How-To on The Final Artefact

Building Apache Superset Home Setup

Thu, 30 Apr 2026 00:00:00 +0000

Background

I was recently asked to help, pro bono, with analysing some life science data. From the outset I expected a steady stream of feedback along the lines of can you show this like that, and that prospect pulled me back to my early career days working as a researcher and analyst for various outfits. What those years taught me is that the bottleneck is rarely the analysis itself; it is the loop of sharing a result, hearing how someone would prefer to see it, and turning that around quickly. A small, self-service tool that lets people poke at the data themselves removes most of that friction.

Bring your Python ML Model to iOS App in under Three Minutes

Thu, 24 Jul 2025 00:00:00 +0000

Integrating Python-based machine learning models into iOS applications can be challenging, particularly when converting models into a Swift-compatible format. This example will demonstrate a simple image classification task using the Fashion-MNIST dataset and CoreML conversion tools. The goal is to illustrate the effort required to deploy small-to-medium complexity ML models within iOS applications. The demonstration is based on a Convolutional Neural Network (CNN) built with PyTorch, but the concepts apply broadly to other Python-based models as well.

Automatically Refreshing NVim plugins

Fri, 28 Mar 2025 00:00:00 +0000

One of the key benefits of modern editors like NVim, Vim, or Emacs is the rich plugin ecosystem. After years with Vim, I switched to NVim and was immediately impressed by its plugin landscape. The Lazy plugin manager—available for NVim > 0.8—quickly became my favourite. Lazy simplifies plugin discovery and management. It offers an intuitive interface and powerful commands that make it easy to add, remove, or update plugins.

One Line Docker Commands

Wed, 19 Mar 2025 00:00:00 +0000

Setting up a robust data science development environment takes time, and it’s a process that’s rarely ever finished. If you’re the type who likes to get the most out of your tools, you’ll likely enjoy tweaking, optimising, and layering your workspace with productivity enhancements. That might mean refining your Python setup to easily manage multiple language versions and dependencies, or expanding your text editor with plugins for linting, code suggestions, unit test execution, and CI/CD integration.

Version Control your Dotfiles

Fri, 14 Mar 2025 00:00:00 +0000

What are .dotfiles?

Dotfiles are hidden configuration files on Unix-like systems.
Their filenames start with a dot (.), making them hidden by default.
They store preferences and settings for programs like shells, text editors, and version control systems.

Many modern Linux applications follow the XDG Base Directory Specification.
This guideline recommends placing user-specific configuration files in ~/.config (or $XDG_CONFIG_HOME).
Using this standard reduces clutter in home directories and simplifies managing configurations across systems.

Aggressively formatting your Python files

Sat, 30 Sep 2023 00:00:00 +0000

Vim provides a wide range of functions for file formatting, starting with basic features such as reindent.

VimL Implementation

Creating a function within Vim to process the file is likely the most straightforward approach. The primary purpose of this function is to pass the filename to an external command for formatting. Leveraging the rich ecosystem of Python formatting tools available from the command line allows the function to efficiently and consistently format files, tapping into powerful, pre-existing solutions for code aesthetics and standardization. In effect, the role of the function is to pass the filename to the call below:

Using RScript for R Installation Management

Mon, 03 Jan 2022 00:00:00 +0000

Most frequently, users tend to undertake common R installation and management tasks from within the R session. Frequently making use of commands, like install.packages, update.packages or old.packages to obtain or update packages or update/verify the existing packages. Those common tasks can also be accomplished via the GUI offered within RStudio, which provides an effortless mechanism for undertaking basic package management tasks. This is approach is usually sufficient for the vast majority of cases; however, there are some examples when working within REPL^[REPL stands for Read Eval Print Loop and is usually delivered in a form of an interactive shell. While working in Python users would commonly access REPLY by running python or ipython, more details.] to accomplish common installation tasks is not hugely convenient.

R-based metaprogramming strategies for handling Hive/CSV interaction (Part I, imports)

Fri, 13 Aug 2021 00:00:00 +0000

Background

Handling Hive/CSV interaction is a common reality of many analytical and data environments. The question on exporting data from Hive to CSV and other formats is frequently raised on online forums with answers frequently suggestring making use of sed that combined with nifty regular expressions pipes Hive output into a flat CSV files as an exporting solution. Import of large amounts of data is best handled by suitable tools like Apache Flume. That is fine for simpler tables but may prove problematic for tables with a large amount of unstructured text. Frequently analysts and data scientists are faced with a challenge with storing data Hive on a irregular semi-regular basis. For instance, a job may produce new forecastring scenarios that we may want to make available through a Hive tables.

Using R for File Manipulation

Mon, 29 Mar 2021 00:00:00 +0000

Challenge

File manipulation is a frequent task unavoidable in almost every IT business process. Traditionally, file manipulation tasks are accomplished within the ramifications of specific tools native to a given system. As such, the one may consider writing and scheduling shell scriptt to undertake frequent file operations or using more specific purpose-built tools like logrotate in order to archive logs or tools like Kafka are used to build streaming-data pipelines. R is usually though of as a statistical programming language or as an environment for a statistical analysis. The fact that R is a mature programming language able to successfully accomplish a wide array of traditional tasks is frequently ignored. What constitutes a programming language is a valid question. Wikipedia offers somehow wide definition:

Inserting Data into Partitioned Table

Fri, 26 Feb 2021 00:00:00 +0000

Rationale

Maintaining partitioned Hive tables is a frequent practice in a business. Properly structured tables are conducive to achieving robust performance through speeding up query execution (see Costa, Costa, and Santos 2019). Frequent use cases pertain to creating tables with hierarchical partition structure. In context of a data that is refreshed daily, the frequently utilised partition structure reflects years, months and dates.

