R 将一些csv文件合并为一个不同的列数

我已经加载了20个csv文件，功能如下：

tbl = list.files(pattern="*.csv")
for (i in 1:length(tbl)) assign(tbl[i], read.csv(tbl[i]))

或

看起来是这样的：

> head(tbl)
[1] "F1.csv"          "F10_noS3.csv"    "F11.csv"         "F12.csv"         "F12_noS7_S8.csv"
[6] "F13.csv"

我必须把所有这些文件合并成一个。让我们称之为主文件，但让我们尝试用所有名称创建一个表。 在所有这些csv文件中都有一个名为“加入”的列。我想从所有这些csv文件的所有名称表。当然，许多访问可以在不同的csv文件中重复。我想保留与加入相应的所有数据

一些问题：

有些名字是一样的，我不想重复 有些名字几乎是一样的。区别在于有名字和后面变成点和数字。 这些csv文件的列数可以不同。 这是显示这些数据的屏幕截图：

让我给你看看它的样子：

AT3G26450.1 <--
AT5G44520.2
AT4G24770.1
AT2G37220.2
AT3G02520.1
AT5G05270.1
AT1G32060.1
AT3G52380.1
AT2G43910.2
AT2G19760.1
AT3G26450.2 <--

---

我尝试了将近两个星期，但我没能做到。所以请帮帮我。

你的问题似乎包含多个子问题。我鼓励你把它们分开

显然，您首先需要将数据帧与不同的列组合起来。您可以使用plyr软件包中的rbind.fill：

---

下面是一个使用一些tidyverse函数和自定义函数的示例，该函数可以将多个缺少列的csv文件合并到一个数据帧中：

library(tidyverse)

# specify the target directory
dir_path <- '~/test_dir/' 

# specify the naming format of the files. 
# in this case csv files that begin with 'test' and a single digit but it could be as just as simple as 'csv'
re_file <- '^test[0-9]\\.csv'

# create sample data with some missing columns 
df_mtcars <- mtcars %>% rownames_to_column('car_name')
write.csv(df_mtcars %>% select(-am), paste0(dir_path, 'test1.csv'), row.names = FALSE)
write.csv(df_mtcars %>% select(-wt, -gear), paste0(dir_path, 'test2.csv'), row.names = FALSE)
write.csv(df_mtcars %>% select(-cyl), paste0(dir_path, 'test3.csv'), row.names = FALSE)

# custom function that takes the target directory and file name pattern as arguments
read_dir <- function(dir_path, file_name){
  x <- read_csv(paste0(dir_path, file_name)) %>% 
    mutate(file_name = file_name) %>% # add the file name as a column              
    select(file_name, everything())   # reorder the columns so file name is first
  return(x)
}

# read the files from the target directory that match the naming format and combine into one data frame
df_panel <-
  list.files(dir_path, pattern = re_file) %>% 
  map_df(~ read_dir(dir_path, .))

# files with missing columns are filled with NAs. 

---

rbind.filllist_的_数据将更快，dplyr:：rbind_alllist_的_数据将更快。两者都工作得很好，速度足够快。任何想法如何删除重复和相同的名称只是不同的数字后的点。谢谢任何关于我做错了什么值得否决的建议？非常有用的答案和有效的！。非常感谢。如果尝试dplyr:：rbind_alllist_of_数据，如果列表元素的长度不相同，R会话将中止。

all_data = do.call(rbind, list_of_data)
Error in rbind(deparse.level, ...) : 
The number of columns is not correct.


all_data$CleanedAccession = str_extract(all_data$Accession, "^[[:alnum:]]+")
all_data = subset(all_data, !duplicated(CleanedAccession))

library(plyr)
all_data = do.call(rbind.fill, list_of_data)

library(tidyverse)

# specify the target directory
dir_path <- '~/test_dir/' 

# specify the naming format of the files. 
# in this case csv files that begin with 'test' and a single digit but it could be as just as simple as 'csv'
re_file <- '^test[0-9]\\.csv'

# create sample data with some missing columns 
df_mtcars <- mtcars %>% rownames_to_column('car_name')
write.csv(df_mtcars %>% select(-am), paste0(dir_path, 'test1.csv'), row.names = FALSE)
write.csv(df_mtcars %>% select(-wt, -gear), paste0(dir_path, 'test2.csv'), row.names = FALSE)
write.csv(df_mtcars %>% select(-cyl), paste0(dir_path, 'test3.csv'), row.names = FALSE)

# custom function that takes the target directory and file name pattern as arguments
read_dir <- function(dir_path, file_name){
  x <- read_csv(paste0(dir_path, file_name)) %>% 
    mutate(file_name = file_name) %>% # add the file name as a column              
    select(file_name, everything())   # reorder the columns so file name is first
  return(x)
}

# read the files from the target directory that match the naming format and combine into one data frame
df_panel <-
  list.files(dir_path, pattern = re_file) %>% 
  map_df(~ read_dir(dir_path, .))

# files with missing columns are filled with NAs.