data.table inherits from data.frame. It offers fast subset, fast grouping, fast update, fast ordered joins and list columns in a short and flexible syntax, for faster development. It is inspired by A[B] syntax in Rwhere A is a matrix and B is a 2-column matrix. Since a data.table is a data.frame, it is compatible with Rfunctions and packages that only accept data.frame.
The 10 minute quick start guide to data.table may be a good place to start: vignette("datatable-intro")}. Or, the first section of FAQs is intended to be read from start to finish and is considered core documentation: vignette("datatable-faq")}. If you have read and searched these documents and the help page below, please feel free to ask questions on bug.report(package="data.table").
Please check the example(data.table) and study the output at the prompt.
*NEW* : help page for :=
keyby argumentdata.table(..., keep.rownames=FALSE, check.names=FALSE, key=NULL)## S3 method for class 'data.table':
[(x, i, j, by, keyby, with=TRUE,
nomatch = getOption("datatable.nomatch"), # default: NA_integer_
mult = "all", roll = FALSE, rolltolast = FALSE,
which = FALSE, .SDcols,
verbose=getOption("datatable.verbose"), # default: FALSE
drop=NULL)
... in data.frame. Usual recycling rules are applied to vectors of different lengths to create a list of equal length vectors.... is a matrix or data.frame, TRUE will retain the rownames of that object in a column named rn.check.names in data.frame.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")data.table.list or data.table. integer and logical vectors work the same way they do in [.data.frame. Other than
list() of expressions of column names, an expression or function call that evaluates to list (including data.frame and data.table which are llist() of expressions of column names, or a single character string containing comma separated column names, or a character vector of column names. The list() of expressions is evaluated within t
by but with an additional setkey() on the by columns of the result, for convenience. Not to be confused with a keyed by as defined above.with=TRUE and j is evaluated within the frame of x. The column names can be used as variables. When with=FALSE, j works as it does in [.data.frame.nomatch in match. When a row in i has no match to x's key, nomatch=NA (default) means NA is returned for x's non-join colux match to the row in i, mult controls which are returned: "all" (default), "first" or "last".roll=TRUE and i's row matches to all but the last x join column, and its value in the last iroll but the data is not rolled forward past the last observation. The value of i must fall in a gap in x but not after the end of the data for that group defined by all but the last join column. TRUE returns the integer row numbers of x that i matches to.x included in .SD. May be character column names or numeric positions. This is useful for speed when applying a function through a subset of (possible very many) columns; e.g., DT[,lapply(TRUE turns on status and information messages to the console. Turn this on by default using options(datatable.verbose=TRUE). The quantity and types of verbosity may be expanded in future.data.table. Do not use. It needs to be here because data.table inherits from data.frame. See vignette("datatable-faq").data.table builds on base Rfunctionality to reduce 2 types of time :
It combines database like operations such as subset, with and by and provides similar joins that merge provides but faster. This is achieved by using R's column based ordered in-memory data.frame structure, eval within the environment of a list, the [.data.table mechanism to condense the features, and compiled C to make certain operations fast.
The package can be used just for rapid programming (compact syntax). Largest compute time benefits are on 64bit platforms with plentiful RAM, or when smaller datasets are repeatedly queried within a loop, or when other methods use so much working memory that they fail with an out of memory error.
As with [.data.frame, compound queries can be concatenated on one line; e.g.,
DT[,sum(v),by=colA][V1<300][tail(order(v1))] 6="" 300="" #="" sum(v)="" by="" cola="" then="" return="" the="" largest="" which="" are="" under="" data.tables (e.g. X, Y and Z) can be joined in many ways; e.g.,
X[Y][Z]
X[Z][Y]
X[Y[Z]]
X[Z[Y]]
A data.table is a list of vectors, just like a data.frame. However :
[.data.tablefor fast joins of keyed tables, fast aggregation, and fast last observation carried forward (LOCF).Since a list is a vector, data.table columns may be type list. Columns of type list can contain mixed types. Each item in a column of type list may be different lengths. This is true of data.frame, too.
Several methods are provided for data.table, including is.na, na.omit,
t, rbind, cbind, merge and others.
data.table homepage: data.frame, [.data.frame, as.data.table, setkey, J, SJ, CJ, merge.data.table, tables, test.data.table, IDateTime, unique.data.table, copy, :=, alloc.col, truelength
example(data.table) # to run these examples at the prompt
DF = data.frame(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9)
DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9)
DF
DT
identical(dim(DT),dim(DF)) # TRUE
identical(DF$a, DT$a) # TRUE
is.list(DF) # TRUE
is.list(DT) # TRUE
is.data.frame(DT) # TRUE
tables()
DT[2] # 2nd row
DT[,v] # v column (as vector)
DT[,list(v)] # v column (as data.table)
DT[2:3,sum(v)] # sum(v) over rows 2 and 3
DT[2:5,cat(v,"")] # just for j's side effect
DT[c(FALSE,TRUE)] # even rows (usual recycling)
DT[,2,with=FALSE] # 2nd column
colNum = 2
DT[,colNum,with=FALSE] # same
setkey(DT,x) # set a 1-column key. No quotes, for convenience.
setkeyv(DT,"x") # same (v in setkeyv stands for vector)
v="x"
setkeyv(DT,v) # same
# key(DT)<-"x" # copies whole table, please use set* functions instead
DT["a"] # binary search (fast)
DT[x=="a"] # vector scan (slow)
DT[,sum(v),by=x] # keyed by
DT[,sum(v),by=key(DT)] # same
DT[,sum(v),by=y] # ad hoc by
DT["a",sum(v)] # j for one group
DT[c("a","b"),sum(v)] # j for two groups
X = data.table(c("b","c"),foo=c(4,2))
X
DT[X] # join
DT[X,sum(v)] # join and eval j for each row in i
DT[X,mult="first"] # first row of each group
DT[X,mult="last"] # last row of each group
DT[X,sum(v)*foo] # join inherited scope
J("a",2) # J() is alias for data.table()
data.table("a",2) # same
setkey(DT,x,y) # 2-column key
setkeyv(DT,c("x","y")) # same
DT["a"] # join to 1st column of key
DT[J("a")] # same
DT[J("a",3)] # join to 2 columns
DT[J("a",3:6)] # join 4 rows (2 missing)
DT[J("a",3:6),nomatch=0] # remove missing
DT[J("a",3:6),roll=TRUE] # rolling join (locf)
DT[,sum(v),by=list(y%%2)] # by expression
DT[,.SD[2],by=x] # 2nd row of each group
DT[,tail(.SD,2),by=x] # last 2 rows of each group
DT[,lapply(.SD,sum),by=x] # applying through columns by group
DT[,list(MySum=sum(v),
MyMin=min(v),
MyMax=max(v)),
by=list(x,y%%2)] # by 2 expressions
DT[,sum(v),x][V1<20] # compound query
DT[,sum(v),x][order(-V1)] # ordering results
DT[,z:=42L] # add new column by reference
DT[,z:=NULL] # remove column
DT["a",v:=42L] # subassign v by reference
DT[,transform(.SD,m=mean(v)),by=x]
DT[,.SD[which.min(v)],by=x]
# Follow posting guide, support is here (not r-help) :
maintainer("data.table")
vignette("datatable-intro")
vignette("datatable-faq")
vignette("datatable-timings")
test.data.table() # over 300 low level tests
update.packages() # keep up to dateRun the code above in your browser using DataLab