1055.02, 1358.24, 1325.94,
1031.91, 1287.14, 1265.36, 931.15, 872.12, 811.48, 755.65, 738.32,
697.41, 682.49, 647.35, 628.25, 620.09, 629.62, 675.22, 709.25,
718.78, 717.42, 551.09, 535.21, 540.98, 534.73, 546.76, 811.96,
823.03, 822.07, 607.4, 626.18, 637.73, 659.87, 756.13, 753.72,
735.91, 720.99, 676.71, 576.6, 508.26, 339.8, 179.53, 121.16,
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-1.02, 59.22, 58.2, 51.73, 54.45, 0.96, 10.59, 138.62, 149.69,
144.87, 142.26, 146.34, 125.24), V2 = c(-446.8, -415.83, -394.43,
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269.76, 455.55, 548.92, 616.3, 691.38, 756.84, 888.72, 1016.97,
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1101.37, 1025.14, 929.84, 852.25, 766.48, 717.47, 733.81, 784.18,
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-3.62, 104.2, 230.8, 154.25, 380.55, 416.62, 260.07, 295.75,
295.75, 251.47, 220.67, 225.96, 180.72, 121.52, 4.14, -127.23,
-176.24, -332.11, -408.35, -494.11, -573.75, -582.62, -678.88,
-730.38, -788.62, -831.94, -846.38, -895.95, -934.46, -968.15,
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-1500.47, -1550.52, -1605.39, -1681.44, -1709.84, -1715.22, -1672.34,
-1607, -1522.59, -1440.57, -1421.18, -1345.62, -1247.95, -1190.77,
-1181.58, -1071.65, -1037.62, -1010.39, -998.82, -986.57, -937.9,
-887.29, -842.05, -831.46, -774.66, -703.91, -573.75, -533.59,
-448.16)), .Names = c("V1", "V2"), class = "data.frame", row.names = c(NA,
-280L))
require(cobs)
xy <- dget(data.txt)
#Cumchord function (from Claude, 2008): Cumulative chordal distance vector
cumchord<-function(M)
{cumsum(sqrt(apply((M-rbind(M[1,],
M[-(dim(M)[1]),]))^2,1,sum)))}
z <- cumchord(xy)
#Calculating B-spline for x and y values separately
x <- cobs(z,xy[,1],nknots=50)
y <- cobs(z,xy[,2],nknots=50)
#Plot spline
plot(xy)
lines(x$fitted,y$fitted)
Image of resulting plot
Following the comments thread, here are some graphs. I use Momocs since I'm familiar with it and it will shorten examples.
I brief, your problem is that there are two outlines in your outline.
I also include original use of spline by Julien Claude, and two additional examples with bezier curves and elliptic Fourier transforms. All 4 can be used to describe an outline (and reconstruct it) and it's probably worth gathering them here.
The picture gathers the original shape and these 4 methods
Now the code. It's not particularly long but quite repetitive.
# devtools::install_github("vbonhomme/Momocs")
library(Momocs) # version 1.0.3
xy <- structure(list(
V1 = c(124.9, 86.44, 97.22, 81.34, 49.09, 57.18, -77.6, -191.95, -284.67, -383.18, -379.27, -492.85, -547.72, -600.67, -713.29, -814.36, -868.27, -926.99, -958.76, -1025.18, -1077.16, -1105.07, -1126.25, -1112.77, -1087.74, -989.56, -911.59, -859.61, -745.06, -656.5, -682.01, -637.25, -601.71, -539.09, -394.79, -219.17, -170.17, -201.48, -122.52, -43.56, 127.97, 344.42, 539.09, 686.11, 987.63, 1253.31, 1283.15, 1536.32, 1741.14, 1832.35, 1700.3, 1787.43, 1911.31, 2017.49, 2097.81, 2135.93, 2093.73, 2066.96, 2063.78, 2022.94, 1978.69, 1919.44, 1904.03, 1895.37, 1854.22, 1810.23, 1771.09, 1741.48, 1642.45, 1553.96, 1472.96, 1396.04, 1141.65, 1085.82, 1055.02, 1358.24, 1325.94, 1031.91, 1287.14, 1265.36, 931.15, 872.12, 811.48, 755.65, 738.32, 697.41, 682.49, 647.35, 628.25, 620.09, 629.62, 675.22, 709.25, 718.78, 717.42, 551.09, 535.21, 540.98, 534.73, 546.76, 811.96, 823.03, 822.07, 607.4, 626.18, 637.73, 659.87, 756.13, 753.72, 735.91, 720.99, 676.71, 576.6, 508.26, 339.8, 179.53, 121.16, 45.6, 12.93, -9.87, -12.59, 16, 27.91, 37.78, 49.35, 8.51, 2.72, -1.02, 59.22, 58.2, 51.73, 54.45, 0.96, 10.59, 138.62, 149.69, 144.87, 142.26, 146.34, 125.24, 124.9, 86.44, 97.22, 81.34, 49.09, 57.18, -77.6, -191.95, -284.67, -383.18, -379.27, -492.85, -547.72, -600.67, -713.29, -814.36, -868.27, -926.99, -958.76, -1025.18, -1077.16, -1105.07, -1126.25, -1112.77, -1087.74, -989.56, -911.59, -859.61, -745.06, -656.5, -682.01, -637.25, -601.71, -539.09, -394.79, -219.17, -170.17, -201.48, -122.52, -43.56, 127.97, 344.42, 539.09, 686.11, 987.63, 1253.31, 1283.15, 1536.32, 1741.14, 1832.35, 1700.3, 1787.43, 1911.31, 2017.49, 2097.81, 2135.93, 2093.73, 2066.96, 2063.78, 2022.94, 1978.69, 1919.44, 1904.03, 1895.37, 1854.22, 1810.23, 1771.09, 1741.48, 1642.45, 1553.96, 1472.96, 1396.04, 1141.65, 1085.82, 1055.02, 1358.24, 1325.94, 1031.91, 1287.14, 1265.36, 931.15, 872.12, 811.48, 755.65, 738.32, 697.41, 682.49, 647.35, 628.25, 620.09, 629.62, 675.22, 709.25, 718.78, 717.42, 551.09, 535.21, 540.98, 534.73, 546.76, 811.96, 823.03, 822.07, 607.4, 626.18, 637.73, 659.87, 756.13, 753.72, 735.91, 720.99, 676.71, 576.6, 508.26, 339.8, 179.53, 121.16, 45.6, 12.93, -9.87, -12.59, 16, 27.91, 37.78, 49.35, 8.51, 2.72, -1.02, 59.22, 58.2, 51.73, 54.45, 0.96, 10.59, 138.62, 149.69, 144.87, 142.26, 146.34, 125.24),
