Technology Tales

Adventures & experiences in contemporary technology

A look at the Julia programming language

19th November 2022

Several open-source computing languages get mentioned when talking about working with data. Among these are R and Python but there are others and Julia is another one of these. It took a while before I got to check out Julia because I felt the need to get acquainted with R and Python beforehand. There are others like Lua to investigate too but that can wait for now.

With the way that R is making an incursion into clinical data reporting analysis following the passage of decades when SAS was predominant, my explorations of Julia are inspired by a certain contrariness on my part. Alongside some small personal projects, there has been some reading in (digital) book form and online. Concerning the latter of these, there are useful tutorials like Introduction to Datascience: Learn Julia Programming, Math & Datascience from Scratch or Julia Programming: a Hands-on Tutorial. Like what happens with R, there are online versions of published books available free of charge and they include Julia Data Science and Interactive Visualization and Plotting with Julia. Video learning can help too and Jane Herriman has recorded and shared useful beginner’s guides on YouTube that start with the basics before heading onto more advanced subjects like multiple dispatch, broadcasting and metaprogramming.

This piece of learning has been made of simple self-inspired puzzles before moving on to anything more complex. That differs from my dalliance with R and Python where I ventured into complexity first, not least because of testing them out with public COVID data. Eventually, I got around to doing that with Julia too though my interest was beginning to wane by then, and Julia’s abilities for creating multi-page PDF files were such that PDF Toolkit was needed to help with this. Along the way, I have made use of such packages as DataFrames.jl, Plots, Gadfly.jl and JSON3.jl, among others. After that, there is PrettyTables.jl to try out and anyone can look at the Beautiful Makie website to see what Makie can do.

So far, my primary usage has been with personal financial data together with automated processing and backup of photo files. The photo file processing has taken advantage of the ability to compile Julia scripts for added speed because just-in-time compilation always means there is a lag before the real work begins.

VSCode is my chosen editor for working with Julia scripts since it has a plugin for the language. That adds the REPL, syntax highlighting, execution and data frame viewing capabilities that once were added to the now defunct Atom editor by its own plugin. While it would be nice to have a keyboard shortcut for script execution, the whole thing works well and is regularly updated.

Naturally, there have been a load of queries as I have gone along and the Julia Documentation has been consulted as well as Julia Discourse and Stack Overflow. The latter pair have become regular landing spots on many a Google search. One example followed a glitch that I encountered after a Julia upgrade when I asked a question about this and was directed to the XLSX.jl Migration Guides where I got the information that I needed to fix my code for it to run properly.

There is more learning to do as I continue to use Julia for various things. Once compiled, it does run fast like it has been promised. The syntax paradigm is akin to R and Python but there are Julia-specific features too. If you have used the others, the learning curve is lessened but not eliminated completely. This is not an object-oriented language as such but its functional nature makes it familiar enough for getting going with it. In short, the project has come a long way since it started more than ten years ago. There is much for the scientific programmer but only time will tell if it usurped its older competitors. For now, I will remain interested in it.

Redirecting a WordPress site to its home page when its loop finds no posts

5th November 2022

Since I created a bespoke theme for this site, I have been tweaking things as I go. The basis came from the WordPress Theme Developer Handbook, which gave me a simpler starting point shorn of all sorts of complexity that is encountered with other themes. Naturally, this means that there are little rough edges that need tidying over time.

One of these is dealing with errors on the site like when content is not found. This could be a wrong address or a search query that finds no matching posts. When that happens, there is a redirection to the home page using some simple JavaScript within the loop fallback code enclosed within script start and end tags (including the whole code triggers the action from this post so it cannot be shown here):

location.href="[blog home page ]";

The bloginfo function can be used with the url keyword to find the home page so this does not get hard coded. For now, this works so long as JavaScript is enabled but a more robust approach may come in time. It is not possible to do a PHP redirect because of the nature of HTTP: when headers have been sent, it is not possible to do server redirects. At this stage, things become client side so using JavaScript is one way to go instead.

Removing a Julia package

5th October 2022

While I have been programming with SAS for a few decades and it remains a lynchpin in the world of clinical development in the pharmaceutical industry, other technologies like R and Python are gaining a foothold. Two years ago, I started to look at those languages with personal projects being a great way of facilitating this. In addition, I got to hear of Julia and got to try that too. That journey continues since I have put it into use for importing and backing up photos, and there are other possible uses too.

