r - Removing all columns with a name on the fly -

i'm using read_excel speed , simplicity import excel file.

unfortunately, there's yet no capability of excluding selected columns won't needed data set; save effort, naming such columns "x" col_names argument, easier trying keep track of x1, x2, , on.

i'd exclude such columns on fly if possible avoid step of copying, in pseudocode:

read_excel("data.xlsx", col_names = c("x", "keep", "x"))[ , !"x"] 

we can use sample data set included readxl package illustration:

library(readxl) df <- read_excel(system.file("extdata/datasets.xlsx", package = "readxl"),                  col_names = c("x", "x", "length", "width", "x"), skip = 1l) 

the approaches i've seen work don't work on fly, e.g., having stored df, can do:

df <- df[ , -grep("^x$", names(df))] 

this works requires making copy of df storing it, overwriting it; i'd prefer remove columns in same command read_excel allocate df ab initio.

other similar approaches require declaring temporary variables, prefer avoid if possible, e.g.,

col_names <- c("x", "x", "length", "width", "x") df <- read_excel(system.file("extdata/datasets.xlsx", package = "readxl"),                  col_names = col_names, skip = 1l)[ , -grep("^x$", col_names)] 

is there way axe these columns without creating unnecessary temporary variables?

(i convert data.table, wondering if there's way without data.table)

there way in readxl::read_excel, though it's little hidden, , have no idea if columns read memory [temporarily] regardless. trick specify column types, putting in "blank" don't want:

readxl::read_excel(system.file("extdata/datasets.xlsx", package = "readxl"),                    col_types = c('blank', 'blank', 'numeric', 'numeric', 'text')) ## # tibble: 150 x 3 ##    petal.length petal.width species ##           <dbl>       <dbl>   <chr> ## 1           1.4         0.2  setosa ## 2           1.4         0.2  setosa ## 3           1.3         0.2  setosa ## 4           1.5         0.2  setosa ## 5           1.4         0.2  setosa ## 6           1.7         0.4  setosa ## 7           1.4         0.3  setosa ## 8           1.5         0.2  setosa ## 9           1.4         0.2  setosa ## 10          1.5         0.1  setosa ## # ... 140 more rows 

the caveat need know data types of columns want, though suppose start text , clean later type.convert or whatnot.