改变多个/连续的列（使用 dplyr 或 base R）
Mutate multiple / consecutive columns (with dplyr or base R)
我正在尝试创建表示重复测量的变量“波”。具体来说，我正在尝试创建代表变量 1 - 10、11 - 20 ... 91-100 的平均值的连续变量。请注意，“...”表示第 3 波到第 9 波的变量，因为避免输入这些是我的目标！
这是一个示例数据框，df，有 10 行和 100 列：
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
dim(df)
> 10 100
我使用了 dplyr::mutate，它在输入所有变量后就可以工作，但是很耗时而且容易出错。如果不诉诸手动输入列的名称，我无法找到这样做的方法，正如我在下面开始做的那样（请注意，“...”代表第 3 波到第 9 波）：
df <- df %>%
mutate(wave_1 = (X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10) / 10,
wave_2 = (X11 + X12 + X13 + X14 + X15 + X16 + X17 + X18 + X19 + X20) / 10,
...
wave_10 = (X91 + X92 + X93 + X94 + X95 + X96 + X97 + X98 + X99 + X100) / 10)
你能 mutate 多个/连续的列 'dplyr' 吗？也欢迎使用其他方法。
这是使用包 zoo 的一种方法：
library(zoo)
t(rollapply(t(df), width = 10, by = 10, function(x) sum(x)/10))
这是使用基数 R 的一种方法：
splits <- 1:100
dim(splits) <- c(10, 10)
splits <- split(splits, col(splits))
results <- do.call("cbind", lapply(splits, function(x) data.frame(rowSums(df[,x] / 10))))
names(results) <- paste0("wave_", 1:10)
results
另一种非常简洁的基数 R 方法（由 G.Grothendieck 提供）：
t(apply(df, 1, tapply, gl(10, 10), mean))
这是 dplyr 和 tidyr 的解决方案：
library(dplyr)
library(tidyr)
df$row <- 1:nrow(df)
df2 <- df %>% gather(column, value, -row)
df2$column <- cut(as.numeric(gsub("X", "", df2$column)),breaks = c(0:10*10))
df2 <- df2 %>% group_by(row, column) %>% summarise(value = sum(value)/10)
df2 %>% spread(column, value) %>% select(-row)
另一种 dplyr 解决方案，它更接近 OP 指示的语法并且不需要重铸数据帧。
4 波计算基本相同，但略有不同，但 向量化（即 rowSums 和 rowMeans）方式：
df <- df %>%
mutate(wave_1 = rowSums(select(., num_range("X", 1:10)))/10,
wave_2 = rowSums(select(., c(11:20)))/10,
wave_3 = rowMeans(select(., X21:X30)),
wave_4 = rowMeans(.[, 31:40]))
编辑：. 可用作当前数据框 df 的占位符（代码已相应更改）。还添加了 wave_4 以证明它可以像数据框一样使用。
如果操作函数不是向量化的（也就是说，它不能用于整个数据帧，例如rowSums），也可以使用rowwise do 函数使用 非矢量化 函数（例如 myfun）
myfun <- function (x) {
sum(x)/10
}
tmp=df %>%
rowwise() %>%
do(data.frame(., wave_1 = myfun(unlist(.)[1:10]))) %>%
do(data.frame(., wave_2 = myfun(unlist(.)[11:20])))
注意：. 更改似乎改变了它的意思，指的是 mutate 的整个数据帧，但仅指 do.
的当前行
使用 dplyr 的另一种方法（IMO 推荐的方法）是在汇总每个波的值之前首先将数据重塑或融化为 tidy data format。
详细来说，这个过程将涉及：
- 将您的数据重塑为长格式 (
tidyr::gather)
- 识别每个变量属于哪些"wave"
- 汇总每个波的值
- 将您的数据重新整形为宽格式 (
tidyr::spread)
在您的示例中，这将如下所示：
library(tidyverse)
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
dim(df)
df %>%
dplyr::mutate(id = dplyr::row_number()) %>%
# reshape to "tidy data" or long format
tidyr::gather(varname, value, -id) %>%
# identify which variables belong to which "wave"
dplyr::mutate(varnum = as.integer(stringr::str_extract(varname, pattern = '\d+')),
wave = floor((varnum-1)/10)+1) %>%
# summarize your value for each wave
dplyr::group_by(id, wave) %>%
dplyr::summarise(avg = sum(value)/n()) %>%
# reshape back to "wide" format
tidyr::spread(wave, avg, sep='_') %>%
dplyr::ungroup()
输出如下：
# A tibble: 10 x 11
id wave_1 wave_2 wave_3 wave_4 wave_5 wave_6 wave_7 wave_8 wave_9 wave_10
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 6.24 4.49 5.85 5.43 5.98 6.04 4.83 6.92 5.43 5.52
2 2 5.16 6.82 5.76 6.66 6.21 5.41 4.58 5.06 5.81 6.93
3 3 7.23 6.28 5.40 5.70 5.13 6.27 5.55 5.84 6.74 5.94
4 4 5.27 4.79 4.39 6.85 5.31 6.01 6.15 3.31 5.73 5.63
5 5 6.48 5.16 5.20 4.71 5.87 4.44 6.40 5.00 5.90 3.78
6 6 4.18 4.64 5.49 5.47 5.75 6.35 4.34 5.66 5.34 6.57
7 7 4.97 4.09 6.17 5.78 5.87 6.47 4.96 4.39 5.99 5.35
8 8 5.50 7.21 5.43 5.15 4.56 5.00 4.86 5.72 6.41 5.65
9 9 5.27 5.71 5.23 5.44 5.12 5.40 5.38 6.05 5.41 5.30
10 10 5.95 4.58 6.52 5.46 7.63 5.56 5.82 7.03 5.68 5.38
这可以连接回您的原始数据以匹配您给出的示例（使用 mutate），如下所示：
df %>%
dplyr::mutate(id = dplyr::row_number()) %>%
tidyr::gather(varname, value, -id) %>%
dplyr::mutate(varnum = as.integer(stringr::str_extract(varname, pattern = '\d+')),
wave = floor((varnum-1)/10)+1) %>%
dplyr::group_by(id, wave) %>%
dplyr::summarise(avg = sum(value)/n()) %>%
tidyr::spread(wave, avg, sep='_') %>%
dplyr::ungroup() %>%
dplyr::right_join(df %>% # <-- join back to original data
dplyr::mutate(id = dplyr::row_number()),
by = 'id')
这种方法的一个好处是您可以检查数据以确认您正确地将变量分配给 "wave"s。
df %>%
dplyr::mutate(id = dplyr::row_number()) %>%
tidyr::gather(varname, value, -id) %>%
dplyr::mutate(varnum = as.integer(stringr::str_extract(varname, pattern = '\d+')),
wave = floor((varnum-1)/10)+1) %>%
dplyr::distinct(varname, varnum, wave) %>%
head()
产生：
varname varnum wave
1 X1 1 1
2 X2 2 1
3 X3 3 1
4 X4 4 1
5 X5 5 1
6 X6 6 1
另一种方法。
set.seed(123)
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
library(tidyverse)
imap_dfc(split.default(df, (((as.numeric(gsub('X', '', names(df)))) -1) %/% 10) +1),
~ .x %>% mutate(!!paste0('wave_', .y) := rowMeans(.)))
