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按組計算唯一值（data.table）不起作用

2022-04-23 16:23:04 移動端開發

我試圖ruc_pk_informante用兩組（year和pn_soc_informante）計算變數（）中的唯一值，但我從（我使用）中data.table得到不同的值。dplyrtidytable

資料表

library(data.table)
library(tidytable)

> tt[, .(count = uniqueN(ruc_pk_informante)), by = .(sort(year),pn_soc_informante)]
# A tidytable: 4 x 3
   sort pn_soc_informante count
  <int> <chr>             <int>
1  2012 PERSONA NATURAL       1
2  2013 PERSONA NATURAL      10 --------->????
3  2014 PERSONA NATURAL       8
4  2015 PERSONA NATURAL       1

dplyr 和手動

> tt %>% arrange.(year) %>% summarise.(n_distinct.(ruc_pk_informante), .by = c(year, pn_soc_informante))
# A tidytable: 4 x 3
   year pn_soc_informante    V1
  <int> <chr>             <int>
1  2012 PERSONA NATURAL       1
2  2013 PERSONA NATURAL      17 --------> OK!
3  2014 PERSONA NATURAL      12
4  2015 PERSONA NATURAL      13
> t1 = tt %>% filter.(year == 2013)
> length(unique(t1$ruc_pk_informante)) # OK!
[1] 17

資料

# data ---------------------------------------------------------------------


tt = structure(list(ruc_pk_informante = c("R3", "R3", "R3", "R3", 
                                     "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", 
                                     "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", 
                                     "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", 
                                     "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", 
                                     "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", "R3", 
                                     "R3", "R3", "R3", "R3", "R3", "R5", "R5", "R7", "R7", "R7", "R7", 
                                     "C9", "C9", "R12", "R12", "R12", "R14", "R16", "R16", "R16", 
                                     "R16", "R16", "R16", "R16", "R16", "R16", "R16", "R16", "R16", 
                                     "R16", "R16", "R16", "R16", "R16", "R16", "R16", "R16", "R16", 
                                     "C21", "R23", "R23", "R23", "R23", "R23", "R23", "R23", "R23", 
                                     "R23", "R27", "R27", "R27", "R27", "R27", "R27", "R27", "R27", 
                                     "R27", "R27", "R27", "R27", "R27", "R27", "R27", "R27", "R27", 
                                     "R27", "R27", "R27", "R27", "R27", "R27", "R30", "R30", "R30", 
                                     "R30", "R33", "R33", "R33", "R33", "R33", "R33", "R34", "R34", 
                                     "R34", "R34", "R37", "R41", "R41", "R41", "R41", "R41", "R41", 
                                     "R41", "R44", "R44", "R44", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", 
                                     "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45", "R45"
), year = c(2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2015L, 2015L, 
            2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 
            2015L, 2015L, 2015L, 2013L, 2015L, 2012L, 2013L, 2014L, 2015L, 
            2013L, 2015L, 2013L, 2014L, 2015L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 
            2013L, 2013L, 2013L, 2013L, 2014L, 2014L, 2014L, 2015L, 2015L, 
            2015L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2014L, 2014L, 2014L, 2014L, 2014L, 2015L, 2015L, 2015L, 
            2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2013L, 2014L, 2015L, 
            2015L, 2013L, 2013L, 2014L, 2014L, 2015L, 2015L, 2013L, 2013L, 
            2014L, 2015L, 2013L, 2013L, 2013L, 2014L, 2014L, 2015L, 2015L, 
            2015L, 2013L, 2014L, 2015L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 
            2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 
            2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L
), pn_soc_informante = c("PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", 
                         "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL", "PERSONA NATURAL"
)), row.names = c(NA, -250L), class = c("tidytable", "data.table", 
                                        "data.frame"), index = structure(integer(0), "`__year`" = c(67L, 
                                                                                                                                                       1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
                                                                                                                                                       15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 65L, 68L, 71L, 73L, 76L, 
                                                                                                                                                       77L, 78L, 79L, 80L, 81L, 82L, 98L, 99L, 100L, 101L, 108L, 109L, 
                                                                                                                                                       110L, 111L, 112L, 113L, 114L, 115L, 116L, 131L, 135L, 136L, 141L, 
                                                                                                                                                       142L, 145L, 146L, 147L, 153L, 156L, 157L, 158L, 159L, 160L, 161L, 
                                                                                                                                                       162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 
                                                                                                                                                       173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 
                                                                                                                                                       184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 
                                                                                                                                                       195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 
                                                                                                                                                       206L, 207L, 208L, 209L, 210L, 211L, 212L, 213L, 23L, 24L, 25L, 
                                                                                                                                                       26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 
                                                                                                                                                       39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 69L, 
                                                                                                                                                       74L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 102L, 103L, 104L, 
                                                                                                                                                       117L, 118L, 119L, 120L, 121L, 132L, 137L, 138L, 143L, 148L, 149L, 
                                                                                                                                                       154L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 
                                                                                                                                                       224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 
                                                                                                                                                       235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 
                                                                                                                                                       246L, 247L, 248L, 249L, 250L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 
                                                                                                                                                       58L, 59L, 60L, 61L, 62L, 63L, 64L, 66L, 70L, 72L, 75L, 91L, 92L, 
                                                                                                                                                       93L, 94L, 95L, 96L, 97L, 105L, 106L, 107L, 122L, 123L, 124L, 
                                                                                                                                                       125L, 126L, 127L, 128L, 129L, 130L, 133L, 134L, 139L, 140L, 144L, 
                                                                                                                                                       150L, 151L, 152L, 155L)))

