39

我有一个 data.table dt。此 data.table 首先按列date（我的分组变量）排序，然后按列排序age：

library(data.table)
setkeyv(dt, c("date", "age")) # Sorts table first by column "date" then by "age"
> dt
         date age     name
1: 2000-01-01   3   Andrew
2: 2000-01-01   4      Ben
3: 2000-01-01   5  Charlie
4: 2000-01-02   6     Adam
5: 2000-01-02   7      Bob
6: 2000-01-02   8 Campbell

我的问题是：我想知道是否可以为每个唯一日期提取前 2 行？或更笼统地说：

如何提取每组中的前 n 行？

在此示例中，结果dt.f将是：

> dt.f = ???????? # function of dt to extract the first 2 rows per unique date
> dt.f
         date age   name
1: 2000-01-01   3 Andrew
2: 2000-01-01   4    Ben
3: 2000-01-02   6   Adam
4: 2000-01-02   7    Bob

ps 下面是创建上述data.table的代码：

install.packages("data.table")
library(data.table)
date <- c("2000-01-01","2000-01-01","2000-01-01",
    "2000-01-02","2000-01-02","2000-01-02")
age <- c(3,4,5,6,7,8)
name <- c("Andrew","Ben","Charlie","Adam","Bob","Campbell")
dt <- data.table(date, age, name)
setkeyv(dt,c("date","age")) # Sorts table first by column "date" then by "age"
4

3 回答 3

60

是的，只需.SD根据需要使用和索引它。

  DT[, .SD[1:2], by=date]

           date age   name
  1: 2000-01-01   3 Andrew
  2: 2000-01-01   4    Ben
  3: 2000-01-02   6   Adam
  4: 2000-01-02   7    Bob

根据@eddi 的建议进行了编辑。

@eddi 的建议是：

使用它来提高速度： 

  DT[DT[, .I[1:2], by = date]$V1]

  # using a slightly larger data set
  > microbenchmark(SDstyle=DT[, .SD[1:2], by=date], IStyle=DT[DT[, .I[1:2], by = date]$V1], times=200L)
  Unit: milliseconds
      expr       min        lq    median        uq      max neval
   SDstyle 13.567070 16.224797 22.170302 24.239881 88.26719   200
    IStyle  1.675185  2.018773  2.168818  2.269292 11.31072   200
于 2013-05-01T20:28:45.487 回答
2

可能不是最快的方法，但如果您不使用键控变量并且需要更多灵活性，它会提供一些灵活性。通过更改选择Row.ID的第一个对象的数量可以根据需要进行调整。

dt[, .( age
        , name
        , Row.ID = rank(age)
        )
   , by = list(date)][Row.ID %in% (1:2), .(date
                                           , age
                                           , name
                                           )]
于 2018-06-21T07:58:50.143 回答
0

这是一个扩展的解决方案。目标是在 1 行代码中从 data.table 中的过滤数据中删除第一行（或其他行）。

我尝试在这里举例说明：

mpg2 <- data.table(mpg)


mpg2



     # manufacturer  model displ year cyl      trans drv cty hwy fl   class
  # 1:         audi     a4   1.8 1999   4   auto(l5)   f  18  29  p compact
  # 2:         audi     a4   1.8 1999   4 manual(m5)   f  21  29  p compact
  # 3:         audi     a4   2.0 2008   4 manual(m6)   f  20  31  p compact
  # 4:         audi     a4   2.0 2008   4   auto(av)   f  21  30  p compact
  # 5:         audi     a4   2.8 1999   6   auto(l5)   f  16  26  p compact
 # ---                                                                     
# 230:   volkswagen passat   2.0 2008   4   auto(s6)   f  19  28  p midsize
# 231:   volkswagen passat   2.0 2008   4 manual(m6)   f  21  29  p midsize
# 232:   volkswagen passat   2.8 1999   6   auto(l5)   f  16  26  p midsize
# 233:   volkswagen passat   2.8 1999   6 manual(m5)   f  18  26  p midsize
# 234:   volkswagen passat   3.6 2008   6   auto(s6)   f  17  26  p midsize


# I wanna first filter the table with year is 1999 and that it is an manual


mpg2[year == "1999" & grepl("manual", trans)]




