r - 使用 mutate_at 缩短多个 dplyr 突变？

Question

我有一个基于 GPS 数据的数据框，我希望在其中创建多个长度的滚动总和变量。我可以使用许多 dplyr::mutate

创建它们

library(dplyr)
library(zoo) # rollsumr

df_1 %<>%
 mutate(1min = rollsumr(Dist, 600, fill = NA),
        2min = rollsumr(Dist, 1200, fill = NA), 
        3min = rollsumr(Dist, 1800, fill = NA), 
        4min = rollsumr(Dist, 2400, fill = NA), 
        5min = rollsumr(Dist, 3000, fill = NA), 
        6min = rollsumr(Dist, 3600, fill = NA),
        7min = rollsumr(Dist, 4200, fill = NA), 
        8min = rollsumr(Dist, 4800, fill = NA), 
        9min = rollsumr(Dist, 5400, fill = NA), 
        10min = rollsumr(Dist, 6000, fill = NA))

但我想知道是否有办法使用 mutate_at 同时创建它们？我自己尝试过，但无法做到。

在处理非常大的数据集时，我确实有一个单独的脚本来处理数据表，但对于较小的数据集，dplyr 和 mutate 速度更快。

setDT(df_1)[paste0(1:15) := lapply((1:15)*600, function(x) rollsumr(Dist, x, fill = NA))][]

示例数据

> dput(head(df_1,40))
structure(list(Seconds = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 
0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 
2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 
3.4, 3.5, 3.6, 3.7, 3.8, 3.9), Dist = c(0, 0.122, 0.116, 0.122, 
0.127, 0.133, 0.144, 0.143, 0.143, 0.133, 0.13, 0.133, 0.139, 
0.146, 0.155, 0.164, 0.159, 0.16, 0.155, 0.156, 0.155, 0.158, 
0.146, 0.148999999999999, 0.154, 0.159, 0.158, 0.166,        0.168999999999999, 
0.156, 0.151, 0.145, 0.152, 0.163999999999999, 0.169, 0.17, 0.169, 
0.177, 0.177999999999999, 0.175)), row.names = c(NA, 40L), class =           "data.frame")

