Adventures & experiences in contemporary technology
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.
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.
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.
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.
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:
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:
R Programming for Data Science
R Markdown: The Definitive Guide
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
Engineering Production-Grade Shiny Apps
Outstanding User Interfaces with Shiny
Happy Git and GitHub for the useR
Outstanding User Interfaces with Shiny
Engineering Production-Grade Shiny Apps
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.
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.
Due to a need to access websites with country blocking, I have decided to give CyberGhost a go and it also will come in handy when connecting devices to other Wi-Fi connections. What I have got is the three year subscription package and all went went well on the first day of use. However, things became unusable on the second and a reboot did not sort it.
The problem seemed to affect a phone running Android too and I even got to suspecting my router an broadband provider. Even terminating the subscription came to mind but it did not come to that. Instead, I did a bit more research and tried changing the maximum transition unit (MTU) for the connection to 1300 as suggested in a CyberGhost help article. Using the Control Panel meant that it was resetting to 1500 on my Windows 10 machine so I then turned to a command line based solution.
To do that, I started PowerShell in administrator mode from the context menu produced by right clicking on the Start Menu icon on the taskbar. Then, I entered the following command to see what connections I had and what the MTU settings were:
netsh interface ipv4 show subinterfaces
From looking through the Settings and Control Panel applications, I already had worked out what network interface belonged to the CyberGhost connection. Seeing that the MTU setting was 1500, I then issued a command like the following to change that to 1300.
netsh interface ipv4 set subinterface "<name of ethernet interface>" mtu=1300 store=persistent
Here, <name of ethernet interface> gets replace by the name of your connection and the string is quoted to avoid spaces in the name causing problems with executing the command. Once that second command had been run, the first one was issued again and the output checked to ensure that the MTU setting was as expected.
This was done when the VPN connection was inactive but it may work also with an active connection. After making the change, I again reconnected to the VPN and all has been as expected since then and I found a better connection for my Android phone too.
Recently, I needed to inactivate blocks of code in a Perl script while doing some testing. This is something that I often do in other computing languages so I sought the same in Perl. To do that, I need to use the POD methodology. This meant enclosing the code as follows.
=start
<< Code to be inactivated by inclusion in a comment >>
=cut
The =start line could use any word after the equality sign but it seems that =cut is needed to close the multi-line comment. If this was actual programming documentation, then the comment block should include some meaningful text for use with perldoc but that was not a concern here since the commenting statements would be removed afterwards anyway and it is good practice not to leave commented code in a production script or program to avoid any later confusion.
In my case, this facility allowed me to isolate the code that I needed to alter and test before putting everything back as needed. It also saved time since I did not need to individually comment out every executable line because multiple lines could be inactivated at a time.
In a previous post, I described how to add Perl modules in Linux Mint while mentioning that I hoped to add another that discusses the use of the Parallel::ForkManager module. This is that second post and I am going to keep things as simple and generic as they can be. There are other articles like one on the Perl Maven website that go into more detail.
The first thing to do is ensure that the Parallel::ForkManager module is called by your script and having the following line near the top will do just that. Without this step, the script will not be able to find the required module by itself and errors will be generated.
use Parallel::ForkManager;
Then, the maximum number of threads needs to be specified. While that can be achieved using a simple variable declaration, the following line reads this from the command used to invoke the script. It even tells a forgetful user what they need to do in its own terse manner. Here $0 is the name of the script and N is the number of threads. Not all these threads will get used and processing capacity will limit how many actually are in use so there is less chance of overwhelming a CPU.
my $forks = shift or die "Usage: $0 N\n";
Once the maximum number of available threads is known, the next step is to instantiate the Parallel::ForkManager object as follows to use these child processes:
my $pm = Parallel::ForkManager->new($forks);
With the Parallel::ForkManager object available, it is now possible to use it as part of a loop. A foreach loop works well though only a single array can be used with hashes being needed when other collections need interrogation. Two extra statements are needed with one to start a child process and another to end it.
foreach $t (@array) {
my $pid = $pm->start and next;
<< Other code to be processed >>
$pm->finish;
}
Since there often is other processing performed by script and it is possible to have multiple threaded loops in one, there needs to be a way of getting the parent process to wait until all the child processes have completed before moving from one step to another in the main script and that is what the following statement does. In short, it adds more control.
$pm->wait_all_children;
To close, there needs to be a comment on the advantages of parallel processing. Modern multi-core processors often get used in single threaded operations and that leaves most of the capacity unused. Utilising this extra power then shortens processing times markedly. To give you an idea of what can be achieved, I had a single script taking around 2.5 minutes to complete in single threaded mode while setting the maximum number of threads to 24 reduced this to just over half a minute while taking up 80% of the processing capacity. This was with an AMD Ryzen 7 2700X CPU with eight cores and a maximum of 16 processor threads. Surprisingly, using 16 as the maximum thread number only used half the processor capacity so it seems to be a matter of performing one’s own measurements when making these decisions.
My online travel photo gallery is a self-coded set of PHP scripts that read data from tables in a MySQL database. These tables are built from input XML files using a Perl script that itself creates and executes an SQL script. The Perl script also does some image processing using GraphicsMagick commands to resize images and to add copyright information and image framing. Because this processed one image at a time sequentially, it was taking several minutes to complete and only partly used the capacity of the PC that I used.
This led me to look at adding parallel processing and that is what brought me to looking at the Parallel::ForkManager Perl module. An alternative approach might have been to add new images in such a way as not to need the full run involving hundreds of image files, but that will take more work and I fancied having a look at parallelising things anyway.
If it was not there already, the first act would have been to install build-essential to get access to the cpan command. The following command accomplishes this:
sudo apt-get install build-essential
Once that is there, the cpan command needs to be run and some questions answered to get things going. The first question to answer is whether you want setup to be as automated as possible and the default answer of yes worked for me. The next question to answer regards the approach that cpan takes when installing modules and I chose sudo here (local::lib is the default value and manual is another option). After this, cpan drops into its own command shell. Here, I issued two more commands to continue the basic setup by updating CPAN.pm to the latest version and adding Bundle::CPAN to optimise the module further:
make install
install Bundle::CPAN
Continuing the last of these may need extra intervention to confirmation the suggested default of exit at one point in its operation and that takes a little time to complete. It is after this that Parallel::ForkManager can be installed using the following command:
install Parallel::ForkManager
That completed quickly and the cpan shell was exited using its exit command. Then, the new module was available in scripting after that. The actual use of this module is something that hope to describe in another post so I am ending this one here and the same process is just as applicable to setting up cpan and adding any other Perl CPAN module.