mean_n {spicy} | R Documentation |
Row Means with Optional Minimum Valid Values
Description
mean_n()
computes row means from a data.frame
or matrix
, handling missing values (NA
s) 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 |
select |
Columns to include. If |
exclude |
Columns to exclude (default: |
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 |
verbose |
If |
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)