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Compute Row Sums Across Items

Source: R/row_operations.R
row_sums.Rd

Calculates the sum across multiple variables for each row. This is the R equivalent of SPSS's COMPUTE total = SUM(var1, var2, var3).

Use min_valid to require a minimum number of non-missing items, mirroring SPSS's SUM.n() syntax.

Usage

row_sums(data, ..., min_valid = NULL, na.rm = TRUE)

Arguments

data

Your survey data (a data frame or tibble). When used inside mutate(), pass . or use pick().

...

The variables to average. Use bare column names separated by commas, or tidyselect helpers like starts_with("trust"). If no variables are specified, all numeric columns in data are used (useful with pick()).

min_valid

Minimum number of non-missing values required to compute a mean. If a row has fewer valid values, NA is returned. Default is NULL (compute mean if at least 1 value is valid).

na.rm

Remove missing values before calculating? Default: TRUE.

Value

A numeric vector with one value per row — the sum across the selected variables.

Details

When to Use row_sums() vs row_means()

  • row_means(): For Likert-type scales where you want an average score (preserves the original scale range)

  • row_sums(): For count-based scores (e.g., number of symptoms endorsed) or when you need a total score

See also

row_means() for row-wise means, row_count() for counting specific values

Other scale: efa(), pomps(), reliability(), row_count(), row_means()

Examples

library(dplyr)
data(survey_data)

# Total score across items
survey_data <- survey_data %>%
  mutate(total = row_sums(., trust_government, trust_media, trust_science))

# With min_valid (like SPSS SUM.3)
survey_data <- survey_data %>%
  mutate(total = row_sums(., trust_government, trust_media,
                          trust_science, min_valid = 3))