dplyr - Canonical way to select columns in R -

i comparing common "tidying" operations in dplyr , in "plain r" (see output here , source here see mean).

i have hard time finding "canonical" and concise way select columns using variable names (by canonical, mean, pure plain r , understandable minimum understanding of r (so no "voodoo trick")).

example:

## subset: columns "var_1" "var_2" excluding "var_3"  ## dplyr:  table %>% select(var_1:var_2, -var_3)  ## plain r:  r <- sapply(c("var_1", "var_2", "var_3"), function(x) which(names(table)==x)) table[ ,setdiff(r[1]:r[2],r[3]) ] 

any suggestions improve plain r syntax?

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edit

i implemented suggestions , compared performance on different syntaxes, , noticed use of match , subset lead surprising falls in performance:

# plain r, v1 system.time(for (i in 1:100) {      r <- sapply(c("size", "country"), function(x) which(names(cran_df)==x))     cran_df[,r[1]:r[2]] } ) ##    user  system elapsed  ##   0.006   0.000   0.007  # plain r, using match system.time(for (i in 1:100) {      r <- match(c("size", "country"), names(cran_df))     cran_df[,r[1]:r[2]] %>% head(n=3) } ) ##    user  system elapsed  ##   0.056   0.028   0.084  # plain r, using match , subset system.time(for (i in 1:100) {     r <- match(c("size", "country"), names(cran_df))     subset(cran_df, select=r[1]:r[2]) %>% head(n=3) } ) ##    user  system elapsed  ##  11.556   1.057  12.640  # dplyr system.time(for (i in 1:100) select(cran_tbl_df,size:country)) ##    user  system elapsed  ##   0.034   0.000   0.034 

looks implementation of subset sub-optimal...

you can use built in subset function, can take select argument follows similar (though not identical) syntax dplyr::select. note dropping columns has done in second step:

t1 <- subset(table, select = var1:var2) t2 <- subset(t1, select = -var_3) 

or:

subset(subset(table, select = var1:var2), select = -var_3) 

for example:

subset(subset(mtcars, select = c(mpg:wt)), select = -hp)