R Data.Table 聚合

Question

library(data.table)
set.seed(01)
DATA = data.table("STUDENT" = c(1:2000),
                  "GROUP" = c(sample(letters[1:3], size=2000, r = T)),
                  "CLASS" = c(sample(1:6, size=2000, r = T)),
                  "SCORE1" = c(sample(50:100,size=2000,r=T)),
                  "SCORE2" = c(sample(40:100, size=2000, r = T)))

假设您有这些数据，并且您希望聚合或折叠多个 data.table...

a) GROUP

平均 SCORE1 和平均 SCORE2

b) 平均 SCORE1 和平均 SCORE2 CLASS

c) 按 GROUP 和按 CLASS

平均 SCORE1 和平均 SCORE2

d) CLASS

的 GROUP 计数

有没有更有效的方法使用data.table创建这4个data.table？或者你只是建议制作所有这些并存储为 4 个单独的 data.tables？

Answer 1

它们都有不同的结构

(一)

DATA[, .(mSCORE1 = mean(SCORE1), mSCORE2= mean(SCORE2)), by=.(GROUP)]

   GROUP  mSCORE1  mSCORE2
1:     a 74.59971 69.98240
2:     c 75.12943 71.30817
3:     b 74.95964 69.97608

（b 和 d 合并）

DATA[, .(mSCORE1 = mean(SCORE1), mSCORE2= mean(SCORE2),nGROUPS =uniqueN(GROUP)), by=.(CLASS)]

   CLASS  mSCORE1  mSCORE2 nGROUPS
1:     6 75.01685 70.14607       3
2:     3 75.76025 70.18927       3
3:     4 74.25532 70.74164       3
4:     5 75.77126 69.46921       3
5:     1 73.28797 71.56646       3
6:     2 75.17595 70.44282       3

(c)

DATA[, lapply(.SD, mean, na.rm=T), by=.(GROUP,CLASS),.SDcols = patterns("SC")]

    GROUP CLASS   SCORE1   SCORE2
 1:     a     6 75.27027 69.00901
 2:     c     3 78.05660 70.18868
 3:     a     4 75.72727 68.95868
 4:     b     6 74.20455 70.78788
 5:     a     5 75.94915 69.78814
 6:     c     1 73.93043 72.63478
 7:     c     2 75.18812 71.80198
 8:     b     2 76.46667 67.89167
 9:     c     5 75.28814 70.63559
10:     a     1 72.43519 69.72222
11:     a     2 73.87500 71.85000
12:     b     1 73.48387 72.38710
13:     b     5 76.11429 67.80000
14:     b     3 75.07477 69.84112
15:     c     6 75.71681 70.51327
16:     b     4 74.26786 71.41964
17:     a     3 74.12500 70.54808
18:     c     4 72.38542 72.19792

如果你想把所有这些都放在一个 table 中，当然你会在组行和 class 之间复制 class-specific 个摘要和 group-specific 个摘要。这是单个管道中的插图

DATA[, .(mSCORE1_grpclass = mean(SCORE1), mSCORE2_grpclass= mean(SCORE2), NSTUDENTS=.N), by=.(GROUP,CLASS)] %>% 
  .[,`:=`(
      mSCORE1_grp=sum(NSTUDENTS*mSCORE1_grpclass)/sum(NSTUDENTS),
      mSCORE2_grp=sum(NSTUDENTS*mSCORE2_grpclass)/sum(NSTUDENTS)),by=.(GROUP)] %>%
  .[,`:=`(
    mSCORE1_class=sum(NSTUDENTS*mSCORE1_grpclass)/sum(NSTUDENTS),
    mSCORE2_class=sum(NSTUDENTS*mSCORE2_grpclass)/sum(NSTUDENTS),
    nGROUPS_class = uniqueN(GROUP)),by=.(CLASS)] %>% 
  .[]

输出：

    GROUP CLASS mSCORE1_grpclass mSCORE2_grpclass NSTUDENTS mSCORE1_grp mSCORE2_grp mSCORE1_class mSCORE2_class nGROUPS_class
 1:     a     6         75.27027         69.00901       111    74.59971    69.98240      75.01685      70.14607             3
 2:     c     3         78.05660         70.18868       106    75.12943    71.30817      75.76025      70.18927             3
 3:     a     4         75.72727         68.95868       121    74.59971    69.98240      74.25532      70.74164             3
 4:     b     6         74.20455         70.78788       132    74.95964    69.97608      75.01685      70.14607             3
 5:     a     5         75.94915         69.78814       118    74.59971    69.98240      75.77126      69.46921             3
 6:     c     1         73.93043         72.63478       115    75.12943    71.30817      73.28797      71.56646             3
 7:     c     2         75.18812         71.80198       101    75.12943    71.30817      75.17595      70.44282             3
 8:     b     2         76.46667         67.89167       120    74.95964    69.97608      75.17595      70.44282             3
 9:     c     5         75.28814         70.63559       118    75.12943    71.30817      75.77126      69.46921             3
10:     a     1         72.43519         69.72222       108    74.59971    69.98240      73.28797      71.56646             3
11:     a     2         73.87500         71.85000       120    74.59971    69.98240      75.17595      70.44282             3
12:     b     1         73.48387         72.38710        93    74.95964    69.97608      73.28797      71.56646             3
13:     b     5         76.11429         67.80000       105    74.95964    69.97608      75.77126      69.46921             3
14:     b     3         75.07477         69.84112       107    74.95964    69.97608      75.76025      70.18927             3
15:     c     6         75.71681         70.51327       113    75.12943    71.30817      75.01685      70.14607             3
16:     b     4         74.26786         71.41964       112    74.95964    69.97608      74.25532      70.74164             3
17:     a     3         74.12500         70.54808       104    74.59971    69.98240      75.76025      70.18927             3
18:     c     4         72.38542         72.19792        96    75.12943    71.30817      74.25532      70.74164             3

R Data.Table 聚合

R Data.Table Aggregate

r

data.table

memory-efficient