uj5u.com熱心網友回復：

上sort正在by破壞行的順序 - 即它正在year單獨重新排列，這將導致來自與“年份”的排序列匹配的其他列的錯誤值。它應該在i

tt[order(year), .(count = uniqueN(ruc_pk_informante)), 
     by = .(year, pn_soc_informante)]
# A tidytable: 4 × 3
   year pn_soc_informante count
  <int> <chr>             <int>
1  2012 PERSONA NATURAL       1
2  2013 PERSONA NATURAL      17
3  2014 PERSONA NATURAL      12
4  2015 PERSONA NATURAL      13

或稍后下訂單

tt[, .(count = uniqueN(ruc_pk_informante)), 
   by = .(year, pn_soc_informante)][order(year)]
# A tidytable: 4 × 3
   year pn_soc_informante count
  <int> <chr>             <int>
1  2012 PERSONA NATURAL       1
2  2013 PERSONA NATURAL      17
3  2014 PERSONA NATURAL      12
4  2015 PERSONA NATURAL      13

檢查排序和未排序的輸出

> tt[, .SD, by = .(year = year,pn_soc_informante)][year == 2013]$ruc_pk_informante
  [1] "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3" 
 [23] "R5"  "R7"  "C9"  "R12" "R14" "R16" "R16" "R16" "R16" "R16" "R16" "C21" "R23" "R23" "R23" "R27" "R27" "R27" "R27" "R27" "R27" "R27"
 [45] "R27" "R27" "R30" "R33" "R33" "R34" "R34" "R37" "R41" "R41" "R44" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45"
 [67] "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45"
 [89] "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45" "R45"
[111] "R45" "R45" "R45"
> tt[, .SD, by = .(year = sort(year),pn_soc_informante)][year == 2013]$ruc_pk_informante
  [1] "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3" 
 [23] "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3" 
 [45] "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R3"  "R5"  "R5"  "R7" 
 [67] "R7"  "R7"  "R7"  "C9"  "C9"  "R12" "R12" "R12" "R14" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16"
 [89] "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "C21" "R23" "R23" "R23" "R23" "R23" "R23" "R23" "R23" "R23" "R27" "R27" "R27" "R27"
[111] "R27" "R27" "R27"

轉載請註明出處，本文鏈接：https://www.uj5u.com/yidong/462249.html

標籤：r dplyr 数据表

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