Recently, I updated Julia to version 1.8.2 but ran into a problem with the DataArrays package that I had installed so I decided to remove it since it was added during experimentation. The Pkg package that is used for package management is documented but I had not gotten to that so some web searching ensued. It turns out that there are two ways of doing this. One uses the REPL: after pressing the ] key, the following command gets issued:

rm DataArrays

When all is done, pressing the delete or backspace keys returns things to normal. This also can be done in a script as well as the REPL and the following line works in both instances:

using Pkg; Pkg.rm("DataArrays")

The semicolon is used to separate two commands issued in the same line but they can be on different lines or issued separately just as well. Naturally, DataArrays is just an example here so you just replace that with the name of whatever other package you need to remove. Since we can get carried away when downloading packages, there are times when a clean-up is needed to remove redundant packages so knowing how to remove any clutter is invaluable.

Accessing Julia REPL command history

4th October 2022

In the BASH shell used on Linux and UNIX, the history command calls up a list of recent commands used and has many uses. There is a .bash_history file in the root of the user folder that logs and provides all this information so there are times when you need to exclude some commands from there but that is another story.

The Julia REPL environment works similarly to many operating system command line interfaces, so I wondered if there was a way to recall or refer to the history of commands issued. So far, I have not come across an equivalent to the BASH history command for the REPL itself but there the command history is retained in a file like .bash_history. The location varies on different operating systems though. On Linux, it is ~/.julia/logs/repl_history.jl while it is %USERPROFILE%\.julia\logs\repl_history.jl on Windows. While I tend to use scripts that I have written in VSCode rather than entering pieces of code in the REPL, the history retains its uses and I am sharing it here for others. In the past, the location changed but these are the ones with Julia 1.8.2, the version that I have at the time of writing.

Changing the Ansible Vault editor from Vi to Nano

15th August 2022

Recently, I got to experimenting with Ansible after reading about the orchestration tool in a copy of Admin magazine. It came in handy for updating a few web servers that I have as well as updating my main Linux workstation. For the former, automated entry of SSH passwords sufficed but the same did not apply for sudo usage on my local machine. This meant that I needed to use Ansible Vault to store the administrator password and doing so opened up a file in the Vi editor. since I am not familiar with Vi and wanted to get things sorted quickly, I fancied using something more user-friendly like Nano.

Doing this meant adding the following line to .bashrc:

export EDITOR=nano

Saving and closing the file followed by reloading the session set me up for what was needed.

Getting custom Python imports to work in Visual Studio Code

18th February 2022

While I continue to use Spyder as my preferred Python code editor, I also tried out Visual Studio Code. Handily, this Integrated Development Environment also has facilities for working with R and Julia code as well as MarkDown text editing and adding the required extensions is enough for these applications; it helps that there is an unofficial Grammarly extension for content creation.

My Python code development makes use of the Pylance extension and it works a little differently from Spyder when it comes to including files using import statements. Spyder will look into the folder where the base script is located but the default behaviour of Pylance is that it looks in the root path of your workspace. This meant that any code that ran successfully in Spyder failed in Visual Studio Code.

The way around this was to add the required location using the python.analysis.extraPaths setting for the workspace. That meant opening Settings by navigating to File > Preferences > Settings in the menu system and entering python.analysis.extraPaths into the search box. That took me to the section that I needed and I then clicked on Add Item before entering the required path and clicking on the OK button. That was enough to fix the problem and all worked as it should after that.

Useful Python packages for working with data

14th October 2021

My response to changes in the technology stack used in clinical research is to develop some familiarity with programming and scripting platforms that complement and compete with SAS, a system with which I have been programming since 2000. One of these has been R but Python is another that has taken up my attention and I now also have Julia in my sights as well. There may be others to assess in the fullness of time.

While I first started to explore the Data Science world in the autumn of 2017, it was in the autumn of 2019 that I began to complete LinkedIn training courses on the subject. Good though they were, I find that I need to actually use a tool in order to better understand it. At that time, I did get to hear about Python packages like Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn and Beautiful Soup  though it took until of spring of this year for me to start gaining some hands-on experience with using any of these.

During the summer of 2020, I attended a BCS webinar on the CodeGrades initiative, a programming mentoring scheme inspired by the way classical musicianship is assessed. In fact, one of the main progenitors is a trained classical musician and teacher of classical music who turned to Python programming when starting a family so as to have a more stable income. The approach is that a student selects a project and works their way through it with mentoring and periodic assessments carried out in a gentle and discursive manner. Of course, the project has to be engaging for the learning experience to stay the course and that point came through in the webinar.

That is one lesson that resonates with me with subjects as diverse as web server performance and the ongoing pandemic pandemic supplying data and there are other sources of public data to examine as well before looking through my own personal archive gathered over the decades. Some subjects are uplifting while others are more foreboding but the key thing is that they sustain interest and offer opportunities for new learning. Without being able to dream up new things to try, my knowledge of R and Python would not be as extensive as it is and I hope that it will help with learning Julia too.

In the main, my own learning has been a solo effort with consultation of documentation along with web searches that have brought me to the likes of Real Python, Stack Abuse, Data Viz with Python and R and others for longer tutorials as well as threads on Stack Overflow. Usually, the web searching begins when I need a steer on a particular or a way to resolve a particular error or warning message but books always are worth reading even if that is the slower route. Those from the Dummies series or from O’Reilly have proved must useful so far but I do need to read them more completely than I already have; it is all too tempting to go with the try the “programming and search for solutions as you go” approach instead.

To get going, many choose the Anaconda distribution to get Jupyter notebook functionality but I prefer a more traditional editor so Spyder has been my tool of choice for Python programming and there are others like PyCharm as well. Spyder itself is written in Python so it can be installed using pip from PyPi like other Python packages. It has other dependencies like Pylint for code management activities but these get installed behind the scenes.

The packages that I first met in 2019 may be the mainstays for doing data science but I have discovered others since then. It also seems that there is porosity between the worlds of R an Python so you get some Python packages aping R packages and R has the Reticulate package for executing Python code. There are Python counterparts to such Tidyverse stables as dply and ggplot2 in the form of Siuba and Plotnine, respectively. The syntax of these packages are not direct copies of what is executed in R but they are close enough for there to be enough familiarity for added user friendliness compared to Pandas or Matplotlib. The interoperability does not stop there for there is SQLAlchemy for connecting to MySQL and other databases (PyMySQL is needed as well) and there also is SASPy for interacting with SAS Viya.

Pyhton may not have the speed of Julia but there are plenty of packages for working with larger workloads. Of these, Dask, Modin and RAPIDS all have there uses for dealing with data volumes that make Pandas code crawl. As if to prove that there are plenty of libraries for various forms of data analytics, data science, artificial intelligence and machine learning, there also are the likes of Keras, TensorFlow and NetworkX. These are just a selection of what is available and there is no need not to check out more. It may be tempting to stick with the most popular packages all the time, especially when they do so much, but it never hurst to keep an open mind either.

Something to watch with the SYSODSESCAPECHAR automatic SAS macro variable

10th October 2021

Recently, a client of mine updated one of their systems from SAS 9.4 M5 to SAS 9.4 M7. In spite of performing due diligence regarding changes between the maintenance release, a change in behaviour of the SYSODSESCAPECHAR automatic macro variable surprised them. The macro variable captures the assignment of the ODS escape character used to prefix RTF codes for page numbering and other things. That setting is made using an ODS ESCAPECHAR statement like the following:

ods escapechar="~";

In the M5 release, the tilde character in this example was output by the automatic macro variable but that changed in the M7 release to 7E, the hexadecimal code for the same and this tripped up one of their validated macro programs used in output production. The adopted solution was to use the escape sequence (*ESC*) that gave the same outcome that was there before the change. That was less verbose than alternative code changing the hexadecimal code into the expected ASCII character that follows.

data _null_;
call symput("new",byte(input("&sysodsescapechar.",hex.)));
run;

The above supplies a hexadecimal code to the BYTE function for correct rendering with the SYMPUT routine assigning the resulting value to a macro variable named new. Just using the escape sequence is far more succinct though there is now an added validation need once user pilot testing has completed. In my line of business, the updating of code is the quickest part of many such changes; documentation and testing always take longer.

Some books and other forms of documentation on R

11th September 2021

The thrust of an exhortation from a computing handbook publisher comes to mind here: don’t just look things up on Google, read a book so you really understand what you are doing. Something like those words was used to sell an eBook on Github but the same sentiment applies to R or any other computing language. Using a search engine will get you going or add to existing knowledge but only a book or a training course will help to embed real competence.