PS - 单个数据框大约包含 70,000 行数据，但我不确定如何提供可重复的样本？

Answer 1

使用purrr::map_dfc做这样的事情怎么样？

windows <- setNames(1:10, paste0("min", 1:10))
df_1 %>%
    bind_cols(
        map_dfc(windows, ~rollsumr(df_1$Dist, .x, fill = NA)) %>%
        setNames(., names(windows)))
#   Seconds  Dist  min1  min2  min3  min4  min5  min6  min7  min8  min9 min10
#1      0.0 0.000 0.000    NA    NA    NA    NA    NA    NA    NA    NA    NA
#2      0.1 0.122 0.122 0.122    NA    NA    NA    NA    NA    NA    NA    NA
#3      0.2 0.116 0.116 0.238 0.238    NA    NA    NA    NA    NA    NA    NA
#4      0.3 0.122 0.122 0.238 0.360 0.360    NA    NA    NA    NA    NA    NA
#5      0.4 0.127 0.127 0.249 0.365 0.487 0.487    NA    NA    NA    NA    NA
#6      0.5 0.133 0.133 0.260 0.382 0.498 0.620 0.620    NA    NA    NA    NA
#7      0.6 0.144 0.144 0.277 0.404 0.526 0.642 0.764 0.764    NA    NA    NA
#8      0.7 0.143 0.143 0.287 0.420 0.547 0.669 0.785 0.907 0.907    NA    NA
#9      0.8 0.143 0.143 0.286 0.430 0.563 0.690 0.812 0.928 1.050 1.050    NA
#10     0.9 0.133 0.133 0.276 0.419 0.563 0.696 0.823 0.945 1.061 1.183 1.183
#11     1.0 0.130 0.130 0.263 0.406 0.549 0.693 0.826 0.953 1.075 1.191 1.313
#12     1.1 0.133 0.133 0.263 0.396 0.539 0.682 0.826 0.959 1.086 1.208 1.324
#13     1.2 0.139 0.139 0.272 0.402 0.535 0.678 0.821 0.965 1.098 1.225 1.347
#14     1.3 0.146 0.146 0.285 0.418 0.548 0.681 0.824 0.967 1.111 1.244 1.371
#15     1.4 0.155 0.155 0.301 0.440 0.573 0.703 0.836 0.979 1.122 1.266 1.399
#16     1.5 0.164 0.164 0.319 0.465 0.604 0.737 0.867 1.000 1.143 1.286 1.430
#17     1.6 0.159 0.159 0.323 0.478 0.624 0.763 0.896 1.026 1.159 1.302 1.445
#18     1.7 0.160 0.160 0.319 0.483 0.638 0.784 0.923 1.056 1.186 1.319 1.462
#19     1.8 0.155 0.155 0.315 0.474 0.638 0.793 0.939 1.078 1.211 1.341 1.474
#20     1.9 0.156 0.156 0.311 0.471 0.630 0.794 0.949 1.095 1.234 1.367 1.497
#21     2.0 0.155 0.155 0.311 0.466 0.626 0.785 0.949 1.104 1.250 1.389 1.522
#22     2.1 0.158 0.158 0.313 0.469 0.624 0.784 0.943 1.107 1.262 1.408 1.547
#23     2.2 0.146 0.146 0.304 0.459 0.615 0.770 0.930 1.089 1.253 1.408 1.554
#24     2.3 0.149 0.149 0.295 0.453 0.608 0.764 0.919 1.079 1.238 1.402 1.557
#25     2.4 0.154 0.154 0.303 0.449 0.607 0.762 0.918 1.073 1.233 1.392 1.556
#26     2.5 0.159 0.159 0.313 0.462 0.608 0.766 0.921 1.077 1.232 1.392 1.551
#27     2.6 0.158 0.158 0.317 0.471 0.620 0.766 0.924 1.079 1.235 1.390 1.550
#28     2.7 0.166 0.166 0.324 0.483 0.637 0.786 0.932 1.090 1.245 1.401 1.556
#29     2.8 0.169 0.169 0.335 0.493 0.652 0.806 0.955 1.101 1.259 1.414 1.570
#30     2.9 0.156 0.156 0.325 0.491 0.649 0.808 0.962 1.111 1.257 1.415 1.570
#31     3.0 0.151 0.151 0.307 0.476 0.642 0.800 0.959 1.113 1.262 1.408 1.566
#32     3.1 0.145 0.145 0.296 0.452 0.621 0.787 0.945 1.104 1.258 1.407 1.553
#33     3.2 0.152 0.152 0.297 0.448 0.604 0.773 0.939 1.097 1.256 1.410 1.559
#34     3.3 0.164 0.164 0.316 0.461 0.612 0.768 0.937 1.103 1.261 1.420 1.574
#35     3.4 0.169 0.169 0.333 0.485 0.630 0.781 0.937 1.106 1.272 1.430 1.589
#36     3.5 0.170 0.170 0.339 0.503 0.655 0.800 0.951 1.107 1.276 1.442 1.600
#37     3.6 0.169 0.169 0.339 0.508 0.672 0.824 0.969 1.120 1.276 1.445 1.611
#38     3.7 0.177 0.177 0.346 0.516 0.685 0.849 1.001 1.146 1.297 1.453 1.622
#39     3.8 0.178 0.178 0.355 0.524 0.694 0.863 1.027 1.179 1.324 1.475 1.631
#40     3.9 0.175 0.175 0.353 0.530 0.699 0.869 1.038 1.202 1.354 1.499 1.650

请注意，我更改了窗口，以便为您提供的示例数据提供合理的非NA值。