从data.table聚合返回多个列 [英] return multiple columns from data.table aggregation

问题描述

我想使用 data.table 替代 aggregate（）或 ddply（），因为这两种方法不能像希望的那样有效地缩放到大对象。不幸的是，我还没有搞清楚如何获得向量返回聚合函数在 data.table 的结果中生成多个列。例如：

 所需的包
库（plyr）
库（data.table）
 
＃模拟数据
x < -  data.table（value = rnorm（100），g = rep（letters [1：5]，each = 20））
 
＃ddply输出我想从data.table获得
 ddply（data.frame（x），'g'，function（i）quantile（i $ value））
 
g 0％25％50％75％100％
 1 a -1.547495 -0.7842795 0.202456288 0.6098762 2.223530 
 2 b -1.366937 -0.4418388 -0.085876995 0.7826863 2.236469 
 3 c -2.064510 -0.6411390 -0.257526983 0.3213343 1.039053 
 4 d -1.773933 -0.5493362 -0.007549273 0.4835467 2.116601 
 5 e -0.780976 -0.2315245 0.194869630 0.6698881 2.207800 
 
＃不是我要找的：
x [ ，quantile（value），by = g] 
 
g V1 
 1：a -1.547495345 
 2：a -0.784279536 
 3：a 0.202456288 
 4 ：a 0.609876241 
 5：a 2.223529739 
 6：b -1.366937074 
 7：b -0.441838791 
 8：b -0.085876995 
 9：b 0.782686277 
 10：b 2.236468703 
  

基本上， ddply 和 aggregate 都是宽格式，而 data.table 的输出是长格式。

解决方案 / div>

尝试强制转到列表：

 > x [，as.list（quantile（value）），by = g] 
g 0％25％50％75％100％
 1：a -1.7507334 -0.632331909 0.07435249 0.7459778 1.428552 
 2 ：b -2.2043481 -0.005652353 0.10534325 0.5769475 1.241754 
 3：c -1.9313985 -1.120737610 -0.26116926 0.6953009 1.360017 
 4：d -0.7434664 -0.055232431 0.22062823 1.1864389 3.021124 
 5：e -2.0101657 -0.468674094 0.20209610 0.6286448 2.433152 
  

I would like to use data.table as an alternative to aggregate() or ddply(), as these two methods aren't scaling to large objects as efficiently as hoped. Unfortunately, I haven't figured out how to get vector-returning aggregate functions to generate multiple columns in the result from data.table. For example:

# required packages
library(plyr)
library(data.table)

# simulated data
x <- data.table(value=rnorm(100), g=rep(letters[1:5], each=20))

# ddply output that I would like to get from data.table
ddply(data.frame(x), 'g', function(i) quantile(i$value))

 g        0%        25%          50%       75%     100%
 1 a -1.547495 -0.7842795  0.202456288 0.6098762 2.223530
 2 b -1.366937 -0.4418388 -0.085876995 0.7826863 2.236469
 3 c -2.064510 -0.6411390 -0.257526983 0.3213343 1.039053
 4 d -1.773933 -0.5493362 -0.007549273 0.4835467 2.116601
 5 e -0.780976 -0.2315245  0.194869630 0.6698881 2.207800

# not quite what I am looking for:
x[, quantile(value), by=g]

g           V1
1: a -1.547495345
2: a -0.784279536
3: a  0.202456288
4: a  0.609876241
5: a  2.223529739
6: b -1.366937074
7: b -0.441838791
8: b -0.085876995
9: b  0.782686277
10: b  2.236468703

Essentially, the output from ddply and aggregate are in wide-format, while the output from the data.table is in long format. Is the answer reshaping the data, or some additional arguments to my data.table object?

解决方案

Try coercing to a list:

> x[, as.list(quantile(value)), by=g]
   g         0%          25%         50%       75%     100%
1: a -1.7507334 -0.632331909  0.07435249 0.7459778 1.428552
2: b -2.2043481 -0.005652353  0.10534325 0.5769475 1.241754
3: c -1.9313985 -1.120737610 -0.26116926 0.6953009 1.360017
4: d -0.7434664 -0.055232431  0.22062823 1.1864389 3.021124
5: e -2.0101657 -0.468674094  0.20209610 0.6286448 2.433152

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