How do you read multiple .txt files into R? [duplicate]

This question already has answers here: How to import multiple .csv files at once? (15 answers) Closed 4 years ago.

I'm using R to visualize some data all of which is in .txt format. There are a few hundred files in a directory and I want to load it all into one table, in one shot.

Any help?

EDIT:

Listing the files is not a problem. But I am having trouble going from list to content. I've tried some of the code from here, but I get a bug with this part:

all.the.data <- lapply( all.the.files,  txt  , header=TRUE)

saying

 Error in match.fun(FUN) : object 'txt' not found

Any snippets of code that would clarify this problem would be greatl开发者_StackOverflowy appreciated.

---

You can try this:

filelist = list.files(pattern = ".*.txt")

#assuming tab separated values with a header    
datalist = lapply(filelist, function(x)read.table(x, header=T)) 

#assuming the same header/columns for all files
datafr = do.call("rbind", datalist) 

---

There are three fast ways to read multiple files and put them into a single data frame or data table

First get the list of all txt files (including those in sub-folders)

list_of_files <- list.files(path = ".", recursive = TRUE,
                            pattern = "\\.txt$", 
                            full.names = TRUE)

1) Use fread() w/ rbindlist() from the data.table package

#install.packages("data.table", repos = "https://cran.rstudio.com")
library(data.table)

# Read all the files and create a FileName column to store filenames
DT <- rbindlist(sapply(list_of_files, fread, simplify = FALSE),
                use.names = TRUE, idcol = "FileName")

2) Use readr::read_table2() w/ purrr::map_df() from the tidyverse framework:

#install.packages("tidyverse", 
#                 dependencies = TRUE, repos = "https://cran.rstudio.com")
library(tidyverse)

# Read all the files and create a FileName column to store filenames
df <- list_of_files %>%
  set_names(.) %>%
  map_df(read_table2, .id = "FileName")

3) (Probably the fastest out of the three) Use vroom::vroom():

#install.packages("vroom", 
#                 dependencies = TRUE, repos = "https://cran.rstudio.com")
library(vroom)

# Read all the files and create a FileName column to store filenames
df <- vroom(list_of_files, .id = "FileName")

   

Note: to clean up file names, use basename or gsub functions

Benchmark: readr vs data.table vs vroom for big data

---

Edit 1: to read multiple csv files and skip the header using readr::read_csv

list_of_files <- list.files(path = ".", recursive = TRUE,
                            pattern = "\\.csv$", 
                            full.names = TRUE)

df <- list_of_files %>%
  purrr::set_names(nm = (basename(.) %>% tools::file_path_sans_ext())) %>%
  purrr::map_df(read_csv, 
                col_names = FALSE,
                skip = 1,
                .id = "FileName")

Edit 2: to convert a pattern including a wildcard into the equivalent regular expression, use glob2rx()

---

There is a really, really easy way to do this now: the readtext package.

readtext::readtext("path_to/your_files/*.txt")

It really is that easy.

---

Look at the help for functions dir() aka list.files(). This allows you get a list of files, possibly filtered by regular expressions, over which you could loop.

If you want to them all at once, you first have to have content in one file. One option would be to use cat to type all files to stdout and read that using popen(). See help(Connections) for more.

---

Thanks for all the answers!

In the meanwhile, I also hacked a method on my own. Let me know if it is any useful:

library(foreign)

setwd("/path/to/directory")

files <-list.files()

data <- 0


for (f in files) {

tempData = scan( f, what="character")

data <- c(data,tempData)    

}