    # manufacturer               model displ year cyl      trans drv cty hwy fl      class
 # 1:         audi                  a4   1.8 1999   4 manual(m5)   f  21  29  p    compact
 # 2:         audi                  a4   2.8 1999   6 manual(m5)   f  18  26  p    compact
 # 3:         audi          a4 quattro   1.8 1999   4 manual(m5)   4  18  26  p    compact
 # 4:         audi          a4 quattro   2.8 1999   6 manual(m5)   4  17  25  p    compact
 # 5:    chevrolet            corvette   5.7 1999   8 manual(m6)   r  16  26  p    2seater
 # 6:        dodge   dakota pickup 4wd   3.9 1999   6 manual(m5)   4  14  17  r     pickup
 # 7:        dodge   dakota pickup 4wd   5.2 1999   8 manual(m5)   4  11  17  r     pickup
 # 8:        dodge ram 1500 pickup 4wd   5.2 1999   8 manual(m5)   4  11  16  r     pickup
 # 9:         ford        explorer 4wd   4.0 1999   6 manual(m5)   4  15  19  r        suv
# 10:         ford     f150 pickup 4wd   4.2 1999   6 manual(m5)   4  14  17  r     pickup
# 11:         ford     f150 pickup 4wd   4.6 1999   8 manual(m5)   4  13  16  r     pickup
# 12:         ford             mustang   3.8 1999   6 manual(m5)   r  18  26  r subcompact
# 13:         ford             mustang   4.6 1999   8 manual(m5)   r  15  22  r subcompact
# 14:        honda               civic   1.6 1999   4 manual(m5)   f  28  33  r subcompact
# 15:        honda               civic   1.6 1999   4 manual(m5)   f  25  32  r subcompact
# 16:        honda               civic   1.6 1999   4 manual(m5)   f  23  29  p subcompact
# 17:      hyundai              sonata   2.4 1999   4 manual(m5)   f  18  27  r    midsize
# 18:      hyundai              sonata   2.5 1999   6 manual(m5)   f  18  26  r    midsize
# 19:      hyundai             tiburon   2.0 1999   4 manual(m5)   f  19  29  r subcompact
# 20:       nissan              altima   2.4 1999   4 manual(m5)   f  21  29  r    compact
# 21:       nissan              maxima   3.0 1999   6 manual(m5)   f  19  25  r    midsize
# 22:       nissan      pathfinder 4wd   3.3 1999   6 manual(m5)   4  15  17  r        suv
# 23:       subaru        forester awd   2.5 1999   4 manual(m5)   4  18  25  r        suv
# 24:       subaru         impreza awd   2.2 1999   4 manual(m5)   4  19  26  r subcompact
# 25:       subaru         impreza awd   2.5 1999   4 manual(m5)   4  19  26  r subcompact
# 26:       toyota         4runner 4wd   2.7 1999   4 manual(m5)   4  15  20  r        suv
# 27:       toyota         4runner 4wd   3.4 1999   6 manual(m5)   4  15  17  r        suv
# 28:       toyota               camry   2.2 1999   4 manual(m5)   f  21  29  r    midsize
# 29:       toyota               camry   3.0 1999   6 manual(m5)   f  18  26  r    midsize
# 30:       toyota        camry solara   2.2 1999   4 manual(m5)   f  21  29  r    compact
# 31:       toyota        camry solara   3.0 1999   6 manual(m5)   f  18  26  r    compact
# 32:       toyota             corolla   1.8 1999   4 manual(m5)   f  26  35  r    compact
# 33:       toyota   toyota tacoma 4wd   2.7 1999   4 manual(m5)   4  15  20  r     pickup
# 34:       toyota   toyota tacoma 4wd   3.4 1999   6 manual(m5)   4  15  17  r     pickup
# 35:   volkswagen                 gti   2.0 1999   4 manual(m5)   f  21  29  r    compact
# 36:   volkswagen                 gti   2.8 1999   6 manual(m5)   f  17  24  r    compact
# 37:   volkswagen               jetta   1.9 1999   4 manual(m5)   f  33  44  d    compact
# 38:   volkswagen               jetta   2.0 1999   4 manual(m5)   f  21  29  r    compact
# 39:   volkswagen               jetta   2.8 1999   6 manual(m5)   f  17  24  r    compact
# 40:   volkswagen          new beetle   1.9 1999   4 manual(m5)   f  35  44  d subcompact
# 41:   volkswagen          new beetle   2.0 1999   4 manual(m5)   f  21  29  r subcompact
# 42:   volkswagen              passat   1.8 1999   4 manual(m5)   f  21  29  p    midsize
# 43:   volkswagen              passat   2.8 1999   6 manual(m5)   f  18  26  p    midsize
    # manufacturer               model displ year cyl      trans drv cty hwy fl      class


