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.
Dates are read as character currently. They can be converted afterwards using the excellent fasttime package or standard base functions. `fread` is for regular delimited files; i.e., where every row has the same number of columns. In future, secondary separator (sep2) may be specified within each column. Such columns will be read as type list where each cell is itself a vector.
fread(input, sep="auto", sep2="auto", nrows=-1L, header="auto", na.strings="NA", file,
stringsAsFactors=FALSE, verbose=getOption("datatable.verbose"), autostart=1L,
skip=0L, select=NULL, drop=NULL, colClasses=NULL,
integer64=getOption("datatable.integer64"), # default: "integer64"
dec=if (sep!=".") "." else ",", col.names,
check.names=FALSE, encoding="unknown", quote="\"",
strip.white=TRUE, fill=FALSE, blank.lines.skip=FALSE, key=NULL,
showProgress=getOption("datatable.showProgress"), # default: TRUE
data.table=getOption("datatable.fread.datatable") # default: TRUE
)fread("grep blah filename")) or the input itself as a string (containing at least one \n), see examples. In both cases, a length 1 character string. A filename input is passed through path.expand for convenience and may be a URL starting http:// or file://. ,\t |;:] that exists on line autostart outside quoted ("") regions, and separates the rows above autostart into a consistent number of fields, too. 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 can be different and is the first character in the same set above [,\t |;:], other than sep, that exists inside each field outside quoted regions on line autostart. NB: sep2 is not yet implemented. 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 nrows if you require the first 10 rows, for example. `nrows=0` is a special case that just returns the column names and types; e.g., a dry run for a large file or to quickly check format consistency of a set of files before starting to read any. NA values. By default ",," for columns read as type character is read as a blank string ("") and ",NA," is read as NA. Typical alternatives might be na.strings=NULL (no coercion to NA at all!) or perhaps na.strings=c("NA","N/A","null"). input argument. sep, sep2 and the number of fields. It's extremely unlikely that autostart should ever need to be changed, we hope. autostart to find the first data row. skip>0 means ignore autostart and take line skip+1 as the first data row (or column names according to header="auto"|TRUE|FALSE as usual). skip="string" searches for "string" in the file (e.g. a substring of the column names row) and starts on that line (inspired by read.xls in package gdata). bit64::integer64. Alternatively, "double"|"numeric" reads as base::read.csv does; i.e., possibly with loss of precision and if so silently. Or, "character". base::read.csv. If not "." (default) then usually ",". See details. FALSE. If TRUE then the names of the variables in the data.table are checked to ensure that they are syntactically valid variable names. If necessary they are adjusted (by make.names) so that they are, and also to ensure that there are no duplicates."unknown". Other possible options are "UTF-8" and "Latin-1". Note: it is not used to re-encode the input, rather enables handling of encoded strings in their native encoding. "\""), if a field starts with a doublequote, fread handles embedded quotes robustly as explained under Details. If it fails, then another attempt is made to read the field as is, i.e., as if quotes are disabled. By setting quote="", the field is always read as if quotes are disabled. TRUE. Strips leading and trailing whitespaces of unquoted fields. If FALSE, only header trailing spaces are removed. FALSE). If TRUE then in case the rows have unequal length, blank fields are implicitly filled.logical, default is FALSE. If TRUE blank lines in the input are ignored.setkey. It may be a single comma separated string such as key="x,y,z", or a vector of names such as key=c("x","y","z"). Only valid when argument data.table=TRUE.TRUE displays progress on the console using \r. It is produced in fread's C code where the very nice (but R level) txtProgressBar and tkProgressBar are not easily available. data.table. FALSE returns a data.frame. data.table by default. A data.frame when argument data.table=FALSE; e.g. options(datatable.fread.datatable=FALSE).
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. 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. Setting skip>0 overrides this feature by setting autostart=skip+1 and turning off the search upwards step.
A sample of 1,000 rows is used to determine column types (100 rows from 10 points). The lowest type for each column is chosen from the ordered list: logical, 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 higher type in rows outside the sample. 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 are no buffers used in fread's C code at all. The field width limit is limited by R itself: the maximum width of a character string (currenly 2^31-1 bytes, 2GB).
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.
If an empty line is encountered then reading stops there, with warning if any text exists after the empty line such as a footer. The first line of any text discarded is included in the warning message.
Line endings: 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.
Decimal separator and locale: fread(...,dec=",") should just work. fread uses C function strtod to read numeric data; e.g., 1.23 or 1,23. strtod retrieves the decimal separator (. or , usually) from the locale of the R session rather than as an argument passed to the strtod function. So for fread(...,dec=",") to work, fread changes this (and only this) R session's locale temporarily to a locale which provides the desired decimal separator.
