Drupal Public Data, Statistics & Silver Linings? An Exploration

This is part 1 of a 5-part series examining Drupal public data in search of actionable insights.

Drupal Installations Analysis

The "Usage statistics for Drupal core" Page

This article analyzes publicly available data presented by the Drupal Project regarding the number of reportedly active Drupal core installations in the period December 22 - 28, 2024.  It is available at: https://www.drupal.org/project/usage/drupal 

Drupal frames the data set in the following way:

"This page provides information about the usage of the Drupal core project, including summaries across all versions and details for each release. For each week beginning on the given date the figures show the number of sites that reported they are using a given version of the project."

Drupal also qualifies the quality data set in this manner:

"These statistics are incomplete; only Drupal websites using the Update Status module are included in the data. This module has been included with the download of Drupal since version 6.x so the data does not include older sites."

A Justification for Using Statistics

Statistics is "the discipline that concerns the collection, organization, analysis, interpretation and presentation of data" (Wikipedia, 2025).  It seems therefore prudent (and perhaps even enjoyable) to attempt applying the approaches and tools of such a discipline to the data set offered by Drupal.  

Digging deeper, Statistics to be the ideal discipline to turn to in circumstances where data has been deemed uncertain, considering that "Statistics is both the science of uncertainty and the technology of extracting information from data" (Hand, 2010), which are the two overriding considerations of undertaking this work. 

Two more things make Statistics stand out as the ideal tool to help power this investigation:  Data Quality, an important sub-discipline of Statistics, was developed to help improve the quality of data sets so as to increase "the capability of data to be used effectively, economically and rapidly to inform and evaluate decisions" (Karr et al, 2002).  Also, Exploratory Data Analysis, yet another sub-discipline of Statistics and one that is frequently used in conjunction with Data Quality, was developed, according to IBM (2025), to:

"...help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables."

With a defence as to the relevance and efficacy of applying Statistics to the Drupal data set finished, we can now move on to the fun stuff:  Examining and interpreting the Usage statistics for the Drupal core data set for the period December 22 - 28, 2024.

The Analysis

Here is the raw data as provided by Drupal:

Table 1:  Data as Provided by Drupal

(the only transformation applied to this data was to reformat it so it appears vertically rather than its original presentation, which was originally presented horizontally) 

(For the purpose of layout, we have moved half the data from columns A and B and continued it on C and D - Editor)

Statistic #1:  Share Percentage

A simple statistical manipulation would be to add percentages to the data under the heading "Share"

Table 2:  Data as Provided by Drupal with Share Percentage

Statistic #2:  Edition Share Percentage

Another simple statistical manipulation would be to gather the "Editions" of Drupal together (i.e. Drupal 5.x, Drupal 6.x, Drupal 7.x, etc...), aggregating them into an "Edition share", while revising the original "Share" to read "Version Share":

Table 2:  Data as Provided by Drupal with Version & Edition Share Percentage

Statistic #3:  Aggregated Edition Share Percentage

Another simple statistical manipulation would be to create a summary table of the different Editions featuring number of installs (#) and Edition Share (%) along with some sanity checks of derived table totals against the original raw data totals, which is a very basic application of Data Quality (DQ).

Table 3:  Aggregated Edition Share Percentage

EDA #1:  Edition Share Percentage as Pie Chart

Drawing on the tools and techniques of Exploratory Data Analysis (EDA), we can now use the summary table to help generate a pie chart that expresses Edition Share in a visual way:

Figure 1:  Edition Share Percentage Pie Chart
 

EDA #2:  EDA's Ability to Power Statistical Inferences

One of the "superpowers" of EDA is harnessing the visual center of the human brain, a finely tuned facility that has benefited from over 250,000 years of evolution.  

From a systems perspective, this is akin to loading a well-tested, comprehensive "library" in order to deal with an existing situation in a completely novel way.  Sometimes, the use of a new approach or perspective can render a heretofore intractable or complicated situation simple and solvable.

What can be immediately inferred from the EDA-powered data projection through a pie chart metaphor is the relative size of the "pie piece" of each Drupal edition:

• The Edition share of Drupal 5 is almost unobservable.
• The Edition Share of Drupal 10 and Drupal 7 are large, and roughly equal in size.
• The Edition Share of Drupal 8 and Drupal 9 are medium, and roughly equal in size.
• The Edition share of Drupal 11 and Drupal 6 are small, and roughly equal in size.

These observations inevitably lead back to a deeper examination of the source data because of the shape of the data.  Presumably, those Drupal Editions were released in series, with Drupal 5 being the earliest recorded Edition and Drupal 11 the most recent.

So what's going on?

The next piece in this series introduces a set of useful statistical models that could be immediately applied to the Drupal data set in the quest to derive actionable insights from their raw numbers.

In 2002, Professor Graham Leach began lecturing at Hong Kong Polytechnic University, the largest school in Hong Kong, by teaching graduate level courses in the Department of Computer Science of the School of Engineering. In 2010, he moved over to the School of Design and remained there until his retirement in 2023.   Graham currently occupies a Professorship of Entrepreneurship in the School of Business of Gratia Christian College, Hong Kong's newest Tertiary educational institution.