No IJulia to add
A new notebook can start on the Julia kernel straight away, with no REPL session first.
JupyterLab 4.6.3 Data
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Jupyter Notebook R and Julia R, Julia and Python kernels, ready before you signย in.
Selfhost.dev runs Jupyter Notebook R and Julia as a one-click template: JupyterLab with the R, Julia and Python kernels registered and the tidyverse installed, on a dedicated server we provision, secure and run. You open a notebook and pick a kernel; nothing gets installed with conda first.
Sign in with your password, then choose R, Julia or Python.
Jupyter Data Scienceone-click template ready
- kernelsR, Julia, Python
- R packagestidyverse
- sign-inyour password, 12+
- HTTPSon from the start
- kept on restartyour notebooks
European 8 GB server$0.03/hr
In one line
What is Jupyter Notebook R on Selfhost.dev?
What it is
Jupyter Data Science is JupyterLab with Python, R and Julia kernels side by side. The R kernel brings the tidyverse; the Python kernel brings scikit-learn.
Where to start. Open the console with Jupyter Data Science selected
Who it is for
R and Julia users who want notebooks without a local setup. Python users who share their work fit here too.
When to use it
When the R or Julia kernel on your laptop keeps breaking. Also when a notebook has to keep running after the lid closes.
Server
Jupyter Notebook R, with no kernel to install
Every guide to R in Jupyter has you install R, add IRkernel with conda or install.packages, then register it with installspec. On this template both kernels are registered before your first sign-in.
The stack, end to end
Callers
Your browser
Edge
HTTPS
Your project server
Container
JupyterLab
Kernel
R
Kernel
Julia
Kernel
Python
To sign in
Your password
| R in Jupyter by hand | Jupyter Notebook R on Selfhost.dev |
|---|---|
| Install R, then conda install r-irkernel | The R kernel, registered |
| Run IRkernel::installspec() to list it | R in the launcher from the first sign-in |
| Pkg.add("IJulia") in the Julia REPL | The Julia kernel, registered |
| Install the tidyverse for that R | The tidyverse, ready for library() |
Only your password opens JupyterLab and its three kernels. Deploy the stack
Container
Julia in Jupyter Notebook, kernel included
Julia notebook guides start in the Julia REPL with Pkg.add("IJulia"), then call notebook() to open Jupyter locally. Here Julia already sits in the launcher beside R and Python, on HTTPS behind your password.
Three languages, one file browser
Keep a Julia model next to an R analysis and a Python script, in the same folders.
Nothing to launch by hand
There is no notebook() call to run. JupyterLab is already serving on its own HTTPS address.
What you need
Jupyter Notebook R requirements
Three language runtimes make a much larger image than plain JupyterLab, so the console starts this template on an 8 GB server, on x86 or ARM. Notebooks run on the CPU.
What it needs
- Server
- 8 GB, the console's default here; x86 or ARM
- Processor
- CPU: tidyverse, Julia and scikit-learn work
- Password
- 12 characters or more, chosen at deploy
- Domain
- Optional; a generated HTTPS address comes first
Server size: 8 GB, enough for Jupyter Data Science. Deploy on an 8 GB server
Quickstart
How to deploy Jupyter with R and Julia in 3 steps
Launch, sign in, pick a kernel.
-
Launch the template
Launch opens Jupyter Data Science. Set a password of 12 characters or more and accept the no-mining terms. Before a first top-up, a platform admin approves it. The template deploys in about twenty minutes.
-
Sign in
Open the HTTPS address shown in the console. Type your password into the Password or token box; there is no username to enter.
-
Pick a kernel
Choose R, Julia or Python in the launcher. In an R notebook, library(tidyverse) loads at once; in a Python one, scikit-learn imports.
Deploy Jupyter Data ScienceStep 1 starts in the console.
