r - 归一化函数没有给出预期的输出：
问题描述
我有一些关于价格的数据，我正在尝试对数据进行规范化。我首先尝试应用以下功能。
divs_normalised <- divs %>%
filter(date >= "1998-09-24") %>%
group_by(symbol) %>%
mutate(norm = 1*dividends/first(dividends))
ggplot(divs_normalised, aes(x = date)) +
geom_point(aes(y = norm, color = symbol, group = symbol), shape = 8)
剧情不对。分数应该从 0 开始，但其中一些开始得更高……我哪里出错了？我尝试了一些归一化函数，但没有运气。
如果需要，我可以提供额外的标准化功能。我的归一化函数正确吗？
数据：
divs <- structure(list(symbol = c("EBAY", "EBAY", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",
"MSFT", "MSFT", "MSFT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT",
"TGT", "TGT", "TGT", "TGT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT", "WMT",
"WMT", "WMT", "WMT", "WMT", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP",
"JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "JCP", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "ORCL", "ORCL",
"ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL",
"ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL",
"ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL",
"ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL",
"ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "ORCL", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM",
"IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "IBM", "ODP",
"ODP", "ODP", "ODP", "ODP", "ODP", "ODP", "ODP", "ODP", "ODP",
"ODP", "ODP", "ODP"), date = structure(c(17955, 18046, 12102,
12340, 12653, 12737, 12829, 12919, 13010, 13102, 13194, 13283,
13375, 13466, 13557, 13648, 13739, 13830, 13928, 14012, 14110,
14201, 14292, 14383, 14474, 14565, 14656, 14747, 14838, 14929,
15020, 15111, 15202, 15293, 15384, 15475, 15566, 15657, 15755,
15839, 15930, 16028, 16119, 16203, 16301, 16392, 16483, 16574,
16665, 16756, 16847, 16938, 17029, 17120, 17211, 17302, 17393,
17485, 17576, 17667, 17758, 17849, 17947, 18031, 4883, 4974,
5065, 5153, 5247, 5339, 5431, 5522, 5612, 5704, 5796, 5893, 5977,
6069, 6161, 6252, 6342, 6434, 6528, 6616, 6710, 6801, 6892, 6983,
7074, 7165, 7257, 7348, 7438, 7530, 7622, 7713, 7803, 7895, 7987,
8078, 8169, 8261, 8355, 8443, 8534, 8628, 8719, 8810, 8901, 8992,
9083, 9174, 9265, 9358, 9450, 9541, 9632, 9724, 9818, 9910, 9997,
10091, 10183, 10275, 10364, 10456, 10548, 10639, 10729, 10821,
10912, 11003, 11094, 11185, 11277, 11368, 11458, 11550, 11642,
11733, 11823, 11915, 12009, 12101, 12188, 12282, 12374, 12466,
12556, 12648, 12739, 12830, 12921, 13012, 13103, 13194, 13285,
13376, 13468, 13559, 13649, 13741, 13833, 13924, 14015, 14109,
14201, 14293, 14382, 14474, 14566, 14657, 14747, 14839, 14930,
15019, 15110, 15202, 15292, 15383, 15474, 15565, 15663, 15751,
15838, 15936, 16027, 16115, 16209, 16300, 16391, 16479, 16573,
16664, 16755, 16843, 16937, 17028, 17119, 17210, 17301, 17392,
17484, 17582, 17666, 17757, 17855, 17946, 18030, 1532, 1713,
1895, 1999, 2076, 2164, 2266, 2363, 2437, 2533, 2636, 2727, 2801,
2883, 2997, 3091, 3165, 3263, 3364, 3462, 3529, 3630, 3739, 3819,
3893, 4009, 4100, 4184, 4260, 4295, 4464, 4548, 4624, 4723, 4824,
4912, 4988, 5087, 5191, 5279, 5353, 5454, 5555, 5646, 5717, 5818,
5920, 6010, 6081, 6182, 6284, 6374, 6447, 6549, 6650, 6738, 6815,
6913, 7015, 7102, 7179, 7278, 7382, 7466, 7543, 7642, 7746, 7831,
7907, 8008, 8111, 8195, 8279, 8373, 8476, 8559, 8643, 8730, 8839,
8923, 9007, 9098, 9204, 9289, 9373, 9463, 9570, 9660, 9737, 9841,
9937, 10030, 10121, 10212, 10303, 10394, 10485, 10569, 10667,
10758, 10849, 10940, 11031, 11122, 11213, 11311, 11402, 11493,
11584, 11675, 11766, 11857, 11948, 12039, 12130, 12221, 12327,
12403, 12494, 12557, 12648, 12767, 12858, 12921, 13012, 13131,
13222, 13285, 13376, 13495, 13586, 13649, 13740, 13859, 13950,
14013, 14104, 14224, 14314, 14377, 14468, 14587, 14678, 14741,
14832, 14951, 15042, 15105, 15196, 15315, 15407, 15469, 15560,
15679, 15772, 15833, 15924, 16043, 16136, 16197, 16288, 16407,
16505, 16561, 16652, 16771, 16869, 16932, 17023, 17142, 17233,
17296, 17387, 17507, 17598, 17661, 17752, 17871, 17969, 18025,
1189, 4386, 4477, 4565, 4659, 4751, 4842, 4933, 5023, 5116, 5207,
5297, 5389, 5482, 5571, 5662, 5755, 5849, 5937, 6031, 6122, 6213,
6304, 6395, 6486, 6578, 6669, 6760, 6850, 6943, 7033, 7123, 7215,
