analysis_missing: Analyse Missing Values in Scales

In jazznbass/wmisc: Wilbert miscellaneous functions

Analyse Missing Values in Scales

Description

This function analyzes missing values for specified scales within a data frame.

Usage

analysis_missing(dat, scale)

Arguments

dat	A data frame containing the dataset to be analyzed.
scale	A list of character vectors, where each list element contains the variable names corresponding to a scale within the dataset.

Value

A data frame summarizing the missing value analysis for each scale. The output includes:

• missing: The total number of missing values for each scale.

• total: The total number of observations expected for each scale.

• p: The proportion of missing values for each scale (missing / total).

• ⁠n cases⁠: The number of cases with at least one missing value within the scale.

• ⁠p cases⁠: The proportion of cases with at least one missing value (⁠n cases / total cases⁠).

• ⁠n all cases⁠: The number of cases where all variables in the scale are missing.

• ⁠p all cases⁠: The proportion of cases where all variables in the scale are missing (⁠n all cases / total cases⁠).

See Also

is.na(), apply()

Examples

# Example dataset
dat <- data.frame(
  scale1_var1 = c(1, 2, NA, 4),
  scale1_var2 = c(NA, 2, 3, 4),
  scale2_var1 = c(1, NA, 3, 4),
  scale2_var2 = c(NA, NA, NA, 4)
)

# Define scales
scales <- list(
  scale1 = c("scale1_var1", "scale1_var2"),
  scale2 = c("scale2_var1", "scale2_var2")
)

# Analyze missing values
analysis_missing(dat, scales)

jazznbass/wmisc documentation built on Jan. 28, 2025, 11:13 p.m.