Autor: Iñaki Úcar

In physics, engineering and other disciplines, a single number, a bare quantity, is useless, meaningless. When you measure something, your results will be within the precision of your equipment, and then you need to propagate those errors up to whatever final indirect measure you are interested in. Finally, you need to express it properly. For instance, the elementary charge:

library(errors)

e <- set_errors(1.6021766208e-19, 0.0000000098e-19)
print(e, digits=2, notation="plus-minus")

## (1.6021766208 +/- 0.0000000098)e-19

print(e, digits=2, notation="parenthesis")

## 1.6021766208(98)e-19

Propagation of uncertainty (or propagation of error) is easy, but too labourious. Lately, for various reasons, I have been growing sick of this task, so I decided to write this small package. In a nutshell, the errors package, which is already on CRAN, provides support for painless automatic error propagation in numerical operations and pretty printing.

With errors, you can add errors to your numeric vectors:

x <- 1:10
errors(x) <- 0.1
x

## errors: 0.1 0.1 0.1 0.1 0.1 ...
##  [1]  1  2  3  4  5  6  7  8  9 10

errors(x)

##  [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

errors(x) <- seq(0.1, 1, 0.1)
errors(x)

##  [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

The set_errors() method is a pipe-friendly version of the above:

(x <- set_errors(1:10, seq(0.1, 1, 0.1)))

## errors: 0.1 0.2 0.3 0.4 0.5 ...
##  [1]  1  2  3  4  5  6  7  8  9 10

Then, you simply work with your quantities without having to worry about errors anymore:

df <- as.data.frame(x)

(df$`3x` <- 3*x)

## errors: 0.3 0.6 0.9 1.2 1.5 ...
##  [1]  3  6  9 12 15 18 21 24 27 30

(df$`x^2` <- x^2)

## errors: 0.2 0.8 1.8 3.2 5 ...
##  [1]   1   4   9  16  25  36  49  64  81 100

(df$`sin(x)` <- sin(x))

## errors: 0.054030230586814 0.0832293673094285 0.296997748980134 0.261457448345445 0.141831092731613 ...
##  [1]  0.8414710  0.9092974  0.1411200 -0.7568025 -0.9589243 -0.2794155
##  [7]  0.6569866  0.9893582  0.4121185 -0.5440211

(df$`cumsum(x)` <- cumsum(x))

## errors: 0.1 0.223606797749979 0.374165738677394 0.547722557505166 0.741619848709566 ...
##  [1]  1  3  6 10 15 21 28 36 45 55

Finally, you probably need to report your data in a consistent way. No problem: just print your table.

df

##         x     3x     x^2  sin(x) cumsum(x)
## 1  1.0(1) 3.0(3)  1.0(2) 0.84(5)    1.0(1)
## 2  2.0(2) 6.0(6)  4.0(8) 0.91(8)    3.0(2)
## 3  3.0(3) 9.0(9)    9(2)  0.1(3)    6.0(4)
## 4  4.0(4)  12(1)   16(3) -0.8(3)   10.0(5)
## 5  5.0(5)  15(2)   25(5) -1.0(1)   15.0(7)
## 6  6.0(6)  18(2)   36(7) -0.3(6)  21.0(10)
## 7  7.0(7)  21(2)  49(10)  0.7(5)     28(1)
## 8  8.0(8)  24(2)  60(10)  1.0(1)     36(1)
## 9  9.0(9)  27(3)  80(20)  0.4(8)     45(2)
## 10  10(1)  30(3) 100(20) -0.5(8)     55(2)

By default, errors uses parenthesis notation (which is more compact) and a single significant digit for errors. If you prefer the plus-minus notation or you need more significant digits, just pass the notation and digits arguments to the print() method, as in the first example, or set them as global options with options(errors.notation="plus-minus", errors.digits=2).

The inner workings of this package have been inspired by Edzer Pebesma and his excellent units package. As a next step, I envision numeric vectors with errors and units for R. Thus, I publicly invite Edzer to collaborate with me in making our packages work together.

