使用多个因子/列将 R 中的长数据集转换为宽数据集

Question

我已经将这个数据集从长转换为宽，但丢失了代码！ 我记得它只有一行。 当我现在看看如何将长转换为宽时，它看起来非常复杂。

在图1 中，您可以看到长格式的数据集。 我想在变量 ObstNamePOST 到 ReactionTimePost 上使用 C_RT 作为依赖变量（值）更改为宽形式。 另一张图2显示了我第一次成功时数据的样子。 现在的区别是我想在传播中再包含一个变量，即 ReacTimePOST。

我试过这个代码

wide_drive <- spread(all_drive_data, 'ObstNamePOST:ReacTimePOST','NC_RT')

没有成功，'obstNamePOST:ReacTimePost' 未被识别。

感谢您的阅读以及您可能留下的任何建议......

structure(list(UsernamePOST = c(604, 604, 604, 604, 604, 604, 
606, 606, 609, 609), ObstNamePOST = c("Barrel", "Barrel", "Barrel", 
"Barrel", "Horse", "Horse", "Barrel", "Barrel", "Barrel", "Horse"
), Obst_DistPOST = c("Far", "Near", "Near", "Near", "Near", "Near", 
"Near", "Near", "Near", "Near"), Obst_SpeedPOST = c("Slow", "Slow", 
"Slow", "Fast", "Slow", "Fast", "Slow", "Fast", "Slow", "Fast"
), HeartBeat_DistancePOST = c(-300, -300, 0, 0, 0, 0, 0, -300, 
-300, -300), ReacTimePOST = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 
C_RT = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), NC_RT = c(100, 
100, 100, 100, 100, 100, 100, 100, 100, 100)), row.names = c(NA, 
-10L), class = c("tbl_df", "tbl", "data.frame"))

复制粘贴：

当前数据集：

HeartBeat_DistancePOST   ReacTimePOST   C_RT     NC_RT
604  Barrel  Far     Slow   -300    1       100
604  Barrel  Near    Slow   -300    1       100
604  Barrel  Near    Slow   0   1       100
604  Barrel  Near    Fast   0   1       100
604  Horse   Near    Slow   0   1       100
604  Horse   Near    Fast   0   1       100
606  Barrel  Near    Slow   0   1       100
606  Barrel  Near    Fast   -300    1       100

所需的数据集，但我想添加 ReacTimePOST 变量：

Participant_Number  Adult_Left_Normal_-300  Adult_Left_Normal_000   Adult_Right_Normal_-300 Adult_Right_Normal_000  Barrel_Far_Fast_-300    Barrel_Far_Fast_000 Barrel_Far_Slow_-300    Barrel_Far_Slow_000 Barrel_Near_Fast_-300   Barrel_Near_Fast_000    Barrel_Near_Slow_-300   Barrel_Near_Slow_000    Child_Left_Normal_-300  Child_Left_Normal_000   Child_Right_Normal_-300 Child_Right_Normal_000  Horse_Far_Fast_-300 Horse_Far_Fast_000  Horse_Far_Slow_-300 Horse_Far_Slow_000  Horse_Near_Fast_-300    Horse_Near_Fast_000 Horse_Near_Slow_-300    Horse_Near_Slow_000
601 0.388346367 0.235300697 0.03155518  0.624582933 0.110802464 0.25957337  0.08679504  0.164433292 0.284582528 0.413972863 0.207617186 0.28161927  0.13972601  0.045308433 0.304697667 0.10031129  0.157257088 0.359689348 0.116680908 0.263032534 0.19115524  0.185128782 0.505271842 0.158193964
602 0.294846867 0.388679503 0.15895842  0.221110027 0.228887962 0.503114284 0.243893454 0.39346924  0.381271356 0.120846548 0.13975677  0.319363408 0.224146543 0.343569433 0.12073006  0.29235078  0.339309712 0.362793736 0.52406006  0.28859633  0.518426446 0.148005672 0.3388977   0.336997982
603 0.174173987 0.239552833 0.234517407 0.16715495  0.27220459  0.179284668 0.107244872 0.187420646 0.051934812 0.214245614 0.1635315   0.192297354 0.072041843 0.098042807 0.243052167 0.082326267 0.198791506 0.078521728 0.216784674 0.103741438 0.091583236 0.171832274 0.302374278 0.460546944
604 0.309366873 1.046732467 0.23858644  0.594436633 0.563371266 0.22630921  0.610015855 0.267286698 0.128946947 0.650960267 0.417259213 0.141574878 0.664484667 0.418242113 0.7192688   0.482289633 0.492884625 0.700427174 0.4072052   0.39345704  0.406260173 0.764027767 0.600029025 0.54421615
605 0.309895843 0.6479008   0.6499176   0.579925533 0.381809986 0.49032592  0.75975724  0.44464418  0.38040314  0.57066642  0.439990252 0.68888845  0.121592207 0.3935903   0.7694243   0.453104667 0.55651854  0.6326355   0.419287102 0.44053956  0.747642608 0.64734956  0.28784026  0.533651724
606 0.62498475  0.340876283 0.448109933 0.803698233 0.225082393 0.612350448 0.7096863   0.72827456  0.419136025 0.769892233 0.48204268  0.626884475 0.49825669  0.996437233 0.753606167 0.745627033 0.62997436  0.7341919   0.3663559   0.6824097   0.8533996   0.271708173 0.458166508 0.54191892
607 0.192169187 0.526224767 0.437194823 0.455769867 0.29140931  0.41169432  0.338650518 0.231164566 0.479882814 0.3833084   0.504272444 0.267279054 0.37011209  0.09752402  0.353108733 0.390748353 0.177707665 0.317674248 0.2109268   0.423828128 0.314648434 0.313227836 0.174044794 0.268264778
608 0.25473659  0.2265676   0.07975769  0.7150548   0.229162594 0.172861085 0.263035584 0.092198184 0.267520126 0.253094474 0.404348766 0.18172761  0.433207173 0.281443287 0.104553227 0.36150105  0.329232798 0.283331298 0.268568432 0.265734874 0.285373688 0.300894158 0.274803175 0.43774414
609 0.700414033 0.7678426   0.490203867 0.523523967 0.46584778  0.56144714  0.55058594  0.40422974  0.45477906  0.53903502  0.4534378   0.68759468  0.565592433 0.7615255   0.6405538   0.667154967 0.50109252  0.275094598 0.62073366  0.34751892  0.518737767 0.741455075 0.527481075 0.54662476

Answer 1

我认为您需要在spread它们之前spread它们unite ：

wide_drive <- all_drive_data %>% 
    unite(unt, ObstNamePOST:ReacTimePOST) %>%
    spread(unt, NC_RT)