drop dplyr and tidyr

Author: Enchufa2

DESCRIPTION

@@ -22,7 +22,7 @@ URL: http://r-simmer.org, https://github.com/r-simmer/simmer
 BugReports: https://github.com/r-simmer/simmer/issues
 Depends: R (>= 3.1.2)
 Imports: Rcpp, R6, magrittr, codetools, utils
-Suggests: simmer.plot, parallel, dplyr, tidyr, testthat, knitr, rmarkdown, rticles
+Suggests: simmer.plot, parallel, testthat, knitr, rmarkdown, rticles
 LinkingTo: Rcpp (>= 0.12.9), BH (>= 1.62.0-1)
 ByteCompile: yes
 RoxygenNote: 6.1.1

vignettes/simmer-02-jss.Rmd

@@ -651,13 +651,12 @@ mm1.envs <- mclapply(1:100, function(i) {
 
 With all these replicas, we could, for instance, perform a t-test over $N$, the average number of customers in the system:
 
-```{r, mm1-replication-test, eval=requireNamespace("dplyr", quietly=TRUE)}
+```{r, mm1-replication-test}
 mm1.data <-
   get_mon_arrivals(mm1.envs) %>%
-  dplyr::group_by(replication) %>%
-  dplyr::summarise(mean = mean(end_time - start_time))
+  aggregate(end_time - start_time ~ replication, data=., mean)
 
-t.test(mm1.data[["mean"]])
+t.test(mm1.data[[2]])
 ```
 
 ## Best practices

vignettes/simmer-04-bank-1.Rmd

@@ -14,11 +14,6 @@ vignette: >
 ```{r, cache = FALSE, include=FALSE}
 knitr::opts_chunk$set(collapse = T, comment = "#>",
                       fig.width = 6, fig.height = 4, fig.align = "center")
-
-required <- c("dplyr")
-
-if (!all(sapply(required, requireNamespace, quietly = TRUE)))
-  knitr::opts_chunk$set(eval = FALSE)
 ```
 
 ## Introduction
@@ -64,7 +59,7 @@ bank <-
   add_generator("Customer", customer, at(5))
 
 bank %>% run(until = 100)
-bank %>% get_mon_arrivals
+bank %>% get_mon_arrivals()
 ```
 
 The short trace printed out by the `get_mon_arrivals` function shows the result.
@@ -99,7 +94,7 @@ bank <-
   add_generator("Customer", customer, at(rexp(1, 1/5)))
 
 bank %>% run(until = 100)
-bank %>% get_mon_arrivals
+bank %>% get_mon_arrivals()
 ```
 
 The trace shows that the customer now arrives at time 7.839305. Changing the
@@ -191,7 +186,7 @@ bank <-
   add_generator("Customer", customer, at(2, 5, 12))
 
 bank %>% run(until = 400)
-bank %>% get_mon_arrivals
+bank %>% get_mon_arrivals()
 ```
 
 Alternatively, we can create three different customer trajectories for the three
@@ -233,7 +228,7 @@ bank <-
   add_generator("Evelyn", Evelyn, at(12))
 
 bank %>% run(until = 400)
-bank %>% get_mon_arrivals
+bank %>% get_mon_arrivals()
 ```
 
 Again, the simulations finish before the 400 specified in the `run` function.
@@ -265,7 +260,7 @@ bank <-
   add_generator("Customer", customer, from_to(0, 41, function() {10}))
 
 bank %>% run(until = 400)
-bank %>% get_mon_arrivals
+bank %>% get_mon_arrivals()
 ```
 
 ### Many random customers
@@ -305,7 +300,7 @@ bank <-
   add_generator("Customer", customer, function() {c(0, rexp(4, 1/10), -1)})
 
 bank %>% run(until = 400)
-bank %>% get_mon_arrivals
+bank %>% get_mon_arrivals()
 ```
 
 ## A Service counter
@@ -336,7 +331,7 @@ step is complete, the `release` function causes the customer to make the counter
 available to other customers in the queue.
 
 Since the activity trace does not produce the waiting time by default, this is
-calculated and appended using the `mutate` function in the dplyr package.
+calculated and appended using the `transform` function.
 
 ```{r, message = FALSE}
 library(simmer)
@@ -362,8 +357,8 @@ bank <-
 
 bank %>% run(until = 400)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 Examining the trace we see that the first two customers get instant service but
@@ -404,8 +399,8 @@ bank <-
 
 bank %>% run(until = 400)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 This model with random arrivals and exponential service times is an example of
@@ -453,8 +448,8 @@ bank <-
 
 bank %>% run(until = 400)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 The waiting times in this model are much shorter than those for the single
@@ -499,12 +494,12 @@ bank <-
 
 bank %>% run(until = 400)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(service_start_time = end_time - activity_time) %>%
-  dplyr::arrange(start_time)
+  get_mon_arrivals() %>%
+  transform(service_start_time = end_time - activity_time) %>%
+  .[order(.$start_time),]
 bank %>%
-  get_mon_resources %>%
-  dplyr::arrange(time)
+  get_mon_resources() %>%
+  .[order(.$time),]
 ```
 
 The results show that the customers chose the counter with the smallest number.
@@ -519,7 +514,7 @@ We now demonstrate how to calculate average waiting times for our customers.  In
 the original SimPy version of this tutorial, this involved using 'Monitors'.  In
 simmer, data is returned by the `get_mon_*` family of functions, as has already
 been demonstrated.  Here, we simply summarise the data frame returned by the
-`get_mon_arrivals` function, using functions from the `dplyr` package.
+`get_mon_arrivals` function, using standard R functions.
 
 We also increase the number of customers to 50 (find the number '49' in the
 code.code).
@@ -544,8 +539,8 @@ bank %>% run(until = 1000)
 
 result <-
   bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 The average waiting time for 50 customers in this 2-counter system is more
@@ -594,8 +589,8 @@ mclapply(c(393943, 100005, 777999555, 319999772), function(the_seed) {
   bank %>% run(until = 400)
   result <-
     bank %>%
-    get_mon_arrivals %>%
-    dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+    get_mon_arrivals() %>%
+    transform(waiting_time = end_time - start_time - activity_time)
   paste("Average wait for ", sum(result$finished), " completions was ",
         mean(result$waiting_time), "minutes.")
 }) %>% unlist()

vignettes/simmer-04-bank-2.Rmd

@@ -15,7 +15,7 @@ vignette: >
 knitr::opts_chunk$set(collapse = T, comment = "#>",
                       fig.width = 6, fig.height = 4, fig.align = "center")
 
-required <- c("simmer.plot", "dplyr")
+required <- c("simmer.plot")
 
 if (!all(sapply(required, requireNamespace, quietly = TRUE)))
   knitr::opts_chunk$set(eval = FALSE)
@@ -55,7 +55,7 @@ priority of 0, we create and activate one special customer, Guido, with priority
 1 who arrives at time 23. This is to ensure that he arrives after Customer2.
 
 Since the activity trace does not produce the waiting time by default, this is
-calculated and appended using the `mutate` function in the dplyr package.
+calculated and appended using the `transform` function.
 
 ```{r, message = FALSE}
 library(simmer)
@@ -84,8 +84,8 @@ bank <-
 
 bank %>% run(until = 400)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 The output above displays the number of customers in the queue just as each one
@@ -131,8 +131,8 @@ bank <-
 
 bank %>% run(until = 400)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 Though Guido arrives at the same time, 23, he no longer has to wait and
@@ -380,16 +380,11 @@ bank <- simmer()
 
 customer <-
   trajectory("Customer's path") %>%
-  log_(function() {
-         capacity <-
-           get_mon_resources(bank) %>%
-           dplyr::filter(resource == "door") %>%
-           .$capacity
-         if (length(capacity) == 0 || tail(capacity, 1) == 0) {
-           return("Here I am but the door is shut.")
-         }
-         "Here I am and the door is open."
-       }) %>%
+  log_(function()
+    if (get_capacity(bank, "door") == 0)
+      "Here I am but the door is shut."
+    else "Here I am and the door is open."
+  ) %>%
   seize("door") %>%
   log_("I can go in!") %>%
   release("door") %>%
@@ -417,8 +412,8 @@ bank <-
 