A new patch release of simmer, the Discrete-Event Simulator for R, is on CRAN. Three months have passed since the last release. The last year was a period of intense development (one release per month). Now, the package has reached some level of maturity, so we intend to extend the release cycle.

In this maintenance release, the replacement operators for trajectories ([<-, [[<-) now work as expected. Also, we have removed previously deprecated plotting capabilities, which are covered and extended by the simmer.plot package.

Last but not least, we have extended the from_to() convenience function with a parameter every, which enables the generation of arrivals in cycles. For instance, let us suppose we want to simulate different patient arrival rates in the morning, evening and night:

library(simmer)
library(simmer.plot)

## Loading required package: ggplot2

set.seed(1234)

# units are hours
# visit time between 10 and 20 minutes
patient <- trajectory() %>%
  seize("doctor", 1) %>%
  timeout(function() runif(1, 10/60, 20/60)) %>%
  release("doctor", 1)

morning <- from_to(start_time = 8,
                   stop_time = 16,
                   dist = function() rexp(1, 60/15),
                   arrive = FALSE, 
                   every = 24)

evening <- from_to(start_time = 16,
                   stop_time = 24,
                   dist = function() rexp(1, 60/30),
                   arrive = FALSE, 
                   every = 24)

night   <- from_to(start_time = 0,
                   stop_time = 8,
                   dist = function() rexp(1, 60/60),
                   arrive = FALSE, 
                   every = 24)

env <- simmer() %>%
  add_resource("doctor", 1) %>%
  add_generator("morning", patient, morning) %>%
  add_generator("evening", patient, evening) %>%
  add_generator("night",   patient, night) %>%
  run(24 * 5) # simulate 5 days

breaks <- c(0, cumsum(rep(8, 3 * 5)))

env %>%
  get_mon_arrivals() %>%
  dplyr::mutate(generator = factor(gsub("\\d", "", name))) %>%
  ggplot(aes(start_time, fill=generator)) + xlab("time") +
  stat_bin(breaks = breaks) +
  scale_x_continuous(breaks = breaks)

plot(env, what="resources", metric="usage", "doctor", steps=TRUE) +
  scale_x_continuous(breaks = breaks)

Minor changes and fixes:

• Recycle logical indexes when subsetting (2526e75).
• Implement replacement operators, [<- and [[<- (#88).
• Provide rep() S3 method for trajectories (7fa515e).
• Remove plotting functions (bb9656b), deprecated since v3.6.0. The new simmer.plot package (on CRAN) already covers these features among others.
• Don’t evaluate vignette chunks if Suggests are not installed (e40e5b6).
• Rewrite DESCRIPTION (3f26516).
• Add an every parameter to the from_to() convenience function (9d68887).

Dirk Eddelbuettel quite rightly reminded us the other day that Suggests is not Depends. I am sorry to say that I am one of those who are using Suggests… “casually”. Mea culpa. I must say that this is restricted to vignettes: there are no tests nor examples using suggested packages. But I am not checking if my suggested packages are available at all, which is definitely wrong. And I understand that it must be frustrating to run reverse dependencies on a package as popular as Rcpp when the rest of us are using Suggests so… casually. So I was definitely determined to solve this, and I finally managed to find a very simple solution that may be helpful to other maintainers.

Our simmer package has seven vignettes. Two of them are very introductory and do not use any external package. But as you try to demonstrate more advanced features and use cases, you start needing some other tools; and their use could be intensive, so that checking suggested packages for every call or every code chunk might not scale. However, I realised that the important thing for those advanced vignettes is just to make the story they tell available to your users, and anyway they are always built and available online on CRAN. Therefore, I decided to add the following at the beginning of each vignette:

required <- c("pkg1", "pkg2", "pkgn")

if (!all(sapply(required, requireNamespace, quietly = TRUE)))
  knitr::opts_chunk$set(eval = FALSE)

Problem solved. Yes, I know, I am still taking knitr for granted. But given that it has its own entry (VignetteBuilder) in the DESCRIPTION, I think this is fair enough. I only hope that Dirk will unblacklist simmer after our next release. ;-)