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.frameormatrix.- select
Columns to include. If
regex = FALSE, use tidyselect syntax (default:dplyr::everything()). Ifregex = 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. IfTRUE, theselectargument is treated as a regular expression.- verbose
Logical. If
FALSE(the default), messages are suppressed. IfTRUE, prints a message about non-numeric columns excluded.
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)
#> [1] NA NA 21.66667 NA 28.33333
# Require at least 2 valid (non-NA) values per row
mean_n(df, min_valid = 2)
#> [1] 7.50000 NA 21.66667 45.00000 28.33333
# Require at least 50% valid (non-NA) values per row
mean_n(df, min_valid = 0.5)
#> [1] 7.50000 NA 21.66667 45.00000 28.33333
# Round the result to 1 decimal
mean_n(df, digits = 1)
#> [1] NA NA 21.7 NA 28.3
# Select specific columns
mean_n(df, select = c(var1, var2))
#> [1] 7.5 NA 22.5 NA 37.5
# Select specific columns using a pipe
df |>
select(var1, var2) |>
mean_n()
#> [1] 7.5 NA 22.5 NA 37.5
# Exclude a column
mean_n(df, exclude = "var3")
#> [1] 7.5 NA 22.5 NA 37.5
# Select columns ending with "1"
mean_n(df, select = ends_with("1"))
#> [1] 10 NA 30 40 50
# Use with native pipe
df |> mean_n(select = starts_with("var"))
#> [1] NA NA 21.66667 NA 28.33333
# Use inside dplyr::mutate()
df |> mutate(mean_score = mean_n(min_valid = 2))
#> # A tibble: 5 × 4
#> var1 var2 var3 mean_score
#> <dbl> <dbl> <dbl> <dbl>
#> 1 10 5 NA 7.5
#> 2 NA NA 30 NA
#> 3 30 15 20 21.7
#> 4 40 NA 50 45
#> 5 50 25 10 28.3
# Select columns directly inside mutate()
df |> mutate(mean_score = mean_n(select = c(var1, var2), min_valid = 1))
#> # A tibble: 5 × 4
#> var1 var2 var3 mean_score
#> <dbl> <dbl> <dbl> <dbl>
#> 1 10 5 NA 7.5
#> 2 NA NA 30 NA
#> 3 30 15 20 22.5
#> 4 40 NA 50 40
#> 5 50 25 10 37.5
# Select columns before mutate
df |>
select(var1, var2) |>
mutate(mean_score = mean_n(min_valid = 1))
#> # A tibble: 5 × 3
#> var1 var2 mean_score
#> <dbl> <dbl> <dbl>
#> 1 10 5 7.5
#> 2 NA NA NA
#> 3 30 15 22.5
#> 4 40 NA 40
#> 5 50 25 37.5
# Show verbose processing info
df |> mutate(mean_score = mean_n(min_valid = 2, digits = 1, verbose = TRUE))
#> mean_n(): Row means computed with min_valid = 2, regex = FALSE
#> # A tibble: 5 × 4
#> var1 var2 var3 mean_score
#> <dbl> <dbl> <dbl> <dbl>
#> 1 10 5 NA 7.5
#> 2 NA NA 30 NA
#> 3 30 15 20 21.7
#> 4 40 NA 50 45
#> 5 50 25 10 28.3
# Add character and grouping columns
df_mixed <- mutate(df,
name = letters[1:5],
group = c("A", "A", "B", "B", "A")
)
df_mixed
#> # A tibble: 5 × 5
#> var1 var2 var3 name group
#> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 10 5 NA a A
#> 2 NA NA 30 b A
#> 3 30 15 20 c B
#> 4 40 NA 50 d B
#> 5 50 25 10 e A
# Non-numeric columns are ignored
mean_n(df_mixed)
#> [1] NA NA 21.66667 NA 28.33333
# Use within mutate() on mixed data
df_mixed |> mutate(mean_score = mean_n(select = starts_with("var")))
#> # A tibble: 5 × 6
#> var1 var2 var3 name group mean_score
#> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 10 5 NA a A NA
#> 2 NA NA 30 b A NA
#> 3 30 15 20 c B 21.7
#> 4 40 NA 50 d B NA
#> 5 50 25 10 e A 28.3
# Use everything() but exclude non-numeric columns manually
mean_n(df_mixed, select = everything(), exclude = "group")
#> [1] NA NA 21.66667 NA 28.33333
# Select columns using regex
mean_n(df_mixed, select = "^var", regex = TRUE)
#> [1] NA NA 21.66667 NA 28.33333
mean_n(df_mixed, select = "ar", regex = TRUE)
#> [1] NA NA 21.66667 NA 28.33333
# Apply to a subset of rows (first 3)
df_mixed[1:3, ] |> mean_n(select = starts_with("var"))
#> [1] NA NA 21.66667
# Store the result in a new column
df_mixed$mean_score <- mean_n(df_mixed, select = starts_with("var"))
df_mixed
#> # A tibble: 5 × 6
#> var1 var2 var3 name group mean_score
#> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 10 5 NA a A NA
#> 2 NA NA 30 b A NA
#> 3 30 15 20 c B 21.7
#> 4 40 NA 50 d B NA
#> 5 50 25 10 e A 28.3
# With a numeric matrix
mat <- matrix(c(1, 2, NA, 4, 5, NA, 7, 8, 9), nrow = 3, byrow = TRUE)
mat
#> [,1] [,2] [,3]
#> [1,] 1 2 NA
#> [2,] 4 5 NA
#> [3,] 7 8 9
mat |> mean_n(min_valid = 2)
#> [1] 1.5 4.5 8.0