single_value: Ensure that a vector has only a single value throughout.

In janitor: Simple Tools for Examining and Cleaning Dirty Data

Ensure that a vector has only a single value throughout.

Description

Missing values are replaced with the single value, and if all values are missing, the first value in missing is used throughout.

Usage

single_value(x, missing = NA, warn_if_all_missing = FALSE, info = NULL)

Arguments

x	The vector which should have a single value
missing	The vector of values to consider missing in x
warn_if_all_missing	Generate a warning if all values are missing?
info	If more than one value is found, append this to the warning or error to assist with determining the location of the issue.

Value

x as the scalar single value found throughout (or an error if more than one value is found).

Examples

# A simple use case with vectors of input

single_value(c(NA, 1))
# Multiple, different values of missing can be given
single_value(c(NA, "a"), missing = c(NA, "a"))

# A typical use case with a grouped data.frame used for input and the output
# (`B` is guaranteed to have a single value and only one row, in this case)
data.frame(A = rep(1:3, each = 2),
           B = c(rep(4:6, each = 2))) %>%
  dplyr::group_by(A) %>%
  dplyr::summarize(
    B = single_value(B)
  )

try(
# info is useful to give when multiple values may be found to see what
# grouping variable or what calculation is causing the error
data.frame(A = rep(1:3, each = 2),
           B = c(rep(1:2, each = 2), 1:2)) %>%
  dplyr::group_by(A) %>%
  dplyr::mutate(
    C = single_value(B, info = paste("Calculating C for group A=", A))
  )
)

janitor documentation built on Feb. 16, 2023, 10:16 p.m.