从下表中我需要通过计算具有相同ID（第2列）的那些行的平均值来组合这些行。
我在考虑plyr函数??
ddply(df, summarize, value = average(ID))
DF：
miRNA ID 100G 100R 106G 106R 122G 122R 124G 124R 126G 126R 134G 134R 141G 141R 167G 167R 185G 185R
1 hsa-miR-106a ID7 1585 423 180 113 598 266 227 242 70 106 2703 442 715 309 546 113 358 309
2 hsa-miR-1185-1 ID2 10 1 3 3 11 8 4 4 28 2 13 3 6 3 6 4 7 5
3 hsa-miR-1185-2 ID2 2 0 2 1 5 1 1 0 4 1 1 1 3 2 2 0 2 1
4 hsa-miR-1197 ID2 2 0 0 5 3 3 0 4 16 0 4 1 3 0 0 2 2 4
5 hsa-miR-127 ID3 29 17 6 55 40 35 6 20 171 10 32 21 23 25 10 14 32 55
原始数据摘要：
> str(ClusterMatrix)
'data.frame': 113 obs. of 98 variables:
$ miRNA: Factor w/ 202 levels "hsa-miR-106a",..: 1 3 4 6 8 8 14 15 15 16 ...
$ ID : Factor w/ 27 levels "ID1","ID10","ID11",..: 25 12 12 12 21 21 12 21 21 6 ...
$ 100G : Factor w/ 308 levels "-0.307749042739963",..: 279 11 3 3 101 42 139 158 215 222 ...
$ 100R : Factor w/ 316 levels "-0.138028803567403",..: 207 7 8 8 18 42 128 183 232 209 ...
$ 106G : Factor w/ 260 levels "-0.103556709881933",..: 171 4 1 3 7 258 95 110 149 162 ...
$ 106R : Factor w/ 300 levels "-0.141810346640204",..: 141 4 6 2 108 41 146 196 244 267 ...
$ 122G : Factor w/ 336 levels "-0.0409548922061764",..: 237 12 4 6 103 47 148 203 257 264 ...
$ 122R : Factor w/ 316 levels "-0.135708706475279",..: 177 1 8 6 36 44 131 192 239 244 ...
$ 124G : Factor w/ 267 levels "-0.348439853247856",..: 210 5 2 3 7 50 126 138 188 249 ...
$ 124R : Factor w/ 303 levels "-0.176414190219115",..: 193 3 7 3 21 52 167 200 238 239 ...
$ 126G : Factor w/ 307 levels "-0.227658806811544",..: 122 88 5 76 169 61 240 220 281 265 ...
$ 126R : Factor w/ 249 levels "-0.271925865853123",..: 119 1 2 3 11 247 78 110 151 193 ...
$ 134G : Factor w/ 344 levels "-0.106333543799583",..: 304 14 8 5 33 48 150 196 248 231 ...
$ 134R : Factor w/ 300 levels "-0.0997616469801097",..: 183 5 7 7 22 298 113 159 213 221 ...
$ 141G : Factor w/ 335 levels "-0.134429748398679",..: 253 7 3 3 24 29 142 137 223 302 ...
$ 141R : Factor w/ 314 levels "-0.143299688877927",..: 210 4 5 7 98 54 154 199 255 251 ...
$ 167G : Factor w/ 306 levels "-0.211181452126958",..: 222 7 4 6 11 292 91 101 175 226 ...
$ 167R : Factor w/ 282 levels "-0.0490740880560127",..: 130 2 6 4 15 282 110 146 196 197 ...
$ 185G : Factor w/ 317 levels "-0.0567841338235346",..: 218 2 7 7 33 34 130 194 227 259 ...
