按组计算唯一值 (data.table) 不起作用
Count unique values by group (data.table) doesn't work
我正在尝试用 data.table 计算两个组（year 和 pn_soc_informante）中变量 (ruc_pk_informante) 的唯一值，但我得到的结果不同来自 dplyr 的值（我使用 tidytable）。
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 和手动
> 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
数据
# 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)))
by 上的 sort 打乱了行的顺序 - 即它正在单独重新排列 year，这将导致与匹配的其他列的错误值'year' 的排序列。它应该在 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
或稍后下单
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
检查已排序和未排序的输出
> 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]$ruc_pk_informante
[1] "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3"
[23] "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3"
[45] "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R3" "R5" "R5" "R7"
[67] "R7" "R7" "R7" "C9" "C9" "R12" "R12" "R12" "R14" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16"
[89] "R16" "R16" "R16" "R16" "R16" "R16" "R16" "R16" "C21" "R23" "R23" "R23" "R23" "R23" "R23" "R23" "R23" "R23" "R27" "R27" "R27" "R27"
[111] "R27" "R27" "R27"
我正在尝试用 data.table 计算两个组（year 和 pn_soc_informante）中变量 (ruc_pk_informante) 的唯一值，但我得到的结果不同来自 dplyr 的值（我使用 tidytable）。
data.table
library(data.table)
library(tidytable)
> tt[, .(count = uniqueN(ruc_pk_informante)), by = .(sort(year),pn_soc_informante)]
# A tidytable: 4 x 3
sort pn_soc_informante count
<int> <chr> <int>
1 2012 PERSONA NATURAL 1
2 2013 PERSONA NATURAL 10 --------->????
3 2014 PERSONA NATURAL 8
4 2015 PERSONA NATURAL 1
dplyr 和手动
> tt %>% arrange.(year) %>% summarise.(n_distinct.(ruc_pk_informante), .by = c(year, pn_soc_informante))
# A tidytable: 4 x 3
year pn_soc_informante V1
<int> <chr> <int>
1 2012 PERSONA NATURAL 1
2 2013 PERSONA NATURAL 17 --------> OK!
3 2014 PERSONA NATURAL 12
4 2015 PERSONA NATURAL 13
> t1 = tt %>% filter.(year == 2013)
> length(unique(t1$ruc_pk_informante)) # OK!
[1] 17
数据
# 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)))
by 上的 sort 打乱了行的顺序 - 即它正在单独重新排列 year，这将导致与匹配的其他列的错误值'year' 的排序列。它应该在 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
或稍后下单
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
检查已排序和未排序的输出
> 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"