Jupyter VS R Shiny
As of 30 October 2026, R Shiny is named by 147 companies and Jupyter by 528; Jupyter skews to companies under 200 people (44% vs 32%), R Shiny to companies over 1,000 (46% vs 31%).
As of 30 October 2026 · refreshed daily · counts move as websites, DNS and career sites are re-read.
The website-and-DNS footprint is not comparable between a marketing suite and a CRM: Jupyter's script and mail records show on hundreds of thousands of small sites, R Shiny rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Jupyter: 2–200 employees
- 44%
- R Shiny: 2–200 employees
- 32%
- Jupyter: 1,000+ employees
- 31%
- R Shiny: 1,000+ employees
- 46%
- Postings per company, 90 days
- 2.0 · 2.5
- Website or DNS footprint
- 0 · 0
Who chooses which
Share of each tool's companies in every segment. "Leans" marks a gap of more than two points.
| Segment | Jupyter | share | R Shiny | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 13 | 2% | 2 | 1% | even |
| 2-10 employees | 35 | 7% | 5 | 3% | Jupyter |
| 11-50 employees | 85 | 16% | 22 | 15% | even |
| 51-200 employees | 110 | 21% | 20 | 14% | Jupyter |
| 201-500 employees | 65 | 12% | 15 | 10% | Jupyter |
| 501-1000 employees | 54 | 10% | 16 | 11% | even |
| 1001-5000 employees | 70 | 13% | 20 | 14% | even |
| 5001-10000 employees | 20 | 4% | 10 | 7% | R Shiny |
| 10,001+ employees | 76 | 14% | 37 | 25% | R Shiny |
| Segment | Jupyter | share | R Shiny | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 253 | 48% | 41 | 28% | Jupyter |
| Business & finance services | 105 | 20% | 25 | 17% | Jupyter |
| Manufacturing & industry | 46 | 9% | 29 | 20% | R Shiny |
| Consumer services & leisure | 29 | 5% | 4 | 3% | Jupyter |
| Public, community & associations | 27 | 5% | 23 | 16% | R Shiny |
| Utilities & infrastructure | 17 | 3% | – | – | outside top 12 |
| Media & content | 15 | 3% | 7 | 5% | even |
| Commerce & trade | 9 | 2% | 3 | 2% | even |
| Transport, logistics & rental | 8 | 2% | – | – | outside top 12 |
| Automotive | 6 | 1% | 2 | 1% | even |
| Hospitality & travel | 5 | 1% | 1 | 1% | even |
| Agriculture & forestry | 3 | 1% | 2 | 1% | even |
| Health care | – | – | 6 | 4% | outside top 12 |
| Property & construction | – | – | 2 | 1% | outside top 12 |
| Segment | Jupyter | share | R Shiny | share | Leans |
|---|---|---|---|---|---|
| United States | 339 | 64% | 97 | 66% | even |
| United Kingdom | 44 | 8% | 10 | 7% | even |
| Germany | 25 | 5% | 7 | 5% | even |
| France | 18 | 3% | 11 | 7% | R Shiny |
| Canada | 12 | 2% | 5 | 3% | even |
| India | 10 | 2% | – | – | outside top 12 |
| Singapore | 9 | 2% | – | – | outside top 12 |
| Poland | 6 | 1% | – | – | outside top 12 |
| Spain | 5 | 1% | – | – | outside top 12 |
| Netherlands | 5 | 1% | – | – | outside top 12 |
| Belgium | 4 | 1% | 1 | 1% | even |
| Brazil | 4 | 1% | – | – | outside top 12 |
| Switzerland | – | – | 2 | 1% | outside top 12 |
| Italy | – | – | 2 | 1% | outside top 12 |
| Japan | – | – | 2 | 1% | outside top 12 |
| AF | – | – | 1 | 1% | outside top 12 |
| Australia | – | – | 1 | 1% | outside top 12 |
| BM | – | – | 1 | 1% | outside top 12 |
| Segment | Jupyter | share | R Shiny | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 738 | 49% | 265 | 55% | R Shiny |
| Software engineering | 212 | 14% | 41 | 8% | Jupyter |
| IT operations & business systems | 70 | 5% | 7 | 1% | Jupyter |
| Engineering (non-software) | 67 | 4% | 8 | 2% | Jupyter |
| Research and science | 29 | 2% | 41 | 8% | R Shiny |
| Finance & accounting | 26 | 2% | 29 | 6% | R Shiny |
| Sales | 22 | 1% | 2 | 0% | even |
| Education & training | 21 | 1% | 4 | 1% | even |
| Product, program & project management | 21 | 1% | 6 | 1% | even |
| Legal, compliance & risk | 19 | 1% | 5 | 1% | even |
| Customer service & success | 14 | 1% | – | – | outside top 12 |
| General management & consulting | 10 | 1% | – | – | outside top 12 |
| Manufacturing and production | – | – | 5 | 1% | outside top 12 |
| Quality, safety & environment | – | – | 2 | 0% | outside top 12 |
The companies behind each column
Companies using Jupyter →
Companies using R Shiny →
data science platforms notebooks ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
R Shiny is named by 147 companies in job postings and Jupyter by 528; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/jupyter-vs-r-shiny)
Read from the source, not self-reported
Every number on this page can be traced to a page we crawled or a posting we read. Full methodology →
- Job postingsRead hourly from career sites. A tool counts when the employer's own text names it as the team's system; both tools can count for one company.
- Website and DNSScripts and embeds on the company's pages, plus SPF, DKIM, MX and CNAME records of its domain that point at the vendor.
- SegmentsCountry, size and industry from the company profile built from its website.
- Not a reviewNo ratings, prices or feature claims. For those, read the vendors.
Jupyter vs R Shiny: questions
Which is more used, Jupyter or R Shiny?
By companies naming it in their own job postings, Jupyter (528) over R Shiny (147). By website-and-DNS footprint, R Shiny (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Jupyter?
44% of Jupyter companies have 2 to 200 employees, against 32% for R Shiny. 31% of Jupyter companies have over 1,000 employees, against 46% for R Shiny.
Do companies run both?
0 companies name both in their postings, usually Jupyter in marketing and R Shiny in sales, or during a migration.
Where do these numbers come from?
From the employer's own text in job postings read hourly from career sites, and from scripts found on company websites in our crawl. No vendor data, no surveys.
Get either list your way
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Every company on either tool,
with the person who owns it
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