按組計算唯一值（data.table）不起作用
[英]Count unique values by group (data.table) doesn't work
問題描述
我試圖用data.table計算兩組（ year和pn_soc_informante ）變量（ ruc_pk_informante ）的唯一值，但我從dplyr得到不同的值（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
數據
# 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)))
1 個解決方案
解決方案1
1 已采納 2022-04-22 21:44:47
by上的sort打破了行的順序 - 即它單獨重新排列year ，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
或者稍后再做訂單
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
從已排序和未排序中查看output
> 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"
R中以data.table列分組並在另一列中計數唯一值的最快方法是什么？
[英]What is the FASTEST way in R to group by a data.table column and count unique values in another column?
在 data.table 中的每個組的行本身之前計算唯一值
[英]Count unique values before a row itself for each group in data.table
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