#> X1 X2 X3 X4 X5 X6 X7 X8
#> 1 3.588198 9.611500 9.005854 9.667218 2.285200 1.412481 6.986037 7.790276
#> 2 8.094746 5.080007 7.235231 9.120691 4.730917 4.979801 1.853566 6.662990
#> 3 4.680792 7.098136 6.764561 7.216348 4.723519 8.190324 4.455727 7.391642
#> 4 8.947157 6.153701 9.948428 8.159207 4.319609 2.097093 3.469453 1.005623
#> 5 9.464206 1.926322 6.901352 1.221523 2.372003 6.048532 8.331760 5.277849
#> 6 1.410008 9.098425 7.376774 5.300164 2.249255 2.858783 5.036647 2.981070
#> 7 5.752949 3.214790 5.896594 7.826136 3.097307 2.147785 8.290579 4.418349
#> 8 9.031771 1.378536 6.347278 2.947671 5.193662 7.779771 8.311506 6.514939
#> 9 5.962915 3.951286 3.602438 3.863629 3.393754 9.055408 8.149081 4.166181
#> 10 5.109533 9.590533 2.324023 3.084632 8.720449 4.370165 4.958485 2.000219
#> X9 X10 wave_1 X11 X12 X13 X14 X15
#> 1 3.192575 2.176261 5.571560 6.399901 9.417698 6.831041 6.573308 3.805320
#> 2 7.012500 6.877917 6.164837 3.995412 3.711060 3.878386 9.022547 4.685275
#> 3 4.758821 4.091648 5.937152 5.397517 1.546485 3.769480 7.056992 1.094204
#> 4 8.093763 6.910823 5.910486 9.590264 9.529542 2.977909 7.633700 2.654646
#> 5 1.925782 3.883359 4.735269 5.346122 7.485366 4.325400 5.690222 8.584564
#> 6 4.914035 2.689220 4.391438 9.013152 2.280649 9.857973 6.938546 3.080456
#> 7 9.864613 8.040649 5.854975 9.229944 5.943562 2.387821 8.396249 3.151900
#> 8 9.037460 1.842355 5.838495 6.478615 9.586821 1.819396 8.076534 1.690220
#> 9 8.978222 5.201011 5.632393 4.696208 6.269350 2.277162 9.818397 3.211513
#> 10 2.575474 5.603549 4.833706 2.323852 4.640593 7.210064 4.954884 7.589217
#> X16 X17 X18 X19 X20 wave_2 X21 X22
#> 1 8.627078 5.520696 6.669757 8.557910 3.467500 6.587021 3.148534 5.338383
#> 2 5.477745 4.185141 2.654456 3.812033 3.893345 4.531540 9.661230 3.276684
#> 3 4.491181 6.849866 8.772797 7.374613 9.870768 5.622390 6.412292 2.946293
#> 4 3.218041 4.372426 7.719112 3.385160 6.579940 5.766074 5.635268 7.069387
#> 5 1.999868 4.199008 7.014562 6.349089 9.435827 6.043003 4.623160 1.428973
#> 6 4.509950 5.803192 6.562161 5.331608 5.198794 5.857648 8.922219 7.307678
#> 7 6.147418 7.663009 4.350143 3.385295 4.661493 5.531683 4.276827 4.166998
#> 8 2.952035 2.989926 5.768521 6.081314 6.933073 5.237646 3.594154 4.680496
#> 9 5.002912 4.714715 8.872141 9.218694 2.371120 5.645221 2.535807 8.388562
#> 10 2.961916 3.391180 6.235751 9.116870 6.155804 5.458013 2.549546 9.269716
#> X23 X24 X25 X26 X27 X28 X29 X30
#> 1 3.542755 6.565161 5.921435 3.314951 9.331372 8.839391 7.827339 3.679676
#> 2 9.649943 4.855794 6.960859 2.996091 8.937798 1.056707 2.233955 1.906494
#> 3 7.555550 5.878723 2.545286 6.337411 7.067682 1.648514 4.569261 1.647137
#> 4 7.177376 1.526306 6.697498 3.407693 9.551503 2.477901 3.024868 8.923965
#> 5 1.475595 3.347712 3.806828 5.779634 5.648004 7.933007 1.521627 7.788227
#> 6 4.556981 4.574368 7.520989 8.067625 6.188671 7.616659 4.563034 8.349453
#> 7 5.300608 2.779703 4.590458 2.512547 4.026981 9.746881 1.584355 9.839263
#> 8 6.042279 8.487348 9.724208 4.639593 4.125922 5.198251 3.032978 1.932397
#> 9 7.284354 2.375985 9.706585 5.244186 1.180219 1.669461 1.491662 1.891376
#> 10 9.241152 8.230767 7.540323 8.812961 5.525317 6.839363 7.032538 8.189485
#> wave_3 X31 X32 X33 X34 X35 X36 X37
#> 1 5.750900 8.061177 4.506284 8.683870 6.555117 4.732618 2.228862 2.294353
#> 2 5.153556 1.084869 5.181993 3.663059 3.576568 2.074643 9.707225 2.735344
#> 3 4.660815 8.011593 7.419511 2.323435 7.640177 5.734267 5.635646 9.070648
#> 4 5.549177 7.564516 1.497717 7.335929 8.506489 3.025660 2.467633 3.773076
#> 5 4.335277 6.671187 4.193048 1.934260 3.828437 5.377706 6.597121 4.269705
#> 6 6.766768 5.328197 8.225310 1.303550 5.433099 4.331933 9.873587 8.055518
#> 7 4.882462 2.409732 8.521380 9.994641 7.276364 9.850152 7.018944 2.740408
#> 8 5.145763 1.073940 3.139745 1.313873 6.773161 4.494872 4.770243 1.159892
#> 9 4.176820 5.072126 4.185875 4.045522 6.795306 3.063204 3.910105 4.659471
#> 10 7.323117 5.430640 8.711969 9.235574 9.800681 6.609678 8.517298 5.348509
#> X38 X39 X40 wave_4 X41 X42 X43 X44
#> 1 4.796605 6.264101 9.793891 5.791688 9.874489 3.852543 5.927135 9.577911
#> 2 4.085279 7.847413 3.666720 4.362311 2.233607 8.484117 6.798162 6.072802
#> 3 8.798350 4.354385 7.533847 6.652186 9.147786 2.936549 6.366372 3.965927
#> 4 5.095972 7.922745 8.071191 5.526093 6.186717 5.481540 3.897436 9.969555
#> 5 5.803884 5.839095 1.948760 4.646320 4.559040 3.484447 9.020029 3.113377
#> 6 9.674590 9.225959 3.156352 6.460810 5.048222 2.728210 6.636313 6.514048
#> 7 7.971324 2.667668 3.434904 6.188551 7.358517 9.555591 3.726144 1.973607
