R语言如何在应用scale()后保留列名？

46qrfjad 于 2023-01-03 发布在其他

关注(0)|答案(2)|浏览(166)

以粘贴的数据库为例，您可以看到以下代码如何将除干预组（分类变量）以外的所有内容传输到scale()，列名将更改为：variable_name[,1]，而我只需要variable_name。看起来这种行为是在缩放数据后使用view()函数和转换后的df。无论指定[，1]的原因是什么，我都希望删除符号
我用的密码是：

library(dplyr)

#1st

df <- df %>% 
  dplyr::mutate(across(.cols = c("ppara":"lrp1"), .fns = scale)) # where c("ppara":"lrp1") is the range of quantitative continouos columns to apply scale

# 2nd
df %>%  dplyr::mutate(across(.cols = c(3:52), .fns = scale, .names = "{.col}"))) 

#Entering the name of the df, seems to work and keep the names, but when I go to the view function 
> df
     id grup_int       ppara       ppard       pparg       nr1h3
1  50109018      LFD  2.88219852  0.21418973  1.29818239  0.40677902
2  50109019      LFD -0.09090266 -0.37583504 -0.17129420  0.24434046
3  50109025      LFD -0.51922065 -0.15165027  0.05554733 -0.42018092
4  50109026     NUTS          NA -0.12085566  0.01702707 -0.01326412
5  50109027      LFD          NA -0.14672313 -0.42809594 -0.82873851
6  50118001       OO          NA  0.68103605  0.25670869  0.49210029

