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faux-naïf (/ˌfoʊ.naɪˈif/): a person who pretends to be simple or innocent
fauxnaif: an R package for simplifying data by
pretending values are NA
fauxnaif provides an extension to
dplyr::na_if(). Unlike dplyr’s
na_if(), na_if_in() allows you to specify
multiple values to be replaced with NA using a single
function. fauxnaif also includes a complementary
function na_if_not() to specify values to keep.
You can install fauxnaif from CRAN:
install.packages("fauxanif")Or the development version from GitHub:
# install.packages("remotes")
remotes::install_github("rossellhayes/fauxnaif")library(dplyr)
library(fauxnaif)Let’s say we want to remove an unwanted negative value from a vector of numbers
-1:10
#> [1] -1 0 1 2 3 4 5 6 7 8 9 10We can replace -1…
… explicitly:
na_if_in(-1:10, -1)
#> [1] NA 0 1 2 3 4 5 6 7 8 9 10… by specifying values to keep:
na_if_not(-1:10, 0:10)
#> [1] NA 0 1 2 3 4 5 6 7 8 9 10… using a formula:
na_if_in(-1:10, ~ . < 0)
#> [1] NA 0 1 2 3 4 5 6 7 8 9 10messy_string <- c("abc", "", "def", "NA", "ghi", 42, "jkl", "NULL", "mno")We can replace unwanted values…
… one at a time:
na_if_in(messy_string, "")
#> [1] "abc" NA "def" "NA" "ghi" "42" "jkl" "NULL" "mno"… or all at once:
na_if_in(messy_string, "", "NA", "NULL", 1:100)
#> [1] "abc" NA "def" NA "ghi" NA "jkl" NA "mno"
na_if_in(messy_string, c("", "NA", "NULL", 1:100))
#> [1] "abc" NA "def" NA "ghi" NA "jkl" NA "mno"
na_if_in(messy_string, list("", "NA", "NULL", 1:100))
#> [1] "abc" NA "def" NA "ghi" NA "jkl" NA "mno"… or using a clever formula:
grepl("[a-z]{3,}", messy_string)
#> [1] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
na_if_not(messy_string, ~ grepl("[a-z]{3,}", .))
#> [1] "abc" NA "def" NA "ghi" NA "jkl" NA "mno"faux_census
#> # A tibble: 5 × 4
#> state age income gender
#> <chr> <dbl> <dbl> <chr>
#> 1 TX 57 9999999 Gender is a social construct
#> 2 Canada 49 149000 Male
#> 3 NY 557 90750 f
#> 4 LA 2 61000 Male
#> 5 TN 64 9999999 Mna_if_in() is particularly useful inside
dplyr::mutate():
faux_census %>%
mutate(
income = na_if_in(income, 9999999),
age = na_if_in(age, ~ . < 18, ~ . > 120),
state = na_if_not(state, ~ grepl("^[A-Z]{2,}$", .)),
gender = na_if_in(gender, ~ nchar(.) > 20)
)
#> # A tibble: 5 × 4
#> state age income gender
#> <chr> <dbl> <dbl> <chr>
#> 1 TX 57 NA <NA>
#> 2 <NA> 49 149000 Male
#> 3 NY NA 90750 f
#> 4 LA NA 61000 Male
#> 5 TN 64 NA MOr you can use dplyr::across() on data frames:
faux_census %>%
mutate(
across(age, na_if_in, ~ . < 18, ~ . > 120),
across(state, na_if_not, ~ grepl("^[A-Z]{2,}$", .)),
across(where(is.character), na_if_in, ~ nchar(.) > 20),
across(everything(), na_if_in, 9999999)
)
#> # A tibble: 5 × 4
#> state age income gender
#> <chr> <dbl> <dbl> <chr>
#> 1 TX 57 NA <NA>
#> 2 <NA> 49 149000 Male
#> 3 NY NA 90750 f
#> 4 LA NA 61000 Male
#> 5 TN 64 NA MHex sticker fonts are Bodoni* by indestructible type* and Source Code Pro by Adobe.
Image adapted from icon made by Freepik from flaticon.com.
Please note that fauxnaif is released with a Contributor Code of Conduct.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.