# maybe I wanna order it


mpg2[year == "1999" & grepl("manual", trans)][order(model, -displ)]




    # manufacturer               model displ year cyl      trans drv cty hwy fl      class
 # 1:       toyota         4runner 4wd   3.4 1999   6 manual(m5)   4  15  17  r        suv
 # 2:       toyota         4runner 4wd   2.7 1999   4 manual(m5)   4  15  20  r        suv
 # 3:         audi                  a4   2.8 1999   6 manual(m5)   f  18  26  p    compact
 # 4:         audi                  a4   1.8 1999   4 manual(m5)   f  21  29  p    compact
 # 5:         audi          a4 quattro   2.8 1999   6 manual(m5)   4  17  25  p    compact
 # 6:         audi          a4 quattro   1.8 1999   4 manual(m5)   4  18  26  p    compact
 # 7:       nissan              altima   2.4 1999   4 manual(m5)   f  21  29  r    compact
 # 8:       toyota               camry   3.0 1999   6 manual(m5)   f  18  26  r    midsize
 # 9:       toyota               camry   2.2 1999   4 manual(m5)   f  21  29  r    midsize
# 10:       toyota        camry solara   3.0 1999   6 manual(m5)   f  18  26  r    compact
# 11:       toyota        camry solara   2.2 1999   4 manual(m5)   f  21  29  r    compact
# 12:        honda               civic   1.6 1999   4 manual(m5)   f  28  33  r subcompact
# 13:        honda               civic   1.6 1999   4 manual(m5)   f  25  32  r subcompact
# 14:        honda               civic   1.6 1999   4 manual(m5)   f  23  29  p subcompact
# 15:       toyota             corolla   1.8 1999   4 manual(m5)   f  26  35  r    compact
# 16:    chevrolet            corvette   5.7 1999   8 manual(m6)   r  16  26  p    2seater
# 17:        dodge   dakota pickup 4wd   5.2 1999   8 manual(m5)   4  11  17  r     pickup
# 18:        dodge   dakota pickup 4wd   3.9 1999   6 manual(m5)   4  14  17  r     pickup
# 19:         ford        explorer 4wd   4.0 1999   6 manual(m5)   4  15  19  r        suv
# 20:         ford     f150 pickup 4wd   4.6 1999   8 manual(m5)   4  13  16  r     pickup
# 21:         ford     f150 pickup 4wd   4.2 1999   6 manual(m5)   4  14  17  r     pickup
# 22:       subaru        forester awd   2.5 1999   4 manual(m5)   4  18  25  r        suv
# 23:   volkswagen                 gti   2.8 1999   6 manual(m5)   f  17  24  r    compact
# 24:   volkswagen                 gti   2.0 1999   4 manual(m5)   f  21  29  r    compact
# 25:       subaru         impreza awd   2.5 1999   4 manual(m5)   4  19  26  r subcompact
# 26:       subaru         impreza awd   2.2 1999   4 manual(m5)   4  19  26  r subcompact
# 27:   volkswagen               jetta   2.8 1999   6 manual(m5)   f  17  24  r    compact
# 28:   volkswagen               jetta   2.0 1999   4 manual(m5)   f  21  29  r    compact
# 29:   volkswagen               jetta   1.9 1999   4 manual(m5)   f  33  44  d    compact
# 30:       nissan              maxima   3.0 1999   6 manual(m5)   f  19  25  r    midsize
# 31:         ford             mustang   4.6 1999   8 manual(m5)   r  15  22  r subcompact
# 32:         ford             mustang   3.8 1999   6 manual(m5)   r  18  26  r subcompact
# 33:   volkswagen          new beetle   2.0 1999   4 manual(m5)   f  21  29  r subcompact
# 34:   volkswagen          new beetle   1.9 1999   4 manual(m5)   f  35  44  d subcompact
# 35:   volkswagen              passat   2.8 1999   6 manual(m5)   f  18  26  p    midsize
# 36:   volkswagen              passat   1.8 1999   4 manual(m5)   f  21  29  p    midsize
# 37:       nissan      pathfinder 4wd   3.3 1999   6 manual(m5)   4  15  17  r        suv
# 38:        dodge ram 1500 pickup 4wd   5.2 1999   8 manual(m5)   4  11  16  r     pickup
# 39:      hyundai              sonata   2.5 1999   6 manual(m5)   f  18  26  r    midsize
# 40:      hyundai              sonata   2.4 1999   4 manual(m5)   f  18  27  r    midsize
# 41:      hyundai             tiburon   2.0 1999   4 manual(m5)   f  19  29  r subcompact
# 42:       toyota   toyota tacoma 4wd   3.4 1999   6 manual(m5)   4  15  17  r     pickup
# 43:       toyota   toyota tacoma 4wd   2.7 1999   4 manual(m5)   4  15  20  r     pickup
    # manufacturer               model displ year cyl      trans drv cty hwy fl      class


