Izolacja w zespole i co z tego wynika

Andrzej Blikle w książce “Doktryna jakości” (dostępna do pobrania za darmo ze strony Autora) podaje przykłady organizacji, firm, instytucji, w których zarządzanie za pomocą “kija i marchewki”, zamiast spodziewanego wzrostu wydajności, przyniosło szkody. Powodów ku temu jest wiele, lecz wszystkie mają wspólny mianownik: w tak “ustawionym” systemie jego uczestnicy zatracają poczucie przynależności, współodpowiedzialności za firmę, odpowiedzialności

Michał Łuczewski: After the plane crash in Smolensk we saw the most primitive instincts turn up in the Polish society

Michał Płociński interviews Dr Michał Łuczewski Plus Minus: Just after the 10th of April 2010 it seemed that the Polish nation was united for a while. It is often mentioned that after the tragedy in Smolensk there was national mourning, silence of disputes, that conciliatory gestures appeared. Did it actually happen or is it only our

Deferred & Remote Function Execution in R

What would you say if you could automatically wrap your R pipeline – which consists of numerous functions and variables – into a single function? What would you say if you could do it repeatedly, with no extra effort regardless of its complexity? Would you store it to document your progress at that particular moment in

Inspecting R in GDB (with Python)

Today I spent a few hours debugging a hanging R process that left a zombie sh which so far suggests bug (race condition?) in R’s system2() call. Anyway, it soon turned out that the only way to see what’s happening with R is to use gdb, which I personally dread. It is so because I haven’t found

(A Very) Experimental Threading in R

I’ve been trying to find a way to introduce threads to R. I guess there can be many reasons to do that, among which I could mention simplified input/output logic, sending tasks to the background (e.g. building a model asynchronously), running computation-intensive tasks in parallel (e.g. parallel, chunk-wise var() on a large vector). Finally, it’s

A new `subprocess` package for R

Here’s a new package that brings to R new API to handle child processes – similar to how Python handles them. Unlike the already available system() and system2() calls from the base package or the mclapply() function from the parallel package, this new API is aimed at handling long-lived child processes that can be controlled by the parent R process in a

Mt. Rainier

Pod koniec października, korzystając z ostatnich w tym roku dni bez deszczu, wybraliśmy się do Parku Narodowego Mt. Rainier. Sama góra to wciąż czynny wulkan (chyba też dość niebezpieczny…). Na szczęście tym razem spał i tylko swoimi rozmiarami przypominał, kto tak naprawdę jest tu górą. Park jest olbrzymi! Piesza trasa wokół wulkanu to jakieś 5 dni

Key to success (in software development)

I’ve been trying to identify the few basic ingredients that make a software product successful and I think I have the three points your future package cannot go without. These would be: some domain knowledge that will help you identify and address a real problem someone working in that domain has to face interface that

Subprocess in R?

I’ve been trying to find a R equivalent to Python’s subprocess and so far I’ve failed. Since a capability to handle child processes in a way that’s more sophisticated than a simple system() call (R has two of them, system() and system2()) might turn out handy I decided to build a new package for R. It’s name is