The R package lazybar provides progress bar showing estimated remaining time. Multiple forecast methods and user defined forecast method for the remaining time are supported.
You can install the development version from Github with:
# install.packages("devtools") devtools::install_github("FinYang/lazybar")
pb <- lazyProgressBar(4) pb$tick() pb$tick() pb$tick() pb$tick() # With linearly increasing run time pb <- lazyProgressBar(4, method = "drift") for(i in 1:4){ Sys.sleep(i * 0.2) pb$tick()$print() } # With user defined forecast function # The forecast function itself will # require certain computational power forecast_fn <- function(dtime, i, n, s = 10){ # When the number of ticks is smaller than s # Estimate the future run time # as the average of the past if(i<s){ eta <- mean(dtime)*(n-i) } # When the number of ticks is larger than s # Fit an arima model every s ticks # using forecast package if(i>=s){ if(i %% s ==0){ model <- forecast::auto.arima(dtime) } runtime <- forecast::forecast(model, h=n-i)$mean if(i %% s !=0){ runtime <- runtime[-seq_len(i %% s)] } eta <- sum(runtime) } return(eta) } pb <- lazyProgressBar(10, fn = forecast_fn, s=3) for(i in 1:10){ Sys.sleep(i * 0.2) pb$tick()$print() }