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
We can do this with chartr
from base R
chartr("ac", "AC", x)
x <- c("a", "b", "c")
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.
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)]
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"