但是，当我像图1那样查看（df）时，请查看 Dataframe

df：

df <- structure(list(id = c(50109018, 50109019, 50109025, 50109026, 
50109027, 50118001, 50202099, 50203004, 50203006, 50203008, 50203009, 
50203010, 50203011, 50203012, 50203013, 50203014, 50203015, 50203016, 
50203017, 50203019, 50203020, 50203022, 50203026, 50203027, 50203029, 
50203030, 50203031, 50203032, 50430001, 50508026, 50508027, 50521001, 
50521002, 50527001, 50601001, 50705001, 60901020, 60901021, 60901023, 
60901024, 60901026, 60901027, 60901028, 60901029, 60901030, 60901031, 
60901033, 60901034, 60901035, 60901036, 60901037, 60901038, 70107034, 
70111021, 70111022, 70111023, 70111024, 70201047, 70204055, 70204056, 
70211014, 70710002, 70713001, 70713002, 70802011, 70802012, 70802013, 
70802015, 71801001, 71801002, 71801003, 110104017, 110104019, 
110104023, 110104024, 110104027, 110104028, 110104029, 110104030, 
110110005, 110113001, 110113003, 110113005, 110113006, 110113007, 
110113008, 110606056, 110606061, 111201006, 111201007, 111201014, 
111201017, 111201019, 111201026, 111202007, 111202009, 111202015, 
120715011, 120715012, 120715019, 120715020, 120715021, 120715022, 
120715025, 120715026, 120715027, 120715029, 120715030, 120715032, 
120715033, 120715034, 120715035, 120715037, 130102008, 130102009, 
130102010, 130102012, 130102013, 130102014, 130104004, 130105044, 
130105045, 130106034, 130106037, 130106038, 130108008, 130108009, 
140101088, 140101091, 140101096, 140101097, 140101099, 140102087, 
140102088, 140102089, 140102090, 140102092, 140102095, 140103019, 
140103020, 140103023, 140103024, 140103026, 140103027, 140103028, 
140103029, 140103030, 140103033, 140103035, 140103036, 140103038
), grup_int = structure(c(3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 1L, 
3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 
1L, 3L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 
3L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 
2L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 
3L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 3L), levels = c("OO", 
"NUTS", "LFD"), label = "GENERAL: Grupo de intervención", class = "factor"), 
    ppara = c(3.761, 1.276, 0.918, NA, NA, NA, 1.437, NA, NA, 
    1.108, NA, 1.962, 1.816, 1.26, 0.498, 1.329, 1.447, NA, 1.848, 
    0.71, 0.909, NA, NA, 1.165, 1.079, 0.556, 1.197, NA, NA, 
    0.917, 0.37, 1.591, NA, 1.019, NA, 1.232, NA, NA, NA, 0.597, 
    NA, 1.136, 0.602, 0.724, 0.799, 0.827, 0.701, NA, NA, 1.973, 
    1.471, NA, NA, NA, NA, 0.845, 0.734, 0.987, NA, 0.389, 0.289, 
    1.86, 2.453, 0.895, 3.927, 1.893, 0.684, 0.782, 1.056, NA, 
    1.378, NA, 3.017, 4.415, NA, NA, 1.329, 1.086, 0.586, NA, 
    NA, NA, 1.093, 0.422, 1.278, 1.025, NA, 1.464, 0.872, 1.109, 
    1.342, NA, NA, 0.845, NA, 0.501, 0.765, NA, 2.709, 2.055, 
    2.078, NA, 1.664, 0.636, 1.358, 1.435, NA, 0.84, 1.348, 2.237, 
    2.627, 3.445, NA, NA, 0.736, 0.63, NA, NA, 0.599, 1.137, 
    2.161, NA, 0.731, 1.333, NA, NA, NA, 1.169, NA, 1.541, 1.046, 
    0.693, NA, 0.565, 1.172, NA, 0.801, 0.907, 3.903, 2.544, 
    NA, 1.658, 0.917, 1.667, NA, 2.54, NA, NA, NA, NA, NA), ppard = c(1.575, 
    1.096, 1.278, 1.303, 1.282, 1.954, 1.704, 1.518, 1.637, 0.951, 
    1.122, 1.025, 1.86, 0.904, 0.53, 1.37, 1.704, 0.751, 1.443, 
    0.78, 0.678, 1.856, 1.449, 1.08, 1.038, 0.727, 1.33, 0.96, 
    1.299, 0.851, 0.777, 1.465, NA, 1.121, NA, 1.304, 0.715, 
    1.549, 1.211, 0.764, 1.61, 1.373, 1.055, 1.054, 0.859, 0.987, 
    0.893, 1.187, 0.924, 0.989, 1.252, NA, 3.55, 1.074, 0.597, 
    0.811, 1.439, 1.006, 1.169, 0.699, 0.718, 4.058, 1.931, 1.045, 