In the case of R, there is a myriad of blogs out there that can be consulted as well as function and package documentation on RDocumentation or rrdr.io. For the former, R-bloggers or R Weekly can make good places to start while ones like Stats and R, Statistics Globe, STHDA, PSI’s VIS-SIG and anything from Posit (including their main blog as well as their AI one) can be worth consulting. Additionally, there is also RStudio Education and the NHS-R Community, which also have a Github repository together with a YouTube channel. Many packages have dedicated websites as well so there is no lack of documentation with all of these so here is a selection:

Tidyverse

forcats

tidyr

Distill for R Markdown

Databases using R

RMariaDB

R Markdown

xaringanExtra

Shiny

formattable

reactable

DT

rhandsontable

thematic

bslib

plumber

ggforce

officeverse

officer

pharmaRTF

COVID-19 Data Hub

To come to the real subject of this post, R is unusual in that books that you can buy also have companions websites that contain the same content with the same structure. Whatever funds this approach (and some appear to be supported by RStudio itself by the looks of things), there certainly are a lot of books available freely online in HTML as you will see from the list below while a few do not have a print counterpart as far as I know:

Big Book of R

R Programming for Data Science

Hands-On Programming with R

Advanced R

Cookbook for R

R Graphics Cookbook

R Markdown: The Definitive Guide

R Markdown Cookbook

RMarkdown for Scientists

bookdown: Authoring Books and Technical Documents with R Markdown

blogdown: Creating Websites with R Markdown

pagedown: Create Paged HTML Documents for Printing from R Markdown

Dynamic Documents with R and knitr

Mastering Shiny

Engineering Production-Grade Shiny Apps

Outstanding User Interfaces with Shiny

R Packages

Mastering Spark with R

Happy Git and GitHub for the useR

JavaScript for R

HTTP Testing in R

Outstanding User Interfaces with Shiny

Engineering Production-Grade Shiny Apps

The Shiny AWS Book

Many of the above have counterparts published by O’Reilly or Chapman & Hall, to name the two publishers that I have found so far. Aside from sharing these with you, there is also the personal motivation of having the collection of links somewhere so I can close tabs in my Firefox session. There are other web articles open in other tabs that I need to retain and share but these will need to do for now and I hope that you find them as useful as I do.

Self-learning new computing languages

10th April 2021

Over the years, I have taught myself a number of computing languages with some coming in useful for professional work while others came in handy for website development and maintenance. The collection has grown to include HTML, CSS, XML, Perl, PHP and UNIX Shell Scripting. The ongoing pandemic allowed to me added two more to the repertoire: R and Python.

My interest in these arose from my work as an information professional concerned with standardisation and automation of statistical results delivery. To date, the main focus has been on clinical study data but ongoing changes in the life sciences sector could mean that I may need to look further afield so having extra knowledge never hurts. Though I have been a SAS programmer for more than twenty years, its predominance in the clinical research field is not what it was so that I am having to rethink things.

As it happens, I would like to continue working with SAS since it does so much and thoughts of leaving it after me bring sadness. It also helps to know what the alternatives might be and to reject some management hopes about any newcomers, especially with regard to the amount of code being produced and the quality of graphs being created. Use cases need to be assessed dispassionately even when emotions loom behind the scenes.

Both R and Python bring large scripting ecosystems with active communities so the attraction of their adoption makes a deal of sense. SAS is comparable in the scale of its own ecosystem though there are considerable differences and the platform is catching up when it comes to Data Science. The aforementioned open source languages may have had a head start but it seems that others are not standing still either. It is a time to have wider awareness and online conference attendance helps with that.

The breadth of what is available for any programming language more than stymies any attempt to create a truly all encompassing starting point and I have abandoned thoughts of doing anything like that for R. Similarly, I will not even try such a thing for Python. Consequently, this means that my sharing of anything learned will be in the form of discrete postings from time to time, especially given ho easy it is to collect numerous website links for sharing.

The learning has been facilitated by ongoing pandemic restrictions though things are opening up a little now. The pandemic also has given us public data that can be used for practice since much can be gained from having one’s own project instead of completing exercises from a book. Having an interesting data set with which to work is a must and COVID-19 data contain a certain self-interest as well though one always is mindful of the suffering and loss of life that has been happening since the pandemic first took hold.

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