r - ifelse 不对数据帧执行子集

Question

我从 ifelse() 得到了一个完全出乎意料的结果，如果能解释原因，我将不胜感激。请参阅底部的可重现数据。

split_ratio = 0.8
target_label = "DV"
training.index <- caret::createDataPartition(dataset[[target_label]], p = split_ratio, list = FALSE)
training.set <- dataset[training.index, ]

以下按预期工作:

testing.set <- if (split_ratio != 1.0) dataset[-training.index, ] else NULL
testing.set
# A tibble: 2 x 6
     DV    nn    ee    oo    aa    cc
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1    89    87   135   112   118   139
2    80    82   134   111   136   128

假设相同的代码，包裹在 ifelse 中，返回一个包含向量的列表

testing.set <- ifelse(split_ratio != 1.0, dataset[-training.index, ], NULL)
testing.set
[[1]]
[1] 89 80

是否在 ifelse 中不允许对数据帧/tibble 进行子集化，还是有其他原因？从帮助页面看并不是很明白...

这是上面变量 dataset 的可重现内容:

structure(list(DV = c(98, 89, 93, 80, 80, 65, 92, 85, 80, 90, 
77, 80, 80, 75, 90, 88, 88, 90, 90, 88), nn = c(65, 87, 61, 82, 
67, 75, 79, 56, 82, 80, 63, 77, 68, 82, 82, 87, 83, 73, 60, 60
), ee = c(149, 135, 149, 134, 153, 143, 129, 167, 168, 121, 138, 
136, 129, 141, 116, 135, 142, 122, 134, 145), oo = c(118, 112, 
109, 111, 79, 118, 101, 107, 134, 112, 125, 120, 108, 125, 110, 
116, 94, 93, 108, 104), aa = c(129, 118, 140, 136, 99, 123, 119, 
122, 122, 124, 89, 123, 120, 162, 116, 126, 140, 122, 129, 123
), cc = c(162, 139, 155, 128, 137, 126, 120, 154, 155, 143, 137, 
137, 136, 138, 99, 119, 135, 138, 145, 147)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -20L), na.action = structure(c(`3` = 3L, 
`11` = 11L, `15` = 15L, `17` = 17L, `19` = 19L, `20` = 20L, `29` = 29L, 
`40` = 40L, `48` = 48L, `52` = 52L, `70` = 70L, `77` = 77L, `88` = 88L, 
`119` = 119L, `124` = 124L, `152` = 152L, `163` = 163L, `169` = 169L, 
`182` = 182L, `192` = 192L, `219` = 219L, `225` = 225L, `242` = 242L, 
`244` = 244L, `247` = 247L, `253` = 253L, `265` = 265L, `267` = 267L, 
`274` = 274L, `309` = 309L, `317` = 317L, `324` = 324L, `330` = 330L, 
`341` = 341L, `364` = 364L, `366` = 366L, `386` = 386L, `411` = 411L, 
`421` = 421L, `426` = 426L, `430` = 430L, `437` = 437L, `440` = 440L, 
`450` = 450L, `454` = 454L, `460` = 460L, `462` = 462L, `476` = 476L, 
`483` = 483L, `505` = 505L, `506` = 506L, `515` = 515L, `533` = 533L, 
`535` = 535L, `540` = 540L, `552` = 552L, `563` = 563L, `578` = 578L, 
`584` = 584L, `589` = 589L, `594` = 594L, `596` = 596L, `597` = 597L, 
`604` = 604L, `609` = 609L, `614` = 614L, `671` = 671L, `683` = 683L, 
`688` = 688L, `701` = 701L, `702` = 702L, `713` = 713L, `715` = 715L, 
`719` = 719L, `752` = 752L, `773` = 773L, `793` = 793L, `794` = 794L, 
`795` = 795L, `799` = 799L, `800` = 800L, `817` = 817L, `823` = 823L, 
`829` = 829L, `834` = 834L, `849` = 849L, `850` = 850L, `851` = 851L, 
`854` = 854L, `875` = 875L, `882` = 882L, `891` = 891L, `892` = 892L, 
`895` = 895L, `910` = 910L, `924` = 924L, `925` = 925L, `936` = 936L, 
`955` = 955L, `958` = 958L, `968` = 968L, `972` = 972L, `984` = 984L, 
`989` = 989L, `991` = 991L, `992` = 992L, `994` = 994L, `1007` = 1007L, 
`1018` = 1018L, `1029` = 1029L, `1030` = 1030L, `1049` = 1049L, 
`1065` = 1065L, `1084` = 1084L, `1085` = 1085L, `1086` = 1086L, 
`1095` = 1095L, `1096` = 1096L, `1097` = 1097L, `1100` = 1100L, 
`1102` = 1102L, `1110` = 1110L, `1117` = 1117L, `1125` = 1125L, 
`1127` = 1127L, `1145` = 1145L, `1160` = 1160L, `1161` = 1161L, 
`1164` = 1164L, `1166` = 1166L, `1171` = 1171L, `1187` = 1187L, 
`1191` = 1191L, `1194` = 1194L, `1212` = 1212L, `1215` = 1215L, 
`1239` = 1239L, `1254` = 1254L, `1262` = 1262L, `1263` = 1263L, 
`1274` = 1274L, `1297` = 1297L, `1308` = 1308L, `1325` = 1325L, 
`1328` = 1328L, `1331` = 1331L, `1337` = 1337L, `1338` = 1338L, 
`1340` = 1340L, `1342` = 1342L, `1348` = 1348L, `1354` = 1354L, 
`1361` = 1361L, `1367` = 1367L, `1373` = 1373L, `1379` = 1379L, 
`1389` = 1389L, `1406` = 1406L, `1411` = 1411L, `1422` = 1422L, 
`1423` = 1423L, `1436` = 1436L, `1439` = 1439L, `1441` = 1441L, 
`1446` = 1446L, `1449` = 1449L, `1476` = 1476L, `1480` = 1480L, 
`1481` = 1481L, `1483` = 1483L, `1503` = 1503L, `1511` = 1511L, 
`1516` = 1516L, `1521` = 1521L, `1524` = 1524L, `1527` = 1527L, 
`1544` = 1544L, `1550` = 1550L, `1567` = 1567L, `1580` = 1580L, 
`1582` = 1582L, `1586` = 1586L, `1595` = 1595L, `1601` = 1601L, 
`1609` = 1609L, `1612` = 1612L, `1619` = 1619L), class = "omit"))

Answer 1

ifelse 是一个向量化函数。参见 ?ifelse:

ifelse returns a value with the same shape as test which is filled with elements selected from either yes or no depending on whether the element of test is TRUE or FALSE.

由于split_ratio != 1.0是一个向量，其第一个值为TRUE，因此返回值的第一个值取自dataset[-training.index , ]，即dataset[-training.index, ][1]。因此，您得到了一个长度为 1 的列表。

ifelse 和 if{}else{} 不相等。该文档明确为您推荐 if{} else{} 案例:

Further note that if(test) yes else no is much more efficient and often much preferable to ifelse(test, yes, no) whenever test is a simple true/false result, i.e., when length(test) == 1.