reading in local files with different extensions – efficient way?

I have a list of 20+ data files with different file extensions (.csv, .xls, .xlsx). I want to store each data file as a data frame in a list. I wrote a for loop to do the task.

My "data" folder contains subfolders with different data files. Then within the loop, I called different functions accordingly depending on the file extension types. I was wondering if there is a way to read in files and store them in a single list without using a for loop?

Below is my code:

library(dplyr)
library(readxl)
library(XML)

# grab all file names
files <- list.files(path="data", recursive=T, pattern="*.(csv|xls|xlsx)$", full.names=T)

### example imported file names from above
files <- c("data/Bills/BillHistory_Account1.csv", "data/Bills/BillHistory_2.xls", "data/Usages/UsageHistory_3.xls", "data/Usages/UsageHistory_4.xls.xlsx")

# store each data frame in a list
df_list <- list()
for(i in seq_along(files)){
  if(grepl("*.csv$", files[[i]])){
    df_list[[i]] <- read.csv(files[[i]], sep="\t")
  } else if(grepl("*.xls$", files[[i]])){
    ### some .xls files actually contain html codes
    ### need to read using XML::readHTMLTable
    df_list[[i]] <- readHTMLTable(files[[i]])$tblMain
  } else if(grepl("*.xlsx$", files[[i]])){
    df_list[[i]] <- read_excel(files[[i]])
  }
}

Thank you in advance for helping!

P.S. I was also wondering if there is a way to create a reproducible example for reading in local data files? I can’t think of any means other than having the reader create arbitrary local csv, or excel files.

>Solution :

Package {rio} is a universal wrapper around various import packages. A simple import(filename.ext) will guess the file format, so the whole task might boil down to:

# grab all file names
files <- list.files(path="data", recursive=T, pattern="*.(csv|xls|xlsx)$", full.names=T)

dataframe_list <- Map(files, f = \(filename) rio::import(filename))

If needed, you can still add reader-specific named arguments (sep, header, skip …) to import.