#> 8 2.879887 3.539966 1.909526 3.105511 1.742525 3.895530 4.493842 5.383293
#> 9 3.779081 1.854662 2.061225 3.942658 4.053813 5.306107 2.444276 1.895012
#> 10 9.742082 2.894384 9.921129 7.621194 7.127088 1.251933 8.762967 2.450492
#> X45 X46 X47 X48 X49 X50 wave_5 X51
#> 1 3.546936 5.714403 9.772532 1.845249 2.936498 9.742943 6.279064 4.182455
#> 2 6.254851 4.148216 4.925170 8.398271 8.325403 1.634401 5.727500 4.297973
#> 3 7.585369 3.164776 5.176150 4.846855 3.769875 5.138067 5.209772 3.583901
#> 4 2.489688 1.523726 2.487683 7.802985 7.189684 7.314266 5.434328 1.719756
#> 5 8.798210 3.129578 6.264429 6.961470 9.394130 1.782462 5.650717 4.289088
#> 6 7.377167 9.010701 3.437002 5.000747 2.042017 9.936496 5.773092 2.602124
#> 7 7.843596 8.306447 3.070872 6.644316 2.149351 3.277891 5.390633 5.824483
#> 8 2.323757 7.727647 7.220870 1.004188 7.104015 1.445846 4.234151 5.535538
#> 9 4.222513 2.394206 3.545672 2.955192 4.860537 7.176925 3.885425 9.505316
#> 10 7.059992 2.122679 8.293583 7.343850 8.509609 8.082346 6.100454 4.071892
#> X52 X53 X54 X55 X56 X57 X58 X59
#> 1 5.182424 3.710283 9.452246 8.208534 3.948377 1.429390 5.979826 8.908066
#> 2 1.742781 4.282039 7.188468 1.824802 2.844448 9.364156 9.154433 8.315674
#> 3 8.740962 3.809015 5.023333 8.488970 6.124444 7.923570 6.287152 8.686900
#> 4 4.560946 1.336330 8.348303 3.491696 8.992497 2.809726 4.811173 4.311064
#> 5 7.623094 5.669244 1.355493 7.777991 5.767427 6.852354 9.546268 8.865549
#> 6 2.545691 7.111121 7.651285 9.677373 6.282628 6.883900 7.381341 2.362044
#> 7 5.092855 9.129102 4.138475 1.733199 6.991616 4.557284 4.719749 3.536305
#> 8 7.931843 1.229740 8.463257 8.689283 5.769032 8.310743 1.165277 7.000346
#> 9 1.563850 9.901704 5.819873 8.220144 5.588586 5.923211 6.100607 9.796452
#> 10 8.335733 3.725989 3.470908 4.466562 1.145444 8.966194 5.410571 6.244658
#> X60 wave_6 X61 X62 X63 X64 X65 X66
#> 1 5.739311 5.674091 3.135067 6.667709 9.130608 7.832218 6.344546 7.776389
#> 2 1.547040 5.056182 7.178413 1.869704 8.789507 8.603512 8.189412 9.253514
#> 3 9.721350 6.838960 3.032366 1.197943 7.978668 5.121294 8.995678 5.281755
#> 4 2.082134 4.246362 3.866451 9.937403 4.391348 7.566685 4.466724 6.104075
#> 5 1.795270 5.954178 2.565854 6.255450 1.378972 1.936708 1.812899 7.629583
#> 6 8.926877 6.142438 8.212866 8.036408 4.279700 2.979849 6.631693 8.717027
#> 7 5.575336 5.129840 2.316539 9.027065 3.463761 9.585561 7.710726 9.182319
#> 8 4.037455 5.813252 8.404457 7.793789 8.654207 7.768338 1.768406 1.507440
#> 9 9.049012 7.146875 3.978980 9.812834 4.261615 8.370598 3.704659 5.526175
#> 10 1.287745 4.712570 4.367524 1.397324 3.740300 4.759994 6.531059 4.154903
#> X67 X68 X69 X70 wave_7 X71 X72 X73
#> 1 8.610005 1.180666 7.603730 1.358026 5.963896 8.604402 6.676962 4.768022
#> 2 8.257916 5.691825 7.432793 6.713205 7.197980 3.341192 5.687581 7.460961
#> 3 2.055981 1.776501 8.480998 5.856048 4.977723 1.208300 6.936592 7.682852
#> 4 7.414179 3.547021 8.979095 2.260932 5.853391 8.761596 7.564248 8.847989
#> 5 3.117420 4.783914 9.573801 3.553406 4.260801 4.011292 5.381406 6.470812
#> 6 1.674611 6.285503 5.955550 6.247275 5.902048 6.686100 4.460110 7.805830
#> 7 9.420815 8.260250 5.701022 2.485233 6.715329 5.917836 1.061502 8.625172
#> 8 2.414510 2.817957 2.536284 1.866710 4.553210 4.388000 1.033158 6.515017
#> 9 6.823516 5.134822 5.313444 4.857518 5.778416 2.672857 9.954428 8.139018
#> 10 2.561660 5.033288 3.284387 4.201854 4.003229 4.860465 1.970982 1.205692
#> X74 X75 X76 X77 X78 X79 X80 wave_8
#> 1 4.752231 9.929536 3.499924 5.624030 9.243877 3.329211 9.403269 6.583146
#> 2 8.873941 2.335172 3.244659 3.127059 8.318018 8.375204 5.882785 5.664657
#> 3 6.826123 1.444279 2.434919 6.178921 7.083010 1.139299 2.974803 4.390910
#> 4 9.320311 6.376181 1.644671 5.340321 8.267492 6.889607 5.404850 6.841727
#> 5 2.550026 3.891956 6.016938 6.124174 2.152580 8.305476 8.210339 5.311500
#> 6 3.842315 5.751361 5.408533 2.297371 3.257007 5.152320 4.668676 4.932962
#> 7 8.238307 8.158798 5.448631 2.311379 3.983923 2.830754 1.933404 4.850971
#> 8 9.900671 1.620393 8.995344 4.710548 4.665424 1.168914 3.525087 4.652256
#> 9 3.901470 7.255186 1.330339 7.147093 6.718308 3.548194 4.253196 5.492009
#> 10 1.010725 9.537684 2.833694 6.880850 8.277508 9.237454 3.333204 4.914826
#> X81 X82 X83 X84 X85 X86 X87 X88
#> 1 5.236137 1.444740 7.198490 1.036962 9.661792 3.936098 4.820069 2.265816
#> 2 4.292609 7.255947 3.801437 7.947185 7.627006 7.537590 1.381246 2.527921
#> 3 2.091448 4.269354 6.450282 5.166777 6.514511 9.925707 6.826979 7.857868
#> 4 1.422943 8.957202 9.919309 7.487563 2.079360 7.436202 5.217572 5.746554
#> 5 3.365167 7.977675 7.688844 6.998551 5.952331 5.539958 6.561335 8.748904
#> 6 9.717771 2.252833 1.682714 6.148663 3.364807 4.924429 3.437339 7.061995
#> 7 5.396459 3.655083 5.060520 7.334317 9.085247 9.539427 2.415658 1.117369
#> 8 5.300398 2.134745 1.481832 6.914990 1.082620 2.081633 2.028288 7.238790
#> 9 7.739136 6.309115 4.056000 3.604169 3.126114 1.676307 5.569146 9.025424
#> 10 7.008762 6.055080 7.605569 1.875155 2.170401 9.001193 5.932291 6.686652
#> X89 X90 wave_9 X91 X92 X93 X94 X95
#> 1 1.965652 6.090889 4.365665 9.313293 1.035067 8.275476 8.981763 9.783007
#> 2 9.289587 7.809851 5.947038 5.883385 1.469788 1.377275 2.836863 5.412018
#> 3 7.078261 7.026432 6.320762 8.671281 8.799042 4.273684 7.935607 4.502533
#> 4 2.337343 5.918705 5.652275 6.252066 6.186207 8.709373 6.367267 4.757995
#> 5 7.708077 8.303173 6.884402 7.014913 3.824583 7.281519 9.619028 1.836332
#> 6 9.483390 7.832501 5.590644 5.601831 9.635192 7.160378 2.428196 2.456283
#> 7 4.787050 1.180616 4.957175 7.864755 6.320744 4.132135 5.733768 4.648748
#> 8 3.679508 4.428133 3.637094 9.130261 5.782684 5.992136 8.858362 4.076330
#> 9 3.334840 1.457921 4.589817 8.384271 4.455430 2.235193 8.827355 4.737317
#> 10 3.005932 8.181126 5.752216 1.642887 3.875979 8.064384 1.213198 3.736472
#> X96 X97 X98 X99 X100 wave_10
#> 1 6.042525 5.573423 8.037720 5.120891 1.784974 6.394814
#> 2 2.402867 5.046399 1.414239 7.405233 3.014020 3.626209
#> 3 9.609218 6.609352 8.378602 9.278633 6.149338 7.420729
#> 4 1.395700 2.259801 3.424676 6.643996 4.601523 5.059860
#> 5 4.349420 9.171518 3.545654 9.119636 6.089188 6.185179
#> 6 9.663538 6.124989 6.788994 7.815962 8.466615 6.614198
#> 7 6.808848 5.934525 9.533070 2.240725 6.779024 5.999634
#> 8 1.551364 2.051449 1.062986 2.379294 4.523489 4.540835
#> 9 4.689513 7.858255 4.164559 2.721810 7.386219 5.545992
#> 10 4.833146 5.305325 4.771412 4.898666 1.979417 4.032089
由 reprex package (v2.0.0)
于 2021-07-24 创建
我们也可以用 purrr::map2:
library(purrr)
set.seed(123)
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
data.frame(from = seq(10, 100, 10) - 9,
to = seq(10, 100, 10)) %>%
{map2_dfc(.$from, .$to, ~ rowMeans(df[, .x:.y]))} %>%
set_names(paste0("Wave_", seq_along(.)))
# A tibble: 10 x 10
Wave_1 Wave_2 Wave_3 Wave_4 Wave_5 Wave_6 Wave_7 Wave_8 Wave_9 Wave_10
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 5.57 6.59 5.75 5.79 6.28 5.67 5.96 6.58 4.37 6.39
2 6.16 4.53 5.15 4.36 5.73 5.06 7.20 5.66 5.95 3.63
3 5.94 5.62 4.66 6.65 5.21 6.84 4.98 4.39 6.32 7.42
4 5.91 5.77 5.55 5.53 5.43 4.25 5.85 6.84 5.65 5.06
5 4.74 6.04 4.34 4.65 5.65 5.95 4.26 5.31 6.88 6.19
6 4.39 5.86 6.77 6.46 5.77 6.14 5.90 4.93 5.59 6.61
7 5.85 5.53 4.88 6.19 5.39 5.13 6.72 4.85 4.96 6.00
8 5.84 5.24 5.15 3.11 4.23 5.81 4.55 4.65 3.64 4.54
9 5.63 5.65 4.18 3.94 3.89 7.15 5.78 5.49 4.59 5.55
10 4.83 5.46 7.32 7.62 6.10 4.71 4.00 4.91 5.75 4.03
而且在 base R 中我们可以这样做：
tmp <- split.default(df, ceiling(seq_along(df) / 10))
as.data.frame(Reduce(function(x, y) {
cbind(x, rowMeans(tmp[[y]]))
}, 2:length(tmp), init = rowMeans(tmp[[1]]))) |>
setNames(paste0("wave_", 1:length(tmp)))
wave_1 wave_2 wave_3 wave_4 wave_5 wave_6 wave_7 wave_8 wave_9 wave_10
1 5.571560 6.587021 5.750900 5.791688 6.279064 5.674091 5.963896 6.583146 4.365665 6.394814
2 6.164837 4.531540 5.153556 4.362311 5.727500 5.056182 7.197980 5.664657 5.947038 3.626209
3 5.937152 5.622390 4.660815 6.652186 5.209772 6.838960 4.977723 4.390910 6.320762 7.420729
4 5.910486 5.766074 5.549177 5.526093 5.434328 4.246362 5.853391 6.841727 5.652275 5.059860
5 4.735269 6.043003 4.335277 4.646320 5.650717 5.954178 4.260801 5.311500 6.884402 6.185179
6 4.391438 5.857648 6.766768 6.460810 5.773092 6.142438 5.902048 4.932962 5.590644 6.614198
7 5.854975 5.531683 4.882462 6.188551 5.390633 5.129840 6.715329 4.850971 4.957175 5.999634
8 5.838495 5.237646 5.145763 3.105511 4.234151 5.813252 4.553210 4.652256 3.637094 4.540835
9 5.632393 5.645221 4.176820 3.942658 3.885425 7.146875 5.778416 5.492009 4.589817 5.545992
10 4.833706 5.458013 7.323117 7.621194 6.100454 4.712570 4.003229 4.914826 5.752216 4.032089
我正在尝试创建表示重复测量的变量“波”。具体来说，我正在尝试创建代表变量 1 - 10、11 - 20 ... 91-100 的平均值的连续变量。请注意，“...”表示第 3 波到第 9 波的变量，因为避免输入这些是我的目标！
这是一个示例数据框，df，有 10 行和 100 列：
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
dim(df)
> 10 100
我使用了 dplyr::mutate，它在输入所有变量后就可以工作，但是很耗时而且容易出错。如果不诉诸手动输入列的名称，我无法找到这样做的方法，正如我在下面开始做的那样（请注意，“...”代表第 3 波到第 9 波）：
df <- df %>%
mutate(wave_1 = (X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10) / 10,
wave_2 = (X11 + X12 + X13 + X14 + X15 + X16 + X17 + X18 + X19 + X20) / 10,
...
