R package: resultcheck

Overview

resultcheck provides lightweight helpers for checking whether empirical results remain unchanged across code revisions, platform differences, and package updates. Call snapshot() on key outputs (models, tables, derived datasets) in your analysis scripts to detect unintended result drift automatically during CI or local testing.

Installation

# install.packages("devtools")
devtools::install_github("kv9898/resultcheck")

Workflow

The package supports a two-phase workflow:

1. Interactive development — run your analysis script and call snapshot() on objects you care about. On first run the snapshot is saved as a human-readable .md file. On subsequent interactive runs, differences are shown and you are prompted to update.

2. Automated testing — wrap your script in setup_sandbox() / run_in_sandbox() / cleanup_sandbox(). Inside run_in_sandbox(), snapshot() switches to testing mode: it errors immediately if a snapshot is missing or has changed, making the test fail.

Integrated Example

Consider a project with this layout:

myproject/
├── resultcheck.yml       # marks the project root
├── data/
│   └── income.csv        # input data
├── analysis.R            # analysis script
└── tests/
    └── testthat/
        └── test-analysis.R

analysis.R — snapshot key results

data <- read.csv("data/income.csv")
model <- lm(income ~ age + education, data = data)

# Snapshot detects unexpected result changes across code revisions.
# Interactive: warns and prompts to update when differences are found.
# Inside run_in_sandbox(): errors if snapshot is missing or doesn't match.
resultcheck::snapshot(model, "income_model")

dir.create("output", showWarnings = FALSE)
write.csv(
  as.data.frame(coef(summary(model))),
  "output/model_summary.csv"
)

tests/testthat/test-analysis.R — automated test

library(testthat)
library(resultcheck)

test_that("analysis produces stable results", {
  sandbox <- setup_sandbox("data")
  on.exit(cleanup_sandbox(sandbox), add = TRUE)

  # Errors immediately if any snapshot inside analysis.R doesn't match.
  run_in_sandbox("analysis.R", sandbox)

  expect_true(
    file.exists(file.path(sandbox$path, "output", "model_summary.csv"))
  )
})

When analysis.R changes in a way that alters the model, run_in_sandbox() errors immediately. To accept the change, re-run the script interactively, review the diff, and confirm the update.

Function Reference

snapshot(value, name, script_name = NULL, method = c("both", "print", "str"))

Creates or verifies a snapshot of any R object.

• First interactive run: saves the object as a human-readable .md file under _resultcheck_snapshots/<script>/ at the project root.
• Subsequent interactive runs: shows a diff and prompts to update.
• Inside run_in_sandbox(): errors if the snapshot is missing or doesn’t match.

The method argument controls how the object is serialized:

Value	Behavior
"both" (default)	Type-specific logic using both print() and str()
"print"	Only print() output is captured
"str"	Only str() output is captured

setup_sandbox(files, temp_base = NULL)

Creates a temporary directory and copies the listed files and/or directories into it, preserving their path structure relative to the project root. Snapshot files do not need to be listed separately.

run_in_sandbox(script_path, sandbox = NULL, ...)

Runs an R script inside the sandbox with the working directory set to the sandbox, while find_root() and snapshot() transparently resolve back to the original project root.

cleanup_sandbox(sandbox = NULL, force = TRUE)

Removes the sandbox directory. Omit the argument to clean up the most recently created sandbox.

find_root(start_path = NULL)

Locates the project root by searching upward for a resultcheck.yml, .Rproj, or .git marker.

Place an empty resultcheck.yml at your project root to make detection reliable:

# resultcheck.yml

If you find this package useful, please give it a ⭐ on GitHub!