Convenience features of fread

The fread function in the data.table R package is not just for speed on large files. Here we highlight its convenience features for small data.

1. Using command line tools directly

For example, from this answer :

fread("grep -v TRIAL sum_data.txt")
#      V1   V2 V3      V4      V5      V6      V7
#  1:   2  0.1  0 -0.0047 -0.0168 -0.9938 -0.0087
#  2:   2  0.2  0 -0.0121  0.0002 -0.9898 -0.0364
#  3:   2  0.3  0  0.0193 -0.0068 -0.9884  0.0040
   ...

The -v makes grep return all lines except lines containing the string TRIAL. Given the number of high quality engineers that have looked at the command tool grep over the years, it is most likely that it is as fast as you can get, as well as being correct, convenient, well documented online, easy to learn and search for solutions for specific tasks. If you need to do more complicated string filters (e.g. strings at the beginning or the end of the lines, etc) then grep syntax is very powerful. Learning its syntax is a transferable skill to other languages and environments.

Here's another example, from this answer :

fread('unzip -cq mtcars.csv.zip')
#                      V1  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#  1:           Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#  2:       Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#  ...

The manual page ?fread contains :

input ..., a shell command that preprocesses the file (e.g. fread("grep blah filename")), ...

On Windows we recommend Cygwin (run one .exe to install) which includes the command line tools such as grep. In March 2016, Microsoft announced they will include these tools in Windows 10 natively. On Linux and Mac they have always been included in the operating system. You can find many examples and tutorials online about command line tools. We recommend Data Science at the Command Line. Some advantages of this approach were highlighted in slides 10-12 here.

2. Automatic separator detection

Given a tab, comma, pipe, space, colon or semi-colon delimited file, you don't need to find or remember the function name or argument name to use. fread is a single command that will look at its input and observe the separator for you. All you as the user has to do is pass the file name to the function. The rest is done for you. Of course, if you do wish to override the separator, or use a very unusual one, you can pass it to the sep= parameter.

The manual page ?fread contains :

sep The separator between columns. Defaults to the first character in the set [,\t |;:] that exists on line autostart outside quoted ("") regions, and separates the rows above autostart into a consistent number of fields too.

3. High quality automatic column type detection

Some files start off with blanks or zeros in some columns and only become fully populated later in the file. For this reason fread doesn't just look at the top of the file, but it looks at the middle and the end too. It is possible to jump directly to the middle and the end of the file efficiently because fread uses the operating system's mmap which does not need to read from the beginning of the file to get to a particular point. Currently fread reads the top 5, middle 5 and bottom 5 rows. We intend to increase this to 1000 and also increase the number of points from 3 to 10. The increase from 15 to 10,000 rows used for type detection is not expected to add much overhead because even 10,000 rows is not very large. If it becomes clear that all columns contain character strings then the detection can return early.

The manual page ?fread contains :

The first 5 rows, middle 5 rows and last 5 rows are then read to determine column types. The lowest type for each column is chosen from the ordered list integer, integer64, double, character. This enables fread to allocate exactly the right number of rows, with columns of the right type, up front once. The file may of course still contain data of a different type in rows other than first, middle and last 5. In that case, the column types are bumped mid read and the data read on previous rows is coerced. Setting verbose=TRUE reports the line and field number of each mid read type bump, and how long this type bumping took (if any).

4. Reading SQL insert scripts

There is no built-in feature for reading SQL insert scripts but they are easily handled with command line pre-processing. For example, given SQL insert script insert_script.sql:

INSERT INTO tbl VALUES (1, 'asd', 923123123, 'zx');
INSERT INTO tbl VALUES (1, NULL, 923123123, 'zxz');
INSERT INTO tbl VALUES (3, 'asd3', 923123123, NULL);

you can fread it using the following command (from this question):

fread('awk -F\' *[(),]+ *\' -v OFS=, \'{for (i=2;i<NF;i++) printf "%s%s", ($i=="NULL"?"":$i), (i<(NF-1)?OFS:ORS)}\' insert_script.sql')
#    V1     V2        V3    V4
# 1:  1  'asd' 923123123  'zx'
# 2:  1        923123123 'zxz'
# 3:  3 'asd3' 923123123      

5. integer64 support

fread automatically detects large integers (> 2^31) and reads them as type integer64 from the bit64 package. This retains full precision by storing the integers as true and accurate integers. This default behaviour can be overridden on a per-column basis using the colClasses argument, for all columns in the call to fread using its integer64 argument, or always by setting options(datatable.integer64="double") or options(datatable.integer64="character").

The manual page ?fread contains:

integer64 "integer64" (default) reads columns detected as containing integers larger than 2^31 as type bit64::integer64. Alternatively, "double"|"numeric" reads as base::read.csv does; i.e., possibly with loss of precision and if so silently. Or, "character".

To know that the default can be changed globally (e.g. in your personal .Rprofile file), you can look at the Usage section of ?fread which contains the default arguments :

integer64=getOption("datatable.integer64") # default: "integer64"

6. drop columns by name or number, as well as select

Rather than read all columns in to R (using up memory) only to use a subset of columns in your analysis, you can use these arguments to tell fread to skip over a set of columns. If you only have a few to discard, it's easier to just drop those. Easier because you don't have to type out the columns to keep or think how to write an R expression that correctly results in the columns to keep. fread will warn you if you specify a column that isn't present in the file. You use either select or drop but not both at the same time.

The manual page ?fread contains:

select Vector of column names or numbers to keep, drop the rest.

drop Vector of column names or numbers to drop, keep the rest.