data.table (version 1.8.8)

fread: Fast and friendly file finagler

Description

Similar to read.table but faster and more convenient. All controls such as sep, colClasses and nrows are automatically detected. bit64::integer64 types are also detected and read directly without needing to read as character before converting. This function is still under development. For example, dates are read as character (they can be converted afterwards using the excellent fasttime package or standard base functions) and embedded quotes (""" and """") have problems. There are other known issues that haven't been fixed and features not yet implemented. But, you may find it works in many cases. Please report problems to datatable-help or Stack Overflow's data.table tag. Not for production use yet. Not because it's unstable in the sense that it crashes or is buggy (your testing will show whether it is stable in your cases or not) but because fread's arguments and behaviour is likely to change in future; i.e., we expect to make (hopefully minor) non-backwards-compatible changes. Why has it been released to CRAN then? Because a maintenance release was asked for by CRAN maintainers to comply with new stricter tests in R-devel, and a few Bioconductor packages depend on data.table and Bioconductor requires packages to pass R-devel checks. It was quicker to leave fread in and write these paragraphs, than take fread out.

Usage

fread(input, sep="auto", sep2="auto", nrows=-1, header="auto", na.strings="NA", stringsAsFactors=FALSE, verbose=FALSE, autostart=30)

Arguments

input

Either the file name to read (containing no \n character) or the input itself as a string (containing at least one \n), see examples. In both cases, a length 1 character string. A filename may be a URL starting http:// or file://.

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 num

sep2

The separator within columns. A list column will be returned where each cell is a vector of values. This is much faster using less working memory than strsplit afterwards or similar techniques. For each column sep2

nrows

The number of rows to read, by default -1 means all. Unlike read.table, it doesn't help speed to set this to the number of rows in the file (or an estimate), since the number of rows is automatically determined and is already fast. Only set <

header

Does the first data line contain column names? Defaults according to whether every field on the first data line is type character.

na.strings

A character vector of strings to convert to NA_character_. By default for columns read as type character ",," is read as a blank string ("") and ",NA," is read as NA_character_. Typical alte

stringsAsFactors

Convert all character columns to factors?

verbose

Be chatty and report timings?

autostart

Any line number within the region of machine readable delimited text, by default 30. If the file is shorter or this line is empty (e.g. short files with trailing blank lines) then the last non empty line is used. This line and the lines above it are used

Value

• A data.table.

Details

Once the separator is found on line autostart, the number of columns is determined. Then the file is searched backwards from autostart until a row is found that doesn't have that number of columns, or the start of file is reached. Thus, the first data row is found and any human readable banners are automatically skipped. This feature can be particularly useful for loading a set of files which may not all have consistently sized banners.

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).

There is no line length limit, not even a very large one. Since we are encouraging list columns (i.e. sep2) this has the potential to encourage longer line lengths. So the approach of scanning each line into a buffer first and then rescanning that buffer is not used. There is no field width limit either, not even a very large one. There are no buffers used in fread's C code at all. The only limits are those imposed by R itself: the maximum width of a character string perhaps, for example.

character columns can be quoted (...,2,"Joe Bloggs",3.14,...) or not quoted (...,2,Joe Bloggs,3.14,...). Spaces and other whitepace (other than sep and \n) may appear in an unquoted character field, provided the field doesn't contain sep itself. Therefore quoting character values is only required if sep itself appears in the string value. Quoting may also be used to signify that numeric data should be read as text (or that can be achieved by specifying the column type via colClasses). Field quoting is automatically detected and no arguments are needed to control it.

The filename extension (such as .csv) is irrelevant for "auto" sep and sep2. Separator detection is entirely driven by the file contents. This can be useful when loading a set of different files which may not be named consistently, or may not have the extension .csv despite being csv. Some datasets have been collected over many years, one file per day for example. Sometimes the file name format has changed at some point in the past or even the format of the file itself. So the idea is that you can loop fread through a set of files and as long as each file is regular and delimited, fread can read them all. Whether they all stack is another matter but at least each one is read quickly without you needing to vary colClasses in read.table or read.csv.

All known line endings are detected automatically: \n (*NIX including Mac), \r\n (Windows CRLF), \r (old Mac) and \n\r (just in case). There is no need to convert input files first. fread running on any architecture will read a file from any architecture. Both \r and \n may be embedded in character strings (including column names) provided the field is quoted.

Furthermore, these few features are for fostering friendliness. Facilitated by a fair farthingsworth of (far from flaky, flawed or fatuous) finagling. Finally, it's frustrating to forget but fear not fine friends, fortunately the (free) fread function's first facet is f; for fast, friendly, file or finagle.

