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R 按组计算唯一值(data.table)不起作用

我试图用data.table分两组(year和pn_soc_informante)计算变量(ruc_pk_informante)的唯一值，但我从dplyr(我用tidytable)得到不同的值.

data.table

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 and manually

> 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

Data

# 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)))

推荐答案

by上的sort打破了行的顺序，也就是说，它分别重新排列了year，这将导致其他列的值与"年"的排序列相匹配.应该在i号公路上

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

或者稍后再点菜

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

判断已排序和未排序数据的输出

> 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"

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