How to Select Columns in Data.Table Using a Character Vector of Certain Column Names

How to select columns in data.table using a character vector of certain column names?

We can use .. notation to find myVector as a vector of column positions, like it would work in data.frame

mtcarsDT[, ..myVector]

According to ?data.table

In case of overlapping variables names inside dataset and in parent scope you can use double dot prefix ..cols to explicitly refer to 'cols variable parent scope and not from your dataset.

Selecting columns of a data.table using a vector of column names or column positions without using with = F

An option is to use double dots

DT[, ..mycols]
#          A           C
#1:  0.1188208 -0.17328827
#2: -0.5622505  0.84231231
#3:  0.8111072 -1.59802306
#4:  0.7968823  2.08468489
# ...

Or specify it in .SDcols

DT[, .SD, .SDcols = mycols]

or else with = FALSE as the OP mentioned in the post

How to select data.table columns whose name is variable

Add in the , with = FALSE

dt <- data.table(x = 1:10, y = 11:20, z = 1:10)
col <- "x"
dt[, c(col, "y"), with=FALSE]

Select subset of columns in data.table R

Use with=FALSE:

cols = paste("V", c(1,2,3,5), sep="")

dt[, !cols, with=FALSE]

I suggest going through the "Introduction to data.table" vignette.

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Update: From v1.10.2 onwards, you can also do:

dt[, ..cols]

See the first NEWS item under v1.10.2 here for additional explanation.

Accessing columns in data.table using a character vector of column names

You can use the data.table syntax .. which "looks up one level" (as in the Unix terminal) for the variable:

> all.equal(DT[,list(x,y)], DT[, ..cols])
[1] TRUE
> all.equal(DT[,.SD[,list(x,y)][min(v)]], DT[,.SD[ ,min(v)], .SDcols = cols])
[1] TRUE

More details under FAQ 1.6 I believe: http://datatable.r-forge.r-project.org/datatable-faq.pdf

Selecting a subset of columns in a data.table

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

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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.

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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).

How to select columns programmatically in a data.table?

This is covered in FAQ 1.1, 1.2 and 2.17.

Some possibilities:

DT[, keep, with = FALSE]
DT[, c('V1', 'V3'), with = FALSE]
DT[, c(1, 3), with = FALSE]
DT[, list(V1, V3)]

The reason DF[c('V1','V3')] works as it does for a data.frame is covered in ?`[.data.frame`

Data frames can be indexed in several modes. When [ and [[ are used
with a single vector index (x[i] or x[[i]]), they index the data frame
as if it were a list. In this usage a drop argument is ignored, with a
warning.

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From data.table 1.10.2, you may use the .. prefix when subsetting columns programmatically:

When j is a symbol prefixed with .. it will be looked up in calling scope and its value taken to be column names or numbers [...] It is experimental.

Thus:

DT[ , ..keep]
#    V1 V3
# 1:  1  7
# 2:  2  8
# 3:  3  9

Specific column selection from data.table in R

You can try like this:

dt[, .SD, .SDcols = colnames]

Meanwhile, data.table gives an alternative choice in recent version:

dt[, ..colnames]

dplyr r : selecting columns whose names are in an external vector

We could use any_of with select

library(dplyr)
data %>%
     select(any_of(col_names))

-output

 a b
1  1 e
2  4 e
3 13 f
4  8 m
5 10 z
6  3 y
...

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