Quickly Reading Very Large Tables as Dataframes

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.

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.

Is there a faster way than fread() to read big data?

You can use select = columns to only load the relevant columns without saturating your memory. For example:

dt <- fread("./file.csv", select = c("column1", "column2", "column3"))

I used read.delim() to read a file that fread() could not load completely. So you could convert your data into .txt and use read.delim().

However, why don't you open a connection to the SQL server you're pulling your data from. You can open connections to SQL servers with library(odbc) and write your query like you normally would. You can optimize your memory usage that way.

Check out this short introduction to odbc.

How to save a large dataframe and quickly load it in R?

You can serialize it easily with:

readr::write_rds(pageInfo_df, "pageInfo_df.Rds")

and then deserialize it like so:


this should handle every valid R object of an arbitrary complexity.

Load large datasets into data frame

I recommend data.table although you will end up with a data table after this. If you choose not to use the data table, you can simply convert back to a normal data frame.


How to properly import CSV files with PySpark

If you can't correct the input file, then you can try to load it as text then split the values to get the desired columns. Here's an example:

input file
read and parse
from pyspark.sql import functions as F

nb_cols = 5

df = spark.read.text("file.csv")

df = df.withColumn(
F.split("value", ",")
*[F.col("values")[i].alias(f"col_{i}") for i in range(nb_cols)],
F.array_join(F.expr(f"slice(values, {nb_cols + 1}, size(values))"), ",").alias(f"col_{nb_cols}")

#|col_0|col_1|col_2|col_3|col_4| col_5|
#| 1| 2| 3| 4| 5|6,7,8,9,10,0,12,121|
#| 1| 2| 3| 4| 5|6,7,8,9,10,0,12,121|

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