noah (no animals were harmed) generates pseudonyms that are delightful and easy to remember. It creates adorable anonymous animals like the Likable Leech and the Proud Chickadee.
Noah is not yet on CRAN, but you can install it from Github with:
Use pseudonymize() to generate a unique pseudonym for every unique element / row in a vector or data frame. pseudonymize() accepts multiple vectors and data frames as arguments, and will pseudonymize them row by row.
library(noah)
pseudonymize(1:9)
#> [1] "Impartial Rat" "Superficial Bird" "Royal Orca"
#> [4] "Earsplitting Python" "Fascinated Donkey" "Defeated Trout"
#> [7] "Encouraging Stoat" "Null Grouse" "Axiomatic Octopus"
pseudonymize(
c("🐰", "🐰", "🐰"),
c("🥕", "🥕", "🍰")
)
#> [1] "Bloody Clam" "Bloody Clam" "Depressed Egret"For extra delight, we can ask noah to generate only alliterations:
pseudonymize(1:9, .alliterate = TRUE)
#> [1] "Safe Sole" "Callous Clownfish" "Polite Panda"
#> [4] "Best Badger" "Like Leopard" "Many Mole"
#> [7] "Smiling Slug" "Sweltering Silverfish" "Sick Sloth"You can use pseudonymize() with dplyr::mutate() to add a column with pseudonyms to a data frame. In this example we use the diabetic retinopathy dataset from the package survival and add a new column with a pseudonym for each unique id. We also use dplyr::relocate() to move the pseudonyms to the first column:
library(dplyr)
diabetic <- as_tibble(survival::diabetic)
diabetic %>%
mutate(pseudonym = pseudonymize(id)) %>%
relocate(pseudonym)
#> # A tibble: 394 x 9
#> pseudonym id laser age eye trt risk time status
#> <chr> <int> <fct> <int> <fct> <int> <int> <dbl> <int>
#> 1 Possessive Armadillo 5 argon 28 left 0 9 46.2 0
#> 2 Possessive Armadillo 5 argon 28 right 1 9 46.2 0
#> 3 Crowded Vole 14 xenon 12 left 1 8 42.5 0
#> 4 Crowded Vole 14 xenon 12 right 0 6 31.3 1
#> 5 Productive Heron 16 xenon 9 left 1 11 42.3 0
#> 6 Productive Heron 16 xenon 9 right 0 11 42.3 0
#> 7 Frequent Okapi 25 xenon 9 left 0 11 20.6 0
#> 8 Frequent Okapi 25 xenon 9 right 1 11 20.6 0
#> 9 Giant Lobster 29 xenon 13 left 0 10 0.3 1
#> 10 Giant Lobster 29 xenon 13 right 1 9 38.8 0
#> # ... with 384 more rowsFor your convenience, noah also provides add_pseudonyms(), which wraps mutate() and relocate() and supports tidyselect syntax for selecting the key columns:
diabetic %>%
add_pseudonyms(id, where(is.factor))
#> # A tibble: 394 x 9
#> pseudonym id laser age eye trt risk time status
#> <chr> <int> <fct> <int> <fct> <int> <int> <dbl> <int>
#> 1 Doubtful Horse 5 argon 28 left 0 9 46.2 0
#> 2 Caring Heron 5 argon 28 right 1 9 46.2 0
#> 3 Grey Chicken 14 xenon 12 left 1 8 42.5 0
#> 4 Giddy Vole 14 xenon 12 right 0 6 31.3 1
#> 5 Overrated Caterpillar 16 xenon 9 left 1 11 42.3 0
#> 6 Angry Oribi 16 xenon 9 right 0 11 42.3 0
#> 7 Roasted Sawfish 25 xenon 9 left 0 11 20.6 0
#> 8 Spectacular Lion 25 xenon 9 right 1 11 20.6 0
#> 9 Panoramic Owl 29 xenon 13 left 0 10 0.3 1
#> 10 Orange Bear 29 xenon 13 right 1 9 38.8 0
#> # ... with 384 more rowsTo make sure that all pseudonyms are unique and consistent, pseudonymize() and add_pseudonyms() use an object of class Ark (a pseudonym archive). By default, a new Ark is created for each function call, but you can also provide an Ark yourself. This allows you to keep track of the pseudonyms that have been used and make sure that the same keys always get assigned the same pseudonym:
ark <- Ark$new()
# split dataset into left and right eye and pseudonymize separately
diabetic_left <- diabetic %>%
filter(eye == "left") %>%
add_pseudonyms(id, .ark = ark)
diabetic_right <- diabetic %>%
filter(eye == "right") %>%
add_pseudonyms(id, .ark = ark)
# reunite the data sets again
bind_rows(diabetic_left, diabetic_right) %>%
arrange(id)
#> # A tibble: 394 x 9
#> pseudonym id laser age eye trt risk time status
#> <chr> <int> <fct> <int> <fct> <int> <int> <dbl> <int>
#> 1 Faulty Swift 5 argon 28 left 0 9 46.2 0
#> 2 Faulty Swift 5 argon 28 right 1 9 46.2 0
#> 3 Tart Crab 14 xenon 12 left 1 8 42.5 0
#> 4 Tart Crab 14 xenon 12 right 0 6 31.3 1
#> 5 Sticky Barnacle 16 xenon 9 left 1 11 42.3 0
#> 6 Sticky Barnacle 16 xenon 9 right 0 11 42.3 0
#> 7 Brainy Moth 25 xenon 9 left 0 11 20.6 0
#> 8 Brainy Moth 25 xenon 9 right 1 11 20.6 0
#> 9 Poised Urial 29 xenon 13 left 0 10 0.3 1
#> 10 Poised Urial 29 xenon 13 right 1 9 38.8 0
#> # ... with 384 more rowsThe ark now contains 197 pseudonyms – as many as there are unique id’s in the dataset.
Building your own Ark allows you to customize the name parts that are used to create pseudonyms (by default, adjectives and animals). It also allow you to use names with more than two parts:
ark <- Ark$new(parts = list(
c("Charles", "Louis", "Henry", "George"),
c("I", "II", "III", "IV"),
c("The Good", "The Wise", "The Brave", "The Mad", "The Beloved")
))
pseudonymize(1:8, .ark = ark)
#> [1] "Louis IV The Brave" "George II The Good" "Louis I The Good"
#> [4] "Charles IV The Wise" "Charles IV The Brave" "Louis II The Mad"
#> [7] "Charles I The Brave" "George I The Beloved"You can also configure an Ark so that it generates only alliterations. Note that this behavior can still be overridden temporarily by using .alliterate = FALSE when you call pseudonymize().
ark <- Ark$new(alliterate = TRUE)
pseudonymize(1:12, .ark = ark)
#> [1] "Hard-To-Find Hyena" "Well-Made Whippet" "Momentous Mosquito"
#> [4] "Mushy Macaw" "Complete Clownfish" "Three Tahr"
#> [7] "Phobic Pheasant" "Squealing Swallow" "Subdued Swan"
#> [10] "Mundane Marsupial" "Complex Centipede" "Cruel Crane"Noah will treat numerically identical whole numbers of type double and integer as different and give them different pseudonyms. This can cause some unexpected behavior. Consider this example:
ark <- Ark$new()
pseudonymize(1:2, .ark = ark) # creates a vector of integers c(1L, 2L)
pseudonymize(1, .ark = ark) # creates a doubleYou might expect to get 2 different pseudonyms, because in the second pseudonymize() you are requesting a pseudonym for the number 1, which is already in the Ark. Instead you get three pseudonyms:
Noah will warn you when it thinks you are making this mistake, but it might not catch it all the time. A workaround is to coerce types explicitly, for example by using as.double(), as.integer(), or 1L to create integers.
There are multiple R packages that generate fake data, including fake names, phone numbers, addresses, credit card numbers, gene sequences and more:
If you need watertight anonymization you should check out these packages for anonymizing personal identifiable information in data sets: