Preparing STEPS Data for Analysis

Introduction

This guide helps you prepare your WHO STEPS survey data file for use with the stepssurvey package. It covers the variables the package expects, how auto-detection works, common data quality issues, and how to resolve mismatches between your data and the package expectations.

The package is designed to work with STEPS data from any country, regardless of instrument version (v3.1 or v3.2) or data management system (Epi Info, SPSS, Stata, Excel).

Supported file formats

Recommendation: Use the .sav file directly as exported from your data management system. The package preserves SPSS variable labels during column detection (to disambiguate codes like A1 that mean different things across versions) and then strips them before analysis to avoid compatibility issues.

Minimum required variables

At a minimum, the package needs age and sex to produce any output. Beyond that, each additional variable you provide enables more indicators and tables.

How auto-detection works

When you call detect_steps_columns(data) (or upload a file in the Shiny app), the package searches for each expected variable using a prioritised alias list. For example, the fasting glucose variable is searched for as:

b5, b5_mmol, c1_mmol, fasting_glucose, glucose_fasting, fbg, fpg

The search is case-insensitive and uses the column names after janitor::clean_names() has standardised them.

For ambiguous codes (like A1 which means different things in v3.1 vs v3.2), the package also checks the SPSS variable label to disambiguate. This is why .sav files produce the most reliable auto-detection.

After detection, you can inspect the mapping:

raw  <- import_steps_data("my_data.sav")
cols <- detect_steps_columns(raw)

# See all detected columns
str(cols[!sapply(cols, is.null)])

# See what was NOT detected
names(cols[sapply(cols, is.null)])

Common data issues and solutions

# Glucose: mg/dL to mmol/L (divide by 18)
raw$b5 <- raw$b5 / 18

# Cholesterol: mg/dL to mmol/L (divide by 38.67)
raw$b8 <- raw$b8 / 38.67

step1 <- haven::read_spss("step1_interview.sav")
step2 <- haven::read_spss("step2_measurements.sav")
step3 <- haven::read_spss("step3_biochemistry.sav")

combined <- dplyr::left_join(step1, step2, by = "pid") |>
  dplyr::left_join(step3, by = "pid")

# Save combined file
haven::write_sav(combined, file.path(tempdir(), "steps_combined.sav"))

# Or import directly
raw <- combined |> janitor::clean_names()

Pre-flight checklist

Before running the analysis, verify these items:

1. File format: Preferably .sav (preserves variable labels for better auto-detection). CSV works but may require more manual column mapping.

2. One row per respondent: The data should be in wide format with one row per survey participant and columns for each variable.

3. Age and sex present: These are the only truly required variables. Verify they contain reasonable values (age should be numeric, sex should have exactly two levels).

4. Weights present: Check for columns named WStep1, WStep2, WStep3, or similar. If using a single weight, ensure it maps to weight_step1.

5. Biochemical units: Glucose should be in mmol/L (typical values 3–15), cholesterol in mmol/L (typical values 2–10). Values in hundreds suggest mg/dL units that need conversion.

6. Blood pressure readings: Should be in mmHg. Typical SBP range is 80–250, typical DBP range is 40–150. Values outside this range are set to NA during cleaning.

7. No duplicate respondent IDs: If the same person appears multiple times, prevalence estimates will be biased.

8. Consistent coding: Binary variables should use the same coding scheme throughout (ideally 1 = Yes, 2 = No per WHO convention).

Quick diagnostic script

Run this after importing to check data quality:

library(stepssurvey)

raw  <- import_steps_data("my_data.sav")
cols <- detect_steps_columns(raw)

# Summary
cat("Rows:", nrow(raw), "\n")
cat("Columns:", ncol(raw), "\n")
cat("Detected:", sum(!sapply(cols, is.null)), "/", length(cols), "\n")

# Check key variables
if (!is.null(cols$age)) {
  cat("\nAge range:", range(raw[[cols$age]], na.rm = TRUE), "\n")
  cat("Age NAs:", sum(is.na(raw[[cols$age]])), "\n")
}
if (!is.null(cols$sex)) {
  cat("\nSex distribution:\n")
  print(table(raw[[cols$sex]], useNA = "ifany"))
}
if (!is.null(cols$weight_step1)) {
  wt <- raw[[cols$weight_step1]]
  cat("\nWeight range:", round(range(wt, na.rm = TRUE), 3), "\n")
  cat("Weight NAs:", sum(is.na(wt)), "\n")
}

# List undetected variables
missing <- names(cols[sapply(cols, is.null)])
cat("\nUndetected variables (", length(missing), "):\n")
cat(paste(" ", missing, collapse = "\n"), "\n")

Further reading

• vignette("stepssurvey-guide") – full API documentation
• vignette("shiny-walkthrough") – interactive Shiny app guide
• WHO STEPS Manual, Part 4: Data Analysis
• WHO STEPS Instrument v3.2