dcast 数据框没有给出正确的列
dcast a dataframe not giving correct columns
我正在尝试使用 dcast 传播我的数据 d:
看起来像：
row_id variable value
1 27 feature1 0.006960242
2 35 feature1 -0.002475289
3 27 feature2 -0.016615848
4 35 feature2 0.010806291
5 27 feature3 0.014437451
6 35 feature3 -0.009046077
我运行的代码是：
mutated_d <- d %>%
group_by(row_id) %>%
mutate(NewVar = sqrt(abs(value)))
mydcasted <- dcast(mutated_d, row_id ~ variable, value.var = c("value", "NewVar"))
给我这个错误：
Error in .subset2(x, i) : subscript out of bounds In addition: Warning
message: In if (!(value.var %in% names(data))) { : the condition has
length > 1 and only the first element will be used
好的，所以我尝试以下操作：
mydcasted <- dcast(mutated_d, row_id ~ variable, value.var = "value")
效果很好。但是它不包含我新创建的变量 NewVar。所以我尝试：
mydcasted <- dcast(mutated_d, row_id ~ variable, value.var = "NewVar")
这给了我与以前相同的输出...列为 feature1、feature2...featureN。我只想要 NewVar 的数据。 （还有 NewVar2、NewVar3 .. NewVarN）。
感谢任何帮助！
d <- structure(list(row_id = c(27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
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35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L), variable = structure(c(1L, 1L,
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23L, 23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L, 29L,
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36L, 36L, 37L, 37L, 38L, 38L, 39L, 39L, 40L, 40L, 41L, 41L, 42L,
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49L, 49L, 50L, 50L, 51L, 51L, 52L, 52L, 53L, 53L, 54L, 54L, 55L,
55L, 56L, 56L, 57L, 57L, 58L, 58L, 59L, 59L, 60L, 60L, 61L, 61L,
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75L, 75L, 76L, 76L, 77L, 77L, 78L, 78L, 79L, 79L, 80L, 80L, 81L,
81L, 82L, 82L, 83L, 83L, 84L, 84L, 85L, 85L, 86L, 86L, 87L, 87L,
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254L, 255L, 255L, 256L, 256L, 257L, 257L, 258L, 258L, 259L, 259L,
260L, 260L), .Label = c("feature1", "feature2", "feature3", "feature4",
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0.00745157222213444, -0.0180410738791992, -0.00473373532746524,
0.00227452329969657, 0.00475625015685899)), row.names = c(NA,
-520L), class = "data.frame")
此问题与从 reshape2 而不是 data.table 调用的 dcast 有关。 reshape2::dcast 不会占用多个 value.var，而 data.table::dcast 会占用
library(data.table)
dcast(setDT(mutated_d), row_id ~ variable, value.var = c('value', 'NewVar'))
此外，这主要可以在 data.table
中完成
dcast(setDT(d)[, NewVar := sqrt(abs(value))],
row_id ~ variable, value.var = c('value', 'NewVar'))
此外，从 tidyr (‘0.8.3.9000’) 的 dev 版本开始，我们可以将 pivot_wider 用于多个值列
library(tidyr)
libary(dplyr)
mutated_d %>%
ungroup %>%
pivot_wider(names_from = variable, values_from = c('value', 'NewVar'))
# A tibble: 2 x 521
# row_id value_feature1 value_feature2 value_feature3 value_feature4 value_feature5 value_feature6 value_feature7 value_feature8 value_feature9
