Using dplyr filter() in programming

I agree with @hrbrmstr's standard evaluation solution. As suggested by @hadley today here's NSE solution:

library(tidyverse)

df <-data.frame(x = sample(1:100, 50), 
                y = rnorm(50), 
                z = sample(1:100,50), 
                w = sample(1:100, 50),
                p = sample(1:100,50))

new <- function(ang, brad, drau){
  ang  <- enquo(ang)
  brad <- enquo(brad)
  drau <- enquo(drau)

  df %>% filter(UQ(drau) %in% 1:50) %>%
    select(UQ(ang),UQ(brad)) 
}

brand <- c("z","w","p")
brand <- rlang::syms(brand)

map_df(brand, ~new(ang = x, brad = y, drau = UQ(.x)))

This appears to do what you want (but it needs confirmation by you):

library(tidyverse)
library(rlang)

set.seed(1492)

xdf <- data_frame(
  x = sample(1:100, 50),
  y = rnorm(50), 
  z = sample(1:100,50), 
  w = sample(1:100, 50),
  p = sample(1:100,50)
)

new_df <- function(ang, brad, drau) {
  drau <- sym(drau)
  filter(xdf, UQE(drau) %in% 1:50) %>% 
    select(ang, brad)
}

brand <- c("z", "w", "p")

map_df(brand, ~new_df(ang = "x", brad = "y", drau = .x))

Despite there being a plethora of "official" "tidyverse" examples using df, it's a function in the stats pkg and I try to avoid using it anymore.

Since you're using the tidyverse, might as well take advantage of map_df() from purrr.