na_summaries: Determine Amount of Missing Values in Data

In coatless/balamuta: Balamuta Miscellaneous

Determine Amount of Missing Values in Data

Description

Computes the proportion of data that is missing in a given data set.

Usage

na_prop_overall(x)

na_prop_by_variable(x)

na_prop_by_observation(x)

na_count_overall(x)

na_count_by_variable(x)

na_count_by_observation(x)

Arguments

x	A vector of length N or a matrix with dimensions N \times P.

Value

• Overall: a single numeric value between ⁠[0, 1]⁠ or a count between ⁠[0, N]⁠.

• Variable: P different numeric values between ⁠[0, 1]⁠ or counts between ⁠[0, N]⁠.

• Observation: N different numeric values between ⁠[0, 1]⁠ or counts between ⁠[0, P]⁠.

Examples

# By vector
x = c(1, 2, NA, 4)
na_prop_overall(x)
na_count_overall(x)

# By Data Frame
missing_df = data.frame(
 a = c(1, 2, NA, 4),
 b = c(3, NA, 2, NA)
)

# Proportion
na_prop_overall(missing_df)
na_prop_by_variable(missing_df)
na_prop_by_observation(missing_df)

# Counts
na_count_overall(missing_df)
na_count_by_variable(missing_df)
na_count_by_observation(missing_df)

coatless/balamuta documentation built on Nov. 16, 2023, 5:30 a.m.