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我正在处理的 20 行数据：

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Zv9_NA110   11219   12319   5'to3'ExonIntron    0   +   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
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Zv9_NA110   18923   20023   5'to3'IntronExon    0   +   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA110   21069   22169   5'to3'ExonIntron    0   +   1100    0 135   115 65  54  45  36  27  16  9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
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Zv9_NA113   5398    6498    5'to3'IntronExon    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA113   7173    8273    5'to3'ExonIntron    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA118   11674   12774   5'to3'IntronExon    0   +   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA118   12711   13811   5'to3'ExonIntron    0   +   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA123   38151   39251   5'to3'ExonIntron    0   -   1100    0 1061  958 844 796 695 600 464 346 265 210 150 133 94  81  72  46  18  4   0   0   0   0   0   0   0   0   0   7   9   9   9   11  21  35  43  58  91  108 180 268 406 547 712 833 882 960 1094    1172    1245    1331    1432    1510    1604    1711    1810    1830    1837    1823    1781    1690    1638    1560    1489    1257    854 731 631 589 551 497 439 404 369 301 231 168 123 76  58  50  42  28  20  11  9   9   24  27  27  27  27  27  25  18  18  18  18  18  18  18  18  18  18  18  18  18  14  5   0   0
Zv9_NA124   2578    3678    5'to3'ExonIntron    0   +   1100    0 423   407 401 377 357 345 324 304 249 185 111 54  30  12  0   0   0   0   0   0   0   0   0   0   0   0   0   1   9   9   9   9   14  18  25  27  27  27  27  27  27  27  27  27  27  27  26  18  18  18  18  18  18  16  4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   8   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA129   4939    6039    5'to3'IntronExon    0   +   1100    226 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   4   9   9   9   9   9   9   9   9   9   9   9   9   14  34  45  60  97  128 175 293 395 524 621 764 894 1036    1164    1334    1469    1639    1801    1885    1983
Zv9_NA132   12589   13689   5'to3'ExonIntron    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA132   13634   14734   5'to3'IntronExon    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA132   14481   15581   5'to3'ExonIntron    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   5   9   9   9   9   9   9   9   9   9   5   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA132   19534   20634   5'to3'IntronExon    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
Zv9_NA132   28708   29808   5'to3'ExonIntron    0   -   1100    0 0 0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   5   9   15  18  24  27  42  46  73  112 142 157 162 162 162 162 162 162 162 162 159 153 153 153 153 153 150 144 132 112 76  52  30  25  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0

我按如下方式将其放入 R 中：

> dat <- read.table("dat.dat",header=F)

我需要得到第 9 到 118 列的平均值，由第 4 列解析。

这有效：

> all_means <- aggregate(cbind(V9,V10,V11)~V4,data=dat,FUN=mean)

                V4 V9  V10 V11
1 5'to3'ExonIntron  0 0.00   0
2 5'to3'IntronExon  0 0.75   1

但是我不可能把它输入到 V118。

我试过这个：

> aggregate(cbind(9:118)~V4,data=blah,FUN=mean)

但我得到这个错误：

Error in model.frame.default(formula = cbind(9:118) ~ V4, data = blah) : 
  variable lengths differ (found for 'V4')

我错过了什么愚蠢的东西吗？

4

3 回答 3

3

你有很多选择。

.使用并传递数据的子集创建公式

aggregate( . ~ V4, data = dat[,c(4,9:118)], FUN = mean)

您还可以使用创建列名向量paste

nn <- paste0('V', 9:118)

并按列名引用

aggregate( . ~ V4, data = dat[,c('V4',nn)], FUN = mean)

考虑到公式方法有效，在这里使用没有多大意义cbind，但是例如。

aggregate( do.call(cbind,lapply(nn, as.name)) ~ V4, data = dat, FUN = mean)

但这很混乱，因为它不能很好地命名列。（而且很难理解）

于 2013-03-27T03:01:14.987 回答
3

如果速度通常是一个问题（此操作不是必需的）并且您想使用该data.table包，请按如下方式完成：

更安全的解决方案

感谢 mnel 的评论，我会使用它：

library(data.table)
dat <- as.data.table(dat)
dat[,lapply(.SD,mean),by="V4",.SDcols=paste0("V", 9:118)]

旧解决方案

dat[,lapply(.SD,mean),by="V4",.SDcols=9:118]
于 2013-03-27T03:11:15.307 回答
1

您可以使用

## S3 method for class 'data.frame'
aggregate(x, by, FUN, ..., simplify = TRUE)

假设您的数据在数据框中DF

DF <- read.table(text = txt, header = FALSE, stringsAsFactors = FALSE)
result <- aggregate(DF[, 9:118], by = list(DF[, 4]), FUN = mean)

