studyframe keeps observations together with the information needed to interpret them: observational units, measurements, sampling, weights, assignment, and derivations. An analysis selects the relevant declarations and checks its requirements before calling an established statistical package.
This is an experimental, breaking revision of the original metadata-only package. It implements a bounded schema and tested adapters, not a complete scientific metadata standard. A well-formed declaration does not establish that its scientific claims or identification assumptions are true.
The R package is now named studyframe, formerly SuperDF. The GitHub repository currently retains its original name. This development revision is not on CRAN.
Install the R package
From a local checkout:
The core package declares and validates data and context. Install survey for survey analysis, estimatr for experimental analysis, and arrow plus zip for portable bundle I/O:
install.packages(c("survey", "estimatr", "arrow", "zip"))The collection-to-inference vignette executes a complete simulated handoff. Schema 0.3 and .studyframe bundles are experimental; this rename does not provide compatibility aliases for the earlier unreleased API or bundle format.
Declare once, use across analyses
library(studyframe)
observations <- data.frame(
id = 1:8,
school = rep(1:4, each = 2),
district = rep(1:2, each = 4),
weight = rep(2, 8),
income = c(12, 15, 18, 11, 20, 24, 16, 22)
)
study <- studyframe(
observations,
keys = "id",
unit = "pupil",
dataset_id = "pupil_survey",
variables = list(variable_spec("income", units = "USD/month")),
sampling = list(sampling_spec(
"schools",
population = "Pupils in the study districts",
stages = list(sampling_stage(
"school", method = "srs", strata = "district", replacement = "with"
)),
source = "Study sampling protocol"
)),
weights = list(weight_spec(
"main", "weight", sampling = "schools", source = "Released design weights"
))
)
result <- estimate_mean(study, outcome = "income", weight = "main")
result$fit
result$analysisWeights are selected explicitly. Measurement waves and row applicability are checked. Sampling and assignment are separate declarations. Missing information stays unknown; it does not silently become simple random sampling.
Preserve the collection roster
district_one <- study[study$district == 1, ]
estimate_mean(district_one, "income", "main", domain = TRUE)
renamed <- rename_studyframe(study, c(income = "monthly_income"))
estimate_mean(renamed, "income", "main")Stable variable IDs survive renaming. The survey design is constructed on the full applicable roster before the domain is selected. domain = TRUE is an analyst declaration, not a claim that any arbitrary restriction is ignorable.
A filtered view still contains and exports the full collection roster. It is not a redacted release. To distribute fewer original records, explicitly construct a new dataset and reconsider its design declarations.
derive_studyframe() adds a defined measurement without overwriting original facts. recode_studyframe() records an explicit value mapping into a new measurement and requires a policy for unmapped values. join_studyframe() requires keys and a unique right-hand match. rbind() accepts disjoint views of the same collection; it rejects incompatible collections. Aggregation requires an explicit new observational unit and dataset. Direct replacement operations are refused, and adapter-time validation detects mutations that bypass the supported methods.
Data and context travel together
Schema 0.3 separates received responses from invited, sampled, and randomized rosters. Ordinary dataframe imports start with unknown completeness. Experimental inference requires an asserted-complete randomized roster scoped to its assignment, with source evidence. Evidence records carry source locations, optional hashes, capture times, producer identities, and versions. Instrument references connect measurements to question and subquestion IDs. Unknown facts remain unknown; planned sampling or assignment declarations cannot stand in for realized designs.
write_studyframe(study, "study.studyframe")
received <- read_studyframe("study.studyframe")
write_codebook(received, "codebook.md")One ZIP bundle contains data.parquet, roster.parquet, context.json, and an integrity manifest. It contains no executable R expressions. Writers refuse to overwrite an existing destination. RDS also preserves the R object, but the bundle is the cross-language format. Bare data extraction is explicit: as.data.frame(study) removes context.
This repository ships the R package. The bundle format and JSON schema remain language-independent. The producer example writes bundles with PyArrow and Python’s standard library, without importing studyframe. R tests read small checked-in bundles from that producer and verify both accepted and rejected cases. Running the R package’s tests does not require Python.
The earlier Python reader is preserved on archive/python-reader. It is deferred work, outside this package’s build and release process.
