📊 Too much global development data still lives in spreadsheets. But modern analysis requires data acquisition as code. Today I’m pleased to share that the unicefData R package is now available on CRAN, providing direct programmatic access to the UNICEF Data Warehouse. Researchers, policymakers, and analysts can now retrieve 700+ internationally comparable indicators on children and women directly from R — enabling reproducible analytics, transparent workflows, and faster evidence generation. Install install.packages("unicefData") Example library(unicefData) df <- unicefData( indicator = "CME_MRY0T4", countries = c("BRA","USA","IND"), year = "2015:2023" ) Why this matters Reliable evidence depends not only on good data, but also on how easily that data can be accessed, reproduced, and reused. The unicefData package helps bridge the gap between official statistics and modern data science workflows by enabling: • Access to 700+ indicators across countries and years • Integration with reproducible research pipelines • Use of SDMX international statistical standards • Consistent interfaces across R, Python, and Stata Making official statistics more accessible, reproducible, and interoperable is an important step toward strengthening evidence for policies that improve outcomes for children. 📦 CRAN cran.r-project.org/package=u… #OpenData #OfficialStatistics #RStats #DataForChildren #ReproducibleResearch