How to Deal with a 50Gb Large CSV File in R Language

How to deal with a 50GB large csv file in r language?

You can use R with SQLite behind the curtains with the sqldf package. You'd use the read.csv.sql function in the sqldf package and then you can query the data however you want to obtain the smaller data frame.

The example from the docs:

library(sqldf)

iris2 <- read.csv.sql("iris.csv", 
    sql = "select * from file where Species = 'setosa' ")

I've used this library on VERY large CSV files with good results.

How to read only lines that fulfil a condition from a csv into R?

You could use the read.csv.sql function in the sqldf package and filter using SQL select. From the help page of read.csv.sql:

library(sqldf)
write.csv(iris, "iris.csv", quote = FALSE, row.names = FALSE)
iris2 <- read.csv.sql("iris.csv", 
    sql = "select * from file where `Sepal.Length` > 5", eol = "\n")

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.

Saving a flat file as an SQL database in R without loading it 100% into RAM

Apparently there is already a function for that

https://raw.githubusercontent.com/inbo/inborutils/master/R/csv_to_sqlite.R

I am testing it. I do not see any progress bar even if the corresponding option is selected, but it appears to get the job done.

Buying ram to avoid chunking for 30-50Gb plus files

I think you have quite a few things which can be optimized:

• first of all read only those columns that you really need instead of reading and then dropping them - use usecols=list_of_needed_columns parameter

• increase your chunksize - try it with different values - i would start with 10**5

• don't use chunk.apply(...) for converting your datetimes - it's very slow - use pd.to_datetime(column, format='...') instead

• you can filter your data bit more efficiently when combining multiple conditions instead of doing it step-by-step:

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