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491, 582, 673, 763, 854, 945, 1042, 1133, 1219, 1315, 1407, 1497,
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dividends = c(0.14, 0.14, 0.08, 0.16, 0.08, 0.08, 0.08, 0.08,
0.08, 0.08, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11,
0.11, 0.11, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13,
0.16, 0.16, 0.16, 0.16, 0.2, 0.2, 0.2, 0.2, 0.23, 0.23, 0.23,
0.23, 0.28, 0.28, 0.28, 0.28, 0.31, 0.31, 0.31, 0.31, 0.36,
0.36, 0.36, 0.36, 0.39, 0.39, 0.39, 0.39, 0.42, 0.42, 0.42,
0.42, 0.46, 0.46, 0.46, 0.3, 0.15, 0.162, 0.162, 0.162, 0.16296,
0.18504, 0.18504, 0.18504, 0.18504, 0.20496, 0.21, 0.21,
0.21, 0.21, 0.23004, 0.23004, 0.23004, 0.23004, 0.255, 0.255,
0.255, 0.255, 0.27996, 0.27996, 0.27996, 0.27996, 0.33, 0.33,
0.33, 0.33, 0.36, 0.36, 0.36, 0.36, 0.38004, 0.38004, 0.38004,
0.38004, 0.39996, 0.39996, 0.39996, 0.39996, 0.42, 0.42,
0.42, 0.42, 0.42, 0.44004, 0.44004, 0.44004, 0.44004, 0.44004,
0.16, 0.16, 0.16, 0.16, 0.16, 0.18, 0.18, 0.09, 0.09, 0.09,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.055, 0.055, 0.055, 0.055,
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0.16, 0.17, 0.17, 0.17, 0.17, 0.25, 0.25, 0.25, 0.25, 0.3,
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0.2375, 0.2725, 0.2725, 0.2725, 0.2725, 0.3025, 0.3025, 0.3025,
0.3025, 0.365, 0.365, 0.365, 0.365, 0.3975, 0.3975, 0.3975,
0.3975, 0.47, 0.47, 0.47, 0.47, 0.48, 0.48, 0.48, 0.48, 0.49,
0.49, 0.49, 0.49, 0.5, 0.5, 0.5, 0.5, 0.51, 0.51, 0.51, 0.51,
0.52, 0.52, 0.52, 0.52, 0.53, 0.53, 0.27, 0.46, 0.5, 0.5,
0.5, 0.5, 0.54, 0.54, 0.54, 0.54, 0.59, 0.59, 0.59, 0.59,
0.59, 0.59, 0.59, 0.59, 0.62, 0.62, 0.62, 0.62, 0.74, 0.37,
0.74, 0.37, 0.5, 0.5, 0.5, 0.5, 0.56, 0.56, 0.56, 0.56, 0.1,
0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66, 0.66,
0.66, 0.66, 0.72, 0.36, 0.36, 0.36, 0.42, 0.42, 0.42, 0.42,
0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.52, 0.52, 0.52, 0.52,
0.52, 0.535, 0.535, 0.535, 0.535, 0.545, 0.545, 0.545, 0.545,
0.545, 0.545, 0.546, 0.545, 0.288, 0.157, 0.288, 0.288, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,
0.125, 0.125, 0.125, 0.18, 0.18, 0.18, 0.18, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.125, 0.125, 0.125, 0.125,
0.135, 0.135, 0.135, 0.135, 0.135, 0.25, 0.25, 0.25, 0.128,
0.128, 0.128, 0.128, 0.13, 0.13, 0.13, 0.13, 0.133, 0.133,
0.133, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.25, 0.25, 0.25,
0.25, 0.313, 0.313, 0.313, 0.313, 0.36, 0.36, 0.36, 0.36,
0.378, 0.378, 0.378, 0.378, 0.378, 0.378, 0.378, 0.378, 0.378,
0.378, 0.378, 0.378, 0.378, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06,
0.18, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.15, 0.15,
0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.19, 0.19, 0.19, 0.19,
0.19, 0.19, 0.19, 0.19, 0.24, 1.2, 1.2, 1.2, 1.3, 1.3, 1.3,
1.3, 1.35, 1.35, 1.35, 1.35, 1.4, 1.4, 1.12, 1.12, 1.28,
1.28, 1.5, 1.5, 1.5, 1.5, 1.75008, 1.75008, 1.75008, 1.75008,
2.25008, 2.25008, 2.5, 2.5, 2.5, 2.5, 2.88, 2.88, 2.88, 2.88,
3.44, 3.44, 0.86, 0.86, 0.86, 0.86, 0.86, 0.86, 0.86, 0.86,
0.86, 0.86, 0.86, 0.86, 0.86, 0.86, 0.86, 0.95, 0.95, 0.95,
0.95, 0.95, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1,
1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.21, 1.21,
1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21,
1.21, 1.21, 1.21, 0.54, 0.54, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.35, 0.35, 0.35, 0.35,
0.4, 0.2, 0.2, 0.2, 0.22, 0.22, 0.22, 0.22, 0.24, 0.12, 0.12,
0.12, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.15,
0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.16, 0.18, 0.18, 0.18,
0.18, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4,
0.4, 0.5, 0.5, 0.5, 0.5, 0.55, 0.55, 0.55, 0.55, 0.65, 0.65,
0.65, 0.65, 0.75, 0.75, 0.75, 0.75, 0.85, 0.85, 0.85, 0.85,
0.95, 0.95, 0.95, 0.95, 1.1, 1.1, 1.1, 1.1, 1.3, 1.3, 1.3,
1.3, 1.3, 1.4, 1.4, 1.4, 1.4, 1.4, 1.5, 1.5, 1.5, 1.5, 1.57,
1.57, 1.57, 1.57, 1.62, 0.025, 0.025, 0.025, 0.025, 0.025,
0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025)), row.names = c(NA,
-835L), class = c("tbl_df", "tbl", "data.frame"))
解决方案
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