Selecting a subset of columns in a data.table - r

Question

I'd like to print all the columns of a data table dt except one of them named V3 but don't want to refer to it by number but by name. This is the code that I have:
dt = data.table(matrix(sample(c(0,1),5,rep=T),50,10))
dt[,-3,with=FALSE] # Is this the only way to not print column "V3"?
Using the data frame way, one could do this through the code:
df = data.frame(matrix(sample(c(0,1),5,rep=T),50,10))
df[,!(colnames(df)%in% c("X3"))]
So, my question is: is there another way to not print one column in a data table without the necessity of refer to it by number? I'd like to find something similar to the data frame syntax I used above but using data table.

Answer 1

Use a very similar syntax as for a data.frame, but add the argument with=FALSE:
dt[, setdiff(colnames(dt),"V9"), with=FALSE]
V1 V2 V3 V4 V5 V6 V7 V8 V10
1: 1 1 1 1 1 1 1 1 1
2: 0 0 0 0 0 0 0 0 0
3: 1 1 1 1 1 1 1 1 1
4: 0 0 0 0 0 0 0 0 0
5: 0 0 0 0 0 0 0 0 0
6: 1 1 1 1 1 1 1 1 1
The use of with=FALSE is nicely explained in the documentation for the j argument in ?data.table:
j: A single column name, single expresson of column names, list() of expressions of column names, an expression or function call that evaluates to list (including data.frame and data.table which are lists, too), or (when with=FALSE) same as j in [.data.frame.
From v1.10.2 onwards it is also possible to do this as follows:
keep <- setdiff(names(dt), "V9")
dt[, ..keep]
Prefixing a symbol with .. will look up in calling scope (i.e. the Global Environment) and its value taken to be column names or numbers (source).

Answer 2

Edit 2019-09-27 with a more modern approach
You can do this with patterns as mentioned above; or, you can do it with ! if there's a vector of names already:
dt[ , !'V3']
# or
drop_cols = 'V3'
dt[ , !..drop_cols]
.. means "look up one level"
Older version using with=FALSE (data.table is moving away from this argument steadily)
Here's a way that uses grep to convert to numeric and allow negative column indexing:
dt[, -grep("^V3$", names(dt)), with=FALSE]
You did say "V3" was to be excluded, right?

Answer 3

Maybe it's only in recent versions of data.table (I'm using 1.9.6), but you can do:
dt[, -'V3']
For several columns:
dt[, -c('V3', 'V9')]
Note that the quotes around the variable names are necessary.
Also, if your column names are stored in a variable, say cols, you'll need to do dt[, -cols, with=FALSE].

Answer 4

From version 1.12.0 onwards, it is also possible to select columns using regular expressions on their names:
iris_DT <- as.data.table(iris)
iris_DT[, .SD, .SDcols = patterns(".e.al")]

Answer 5

To summarize the answer to this question, and also to make it
a) negation-friendly (so that you can also select columns by negation),
b) pipe-line friendly (so that you can use in a pipeline with %>% operator), and
c) so that you can select using both column numbers and column names,
these are available options:
library(data.table);
select1 <- function (dt, range) dt[, range, with=F]
select2 <- function (dt, range) dt[, ..range]
select3 <- function (dt, range) dt[, .SD, .SDcols=range]
dt <- ggplot2::diamonds
range <- 1:3 # or
range <- dt %>% names %>% .[1:3]
dt %>% select1(range);
dt %>% select2(range);
dt %>% select3(range);
dt %>% select1(-range);
dt %>% select2(-range);
dt %>% select3(-range); # DOES NOT WORK
Also we note that this
dt %>% .[, ..(names(dt)[1:3])] # DOES NOT WORK
Therefore the best (most universal and fast) way to select multiple columns in data.table is the following:
# columns are selected using column numbers:
range <- 1:3
dt %>% select1(range);
dt %>% .[, range, with=F]
# The same works if columns are selected using column names:
range <- names(dt) [1:3]
dt %>% select1(range);
dt %>% .[, range, with=F]
PS.
If, instead of selecting multiple columns, you want to efficiently delete multiple columns from data.table by reference (i.e. instead of copying the entire data.table), then you can use data.table's := operator. But I don't know how to do it for multiple columns in one line