This provides updates to the impute functions by providing details of using across with these functions.
This also involved deprecating shadow shift in favour of impute_below and changing various part opf the code base for that.
Related Issue
resolves #262
resolves #193
Example
Just providing more detail for the impute functions and how to use them with across, e.g.,
library(naniar)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
vec_fct <- factor(LETTERS[1:10])
vec_fct[sample(1:10, 3)] <- NA
vec_fct
#> [1] A B C <NA> E F G <NA> I <NA>
#> Levels: A B C D E F G H I J
impute_factor(vec_fct, "wat")
#> [1] A B C wat E F G wat I wat
#> Levels: A B C D E F G H I J wat
vec <- rnorm(10)
vec[sample(1:10, 3)] <- NA
vec
#> [1] 0.5629153 NA -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 NA NA
impute_below(vec)
#> [1] 0.5629153 -0.2858669 -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 -0.3127560 -0.3335501
impute_fixed(vec, -99)
#> [1] 0.5629153 -99.0000000 -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 -99.0000000 -99.0000000
impute_mean(vec)
#> [1] 0.5629153 0.6471627 -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 0.6471627 0.6471627
impute_median(vec)
#> [1] 0.5629153 0.4184700 -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 0.4184700 0.4184700
impute_mode(vec)
#> [1] 0.5629153 0.4307643 -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 0.4307643 0.4307643
impute_mode(vec_fct)
#> [1] A B C A E F G A I A
#> Levels: A B C D E F G H I J
impute_zero(vec)
#> [1] 0.5629153 0.0000000 -0.0867603 0.3899923 2.0253902 0.8396355
#> [7] 0.4184700 0.3804955 0.0000000 0.0000000
dat <- tibble(
num = rnorm(10),
int = rpois(10, 5),
unif = runif(10),
small = rpois(10, 8) / 1000,
large = rpois(10, 1e6),
neg = -rpois(10, 10),
geom = rgeom(10, 0.1),
fct = factor(LETTERS[1:10])
) %>%
mutate(
across(
everything(),
\(x) set_prop_miss(x, prop = 0.25)
)
)
dat
#> # A tibble: 10 × 8
#> num int unif small large neg geom fct
#> <dbl> <int> <dbl> <dbl> <int> <int> <int> <fct>
#> 1 0.0791 3 0.750 0.01 NA -10 2 A
#> 2 0.295 7 0.0258 NA NA -8 2 B
#> 3 1.10 5 0.406 0.012 999918 -13 18 C
#> 4 -1.57 1 0.677 0.01 1000710 NA 0 D
#> 5 -0.324 NA 0.116 0.007 999753 -13 3 E
#> 6 NA 1 NA 0.007 999087 -6 NA F
#> 7 -1.62 3 0.262 0.008 1000407 -21 12 G
#> 8 -1.50 NA NA 0.004 1001663 NA NA H
#> 9 -0.721 7 0.323 0.008 999725 -4 1 <NA>
#> 10 NA 6 0.375 NA 1001373 -13 1 <NA>
dat %>%
nabular() %>%
mutate(
num = impute_fixed(num, -9999),
int = impute_zero(int),
fct = impute_factor(fct, "out")
)
#> # A tibble: 10 × 16
#> num int unif small large neg geom fct num_NA int_NA unif_NA
#> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <fct> <fct> <fct> <fct>
#> 1 7.91e-2 3 0.750 0.01 NA -10 2 A !NA !NA !NA
#> 2 2.95e-1 7 0.0258 NA NA -8 2 B !NA !NA !NA
#> 3 1.10e+0 5 0.406 0.012 999918 -13 18 C !NA !NA !NA
#> 4 -1.57e+0 1 0.677 0.01 1000710 NA 0 D !NA !NA !NA
#> 5 -3.24e-1 0 0.116 0.007 999753 -13 3 E !NA NA !NA
#> 6 -1.00e+4 1 NA 0.007 999087 -6 NA F NA !NA NA
#> 7 -1.62e+0 3 0.262 0.008 1000407 -21 12 G !NA !NA !NA
#> 8 -1.50e+0 0 NA 0.004 1001663 NA NA H !NA NA NA
#> 9 -7.21e-1 7 0.323 0.008 999725 -4 1 out !NA !NA !NA
#> 10 -1.00e+4 6 0.375 NA 1001373 -13 1 out NA !NA !NA
#> # ℹ 5 more variables: small_NA <fct>, large_NA <fct>, neg_NA <fct>,
#> # geom_NA <fct>, fct_NA <fct>
dat %>%
mutate(