# My wish would be to extract the model from 1999 with the highest displ (I could use max, but I will extract the first row)


mpg2[year == "1999" & grepl("manual", trans)][order(model, -displ), .SD[1], model]




                  # model manufacturer displ year cyl      trans drv cty hwy fl      class
 # 1:         4runner 4wd       toyota   3.4 1999   6 manual(m5)   4  15  17  r        suv
 # 2:                  a4         audi   2.8 1999   6 manual(m5)   f  18  26  p    compact
 # 3:          a4 quattro         audi   2.8 1999   6 manual(m5)   4  17  25  p    compact
 # 4:              altima       nissan   2.4 1999   4 manual(m5)   f  21  29  r    compact
 # 5:               camry       toyota   3.0 1999   6 manual(m5)   f  18  26  r    midsize
 # 6:        camry solara       toyota   3.0 1999   6 manual(m5)   f  18  26  r    compact
 # 7:               civic        honda   1.6 1999   4 manual(m5)   f  28  33  r subcompact
 # 8:             corolla       toyota   1.8 1999   4 manual(m5)   f  26  35  r    compact
 # 9:            corvette    chevrolet   5.7 1999   8 manual(m6)   r  16  26  p    2seater
# 10:   dakota pickup 4wd        dodge   5.2 1999   8 manual(m5)   4  11  17  r     pickup
# 11:        explorer 4wd         ford   4.0 1999   6 manual(m5)   4  15  19  r        suv
# 12:     f150 pickup 4wd         ford   4.6 1999   8 manual(m5)   4  13  16  r     pickup
# 13:        forester awd       subaru   2.5 1999   4 manual(m5)   4  18  25  r        suv
# 14:                 gti   volkswagen   2.8 1999   6 manual(m5)   f  17  24  r    compact
# 15:         impreza awd       subaru   2.5 1999   4 manual(m5)   4  19  26  r subcompact
# 16:               jetta   volkswagen   2.8 1999   6 manual(m5)   f  17  24  r    compact
# 17:              maxima       nissan   3.0 1999   6 manual(m5)   f  19  25  r    midsize
# 18:             mustang         ford   4.6 1999   8 manual(m5)   r  15  22  r subcompact
# 19:          new beetle   volkswagen   2.0 1999   4 manual(m5)   f  21  29  r subcompact
# 20:              passat   volkswagen   2.8 1999   6 manual(m5)   f  18  26  p    midsize
# 21:      pathfinder 4wd       nissan   3.3 1999   6 manual(m5)   4  15  17  r        suv
# 22: ram 1500 pickup 4wd        dodge   5.2 1999   8 manual(m5)   4  11  16  r     pickup
# 23:              sonata      hyundai   2.5 1999   6 manual(m5)   f  18  26  r    midsize
# 24:             tiburon      hyundai   2.0 1999   4 manual(m5)   f  19  29  r subcompact
# 25:   toyota tacoma 4wd       toyota   3.4 1999   6 manual(m5)   4  15  17  r     pickup
                  # model manufacturer displ year cyl      trans drv cty hwy fl      class


# my goal now is actually from the orginal table to remove those rows, we will you then .I which will return the frow ID from those ones. However it should be a bit differently written. All of this in 1 line of code:
# mpg2[year == "1999" & grepl("manual", trans)] is same as mpg2[mpg2[, .I[year == "1999" & grepl("manual", trans)]]]
# mpg2[year == "1999" & grepl("manual", trans)] is same as mpg2[mpg2[, .I[year == "1999" & grepl("manual", trans)], model]$V1]

# goal is to first get row ID (V1) of filtered data and keep our important variable for future steps


mpg2[, .I[year == "1999" & grepl("manual", trans)], .(model, displ)]



