How do you delete a column by name in data.table?

Question

To get rid of a column named \"foo\" in a data.frame, I can do:

df <- df[-grep(\'foo\', colnames(df))]

However, once df

Answer 1

Here is a way when you want to set a # of columns to NULL given their column names a function for your usage :)

deleteColsFromDataTable <- function (train, toDeleteColNames) {

       for (myNm in toDeleteColNames)

       train <- train [,(myNm):=NULL]

       return (train)
}

Answer 2

DT[,c:=NULL] # remove column c

Answer 3

Suppose your dt has columns col1, col2, col3, col4, col5, coln.

To delete a subset of them:

vx <- as.character(bquote(c(col1, col2, col3, coln)))[-1]
DT[, paste0(vx):=NULL]

Answer 4

For a data.table, assigning the column to NULL removes it:

DT[,c("col1", "col1", "col2", "col2")] <- NULL
^
|---- Notice the extra comma if DT is a data.table

... which is the equivalent of:

DT$col1 <- NULL
DT$col2 <- NULL
DT$col3 <- NULL
DT$col4 <- NULL

The equivalent for a data.frame is:

DF[c("col1", "col1", "col2", "col2")] <- NULL
      ^
      |---- Notice the missing comma if DF is a data.frame

Q. Why is there a comma in the version for data.table, and no comma in the version for data.frame?

A. As data.frames are stored as a list of columns, you can skip the comma. You could also add it in, however then you will need to assign them to a list of NULLs, DF[, c("col1", "col2", "col3")] <- list(NULL).

Answer 5

Very simple option in case you have many individual columns to delete in a data table and you want to avoid typing in all column names #careadviced

dt <- dt[, -c(1,4,6,17,83,104)]

This will remove columns based on column number instead.

It's obviously not as efficient because it bypasses data.table advantages but if you're working with less than say 500,000 rows it works fine

Answer 6

Any of the following will remove column foo from the data.table df3:

# Method 1 (and preferred as it takes 0.00s even on a 20GB data.table)
df3[,foo:=NULL]

df3[, c("foo","bar"):=NULL]  # remove two columns

myVar = "foo"
df3[, (myVar):=NULL]   # lookup myVar contents

# Method 2a -- A safe idiom for excluding (possibly multiple)
# columns matching a regex
df3[, grep("^foo$", colnames(df3)):=NULL]

# Method 2b -- An alternative to 2a, also "safe" in the sense described below
df3[, which(grepl("^foo$", colnames(df3))):=NULL]

data.table also supports the following syntax:

## Method 3 (could then assign to df3, 
df3[, !"foo"]  

though if you were actually wanting to remove column "foo" from df3 (as opposed to just printing a view of df3 minus column "foo") you'd really want to use Method 1 instead.

(Do note that if you use a method relying on grep() or grepl(), you need to set pattern="^foo$" rather than "foo", if you don't want columns with names like "fool" and "buffoon" (i.e. those containing foo as a substring) to also be matched and removed.)

Less safe options, fine for interactive use:

The next two idioms will also work -- if df3 contains a column matching "foo" -- but will fail in a probably-unexpected way if it does not. If, for instance, you use any of them to search for the non-existent column "bar", you'll end up with a zero-row data.table.

As a consequence, they are really best suited for interactive use where one might, e.g., want to display a data.table minus any columns with names containing the substring "foo". For programming purposes (or if you are wanting to actually remove the column(s) from df3 rather than from a copy of it), Methods 1, 2a, and 2b are really the best options.

# Method 4:
df3[, .SD, .SDcols = !patterns("^foo$")]

---

Lastly there are approaches using with=FALSE, though data.table is gradually moving away from using this argument so it's now discouraged where you can avoid it; showing here so you know the option exists in case you really do need it:

# Method 5a (like Method 3)
df3[, !"foo", with=FALSE] 
# Method 5b (like Method 4)
df3[, !grep("^foo$", names(df3)), with=FALSE]
# Method 5b (another like Method 4)
df3[, !grepl("^foo$", names(df3)), with=FALSE]