across(
c("num", "int", "unif"),
\(x) impute_mean(x)
)
)
#> # A tibble: 10 × 8
#> num int unif small large neg geom fct
#> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <fct>
#> 1 0.0791 3 0.750 0.01 NA -10 2 A
#> 2 0.295 7 0.0258 NA NA -8 2 B
#> 3 1.10 5 0.406 0.012 999918 -13 18 C
#> 4 -1.57 1 0.677 0.01 1000710 NA 0 D
#> 5 -0.324 4.12 0.116 0.007 999753 -13 3 E
#> 6 -0.533 1 0.367 0.007 999087 -6 NA F
#> 7 -1.62 3 0.262 0.008 1000407 -21 12 G
#> 8 -1.50 4.12 0.367 0.004 1001663 NA NA H
#> 9 -0.721 7 0.323 0.008 999725 -4 1 <NA>
#> 10 -0.533 6 0.375 NA 1001373 -13 1 <NA>
dat %>%
mutate(
across(
where(is.numeric),
\(x) impute_mean(x)
)
)
#> # A tibble: 10 × 8
#> num int unif small large neg geom fct
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 0.0791 3 0.750 0.01 1000330. -10 2 A
#> 2 0.295 7 0.0258 0.00825 1000330. -8 2 B
#> 3 1.10 5 0.406 0.012 999918 -13 18 C
#> 4 -1.57 1 0.677 0.01 1000710 -11 0 D
#> 5 -0.324 4.12 0.116 0.007 999753 -13 3 E
#> 6 -0.533 1 0.367 0.007 999087 -6 4.88 F
#> 7 -1.62 3 0.262 0.008 1000407 -21 12 G
#> 8 -1.50 4.12 0.367 0.004 1001663 -11 4.88 H
#> 9 -0.721 7 0.323 0.008 999725 -4 1 <NA>
#> 10 -0.533 6 0.375 0.00825 1001373 -13 1 <NA>
Description
This provides updates to the impute functions by providing details of using
acrosswith these functions.This also involved deprecating shadow shift in favour of
impute_belowand changing various part opf the code base for that.Related Issue
resolves #262 resolves #193
Example
Just providing more detail for the
imputefunctions and how to use them withacross, e.g.,Created on 2023-04-13 with reprex v2.0.2
Session info
``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.3 (2023-03-15) #> os macOS Ventura 13.2 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz Australia/Sydney #> date 2023-04-13 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0) #> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0) #> digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.0) #> dplyr * 1.1.1 2023-03-22 [1] CRAN (R 4.2.0) #> evaluate 0.20 2023-01-17 [1] CRAN (R 4.2.0) #> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0) #> forcats 1.0.0 2023-01-29 [1] CRAN (R 4.2.0) #> fs 1.6.1 2023-02-06 [1] CRAN (R 4.2.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0) #> ggplot2 3.4.2 2023-04-03 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0) #> htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0) #> knitr 1.42 2023-01-25 [1] CRAN (R 4.2.0) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0) #> naniar * 1.0.0.9000 2023-04-10 [1] local #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0) #> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.0) #> rlang 1.1.0 2023-03-14 [1] CRAN (R 4.2.0) #> rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.0) #> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0) #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> styler 1.9.1 2023-03-04 [1] CRAN (R 4.2.0) #> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0) #> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0) #> vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.2.0) #> visdat 0.6.0 2023-02-02 [1] local #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.38 2023-03-24 [1] CRAN (R 4.2.0) #> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```Tests
Yes
NEWS + DESCRIPTION
Yes