Compute Row Sums Across Items

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). Inside mutate(), the recommended form is pick(): mutate(score = row_means(pick(item1, item2, item3))). It also works in a grouped mutate(); . (the magrittr placeholder) is the whole data set and therefore only works without groups.
...: 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(); non-numeric columns are ignored with a warning).
min_valid: Minimum number of non-missing values required to compute a mean (a whole number). 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

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))