Autor: Iñaki Úcar

The 4.1.0 release of simmer, the Discrete-Event Simulator for R, is on CRAN. As per request in the mailing list, now get_global() is able to work inside a generator function. Moreover, the new add_global() method attaches a global attribute to a simulator.

library(simmer)

env <- simmer()

hello_sayer <- trajectory() %>%
  log_("hello world!") %>%
  set_global("interarrival", 1, mod="+")

generator <- function() get_global(env, "interarrival")

env %>%
  add_global("interarrival", 1) %>%
  add_generator("dummy", hello_sayer, generator) %>%
  run(7) %>%
  get_global("interarrival")

## 1: dummy0: hello world!
## 3: dummy1: hello world!
## 6: dummy2: hello world!

## [1] 4

Compared to plain global variables, these ones are automatically managed and thus reinitialised if the environment is reset.

env %>%
  reset() %>%
  get_global("interarrival")

## [1] 1

env %>%
  run(7) %>%
  get_global("interarrival")

## 1: dummy0: hello world!
## 3: dummy1: hello world!
## 6: dummy2: hello world!

## [1] 4

There has been a small refactoring of some parts of the C++ core, which motivates the minor version bump, but this shouldn’t be noticeable to the users. Finally, several bug fixes and improvements complete this release. See below for a complete list.

The 4.0.1 release of simmer, the Discrete-Event Simulator for R, is on CRAN since a couple of weeks ago. There are few changes, notably new getters (get_sources(), get_resources(), get_trajectory()) for simmer environments and some improvements in resource selection policies (see details in help(select)).

A new convenience function, when_activated, makes it easier to generate arrivals on demand, triggered from trajectories. Let us consider, for instance, a simple restocking pattern:

library(simmer)

restock <- trajectory() %>%
  log_("restock")

serve <- trajectory() %>%
  log_("serve") %>%
  activate("Restock")

env <- simmer() %>%
  add_generator("Customer", serve, at(1, 2, 3)) %>%
  add_generator("Restock", restock, when_activated()) %>%
  run()

## 1: Customer0: serve
## 1: Restock0: restock
## 2: Customer1: serve
## 2: Restock1: restock
## 3: Customer2: serve
## 3: Restock2: restock

Finally, this release leverages the new fast evaluation framework offered by Rcpp (>= 0.12.18) by default, and includes some minor improvements and bug fixes.

The latest R tip in Win-Vector Blog encourages you to Use Radix Sort based on a simple benchmark showing a x35 speedup compared to the default method, but with no further explanation. In my opinion, though, the complete tip would be, instead, use radix sort… if you know what you are doing, because a quick benchmark shouldn’t spare you the effort of actually reading the docs. And here is a spoiler: you are already using it.

One may wonder why R’s default sorting algorithm is so bad, and why was even chosen. The thing is that there is a trick here, and to understand it, first we must understand the benchmark’s data and then read the docs. This is the function from the original code (slightly modified for subsequent reuse) that generates the data:

mk_data <- function(nrow, stringsAsFactors = FALSE) {
  alphabet <- paste("sym", seq_len(max(2, floor(nrow^(1/3)))), sep = "_")
  data.frame(col_a = sample(alphabet, nrow, replace=TRUE),
             col_b = sample(alphabet, nrow, replace=TRUE),
             col_c = sample(alphabet, nrow, replace=TRUE),
             col_x = runif(nrow),
             stringsAsFactors = stringsAsFactors)
}

set.seed(32523)
d <- mk_data(1e+6)

summary(d)

##     col_a              col_b              col_c          
##  Length:1000000     Length:1000000     Length:1000000    
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##      col_x          
##  Min.   :0.0000002  
##  1st Qu.:0.2496717  
##  Median :0.4991010  
##  Mean   :0.4996031  
##  3rd Qu.:0.7494089  
##  Max.   :0.9999999

length(table(d$col_a))

## [1] 99

There are three character columns sampled from 99 symbols (sym_1, sym_2, …, sym_99) and a numeric column sampled from a uniform. The first three columns are thus clearly factors, but they are not treated as such. Let’s see now what help(sort) has to tell us about the sorting method, which by default is method="auto":

The “auto” method selects “radix” for short (less than 2^31 elements) numeric vectors, integer vectors, logical vectors and factors; otherwise, “shell”.

So, as I said in the opening paragraph, you are already using radix sort, except for characters. Let’s see then what happens if we treat such columns as proper factors:

library(microbenchmark)

set.seed(32523)
d <- mk_data(1e+6, stringsAsFactors = TRUE)

timings <- microbenchmark(
  order_default = d[order(d$col_a, d$col_b, d$col_c, d$col_x), , 
                    drop = FALSE],
  order_radix = d[order(d$col_a, d$col_b, d$col_c, d$col_x,
                        method = "radix"), ,
                  drop = FALSE],
  times = 10L)

print(timings)

## Unit: milliseconds
##           expr      min       lq     mean   median       uq      max neval
##  order_default 289.4685 312.0257 388.5259 387.8308 418.2673 584.4771    10
##    order_radix 265.6491 321.8337 421.2072 376.1166 512.0047 667.0545    10
##  cld
##    a
##    a

Unsurprisingly, timings are the same, because R automatically selects "radix" for you when appropriate. But when is it considered appropriate and why isn’t it appropriate in general for character vectors? We should go back to the docs:

The implementation is orders of magnitude faster than shell sort for character vectors, in part thanks to clever use of the internal CHARSXP table.

However, there are some caveats with the radix sort:

• If x is a character vector, all elements must share the same encoding. Only UTF-8 (including ASCII) and Latin-1 encodings are supported. Collation always follows the “C” locale.
• Long vectors (with more than 2^32 elements) and complex vectors are not supported yet.

An there it is: R is doing the right thing for you for the general case. So let us round up the tip: enforce method="radix" for character vectors if you know what you are doing. And, please, do read the docs.