如何通过使用R将每个文件的数据添加为附加行来将不同的.csv文件组合为一个完整文件？

Question

I have several different folders which all contain one single .csv file. All of those .csv files have one single column containing the data of one condition of an experiment. I would like to merge those .csv files in such a way that the data of every file is added as a new column.

At the moment, it Looks somehow like this:

C1.csv
102
106
152
196
223
486
553

C2.csv
296
299
843
1033
1996

However, it would like to have one single .csv file, where all the separate files are copied into a new column containing the name of the source file, like:

C1     C2     ...    Cn
102    296    ...    ...
106    299    ...
152    843    ...
196    1033   ...
223    1996   ...
486           ...
553           ...

So far, I the following code:

myFiles = list.files(path = ".", recursive = TRUE, pattern = ".csv", full.names = TRUE)
data <- lapply(myFiles, read.table, sep="\t", header=FALSE)
Max <- max(sapply(data, length))
data <- lapply(data, function(x) c(x, rep(NA, Max - length(x))))
data <- do.call(cbind, data)
names(data) <- sub("^[^[:alnum:]]*([[:alnum:]]+)\\.csv$", "\\1", myFiles)

write.csv(data, "outfile.csv")

It yielded a document that looks like this instead of adding the data of every .csv file in a new column:

enter image description here

Answer 1

Is this what you want?
Note that I read the files in with scan . Since the files have only one column there is no need for a complex function like read.csv .

myFiles <- list.files(path = ".", pattern = "^C.*\\.csv", full.names = TRUE, recursive = TRUE)
data <- lapply(myFiles, scan)
Max <- max(sapply(data, length))
data <- lapply(data, function(x) c(x, rep(NA, Max - length(x))))
data <- do.call(cbind, data)
names(data) <- sub("^[^[:alnum:]]*([[:alnum:]]+)\\.csv$", "\\1", myFiles)

write.csv(data, "outfile.csv")

The contents of "outfile.csv" are

"","V1","V2"
"1",102,296
"2",106,299
"3",152,843
"4",196,1033
"5",223,1996
"6",486,NA
"7",553,NA

Answer 2

One can read all files using read.table in a list. Combine all data using dplyr::bind_rows . Afterwards, use reshape2::dcast to spread data in wide format with a column for data from every file.

# Get list of files in directory
fileList <- list.files(".", "*.csv", full.names = TRUE)

# Read file data. This will generate a list containing dataframes
listData <- lapply(fileList, read.table)

# Name list using name of files
names(listData) <- gsub(".csv","",basename(fileList))

library(tidyverse)
library(reshape2)

bind_rows(listData, .id = "FileName") %>%
  group_by(FileName) %>%
  mutate(rowNum = row_number()) %>%
  dcast(rowNum~FileName, value.var = "V1") %>%
  select(-rowNum) %>%
  write.csv(file="Result.csv")

# Content of Result.csv
# "","C1","C2"
# "1",102,296
# "2",106,299
# "3",152,843
# "4",196,1033
# "5",223,1996
# "6",486,NA
# "7",553,NA