References

Background : http://cran.r-project.org/doc/manuals/R-data.html http://stackoverflow.com/questions/1727772/quickly-reading-very-large-tables-as-dataframes-in-r www.biostat.jhsph.edu/~rpeng/docs/R-large-tables.html https://stat.ethz.ch/pipermail/r-help/2007-August/138315.html http://www.cerebralmastication.com/2009/11/loading-big-data-into-r/ http://stackoverflow.com/questions/9061736/faster-than-scan-with-rcpp http://stackoverflow.com/questions/415515/how-can-i-read-and-manipulate-csv-file-data-in-c http://stackoverflow.com/questions/9352887/strategies-for-reading-in-csv-files-in-pieces http://stackoverflow.com/questions/11782084/reading-in-large-text-files-in-r http://stackoverflow.com/questions/45972/mmap-vs-reading-blocks http://stackoverflow.com/questions/258091/when-should-i-use-mmap-for-file-access http://stackoverflow.com/a/9818473/403310 http://stackoverflow.com/questions/9608950/reading-huge-files-using-memory-mapped-files

finagler = "to get or achieve by guile or manipulation" http://dictionary.reference.com/browse/finagler

See Also

read.csv, url html{}

Examples

# Demo speedup
n=1e6
DT = data.table( a=sample(1:1000,n,replace=TRUE),
                 b=sample(1:1000,n,replace=TRUE),
                 c=rnorm(n),
                 d=sample(c("foo","bar","baz","qux","quux"),n,replace=TRUE),
                 e=rnorm(n),
                 f=sample(1:1000,n,replace=TRUE) )
DT[2,b:=NA_integer_]
DT[4,c:=NA_real_]
DT[3,d:=NA_character_]
DT[5,d:=""]
DT[2,e:=+Inf]
DT[3,e:=-Inf]

write.table(DT,"test.csv",sep=",",row.names=FALSE,quote=FALSE)
cat("File size (MB):",round(file.info("test.csv")$size/1024^2),"\n")    # 50 MB (1e6 rows x 6 columns)

system.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))         # 60 sec (first time in fresh R session)
system.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))         # 30 sec (immediate repeat is faster, varies)

system.time(DF2 <- read.table("test.csv",header=TRUE,sep=",",quote="",  # 10 sec (consistently)
    stringsAsFactors=FALSE,comment.char="",nrows=n,                     # ( All known tricks and known
    colClasses=c("integer","integer","numeric",                         #   nrows, see references )
                 "character","numeric","integer")))

require(data.table)
system.time(DT <- fread("test.csv"))                                    #  3 sec (faster and friendlier)

require(sqldf)
system.time(SQLDF <- read.csv.sql("test.csv",dbname=NULL))              # 20 sec (friendly too, good defaults)

require(ff)
system.time(FFDF <- read.csv.ffdf(file="test.csv",nrows=n))             # 20 sec (friendly too, good defaults)

identical(DF1,DF2)                                                      # TRUE
all.equal(as.data.table(DF1), DT)                                       # TRUE
identical(DF1,within(SQLDF,{b<-as.integer(b);c<-as.numeric(c)}))        # TRUE
identical(DF1,within(as.data.frame(FFDF),d<-as.character(d)))           # TRUE

# Scaling up ...
l = vector("list",10)
for (i in 1:10) l[[i]] = DT
DTbig = rbindlist(l)
tables()
write.table(DTbig,"testbig.csv",sep=",",row.names=FALSE,quote=FALSE)    # 500MB (10 million rows x 6 columns)

system.time(DF <- read.table("testbig.csv",header=TRUE,sep=",",         # 100-200 sec (varies)  
    quote="",stringsAsFactors=FALSE,comment.char="",nrows=1e7,                     
    colClasses=c("integer","integer","numeric",
                 "character","numeric","integer")))

system.time(DT <- fread("testbig.csv"))                                 # 30-40 sec
all(mapply(all.equal, DF, DT))                                          # TRUE


# Real data example (Airline data)
# http://stat-computing.org/dataexpo/2009/the-data.html

download.file("http://stat-computing.org/dataexpo/2009/2008.csv.bz2",
              destfile="2008.csv.bz2")                                  # 109MB (compressed)
system("bunzip2 2008.csv.bz2")                                          # 658MB (7,009,728 rows x 29 columns)
colClasses = sapply(read.csv("2008.csv",nrows=100),class)               # 4 character, 24 integer, 1 logical. Incorrect.
colClasses = sapply(read.csv("2008.csv",nrows=200),class)               # 5 character, 24 integer. Correct. Might have missed data
system.time(DF <- read.table("2008.csv", header=TRUE, sep=",",          
    quote="",stringsAsFactors=FALSE,comment.char="",nrows=7009730,      
    colClasses=colClasses)                                              # 360 secs
system.time(DT <- fread("2008.csv"))                                    #  40 secs
table(sapply(DT,class))                                                 # 5 character and 24 integer columns


# Reads URLs directly :
fread("http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat")

# Reads text input directly :
fread("A,B\n1,2\n3,4")

# Reads pasted input directly :
fread("A,B
1,2
3,4
")

# Finds the first data line automatically :
fread("
This is perhaps a banner line or two or ten.
A,B
1,2
3,4
")

# Detects whether column names are present automatically :
fread("
1,2
3,4
")