我正在处理的20行数据：
Zv9_NA110 6176 7276 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 10126 11226 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 7 9 9 15 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 13 0 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 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
Zv9_NA110 14887 15987 5'to3'IntronExon 0 + 1100 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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
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
Zv9_NA113 1615 2715 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 2335 3435 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 7 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 3 0 0 0 0 0 0 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 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
我把它变成R如下：
> dat <- read.table("dat.dat",header=F)
我需要获得第9列到第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
但我无法将其输入V118。
我试过这个：
> aggregate(cbind(9:118)~V4,data=blah,FUN=mean)
但是我收到了这个错误：
Error in model.frame.default(formula = cbind(9:118) ~ V4, data = blah) :
variable lengths differ (found for 'V4')
有什么东西是我丢失的吗？
答案 0 :(得分：3)
您有很多选择。
使用.创建公式并传递数据的子集
aggregate( . ~ V4, data = dat[,c(4,9:118)], FUN = mean)
您还可以使用paste
nn <- paste0('V', 9:118)
并按列名称引用
aggregate( . ~ V4, data = dat[,c('V4',nn)], FUN = mean)
如果公式方法有效，那么在这里使用cbind没什么意义，但是例如。
aggregate( do.call(cbind,lapply(nn, as.name)) ~ V4, data = dat, FUN = mean)
但这很麻烦，因为它没有很好地命名列。 （并且很难遵循）
答案 1 :(得分：3)
如果速度通常是一个问题（对于此操作不是必需的）并且您想要使用data.table包，则执行如下操作：
更安全的解决方案
感谢mnel的评论，我会用它：
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]
答案 2 :(得分：1)
您可以使用
## S3 method for class 'data.frame'
aggregate(x, by, FUN, ..., simplify = TRUE)
假设您的数据位于数据框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
## -------------------------------------------------------
##