mean_n: Row Means with Optional Minimum Valid Values

In spicy: Descriptive Statistics and Data Management Tools

Row Means with Optional Minimum Valid Values

Description

mean_n() computes row means from a data.frame or matrix, handling missing values (NAs) automatically. Row-wise means are calculated across selected numeric columns, with an optional condition on the minimum number (or proportion) of valid (non-missing) values required for a row to be included. Non-numeric columns are excluded automatically and reported.

Usage

mean_n(
  data = NULL,
  select = dplyr::everything(),
  exclude = NULL,
  min_valid = NULL,
  digits = NULL,
  regex = FALSE,
  verbose = FALSE
)

Arguments

data	A data.frame or matrix.
select	Columns to include. If regex = FALSE, use tidyselect syntax (default: dplyr::everything()). If regex = TRUE, provide a regular expression pattern (character string).
exclude	Columns to exclude (default: NULL).
min_valid	Minimum number of valid (non-NA) values required per row. If a proportion, it's applied to the number of selected columns. Defaults to NULL (all values must be valid).
digits	Optional number of decimal places to round the result. Defaults to NULL (no rounding).
regex	Logical. If FALSE (the default), uses tidyselect helpers. If TRUE, the select argument is treated as a regular expression.
verbose	Logical. If FALSE (the default), messages are suppressed. If TRUE, prints a message about non-numeric columns excluded.

Value

A numeric vector of row-wise means.

Examples

library(dplyr)

# Create a simple numeric data frame
df <- tibble(
  var1 = c(10, NA, 30, 40, 50),
  var2 = c(5, NA, 15, NA, 25),
  var3 = c(NA, 30, 20, 50, 10)
)

# Compute row-wise mean (all values must be valid by default)
mean_n(df)

# Require at least 2 valid (non-NA) values per row
mean_n(df, min_valid = 2)

# Require at least 50% valid (non-NA) values per row
mean_n(df, min_valid = 0.5)

# Round the result to 1 decimal
mean_n(df, digits = 1)

# Select specific columns
mean_n(df, select = c(var1, var2))

# Select specific columns using a pipe
df |>
  select(var1, var2) |>
  mean_n()

# Exclude a column
mean_n(df, exclude = "var3")

# Select columns ending with "1"
mean_n(df, select = ends_with("1"))

# Use with native pipe
df |> mean_n(select = starts_with("var"))

# Use inside dplyr::mutate()
df |> mutate(mean_score = mean_n(min_valid = 2))

# Select columns directly inside mutate()
df |> mutate(mean_score = mean_n(select = c(var1, var2), min_valid = 1))

# Select columns before mutate
df |>
  select(var1, var2) |>
  mutate(mean_score = mean_n(min_valid = 1))

# Show verbose processing info
df |> mutate(mean_score = mean_n(min_valid = 2, digits = 1, verbose = TRUE))

# Add character and grouping columns
df_mixed <- mutate(df,
  name = letters[1:5],
  group = c("A", "A", "B", "B", "A")
)
df_mixed

# Non-numeric columns are ignored
mean_n(df_mixed)

# Use within mutate() on mixed data
df_mixed |> mutate(mean_score = mean_n(select = starts_with("var")))

# Use everything() but exclude non-numeric columns manually
mean_n(df_mixed, select = everything(), exclude = "group")

# Select columns using regex
mean_n(df_mixed, select = "^var", regex = TRUE)
mean_n(df_mixed, select = "ar", regex = TRUE)

# Apply to a subset of rows (first 3)
df_mixed[1:3, ] |> mean_n(select = starts_with("var"))

# Store the result in a new column
df_mixed$mean_score <- mean_n(df_mixed, select = starts_with("var"))
df_mixed

# With a numeric matrix
mat <- matrix(c(1, 2, NA, 4, 5, NA, 7, 8, 9), nrow = 3, byrow = TRUE)
mat
mat |> mean_n(min_valid = 2)

spicy documentation built on March 14, 2026, 5:06 p.m.