答案 0 :(得分：1)
我们可以使用dplyr。我们按ID＆＃39;分组，使用mutate_each创建显示mean值为＆＃39; 100G＆＃39;的列。到＆＃39; 185R＆＃39;。我们使用mutate_each中的正则表达式模式选择matches中的列。然后cbind（bind_cols）包含mutate d列的原始数据集，并根据需要转换为data.frame。我们还可以更改mean列的列名。
library(dplyr)
out <- df1 %>%
group_by(ID) %>%
mutate_each(funs(mean=mean(., na.rm=TRUE)), matches('^\\d+')) %>%
setNames(., c(names(.)[1:2], paste0('Mean_', names(.)[3:ncol(.)]))) %>%
as.data.frame()
out1 <- bind_cols(df1, out[-(1:2)])
out1
# miRNA ID 100G 100R 106G 106R 122G 122R 124G 124R 126G 126R 134G
#1 hsa-miR-106a ID7 1585 423 180 113 598 266 227 242 70 106 2703
#2 hsa-miR-1185-1 ID2 10 1 3 3 11 8 4 4 28 2 13
#3 hsa-miR-1185-2 ID2 2 0 2 1 5 1 1 0 4 1 1
#4 hsa-miR-1197 ID2 2 0 0 5 3 3 0 4 16 0 4
#5 hsa-miR-127 ID3 29 17 6 55 40 35 6 20 171 10 32
# 134R 141G 141R 167G 167R 185G 185R Mean_100G Mean_100R Mean_106G
#1 442 715 309 546 113 358 309 1585.000000 423.0000000 180.000000
#2 3 6 3 6 4 7 5 4.666667 0.3333333 1.666667
#3 1 3 2 2 0 2 1 4.666667 0.3333333 1.666667
#4 1 3 0 0 2 2 4 4.666667 0.3333333 1.666667
#5 21 23 25 10 14 32 55 29.000000 17.0000000 6.000000
# Mean_106R Mean_122G Mean_122R Mean_124G Mean_124R Mean_126G Mean_126R
#1 113 598.000000 266 227.000000 242.000000 70 106
#2 3 6.333333 4 1.666667 2.666667 16 1
#3 3 6.333333 4 1.666667 2.666667 16 1
#4 3 6.333333 4 1.666667 2.666667 16 1
#5 55 40.000000 35 6.000000 20.000000 171 10
# Mean_134G Mean_134R Mean_141G Mean_141R Mean_167G Mean_167R Mean_185G
#1 2703 442.000000 715 309.000000 546.000000 113 358.000000
#2 6 1.666667 4 1.666667 2.666667 2 3.666667
#3 6 1.666667 4 1.666667 2.666667 2 3.666667
#4 6 1.666667 4 1.666667 2.666667 2 3.666667
#5 32 21.000000 23 25.000000 10.000000 14 32.000000
# Mean_185R
#1 309.000000
#2 3.333333
#3 3.333333
#4 3.333333
#5 55.000000
编辑：如果我们需要为每个ID＆＃39;添加一行mean，我们可以使用summarise_each
df1 %>%
group_by(ID) %>%
summarise_each(funs(mean=mean(., na.rm=TRUE)), matches('^\\d+'))
EDIT2：根据OP的更新，原始数据集（＆＃39; ClusterMatrix＆＃39;）列都是factor类。在获取numeric之前，我们需要将列转换为mean类。有两种方法可以将factor转换为numeric - 1）as.numeric(as.character(..，这可能会慢一些，2）as.numeric(levels(..更快。在这里，我使用第一种方法，因为它可能更清楚。
ClusterMatrix %>%
group_by(ID) %>%
summarise_each(funs(mean= mean(as.numeric(as.character(.)),
na.rm=TRUE)), matches('^\\d+'))
df1 <- structure(list(miRNA = c("hsa-miR-106a", "hsa-miR-1185-1",
"hsa-miR-1185-2",
"hsa-miR-1197", "hsa-miR-127"), ID = c("ID7", "ID2", "ID2", "ID2",
"ID3"), `100G` = c(1585L, 10L, 2L, 2L, 29L), `100R` = c(423L,
1L, 0L, 0L, 17L), `106G` = c(180L, 3L, 2L, 0L, 6L), `106R` = c(113L,
3L, 1L, 5L, 55L), `122G` = c(598L, 11L, 5L, 3L, 40L), `122R` = c(266L,
8L, 1L, 3L, 35L), `124G` = c(227L, 4L, 1L, 0L, 6L), `124R` = c(242L,
4L, 0L, 4L, 20L), `126G` = c(70L, 28L, 4L, 16L, 171L), `126R` = c(106L,
2L, 1L, 0L, 10L), `134G` = c(2703L, 13L, 1L, 4L, 32L), `134R` = c(442L,
3L, 1L, 1L, 21L), `141G` = c(715L, 6L, 3L, 3L, 23L), `141R` = c(309L,
3L, 2L, 0L, 25L), `167G` = c(546L, 6L, 2L, 0L, 10L), `167R` = c(113L,
4L, 0L, 2L, 14L), `185G` = c(358L, 7L, 2L, 2L, 32L), `185R` = c(309L,
5L, 1L, 4L, 55L)), .Names = c("miRNA", "ID", "100G", "100R",
"106G", "106R", "122G", "122R", "124G", "124R", "126G", "126R",
"134G", "134R", "141G", "141R", "167G", "167R", "185G", "185R"
), class = "data.frame", row.names = c("1", "2", "3", "4", "5"
))