As an impatient person, I typically use progress bars for any code that takes more than a few minutes to run. In a previous post, I wrote about creating ASCII progress bars in R and Racket.
Categorized as "Racket"
Over the last 6 months, I have been learning Racket in my free time. One of my first posts on this blog laid out my reasons for choosing Racket. The relatively low barrier to entry (e.
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.
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.
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.
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).
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
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.
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.
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.
When building a simulation model in R, I might want to group related input parameters into a data structure. For example, in a life cycle model with resident and anadromous fish, you might use different fecundity parameters for each life history type.
I have recently started learning Racket. For a first task, I tried to build a simple age-structured population model. I hit a stumbling block and reached out to the helpful folks on the Racket mailing list.