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Compute Row Means Across Items

Source: R/row_operations.R
row_means.Rd

Calculates the mean across multiple variables for each row. This is the standard way to create scale scores from survey items — the R equivalent of SPSS's COMPUTE score = MEAN(var1, var2, var3).

Use min_valid to require a minimum number of non-missing items, mirroring SPSS's MEAN.n() syntax: min_valid = 2 corresponds to MEAN.2(a, b, c).

Usage

row_means(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 mean across the selected variables. Use inside dplyr::mutate() to add it as a new column.

Details

How It Works

For each row, row_means() computes the arithmetic mean of the selected variables. Missing values are ignored by default, so a respondent who answered 2 out of 3 items still gets a score.

The min_valid Parameter

In practice, you often want to require a minimum number of valid responses. If someone only answered 1 out of 5 items, a mean based on a single item may not be reliable. Set min_valid to control this:

  • min_valid = NULL (default): Compute mean with any number of valid values (at least 1)

  • min_valid = 2: Require at least 2 valid values

  • min_valid = 3: Require at least 3 valid values

When to Use This

Use row_means() after checking reliability with reliability:

  1. Run reliability() to check if items form a reliable scale

  2. If Cronbach's Alpha is acceptable (typically > .70), create the index

  3. Use the index in further analyses (t-tests, correlations, regression)

See also

row_sums() for row-wise sums, row_count() for counting specific values, pomps() for rescaling to 0-100

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

Examples

library(dplyr)
data(survey_data)

# Create a trust scale from 3 items
survey_data <- survey_data %>%
  mutate(m_trust = row_means(., trust_government, trust_media, trust_science))

# Using pick()
survey_data <- survey_data %>%
  mutate(m_trust = row_means(pick(starts_with("trust"))))

# Require at least 2 valid items (like SPSS MEAN.2)
survey_data <- survey_data %>%
  mutate(m_trust = row_means(., trust_government, trust_media,
                             trust_science, min_valid = 2))