spicy (version 0.3.0)

sum_n: Row Sums with Optional Minimum Valid Values

Description

sum_n() computes row sums from a data.frame or matrix, handling missing values (NAs) automatically. Row-wise sums 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

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

Value

A numeric vector of row-wise sums

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.

digits

Optional number of decimal places to round the result.

regex

If TRUE, the select argument is treated as a regular expression. If FALSE, uses tidyselect helpers.

verbose

If TRUE, prints a message about processing.

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 sums (all values must be valid by default)
sum_n(df)

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

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

# Round the results to 1 decimal
sum_n(df, digits = 1)

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

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

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

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

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

# Use inside dplyr::mutate()
df |> mutate(sum_score = sum_n(min_valid = 2))

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

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

# Show verbose message
df |> mutate(sum_score = sum_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
sum_n(df_mixed)

# Use inside mutate with mixed data
df_mixed |> mutate(sum_score = sum_n(select = starts_with("var")))

# Use everything(), but exclude known non-numeric
sum_n(df_mixed, select = everything(), exclude = "group")

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

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

# Store the result in a new column
df_mixed$sum_score <- sum_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 |> sum_n(min_valid = 2)