    4.25, 1.931, 0.613, 1.159, 1.123, NA, 1.758, 1.517, 2.089, 
    5.654, 2.191, 3.724, 1.693, 0.964, 4.672, 1.276, 1.108, 1.363, 
    1.221, 0.344, 1.363, 1.397, NA, 1.813, 1.203, 1.093, 1.307, 
    0.623, NA, 1.244, NA, 0.822, 0.997, 1.595, 1.73, 2.223, 1.841, 
    1.58, 2.088, 1.032, 1.399, 1.788, 1.323, 1.157, 2.091, 1.868, 
    1.893, 1.98, NA, 0.429, 0.779, 0.714, 0.928, 0.971, 1.201, 
    0.539, 1.222, 1.186, 0.522, 1.037, 0.494, 0.671, 1.52, 0.946, 
    1.019, 1.54, 1.188, 1.133, 1.424, 0.894, 0.943, 4.713, 1.041, 
    1.476, 1.873, 1.515, 1.31, 1.118, 1.562, 1.464, NA, 1.069, 
    NA, 1.442, 1.798, NA, 2.134), pparg = c(2.201, 1.171, 1.33, 
    1.303, 0.991, 1.471, 1.795, 1.221, 2.188, 1.158, 1.532, 0.85, 
    2.393, 0.791, 0.773, 2.011, 1.445, 1.066, 0.392, 0.392, 1.361, 
    1.025, 1.471, 1.063, 0.89, 0.797, 0.981, 0.5, NA, 0.5, 0.682, 
    2.783, NA, 1.322, NA, 1.044, 0.893, 2.676, 0.714, 0.7, 1.662, 
    1.259, 1.135, 0.838, 1.075, 1.45, 1.311, 1.044, 1.223, 1.329, 
    0.932, NA, 3.831, 1.571, 0.879, 0.6, 0.82, 0.862, 1.545, 
    0.451, 0.663, 0.818, 1.539, 0.842, 3.584, 1.501, 0.453, 0.738, 
    1.091, NA, 1.12, 1.182, 2.528, 4.489, 2.161, NA, 1.006, 0.702, 
    3.117, 1.302, 0.857, 1.069, 0.96, 0.354, 0.902, 0.638, NA, 
    1.51, 1.011, 0.421, 1.445, 0.634, NA, 1.197, NA, 1.122, 1.168, 
    1.388, 0.789, 1.51, 1.334, 0.813, 1.718, 1.413, 1.242, 0.903, 
    1.134, 1.366, 1.451, 1.635, 3.1, 2.107, NA, 1.033, 0.719, 
    0.961, 1.203, 1.19, 1.094, 0.846, 1.724, 1.746, 0.169, 0.651, 
    0.4, 0.761, 1.662, 1.324, 0.645, 2.503, 0.555, 1.298, 1.852, 
    2.334, 1.608, 2.606, 1.2, 1.631, 1.262, 1.439, 0.689, 0.856, 
    0.93, 1.167, NA, 0.848, NA, 0.909, 2.218, NA, 2.414), nr1h3 = c(1.55, 
    1.451, 1.046, 1.294, 0.797, 1.602, 1.839, 1.406, 0.87, 1.436, 
    1.318, 1.028, 1.462, 1.919, 0.786, 1.597, 0.932, 0.936, 0.802, 
    1.184, 0.956, 1.712, 0.932, 1.023, 1.001, 1.002, 1.682, 0.735, 
    1.267, 1.052, 0.824, 1.334, 0.026, 1.345, NA, 1.468, 1.317, 
    1.581, 1.809, 0.845, 1.347, 1.769, 1.206, 1.584, 1.19, 1.016, 
    1.138, 1.066, 1.097, 1.137, 2.099, NA, NA, 0.999, 0.727, 
    0.689, 0.678, 0.825, 1.47, 0.612, 0.798, 1.093, 2.946, 0.999, 
    NA, 1.849, NA, 1.123, 1.617, NA, 1.446, 1.449, 1.716, NA, 
    3.037, 2.588, 1.344, 2.2, 4.011, NA, NA, NA, 1.176, 0.268, 
    1.26, 1.366, NA, 1.914, NA, NA, 1.041, 0.536, NA, 2.36, NA, 
    0.881, 1.357, 1.44, 1.713, 0.967, 1.728, 1.145, 1.854, 0.881, 
    1.77, 1.597, 1.181, 0.924, 1.38, 1.754, 2.612, 1.751, NA, 
    0.512, NA, 0.97, 1.604, 0.761, 0.738, 0.711, 1.132, 1.285, 
    0.451, 0.497, 0.538, 0.779, 0.941, 0.881, 0.763, 1.349, 1.693, 
    0.91, 1.128, 0.734, 0.833, 3.879, 0.943, 1.457, 1.879, 1.021, 
    1.016, 1.316, 1.012, 1.259, NA, 1.031, NA, 1.176, 1.588, 
    NA, 2.899), nr1h2 = c(0.861, 1.744, 0.824, 0.665, 0.913, 
    1.355, 1.927, 1.568, 1.18, 0.699, 0.952, 1.397, 2.622, 1.389, 
    0.874, 0.835, 1.304, 0.733, 0.953, 0.803, 0.921, 1.867, 1.56, 
    0.89, 0.907, 0.518, 1.162, 1.178, 2.069, 0.929, 0.994, 1.16, 
    0.044, 1.167, NA, 1.383, 1.089, 1.415, 1.571, 0.777, 1.754, 
    2.031, 0.885, 1.114, 1.388, 1.485, 0.941, 1.146, 0.831, 1.005, 
    0.957, NA, 3.479, 0.736, 0.613, 0.871, 0.984, 0.851, 1.376, 
    0.505, 0.632, 1.397, 2.122, 0.927, 8.337, 1.616, 1.063, 1.407, 
    1.04, NA, 1.993, 1.859, 1.679, 4.436, 1.825, 6.177, 1.51, 
    1.161, 6.476, 1.734, 0.901, 0.82, 1.325, 0.376, 0.89, 1.25, 
    NA, 1.447, 1.019, 1.333, 0.809, 0.545, NA, 1.036, NA, 0.887, 
    0.944, 1.192, 1.848, 1.974, 1.281, 1.992, 1.909, 0.774, 1.436, 
    1.656, 1.508, 1.796, 1.754, 2.372, 1.617, 1.851, NA, 0.575, 
