Raycast Extension Store Analysis 05/2022-05/2023

9-minute read
Raycast
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A year ago I started the development of a Raycast extension which give me more details about the Raycast Store. During the development I quickly realized that I need to store the data of the Raycast Store per day to be able to calculate e.g. growth rates and other indicators.

But I don’t want to host a full database for such a small extension. Instead I went with git-scraping. My scraping repository just get triggered once a day via GitHub Workflows and store the data in a nice structure in the repo. That way it is very easy to process the data for my extension.

A year later

I create the extension one year ago and I collected the data from the Raycast store since. I thought it would be very interesting to make some simple data analysis on the dataset to learn more about the eco system and maybe find some hidden gems in the Raycast Store. There are more and more extensions and finding the interesting ones is not that easy.

Preparation

If you wanna skip the data preparation part you can directly jump to the Results

Jupyter Notebook + VS Code

My tool of choice for the analysis was Jupyter Notebook because it is easy to use. I prefer the VS Code Integration so I went with it.

For the data processing I use pandas and numpy. For plotting I used plotly because the default plot engine Matplotlib can only paint images and plotly make interactive ones which make the analysis way easier.

The Plotly graphs are embedded in this article so you can interactively play with them.

Checking and fixing the dataset from git-scraping repo

First I need to clean the data. Data analysis without inspecting and fixing your dataset first don’t make much sense and you can have heavy side effects in the analysis part if you don’t check your data.

So first things first.

Interpolate missing data

Sometimes my scraping process was not able to get all the data, so I add a linear interpolation to fill the gaps. This type of interpolation should be good enough. Thanks to pandas this is a simple function call.

Renamed Extensions

I need to join the extensions which got renamed as well.

  • 1password

    1password7 was the old name. The new one is 1password. I joined the 2 datasets under the new name.

  • openai-gpt

    openai-gpt3 was the old name. The new one is OpenAI GPT. I joined the 2 datasets and used slinear interpolation to fill the gaps.

Native vs TypeScript Extensions

Most native extensions got replaced with the TypeScript version. I decided to cutoff the old data because the TypeScript version have the same extension id and it would be very confusing to have a minus growth rate.

  • GitHub

    Data was taken since 2022-12-15

  • Zoom

    Data was taken since 2022-08-04

  • Jira

    Data was taken since 2023-03-29

  • Google Workspace

    Data was taken since 2022-08-04

  • Linear

    Data was taken since 2022-07-21

  • Asana

    Data was taken since 2022-09-28

  • Browser Bookmarks

    Data was taken from the native version until 2022-04-28 because the TypeScript version was released on 2022-04-29

Extensions removed from the Analysis

It seems that some extensions got removed completely and therefore I removed it from the analysis as well.

  • paste-without-formatting
  • quick-surf
  • api-icon-list
  • case-word
  • menubar-message
  • luent-outdoors
  • chatgpt

The old native version got removed from the analysis too because I assume that most user had switched to the TypeScript versions.

  • asana-legacy-native
  • github-legacy
  • jira-legacy
  • asana-legacy
  • linear-legacy
  • google-workspace-legacy
  • zoom-legacy

The beta versions are also removed because we have the production version as well.

  • github-beta
  • jira-beta
  • spotify-beta

Results

The analysis is mainly done based on the install/download count of the extensions. Be aware that this does not necessarily correspond to the continuous use after the initial download. I’m also not aware if the install number only includes the initial install or if it count any install including update. This analysis assume that only the first install is included!

The data of the analysis was recorded between 2022-05-01 and 2023-05-01, exactly 12 months.

The graphs are interactive ones. Make sure you have enough screen size, especially on mobile devices otherwise it could happen the the graph are to small. Desktops or tablets works best.

WARNING: Be aware that the analysis can contain errors because missing data got interpolated and may be more imprecise in certain time periods

Downloads per Day

Most Downloads within a Day

Here is the list of the most downloads within a day.

The most downloaded extension within a day was OpenAI GPT. To my surprise Arc was second. Seeing Color Picker in the Top 5 was a surprise, because this was not on my radar at all. Seems to be a hidden gem which I should try out.

Average Downloads per Day

The average downloads per day looks a little bit different.

The clear winner is Google Translate. GitHub is second. Color Picker is in the Top 20 too.

Pomodoro seems to be very popular.

Downloads per Month

Here are the same numbers, but now for the month interval.

Most Downloads within a Month

Here we can see the hype around OpenAI GPT which beat Google Translate. Color Picker is on place 3.

Average Downloads per Month

In the average view Google Translate is cleary the winner. OpenAI GPT is one place 3. Timers is the surprise in this plot.

Seeing my 2 extensions YouTube and Speedtest in the Top 20 is cool 😊.

