In a previous post, I used GUI toolkits to make progress bars in R and Racket. However, I usually prefer the ASCII progress bars of the progress package in R. The progress package includes several options for formatting the progress bar. I particularly like the option to display the estimated time remaining. However, for this post, we will stick to the basic progress bar.
library(progress) pb <- progress_bar$new(total = 100) for (i in 1:100) { pb$tick() Sys.
As an impatient person and an insecure programmer, I typically use progress bars for any code that takes more than a few minutes to run. In R, a progress bar widget is available through the tcltk package.
library(tcltk) pb <- tkProgressBar(title = "Progress Bar", label = "0%", max = 100) for(i in 1:100) { Sys.sleep(0.05) setTkProgressBar(pb, value = i, label = paste0(i, "%")) } close(pb) In this code, we initialize a progress bar and assign it to pb.
In learning about reading CSV files in Racket, I have started to reconsider whether storing small(ish) datasets in CSV files is the best default behavior.1 My default was set by primarily working in R, where reading and writing CSV files plays a central role in data analysis. When working solely in R, I expect that my old habits will die hard and CSV files will continue to play a prominent role.
In a previous post, I wrote about reading and writing data to file while retaining the structure and attributes of the data (i.e., data serialization). However, I more commonly pass data around as text files (usually, CSV files). For this post, I created an example CSV file with a tool for generating test data, which allows for including different data types (e.g., dates, integers, names, phone numbers) in the output file.
One of the areas of expertise at Cramer Fish Sciences is watershed and habitat restoration. In the context of that work, we are often faced with estimating how much rearing habitat is needed to support a specified number of juvenile salmonids (or how many juvenile salmonids are supported by a specified amount of rearing habitat). Our typical approach for generating these estimates is to use the territory size-fork length relationship from Grant and Kramer (1990).
In a previous post, I wrote a function to perform repeated timings of untyped and typed versions of the same Racket functions.
#lang racket (require math) (define (time-apply-cpu-old proc lst reps) (define out (for/list ([i (in-range reps)]) (define-values (results cpu-time real-time gc-time) (time-apply proc lst)) cpu-time)) (displayln (string-append "min: " (number->string (apply min out)) " mean: " (number->string (round (mean out))) " max: " (number->string (apply max out)) " function: " (symbol->string (object-name proc))))) time-apply-cpu-old is a wrapper function for time-apply from racket/base that runs time-apply repeatedly and prints the min, mean, and max cpu time.
In a previous post, I wrote that Racket’s if is similar to ifelse in R. That’s not quite right. This is a short post to clarify the comparison. The more accurate description is that this Racket code
(if (test-expr) true-expr false-expr) is the same as this R code.
if (test_expr){ true_expr }else{ false_expr } In contrast, R’s ifelse function is vectorized meaning that the same operation is applied to multiple elements of a vector.
On my journey to learn Racket, I look for small pieces of R code to try to implement in Racket. A blog post about speeding up population simulations in R with the Rcpp package seemed like a good candidate for implementing in Racket. I was particularly interested in the (superficial?) parallels between R/Rcpp and Racket/Typed Racket.
Running a single simulation First, let’s get some of the setup code1 out of the way.
R makes it easy to generate random numbers from a wide variety of distributions with a consistent interface. For example, runif, rnorm, and rpois generate random numbers from uniform, normal, and Poisson distributions, respectively.
> x = runif(n = 1000, min = 4.6, max = 9.3) > min(x) [1] 4.60374 > max(x) [1] 9.288063 > > y = rnorm(n = 1000, mean = 81, sd = 9) > mean(y) [1] 81.
When programming in R, I generally pass data around by reading and writing text files (typically, CSV files). The ubiquity of CSV files means that many different types of software will open them easily (e.g., Notepad, Excel, TextEdit, etc.). However, if the data structure is not flat or contains other attributes, then writing to CSV requires flattening and/or dropping attributes. The general solution to writing data to a file while retaining structure and attributes is serialization.