                  # model displ  V1
 # 1:                  a4   1.8   2
 # 2:                  a4   2.8   6
 # 3:          a4 quattro   1.8   8
 # 4:          a4 quattro   2.8  13
 # 5:            corvette   5.7  24
 # 6:   dakota pickup 4wd   3.9  52
 # 7:   dakota pickup 4wd   5.2  56
 # 8: ram 1500 pickup 4wd   5.2  72
 # 9:        explorer 4wd   4.0  79
# 10:     f150 pickup 4wd   4.2  85
# 11:     f150 pickup 4wd   4.6  86
# 12:             mustang   3.8  91
# 13:             mustang   4.6  96
# 14:               civic   1.6 100
# 15:               civic   1.6 102
# 16:               civic   1.6 103
# 17:              sonata   2.4 110
# 18:              sonata   2.5 114
# 19:             tiburon   2.0 117
# 20:              altima   2.4 142
# 21:              maxima   3.0 149
# 22:      pathfinder 4wd   3.3 152
# 23:        forester awd   2.5 160
# 24:         impreza awd   2.2 167
# 25:         impreza awd   2.5 168
# 26:         4runner 4wd   2.7 174
# 27:         4runner 4wd   3.4 177
# 28:               camry   2.2 180
# 29:               camry   3.0 185
# 30:        camry solara   2.2 188
# 31:        camry solara   3.0 192
# 32:             corolla   1.8 196
# 33:   toyota tacoma 4wd   2.7 201
# 34:   toyota tacoma 4wd   3.4 204
# 35:                 gti   2.0 208
# 36:                 gti   2.8 212
# 37:               jetta   1.9 213
# 38:               jetta   2.0 214
# 39:               jetta   2.8 221
# 40:          new beetle   1.9 222
# 41:          new beetle   2.0 224
# 42:              passat   1.8 228
# 43:              passat   2.8 233
                  # model displ  V1


# then order and get the first row


mpg2[, .I[year == "1999" & grepl("manual", trans)], .(model, displ)][order(model, -displ), .SD[1], model]


                  # model displ  V1
 # 1:         4runner 4wd   3.4 177
 # 2:                  a4   2.8   6
 # 3:          a4 quattro   2.8  13
 # 4:              altima   2.4 142
 # 5:               camry   3.0 185
 # 6:        camry solara   3.0 192
 # 7:               civic   1.6 100
 # 8:             corolla   1.8 196
 # 9:            corvette   5.7  24
# 10:   dakota pickup 4wd   5.2  56
# 11:        explorer 4wd   4.0  79
# 12:     f150 pickup 4wd   4.6  86
# 13:        forester awd   2.5 160
# 14:                 gti   2.8 212
# 15:         impreza awd   2.5 168
# 16:               jetta   2.8 221
# 17:              maxima   3.0 149
# 18:             mustang   4.6  96
# 19:          new beetle   2.0 224
# 20:              passat   2.8 233
# 21:      pathfinder 4wd   3.3 152
# 22: ram 1500 pickup 4wd   5.2  72
# 23:              sonata   2.5 114
# 24:             tiburon   2.0 117
# 25:   toyota tacoma 4wd   3.4 204
                  # model displ  V1


# V1 is the row ID from original table, we have then just to remove it from it 


mpg2[!mpg2[, .I[year == "1999" & grepl("manual", trans)], .(model, displ)][order(model, -displ), .SD[1], model]$V1]


     # manufacturer  model displ year cyl      trans drv cty hwy fl   class
  # 1:         audi     a4   1.8 1999   4   auto(l5)   f  18  29  p compact
  # 2:         audi     a4   1.8 1999   4 manual(m5)   f  21  29  p compact
  # 3:         audi     a4   2.0 2008   4 manual(m6)   f  20  31  p compact
  # 4:         audi     a4   2.0 2008   4   auto(av)   f  21  30  p compact
  # 5:         audi     a4   2.8 1999   6   auto(l5)   f  16  26  p compact
 # ---                                                                     
# 205:   volkswagen passat   1.8 1999   4   auto(l5)   f  18  29  p midsize
# 206:   volkswagen passat   2.0 2008   4   auto(s6)   f  19  28  p midsize
# 207:   volkswagen passat   2.0 2008   4 manual(m6)   f  21  29  p midsize
# 208:   volkswagen passat   2.8 1999   6   auto(l5)   f  16  26  p midsize
# 209:   volkswagen passat   3.6 2008   6   auto(s6)   f  17  26  p midsize
于 2021-01-20T15:03:32.250 回答