    0.624, 0.683, 0.824, 0.87, 1.099, 0.621, 1.278, 1.486, 0.858, 
    0.715, 0.481, 0.636, 0.872, 0.953, 0.67, 1.356, 1.601, 0.844, 
    1.303, 0.486, 0.808, 5.391, 0.994, 1.037, 1.677, 1.6, 0.718, 
    0.846, 2.001, 1.246, NA, 1.159, NA, 1.485, 1.574, NA, 2.04
    ), rxra = c(1.402, 1.28, 1.076, 1.105, 1.4, 1.327, 1.722, 
    1.365, 1.151, 0.785, 1.047, 1.458, 1.578, 0.952, 0.982, 1.182, 
    1.156, 1.045, 0.835, 0.76, 0.772, 1.739, 1.392, 1.907, 0.95, 
    0.938, 1.166, 1.047, NA, 1.738, 1.378, 1.382, NA, 1.296, 
    NA, 0.89, 1.009, 1.267, 1.101, 1.29, 0.94, 1.41, 0.731, 1.464, 
    1.214, 0.964, 0.857, 0.876, 0.663, 0.83, 0.861, NA, 3.695, 
    0.735, 0.59, 0.675, 1.472, 1.343, 1.282, 0.73, 0.821, 2.176, 
    1.872, 0.954, 8.058, 1.496, 1.141, 1.615, 1.134, NA, 1.435, 
    1.847, 1.888, 5.043, 2.009, 2.9, 1.077, 1.204, 3.304, 1.454, 
    0.779, 1.442, 1.442, 0.444, 0.639, 1.312, NA, 1.267, 1.349, 
    1.472, 0.909, 0.973, NA, 1.519, NA, 0.885, 0.72, 1.428, 1.543, 
    1.539, 1.9, 2.659, 3.304, 1.217, 1.241, 1.875, 1.541, 1.449, 
    2.359, 2.131, 1.58, 2.217, NA, 0.689, 0.927, 1.052, 1.1, 
    0.75, 0.694, 0.463, 1.109, 1.671, 0.929, 0.768, 0.641, 1.082, 
    1.198, 1.434, 1.109, 1.303, 1.135, 0.986, 1.549, 1.061, 1.197, 
    4.063, 1.309, 1.345, 1.357, 1.996, 0.66, 1.188, 1.314, 1.458, 
    NA, 1.333, NA, 1.497, 1.772, NA, 1.609), rxrb = c(0.987, 
    0.938, 1.882, 1.506, 2.067, 1.526, 1.223, 1.192, 1.054, 0.854, 
    1.192, 2.21, 1.78, 0.895, 0.255, 0.78, 0.633, 0.924, 0.868, 
    0.702, 0.721, 1.814, 1.572, 1.714, 1.337, 0.884, 1.347, 1.239, 
    1.737, 2.171, 1.062, 1.48, 0.024, 0.645, NA, 1.207, 1.153, 
    0.922, 1.574, 0.887, 0.636, 1.64, 0.777, 1.241, 1.184, 0.813, 
    0.372, 1.036, 1.146, 0.842, 1.166, NA, 4.097, 1.564, 0.689, 
    1.183, 1.281, 0.761, 1.563, 0.818, 0.873, 2.011, 1.277, 1.357, 
    4.742, 1.925, 1.128, 1.26, 1.239, NA, 1.461, 1.723, 1.514, 
    6.87, 1.437, 0.09, 1.99, 1.385, 0.069, 1.341, 2.724, 1.618, 
    1.252, 0.449, 2.379, 1.635, NA, 1.555, 1.71, 0.952, 1.409, 
    4.682, NA, 1.596, NA, 1.265, 0.97, 1.461, 2.087, 1.55, 1.762, 
    1.461, 1.574, 2.781, 1.1, 1.741, 1.257, 1.468, 2.02, 1.91, 
    2.047, 1.462, NA, 0.6, 0.75, 1.177, 1.023, 0.648, 1.029, 
    0.802, 0.622, 0.906, 0.632, 1.259, 1.054, 1.213, 1.135, 1.253, 
    1.481, 1.517, 1.918, 2.099, 1.541, 1.003, 0.733, 0.122, 1.748, 
    1.264, 1.871, 2.006, 1.552, 1.448, 1.116, 1.379, NA, 1.387, 
    NA, 1.436, 2.086, NA, 1.173), cyp27a1 = c(1.058, 1.079, 1.152, 
    0.743, 1.803, 1.157, 2.25, 1.193, 1.448, 0.649, 1.718, 0.972, 
    1.393, 0.908, 1.3, 1.389, 1.16, 0.666, 0.799, 0.665, 0.991, 
    1.535, 1.257, 1, 1.8, 0.882, 1.087, 1.298, NA, 1.37, 0.765, 
    1.807, NA, 1.104, NA, 0.967, 1.009, 1.03, 1.139, 0.666, 1.462, 
    1.532, 0.767, 1.24, 0.997, 1.005, 0.736, 1.122, 0.868, 0.878, 
    1.129, NA, NA, 1.011, 0.425, 0.615, 0.919, 0.848, 1.178, 
    0.66, 0.714, 6.647, 2.254, 0.804, NA, 1.831, 1.008, 1.421, 
    1.215, NA, 1.92, 1.688, 1.734, NA, 1.571, 3.858, 1.146, 1.128, 
    4.592, 1.865, 1.419, 1.056, 1.201, 0.324, 1.159, 1.099, NA, 
    1.693, 1.503, 1.175, 1.596, 0.836, NA, 1.532, NA, 1.097, 
    0.913, 1.837, 1.334, 2.072, 2.149, 2.595, 2.654, 1.237, 1.28, 
    2.118, 1.153, 1.616, 2.185, 2.29, 3.089, 1.429, NA, 0.981, 
    0.546, 0.997, 0.807, 0.666, 1.393, 0.574, 0.665, 2.131, 0.348, 
    1.269, 0.271, 0.674, 0.81, 0.908, 0.877, 1.144, 1.469, 1, 
    1.309, 0.841, 0.785, 5.332, 1.502, 1.248, 1.705, 2.165, 1.046, 
    1.227, 1.444, 1.114, NA, 1.222, NA, 1.49, 1.368, NA, 3.854
    )), row.names = c(NA, -151L), class = "data.frame")

