Closed njtierney closed 1 year ago
Rework examples to demonstrate workflow for finding complete variables.
any_na()
any_miss()
any_complete()
all_complete
!anyNA(x)
all(complete.cases(x))
# for vectors library(naniar) misses <- c(NA, NA, NA) complete <- c(1, 2, 3) mixture <- c(NA, 1, NA) all_na(misses) #> [1] TRUE all_na(complete) #> [1] FALSE all_na(mixture) #> [1] FALSE all_complete(misses) #> [1] FALSE all_complete(complete) #> [1] TRUE all_complete(mixture) #> [1] FALSE any_na(misses) #> [1] TRUE any_na(complete) #> [1] FALSE any_na(mixture) #> [1] TRUE # for data frames all_na(airquality) #> [1] FALSE # an alias of all_na all_miss(airquality) #> [1] FALSE all_complete(airquality) #> [1] FALSE any_na(airquality) #> [1] TRUE any_complete(airquality) #> [1] TRUE # use in identifying columns with all missing/complete 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 # for printing aq <- as_tibble(airquality) aq #> # A tibble: 153 × 6 #> Ozone Solar.R Wind Temp Month Day #> <int> <int> <dbl> <int> <int> <int> #> 1 41 190 7.4 67 5 1 #> 2 36 118 8 72 5 2 #> 3 12 149 12.6 74 5 3 #> 4 18 313 11.5 62 5 4 #> 5 NA NA 14.3 56 5 5 #> 6 28 NA 14.9 66 5 6 #> 7 23 299 8.6 65 5 7 #> 8 19 99 13.8 59 5 8 #> 9 8 19 20.1 61 5 9 #> 10 NA 194 8.6 69 5 10 #> # ℹ 143 more rows # select variables with all missing values aq %>% select(where(all_na)) #> # A tibble: 153 × 0 # there are none! # select columns with any NA values aq %>% select(where(any_na)) #> # A tibble: 153 × 2 #> Ozone Solar.R #> <int> <int> #> 1 41 190 #> 2 36 118 #> 3 12 149 #> 4 18 313 #> 5 NA NA #> 6 28 NA #> 7 23 299 #> 8 19 99 #> 9 8 19 #> 10 NA 194 #> # ℹ 143 more rows # select only columns with all complete data aq %>% select(where(all_complete)) #> # A tibble: 153 × 4 #> Wind Temp Month Day #> <dbl> <int> <int> <int> #> 1 7.4 67 5 1 #> 2 8 72 5 2 #> 3 12.6 74 5 3 #> 4 11.5 62 5 4 #> 5 14.3 56 5 5 #> 6 14.9 66 5 6 #> 7 8.6 65 5 7 #> 8 13.8 59 5 8 #> 9 20.1 61 5 9 #> 10 8.6 69 5 10 #> # ℹ 143 more rows # select columns where there are any complete cases (all the data) aq %>% select(where(any_complete)) #> # A tibble: 153 × 6 #> Ozone Solar.R Wind Temp Month Day #> <int> <int> <dbl> <int> <int> <int> #> 1 41 190 7.4 67 5 1 #> 2 36 118 8 72 5 2 #> 3 12 149 12.6 74 5 3 #> 4 18 313 11.5 62 5 4 #> 5 NA NA 14.3 56 5 5 #> 6 28 NA 14.9 66 5 6 #> 7 23 299 8.6 65 5 7 #> 8 19 99 13.8 59 5 8 #> 9 8 19 20.1 61 5 9 #> 10 NA 194 8.6 69 5 10 #> # ℹ 143 more rows
Created on 2023-04-28 with reprex v2.0.2
Yes
Done
Rework examples to demonstrate workflow for finding complete variables.
Description
any_na()(andany_miss()) andany_complete(). Rework examples to demonstrate workflow for finding complete variables.all_complete, which was implemented as!anyNA(x)but should beall(complete.cases(x)).Related Issue
243
Example
Created on 2023-04-28 with reprex v2.0.2
Session info
``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.3.0 (2023-04-21) #> os macOS Ventura 13.2 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Los_Angeles #> date 2023-04-28 #> 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 4.3.0) #> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.0) #> digest 0.6.31 2022-12-11 [1] CRAN (R 4.3.0) #> dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.3.0) #> evaluate 0.20 2023-01-17 [1] CRAN (R 4.3.0) #> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.3.0) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0) #> fs 1.6.1 2023-02-06 [1] CRAN (R 4.3.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0) #> ggplot2 3.4.2 2023-04-03 [1] CRAN (R 4.3.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.3.0) #> gtable 0.3.3 2023-03-21 [1] CRAN (R 4.3.0) #> htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.3.0) #> knitr 1.42 2023-01-25 [1] CRAN (R 4.3.0) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.3.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.3.0) #> naniar * 1.0.0.9000 2023-04-28 [1] local #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0) #> purrr 1.0.1 2023-01-10 [1] CRAN (R 4.3.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.3.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.3.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.3.0) #> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.3.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.3.0) #> rlang 1.1.0 2023-03-14 [1] CRAN (R 4.3.0) #> rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.3.0) #> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.3.0) #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.3.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0) #> styler 1.9.1 2023-03-04 [1] CRAN (R 4.3.0) #> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.0) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0) #> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.3.0) #> vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.3.0) #> visdat 0.6.0 2023-02-02 [1] CRAN (R 4.3.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.3.0) #> xfun 0.39 2023-04-20 [1] CRAN (R 4.3.0) #> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.3.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```Tests
Yes
NEWS + DESCRIPTION
Done