V2 = c(-446.8, -415.83, -394.43, -259.19, -104.69, -4.03, 58.59, -80.26, 52.11, -48.33, -142.23, -176.89, -233.68, -321.28, -416.57, -457.97, -458.93, -429.09, -422.35, -450.27, -431.98, -379.03, -260.63, -123.94, -2.65, 269.76, 455.55, 548.92, 616.3, 691.38, 756.84, 888.72, 1016.97, 1157.18, 1198.02, 1101.37, 1025.14, 929.84, 852.25, 766.48, 717.47, 733.81, 784.18, 835.91, 1225.63, 1198.68, 925.3, 742.4, 814.13, 732.45, 586.79, 394.84, 212.42, 28.64, -111.58, -337.56, -490.03, -526.07, -528.82, -547.2, -551.97, -552.3, -585.51, -551.34, -543.16, -526.1, -494.11, -466.88, -355.93, -274.94, -215.04, -114.3, -194.21, -103.73, -3.62, 104.2, 230.8, 154.25, 380.55, 416.62, 260.07, 295.75, 295.75, 251.47, 220.67, 225.96, 180.72, 121.52, 4.14, -127.23, -176.24, -332.11, -408.35, -494.11, -573.75, -582.62, -678.88, -730.38, -788.62, -831.94, -846.38, -895.95, -934.46, -968.15, -1033.12, -1097.62, -1150.08, -1157.3, -1254.04, -1340.2, -1441.75, -1500.47, -1550.52, -1605.39, -1681.44, -1709.84, -1715.22, -1672.34, -1607, -1522.59, -1440.57, -1421.18, -1345.62, -1247.95, -1190.77, -1181.58, -1071.65, -1037.62, -1010.39, -998.82, -986.57, -937.9, -887.29, -842.05, -831.46, -774.66, -703.91, -573.75, -533.59, -448.16, -446.8, -415.83, -394.43, -259.19, -104.69, -4.03, 58.59, -80.26, 52.11, -48.33, -142.23, -176.89, -233.68, -321.28, -416.57, -457.97, -458.93, -429.09, -422.35, -450.27, -431.98, -379.03, -260.63, -123.94, -2.65, 269.76, 455.55, 548.92, 616.3, 691.38, 756.84, 888.72, 1016.97, 1157.18, 1198.02, 1101.37, 1025.14, 929.84, 852.25, 766.48, 717.47, 733.81, 784.18, 835.91, 1225.63, 1198.68, 925.3, 742.4, 814.13, 732.45, 586.79, 394.84, 212.42, 28.64, -111.58, -337.56, -490.03, -526.07, -528.82, -547.2, -551.97, -552.3, -585.51, -551.34, -543.16, -526.1, -494.11, -466.88, -355.93, -274.94, -215.04, -114.3, -194.21, -103.73, -3.62, 104.2, 230.8, 154.25, 380.55, 416.62, 260.07, 295.75, 295.75, 251.47, 220.67, 225.96, 180.72, 121.52, 4.14, -127.23, -176.24, -332.11, -408.35, -494.11, -573.75, -582.62, -678.88, -730.38, -788.62, -831.94, -846.38, -895.95, -934.46, -968.15, -1033.12, -1097.62, -1150.08, -1157.3, -1254.04, -1340.2, -1441.75, -1500.47, -1550.52, -1605.39, -1681.44, -1709.84, -1715.22, -1672.34, -1607, -1522.59, -1440.57, -1421.18, -1345.62, -1247.95, -1190.77, -1181.58, -1071.65, -1037.62, -1010.39, -998.82, -986.57, -937.9, -887.29, -842.05, -831.46, -774.66, -703.91, -573.75, -533.59, -448.16)), .Names = c("V1", "V2"), class = "data.frame", row.names = c(NA, -280L))
### First thing first: double outline ---------------------
coo_plot(xy)
ldk_labels(xy) # blurry since superimposed
coo_plot(xy[1:140, ], lwd=3) # first shape
coo_draw(xy[-(1:140), ], border="white") # second shape
# so from here, we will use the first 140th points from xy and name it 'shp' to avoid confusion
# if you're bored with dinos footprints, you can use beer bottles with shp <- bot[9] for a guinness
shp <- xy[1:140, ]
### 1.Natural splines ---------------------
shp_cumchord <- coo_perimcum(shp) # cumchord equivalent
shp_spline <- cbind(spline(shp_cumchord, shp[, 1], method="natural", n=120)$y,
spline(shp_cumchord, shp[, 2], method="natural", n=120)$y)
coo_plot(shp, main="natural spline", zoom=1.2)
coo_draw(shp_spline, border="blue", lwd=2)
### 2. B-splines with cobs ---------------------
library(cobs)
shp_bspline <- cbind(cobs(shp_cumchord, shp[, 1], nknots=50)$fitted,
cobs(shp_cumchord, shp[, 2], nknots=50)$fitted)
coo_plot(shp, main = "bspline", zoom=1.2)
coo_draw(shp_bspline, border="blue")
### 3. Bezier curves ---------------------
# built in function so it's shorter
shp_bezier <- shp %>% bezier() %>% bezier_i()
coo_plot(shp, main = "bezier", zoom=1.2)
coo_draw(shp_bezier, border="blue")
### 4. elliptic Fourier transforms ---------------------
# another built in function
shp_eft <- shp %>% efourier() %>% efourier_i()
coo_plot(shp, main = "bspline", zoom=1.2)
coo_draw(shp_eft, border="blue")
### 5. A panel of original shape and 4 methods ---------
Out(list(original=shp,
nat_spline=shp_spline, bspline=shp_bspline,
bezier=shp_bezier, eft=shp_eft)) %>%
panel(names=TRUE, dim=c(1, 5))
I am having trouble picking up the seasonality the seems to be implied in the data. I think (though its just a guess that its using additive and not multiplicative seasonality). I am using the forecast function and thought it would automatically pick what I need based on a lecture from Dr. Hyndman. The following snipet of code plots the chart and I would have expected the forecast to be higher then it is. Am I missing a model parameter or something? Any help would be appreciated.
sw<-c(2280, 1754, 1667, 1359, 1285, 1379, 2166, 1053, 1076, 1149, 1277, 1577, 1639, 1719, 1592, 2306, 3075, 2897, 1875, 1966, 2927, 3528, 2948, 2890, 3947, 3913, 3885, 4148, 5293, 5752, 6001, 7719, 5512, 6782, 6320, 6425, 6406, 7237, 8655, 9269, 12447, 13470, 13469, 13949, 17753, 17653, 14531, 14496, 13643, 12652, 12665, 10629, 8962, 8198, 6833, 5027, 4407, 4449, 4399, 5896, 6589, 3786, 4386, 4847, 5597, 5407, 4800, 7803, 9255, 10423, 5523, 8121, 6944, 8434, 9847, 9292, 9794, 10195, 10124, 11310, 12245, 12798, 14611, 15402, 13532, 16154, 15101, 14755, 17139, 16475, 19935, 19980, 25173, 28568, 27839, 28991, 27073, 29615, 25849, 27910, 27067, 21303, 20544, 15188, 13706, 9277, 10815, 7228, 4608, 4409, 9866, 8471, 8223, 6445, 6641, 6833, 11421, 8945, 8127, 10380, 12005, 13272, 9431, 12144, 14934, 14052, 11712, 14888, 15824, 17275, 18067, 19839, 21192, 22763, 22976, 23721, 22681, 20131, 19965, 20539, 19517, 22022, 23076, 30574, 40247, 43111, 39577, 40724, 44982, 44388, 46372, 43153, 36821, 32258, 31256, 27153, 23180, 18252, 16381, 13220, 12500, 10727, 9636, 8892, 8644, 9482, 9170, 10937, 12299, 15781, 11477, 16524, 16752, 18072, 14776, 13388, 18056, 19815, 21263, 22046, 26415, 24247, 25403, 30058, 26331, 32533, 31891, 35973, 27558, 24554, 25692, 25955, 24284, 24930, 28354, 34840, 40055, 42099, 42768, 48279, 50086, 56466, 42244, 51451, 44583, 39091, 33391, 29452, 25533)
swts <- ts(sw, frequency=52, start=c(2006,30))
swfc <- forecast(swts,h=52)
plot(swfc)
Did you data have multiple seasonal periods? If so you could check the tbats function.
Anyway, your seasonal period is greater than 12, so forecast is using a stl decomposition to adjust your seasonal data. Maybe you wanna check ?stlf for more info on what parameters you can change, or try a BoxCox transformation:
lambda <- BoxCox.lambda(sw)
swfc <- forecast(swts,h=52, lambda = lambda, robust = TRUE)
plot(swfc)
This question already has answers here:
Remove NA values from a vector
(8 answers)
Closed 8 years ago.
I have a vector which stores over 1000 values. The first 50 values are NAs, how can I get rid of it?
c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 1.5741, 1.583, 1.605, 1.633, 1.6465, 1.6475, 1.6329,
1.6413, 1.685, 1.692, 1.7087, 1.7055, 1.6985, 1.6807, 1.6745,
1.673, 1.6625, 1.6805, 1.689, 1.667, 1.684, 1.6675, 1.6867, 1.6688,
1.6643, 1.6685, 1.7025, 1.737, 1.7663, 1.742, 1.7535, 1.749,
1.7494, 1.75, 1.711, 1.7145, 1.7205, 1.751, 1.7295, 1.7205, 1.7325,
1.731, 1.7235, 1.712, 1.6967, 1.6872, 1.696, 1.7354, 1.729, 1.712,
1.7208, 1.7115, 1.7035, 1.7032, 1.6947, 1.715, 1.7181, 1.742,
1.7471, 1.7438, 1.7493, 1.7525, 1.773, 1.7695, 1.7735, 1.7895,
1.7905, 1.7945, 1.798, 1.8138, 1.791, 1.7915, 1.802, 1.7812,
1.7925, 1.7873, 1.7952, 1.808, 1.8265, 1.8369, 1.83, 1.8347,
1.826, 1.8079, 1.8165, 1.8104, 1.8333, 1.7864, 1.7878, 1.801,
1.79, 1.7745, 1.7493, 1.7625, 1.7575, 1.739, 1.7615, 1.739, 1.7521,
1.752, 1.7445, 1.7585, 1.7375, 1.7188, 1.709, 1.7092, 1.7154,
1.7273, 1.724, 1.7323, 1.7365, 1.7495, 1.7643, 1.8165, 1.811,
1.7395, 1.7365, 1.749, 1.7485, 1.748, 1.738, 1.743, 1.747, 1.7457,
1.7375, 1.738, 1.7391, 1.7155, 1.6909, 1.6953, 1.6984, 1.6795,
1.6885, 1.672, 1.669, 1.6815, 1.6895, 1.6855, 1.6697, 1.6845,
1.683, 1.6865, 1.6715, 1.6628, 1.6687, 1.6674, 1.6638, 1.682,
1.6825, 1.6953, 1.6915, 1.6955, 1.69, 1.7, 1.7105, 1.704, 1.705,
1.6867, 1.6917, 1.6954, 1.71, 1.702, 1.6985, 1.7185, 1.6898,
1.6725, 1.6725, 1.6515, 1.6305, 1.6355, 1.642, 1.631, 1.6425,
1.6375, 1.6355, 1.634, 1.6285, 1.6358, 1.6069, 1.602, 1.5995,
1.595, 1.582, 1.584, 1.5825, 1.6193, 1.6163, 1.6248, 1.6073,
1.615, 1.6125, 1.5865, 1.5718, 1.5714, 1.575, 1.579, 1.5835,
1.586, 1.5774, 1.5755, 1.5715, 1.557, 1.5345, 1.5115, 1.5208,