r

来源：https://stackoverflow.com/questions/74980587/how-to-preserve-column-names-after-applying-scale

2条答案

按热度按时间

pdtvr36n1#

问题是scale返回的是matrix，而不是向量，也就是说，当应用于向量时，结果是一个单列矩阵，这就是[,1]所告诉你的。
这可以在调用str()时看到：
注意：我将示例数据简化为前四列。

library(dplyr)

df <- df |> select(1:4)

df %>% 
  dplyr::mutate(across(.cols = c(ppara:ppard), .fns = scale)) |> 
  str()
#> 'data.frame':    151 obs. of  4 variables:
#>  $ id      : num  50109018 50109019 50109025 50109026 50109027 ...
#>  $ grup_int: Factor w/ 3 levels "OO","NUTS","LFD": 3 3 3 2 3 1 1 3 3 3 ...
#>   ..- attr(*, "label")= chr "GENERAL: Grupo de intervención"
#>  $ ppara   : num [1:151, 1] 2.8822 -0.0909 -0.5192 NA NA ...
#>   ..- attr(*, "scaled:center")= num 1.35
#>   ..- attr(*, "scaled:scale")= num 0.836
#>  $ ppard   : num [1:151, 1] 0.214 -0.376 -0.152 -0.121 -0.147 ...
#>   ..- attr(*, "scaled:center")= num 1.4
#>   ..- attr(*, "scaled:scale")= num 0.812

从输出中我们可以看到，缩放后的列是维数为151 x 1的数值矩阵。
为了避免这种情况，您必须通过.[,1]或使用as.numeric拾取列来显式转换为向量：

df %>% 
  dplyr::mutate(across(.cols = c(ppara:ppard), .fns = ~ scale(.x)[,1])) |> 
  str()
#> 'data.frame':    151 obs. of  4 variables:
#>  $ id      : num  50109018 50109019 50109025 50109026 50109027 ...
#>  $ grup_int: Factor w/ 3 levels "OO","NUTS","LFD": 3 3 3 2 3 1 1 3 3 3 ...
#>   ..- attr(*, "label")= chr "GENERAL: Grupo de intervención"
#>  $ ppara   : num  2.8822 -0.0909 -0.5192 NA NA ...
#>  $ ppard   : num  0.214 -0.376 -0.152 -0.121 -0.147 ...

df %>% 
  dplyr::mutate(across(.cols = c(ppara:ppard), .fns = ~ as.numeric(scale(.x)))) |> 
  str()
#> 'data.frame':    151 obs. of  4 variables:
#>  $ id      : num  50109018 50109019 50109025 50109026 50109027 ...
#>  $ grup_int: Factor w/ 3 levels "OO","NUTS","LFD": 3 3 3 2 3 1 1 3 3 3 ...
#>   ..- attr(*, "label")= chr "GENERAL: Grupo de intervención"
#>  $ ppara   : num  2.8822 -0.0909 -0.5192 NA NA ...
#>  $ ppard   : num  0.214 -0.376 -0.152 -0.121 -0.147 ...

赞(0）回复(0）举报 2023-01-03

wlsrxk512#

这些都是@stefan说的，因此还有另一种方法：

我们可以重写scale()函数：

library(dplyr)

my_scale <- function(x){
  require(caret)
  var <-  preProcess(data.frame(x),c("center", "scale"))
  predict(var, data.frame(x))$x
}

df1 <- df %>%
  dplyr::mutate(across(.cols = c("ppara":"cyp27a1"), .fns = my_scale))

View(df1)

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R语言如何在应用scale()后保留列名？

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R语言 如何在应用scale()后保留列名？

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R语言如何在应用scale()后保留列名？