wave_10 = (X91 + X92 + X93 + X94 + X95 + X96 + X97 + X98 + X99 + X100) / 10)
你能 mutate 多个/连续的列 'dplyr' 吗？也欢迎使用其他方法。
这是使用包 zoo 的一种方法：
library(zoo)
t(rollapply(t(df), width = 10, by = 10, function(x) sum(x)/10))
这是使用基数 R 的一种方法：
splits <- 1:100
dim(splits) <- c(10, 10)
splits <- split(splits, col(splits))
results <- do.call("cbind", lapply(splits, function(x) data.frame(rowSums(df[,x] / 10))))
names(results) <- paste0("wave_", 1:10)
results
另一种非常简洁的基数 R 方法（由 G.Grothendieck 提供）：
t(apply(df, 1, tapply, gl(10, 10), mean))
这是 dplyr 和 tidyr 的解决方案：
library(dplyr)
library(tidyr)
df$row <- 1:nrow(df)
df2 <- df %>% gather(column, value, -row)
df2$column <- cut(as.numeric(gsub("X", "", df2$column)),breaks = c(0:10*10))
df2 <- df2 %>% group_by(row, column) %>% summarise(value = sum(value)/10)
df2 %>% spread(column, value) %>% select(-row)
另一种 dplyr 解决方案，它更接近 OP 指示的语法并且不需要重铸数据帧。
4 波计算基本相同，但略有不同，但 向量化（即 rowSums 和 rowMeans）方式：
df <- df %>%
mutate(wave_1 = rowSums(select(., num_range("X", 1:10)))/10,
wave_2 = rowSums(select(., c(11:20)))/10,
wave_3 = rowMeans(select(., X21:X30)),
wave_4 = rowMeans(.[, 31:40]))
编辑：. 可用作当前数据框 df 的占位符（代码已相应更改）。还添加了 wave_4 以证明它可以像数据框一样使用。
如果操作函数不是向量化的（也就是说，它不能用于整个数据帧，例如rowSums），也可以使用rowwise do 函数使用 非矢量化 函数（例如 myfun）
myfun <- function (x) {
sum(x)/10
}
tmp=df %>%
rowwise() %>%
do(data.frame(., wave_1 = myfun(unlist(.)[1:10]))) %>%
do(data.frame(., wave_2 = myfun(unlist(.)[11:20])))
注意：. 更改似乎改变了它的意思，指的是 mutate 的整个数据帧，但仅指 do.
使用 dplyr 的另一种方法（IMO 推荐的方法）是在汇总每个波的值之前首先将数据重塑或融化为 tidy data format。
详细来说，这个过程将涉及：
- 将您的数据重塑为长格式 (
tidyr::gather) - 识别每个变量属于哪些"wave"
- 汇总每个波的值
- 将您的数据重新整形为宽格式 (
tidyr::spread)
在您的示例中，这将如下所示：
library(tidyverse)
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
dim(df)
df %>%
dplyr::mutate(id = dplyr::row_number()) %>%
# reshape to "tidy data" or long format
tidyr::gather(varname, value, -id) %>%
# identify which variables belong to which "wave"
dplyr::mutate(varnum = as.integer(stringr::str_extract(varname, pattern = '\d+')),
wave = floor((varnum-1)/10)+1) %>%
# summarize your value for each wave
dplyr::group_by(id, wave) %>%
dplyr::summarise(avg = sum(value)/n()) %>%
# reshape back to "wide" format
tidyr::spread(wave, avg, sep='_') %>%
dplyr::ungroup()
输出如下：
# A tibble: 10 x 11
id wave_1 wave_2 wave_3 wave_4 wave_5 wave_6 wave_7 wave_8 wave_9 wave_10
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 6.24 4.49 5.85 5.43 5.98 6.04 4.83 6.92 5.43 5.52
2 2 5.16 6.82 5.76 6.66 6.21 5.41 4.58 5.06 5.81 6.93
3 3 7.23 6.28 5.40 5.70 5.13 6.27 5.55 5.84 6.74 5.94
4 4 5.27 4.79 4.39 6.85 5.31 6.01 6.15 3.31 5.73 5.63
5 5 6.48 5.16 5.20 4.71 5.87 4.44 6.40 5.00 5.90 3.78
6 6 4.18 4.64 5.49 5.47 5.75 6.35 4.34 5.66 5.34 6.57
7 7 4.97 4.09 6.17 5.78 5.87 6.47 4.96 4.39 5.99 5.35
8 8 5.50 7.21 5.43 5.15 4.56 5.00 4.86 5.72 6.41 5.65
9 9 5.27 5.71 5.23 5.44 5.12 5.40 5.38 6.05 5.41 5.30
10 10 5.95 4.58 6.52 5.46 7.63 5.56 5.82 7.03 5.68 5.38
这可以连接回您的原始数据以匹配您给出的示例（使用 mutate），如下所示：
df %>%
dplyr::mutate(id = dplyr::row_number()) %>%
tidyr::gather(varname, value, -id) %>%
dplyr::mutate(varnum = as.integer(stringr::str_extract(varname, pattern = '\d+')),
wave = floor((varnum-1)/10)+1) %>%
dplyr::group_by(id, wave) %>%
dplyr::summarise(avg = sum(value)/n()) %>%
tidyr::spread(wave, avg, sep='_') %>%
dplyr::ungroup() %>%
dplyr::right_join(df %>% # <-- join back to original data
dplyr::mutate(id = dplyr::row_number()),
by = 'id')
这种方法的一个好处是您可以检查数据以确认您正确地将变量分配给 "wave"s。
df %>%
dplyr::mutate(id = dplyr::row_number()) %>%
tidyr::gather(varname, value, -id) %>%
dplyr::mutate(varnum = as.integer(stringr::str_extract(varname, pattern = '\d+')),
wave = floor((varnum-1)/10)+1) %>%
dplyr::distinct(varname, varnum, wave) %>%
head()
产生：
varname varnum wave
1 X1 1 1
2 X2 2 1
3 X3 3 1
4 X4 4 1
5 X5 5 1
6 X6 6 1
另一种方法。
set.seed(123)
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
library(tidyverse)
imap_dfc(split.default(df, (((as.numeric(gsub('X', '', names(df)))) -1) %/% 10) +1),
~ .x %>% mutate(!!paste0('wave_', .y) := rowMeans(.)))