1.508, 1.5175, 1.5165, 1.517, 1.5268, 1.5444, 1.5195, 1.518,
1.5135, 1.5658, 1.583, 1.5757, 1.5945, 1.6245, 1.6135, 1.609,
1.5923, 1.586, 1.5915, 1.6032, 1.5893, 1.6125, 1.5965, 1.5972,
1.6142, 1.6085, 1.5995, 1.5907, 1.585, 1.5755, 1.563, 1.5768,
1.5917, 1.607, 1.6273, 1.623, 1.6223, 1.6455, 1.6438, 1.6435,
1.653, 1.6518, 1.647, 1.651, 1.6415, 1.6367, 1.6415, 1.6579,
1.672, 1.6737, 1.669, 1.6615, 1.6715, 1.663, 1.668, 1.6665, 1.662,
1.6495, 1.649, 1.6715, 1.6725, 1.6691, 1.6655, 1.6502, 1.6605,
1.6425, 1.6465, 1.645, 1.6545, 1.644, 1.6231, 1.6245, 1.6243,
1.6256, 1.616, 1.637, 1.6572, 1.652, 1.663, 1.669, 1.6685, 1.6693,
1.667, 1.6633, 1.662, 1.6495, 1.6525, 1.6545, 1.6588, 1.6495,
1.6395, 1.6482, 1.6391, 1.629, 1.637, 1.6462, 1.64, 1.6235, 1.6165,
1.6105, 1.6125, 1.5965, 1.5907, 1.6027, 1.6145, 1.6175, 1.6135,
1.6125, 1.639, 1.629, 1.607, 1.612, 1.6051, 1.6049, 1.603, 1.588,
1.5883, 1.591, 1.5944, 1.5793, 1.577, 1.575, 1.5645, 1.5769,
1.5665, 1.5737, 1.5665, 1.5639, 1.5504, 1.5402, 1.536, 1.5158,
1.5246, 1.5215, 1.5102, 1.5183, 1.5117, 1.4945, 1.4945, 1.5135,
1.495, 1.4772, 1.4832, 1.4793, 1.4785, 1.4589, 1.4965, 1.4865,
1.4872, 1.4835, 1.5037, 1.4815, 1.4745, 1.4815, 1.4835, 1.478,
1.4753, 1.475, 1.4775, 1.477, 1.4707, 1.4661, 1.4684, 1.4626,
1.4558, 1.467, 1.463, 1.4568, 1.453, 1.4478, 1.4275, 1.4035,
1.399, 1.4065, 1.4083, 1.4062, 1.4001, 1.3924, 1.3915, 1.4133,
1.4032, 1.4015, 1.3908, 1.41, 1.4095, 1.4482, 1.483, 1.4862,
1.524, 1.4861, 1.5, 1.4815, 1.4938, 1.5, 1.486, 1.484, 1.451,
1.424, 1.417, 1.4235, 1.409, 1.4164, 1.432, 1.4435, 1.4728, 1.493,
1.4685, 1.47, 1.466, 1.4526, 1.4815, 1.4875, 1.5215, 1.5105,
1.5063, 1.5318, 1.537, 1.5345, 1.5374, 1.538, 1.5362, 1.569,
1.5625, 1.569, 1.5795, 1.5945, 1.589, 1.594, 1.5845, 1.5875,
1.567, 1.5885, 1.5995, 1.597, 1.5795, 1.599, 1.6002, 1.6015,
1.5935, 1.5955, 1.6005, 1.5945, 1.576, 1.5705, 1.5818, 1.596,
1.5625, 1.5575, 1.5665, 1.579, 1.5775, 1.569, 1.5675, 1.554,
1.5605, 1.566, 1.567, 1.5875, 1.5945, 1.598, 1.6092, 1.617, 1.6142,
1.6195, 1.637, 1.6265, 1.6345, 1.635, 1.639, 1.6305, 1.6325,
1.6325, 1.6195, 1.6325, 1.6135, 1.6115, 1.6055, 1.615, 1.5935,
1.573, 1.579, 1.586, 1.585, 1.611, 1.6365, 1.643, 1.6465, 1.656,
1.6545, 1.6555, 1.6545, 1.6595, 1.6575, 1.6615, 1.6587, 1.63,
1.628, 1.633, 1.6365, 1.6285, 1.614, 1.6195, 1.6335, 1.6455,
1.646, 1.643, 1.6475, 1.6355, 1.672, 1.6625, 1.668, 1.665, 1.661,
1.6665, 1.662, 1.6615, 1.6645, 1.654, 1.6335, 1.6375, 1.6305,
1.6265, 1.6425, 1.6315, 1.629, 1.618, 1.6085, 1.596, 1.6105,
1.5965, 1.611, 1.617, 1.6065, 1.6035, 1.6035, 1.578, 1.5925,
1.606, 1.6147, 1.5995, 1.595, 1.6035, 1.606, 1.582, 1.567, 1.5805,
1.5855, 1.5815, 1.5845, 1.5815, 1.5715, 1.5775, 1.5795, 1.5825,
1.6055, 1.6084, 1.6135, 1.616, 1.602, 1.6165, 1.624, 1.624, 1.6145,
1.6255, 1.6378, 1.63, 1.6315, 1.607, 1.5868, 1.5895, 1.5927,
1.5948, 1.6004, 1.626, 1.6195, 1.6225, 1.637, 1.629, 1.6235,
1.628, 1.6375, 1.6605, 1.6568, 1.681, 1.6895, 1.6955, 1.6925,
1.7095, 1.7032, 1.6987, 1.692, 1.704, 1.6976, 1.6965, 1.696,
1.698, 1.7091, 1.707, 1.721, 1.7286, 1.7204, 1.7165, 1.7241,
1.7205, 1.7037, 1.7053, 1.6975, 1.7075, 1.72, 1.7245, 1.7243,
1.7185, 1.7385, 1.7402, 1.712, 1.7057, 1.71, 1.712, 1.6975, 1.7,
1.7115, 1.721, 1.7158, 1.7132, 1.6904, 1.6965, 1.6782, 1.6865,
1.6767, 1.686, 1.679, 1.6868, 1.6665, 1.6645, 1.6738, 1.677,
1.658, 1.6445, 1.623, 1.611, 1.6075, 1.6177, 1.5985, 1.5935,
1.612, 1.6085, 1.5935, 1.6047, 1.6092, 1.608, 1.6187, 1.6325,
1.6443, 1.645, 1.6295, 1.6178, 1.6133, 1.6335, 1.6265, 1.623,
1.6255, 1.6221, 1.6215, 1.601, 1.604, 1.5935, 1.604, 1.6145,
1.6137, 1.6285, 1.6377, 1.647, 1.663, 1.676, 1.673, 1.682, 1.6794,
1.6788, 1.6774, 1.694, 1.6965, 1.6937, 1.6957, 1.691, 1.6842,
1.696, 1.6925, 1.691, 1.6908, 1.6865, 1.7023, 1.706, 1.7095,
