R Food Security Survey Analysis Training Course

Course overview

Food security teams routinely receive household survey exports that contain critical evidence but are difficult to turn into defensible decisions. Analysts must calculate Food Consumption Score (FCS), Household Dietary Diversity Score (HDDS), reduced Coping Strategies Index (rCSI), Household Hunger Scale (HHS), and livelihoods-based indicators while accounting for skip patterns, duplicate records, sampling weights, and missing values. Without a reproducible workflow, situation reports can contain inconsistent classifications, weak subgroup comparisons, and results that cannot be traced back to the original questionnaire or checked during donor, cluster, or government review.

This course teaches participants to use R for the complete analysis of food security survey data. Participants import and structure KoboToolbox or CSV survey exports, build data dictionaries, apply cleaning rules, recode response categories, calculate standard indicators, and produce weighted estimates with confidence intervals. They use the tidyverse and survey packages to create reproducible scripts for disaggregation by sex, age group, disability status, livelihood zone, displacement status, and administrative area. The course also covers practical interpretation: identifying meaningful differences, avoiding misleading comparisons from small samples, documenting assumptions, and presenting findings for programme targeting and response design.

Teaching combines short instructor demonstrations with guided coding labs based on a realistic humanitarian household assessment dataset. Each participant works in Posit RStudio, progressively building an annotated R script, a quality-assurance log, indicator tables, and publication-ready charts. By the final day, participants produce a reusable food security survey analysis template that their team can adapt for baseline, monitoring, rapid assessment, or post-distribution monitoring work.

The course is designed for development and humanitarian professionals who need to analyse or quality-assure household food security data rather than rely solely on spreadsheet outputs or external analysts.

Course objectives

By the end of this course, participants will be able to:

• Import and document household food security survey data from KoboToolbox and CSV exports in R
• Clean, validate, and recode survey variables using reproducible tidyverse workflows
• Calculate FCS, HDDS, rCSI, HHS, and livelihoods coping indicators from questionnaire responses
• Apply sampling weights and design-based estimation using the R survey package
• Produce disaggregated food security estimates with confidence intervals by population group and location
• Detect duplicates, skip-pattern errors, outliers, and missing-data risks through structured quality checks
• Create decision-ready indicator tables, bar charts, and maps for assessment reports and briefings
• Build an annotated R analysis script and quality-assurance log for reuse and peer review

Benefits of attending

Target competencies

Who should attend

• Food Security and Livelihoods Officers — who need to convert household assessment data into targeting and response evidence
• MEAL Officers — who manage survey datasets and must produce traceable indicator results
• Humanitarian Data Analysts — who need reproducible R workflows for multi-sector household surveys
• Assessment and Information Management Officers — who quality-assure field data before analysis and reporting
• Programme Managers — who need to interpret food security findings and challenge unsupported conclusions
• Research and Monitoring Consultants — who deliver defensible food security analysis for donors, NGOs, and government counterparts

Requirements and prerequisites

Participants should be comfortable using a spreadsheet to inspect rows, columns, filters, and basic formulas, and should understand that a household survey contains questions, responses, and coded variables. Familiarity with food security concepts such as food consumption, coping, and household-level sampling is helpful but not essential. No prior R programming, statistics qualification, SQL knowledge, or advanced mathematics is required. Complete beginners should expect to write and run R code from the first day, with guided explanations of objects, scripts, functions, and error messages. Participants should bring a laptop on which they can install R and Posit RStudio.

Training methodology

The course uses a realistic household food security assessment dataset throughout the five days. Instructor-led sessions introduce each analytical decision, followed by live R demonstrations and individually completed coding labs in Posit RStudio. Participants inspect questionnaire logic, clean raw records, calculate indicators, apply weights, and interpret disaggregated results in small review groups. Short case discussions examine common field-data problems, including inconsistent recall periods and missing meal-frequency responses. On the final day, each participant adapts the workflow into an application plan and reusable analysis template for a current or anticipated survey.

Course outline

Day 1: Preparing household survey data for analysis

• Food security assessment questions and indicator measurement logic
• R and Posit RStudio project setup for reproducible analysis
• Importing CSV and KoboToolbox survey exports into R
• Inspecting variable names, value labels, and data types
• Creating a survey data dictionary and codebook
• Using dplyr pipelines to filter, rename, and transform records
• Identifying duplicate household records and incomplete interviews

Workshop: Participants import a household survey export, create a data dictionary, and produce a first-pass data-quality summary in R.

Day 2: Cleaning and calculating food security indicators

• Questionnaire skip patterns and conditional-response validation
• Missing-value coding and non-response treatment rules
• Food group aggregation for Food Consumption Score calculation
• Frequency weighting and threshold classification for FCS
• Household Dietary Diversity Score construction
• Reduced Coping Strategies Index calculation and interpretation
• Household Hunger Scale scoring and severity categories

Workshop: Participants build functions that calculate FCS, HDDS, rCSI, and HHS and generate a household-level indicator file.

Day 3: Sampling, weights, and population estimates

• Probability sampling concepts for humanitarian household surveys
• Base weights, non-response adjustment, and post-stratification concepts
• Creating survey design objects with the survey package
• Weighted proportions and means with confidence intervals
• Design effects and implications for result interpretation
• Sample-size checks for administrative and demographic disaggregation
• Comparing groups without overstating uncertain differences

Workshop: Participants apply survey weights to estimate FCS categories and compare weighted and unweighted findings by district.

Day 4: Analysing vulnerability and presenting findings

• Livelihoods-based coping indicator coding and severity grouping
• Disaggregating results by sex, displacement status, disability, and livelihood group
• Cross-tabulations and uncertainty-aware subgroup comparisons
• Outlier review for expenditure, food-source, and household-size variables
• Building publication-ready charts with ggplot2
• Creating indicator tables for situation reports and donor briefs
• Mapping administrative-area results with sf objects

Workshop: Participants produce a disaggregated results table, two report-ready charts, and a district-level food security map.

Day 5: Quality assurance, interpretation, and reusable reporting

• Interpreting indicator patterns for response analysis
• Triangulating food security results with market, nutrition, and qualitative evidence
• Documenting assumptions, exclusions, and indicator thresholds
• Peer-review checklist for R scripts and analytical outputs
• Version control principles for assessment analysis files
• Structuring an automated analysis report with R Markdown
• Adapting the workflow for baseline, rapid assessment, and post-distribution monitoring

Workshop: Participants complete a peer review and assemble an annotated R analysis template, quality-assurance log, and action plan for a live work context.

Tools & standards covered

R, Posit RStudio, tidyverse, survey

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions