适当/最快的方式重塑数据表

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问题描述

我在R中有：
  library（data.table）
 set.seed（1234）
 DT<  -  data。 （x = rep（c（1,2,3），each = 4），y = c（A，B），v = sample（1：100,12））
 DT 
xyv 
 [1，] 1 A 12 
 [2，] 1 B 62 
 [3，] 1 A 60 
 [4，] 1 B 61 
 [5，] 2 A 83 
 [6，] 2 B 97 
 [7，] 2 A 1 
 [8，] 2 B 22 
 [9，] 3 A 99 
 [10，] 3 B 47 
 [11，] 3 A 63 
 [12，] 3 B 49 
  $ p> 
 
 我可以通过data.table中的组轻松求和变量v：
  out<  -  DT [，list（SUM = sum（v）），by = list（x，y）] 
 out 
xy SUM 
 [1，] 1 A 72 
 [2，] 1 B 123 
 [3，] 2 A 84 
 [4，] 2 B 119 
 [5，] 3 A 162 
 [ 6，] 3 B 96 
  
但是，我想将组而不是行。我可以使用 reshape ：
  out，direction ='wide'，idvar ='x'，timevar ='y'）
 out 
x SUM.A SUM.B 
 [1，] 1 72 123 
 [2，] 2 84 119 
 [3，] 3 162 96 
  
 
 
更有效的方式重整数据后聚合它？有没有办法使用data.table操作将这些操作合并为一个步骤？
解决方案
  data.table 包实现更快 melt / dcast 函数（在C中）。它还具有其他功能，允许融化和铸造多个列 。请参阅Github上的新。 p> 
 
 
熔化/ dcast函数for data.table自v1.9.0起已可用，其功能包括：
 
 
  
  在投放之前，不需要加载 reshape2 包。但是如果您希望加载其他操作，请在加载 data.table 之前加载。
 
 
    dcast 也是一个S3通用。没有更多 dcast.data.table（）。只需使用 dcast（）
。
 
 
    >：
 
 
  
  能够在list类型的列上融化。
 
 
  获得 variable.factor 和 value.factor ，默认情况下 TRUE 和 FALSE ，以与 reshape2 兼容。这允许直接控制 variable 和 value 列的输出类型（作为因子或不是因子）。 
 
 
    melt.data.table 的 na.rm = TRUE 参数在内部进行优化，以便在熔化过程中直接删除NA，因此效率更高。
 
 
   NEW： 可以接受 measure.vars 的列表，并且列表中每个元素中指定的列将合并在一起。这通过使用 patterns（）进一步实现。 
 
 
 
 
 
    dcast ：
 
 
  
  接受多个 fun.aggregate 和多个 value.var 。请参阅vignette或？dcast 。
 
 
  使用 rowid c $ c>函数直接在公式中生成id列，有时需要唯一标识行。 
 
 
 
 
 
  旧基准：
 
 
  
   melt ：1000万行和5列，61.3秒减少到1.2秒。 
 
   dcast ：100万行4列，192秒减少到3.6秒。 
 
 
 
 
 
 
 
 科隆提醒（2013年12月）简报幻灯片32： 
 
I have a data table in R:
library(data.table)
set.seed(1234)
DT <- data.table(x=rep(c(1,2,3),each=4), y=c("A","B"), v=sample(1:100,12))
DT
      x y  v
 [1,] 1 A 12
 [2,] 1 B 62
 [3,] 1 A 60
 [4,] 1 B 61
 [5,] 2 A 83
 [6,] 2 B 97
 [7,] 2 A  1
 [8,] 2 B 22
 [9,] 3 A 99
[10,] 3 B 47
[11,] 3 A 63
[12,] 3 B 49
I can easily sum the variable v by the groups in the data.table:
out <- DT[,list(SUM=sum(v)),by=list(x,y)]
out
     x  y SUM
[1,] 1 A  72
[2,] 1 B 123
[3,] 2 A  84
[4,] 2 B 119
[5,] 3 A 162
[6,] 3 B  96
However, I would like to have the groups (y) as columns, rather than rows.  I can accomplish this using reshape:
out <- reshape(out,direction='wide',idvar='x', timevar='y')
out
     x SUM.A SUM.B
[1,] 1    72   123
[2,] 2    84   119
[3,] 3   162    96
Is there a more efficient way to reshape the data after aggregating it?  Is there any way to combine these operations into one step, using the data.table operations?
 解决方案 
The data.table package implements faster melt/dcast functions (in C). It also has additional features by allowing to melt and cast multiple columns. Please see the new Efficient reshaping using data.tables on Github.
melt/dcast functions for data.table have been available since v1.9.0 and the features include:
There is no need to load reshape2 package prior to casting. But if you want it loaded for other operations, please load it before loading data.table.
dcast is also a S3 generic. No more dcast.data.table(). Just use dcast().
melt:
is capable of melting on columns of type 'list'.
gains variable.factor and value.factor which by default are TRUE and FALSE respectively for compatibility with reshape2. This allows for directly controlling the output type of variable and value columns (as factors or not).  
melt.data.table's na.rm = TRUE parameter is internally optimised to remove NAs directly during melting and is therefore much more efficient.
NEW: melt can accept a list for measure.vars and columns specified in each element of the list will be combined together. This is faciliated further through the use of patterns(). See vignette or ?melt.
dcast: 
accepts multiple fun.aggregate and multiple  value.var. See vignette or ?dcast.
use rowid() function directly in formula to generate an id-column, which is sometimes required to identify the rows uniquely. See ?dcast.
Old benchmarks:   
melt  : 10 million rows and 5 columns, 61.3 seconds reduced to 1.2 seconds.  
dcast : 1 million rows and 4 columns, 192 seconds reduced to 3.6 seconds.  
Reminder of Cologne (Dec 2013) presentation slide 32 : Why not submit a dcast pull request to reshape2?
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