1.7145, 1.7032, 1.7005, 1.7027, 1.7082, 1.7118, 1.707, 1.7148,
1.7165, 1.7211, 1.7208, 1.7062, 1.7045, 1.704, 1.7055, 1.6985,
1.706, 1.7125, 1.7163, 1.7078, 1.705, 1.7125, 1.7077, 1.701,
1.6935, 1.6965, 1.7019, 1.7008, 1.7175, 1.735, 1.7365, 1.7371,
1.7398, 1.7399, 1.7397, 1.7333, 1.7308, 1.7398, 1.7366, 1.752,
1.7505, 1.7553, 1.7487, 1.744, 1.7358, 1.7474, 1.7504, 1.7528,
1.748, 1.7441, 1.7273, 1.7444, 1.727, 1.7343, 1.7314, 1.736,
1.763, 1.7658, 1.7603, 1.7534, 1.7517, 1.7438, 1.7255, 1.7219,
1.734, 1.718, 1.7275, 1.7269, 1.7279, 1.7306, 1.7055, 1.7069,
1.709, 1.7037, 1.7088, 1.7198, 1.7184, 1.7155, 1.707, 1.6893,
1.6779, 1.6898, 1.6963, 1.691, 1.68, 1.6961, 1.6979, 1.6885,
1.685, 1.6726, 1.668, 1.6728, 1.6675, 1.6788, 1.6695, 1.6952,
1.6985, 1.7071, 1.7151, 1.7181, 1.7134, 1.708, 1.7188, 1.7135,
1.7088, 1.7124, 1.7202, 1.701, 1.694, 1.6882, 1.6947, 1.6794,
1.6801, 1.6727, 1.6697, 1.657, 1.6501, 1.6461, 1.662, 1.6682,
1.6661, 1.6579, 1.6705, 1.6735, 1.6708, 1.6671, 1.674, 1.6683,
1.6596, 1.6551, 1.6463, 1.6456, 1.6478, 1.6447, 1.6438, 1.6444,
1.6466, 1.6435, 1.6428, 1.6515, 1.6665, 1.6692, 1.6695, 1.6698,
1.6721, 1.6672, 1.65, 1.6443, 1.6354, 1.6319, 1.6299, 1.5986,
1.6009, 1.6006, 1.6035, 1.5972, 1.5758, 1.5849, 1.5825, 1.5915,
1.5946, 1.5965, 1.5826, 1.5685, 1.5745, 1.5741, 1.54, 1.5235,
1.5383, 1.5457, 1.5558, 1.5428, 1.5569, 1.5662, 1.576, 1.5955,
1.5849, 1.5865, 1.576, 1.5795, 1.5888, 1.5743, 1.5814, 1.5792,
1.581, 1.5822, 1.5811, 1.582, 1.5745, 1.5872, 1.5557, 1.5533,
1.5552, 1.5603, 1.5456, 1.5396, 1.5309, 1.5377, 1.5411, 1.5462,
1.5615, 1.5831, 1.5805, 1.5783, 1.577, 1.5602, 1.5537, 1.5426,
1.5486, 1.5562, 1.5419, 1.5435, 1.5493, 1.5374, 1.5467, 1.5376,
1.5528, 1.5508, 1.5449, 1.5488, 1.5443, 1.5551, 1.547, 1.5467,
1.5461, 1.5515, 1.5573, 1.5519, 1.5426, 1.5444, 1.5411, 1.55,
1.5466, 1.5421, 1.5423, 1.5201, 1.5036, 1.5003, 1.5009, 1.5016,
1.4954, 1.497, 1.492, 1.4953, 1.4972, 1.5094, 1.5077, 1.4994,
1.4945, 1.5146, 1.5235, 1.5172, 1.5097, 1.53, 1.5348, 1.528,
1.5453, 1.5454, 1.551, 1.548, 1.5539, 1.5594, 1.5536, 1.5537,
1.5582, 1.5589, 1.5643, 1.5612, 1.5703, 1.5722, 1.5778, 1.5741,
1.5725, 1.571, 1.5777, 1.5773, 1.5728, 1.5728, 1.5691, 1.5718,
1.5714, 1.5738, 1.572, 1.5703, 1.5805, 1.5783, 1.5768, 1.5737,
1.5507, 1.55, 1.5539, 1.559, 1.5535, 1.5575, 1.5539, 1.5341,
1.5354, 1.5295, 1.5327, 1.5282, 1.532, 1.53, 1.5303, 1.5087,
1.5095, 1.5127, 1.5183, 1.5147, 1.5119, 1.5099, 1.5148, 1.5228,
1.52, 1.525, 1.5309, 1.5355, 1.5312, 1.5291, 1.5241, 1.5184,
1.5137, 1.5084, 1.4914, 1.4887, 1.4729, 1.4796, 1.4679, 1.4727,
1.4749, 1.458, 1.4644, 1.465, 1.4601, 1.4329, 1.4028, 1.3926,
1.3855, 1.3904, 1.4145, 1.4053, 1.4136, 1.3926, 1.3882, 1.3865,
1.3973, 1.4125, 1.4061, 1.4015, 1.4128, 1.4087, 1.3997, 1.3773,
1.4107, 1.3685, 1.3723, 1.3854, 1.3835, 1.3763, 1.3811, 1.4055,
1.401, 1.4048, 1.3892, 1.39, 1.3715, 1.3677, 1.3542, 1.3704,
1.3766, 1.3699, 1.365, 1.3811, 1.3734, 1.3823, 1.3902, 1.3753,
1.3746, 1.3697, 1.3711, 1.3646, 1.3701, 1.3906, 1.4135, 1.4433,
1.4466, 1.4414, 1.4368, 1.4597, 1.4404, 1.4482, 1.4362, 1.439,
1.4043, 1.3829, 1.3886, 1.3899, 1.413, 1.4233, 1.406, 1.4074,
1.4188, 1.4074, 1.4198, 1.3973, 1.4029, 1.4044, 1.3974, 1.4106,
1.4007, 1.3991, 1.3924, 1.3921, 1.3845, 1.3877, 1.3942, 1.3846,
1.3884, 1.3891, 1.3841, 1.3782, 1.3817, 1.3833, 1.3816, 1.3935,
1.3988, 1.402, 1.4042, 1.3923, 1.391, 1.3963, 1.3883, 1.3887,
1.3767, 1.3865, 1.3837, 1.393, 1.3849, 1.3863, 1.3792, 1.3807,
1.3805, 1.3967, 1.3915, 1.3928, 1.4048, 1.4067, 1.4073, 1.423,
1.4334, 1.4341, 1.4749, 1.4756, 1.4771, 1.4702, 1.4723, 1.4852,
1.4791, 1.4803, 1.4786, 1.4615, 1.4714, 1.4758, 1.4695, 1.4633,
1.4647, 1.4631, 1.479, 1.475, 1.481, 1.4717, 1.4714, 1.4908,