All-Time Downloads Charts

Let’s see which are the most installed extensions total (2023-05-01).

The winner is Google Translate 🎉. Followed by Home Brew and Spotify Player.

The most surprising to me is Visual Studio Code - Project Manager because I personally use VSCode Recent Projects which is not in the Top 20 at all. Visual Studio Code - Project Manager seems to be the second hidden gem for me and I will definitely check it out in the near future.

Gained Extension Downloads in the last 12 Months

No big surprises here. The winner is Google Translate 🎉 again.

My hidden gem Visual Studio Code - Project Manager on place number 11. In the total installations charts it is place number 12, so a good sign for this extension.

All-Time Downloads by Max. Downloads

I added the following charts by max. installs to not only display the most downloaded. They could be over time always the same and I wanna see what hidden gems in the store.

Max. 10K

Pomodoro take the lead and seems to be the next extension with >= 10K downloads.

File Manager sound interesting and I put it to my hidden gems list as well.

WhatsApp is surprising, last time I check it it was not that useful for my daily stuff.

Max. 5K

Nice to see my Twitter Extension at place number one. Thanks to the new pricing I could not really maintain it or use it myself. Hope users with a paid subscription like it.

ChatGPT3 Prompt is an extension which give you chatGPT prompts from awesome-chatgpt-prompts. This move as well to my hidden gem list.

Happy to see my Home Assistant Extension around 5K installs. Cool to have so many people who control the Smarthome via Raycast 🚀.

Fun fact: Home Assistant was the first extension available in the Raycast Store.

Max. 1K

KeePassXC take lead here.

Linguee move to my hidden gems list to try the extension in the near future.

Downloads per Weekday

The total downloads per weekday are interesting. There are less downloads on a Thursday in comparison to all other work days. Saturday and sunday are not really a big surprise 😀.

Friday has the most downloads 🤔.

Available Extensions

The available extension count growth is pretty constant. I was surprised that it shows clearly a linear trend. This indicates that the interest from the extension developers is still high and invest a lot into the eco system.

The linearity will also come from the code review process which every extension goes through which hold the growth rate a little bit down but this is not really a bad thing here. Great job to get so much extensions through the process and still have this high growth rate and amazing quality of the extensions.

Amazing job by the Raycast Team 👏.

On 2022-05-01 the total extension available was 367. The total extension count on 2023-05-01 is 956, which is approximately 2.6 times more 🎉.

On average 1.6 new extensions per day (or 49 per month), pretty impressive 🤯.

Downloads Distribution

Let checkout how the installs are distributed in the Raycast Store.

All-Time

42 extension have >10K downloads. 914 has <= 10K.

The sum of total installs of the extensions <=10K is 889K and >10K 848K. The delta is about 41K downloads.

Therefore the top most 42 top-most extension make approximately 49% of all downloads.

Last 12 Months

29 extension have >10K downloads. 929 has <= 10K.

The sum of total installs of the extensions <=10K is 500K and >10K 573K. The delta is about 73K downloads.

Therefore the top most 29 top-most extension make approximately 53% of all downloads.

Stats

Here is a short summary of the data above.

Most Downloads by a single Extension

Most Downloads on Average by a single Extension

Extension Downloads in the last 12 Months

2022-05-01: 290K (290622)

2023-05-01: 1.74M (1736969)

Delta: +1.4M (1446347)

This are 6 times more downloads as a year before.

On average the extension installs are 3960 installs more each day.

Available Extensions

2022-05-01: 367

2023-05-01: 956

Delta: +589

2.6 times more as a year before.

All-Time Extension Downloads Distribution

  • <= 10K

    Installs: 889K (888948)

    ~51% of all downloads

  • > 10K

    Installs: 848K (848021)

    ~49% of all downloads

Last 12 months Extension Downloads Distribution

  • <= 10K

    929 Extensions

    Installs: 500K (500094)

    ~47% of all downloads

  • > 10K

    29 Extensions

    Installs: 573K (573362)

    ~53% of all downloads

Extension growth in Detail per Month

Here are all extensions in detail per month if you wanna play around with the data.

You can isolate datasets by double-click onto an entry in the legend. Click only once will hide/show the clicked dataset.

Conclusion

The Raycast extension eco system has a very rapid growth rate and the extension developers and users has still high interest. More than happy to see that and be also be part of it 👏.

Google Translate is the most downloaded extension. OpenAI GPT was the fastest growing one which come not really as a surprise when we think about the hype at the end of last year. The upcoming public release of Raycast AI could make the download rate of OpenAI GPT much lower.

During the analysis I found some hidden gems which I will definitely try

The analysis of the Raycast Extension eco system was really fun. I need to collect more data to make a deeper analysis next year.

Will be interesting to see the data in one year from now 😀.