Check a request before fitting
request <- analysis_request("mean", "income", weight = "main")
requirements <- check_analysis(study, request)
requirements$issues
if (requirements$ok) result <- run_analysis(study, request)Checking prepares the design without fitting an outcome estimator. Issues include stable codes, categories, affected declarations, evidence references, and remedies. Estimation uses the same requirements and returns a native statistical fit together with the request, input identity, selected declarations, assumptions, omissions, and interval convention. A passing check supports execution of the declared analysis; it does not verify the study’s scientific assertions.
run_analysis() raises studyframe_analysis_error with an issues field when requirements fail. Catch that class to inspect the same issue records returned by check_analysis(); underlying errors are retained in parent for debugging. Issue records are plain data and can be serialized without the R condition or its backtrace. Import errors similarly provide issues under studyframe_import_error; both inherit from studyframe_error.
Request scalar fields must be ordinary strings or logical values. Classed scalars and one-cell matrices are rejected because they can change shape during JSON serialization. Missing-data, preview, and recode policy names require exact matches rather than abbreviations.
For an observational linear model, use regression_request() with numeric predictor identifiers, an explicit target, and the assumptions supporting the analysis. The adapter uses estimatr::lm_robust() with HC2 uncertainty for unclustered observations or CR2/stata uncertainty for declared clusters. It records the selected observations, joint missingness exclusions, clustering, and model assumptions. Its coefficients describe associations; this route does not certify causal identification or population representativeness, and it does not apply sampling weights.
Start with a Qualtrics export
responses <- read_qualtrics("responses.csv", "instrument.qsf")
write_codebook(responses, "codebook.md")
context(responses)$diagnosticsThe importer maps supported questions through stable import IDs, preserves explicit recodes and source hashes, and can join responses to an authoritative external roster. Response-only imports are also supported when the QSF is unavailable; instrument identity and coding remain unresolved. Preview records require an explicit policy. An exported response table does not establish the original sampling or randomized roster.
See the collection workflow for supported question types, a complete executable example, and composition with qualtRics downloads.
Supported analyses and limits
| Component | Implemented |
|---|---|
| Sampling | Explicit SRS stages; one-stage with replacement; multistage without replacement with FPC at every stage |
| Replicate weights | BRR, Fay, jackknife, bootstrap, and explicitly scaled other specifications through R survey
|
| Survey analysis | Numeric means; explicit weight, wave, domain, and missing-data policies |
| Assignment | Two-condition complete randomization, including blocks and clusters, with constant declared assignment probability |
| Experimental analysis |
estimatr::difference_in_means() on a complete roster and complete numeric outcomes |
| Regression | Unweighted numeric linear models with an intercept; HC2 or clustered CR2/stata uncertainty, explicit target and assumptions |
| Measurement | Definitions, units, category labels, named missingness codes, measurement occasion |
| Portability | Primitive integer, double, string, and boolean columns; one current table and its attached roster |
PPS, unknown collection mechanisms, and Bernoulli assignment can be recorded but are not translated by the current adapters. Taylor analysis of explicitly adjusted weights is refused because calibration and adjustment variance structures are not yet represented. Replicate specifications can carry adjustment effects when the producer supplies the appropriate replicates. Multiple imputation, variable eligibility logic, longitudinal estimators, arbitrary joint inclusion probabilities, and general multi-table studies are future work. Collection import currently covers a bounded set of Qualtrics CSV/QSF representations.
Factors, dates, nested columns, infinity, and NaN require explicit conversion. Numeric missing codes remain in storage and are converted only for analysis. missing = "omit" records an omission; it does not justify a nonresponse assumption. Unsupported dataframe libraries may drop the class or alter its contents; only documented studyframe operations provide the preservation contract.
See the schema and requirements, four reproducible workflows, and API documentation.
Development
make ci runs R linting, statistical and bundle-conformance tests, R CMD check, and an R source-package build. make document regenerates the R reference documentation. The README is the package website’s main page. make format uses Air for R.
The four deterministic workflows cover stratified cluster sampling and domains, replicate-weight inference, blocked cluster randomization, and wave-specific weights. Tests compare estimates, standard errors, and degrees of freedom against direct backend calls and deliberately break alignment and applicability.
For a stochastic collection-to-inference demonstration, run:
source(system.file("examples", "simulated_study.R", package = "studyframe"))
run_simulated_study("simulation", repetitions = 500L)This creates a finite population, samples schools, randomizes treatment, collects observed outcomes, joins shuffled records, and writes and reloads a portable bundle before analysis. Repeated runs report bias, estimated versus empirical uncertainty, and interval coverage against known truth. See the simulation design for the two inferential targets and limitations.
License
MIT. See LICENSE.