R List Object Manipulation

R List Object Manipulation

Summary

Convert R list objects returned from binary file readers (such as rawrr::readFileHeader()) into serialized formats like JSON, enabling interoperability with downstream analysis tools and reproducible storage of extracted metadata. This skill bridges proprietary binary data access with standard data exchange formats.

When to use

You have extracted metadata or spectral information from a Thermo Fisher Scientific .raw file (or similar binary format) as an R list object using functions like readFileHeader(), readIndex(), or readChromatogram(), and you need to persist, share, or pass that structured data to non-R tools or store it in a language-agnostic format for reproducibility and version control.

When NOT to use

• Input is already in a standard serialized format (e.g., mzML, NetCDF, or CSV); conversion is unnecessary.
• You are performing statistical analysis within R and do not need to export intermediate metadata structures.
• The list object contains non-serializable elements (e.g., function pointers, external pointers) that jsonlite cannot handle.

Inputs

• R list object (output from rawrr::readFileHeader(), readIndex(), readChromatogram(), or readSpectrum())
• File path or connection to write output

Outputs

• .json file containing serialized list metadata
• JSON-formatted string representation of the R list

How to apply

After calling a rawrr reader function (e.g., rawrr::readFileHeader()) to obtain an R list object containing dynamic items such as instrument model, file name, time range, and number of scans, apply a JSON serialization function (e.g., jsonlite::toJSON()) to convert the list into a portable JSON string. Write the serialized output to a named .json file using standard R I/O functions (e.g., write() or jsonlite::write_json()). Verify the JSON is well-formed and contains all expected keys and values by parsing it back or inspecting the file structure. This workflow ensures metadata extracted from binary files is captured in a format compatible with downstream statistical tools, documentation systems, and cross-platform pipelines.

Related tools

• rawrr (R package providing reader functions (readFileHeader, readIndex, readSpectrum, readChromatogram) that return list objects from Thermo Fisher Scientific .raw files) — https://github.com/fgcz/rawrr
• jsonlite (R package for JSON serialization and deserialization of R objects, including toJSON() and write_json() functions)
• RawFileReader (.NET assembly providing low-level binary access to Thermo Fisher Scientific raw files; wrapped by rawrr) — https://github.com/thermofisherlsms/RawFileReader

Examples

H <- rawrr::readFileHeader('sample.raw'); jsonlite::write_json(H, 'sample_header.json', pretty=TRUE)

Evaluation signals

• Output .json file exists and is readable without parse errors (validate with a JSON parser).
• JSON keys match the original R list names; all numeric, string, and logical values are preserved.
• Parsing the JSON back into R (e.g., jsonlite::fromJSON()) recovers an equivalent or structurally identical list.
• File size and content match expectations (e.g., presence of expected metadata fields such as 'instrument model', 'time range', 'number of scans').
• JSON output is consumable by downstream tools or workflows that require standard data exchange formats.

Limitations

• jsonlite serialization may lose R-specific type information (e.g., factors, POSIXct datetime objects may be coerced to strings); round-trip fidelity should be verified.
• Very large list objects (e.g., from readSpectrum() on thousands of scans) may produce large JSON files; streaming or chunked serialization may be necessary.
• Non-serializable R objects (function closures, external pointers) within a list will cause serialization to fail; such elements must be removed or handled separately.
• JSON does not natively support all R data structures (e.g., matrices, arrays); conversion may flatten or reshape nested data.

Evidence

• [other] readFileHeader() extraction and metadata serialization workflow: "Call rawrr::readFileHeader() on the raw file to retrieve header metadata as an R list object. 3. Serialize the list object to JSON format using standard R JSON serialization (e.g.,"
• [results] readFileHeader() returns list with dynamic metadata fields: "The respective function is called readFileHeader() and returns a simple R object of type list"
• [other] List contents include instrument model, file name, time range, and scan counts: "rawrr::readFileHeader() reads meta information from a raw file header and returns a list object containing dynamic data items such as instrument model, file name, time range, and number of scans"
• [readme] rawrr provides access to Thermo Fisher Scientific Orbitrap data as R list objects: "The package provides access to proprietary Thermo Fisher Scientific Orbitrap instrument data as a stand-alone R package or serves as MsRawFileReaderBackend for the Bioconductor Spectra package."
• [methods] Extracted data undergoes file I/O serialization workflow: "In order to return extracted data back to the R layer we use file I/O. More specifically, the extracted information is written to a temporary location on the harddrive, read back into memory and"