Helpers to identify which rows and variables have over a certain % or number of missingns #317
Description
As discussed with @dicook , these helpers could be used to identify which rows and variables need imputation/dropping/exploring.
Perhaps something like this, with an example workflow
library(tidyverse)
# which row ID has > X% missing
# which variables have > x% missing
which_prop_miss_row <- function(data, prop){
naniar::prop_miss_row(data) > prop
}
prop_miss_cols <- function(data){
colMeans(is.na(data))
}
which_prop_miss_var <- function(data, prop){
prop_miss_cols(data) > prop
}
which_prop_miss_row(airquality, 0.1)
#> [1] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [37] TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [49] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [61] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [73] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [97] TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
#> [109] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
#> [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [145] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
which_prop_miss_var(airquality, 0.1)
#> Ozone Solar.R Wind Temp Month Day
#> TRUE FALSE FALSE FALSE FALSE FALSE
nrow(airquality)
#> [1] 153
airquality %>%
filter(
which_prop_miss_row(., 0.1)
) %>%
nrow()
#> [1] 42
vars_over_10_pct <- which(which_prop_miss_var(airquality, 0.1))
vars_under_10_pct <- which(!which_prop_miss_var(airquality, 0.1))
airquality %>%
select(
all_of(vars_over_10_pct)
) %>%
as_tibble()
#> # A tibble: 153 × 1
#> Ozone
#> <int>
#> 1 41
#> 2 36
#> 3 12
#> 4 18
#> 5 NA
#> 6 28
#> 7 23
#> 8 19
#> 9 8
#> 10 NA
#> # … with 143 more rows
airquality %>%
select(
all_of(vars_under_10_pct)
) %>%
as_tibble()
#> # A tibble: 153 × 5
#> Solar.R Wind Temp Month Day
#> <int> <dbl> <int> <int> <int>
#> 1 190 7.4 67 5 1
#> 2 118 8 72 5 2
#> 3 149 12.6 74 5 3
#> 4 313 11.5 62 5 4
#> 5 NA 14.3 56 5 5
#> 6 NA 14.9 66 5 6
#> 7 299 8.6 65 5 7
#> 8 99 13.8 59 5 8
#> 9 19 20.1 61 5 9
#> 10 194 8.6 69 5 10
#> # … with 143 more rowsCreated on 2023-04-06 with reprex v2.0.2
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Australia/Hobart
#> date 2023-04-06
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#>
#> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#>
#> ──────────────────────────────────────────────────────────────────────────────There could also be equivalent which_n_miss_row/var functions