Fix Memory Leak in R

Memory Leaks in R

R's copy-on-modify semantics and environment scoping can lead to unexpected memory growth, especially in long-running Shiny applications and batch processing scripts.

Growing Environments

R closures capture their enclosing environment. Nested functions in loops create chains of environments that can't be garbage collected:

# BAD — each closure captures the loop environment
functions <- list()
for (i in 1:10000) {
  large_data <- rnorm(10000)
  functions[[i]] <- function() sum(large_data)
}

# GOOD — force evaluation and minimize captured scope
functions <- lapply(1:10000, function(i) {
  val <- sum(rnorm(10000))  # Compute immediately
  function() val            # Only captures the scalar
})

Shiny Reactive Leaks

Reactive values that accumulate data without cleanup:

# BAD — log grows without bound
server <- function(input, output, session) {
  log <- reactiveVal(list())

  observeEvent(input$action, {
    current <- log()
    log(c(current, list(Sys.time())))
  })
}

# GOOD — keep only recent entries
  observeEvent(input$action, {
    current <- log()
    log(tail(c(current, list(Sys.time())), 100))
  })

Explicit Cleanup

# Remove large objects when done
result <- process_large_dataset(big_data)
rm(big_data)
gc()  # Suggest garbage collection

Profiling Memory

library(profmem)
p <- profmem({
  result <- my_function()
})
print(p)

Bugsly can receive error reports from R via its HTTP API — use httr to post exceptions and memory metrics from Shiny apps or batch R processes.