R for Monitoring & Evaluation: 7 Workflows That Will Change Your Life – Nic | Data

Author

Nichodemus Amollo

Published

November 2, 2025

Why R Is a Secret Weapon for M&E

Most M&E teams live in:

Excel
SPSS or Stata
Manually updated Word/PDF reports

R lets you:

Clean data reproducibly
Reuse scripts across projects
Generate entire reports with one command

7 High-Impact Workflows

Automated Cleaning of Survey Data
- Handle missing values
- Recode factors
- Detect outliers
Indicator Calculation
- Coverage, quality, equity indicators
- By district/facility/age/sex
Data Quality Dashboards
- Missingness, duplicates, logical checks
Monitoring Dashboards
- Monthly or quarterly indicator updates
Impact Analysis
- Before/after comparisons
- Difference-in-differences
Reproducible Reports
- Quarto reports by country/partner
Small Simulation Studies
- Sample size sensitivity
- Scenario planning

Example: From Kobo Export to Clean Dataset

Conceptually:

Read raw CSV from Kobo into R
Clean variable names and types
Recode key variables (e.g., facility IDs, districts)
Save a versioned clean file into data/clean/

If you can turn messy raw exports into clean analysis-ready data with one script, you become extremely valuable.

How to Learn R for M&E Quickly

Start with:
- tidyverse for data wrangling
- janitor for quick cleaning
- readr for imports
Practice on:
- DHS, MICS, or other public health datasets
- Your own anonymized program data (if allowed)

Focus on real workflows, not isolated functions.

---
title: "R for Monitoring & Evaluation: 7 Workflows That Will Change Your Life"
subtitle: "From cleaning survey data to automating donor reports with a single script"
author: "Nichodemus Amollo"
date: "2025-11-02"
categories: [R, Monitoring & Evaluation, Automation]
image: "/img/blog-mande.svg"
---

## Why R Is a Secret Weapon for M&amp;E

Most M&amp;E teams live in:

- Excel
- SPSS or Stata
- Manually updated Word/PDF reports

R lets you:

- Clean data reproducibly
- Reuse scripts across projects
- Generate entire reports with **one command**

---

## 7 High-Impact Workflows

1. **Automated Cleaning of Survey Data**
   - Handle missing values
   - Recode factors
   - Detect outliers

2. **Indicator Calculation**
   - Coverage, quality, equity indicators
   - By district/facility/age/sex

3. **Data Quality Dashboards**
   - Missingness, duplicates, logical checks

4. **Monitoring Dashboards**
   - Monthly or quarterly indicator updates

5. **Impact Analysis**
   - Before/after comparisons
   - Difference-in-differences

6. **Reproducible Reports**
   - Quarto reports by country/partner

7. **Small Simulation Studies**
   - Sample size sensitivity
   - Scenario planning

---

## Example: From Kobo Export to Clean Dataset

Conceptually:

1. Read raw CSV from Kobo into R
2. Clean variable names and types
3. Recode key variables (e.g., facility IDs, districts)
4. Save a versioned clean file into `data/clean/`

If you can turn **messy raw exports into clean analysis-ready data** with one script, you become extremely valuable.

---

## How to Learn R for M&amp;E Quickly

- Start with:
  - `tidyverse` for data wrangling
  - `janitor` for quick cleaning
  - `readr` for imports
- Practice on:
  - DHS, MICS, or other public health datasets
  - Your own anonymized program data (if allowed)

Focus on **real workflows**, not isolated functions.