Plot Methods for simmer Monitoring Statistics

Methods for the plot generic. See below for details about each metric available.

# S3 method for arrivals
plot(x, metric = c("activity_time", "waiting_time", "flow_time"), ...)

# S3 method for attributes
plot(x, metric = NULL, keys, ...)

# S3 method for resources
plot(x, metric = c("usage", "utilization"), names, ...)

Arguments

x	a data frame of class arrivals/attributes/resources (see get_mon).
metric	specific metric to compute.
...	further arguments items, for plot.resources(metric="usage") components of the resource to plot, one or more of c("system", "queue", "server"). steps, for plot.resources(metric="usage") if TRUE, shows the instantaneous usage instead of the cumulative average.
keys	attributes to plot (if left empty, all attributes are shown).
names	resources to plot (if left empty, all resources are shown).

Value

Returns a ggplot2 object.

Details

The S3 method for 'arrivals' provides three metrics: "activity_time", "waiting_time", and "flow_time". The "activity_time" is the amount of time spent in active state (i.e., in timeout activities), and it is already provided in the output of get_mon_arrivals. The "flow_time" is the amount of time spent in the system, and it is computed as follows: flow = end_time - start_time. Finally, the "waiting_time" is the amount of time spent waiting (e.g., in resources' queues, or due to a wait activity...), and it is computed as follows: waiting_time = flow_time - activity_time. This method does not apply any summary, but just shows a line plot of the values throughout the simulation.

The S3 method for 'attributes' does not support any metric. It simply shows a stairstep graph of the values throughout the simulation for the keys provided (or all the collected attributes if no key is provided).

The S3 method for 'resources' provides two metrics: "usage" and "utilization". The "usage" metric shows a line graph of the cumulative average resource usage throughout the simulation, for each resource, replication and item (by default, queue, server and system, which is the sum of queue and server). If steps=TRUE, a stairstep graph with the instantaneous values is provided instead. The "utilization" metric shows a bar plot of the average resource utilization (total time in use divided by the total simulation time). For multiple replications, the bar represents the median, and the error bars represent the quartiles. Thus, if a single replication is provided, the bar and the error bar coincide.

Examples

t0 <- trajectory("my trajectory") %>%
  ## add an intake activity
  seize("nurse", 1) %>%
  timeout(function() rnorm(1, 15)) %>%
  release("nurse", 1) %>%
  ## add a consultation activity
  seize("doctor", 1) %>%
  timeout(function() rnorm(1, 20)) %>%
  release("doctor", 1) %>%
  ## add a planning activity
  seize("administration", 1) %>%
  timeout(function() rnorm(1, 5)) %>%
  release("administration", 1)

env <- simmer("SuperDuperSim") %>%
  add_resource("nurse", 1) %>%
  add_resource("doctor", 2) %>%
  add_resource("administration", 1) %>%
  add_generator("patient", t0, function() rnorm(1, 10, 2)) %>%
  run(until=80)

resources <- get_mon_resources(env)
arrivals <- get_mon_arrivals(env)

plot(resources, metric="usage", "doctor", items = "server", steps = TRUE)
plot(resources, metric="utilization", c("nurse", "doctor", "administration"))
plot(arrivals, metric="waiting_time")
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#> Warning: span too small.   fewer data values than degrees of freedom.
#> Warning: pseudoinverse used at 45.221
#> Warning: neighborhood radius 13.017
#> Warning: reciprocal condition number  0
#> Warning: There are other near singularities as well. 210.04
#> Warning: span too small.   fewer data values than degrees of freedom.
#> Warning: pseudoinverse used at 45.221
#> Warning: neighborhood radius 13.017
#> Warning: reciprocal condition number  0
#> Warning: There are other near singularities as well. 210.04
#> Warning: no non-missing arguments to max; returning -Inf