Creating partitioned table

In HiveQL we would create the table with the following structure using the syntax below. In order to keep the development tidy, I’m creating a separate database on Hive which I will use for the purpose of creating tables for this article.

Poor Man's Robust Shiny App Deployment (Part II)

Fri, 12 Feb 2021 00:00:00 +0000

Introduction

This article draws on the past post concerned with utilisation of golem for robust deployment of analytical and reporting solutions. For this article, we will assume that we are working with defined working requirements that utilise some of the Labour Market Statistics disseminated through the nomis portal.

Change Plan

What we have

Reporting requirements
Past scriptts we used to create reports with accompanying instructions

What we want

Stronger business continuity - we want to be able to give some access to this project and don’t be concerned with missing files, outdated unavailable documentation and questions on how to produce updated reports. We want self-encompassing entity that takes of care of its technical requirements and user-interaction^[Good parallel can be drawn between this approach and manuals available with life-saving equipment. Equipment delivers technical capacity and manual ensures operational capacity. In case of an inexperienced user one is not useful without the other. We want to ensure that user with minimum required capacity can use the tools correctly.]
Better reproducibility - Easier way to re-run reports on custom parameters
Improved efficiency - We want to have a possibility of quickly creating updated and re-running past reports using the app.
Better development:
- We want to ensure that any change requests to our reporting/analytical stack won’t break crucial functionalities.
- We want to modularise development so new corporate branding or visualisation requirements can be applied with no (or minimal) integration in analytical function

Framework

Package

Future robust development owes a lot to solid foundations. As the aim is to capitalise on the robust R package architecture, we will look to leverage available supporting packages. As a first step, we will construct a new Shiny/R package infrastructure using golem.

Poor Man's Robust Shiny App Deployment

Thu, 23 Jul 2020 00:00:00 +0000

Not so uncommon problem

RStudio Connect and more modest Shiny Proxy come to mind as most obvious solutions for deploying Shiny applications in production. Application servers are ideal for deploying applications that are to be consumed on a regular basis by larger audiences. In addition to serving the application, managing dependencies and user access or logging user activity are common tasks we would expect for a publishing platform to address. Frequently, however, deployment of Shiny application is directed at smaller audiences and less frequent usage. In such a situation, are availability, accessibility and user access management requirements will be often more modest. Commonly,in business a modelling or analytical solution can be packaged in Shiny application facilitating periodical re-run of models with different parameters and updated data sets. Such solutions can be conveniently utilised to facilitated development of monthly or quarterly reports. If the app is used once per month/quarter by a narrow user group the need to deploy it on the server is not well articulated. In that particular case we are mostly interested in ensuring that we can:

Installing Hortonworks Sanbox on Mac with Docker

Sat, 23 Feb 2019 00:00:00 +0000

Background

The post covers installation of Hortonworks Sandbox (HD) on Mac using Docker. In software development, sandbox describes a testing environment that can be used to isolate untested code changes from a production code. Hortonworks Sandbox provides such an environment with the Hortonworks Data Platform installed. Hortonworks Data Platform is an open source framework facilitating distributed storage and processing large volumes of data.

Deploying system for distributed processing within a single computer may seem like a counter-intuitive idea but it’s actually a very common practice. Most frequent use cases involve various learning / professional development activities where one may be interested in learning new technology or simply exploring available interfaces. Other frequent use case pertains to various demos, where there may be a need to demonstrate product capabilities and accessing proper, production environment could be cumbersome.

Interactively Loading Shiny Modules

Sat, 24 Nov 2018 00:00:00 +0000

TL;DR

If you want to see the implemented solution, please refer to: GitHub repo.

Context

Shiny is a widely popular web application framework for a R. In simple tearms it enables any R programmer to develop and deploy web application. This application could be simple - an interactive document consisting of a few charts and tables or a c complex “behemoth” with multiple functionalities enabling end-users to run models, query external data, generate exportable reports and sophisticated visuals.

ASCII charts in R

Fri, 05 Jun 2015 00:00:00 +0000

In Stata it is possible to use function plot in order to get a simple scatter plot in Stata console. As of Stata eight, plot is no longer supported but remains a useful tool for quickly exploring relationships between variables. Using plot on the auto data provides the following results:

Now the question is: can we achieve the same level of convenience in R? Of course. The txtplot package authored by Bjoern Bornkamp provides similar functionality. Executing the code below will generate nice text plot straight in the R console:

Managing rows in the ggplot legend

Sat, 28 Mar 2015 00:00:00 +0000

After developing the Shiny App sourcing live labour market data from NOMIS. I wanted to accommodate a convenient way of managing rows in the legend. In particular, I wanted to account for the situation where end-user may select a number of geographies that will only conveniently fit into two or more rows. After transposing the data to long format, guessing the number of elements in the legend is relatively simple as it will correspond to the number of unique geographies passed via the subset command.

Amusing way to get user input windows in R

Wed, 01 Feb 2012 00:00:00 +0000

In an unlikely scenario that beautiful Shiny apps do not meet your analytical requirements and developing a full-blown user interface. in RGtk2 may seem to be a little too much, there is a third, often overlooked solution, - package svDialogs by Philippe Grosjean. The package in a convenient way enables user to create various interface gadgets. For example the code:

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require(svDialogs)

## Let's keep some data in one place
user_figure <- svDialogs::dlg_input()

would result in the following window being presented to the user: