# NOT RUN {
# examples from data.table
dat = data.table(x=rep(c("b","a","c"),each=3), y=c(1,3,6), v=1:9)
dat
# basic row subset operations
query_if(dat, 2) # 2nd row
query_if(dat, 3:2) # 3rd and 2nd row
query_if(dat, order(x)) # no need for order(dat$x)
query_if(dat, y>2) # all rows where dat$y > 2
query_if(dat, y>2 & v>5) # compound logical expressions
query_if(dat, !2:4) # all rows other than 2:4
query_if(dat, -(2:4)) # same
# select|compute columns data.table way
query(dat, v) # v column (as vector)
query(dat, list(v)) # v column (as data.table)
query(dat, sum(v)) # sum of column v, returned as vector
query(dat, list(sum(v))) # same, but return data.table (column autonamed V1)
query(dat, list(v, v*2)) # return two column data.table, v and v*2
# subset rows and select|compute data.table way
query_if(dat, 2:3, sum(v)) # sum(v) over rows 2 and 3, return vector
query_if(dat, 2:3, list(sum(v))) # same, but return data.table with column V1
query_if(dat, 2:3, list(sv=sum(v))) # same, but return data.table with column sv
query_if(dat, 2:5, cat(v, "\n")) # just for j's side effect
# select columns the data.frame way
query(dat, 2, with=FALSE) # 2nd column, returns a data.table always
colNum = 2
query(dat, colNum, with=FALSE) # same, equivalent to DT[, .SD, .SDcols=colNum]
# grouping operations - j and by
query(dat, sum(v), by=x) # ad hoc by, order of groups preserved in result
query(dat, sum(v), keyby=x) # same, but order the result on by cols
query(dat, sum(v), by=x) %>%
query_if(order(x)) # same but by chaining expressions together
# fast ad hoc row subsets (subsets as joins)
# same as x == "a" but uses binary search (fast)
query_if(dat, "a", on="x")
# same, for convenience, no need to quote every column
query_if(dat, "a", on=list(x))
query_if(dat, .("a"), on="x") # same
# same, single "==" internally optimised to use binary search (fast)
query_if(dat, x=="a")
# not yet optimized, currently vector scan subset
query_if(dat, x!="b" | y!=3)
# join on columns x,y of 'dat'; uses binary search (fast)
query_if(dat, .("b", 3), on=c("x", "y"))
query_if(dat, .("b", 3), on=list(x, y)) # same, but using on=list()
query_if(dat, .("b", 1:2), on=c("x", "y")) # no match returns NA
query_if(dat, .("b", 1:2), on=.(x, y), nomatch=0) # no match row is not returned
# locf, nomatch row gets rolled by previous row
query_if(dat, .("b", 1:2), on=c("x", "y"), roll=Inf)
query_if(dat, .("b", 1:2), on=.(x, y), roll=-Inf) # nocb, nomatch row gets rolled by next row
# on rows where dat$x=="b", calculate sum(v*y)
query_if(dat, "b", sum(v*y), on="x")
# all together now
query_if(dat, x!="a", sum(v), by=x) # get sum(v) by "x" for each i != "a"
query_if(dat, !"a", sum(v), by=.EACHI, on="x") # same, but using subsets-as-joins
query_if(dat, c("b","c"), sum(v), by=.EACHI, on="x") # same
query_if(dat, c("b","c"), sum(v), by=.EACHI, on=.(x)) # same, using on=.()
# joins as subsets
X = data.table(x=c("c","b"), v=8:7, foo=c(4,2))
X
query_if(dat, X, on="x") # right join
query_if(X, dat, on="x") # left join
query_if(dat, X, on="x", nomatch=0) # inner join
query_if(dat, !X, on="x") # not join
# join using column "y" of 'dat' with column "v" of X
query_if(dat, X, on=c(y="v"))
query_if(dat,X, on="y==v") # same as above (v1.9.8+)
query_if(dat, X, on = .(y<=foo)) # NEW non-equi join (v1.9.8+)
query_if(dat, X, on="y<=foo") # same as above
