This sample R code is an implementation one of possible solutions for generating timetable schedule. The proposed solution is based on methods of evolutionary computing, and it uses (1+1) evolutionary strategy and simulated hardening. The success of solution is estimated on fulfillment of given constraints and criteria. Results of testing the algorithm show that all hard constraints are satisfied, while additional criteria are optimized to a certain extent. Problem: Each meeting slot is represented as block (lasts arbitrary number of hours, mostly form 1 to 4). For conducting every block requi ..read more

Let’s play with some dragons. Dragons from the Jurassic park or the board game dungeon and dragons. The algorithm is a fractal curve of Hausdorff dimension 2. One starts with one segment. In each iteration the number of segments is doubled by taking each segment as the diagonal of a square and replacing it by half the square (90 degrees). Alternating and doing the left and right function / direction to complement in order to get the shape. Assume that we start with just a horizontal line (called Dn) as a segment. Since we created two dragons, we will introduce another line (called Cn, which is ..read more

A simple, yet effective way to set your colour palette in R using ggplot library. library(ggplot2) set.seed(2908) my_palette <- c("red", "limegreen", "#3357FF", "goldenrod1", "#33FFFF", "brown") data <- data.frame( x = 1:25, y = rnorm(25), group = rep(c("A", "B", "C", "D", "E"), each = 5) ) After that, we can start “chaining” ggplot graphs. Scatter plot ggplot(data, aes(x = x, y = y, color = group)) + geom_point(size = 3) + scale_color_manual(values = my_palette, na.value = "grey45") + theme_minimal() Barchart / Histogram ggplot(data, aes(x = x, y = y, color = gro ..read more

Calculating cumulative percentage or percentage per group for each time can sometimes be a task with a slight twist. Let’s check this with ggplot2 and tidyverse. library(ggplot2) library(tidyverse) data <- data.frame( sector = rep(1:20, each = 5), item = rep(1:5, times = 20), value = rpois(100, 10) ) Three (out of many more) ways to show how this can be achieved. using simple calculation # using simple calculation ggplot(data, aes(x = factor(sector), y = value / sum(value) * 100, fill = factor(item))) + geom_bar(stat = "identity", position = "fill") + scale_y_con ..read more

You know the feeling after long vacation and finally sitting in front of your favourite UI and even forgot how to write simplest “hello world” or “foo bar” function? Well, we got you covered! The reverse Hello world function is for all the people returning to the office after rather long vacation. Create the function: # reverse Hello World hello_world <- function(print){ if (print == "print"){ print("Hello World") } else { cat("\rWell ...") } } And be confused for a split second, when you want to use this function correctly Welcome back! As always, the complete code is av ..read more

In this Microsoft Fabric series: Dec 01: What is Microsoft Fabric? Dec 02: Getting started with Microsoft Fabric Dec 03: What is lakehouse in Fabric? Dec 04: Delta lake and delta tables in Microsoft Fabric Dec 05: Getting data into lakehouse Dec 06: SQL Analytics endpoint Dec 07: SQL commands in SQL Analytics endpoint Dec 08: Using Lakehouse REST API Dec 09: Building custom environments Dec 10: Creating Job Spark definition Dec 11: Starting data science with Microsoft Fabric Dec 12: Creating data science ..read more

In this Microsoft Fabric series: Dec 01: What is Microsoft Fabric? Dec 02: Getting started with Microsoft Fabric Dec 03: What is lakehouse in Fabric? Dec 04: Delta lake and delta tables in Microsoft Fabric Dec 05: Getting data into lakehouse Dec 06: SQL Analytics endpoint Dec 07: SQL commands in SQL Analytics endpoint Dec 08: Using Lakehouse REST API Dec 09: Building custom environments Dec 10: Creating Job Spark definition Dec 11: Starting data science with Microsoft Fabric Dec 12: Creating data science ..read more

In this Microsoft Fabric series: Dec 01: What is Microsoft Fabric? Dec 02: Getting started with Microsoft Fabric Dec 03: What is lakehouse in Fabric? Dec 04: Delta lake and delta tables in Microsoft Fabric Dec 05: Getting data into lakehouse Dec 06: SQL Analytics endpoint Dec 07: SQL commands in SQL Analytics endpoint Dec 08: Using Lakehouse REST API Dec 09: Building custom environments Dec 10: Creating Job Spark definition Dec 11: Starting data science with Microsoft Fabric Dec 12: Creating data science ..read more

In this Microsoft Fabric series: Dec 01: What is Microsoft Fabric? Dec 02: Getting started with Microsoft Fabric Dec 03: What is lakehouse in Fabric? Dec 04: Delta lake and delta tables in Microsoft Fabric Dec 05: Getting data into lakehouse Dec 06: SQL Analytics endpoint Dec 07: SQL commands in SQL Analytics endpoint Dec 08: Using Lakehouse REST API Dec 09: Building custom environments Dec 10: Creating Job Spark definition Dec 11: Starting data science with Microsoft Fabric Dec 12: Creating data science ..read more

In this Microsoft Fabric series: Dec 01: What is Microsoft Fabric? Dec 02: Getting started with Microsoft Fabric Dec 03: What is lakehouse in Fabric? Dec 04: Delta lake and delta tables in Microsoft Fabric Dec 05: Getting data into lakehouse Dec 06: SQL Analytics endpoint Dec 07: SQL commands in SQL Analytics endpoint Dec 08: Using Lakehouse REST API Dec 09: Building custom environments Dec 10: Creating Job Spark definition Dec 11: Starting data science with Microsoft Fabric Dec 12: Creating data science ..read more