#> X1 X2 X3 X4 X5 X6 X7 X8
#> 1 3.588198 9.611500 9.005854 9.667218 2.285200 1.412481 6.986037 7.790276
#> 2 8.094746 5.080007 7.235231 9.120691 4.730917 4.979801 1.853566 6.662990
#> 3 4.680792 7.098136 6.764561 7.216348 4.723519 8.190324 4.455727 7.391642
#> 4 8.947157 6.153701 9.948428 8.159207 4.319609 2.097093 3.469453 1.005623
#> 5 9.464206 1.926322 6.901352 1.221523 2.372003 6.048532 8.331760 5.277849
#> 6 1.410008 9.098425 7.376774 5.300164 2.249255 2.858783 5.036647 2.981070
#> 7 5.752949 3.214790 5.896594 7.826136 3.097307 2.147785 8.290579 4.418349
#> 8 9.031771 1.378536 6.347278 2.947671 5.193662 7.779771 8.311506 6.514939
#> 9 5.962915 3.951286 3.602438 3.863629 3.393754 9.055408 8.149081 4.166181
#> 10 5.109533 9.590533 2.324023 3.084632 8.720449 4.370165 4.958485 2.000219
#> X9 X10 wave_1 X11 X12 X13 X14 X15
#> 1 3.192575 2.176261 5.571560 6.399901 9.417698 6.831041 6.573308 3.805320
#> 2 7.012500 6.877917 6.164837 3.995412 3.711060 3.878386 9.022547 4.685275
#> 3 4.758821 4.091648 5.937152 5.397517 1.546485 3.769480 7.056992 1.094204
#> 4 8.093763 6.910823 5.910486 9.590264 9.529542 2.977909 7.633700 2.654646
#> 5 1.925782 3.883359 4.735269 5.346122 7.485366 4.325400 5.690222 8.584564
#> 6 4.914035 2.689220 4.391438 9.013152 2.280649 9.857973 6.938546 3.080456
#> 7 9.864613 8.040649 5.854975 9.229944 5.943562 2.387821 8.396249 3.151900
#> 8 9.037460 1.842355 5.838495 6.478615 9.586821 1.819396 8.076534 1.690220
#> 9 8.978222 5.201011 5.632393 4.696208 6.269350 2.277162 9.818397 3.211513
#> 10 2.575474 5.603549 4.833706 2.323852 4.640593 7.210064 4.954884 7.589217
#> X16 X17 X18 X19 X20 wave_2 X21 X22
#> 1 8.627078 5.520696 6.669757 8.557910 3.467500 6.587021 3.148534 5.338383
#> 2 5.477745 4.185141 2.654456 3.812033 3.893345 4.531540 9.661230 3.276684
#> 3 4.491181 6.849866 8.772797 7.374613 9.870768 5.622390 6.412292 2.946293
#> 4 3.218041 4.372426 7.719112 3.385160 6.579940 5.766074 5.635268 7.069387
#> 5 1.999868 4.199008 7.014562 6.349089 9.435827 6.043003 4.623160 1.428973
#> 6 4.509950 5.803192 6.562161 5.331608 5.198794 5.857648 8.922219 7.307678
#> 7 6.147418 7.663009 4.350143 3.385295 4.661493 5.531683 4.276827 4.166998
#> 8 2.952035 2.989926 5.768521 6.081314 6.933073 5.237646 3.594154 4.680496
#> 9 5.002912 4.714715 8.872141 9.218694 2.371120 5.645221 2.535807 8.388562
#> 10 2.961916 3.391180 6.235751 9.116870 6.155804 5.458013 2.549546 9.269716
#> X23 X24 X25 X26 X27 X28 X29 X30
#> 1 3.542755 6.565161 5.921435 3.314951 9.331372 8.839391 7.827339 3.679676
#> 2 9.649943 4.855794 6.960859 2.996091 8.937798 1.056707 2.233955 1.906494
#> 3 7.555550 5.878723 2.545286 6.337411 7.067682 1.648514 4.569261 1.647137
#> 4 7.177376 1.526306 6.697498 3.407693 9.551503 2.477901 3.024868 8.923965
#> 5 1.475595 3.347712 3.806828 5.779634 5.648004 7.933007 1.521627 7.788227
#> 6 4.556981 4.574368 7.520989 8.067625 6.188671 7.616659 4.563034 8.349453
#> 7 5.300608 2.779703 4.590458 2.512547 4.026981 9.746881 1.584355 9.839263
#> 8 6.042279 8.487348 9.724208 4.639593 4.125922 5.198251 3.032978 1.932397
#> 9 7.284354 2.375985 9.706585 5.244186 1.180219 1.669461 1.491662 1.891376
#> 10 9.241152 8.230767 7.540323 8.812961 5.525317 6.839363 7.032538 8.189485
#> wave_3 X31 X32 X33 X34 X35 X36 X37
#> 1 5.750900 8.061177 4.506284 8.683870 6.555117 4.732618 2.228862 2.294353
#> 2 5.153556 1.084869 5.181993 3.663059 3.576568 2.074643 9.707225 2.735344
#> 3 4.660815 8.011593 7.419511 2.323435 7.640177 5.734267 5.635646 9.070648
#> 4 5.549177 7.564516 1.497717 7.335929 8.506489 3.025660 2.467633 3.773076
#> 5 4.335277 6.671187 4.193048 1.934260 3.828437 5.377706 6.597121 4.269705
#> 6 6.766768 5.328197 8.225310 1.303550 5.433099 4.331933 9.873587 8.055518
#> 7 4.882462 2.409732 8.521380 9.994641 7.276364 9.850152 7.018944 2.740408
#> 8 5.145763 1.073940 3.139745 1.313873 6.773161 4.494872 4.770243 1.159892
#> 9 4.176820 5.072126 4.185875 4.045522 6.795306 3.063204 3.910105 4.659471
#> 10 7.323117 5.430640 8.711969 9.235574 9.800681 6.609678 8.517298 5.348509
#> X38 X39 X40 wave_4 X41 X42 X43 X44
#> 1 4.796605 6.264101 9.793891 5.791688 9.874489 3.852543 5.927135 9.577911
#> 2 4.085279 7.847413 3.666720 4.362311 2.233607 8.484117 6.798162 6.072802
#> 3 8.798350 4.354385 7.533847 6.652186 9.147786 2.936549 6.366372 3.965927
#> 4 5.095972 7.922745 8.071191 5.526093 6.186717 5.481540 3.897436 9.969555
#> 5 5.803884 5.839095 1.948760 4.646320 4.559040 3.484447 9.020029 3.113377