 bank %>% run(until = maxTime)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 The output above programs shows how the first two customers have to wait until
@@ -482,8 +477,8 @@ bank %>%
 
 bank %>% run(until = maxTime)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 The output run for this program shows how the first two customers have to wait
@@ -535,8 +530,8 @@ bank %>%
 
 bank %>% run(until = maxTime)
 bank %>%
-  get_mon_arrivals %>%
-  dplyr::mutate(waiting_time = end_time - start_time - activity_time)
+  get_mon_arrivals() %>%
+  transform(waiting_time = end_time - start_time - activity_time)
 ```
 
 This second method gives the same output as the first.
@@ -641,7 +636,7 @@ bank <-
 bank %>% run(until = 400)
 customer_monitor <-
   get_mon_arrivals(bank) %>%
-  dplyr::mutate(wait = end_time - start_time - activity_time)
+  transform(wait = end_time - start_time - activity_time)
 mean_waiting_time <- mean(customer_monitor$wait)
 
 resource_monitor <- get_mon_resources(bank)

vignettes/simmer-05-simpy.Rmd

@@ -14,11 +14,6 @@ vignette: >
 ```{r, cache = FALSE, include=FALSE}
 knitr::opts_chunk$set(collapse = T, comment = "#>",
                       fig.width = 6, fig.height = 4, fig.align = "center")
-
-required <- c("dplyr")
-
-if (!all(sapply(required, requireNamespace, quietly = TRUE)))
-  knitr::opts_chunk$set(eval = FALSE)
 ```
 
 ## Introduction
@@ -118,7 +113,6 @@ The `make_parts` trajectory defines a machine's operating loop. A worker seizes
 ```{r, message=FALSE, warning=FALSE}
 make_parts <- function(machine)
   trajectory() %>%
-    set_attribute("parts", 0) %>%
     seize(machine, 1) %>%
     timeout(function() rnorm(1, PT_MEAN, PT_SIGMA)) %>%
     set_attribute("parts", 1, mod="+") %>%
@@ -178,10 +172,7 @@ env %>%
 The last value per worker from the attributes table reports the number of parts made:
 
 ```{r, message=FALSE, warning=FALSE}
-get_mon_attributes(env) %>%
-  dplyr::group_by(name) %>%
-  dplyr::slice(n()) %>%
-  dplyr::arrange(name)
+aggregate(value ~ name, get_mon_attributes(env), max)
 ```
 
 ## Movie Renege
@@ -287,28 +278,25 @@ env %>%
   run(SIM_TIME)
 ```
 
-The analysis is performed with the help of `dplyr`:
+The analysis is performed with standard R tools:
 
 ```{r, message=FALSE, warning=FALSE}
 # get the three rows with the sold out instants
 sold_time <- get_mon_resources(env) %>%
-  dplyr::filter(resource != "counter" & capacity == 0)
+  subset(resource != "counter" & capacity == 0)
 
 # get the arrivals that left at the sold out instants
 # count the number of arrivals per movie
-n_reneges <- dplyr::left_join(
-  get_mon_arrivals(env) %>%
-    dplyr::filter(finished == FALSE & end_time %in% sold_time$time),
-  get_mon_attributes(env)
-) %>%
-  dplyr::mutate(resource = movies[value]) %>%
-  dplyr::group_by(resource) %>%
-  dplyr::count()
+n_reneges <- get_mon_arrivals(env) %>%
+  subset(finished == FALSE & end_time %in% sold_time$time) %>%
+  merge(get_mon_attributes(env)) %>%
+  transform(resource = movies[value]) %>%
+  aggregate(value ~ resource, data=., length)
 
 # merge the info  and print
-invisible(apply(dplyr::left_join(sold_time, n_reneges), 1, function(i) {
+invisible(apply(merge(sold_time, n_reneges), 1, function(i) {
   cat("Movie '", i["resource"], "' was sold out in ", i["time"], " minutes.\n", 
-      "  Number of people that left the queue: ", i["n"], "\n", sep="")
+      "  Number of people that left the queue: ", i["value"], "\n", sep="")
 }))
 ```