如何一次导入多个 .csv 文件? [英] How to import multiple .csv files at once?

问题描述

假设我们有一个文件夹，其中包含多个 data.csv 文件，每个文件包含相同数量的变量，但每个都来自不同的时间.R 中有没有办法同时导入它们，而不必单独导入它们?

Suppose we have a folder containing multiple data.csv files, each containing the same number of variables but each from different times. Is there a way in R to import them all simultaneously rather than having to import them all individually?

我的问题是我有大约 2000 个数据文件要导入，并且必须使用代码单独导入它们:

My problem is that I have around 2000 data files to import and having to import them individually just by using the code:

read.delim(file="filename", header=TRUE, sep="	")

效率不高.

推荐答案

类似于下面的内容应该导致每个数据框作为单个列表中的单独元素:

Something like the following should result in each data frame as a separate element in a single list:

temp = list.files(pattern="*.csv")
myfiles = lapply(temp, read.delim)

这假设您在单个目录(您当前的工作目录)中拥有这些 CSV，并且所有这些文件的扩展名都是小写的 .csv.

This assumes that you have those CSVs in a single directory--your current working directory--and that all of them have the lower-case extension .csv.

如果您随后想将这些数据帧合并为一个数据帧，请使用 do.call(rbind,...)、dplyr 之类的内容查看其他答案中的解决方案::bind_rows() 或 data.table::rbindlist().

If you then want to combine those data frames into a single data frame, see the solutions in other answers using things like do.call(rbind,...), dplyr::bind_rows() or data.table::rbindlist().

如果您确实希望将每个数据框放在一个单独的对象中，即使这通常是不可取的，您也可以使用 assign 执行以下操作:

If you really want each data frame in a separate object, even though that's often inadvisable, you could do the following with assign:

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

或者，不使用 assign，并演示 (1) 如何清理文件名和 (2) 演示如何使用 list2env，您可以尝试以下:

Or, without assign, and to demonstrate (1) how the file name can be cleaned up and (2) show how to use list2env, you can try the following:

temp = list.files(pattern="*.csv")
list2env(
  lapply(setNames(temp, make.names(gsub("*.csv$", "", temp))), 
         read.csv), envir = .GlobalEnv)

但同样，将它们放在一个列表中通常会更好.

But again, it's often better to leave them in a single list.

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