#> 6 9.674590 9.225959 3.156352 6.460810 5.048222 2.728210 6.636313 6.514048
#> 7 7.971324 2.667668 3.434904 6.188551 7.358517 9.555591 3.726144 1.973607
#> 8 2.879887 3.539966 1.909526 3.105511 1.742525 3.895530 4.493842 5.383293
#> 9 3.779081 1.854662 2.061225 3.942658 4.053813 5.306107 2.444276 1.895012
#> 10 9.742082 2.894384 9.921129 7.621194 7.127088 1.251933 8.762967 2.450492
#> X45 X46 X47 X48 X49 X50 wave_5 X51
#> 1 3.546936 5.714403 9.772532 1.845249 2.936498 9.742943 6.279064 4.182455
#> 2 6.254851 4.148216 4.925170 8.398271 8.325403 1.634401 5.727500 4.297973
#> 3 7.585369 3.164776 5.176150 4.846855 3.769875 5.138067 5.209772 3.583901
#> 4 2.489688 1.523726 2.487683 7.802985 7.189684 7.314266 5.434328 1.719756
#> 5 8.798210 3.129578 6.264429 6.961470 9.394130 1.782462 5.650717 4.289088
#> 6 7.377167 9.010701 3.437002 5.000747 2.042017 9.936496 5.773092 2.602124
#> 7 7.843596 8.306447 3.070872 6.644316 2.149351 3.277891 5.390633 5.824483
#> 8 2.323757 7.727647 7.220870 1.004188 7.104015 1.445846 4.234151 5.535538
#> 9 4.222513 2.394206 3.545672 2.955192 4.860537 7.176925 3.885425 9.505316
#> 10 7.059992 2.122679 8.293583 7.343850 8.509609 8.082346 6.100454 4.071892
#> X52 X53 X54 X55 X56 X57 X58 X59
#> 1 5.182424 3.710283 9.452246 8.208534 3.948377 1.429390 5.979826 8.908066
#> 2 1.742781 4.282039 7.188468 1.824802 2.844448 9.364156 9.154433 8.315674
#> 3 8.740962 3.809015 5.023333 8.488970 6.124444 7.923570 6.287152 8.686900
#> 4 4.560946 1.336330 8.348303 3.491696 8.992497 2.809726 4.811173 4.311064
#> 5 7.623094 5.669244 1.355493 7.777991 5.767427 6.852354 9.546268 8.865549
#> 6 2.545691 7.111121 7.651285 9.677373 6.282628 6.883900 7.381341 2.362044
#> 7 5.092855 9.129102 4.138475 1.733199 6.991616 4.557284 4.719749 3.536305
#> 8 7.931843 1.229740 8.463257 8.689283 5.769032 8.310743 1.165277 7.000346
#> 9 1.563850 9.901704 5.819873 8.220144 5.588586 5.923211 6.100607 9.796452
#> 10 8.335733 3.725989 3.470908 4.466562 1.145444 8.966194 5.410571 6.244658
#> X60 wave_6 X61 X62 X63 X64 X65 X66
#> 1 5.739311 5.674091 3.135067 6.667709 9.130608 7.832218 6.344546 7.776389
#> 2 1.547040 5.056182 7.178413 1.869704 8.789507 8.603512 8.189412 9.253514
#> 3 9.721350 6.838960 3.032366 1.197943 7.978668 5.121294 8.995678 5.281755
#> 4 2.082134 4.246362 3.866451 9.937403 4.391348 7.566685 4.466724 6.104075
#> 5 1.795270 5.954178 2.565854 6.255450 1.378972 1.936708 1.812899 7.629583
#> 6 8.926877 6.142438 8.212866 8.036408 4.279700 2.979849 6.631693 8.717027
#> 7 5.575336 5.129840 2.316539 9.027065 3.463761 9.585561 7.710726 9.182319
#> 8 4.037455 5.813252 8.404457 7.793789 8.654207 7.768338 1.768406 1.507440
#> 9 9.049012 7.146875 3.978980 9.812834 4.261615 8.370598 3.704659 5.526175
#> 10 1.287745 4.712570 4.367524 1.397324 3.740300 4.759994 6.531059 4.154903
#> X67 X68 X69 X70 wave_7 X71 X72 X73
#> 1 8.610005 1.180666 7.603730 1.358026 5.963896 8.604402 6.676962 4.768022
#> 2 8.257916 5.691825 7.432793 6.713205 7.197980 3.341192 5.687581 7.460961
#> 3 2.055981 1.776501 8.480998 5.856048 4.977723 1.208300 6.936592 7.682852
#> 4 7.414179 3.547021 8.979095 2.260932 5.853391 8.761596 7.564248 8.847989
#> 5 3.117420 4.783914 9.573801 3.553406 4.260801 4.011292 5.381406 6.470812
#> 6 1.674611 6.285503 5.955550 6.247275 5.902048 6.686100 4.460110 7.805830
#> 7 9.420815 8.260250 5.701022 2.485233 6.715329 5.917836 1.061502 8.625172
#> 8 2.414510 2.817957 2.536284 1.866710 4.553210 4.388000 1.033158 6.515017
#> 9 6.823516 5.134822 5.313444 4.857518 5.778416 2.672857 9.954428 8.139018
#> 10 2.561660 5.033288 3.284387 4.201854 4.003229 4.860465 1.970982 1.205692
#> X74 X75 X76 X77 X78 X79 X80 wave_8
#> 1 4.752231 9.929536 3.499924 5.624030 9.243877 3.329211 9.403269 6.583146
#> 2 8.873941 2.335172 3.244659 3.127059 8.318018 8.375204 5.882785 5.664657
#> 3 6.826123 1.444279 2.434919 6.178921 7.083010 1.139299 2.974803 4.390910
#> 4 9.320311 6.376181 1.644671 5.340321 8.267492 6.889607 5.404850 6.841727
#> 5 2.550026 3.891956 6.016938 6.124174 2.152580 8.305476 8.210339 5.311500
#> 6 3.842315 5.751361 5.408533 2.297371 3.257007 5.152320 4.668676 4.932962
#> 7 8.238307 8.158798 5.448631 2.311379 3.983923 2.830754 1.933404 4.850971
#> 8 9.900671 1.620393 8.995344 4.710548 4.665424 1.168914 3.525087 4.652256
#> 9 3.901470 7.255186 1.330339 7.147093 6.718308 3.548194 4.253196 5.492009
#> 10 1.010725 9.537684 2.833694 6.880850 8.277508 9.237454 3.333204 4.914826