1.4895, 1.4875, 1.4873, 1.4876, 1.4675, 1.44, 1.4175, 1.4278,
1.4413, 1.4268, 1.4212, 1.423, 1.4299, 1.4393, 1.4363, 1.4301,
1.427, 1.4119, 1.4176, 1.4249, 1.4223, 1.4291, 1.4195, 1.4168,
1.4235, 1.4141, 1.3979, 1.3851, 1.387, 1.3936, 1.4017, 1.4006,
1.4053, 1.4083, 1.4164, 1.4191, 1.4155, 1.4134, 1.4167, 1.4166,
1.4161, 1.4123, 1.4195, 1.4145, 1.4024, 1.4095, 1.4048, 1.4125,
1.4079, 1.4085, 1.4136, 1.4165, 1.4358, 1.4338, 1.4368, 1.4453,
1.4451, 1.4381, 1.4363, 1.4432, 1.4416, 1.448, 1.4442, 1.4485,
1.4499, 1.4418, 1.4426, 1.4318, 1.4355, 1.4434, 1.4402, 1.4402,
1.4333, 1.4313, 1.4319, 1.4313, 1.4347, 1.4403, 1.4565, 1.4375,
1.4403, 1.4432, 1.4383, 1.437, 1.4407, 1.4466, 1.4576, 1.4646,
1.4696, 1.479, 1.4755, 1.4773, 1.4803, 1.4763, 1.4912, 1.4859,
1.4899, 1.4879, 1.4933, 1.4872, 1.4693, 1.4718, 1.4773, 1.4762,
1.4774, 1.4725, 1.4782, 1.4692, 1.4713, 1.4617, 1.443, 1.4533,
1.4513, 1.4516, 1.4513, 1.4471, 1.4528, 1.4614, 1.4697, 1.4759,
1.4758, 1.4789, 1.475, 1.4789, 1.4836, 1.4823, 1.4805, 1.4718,
1.4709, 1.476, 1.4742, 1.4762, 1.4775, 1.4763, 1.4753, 1.4777,
1.4758, 1.4875, 1.4781, 1.4764, 1.4832, 1.4818, 1.4792, 1.4805,
1.4826, 1.4778, 1.4918, 1.4997, 1.5013, 1.5037, 1.5104, 1.5096,
1.5073, 1.5121, 1.5057, 1.5164, 1.518, 1.5219, 1.5295, 1.5288,
1.5213, 1.5336, 1.5336, 1.5328, 1.5273, 1.5266, 1.5213, 1.5183,
1.5168, 1.5249, 1.5336, 1.5368, 1.5341, 1.5368, 1.5262, 1.5347,
1.5415, 1.5387, 1.5405, 1.5414, 1.5414, 1.5483, 1.5453, 1.5323,
1.5275, 1.5265, 1.5318, 1.5293, 1.5301, 1.5345, 1.536, 1.5363,
1.5363, 1.5353, 1.5229, 1.5177, 1.5148, 1.5207, 1.5244, 1.5289,
1.5315, 1.5322, 1.5287, 1.5218, 1.5222, 1.5235, 1.527, 1.5252,
1.5235, 1.528, 1.5278, 1.5233, 1.5246, 1.5217, 1.5226, 1.5184,
1.4922, 1.4865, 1.4917, 1.4885, 1.4863, 1.4897, 1.4877, 1.4784,
1.4809, 1.4808, 1.4774, 1.4728, 1.4736, 1.4779, 1.4813, 1.4843,
1.4829, 1.4845, 1.4784, 1.4753, 1.4767, 1.4834, 1.4856, 1.4935,
1.4882, 1.4893, 1.4817, 1.4959, 1.4874, 1.4783, 1.4777, 1.476,
1.4802, 1.4778, 1.4869, 1.4844, 1.4823, 1.4848, 1.4885, 1.4918,
1.508, 1.5098, 1.5122, 1.5124, 1.5095, 1.5143, 1.5084, 1.514,
1.5149, 1.5134, 1.5132, 1.5102, 1.5217, 1.5243, 1.5253, 1.5258,
1.527, 1.5306, 1.5296, 1.5304, 1.5272, 1.5285, 1.5298, 1.5315,
1.5293, 1.5405, 1.5375, 1.5437, 1.543, 1.5362, 1.535, 1.5234,
1.5227, 1.5234, 1.5203, 1.5103, 1.5073, 1.5132, 1.5165, 1.5132,
1.5168, 1.5164, 1.5078, 1.5059, 1.499, 1.4991, 1.5068, 1.5093,
1.5083, 1.5017, 1.5028, 1.4977, 1.499, 1.5023, 1.5192, 1.5276,
1.5261, 1.5316, 1.5371, 1.5424, 1.5665, 1.5627, 1.5547, 1.5404,
1.5566, 1.5539, 1.5412, 1.5439, 1.5466, 1.5534, 1.5454, 1.5544,
1.5601, 1.5539, 1.5536, 1.5553, 1.5564, 1.5552, 1.5567, 1.5411,
1.5418, 1.5575, 1.5649, 1.5628, 1.5714, 1.5757, 1.5841, 1.5895,
1.594, 1.5895, 1.5925, 1.6113, 1.624, 1.6244, 1.6407, 1.639,
1.6268, 1.6376, 1.6511, 1.6436, 1.6394, 1.6377, 1.6429, 1.6415,
1.6484, 1.6501, 1.6722, 1.6565, 1.6721, 1.6832, 1.6871, 1.6862,
1.6995, 1.6914, 1.694, 1.6896, 1.6831, 1.6697, 1.6758, 1.689,
1.6897, 1.6882, 1.6934, 1.7094, 1.7113, 1.7185, 1.7189, 1.7032,
1.7055, 1.7014, 1.7012, 1.6993, 1.6901, 1.6782, 1.6815, 1.6867,
1.6834, 1.6882, 1.6907, 1.6922, 1.6775, 1.6762, 1.6684, 1.6697,
1.6703, 1.6683, 1.6758, 1.711, 1.711, 1.7197, 1.7169, 1.7241,
1.7233, 1.7339, 1.7286, 1.7257, 1.7162, 1.7023, 1.7112, 1.7124,
1.717, 1.7235, 1.7273, 1.7248, 1.7317, 1.7186, 1.7285, 1.732,
1.7231, 1.7194, 1.7075, 1.6934, 1.7004, 1.6972, 1.7006, 1.6983,
1.6941, 1.7072, 1.6949, 1.695, 1.6933, 1.6907, 1.6911, 1.7012,
1.6968, 1.6967, 1.7056, 1.7262, 1.7261, 1.7294, 1.7289, 1.7289,
1.7085, 1.7167, 1.7148, 1.724, 1.7378, 1.7295, 1.7338, 1.727,
1.7247, 1.7321, 1.7224, 1.724, 1.7235, 1.7286, 1.7363, 1.7426,
1.7401, 1.7511, 1.749, 1.7539, 1.7448, 1.7572, 1.7619, 1.7512,
