What's wrong with my function to load multiple .csv files into single dataframe in R using rbind?

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I have written the following function to combine 300 .csv files. My directory name is "specdata". I have done the following steps for execution,

x <- function(directory) {     
    dir <- directory    
    data_dir <- paste(getwd(),dir,sep = "/")    
    files  <- list.files(data_dir,pattern = '\\.csv')    
    tables <- lapply(paste(data_dir,files,sep = "/"), read.csv, header = TRUE)    
    pollutantmean <- do.call(rbind , tables)         
}

# Step 2: call the function
x("specdata")

# Step 3: inspect results
head(pollutantmean)

Error in head(pollutantmean) : object 'pollutantmean' not found

What is my mistake? Can anyone please explain?

There's a lot of unnecessary code in your function. You can simplify it to:

load_data <- function(path) { 
  files <- dir(path, pattern = '\\.csv', full.names = TRUE)
  tables <- lapply(files, read.csv)
  do.call(rbind, tables)
}

pollutantmean <- load_data("specdata")

Be aware that do.call + rbind is relatively slow. You might find dplyr::bind_rows or data.table::rbindlist to be substantially faster.

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To update Prof. Wickham's answer above with code from the more recent purrr library which he coauthored with Lionel Henry:

Tbl <-
    list.files(pattern="*.csv") %>% 
    map_df(~read_csv(.))

If the typecasting is being cheeky, you can force all the columns to be as characters with this.

Tbl <-
    list.files(pattern="*.csv") %>% 
    map_df(~read_csv(., col_types = cols(.default = "c")))

If you are wanting to dip into subdirectories to construct your list of files to eventually bind, then be sure to include the path name, as well as register the files with their full names in your list. This will allow the binding work to go on outside of the current directory. (Thinking of the full pathnames as operating like passports to allow movement back across directory 'borders'.)

Tbl <-
    list.files(path = "./subdirectory/",
               pattern="*.csv", 
               full.names = T) %>% 
    map_df(~read_csv(., col_types = cols(.default = "c"))) 

As Prof. Wickham describes here (about halfway down):

map_df(x, f) is effectively the same as do.call("rbind", lapply(x, f)) but under the hood is much more efficient.

and a thank you to Jake Kaupp for introducing me to map_df() here.

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This can be done very succinctly with dplyr and purrr from the tidyverse. Where x is a list of the names of your csv files you can simply use:

bind_rows(map(x, read.csv))

Mapping read.csv to x produces a list of dfs that bind_rows then neatly combines!

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```{r echo = FALSE, warning = FALSE, message = FALSE}

setwd("~/Data/R/BacklogReporting/data/PastDue/global/") ## where file are located

path = "~/Data/R/BacklogReporting/data/PastDue/global/"
out.file <- ""
file.names <- dir(path, pattern = ".csv")
for(i in 1:length(file.names)){
  file <- read.csv(file.names[i], header = TRUE, stringsAsFactors = FALSE)
  out.file <- rbind(out.file, file)
}

write.csv(out.file, file = "~/Data/R/BacklogReporting/data/PastDue/global/global_stacked/past_due_global_stacked.csv", row.names = FALSE) ## directory to write stacked file to

past_due_global_stacked <- read.csv("C:/Users/E550143/Documents/Data/R/BacklogReporting/data/PastDue/global/global_stacked/past_due_global_stacked.csv", stringsAsFactors = FALSE)

files <- list.files(pattern = "\\.csv$") %>%  t() %>% paste(collapse = ", ")
```

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If your csv files are into an other directory, you could use something like this:

readFilesInDirectory <- function(directory, pattern){
  files <- list.files(path = directory,pattern = pattern)
  for (f in files){
    file <- paste(directory,files, sep ="")
    temp <- lapply(file, fread, sep=",")
    data <- rbindlist( temp )
  }
  return(data)
}

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Comments
  • if Coursera indeed brought you here, read through the excellent practice assignment that they've provided first. (As of today, the link is: github.com/rdpeng/practice_assignment/blob/master/…)
  • Or now dplyr::bind_rows instead of dplyr::rbind_list which has been deprecated.
  • also, using readr::read_csv is much faster than read.csv.
  • This is the best answer given the current state of the Tidyverse
  • Code works - modified from Daniel Marcelino's code. Thanks for taking the time to comment.