ff lets R work with supported data stored in files instead of requiring the whole object to reside in ordinary RAM. Its ffdf data-frame interface and chunked import tools can help with delimited files that are too large or awkward to load as a conventional data frame. They do not make every R operation memory-free: chunk size, indexing, copies, and the operation itself still determine how much RAM is needed.
What ff stores—and what it does not do
The ff package stores supported atomic data in flat files and maps sections of that data into main memory when R accesses them. R objects retain metadata such as dimensions and virtual storage mode; the data values themselves are file-backed. The project also provides ffdf, a data-frame-like interface built from file-backed columns, along with import, export, indexing, and chunk-processing tools. See the ff project and the package reference manual.
The practical distinction is between storing an object on disk and materializing it in RAM. File backing can reduce the need to load an entire dataset as an ordinary R vector or data frame, but it does not guarantee constant-memory computation. An operation may create large temporary objects, expand an index in memory, or copy data. Access also goes through package methods rather than turning the file into an ordinary in-memory R object.
The package supports standard atomic types and additional compact or extended storage modes. Its documentation describes persistence across R sessions and sharing ff files across R objects or processes. Those capabilities are not the same as automatic coordination of concurrent writes or a guarantee that every R function accepts ff objects unchanged; check compatibility for the specific operation and package version you intend to use.
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Import a delimited file in row chunks
read.table.ffdf imports a separated flat file into an ffdf by processing rows in chunks rather than building the entire result as a conventional in-memory data frame. In the documented interface, first.rows controls the initial chunk; later chunk sizes are chosen using getOption("ffbatchbytes"). The initial chunk matters: a smaller one can reduce the risk that preallocation is too large for a wide file and limited RAM, while a larger one can help with factor-level ordering. Consult the read.table.ffdf help page for the exact arguments available in the version you install.
library(ff)
# Illustrative import: choose classes and chunk settings for your file
# and confirm the arguments against your installed ff version.
x <- read.table.ffdf(
file = "measurements.csv",
header = TRUE,
sep = ",",
first.rows = 10000
)
This example leaves later chunk sizing to the documented ffbatchbytes option and leaves column-class decisions to you. A reasonable first.rows value depends on row width, available memory, and the file’s contents; the example’s 10,000 rows is illustrative, not a universal recommendation. Inspect the installed help before relying on exact defaults or argument behavior: the hosted reference index identifies version 4.0.12, while the CRAN mirror page reports version 4.5.3 dated 2026-07-21. Documentation for one version should not be assumed to describe another without checking.
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Choose column classes deliberately
The documented read.table.ffdf path does not directly support character columns. Specify supported classes where appropriate, such as Date, POSIXct, factor, or ordered, rather than assuming character input will work as it would with an ordinary read.table call. Review the imported column types and values before using the result downstream.
Account for factor levels across chunks
Factor levels discovered in later chunks are appended during import; the importer does not globally sort and recode all levels as though it had seen the whole file at once. If a particular level order is required, use the documented sortLevels facility afterward and verify that the resulting order matches the analysis you need. This is especially important when level order carries meaning rather than being merely presentational.
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Process data without accidentally rebuilding it in RAM
After import, keep the workflow compatible with file-backed access. The reference index lists chunking and apply helpers, ffdf operations, indexing, sorting, and CSV export methods; see the reference index for the version it documents. Prefer operations that work on bounded pieces when a whole-object result is not required. Before applying a familiar R expression, check whether its method supports ff objects and whether it returns another file-backed object or a conventional in-memory result.
- Estimate the working set, not only the stored file size. Temporary vectors, selected columns, and copies can raise peak RAM use.
- Check the behavior of the specific indexing expression. Some index expressions expand in RAM, and unsorted index positions can require an additional vector.
- Use the package’s chunking or apply helpers when the task can be expressed as repeated work on bounded sections; confirm their signatures in the version installed.
- Test the intended pipeline on a representative subset, including its output types and factor behavior, before committing a long import or transformation.
Check the limits and manage file lifecycles
The package limitations page says ff objects are limited to .Machine$integer.max elements; the documented R code has not ported 64-bit double indices, and operating-system file-size limits also apply. That is an element-count bound, not a promise that an object near the limit will fit comfortably on a particular filesystem or work with every operation. See the ff limitations documentation.
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Use deliberate filenames for durable data
Without filename=, the package creates a temporary file with a delete finalizer. With a named file, it creates a permanent file with a close finalizer. This distinction affects whether data remain available after an R object is finalized or a session ends. For data you need to keep, choose filenames deliberately, manage the files’ lifecycle, and make sure the directory and filesystem have enough space. Do not rely on an unnamed temporary backing file as durable storage.
Check sharing, indexing, and portability assumptions
- Changes to data and physical attributes can be shared between copies, while virtual and class attributes are not necessarily shared. Treat copies as potentially sharing underlying state rather than assuming ordinary copy-on-modify behavior.
- Some
[[methods are documented as having undefined behavior and should not be used in programming. - ff files cannot be transferred between systems with different byte order. Treat them as platform-sensitive storage, not as a portable interchange format; export data to a suitable format when moving between incompatible systems.
- Review index size and ordering before scaling a lookup: apparently small selections can still involve memory-expanding index vectors.
When ff fits—and when another workflow may fit better
Choose based on the access pattern, not just the number of rows. The 2009 ff/bit presentation used the phrase “Data too large for RAM” and described multiple datasets, repeated copies, and sharing among parallel R workers as reasons to consider ff. It also listed small in-memory datasets, B-tree-like search, database-style large queries, transparent locking, and exhausted filesystem cache or excessive swapping as counter-indications or cases for other tools. This is historical design guidance, not a current benchmark or a present-day comparison of packages. The presentation is available at useR! 2009 abstracts.
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| Workload or requirement | What to assess |
|---|---|
| Repeated general R vector or array access | Check whether the required operations have ff-compatible methods and how much data or temporary state each operation materializes. |
| Sequential work over a large delimited file | Chunked import and bounded piece-by-piece processing may suit the workflow; account for row width, column classes, factor levels, and chunk sizing. |
| Database-style filtering, joins, or large queries | Compare the query pattern and indexing needs with a database-oriented workflow rather than assuming file backing provides query-engine behavior. |
| Concurrent workers or writes | Establish the required sharing and locking semantics. Shared files are documented, but transparent locking is not established by that fact. |
| Large or complex indexing | Estimate memory for index vectors and temporary results, including the extra-vector case for unsorted positions. |
| Portability and long-term storage | Account for byte order, operating-system file-size limits, filenames, and the need to export to a portable format. |
There is no contemporary performance comparison established here, so no speed ranking follows from these characteristics. The decision should be tested against the actual R and package versions, data shape, operations, memory pressure, search pattern, and concurrency requirements.
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