1.7696, 1.7909, 1.8003, 1.7974, 1.7922, 1.7907, 1.7964, 1.8127,
1.8273, 1.8306, 1.8372, 1.8445, 1.8291, 1.8377, 1.8368, 1.8567,
1.8639, 1.879, 1.8745, 1.8733, 1.8531, 1.8544, 1.8624, 1.8316,
1.8461, 1.8183, 1.8223, 1.8364, 1.8568, 1.842, 1.8173, 1.8202,
1.8005, 1.8104, 1.7964, 1.8002, 1.8114, 1.8328, 1.8184, 1.8182,
1.8068, 1.8103, 1.8111, 1.8015, 1.7915, 1.7699, 1.7632, 1.7681,
1.7686, 1.7708, 1.7713, 1.793, 1.7954, 1.7717, 1.7678, 1.7647,
1.7589, 1.7662, 1.7756, 1.7671, 1.7625, 1.7622, 1.7539, 1.754,
1.739, 1.7499, 1.7506, 1.7564, 1.7511, 1.7443, 1.7687, 1.7714,
1.7851, 1.7815, 1.7747, 1.7783, 1.7599, 1.7248, 1.7416, 1.7231,
1.7211, 1.7362, 1.721, 1.7232, 1.7209, 1.7055, 1.7116, 1.7113,
1.7238, 1.7233, 1.7308, 1.7336, 1.7252, 1.7327, 1.7314, 1.748,
1.7353, 1.7437, 1.7593, 1.7642, 1.7639, 1.7774, 1.7746, 1.7684,
1.7704, 1.7828, 1.7884, 1.7946, 1.783, 1.7733, 1.7732, 1.7702,
1.7846, 1.7718, 1.7725, 1.7688, 1.7804, 1.7733, 1.7724, 1.7724,
1.7836, 1.7915, 1.7981, 1.8037, 1.8185, 1.8226, 1.8257, 1.8166,
1.8203, 1.8185, 1.8213, 1.8228, 1.8312, 1.8271, 1.8367, 1.8408,
1.8273, 1.8025, 1.7805, 1.7891, 1.7891, 1.8054, 1.8233, 1.828,
1.8223, 1.819, 1.8082, 1.7888, 1.7934, 1.8138, 1.8087, 1.8205,
1.8097, 1.8243, 1.8186, 1.8248, 1.8196, 1.8243, 1.8218, 1.7988,
1.8019, 1.8117, 1.8131, 1.8148, 1.8122, 1.8089, 1.8187, 1.829,
1.8322, 1.827, 1.8312, 1.8366, 1.8278, 1.8167, 1.8208, 1.8138,
1.826, 1.8273, 1.8343, 1.8282, 1.8295, 1.8266, 1.8233, 1.8279,
1.8446, 1.8494, 1.8497, 1.849, 1.8473, 1.8427, 1.8388, 1.8244,
1.8176, 1.8176, 1.823, 1.8037, 1.797, 1.8076, 1.8075, 1.8024,
1.7904, 1.7917, 1.7987, 1.7953, 1.7896, 1.7951, 1.7938, 1.7945,
1.7793, 1.7815, 1.7728, 1.7648, 1.7638, 1.7688, 1.7765, 1.7747,
1.7778, 1.7779, 1.7814, 1.7892, 1.7845, 1.77, 1.7575, 1.7574,
1.7672, 1.7721, 1.7816, 1.778, 1.7828, 1.7803, 1.7818, 1.7719,
1.7637, 1.7731, 1.7775, 1.7796, 1.7966, 1.8041, 1.8044, 1.8132,
1.798, 1.79, 1.7899, 1.7829, 1.7944, 1.8009, 1.8015, 1.7955,
1.8098, 1.8133, 1.805, 1.8185, 1.8221, 1.8185, 1.808, 1.8122,
1.8179, 1.8292, 1.8183, 1.8036, 1.7988, 1.803, 1.792, 1.7881,
1.7785, 1.7876, 1.7907, 1.7926, 1.7788, 1.7824, 1.7754, 1.7736,
1.7746, 1.7786, 1.7844, 1.7832, 1.7719, 1.7682, 1.7727, 1.7796,
1.7813, 1.7747, 1.7844, 1.7969, 1.7938, 1.7973, 1.8004, 1.7995,
1.8023, 1.7964, 1.7993, 1.8076, 1.8076, 1.7764, 1.7635, 1.7493,
1.7508, 1.7244, 1.7346, 1.725, 1.7291, 1.7242, 1.692, 1.6878,
1.7031, 1.687, 1.6918, 1.681, 1.6915, 1.6826, 1.6849, 1.6873,
1.6788, 1.6711, 1.6806, 1.678, 1.6714, 1.6502, 1.6366, 1.6348,
1.6389, 1.6134, 1.6084, 1.6339, 1.6493, 1.6374, 1.6445, 1.624,
1.6232, 1.6288, 1.6421, 1.6482, 1.639, 1.6353, 1.6449, 1.6583,
1.6572, 1.6505, 1.6562, 1.6507, 1.6588, 1.6693, 1.6581, 1.6623,
1.6875, 1.684, 1.6783, 1.6927, 1.6892, 1.6668, 1.6694, 1.6676,
1.68, 1.6887, 1.7044, 1.7045, 1.7047, 1.7036, 1.7084, 1.6908,
1.678, 1.673, 1.6741, 1.6755, 1.6764, 1.671, 1.6679, 1.6595,
1.6536, 1.6523, 1.6591, 1.6672, 1.6645, 1.6643, 1.6732, 1.6753,
1.6742, 1.6803, 1.679, 1.6722, 1.672, 1.6656, 1.6576, 1.6598,
1.6688, 1.6759, 1.693, 1.6957, 1.6945, 1.6712, 1.6716, 1.6875,
1.6865, 1.6852, 1.6904, 1.6885, 1.6887, 1.6916, 1.6899, 1.7003,
1.713, 1.7222, 1.7302, 1.7225, 1.7238, 1.7324, 1.7329, 1.7387,
1.7308, 1.7276, 1.7312, 1.7342, 1.7406, 1.7494, 1.7412, 1.7429,
1.7627, 1.7729, 1.7812, 1.7856, 1.7698, 1.7817, 1.7889, 1.7899,
1.7915, 1.8061, 1.8026, 1.7972, 1.7964, 1.7854, 1.8057, 1.7872,
1.7871, 1.793, 1.7768, 1.7803, 1.7902, 1.7936, 1.7948, 1.7909,
1.7989, 1.8221, 1.825, 1.8174, 1.8116, 1.809, 1.8131, 1.8242,
1.8175, 1.8117, 1.8053, 1.8149, 1.8031, 1.8126, 1.8091, 1.8232,
1.827, 1.8429, 1.8354, 1.8439, 1.8458, 1.8373, 1.8447, 1.8367,
1.8394, 1.8467, 1.8487, 1.8508, 1.8437, 1.8316, 1.8146, 1.813,