# <int> <dbl> <dbl> <dbl> <dbl> #<dbl> <dbl> <dbl> <dbl> <dbl>
#1 27 0.00696 -0.0166 0.0144 0.00192 0.0144 -0.0341 -0.00728 -0.00624 -0.0345
#2 35 -0.00248 0.0108 -0.00905 0.00249 0.00248 -0.0190 -0.0101 0.0136 0.00252
# … with 511 more variables: ...
我正在尝试使用 dcast 传播我的数据 d:
看起来像：
row_id variable value
1 27 feature1 0.006960242
2 35 feature1 -0.002475289
3 27 feature2 -0.016615848
4 35 feature2 0.010806291
5 27 feature3 0.014437451
6 35 feature3 -0.009046077
我运行的代码是：
mutated_d <- d %>%
group_by(row_id) %>%
mutate(NewVar = sqrt(abs(value)))
mydcasted <- dcast(mutated_d, row_id ~ variable, value.var = c("value", "NewVar"))
给我这个错误：
Error in .subset2(x, i) : subscript out of bounds In addition: Warning message: In if (!(value.var %in% names(data))) { : the condition has length > 1 and only the first element will be used
好的，所以我尝试以下操作：
mydcasted <- dcast(mutated_d, row_id ~ variable, value.var = "value")
效果很好。但是它不包含我新创建的变量 NewVar。所以我尝试：
mydcasted <- dcast(mutated_d, row_id ~ variable, value.var = "NewVar")
这给了我与以前相同的输出...列为 feature1、feature2...featureN。我只想要 NewVar 的数据。 （还有 NewVar2、NewVar3 .. NewVarN）。
感谢任何帮助！
d <- structure(list(row_id = c(27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
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27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
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35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L,
35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L, 27L, 35L,
27L, 35L, 27L, 35L, 27L, 35L), variable = structure(c(1L, 1L,
2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L,
10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L,
16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L,
23L, 23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L, 29L,
29L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L, 34L, 35L, 35L,
36L, 36L, 37L, 37L, 38L, 38L, 39L, 39L, 40L, 40L, 41L, 41L, 42L,
42L, 43L, 43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 48L, 48L,
49L, 49L, 50L, 50L, 51L, 51L, 52L, 52L, 53L, 53L, 54L, 54L, 55L,
55L, 56L, 56L, 57L, 57L, 58L, 58L, 59L, 59L, 60L, 60L, 61L, 61L,
62L, 62L, 63L, 63L, 64L, 64L, 65L, 65L, 66L, 66L, 67L, 67L, 68L,
68L, 69L, 69L, 70L, 70L, 71L, 71L, 72L, 72L, 73L, 73L, 74L, 74L,
75L, 75L, 76L, 76L, 77L, 77L, 78L, 78L, 79L, 79L, 80L, 80L, 81L,
81L, 82L, 82L, 83L, 83L, 84L, 84L, 85L, 85L, 86L, 86L, 87L, 87L,
88L, 88L, 89L, 89L, 90L, 90L, 91L, 91L, 92L, 92L, 93L, 93L, 94L,
94L, 95L, 95L, 96L, 96L, 97L, 97L, 98L, 98L, 99L, 99L, 100L,
100L, 101L, 101L, 102L, 102L, 103L, 103L, 104L, 104L, 105L, 105L,
106L, 106L, 107L, 107L, 108L, 108L, 109L, 109L, 110L, 110L, 111L,
111L, 112L, 112L, 113L, 113L, 114L, 114L, 115L, 115L, 116L, 116L,
117L, 117L, 118L, 118L, 119L, 119L, 120L, 120L, 121L, 121L, 122L,
122L, 123L, 123L, 124L, 124L, 125L, 125L, 126L, 126L, 127L, 127L,
128L, 128L, 129L, 129L, 130L, 130L, 131L, 131L, 132L, 132L, 133L,
133L, 134L, 134L, 135L, 135L, 136L, 136L, 137L, 137L, 138L, 138L,
139L, 139L, 140L, 140L, 141L, 141L, 142L, 142L, 143L, 143L, 144L,
144L, 145L, 145L, 146L, 146L, 147L, 147L, 148L, 148L, 149L, 149L,
150L, 150L, 151L, 151L, 152L, 152L, 153L, 153L, 154L, 154L, 155L,
155L, 156L, 156L, 157L, 157L, 158L, 158L, 159L, 159L, 160L, 160L,
161L, 161L, 162L, 162L, 163L, 163L, 164L, 164L, 165L, 165L, 166L,
166L, 167L, 167L, 168L, 168L, 169L, 169L, 170L, 170L, 171L, 171L,
172L, 172L, 173L, 173L, 174L, 174L, 175L, 175L, 176L, 176L, 177L,
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183L, 183L, 184L, 184L, 185L, 185L, 186L, 186L, 187L, 187L, 188L,
188L, 189L, 189L, 190L, 190L, 191L, 191L, 192L, 192L, 193L, 193L,
194L, 194L, 195L, 195L, 196L, 196L, 197L, 197L, 198L, 198L, 199L,
199L, 200L, 200L, 201L, 201L, 202L, 202L, 203L, 203L, 204L, 204L,
205L, 205L, 206L, 206L, 207L, 207L, 208L, 208L, 209L, 209L, 210L,
210L, 211L, 211L, 212L, 212L, 213L, 213L, 214L, 214L, 215L, 215L,
216L, 216L, 217L, 217L, 218L, 218L, 219L, 219L, 220L, 220L, 221L,
221L, 222L, 222L, 223L, 223L, 224L, 224L, 225L, 225L, 226L, 226L,
227L, 227L, 228L, 228L, 229L, 229L, 230L, 230L, 231L, 231L, 232L,
232L, 233L, 233L, 234L, 234L, 235L, 235L, 236L, 236L, 237L, 237L,
238L, 238L, 239L, 239L, 240L, 240L, 241L, 241L, 242L, 242L, 243L,
243L, 244L, 244L, 245L, 245L, 246L, 246L, 247L, 247L, 248L, 248L,
249L, 249L, 250L, 250L, 251L, 251L, 252L, 252L, 253L, 253L, 254L,
254L, 255L, 255L, 256L, 256L, 257L, 257L, 258L, 258L, 259L, 259L,
260L, 260L), .Label = c("feature1", "feature2", "feature3", "feature4",
"feature5", "feature6", "feature7", "feature8", "feature9", "feature10",
"feature11", "feature12", "feature13", "feature14", "feature15",
"feature16", "feature17", "feature18", "feature19", "feature20",
"feature21", "feature22", "feature23", "feature24", "feature25",
"feature26", "feature27", "feature28", "feature29", "feature30",
"feature31", "feature32", "feature33", "feature34", "feature35",
"feature36", "feature37", "feature38", "feature39", "feature40",
"feature41", "feature42", "feature43", "feature44", "feature45",
"feature46", "feature47", "feature48", "feature49", "feature50",
"feature51", "feature52", "feature53", "feature54", "feature55",
"feature56", "feature57", "feature58", "feature59", "feature60",
"feature61", "feature62", "feature63", "feature64", "feature65",
"feature66", "feature67", "feature68", "feature69", "feature70",
"feature71", "feature72", "feature73", "feature74", "feature75",
"feature76", "feature77", "feature78", "feature79", "feature80",
"feature81", "feature82", "feature83", "feature84", "feature85",
"feature86", "feature87", "feature88", "feature89", "feature90",
"feature91", "feature92", "feature93", "feature94", "feature95",
"feature96", "feature97", "feature98", "feature99", "feature100",
"feature101", "feature102", "feature103", "feature104", "feature105",
"feature106", "feature107", "feature108", "feature109", "feature110",
"feature111", "feature112", "feature113", "feature114", "feature115",
"feature116", "feature117", "feature118", "feature119", "feature120",
"feature121", "feature122", "feature123", "feature124", "feature125",
"feature126", "feature127", "feature128", "feature129", "feature130",
"feature131", "feature132", "feature133", "feature134", "feature135",
"feature136", "feature137", "feature138", "feature139", "feature140",
"feature141", "feature142", "feature143", "feature144", "feature145",
"feature146", "feature147", "feature148", "feature149", "feature150",
"feature151", "feature152", "feature153", "feature154", "feature155",
"feature156", "feature157", "feature158", "feature159", "feature160",
"feature161", "feature162", "feature163", "feature164", "feature165",
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"feature186", "feature187", "feature188", "feature189", "feature190",
"feature191", "feature192", "feature193", "feature194", "feature195",
"feature196", "feature197", "feature198", "feature199", "feature200",
"feature201", "feature202", "feature203", "feature204", "feature205",
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"feature211", "feature212", "feature213", "feature214", "feature215",
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"feature241", "feature242", "feature243", "feature244", "feature245",
"feature246", "feature247", "feature248", "feature249", "feature250",
"feature251", "feature252", "feature253", "feature254", "feature255",
"feature256", "feature257", "feature258", "feature259", "feature260"
), class = "factor"), value = c(0.00696024229830469, -0.0024752893900013,
-0.0166158478838581, 0.0108062906243271, 0.0144374512212513,
-0.00904607697992132, 0.00191739489546857, 0.00248965185866812,
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此问题与从 reshape2 而不是 data.table 调用的 dcast 有关。 reshape2::dcast 不会占用多个 value.var，而 data.table::dcast 会占用
library(data.table)
dcast(setDT(mutated_d), row_id ~ variable, value.var = c('value', 'NewVar'))
此外，这主要可以在 data.table
dcast(setDT(d)[, NewVar := sqrt(abs(value))],
row_id ~ variable, value.var = c('value', 'NewVar'))
此外，从 tidyr (‘0.8.3.9000’) 的 dev 版本开始，我们可以将 pivot_wider 用于多个值列
library(tidyr)
libary(dplyr)
mutated_d %>%
ungroup %>%
pivot_wider(names_from = variable, values_from = c('value', 'NewVar'))
# A tibble: 2 x 521
# row_id value_feature1 value_feature2 value_feature3 value_feature4 value_feature5 value_feature6 value_feature7 value_feature8 value_feature9
# <int> <dbl> <dbl> <dbl> <dbl> #<dbl> <dbl> <dbl> <dbl> <dbl>
#1 27 0.00696 -0.0166 0.0144 0.00192 0.0144 -0.0341 -0.00728 -0.00624 -0.0345
#2 35 -0.00248 0.0108 -0.00905 0.00249 0.00248 -0.0190 -0.0101 0.0136 0.00252
# … with 511 more variables: ...