#> X81 X82 X83 X84 X85 X86 X87 X88
#> 1 5.236137 1.444740 7.198490 1.036962 9.661792 3.936098 4.820069 2.265816
#> 2 4.292609 7.255947 3.801437 7.947185 7.627006 7.537590 1.381246 2.527921
#> 3 2.091448 4.269354 6.450282 5.166777 6.514511 9.925707 6.826979 7.857868
#> 4 1.422943 8.957202 9.919309 7.487563 2.079360 7.436202 5.217572 5.746554
#> 5 3.365167 7.977675 7.688844 6.998551 5.952331 5.539958 6.561335 8.748904
#> 6 9.717771 2.252833 1.682714 6.148663 3.364807 4.924429 3.437339 7.061995
#> 7 5.396459 3.655083 5.060520 7.334317 9.085247 9.539427 2.415658 1.117369
#> 8 5.300398 2.134745 1.481832 6.914990 1.082620 2.081633 2.028288 7.238790
#> 9 7.739136 6.309115 4.056000 3.604169 3.126114 1.676307 5.569146 9.025424
#> 10 7.008762 6.055080 7.605569 1.875155 2.170401 9.001193 5.932291 6.686652
#> X89 X90 wave_9 X91 X92 X93 X94 X95
#> 1 1.965652 6.090889 4.365665 9.313293 1.035067 8.275476 8.981763 9.783007
#> 2 9.289587 7.809851 5.947038 5.883385 1.469788 1.377275 2.836863 5.412018
#> 3 7.078261 7.026432 6.320762 8.671281 8.799042 4.273684 7.935607 4.502533
#> 4 2.337343 5.918705 5.652275 6.252066 6.186207 8.709373 6.367267 4.757995
#> 5 7.708077 8.303173 6.884402 7.014913 3.824583 7.281519 9.619028 1.836332
#> 6 9.483390 7.832501 5.590644 5.601831 9.635192 7.160378 2.428196 2.456283
#> 7 4.787050 1.180616 4.957175 7.864755 6.320744 4.132135 5.733768 4.648748
#> 8 3.679508 4.428133 3.637094 9.130261 5.782684 5.992136 8.858362 4.076330
#> 9 3.334840 1.457921 4.589817 8.384271 4.455430 2.235193 8.827355 4.737317
#> 10 3.005932 8.181126 5.752216 1.642887 3.875979 8.064384 1.213198 3.736472
#> X96 X97 X98 X99 X100 wave_10
#> 1 6.042525 5.573423 8.037720 5.120891 1.784974 6.394814
#> 2 2.402867 5.046399 1.414239 7.405233 3.014020 3.626209
#> 3 9.609218 6.609352 8.378602 9.278633 6.149338 7.420729
#> 4 1.395700 2.259801 3.424676 6.643996 4.601523 5.059860
#> 5 4.349420 9.171518 3.545654 9.119636 6.089188 6.185179
#> 6 9.663538 6.124989 6.788994 7.815962 8.466615 6.614198
#> 7 6.808848 5.934525 9.533070 2.240725 6.779024 5.999634
#> 8 1.551364 2.051449 1.062986 2.379294 4.523489 4.540835
#> 9 4.689513 7.858255 4.164559 2.721810 7.386219 5.545992
#> 10 4.833146 5.305325 4.771412 4.898666 1.979417 4.032089
由 reprex package (v2.0.0)
于 2021-07-24 创建我们也可以用 purrr::map2:
library(purrr)
set.seed(123)
mat <- matrix(runif(1000, 1, 10), ncol = 100)
df <- data.frame(mat)
data.frame(from = seq(10, 100, 10) - 9,
to = seq(10, 100, 10)) %>%
{map2_dfc(.$from, .$to, ~ rowMeans(df[, .x:.y]))} %>%
set_names(paste0("Wave_", seq_along(.)))
# A tibble: 10 x 10
Wave_1 Wave_2 Wave_3 Wave_4 Wave_5 Wave_6 Wave_7 Wave_8 Wave_9 Wave_10
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 5.57 6.59 5.75 5.79 6.28 5.67 5.96 6.58 4.37 6.39
2 6.16 4.53 5.15 4.36 5.73 5.06 7.20 5.66 5.95 3.63
3 5.94 5.62 4.66 6.65 5.21 6.84 4.98 4.39 6.32 7.42
4 5.91 5.77 5.55 5.53 5.43 4.25 5.85 6.84 5.65 5.06
5 4.74 6.04 4.34 4.65 5.65 5.95 4.26 5.31 6.88 6.19
6 4.39 5.86 6.77 6.46 5.77 6.14 5.90 4.93 5.59 6.61
7 5.85 5.53 4.88 6.19 5.39 5.13 6.72 4.85 4.96 6.00
8 5.84 5.24 5.15 3.11 4.23 5.81 4.55 4.65 3.64 4.54
9 5.63 5.65 4.18 3.94 3.89 7.15 5.78 5.49 4.59 5.55
10 4.83 5.46 7.32 7.62 6.10 4.71 4.00 4.91 5.75 4.03
而且在 base R 中我们可以这样做：
tmp <- split.default(df, ceiling(seq_along(df) / 10))
as.data.frame(Reduce(function(x, y) {
cbind(x, rowMeans(tmp[[y]]))
}, 2:length(tmp), init = rowMeans(tmp[[1]]))) |>
setNames(paste0("wave_", 1:length(tmp)))
wave_1 wave_2 wave_3 wave_4 wave_5 wave_6 wave_7 wave_8 wave_9 wave_10
1 5.571560 6.587021 5.750900 5.791688 6.279064 5.674091 5.963896 6.583146 4.365665 6.394814
2 6.164837 4.531540 5.153556 4.362311 5.727500 5.056182 7.197980 5.664657 5.947038 3.626209
3 5.937152 5.622390 4.660815 6.652186 5.209772 6.838960 4.977723 4.390910 6.320762 7.420729
4 5.910486 5.766074 5.549177 5.526093 5.434328 4.246362 5.853391 6.841727 5.652275 5.059860
5 4.735269 6.043003 4.335277 4.646320 5.650717 5.954178 4.260801 5.311500 6.884402 6.185179
6 4.391438 5.857648 6.766768 6.460810 5.773092 6.142438 5.902048 4.932962 5.590644 6.614198
7 5.854975 5.531683 4.882462 6.188551 5.390633 5.129840 6.715329 4.850971 4.957175 5.999634
8 5.838495 5.237646 5.145763 3.105511 4.234151 5.813252 4.553210 4.652256 3.637094 4.540835
9 5.632393 5.645221 4.176820 3.942658 3.885425 7.146875 5.778416 5.492009 4.589817 5.545992
10 4.833706 5.458013 7.323117 7.621194 6.100454 4.712570 4.003229 4.914826 5.752216 4.032089