# Using pander to print result table nicely. It's not needed for aggregation :)
require(pander)
pandoc.table(result)
## 
## ----------------------------------------------------
##     Group.1       V9    V10   V11   V12   V13   V14 
## ---------------- ----- ----- ----- ----- ----- -----
## 5'to3'ExonIntron 161.9  148   131  122.7 109.7 98.1 
## 
## 5'to3'IntronExon  0.0    0     0    0.0   0.0   0.0 
## ----------------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V15   V16   V17   V18   V19   V20   V21   V22 
## ----- ----- ----- ----- ----- ----- ----- -----
## 81.5  66.6  52.3  39.5  26.1  18.7  12.4   9.3 
## 
##  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V23   V24   V25   V26   V27   V28   V29   V30 
## ----- ----- ----- ----- ----- ----- ----- -----
##  7.2   4.6   1.8   0.4    0     0     0    0.5 
## 
##  0.0   0.0   0.0   0.0    0     0     0    0.0 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V31   V32   V33   V34   V35   V36   V37   V38 
## ----- ----- ----- ----- ----- ----- ----- -----
##  0.9   1.5   1.8   2.4   2.7    5    6.4   9.1 
## 
##  0.0   0.0   0.0   0.0   0.0    0    0.0   0.0 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V39   V40   V41   V42   V43   V44   V45   V46 
## ----- ----- ----- ----- ----- ----- ----- -----
##  13   16.2  19.2  21.5   23   24.7   28   29.7 
## 
##   0    0.0   0.0   0.0    0    0.0    0    0.0 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V47   V48   V49   V50   V51   V52   V53   V54 
## ----- ----- ----- ----- ----- ----- ----- -----
## 36.9  45.7  59.5  73.3  89.2  101.3 106.2  114 
## 
##  0.0   0.0   0.0   0.0   0.0   0.0   0.0    0  
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V55   V56   V57   V58   V59   V60   V61   V62 
## ----- ----- ----- ----- ----- ----- ----- -----
## 127.3  134  140.7 148.1 156.2 160.4 167.4 175.7
## 
##  0.0    0    0.0   0.0   0.0   0.0   0.0   0.0 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V63   V64   V65   V66   V67   V68   V69   V70 
## ----- ----- ----- ----- ----- ----- ----- -----
## 183.9 183.1 183.7 182.3 178.1  169  163.8 156.7
## 
##  0.0   0.0   0.0   0.0   0.0    0    0.0   0.0 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V71   V72   V73   V74   V75   V76   V77   V78 
## ----- ----- ----- ----- ----- ----- ----- -----
## 149.8 126.6 86.3  74.0  64.0  59.8  56.0  50.6 
## 
##  0.7   0.9   0.9   1.5   1.8   1.8   1.8   1.8 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V79   V80   V81   V82   V83   V84   V85   V86 
## ----- ----- ----- ----- ----- ----- ----- -----
## 45.6  42.2  38.7  31.9  24.9  18.6  14.1   9.4 
## 
##  1.8   1.8   1.8   1.8   1.8   1.8   2.2   2.7 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------
##  V87   V88   V89   V90   V91   V92   V93   V94 
## ----- ----- ----- ----- ----- ----- ----- -----
##  7.6   6.2   5.1   3.7   2.9   2.5   2.7   2.7 
## 
##  2.7   2.7   2.7   2.7   2.7   2.7   2.2   0.9 
## -----------------------------------------------
## 
## Table: Table continues below
## 
##  
## --------------------------------------------------
##  V95   V96   V97   V98   V99   V100   V101   V102 
## ----- ----- ----- ----- ----- ------ ------ ------
##  4.2   4.5   4.5   4.5   4.5   4.5    4.3    2.5  
## 
##  0.9   0.9   0.9   1.4   4.1   5.4    6.9    10.6 
## --------------------------------------------------
## 
## Table: Table continues below
## 
##  
## -------------------------------------------------------
##  V103   V104   V105   V106   V107   V108   V109   V110 
## ------ ------ ------ ------ ------ ------ ------ ------
##  1.8    1.8    1.8    1.8    1.8    1.8    1.8    1.8  
## 
##  13.7   18.4   30.2   40.4   53.3   63.0   77.3   90.3 
## -------------------------------------------------------
## 
## Table: Table continues below
## 
##  
## -------------------------------------------------------
##  V111   V112   V113   V114   V115   V116   V117   V118 
## ------ ------ ------ ------ ------ ------ ------ ------
##  1.8    1.8    1.8    1.8    1.4    0.5    0.0    0.0  
## 
## 104.5  117.3  134.3  147.8  164.8  181.0  189.4  199.2 
## -------------------------------------------------------
##
于 2013-03-27T04:03:47.077 回答