1.8105, 1.8235, 1.8324, 1.8404, 1.8279, 1.8322, 1.834, 1.8352,
1.8408, 1.8529, 1.8499, 1.8435, 1.8667, 1.8701, 1.8705, 1.8755,
1.869, 1.8882, 1.8892, 1.8981, 1.8995, 1.8759, 1.8752, 1.8646,
1.864, 1.8746, 1.8778, 1.8961, 1.8958, 1.8833, 1.8956, 1.8978,
1.8969, 1.8905, 1.8688, 1.8856, 1.892, 1.8966, 1.9088, 1.9138,
1.9118, 1.9082, 1.913, 1.9214, 1.9195, 1.9291, 1.9214, 1.9162,
1.9089, 1.9141, 1.9224, 1.8788, 1.8626, 1.8642, 1.8686, 1.8353,
1.8405, 1.8428, 1.8254, 1.8275, 1.8344, 1.8341, 1.8172, 1.8108,
1.8141, 1.8279, 1.8225, 1.8349, 1.8373, 1.8508, 1.8506, 1.8615,
1.8561, 1.8405, 1.8369, 1.8487, 1.8586, 1.8707, 1.8758, 1.8708,
1.8721, 1.8517, 1.8462, 1.8263, 1.8446, 1.849, 1.849, 1.8452,
1.8517, 1.8678, 1.8828, 1.8752, 1.8815, 1.8804, 1.88, 1.8788,
1.8671, 1.8622, 1.875, 1.8686, 1.8727, 1.8628, 1.8438, 1.8366,
1.8282, 1.828, 1.8311, 1.8268, 1.8223, 1.8404, 1.8383, 1.823,
1.8174, 1.8141, 1.797, 1.803, 1.8116, 1.8149, 1.8095, 1.8293,
1.8319, 1.8472, 1.8577, 1.8577, 1.8609, 1.8641, 1.8641, 1.8662,
1.8726, 1.8786, 1.886, 1.8799, 1.8744, 1.8796, 1.8954, 1.8977,
1.8975, 1.8819, 1.8958, 1.9003, 1.8938, 1.9002, 1.9182, 1.9184,
1.9333, 1.9433, 1.9424, 1.9406, 1.9491, 1.9532, 1.9126, 1.9124,
1.9088, 1.9217, 1.9244, 1.9334, 1.945, 1.9489, 1.9212, 1.9375,
1.9395, 1.9386, 1.9403, 1.9261, 1.9326, 1.9417, 1.9468, 1.9513,
1.9265, 1.8985, 1.8921, 1.8899, 1.9009, 1.9055, 1.8993, 1.904,
1.9042, 1.932, 1.9397, 1.9349, 1.9367, 1.9373, 1.9423, 1.9514,
1.9433, 1.9535, 1.9789, 1.996, 1.9991, 2.0104, 2.0119, 1.9873,
1.9963, 1.9994, 1.9828, 1.9759, 1.9848, 1.9843, 1.9957, 1.9886,
1.9883, 1.9836, 1.9854, 1.9815, 1.9422, 1.9485, 1.9636, 1.9983,
2.0228, 2.0315, 2.0203, 2.0241, 2.0336, 2.0347, 2.0428, 2.0434,
2.0184, 2.0279, 2.0231, 2.0314, 2.0185, 2.0097, 2.0197, 2.0174,
2.0137, 2.0342, 2.0156, 2.0131, 2.0152, 2.0223, 2.0517, 2.0467,
2.044, 2.0451, 2.0488, 2.0254, 2.0394, 2.0403, 2.038, 2.0313,
2.0419, 2.0469, 2.0456, 2.0482, 2.0605, 2.0795, 2.0818, 2.0818,
2.0815, 2.109, 2.1178, 2.1455, 2.1464, 2.1531, 2.1543, 2.1936,
2.1923, 2.1908, 2.1884, 2.1675, 2.1464, 2.1644, 2.1571, 2.1344,
2.1609, 2.1952, 2.1886, 2.1908, 2.1637, 2.1522, 2.1554, 2.1567,
2.1124, 2.1083, 2.0975, 2.109, 2.1007, 2.0716, 2.0708, 2.0397,
2.0435, 2.0514, 2.0568, 2.0505, 2.036, 2.0396, 2.0532, 2.0357,
2.0368, 2.0472, 2.0672, 2.0819, 2.0886, 2.0873, 2.0734, 2.0677,
2.0534, 2.0404, 2.0554, 2.0591, 2.0448, 2.0465, 2.0605, 2.053,
2.0568, 2.0711, 2.0915, 2.0869, 2.0923, 2.098, 2.1162, 2.1157,
2.0976, 2.0962, 2.078, 2.0758, 2.0825, 2.1097, 2.1108, 2.1132,
2.1403, 2.1627, 2.1526, 2.1532, 2.1738, 2.1789, 2.152, 2.1616,
2.1635, 2.1418, 2.1453, 2.1385, 2.1521, 2.1642, 2.1776, 2.188,
2.1665, 2.1614, 2.1751, 2.182, 2.1892, 2.1999, 2.1733, 2.1739,
2.2071, 2.2274, 2.2455, 2.2574, 2.2695, 2.2724, 2.2623, 2.2706,
2.2662, 2.2892, 2.2913, 2.3078, 2.2932, 2.2183, 2.2398, 2.2223,
2.2194, 2.2152, 2.2164, 2.2184, 2.2318, 2.2409, 2.2495, 2.249,
2.2507, 2.2528, 2.2381, 2.2678, 2.2757, 2.3048, 2.3014, 2.3075,
2.3295, 2.3352, 2.3397, 2.3363, 2.3603, 2.3594, 2.3209, 2.3147,
2.3075, 2.2806, 2.274, 2.2648, 2.2711, 2.2724, 2.2841, 2.2772,
2.2651, 2.274, 2.2794, 2.2758, 2.2894, 2.2938, 2.304, 2.313,
2.3194, 2.3226, 2.3289, 2.304, 2.2877, 2.2741, 2.2468, 2.2405,
2.1992, 2.2101, 2.2111, 2.1982, 2.2024, 2.2279, 2.2298, 2.2339,
2.204, 2.1793, 2.1835, 2.195, 2.1569, 2.1398, 2.118, 2.1057,
2.1018, 2.1128, 2.0833)
And even shorter:
v <- na.omit(v)
Try this:
new.v <- v[ !is.na( v ) ]
Either is.na or na.omit are good enough for this situation
x <- c(1,NA,2,NA, 3) # a vector with NA
x[!is.na(x)] # a vector without NA
[1] 1 2 3
as.numeric(na.omit(x))
[1] 1 2 3
Actually as.numeric applied to na.omit is not necessary as you can tell from Dirk's answer :)