query_if(dat, X, on=c("y<=foo")) # same as above
query_if(dat, X, on=.(y>=foo)) # NEW non-equi join (v1.9.8+)
query_if(dat, X, on=.(x, y<=foo)) # NEW non-equi join (v1.9.8+)
query_if(dat, X, .(x,y,x.y,v), on=.(x, y>=foo)) # Select x's join columns as well
query_if(dat, X, on="x", mult="first") # first row of each group
query_if(dat, X, on="x", mult="last") # last row of each group
query_if(dat, X, sum(v), by=.EACHI, on="x") # join and eval j for each row in i
query_if(dat, X, sum(v)*foo, by=.EACHI, on="x") # join inherited scope
query_if(dat, X, sum(v)*i.v, by=.EACHI, on="x") # 'i,v' refers to X's v column
query_if(dat, X, on=.(x, v>=v), sum(y)*foo, by=.EACHI) # NEW non-equi join with by=.EACHI (v1.9.8+)
# more on special symbols, see also ?"special-symbols"
query_if(dat, .N) # last row
query(dat, .N) # total number of rows in DT
query(dat, .N, by=x) # number of rows in each group
query(dat, .SD, .SDcols=x:y) # select columns 'x' and 'y'
query(dat, .SD[1]) # first row of all columns
query(dat, .SD[1], by=x) # first row of 'y' and 'v' for each group in 'x'
query(dat, c(.N, lapply(.SD, sum)), by=x) # get rows *and* sum columns 'v' and 'y' by group
query(dat, .I[1], by=x) # row number in DT corresponding to each group
query(dat, grp := .GRP, by=x) %>% head() # add a group counter column
query(X, query_if(dat, .BY, y, on="x"), by=x) # join within each group
# add/update/delete by reference (see ?assign)
query(dat, z:=42L) %>% head() # add new column by reference
query(dat, z:=NULL) %>% head() # remove column by reference
query_if(dat, "a", v:=42L, on="x") %>% head() # subassign to existing v column by reference
query_if(dat, "b", v2:=84L, on="x") %>% head() # subassign to new column by reference (NA padded)
# NB: postfix [] is shortcut to print()
query(dat, m:=mean(v), by=x)[] # add new column by reference by group
# advanced usage
dat = data.table(x=rep(c("b","a","c"),each=3),
v=c(1,1,1,2,2,1,1,2,2),
y=c(1,3,6),
a=1:9,
b=9:1)
dat
query(dat, sum(v), by=.(y%%2)) # expressions in by
query(dat, sum(v), by=.(bool = y%%2)) # same, using a named list to change by column name
query(dat, .SD[2], by=x) # get 2nd row of each group
query(dat, tail(.SD,2), by=x) # last 2 rows of each group
query(dat, lapply(.SD, sum), by=x) # sum of all (other) columns for each group
query(dat, .SD[which.min(v)], by=x) # nested query by group
query(dat, list(MySum=sum(v),
MyMin=min(v),
MyMax=max(v)),
by=.(x, y%%2)
) # by 2 expressions
query(dat, .(a = .(a), b = .(b)), by=x) # list columns
query(dat, .(seq = min(a):max(b)), by=x) # j is not limited to just aggregations
query(dat, sum(v), by=x) %>%
query_if(V1<20) # compound query
query(dat, sum(v), by=x) %>%
setorder(-V1) %>%
head() # ordering results
query(dat, c(.N, lapply(.SD,sum)), by=x) # get number of observations and sum per group
# anonymous lambda in 'j', j accepts any valid
# expression. TO REMEMBER: every element of
# the list becomes a column in result.
query(dat,
{tmp = mean(y);
.(a = a-tmp, b = b-tmp)
},
by=x)
# }
# NOT RUN {
pdf("new.pdf")
query(dat, plot(a,b), by=x) # can also plot in 'j'
dev.off()
# }
# NOT RUN {
# using rleid, get max(y) and min of all cols in .SDcols for each consecutive run of 'v'
query(dat,
c(.(y=max(y)), lapply(.SD, min)),
by=rleid(v),
.SDcols=v:b
)
# }
Run the code above in your browser using DataLab