Load a Small Random Sample from a Large CSV File into R Data Frame

Importing and extracting a random sample from a large .CSV in R

I think that there is not a good R tool to read a file in a random way (maybe it can be an extension read.table or fread(data.table package)) .

Using perl you can easily do this task. For example , to read 1% of your file in a random way, you can do this :

xx= system(paste("perl -ne 'print if (rand() < .01)'",big_file),intern=TRUE)

Here I am calling it from R using system. xx contain now only 1% of your file.

You can wrap all this in a function:

read_partial_rand <- 
  function(big_file,percent){
    cmd <- paste0("perl -ne 'print if (rand() < ",percent,")'")
    cmd <- paste(cmd,big_file)
    system(cmd,intern=TRUE)
  }

Random Sampling multiple Dataframes by Rows in a folder using R

First, sample the data:

 listdf <- lapply(listdf, FUN = function(i) sample_n(i, 300)) 

Then use an rbind inside do.call to bind all the data, note that this will only function as long as all the dataframes have the same number of columns, as well as the same column names:

 data <- do.call("rbind", listdf)

How to take multiple Sample() vector outputs and combine them into a data frame

Assume that the data is held in files name for their day, like mydata_2020_05_17.csv

library(tidyverse)
readDay <- function(date, dir, sampleN){
  path <- paste0(dir, "/", "mydata_", date, ".csv")
  read_csv(path) %>% 
    as_tibble() %>% 
      # You many not need this if the records already have the date
    mutate(DATE = date) %>% 
    sample_n(sampleN, replace = FALSE)
}

Lets start on the first Sunday of the month

answerWeek = map_df(seq.Date(from = as_date("2020-05-03"), length.out = 6, by = 1),
                    ~ readDay(.x, "~/nefarious/data", sampleN = 20))

NOT RUN because I don't have a folder full of dated csv data.

Let us know if I've mis-interpreted what you're looking for.

Quickly reading very large tables as dataframes

An update, several years later

This answer is old, and R has moved on. Tweaking read.table to run a bit faster has precious little benefit. Your options are:

1. Using vroom from the tidyverse package vroom for importing data from csv/tab-delimited files directly into an R tibble. See Hector's answer.

2. Using fread in data.table for importing data from csv/tab-delimited files directly into R. See mnel's answer.

3. Using read_table in readr (on CRAN from April 2015). This works much like fread above. The readme in the link explains the difference between the two functions (readr currently claims to be "1.5-2x slower" than data.table::fread).

4. read.csv.raw from iotools provides a third option for quickly reading CSV files.

5. Trying to store as much data as you can in databases rather than flat files. (As well as being a better permanent storage medium, data is passed to and from R in a binary format, which is faster.) read.csv.sql in the sqldf package, as described in JD Long's answer, imports data into a temporary SQLite database and then reads it into R. See also: the RODBC package, and the reverse depends section of the DBI package page. MonetDB.R gives you a data type that pretends to be a data frame but is really a MonetDB underneath, increasing performance. Import data with its monetdb.read.csv function. dplyr allows you to work directly with data stored in several types of database.

6. Storing data in binary formats can also be useful for improving performance. Use saveRDS/readRDS (see below), the h5 or rhdf5 packages for HDF5 format, or write_fst/read_fst from the fst package.

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The original answer

There are a couple of simple things to try, whether you use read.table or scan.

1. Set nrows=the number of records in your data (nmax in scan).

2. Make sure that comment.char="" to turn off interpretation of comments.

3. Explicitly define the classes of each column using colClasses in read.table.

4. Setting multi.line=FALSE may also improve performance in scan.

If none of these thing work, then use one of the profiling packages to determine which lines are slowing things down. Perhaps you can write a cut down version of read.table based on the results.

The other alternative is filtering your data before you read it into R.

Or, if the problem is that you have to read it in regularly, then use these methods to read the data in once, then save the data frame as a binary blob with save saveRDS, then next time you can retrieve it faster with load readRDS.

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