You can use mutate_at() from dplyr, You can pass it either explicit column names, column positions as numbers or, with the the help of vars(matches(regex)), a regular expression that matches column names. Here is a very simple example where I make functions to catch values outside of specific limits.
library(dplyr)
df <- data.frame(Alpha = c(6, -5, 42, 7), Beta = c(17, 99, 8, -3),
Gamma = c(100, 23, 19, 22), Delta = c(2, 7, -2, 30))
df
#> Alpha Beta Gamma Delta
#> 1 6 17 100 2
#> 2 -5 99 23 7
#> 3 42 8 19 -2
#> 4 7 -3 22 30
Clean1 <- function(x) ifelse(x < 0 | x > 20, NA, x)
df <- df %>% mutate_at(.vars = c("Alpha", "Delta"), Clean1)
df
#> Alpha Beta Gamma Delta
#> 1 6 17 100 2
#> 2 NA 99 23 7
#> 3 NA 8 19 NA
#> 4 7 -3 22 NA
Clean2 <- function(x) ifelse(x < 0 | x > 90, NA, x)
df <- df %>% mutate_at(.vars = c("Beta", "Gamma"), Clean2)
df
#> Alpha Beta Gamma Delta
#> 1 6 17 NA 2
#> 2 NA NA 23 7
#> 3 NA 8 19 NA
#> 4 7 NA 22 NA
Created on 2019-05-07 by the reprex package (v0.2.1)