impute_below: Impute data with values shifted 10 percent below range.

In njtierney/naniar: Data Structures, Summaries, and Visualisations for Missing Data

Impute data with values shifted 10 percent below range.

Description

It can be useful in exploratory graphics to impute data outside the range of the data. impute_below imputes variables with missings to have values 10 percent below the range for numeric values, plus some jittered noise, to separate repeated values, so that missing values can be visualised along with the rest of the data. For character or factor values, it adds a new string or label.

Usage

impute_below(x, ...)

Arguments

x	a variable of interest to shift
...	extra arguments to pass

See Also

add_shadow_shift() cast_shadow_shift() cast_shadow_shift_label()

Examples

library(dplyr)
vec <- rnorm(10)

vec[sample(1:10, 3)] <- NA

impute_below(vec)
impute_below(vec, prop_below = 0.25)
impute_below(vec,
            prop_below = 0.25,
            jitter = 0.2)

dat <- tibble(
 num = rnorm(10),
 int = as.integer(rpois(10, 5)),
 fct = factor(LETTERS[1:10])
) %>%
 mutate(
   across(
     everything(),
     \(x) set_prop_miss(x, prop = 0.25)
   )
 )

dat

dat %>%
 nabular() %>%
 mutate(
   num = impute_below(num),
   int = impute_below(int),
   fct = impute_below(fct),
 )

dat %>%
 nabular() %>%
 mutate(
   across(
     where(is.numeric),
     impute_below
   )
 )

dat %>%
 nabular() %>%
 mutate(
   across(
     c("num", "int"),
     impute_below
   )
 )

njtierney/naniar documentation built on March 19, 2024, 9:48 p.m.