R LanguageNon-standard evaluation and standard evaluation


Dplyr and many modern libraries in R use non-standard evaluation (NSE) for interactive programming and standard evaluation (SE) for programming1.

For instance, the summarise() function use non-standard evaluation but relies on the summarise_() which uses standard evaluation.

The lazyeval library makes it easy to turn standard evaluation function into NSE functions.

Examples with standard dplyr verbs

NSE functions should be used in interactive programming. However, when developping new functions in a new package, it's better to use SE version.

Load dplyr and lazyeval :



NSE version

filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)
filter(mtcars, cyl < 6 & vs == 1)

SE version (to be use when programming functions in a new package)

filter_(mtcars, .dots = list(~ cyl == 8))
filter_(mtcars, .dots = list(~ cyl < 6))
filter_(mtcars, .dots = list(~ cyl < 6, ~ vs == 1))


NSE version

summarise(mtcars,  mean(disp))
summarise(mtcars,  mean_disp = mean(disp))

SE version

summarise_(mtcars, .dots = lazyeval::interp(~ mean(x), x = quote(disp)))
summarise_(mtcars, .dots = setNames(list(lazyeval::interp(~ mean(x), x = quote(disp))), "mean_disp"))
summarise_(mtcars, .dots = list("mean_disp" = lazyeval::interp(~ mean(x), x = quote(disp))))


NSE version

mutate(mtcars, displ_l = disp / 61.0237)

SE version

    .data = mtcars, 
    .dots = list(
        "displ_l" = lazyeval::interp(
                        ~ x / 61.0237, x = quote(disp)