Is there an alternative to “revalue” function from plyr when using dplyr?

Question

I\'m a fan of the revalue function is plyr for substituting strings. It\'s simple and easy to remember.

However, I\'ve migrated new code to

Answer 1

One alternative that I find handy is the mapvalues function for the data.tables e.g

df[, variable := mapvalues(variable, old = old_names_string_vector, new = new_names_string_vector)]

Answer 2

There is a recode function available starting with dplyr version dplyr_0.5.0 which looks very similar to revalue from plyr.

Example built from the recode documentation Examples section:

set.seed(16)
x = sample(c("a", "b", "c"), 10, replace = TRUE)
x
 [1] "a" "b" "a" "b" "b" "a" "c" "c" "c" "a"

recode(x, a = "Apple", b = "Bear", c = "Car")

   [1] "Car"   "Apple" "Bear"  "Apple" "Car"   "Apple" "Apple" "Car"   "Car"   "Apple"

If you only define some of the values that you want to recode, by default the rest are filled with NA.

recode(x, a = "Apple", c = "Car")
 [1] "Car"   "Apple" NA      "Apple" "Car"   "Apple" "Apple" "Car"   "Car"   "Apple"

This behavior can be changed using the .default argument.

recode(x, a = "Apple", c = "Car", .default = x)
 [1] "Car"   "Apple" "b"     "Apple" "Car"   "Apple" "Apple" "Car"   "Car"   "Apple"

There is also a .missing argument if you want to replace missing values with something else.

Answer 3

I wanted to comment on the answer by @aosmith, but lack reputation. It seems that nowadays the default of dplyr's recode function is to leave unspecified levels unaffected.

x = sample(c("a", "b", "c"), 10, replace = TRUE)
x
[1] "c" "c" "b" "b" "a" "b" "c" "c" "c" "b"

recode(x , a = "apple", b = "banana" )

[1] "c"      "c"      "banana" "banana" "apple"  "banana" "c"      "c"      "c"      "banana"

To change all nonspecified levels to NA, the argument .default = NA_character_ should be included.

recode(x, a = "apple", b = "banana", .default = NA_character_)

[1] "apple"  "banana" "apple"  "banana" "banana" "apple"  NA       NA       NA       "apple" 

Answer 4

We can do this with chartr from base R

chartr("ac", "AC", x)

data

x <- c("a", "b", "c")