Read all files in a folder and apply a function to each data frame

I am doing a relatively simple piece of analysis that I have put into a function on all the files in a particular folder. I was wondering whether anyone had any tips to help me automate the process on a number of different folders.

1. Firstly, I was wondering whether there was a way of reading all the files in a particular folder straight into R. I believe the following command will list all the files:

files <- (Sys.glob("*.csv"))

...which I found from Using R to list all files with a specified extension

And then the following code reads all those files into R.

listOfFiles <- lapply(files, function(x) read.table(x, header = FALSE)) 

…from Manipulating multiple files in R

But the files seem to be read in as one continuous list and not individual files… how can I change the script to open all the csv files in a particular folder as individual dataframes?

2. Secondly, assuming that I can read all the files in separately, how do I complete a function on all these dataframes in one go. For example, I have created four small dataframes so I can illustrate what I want:

    Df.1 <- data.frame(A = c(5,4,7,6,8,4),B = (c(1,5,2,4,9,1)))
    Df.2 <- data.frame(A = c(1:6),B = (c(2,3,4,5,1,1)))
    Df.3 <- data.frame(A = c(4,6,8,0,1,11),B = (c(7,6,5,9,1,15)))
    Df.4 <- data.frame(A = c(4,2,6,8,1,0),B = (c(3,1,9,11,2,16)))
   

I have also made up an example function:

Summary<-function(dfile){
SumA<-sum(dfile$A)
MinA<-min(dfile$A)
MeanA<-mean(dfile$A)
MedianA<-median(dfile$A)
MaxA<-max(dfile$A)

sumB<-sum(dfile$B)
MinB<-min(dfile$B)
MeanB<-mean(dfile$B)
MedianB<-median(dfile$B)
MaxB<-max(dfile$B)

Sum<-c(sumA,sumB)
Min<-c(MinA,MinB)
Mean<-c(MeanA,MeanB)
Median<-c(MedianA,MedianB)
Max<-c(MaxA,MaxB)
rm(sumA,sumB,MinA,MinB,MeanA,MeanB,MedianA,MedianB,MaxA,MaxB)

Label<-c("A","B")
dfile_summary<-data.frame(Label,Sum,Min,Mean,Median,Max)
return(dfile_summary)}

I would ordinarily use the following command to apply the function to each individual dataframe.

Df1.summary<-Summary(dfile)

Is there a way instead of applying the function to all the dataframes, and use the titles of the dataframes in the summary tables (i.e. Df1.summary).

Many thanks,

Katie

Solution 1:

On the contrary, I do think working with list makes it easy to automate such things.

Here is one solution (I stored your four dataframes in folder temp/).

filenames <- list.files("temp", pattern="*.csv", full.names=TRUE)
ldf <- lapply(filenames, read.csv)
res <- lapply(ldf, summary)
names(res) <- substr(filenames, 6, 30)

It is important to store the full path for your files (as I did with full.names), otherwise you have to paste the working directory, e.g.

filenames <- list.files("temp", pattern="*.csv")
paste("temp", filenames, sep="/")

will work too. Note that I used substr to extract file names while discarding full path.

You can access your summary tables as follows:

> res$`df4.csv`
       A              B        
 Min.   :0.00   Min.   : 1.00  
 1st Qu.:1.25   1st Qu.: 2.25  
 Median :3.00   Median : 6.00  
 Mean   :3.50   Mean   : 7.00  
 3rd Qu.:5.50   3rd Qu.:10.50  
 Max.   :8.00   Max.   :16.00  

If you really want to get individual summary tables, you can extract them afterwards. E.g.,

for (i in 1:length(res))
  assign(paste(paste("df", i, sep=""), "summary", sep="."), res[[i]])

Solution 2:

usually i don't use for loop in R, but here is my solution using for loops and two packages : plyr and dostats

plyr is on cran and you can download dostats on https://github.com/halpo/dostats (may be using install_github from Hadley devtools package)

Assuming that i have your first two data.frame (Df.1 and Df.2) in csv files, you can do something like this.

require(plyr)
require(dostats)

files <- list.files(pattern = ".csv")


for (i in seq_along(files)) {

    assign(paste("Df", i, sep = "."), read.csv(files[i]))

    assign(paste(paste("Df", i, sep = ""), "summary", sep = "."), 
           ldply(get(paste("Df", i, sep = ".")), dostats, sum, min, mean, median, max))

}

Here is the output

R> Df1.summary
  .id sum min   mean median max
1   A  34   4 5.6667    5.5   8
2   B  22   1 3.6667    3.0   9
R> Df2.summary
  .id sum min   mean median max
1   A  21   1 3.5000    3.5   6
2   B  16   1 2.6667    2.5   5

Solution 3:

Here is a tidyverse option that might not the most elegant, but offers some flexibility in terms of what is included in the summary:

library(tidyverse)
dir_path <- '~/path/to/data/directory/'
file_pattern <- 'Df\\.[0-9]\\.csv' # regex pattern to match the file name format

read_dir <- function(dir_path, file_name){
  read_csv(paste0(dir_path, file_name)) %>% 
    mutate(file_name = file_name) %>%                # add the file name as a column              
    gather(variable, value, A:B) %>%                 # convert the data from wide to long
    group_by(file_name, variable) %>% 
    summarize(sum = sum(value, na.rm = TRUE),
              min = min(value, na.rm = TRUE),
              mean = mean(value, na.rm = TRUE),
              median = median(value, na.rm = TRUE),
              max = max(value, na.rm = TRUE))
  }

df_summary <- 
  list.files(dir_path, pattern = file_pattern) %>% 
  map_df(~ read_dir(dir_path, .))

df_summary
# A tibble: 8 x 7
# Groups:   file_name [?]
  file_name variable   sum   min  mean median   max
  <chr>     <chr>    <int> <dbl> <dbl>  <dbl> <dbl>
1 Df.1.csv  A           34     4  5.67    5.5     8
2 Df.1.csv  B           22     1  3.67    3       9
3 Df.2.csv  A           21     1  3.5     3.5     6
4 Df.2.csv  B           16     1  2.67    2.5     5
5 Df.3.csv  A           30     0  5       5      11
6 Df.3.csv  B           43     1  7.17    6.5    15
7 Df.4.csv  A           21     0  3.5     3       8
8 Df.4.csv  B           42     1  7       6      16