They say that number Pi holds all infinite possibilities, all the words, all the combinations. Well, easier said to be done. So let's put this to a test. This is my translational table. We will do this in the following steps (for the word "eggs"): Encode the word EGGS to numbers. E = 5, G = 7, G = 7 and S = 19. Together concatenated we get the string of 57719. We store a veeery long string of PI number. Start looking in PI number for substring of "57719".

What motivates human behaviour can be captured in the Maslow's hierarchy of needs (source: Wiki). Maslow and psychologists have articulated these needs in a form of a Pyramid, and ever since the concept had been widely adopted (also criticised), and yet, another adaptation is the Pyramid of R needs 😀 Yes, R needs 🙂 What does it take to create and write an useless-useful R function. Well, let's dive into this reluctant nonsense, with a weary potential. The function is pure ggplot based depiction of pyramid. First version is stacked blocks and the second version is polygon in shape of triangle (resembling the pyramid).

This one is named, yes, you guessed it, after Markov chains. 🙂 The babbler is there to connotate the simplicity of useless R function. It's simple calculation of probability of words chaining and drawing the multiple times appeared chained words reminds of markov chain (although this is not it!). The gist is is tokenization of words, counting the appearances and calculating the probabilities.

How is it that in the year 2025 of our Lord installing a Python package is still such a gamble? This post comes from someone that rarely uses Python, but consider the following: the rare times I need ...

This time, I will do an absurd amount of useless conversions 🙂 Some might make some sense - in the long looong run, but most will for sure have none. It all started with coffee 🙂 and the initial question was, how many Italian macchiatos you need to to distribute a litre of milk. Factor in, an Italian macchiato is an espresso and a small amount of steamed milk, usually with milk foam on top.

Managing your tables and data DDL and CRUD using Github actions (or any DevOps tool) is a great way to have your Azure Databricks Unity catalog developed from ground up with DevOps pipelines. And to evolve further catalogs, schemas or tables for robust development As a prerequisite, you will need an Azure Databricks workspace created with SQL warehouse compute available. On top of that, a…

Exploring graphs is always a fun. Attaching the edges and nodes with real examples of psychological effects and accompany them with useless mental shortcuts is beyond fun. This is why we will call it a "cognitive bias" explorer using DAG. Here are the graphs edges and nodes and we are calling them biases and weird links. Because, yes 🙂 Let's mix the math with psychology.

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Starting with the end of January 2025, DeepSeek R1 model has had a huge impact on how the AI community, developers and general public see Generative Models. Not only that is free (as of writing this blog post), but the refinement of the information made it possible, that the models are smaller than original GPT-4 and similar. Benefiting in smaller costs for training, running and improving reasoning.

Ever wanted to have a clock on the wall or in the office, that is not binary. But it is QR-Code clock 🙂 Well, now you can have it. This useless R function generates new QR Code for every given period and tells the time. Run this function: library(qrcode) while (Sys.time()+1 > Sys.time()){ n_o_w <- paste0("Current time is ",as.character(format(Sys.time(), "%X"))) print(n_o_w) qr_code(n_o_w, ecl = "M" ) |> plot() Sys.sleep(10) }

Let’s play with some words. More in particular with vanishing words. Using two packages: ggplot2 and gganimate we will construct a animation (looped), where sentences will be vanishing, word by word. A nice way to train the ggplot animations. The function is combination of data frame wrangling and visualizations: vanishing_sentence <- function(sentence, output_file = NULL, interval = 0.5) { words <- unlist(strsplit(sentence, " ")) vanishing_order <- sample(seq_along(words)) sentence_data <- data.frame( word = words, position = seq_along(words), vanish_step = match(seq_along(words), vanishing_order) ) # sequence animation_data <- do.call(rbind, lapply(1:(max(sentence_data$vanish_step) + 1), function(step) { sentence_data %>% mutate(visible = ifelse(vanish_step >= step, TRUE, FALSE)) %>% group_by(position) %>% summarize(word = ifelse(visible, word, ""), .groups = "drop") %>% mutate(step = step) })) p <- ggplot(animation_data, aes(x = position, y = 1, label = word)) + geom_text(size = 6, hjust = 0.5, vjust = 0.5, fontface = "bold") + theme_void() + theme( plot.margin = margin(1, 1, 1, 1, "cm"), plot.background = element_rect(fill = "white", color = NA) ) + transition_states(step, transition_length = interval,state_length = 1) + enter_fade() + exit_fade() + ease_aes("linear") # render and save if (!is.null(output_file)) { anim <- animate( p,nframes = length(words) + 10, fps = 10,width = 800,height = 400, renderer = gifski_renderer(output_file) ) return(anim) } else { animate( p, nframes = length(words) + 10, fps = 10, width = 800, height = 400 ) } }

Data Community Weekender is a free online conference running on Saturday March, 1st 2025 for the Microsoft Data Platform Community.Our main organizati...

R functions for every data engineer using Tidyverse Tidyverse has long been an amazing collection of R packages, primarily for data engineering and data science. Common among these packages is the same language grammar, great design and structure, making data science easier. Motivation Data engineering is important step that helps improve data usability, data exploration and data science. Preparing the data needs therefore needs to be done in a manner, that is easy to read, repeat and exchange between others engineers.