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memtoc

Tictoc-style memory tracking for R. Simple start/stop syntax for monitoring RAM usage during code execution with continuous background polling to estimate peak memory. Inspired by the tictoc package for timing.

Installation

# install.packages("pak")
pak::pak("jcoa05/memtoc")
# Or using devtools
devtools::install_github("jcoa05/memtoc")

Quick start

library(memtoc)

# Track memory for any operation
tic_mem("data processing")
data <- read.csv("large_file.csv")
processed <- transform(data)
toc_mem()
#> ✔ data processing: 142.3 MB peak | 89.1 MB current | 2.34 sec | 3 samples

Why memtoc?

R’s built-in memory tools (gc(), object.size()) only show point-in-time snapshots. Prioritizing ease of use, memtoc estimates peak memory by continuously sampling in the background.

tic_mem("matrix operation")
x <- matrix(rnorm(1e8), ncol = 1000)  # ~800 MB temporary allocation
y <- colMeans(x)                        
rm(x)  # A peak is recorded only if a sample captured the allocation
toc_mem()
#> ✔ matrix operation: 812.4 MB peak | 45.2 MB current | 3.21 sec | 7 samples
# without background polling, you'd only see the final 45 MB.

Features

Feature	Description
🎯 Background polling	Background sampling estimates peak usage
📊 Nested tracking	Track pipelines and individual steps simultaneously
⚡ Parallel monitoring	Auto-detect and monitor `future` workers
💾 Crash recovery	Recover data if R crashes mid-computation
⚠️ System warnings	Alerts when system RAM is running low
📝 Logging	Collect results for later analysis

Background polling

tic_mem("job", interval = 0.5)  # Sample every 0.5 seconds
# ... your code ...
result <- toc_mem()
result$trajectory  # Full memory timeline

Nested tracking

tic_mem("full pipeline")
  tic_mem("step 1"); do_step1(); toc_mem()
  tic_mem("step 2"); do_step2(); toc_mem()
  tic_mem("step 3"); do_step3(); toc_mem()
toc_mem()

Parallel worker monitoring

library(future)
plan(multisession, workers = 4)

tic_mem("parallel job", workers = "auto")
result <- future_lapply(1:100, heavy_function)
toc_mem()
#> ✔ parallel job: 1.2 GB peak | 245 MB current | 5.4 sec | 4 workers

Crash recovery

Checkpoints live in R’s session-specific temporary directory. After an R restart, use mem_recover(path = ...) with the actual path to a surviving checkpoint from the previous session. Recovery is impossible if that temporary directory has been removed. Normal completion removes checkpoints.

# List checkpoints in this R session
mem_recover()
#> ℹ Found 1 recovery file: PID 12345 (152 samples)
data <- mem_recover(pid = 12345)

Reference

Function	Description
`tic_mem()`	Start tracking
`toc_mem()`	Stop tracking and report results
`mem_log()`	Get logged results as data frame
`mem_clearlog()`	Clear the log
`mem_clear()`	Clear orphaned tracking entries
`mem_recover()`	Recover data from crashed sessions
`mem_capabilities()`	Check available features
`mem_diagnose()`	Detailed troubleshooting
`mem_parallel_info()`	Check parallel backend status

Documentation

See vignette("memtoc") for a detailed tutorial, or ?tic_mem for function help.

Requirements

R ≥ 4.1.0
Dependencies: ps, cli, and callr installed automatically
Optional: future and parallelly for parallel worker monitoring

tictoc: Timing (memtoc is for memory)
bench: Benchmarking with memory tracking
profmem: Memory profiling

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.