On Windows, "French_France.1252" is tried which should be available as standard (any locale with comma decimal separator would suffice) and on unix "fr_FR.utf8" (you may need to install this locale on unix). fread() is very careful to set the locale back again afterwards, even if the function fails with an error. The choice of locale is determined by options()$datatable.fread.dec.locale. This may be a vector of locale names and if so they will be tried in turn until the desired dec is obtained; thus allowing more than two different decimal separators to be selected. This is a new feature in v1.9.6 and is experimental. In case of problems, turn it off with options(datatable.fread.dec.experiment=FALSE).
Quotes:
When quote is a single character,
sep and \n) may appear in unquoted character fields, e.g., ...,2,Joe Bloggs,3.14,....
character columns are quoted, they must start and end with that quoting character immediately followed by sep or \n, e.g., ...,2,"Joe Bloggs",3.14,.... In essence quoting character fields are required only if sep or \n appears in the string value. Quoting may be used to signify that numeric data should be read as text. Unescaped quotes may be present in a quoted field, e.g., ...,2,"Joe, "Bloggs"",3.14,..., as well as escaped quotes, e.g., ...,2,"Joe \",Bloggs\"",3.14,....
If an embedded quote is followed by the separator inside a quoted field, the embedded quotes up to that point in that field must be balanced; e.g. ...,2,"www.blah?x="one",y="two"",3.14,....
On those fields that do not satisfy these conditions, e.g., fields with unbalanced quotes, fread re-attempts that field as if it isn't quoted. This is quite useful in reading files that contains fields with unbalanced quotes as well, automatically.
To read fields as is instead, use quote = "".
finagler = "to get or achieve by guile or manipulation" http://dictionary.reference.com/browse/finagler
read.csv, url, Sys.setlocale
## Not run:
#
# # 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="",
# stringsAsFactors=FALSE,comment.char="",nrows=n,
# colClasses=c("integer","integer","numeric",
# "character","numeric","integer")))
# # 10 sec (consistently). All known tricks and known nrows, see references.
#
# 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)
# all.equal(as.data.table(DF1), DT)
# identical(DF1,within(SQLDF,{b<-as.integer(b);c<-as.numeric(c)}))
# identical(DF1,within(as.data.frame(FFDF),d<-as.character(d)))
#
# # 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=",",
# quote="",stringsAsFactors=FALSE,comment.char="",nrows=1e7,
# colClasses=c("integer","integer","numeric",
# "character","numeric","integer")))
# # 100-200 sec (varies)
#
# system.time(DT <- fread("testbig.csv"))
# # 30-40 sec
#
# all(mapply(all.equal, DF, DT))
#
#
# # 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 only using 100 rows
# # since read.table assumes colClasses is correct.
#
# 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. Correct without needing to worry about colClasses
# # issue above.
#
#
# # Reads URLs directly :
# fread("http://www.stats.ox.ac.uk/pub/datasets/csb/ch11b.dat")
#
# ## End(Not run)
# 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
")
# Numerical precision :
DT = fread("A\n1.010203040506070809010203040506\n") # silent loss of precision
DT[,sprintf("%.15E",A)] # stored accurately as far as double precision allows
DT = fread("A\n1.46761e-313\n") # detailed warning about ERANGE; read as 'numeric'
DT[,sprintf("%.15E",A)] # beyond what double precision can store accurately to 15 digits
# For greater accuracy use colClasses to read as character, then package Rmpfr.
# colClasses
data = "A,B,C,D\n1,3,5,7\n2,4,6,8\n"
fread(data, colClasses=c(B="character",C="character",D="character")) # as read.csv
fread(data, colClasses=list(character=c("B","C","D"))) # saves typing
fread(data, colClasses=list(character=2:4)) # same using column numbers
# drop
fread(data, colClasses=c("B"="NULL","C"="NULL")) # as read.csv
fread(data, colClasses=list(NULL=c("B","C"))) #
fread(data, drop=c("B","C")) # same but less typing, easier to read
fread(data, drop=2:3) # same using column numbers
# select
# (in read.csv you need to work out which to drop)
fread(data, select=c("A","D")) # less typing, easier to read
fread(data, select=c(1,4)) # same using column numbers
# skip blank lines
fread("a,b\n1,a\n2,b\n\n\n3,c\n", blank.lines.skip=TRUE)
# fill
fread("a,b\n1,a\n2\n3,c\n", fill=TRUE)
fread("a,b\n\n1,a\n2\n\n3,c\n\n", fill=TRUE)
# fill with skip blank lines
fread("a,b\n\n1,a\n2\n\n3,c\n\n", fill=TRUE, blank.lines.skip=TRUE)
# check.names usage
fread("a b,a b\n1,2\n")
fread("a b,a b\n1,2\n", check.names=TRUE) # no duplicates + syntactically valid names
Run the code above in your browser using DataLab