R:在for循环中一次子集多个数据帧

R: subset multiple data frames at once in for loop

我在 R 中有大量数据帧,我想在 for 循环中同时执行一些操作。

数据框包含有关基因表达数据的信息。对于每个基因,都有关于 upregulation/downregulation 的信息和相关的 P 值。最终,我想获得一个新的数据框,其中包含每个数据框显着(P 值 < 0.05)上调和下调基因的数量。

我将分两步进行:

  1. 在仅包含上调和下调基因的子集中对数据帧进行子集化
  2. 计算每个子集数据帧中重要基因的数量

首先,让我们制作两个虚拟数据框:

#data frame 1
gene = c('gene1','gene2','gene3','gene4','gene5','gene6')
direction = c('up','up','down','down','down','up')
Pvalue = as.numeric(c(0.05,0.06,0.001,0.075,0.11,0.12))
df1 = as.data.frame(cbind(gene,direction,Pvalue)) 
> df1 
   gene direction Pvalue
1 gene1        up   0.05
2 gene2        up   0.06
3 gene3      down  0.001
4 gene4      down  0.075
5 gene5      down   0.11
6 gene6        up   0.12
#data frame 2
gene = c('gene1','gene2','gene3','gene4','gene5','gene6')
direction = c('down','up','down','down','up','up')
Pvalue = as.numeric(c(0.043,0.001,0.34,0.96,0.001,0.04))
df2 = as.data.frame(cbind(gene,direction,Pvalue)) 
> df2
   gene direction Pvalue
1 gene1      down  0.043
2 gene2        up  0.001
3 gene3      down   0.34
4 gene4      down   0.96
5 gene5        up  0.001
6 gene6        up   0.04

然后,我制作了一个包含所有数据框名称的列表:

df_summary = c('df1','df2')

然后我在这个列表上使用 for 循环来执行上面概述的步骤 1 和 2:

df3 = data.frame()
for (df in df_summary){
  df_down = df[df$direction == 'down',]
  df_up = df[df$direction == 'up',]
  df_down_sign = length(which(df_down$Pvalue < 0.05))
  df_up_sign = length(which(df_up$Pvalue < 0.05))
  df3 = rbind.data.frame(df3, c(df_down_sign,df_up_sign))
}

此代码在循环外的单个数据帧上工作得很好,但是当我 运行 循环时抛出以下错误:

Error: $ operator is invalid for atomic vectors

我正在寻找的输出应该是这样的:

  dataframe number
1       df1      1
2       df1      0
3       df2      1
4       df2      3

所以我的问题是:为什么我会在 for 循环中收到此错误,如何解决?

事实证明,在发布我的问题后,我遇到了一些看起来像是解决方案的东西。

简单运行

df_summary = list(df1,df2)

而不是

df_summary = c('df1','df2')

似乎解决了我的问题!

以下解决问题。

df_list <- mget(ls(pattern = "^df"))

df3 <- lapply(seq_along(df_list), function(i){
  dftmp <- df_list[[i]]
  dfname <- names(df_list)[i]
  agg <- aggregate(Pvalue ~ direction, dftmp, function(x) sum(x < 0.05))
  cbind.data.frame(dataframe = dfname, agg)
})
df3 <- do.call(rbind, df3)

df3
#  dataframe direction Pvalue
#1       df1      down      1
#2       df1        up      0
#3       df2      down      1
#4       df2        up      3