Launch Jupyter Data Science Billing
Jupyter Notebook R cost
Jupyter Notebook R costs one server, billed by the hour from prepaid credits. R, Julia and Python share it, so a second language adds nothing. On a European 8 GB server that comes to $0.03/hr, about $23 a month.
$0.03
/hr, about $23 a month at 730 hours
European 8 GB server, $0.03/hr Deploy Jupyter Data Science | Item | Cost |
|---|---|
| The Jupyter Data Science template | $0 |
| Bandwidth to your notebooks | $0, included in the server price |
| Optional daily backups | +20% of the server price |
| R, Julia or Python packages you add | $0 |
| Server | RAM, cores, disk | Where | Rate | About a month |
|---|---|---|---|---|
cx33 (recommended) | 8 GB, 4 cores, 80 GB | Europe | $0.03/hr | $23.36 |
cax21 (ARM) | 8 GB, 4 cores, 80 GB | Europe | $0.04/hr | $29.20 |
cx43 (room to grow) | 16 GB, 8 cores, 160 GB | Europe | $0.06/hr | $43.22 |
ccx13 | 8 GB, 2 cores, 80 GB | US | $0.16/hr | $119.28 |
cpx32 | 8 GB, 4 cores, 160 GB | Singapore | $0.19/hr | $135.63 |
Monthly figures assume 730 hours. The US and Singapore servers need a first top-up.
If your balance reaches zero
With a card on file or a past top-up, Jupyter Data Science runs for 5 more days after your balance reaches zero, then stops. Top up within 24 hours to restart it; after that it is deleted, with a 14-day copy you can restore. Welcome-credit-only servers are deleted at zero.
Price a 16 GB server for larger R data frames in the calculator, no signup.
Options
Jupyter Notebook R: your laptop vs a server
On a laptop, R in Jupyter lives in a conda environment you built. It stops when the lid closes. Jupyter Notebook R on Selfhost.dev runs on a server of its own, with the kernels registered and reachable from any browser.
| On your laptop | On Selfhost.dev | |
|---|---|---|
| Setup | Install R, IRkernel and IJulia, then register each | Registered before you sign in |
| In Docker | Pull an image, map port 8888, copy a token | Already on HTTPS behind your password |
| Long jobs | Stop when the laptop sleeps | Keep running after you close the tab |
Status
Five checks before your R notebook opens
- Every service is health-checked every 60 seconds.
- A stalled deploy is retried automatically, without you starting over.
- Done means the URL answers, not just that a container started.
- Locked from the first request. The password you set at deploy is the only way in.
- You see the release it runs. The console flags Update available when a newer one ships.
~20 min to deploy
The console estimates about twenty minutes for the Jupyter Data Science template after the server is ready; first-time server provisioning adds time.
Notes
Jupyter Notebook R limitations
- One password, one shared workspace. Whoever holds the password sees the same notebooks and kernels. Someone who needs a private space gets a deployment of their own.
- The first deploy is the slowest. Three language runtimes make a much larger image than plain JupyterLab, so the console allows about twenty minutes.
- Keep your package installs in a notebook. Put your install.packages() and Pkg.add() lines in a setup notebook, so one run rebuilds your libraries on a new deployment.
Nothing here in your way? The template deploys in about twenty minutes.
Launch Jupyter Data Science Planning
Teaching R or Julia to a group?
Tell us how many notebooks run at once and how large the data is. We reply with the server we would pick and what it costs a month.
Help
Frequently asked questions
Can you use R in a Jupyter Notebook?
Yes. Jupyter runs R through an R kernel, which most guides install with conda or install.packages and then register by hand. On this template the R kernel is already registered with the tidyverse, so you choose R in the launcher and write R in the first cell.
Which R and Python packages come installed?
The R kernel comes with the tidyverse, so dplyr, ggplot2 and readr load with one library(tidyverse) call. The Python kernel comes with scikit-learn. Julia is ready for Pkg.add(). Add anything else from a notebook with install.packages(), Pkg.add() or pip. Keep those lines in a setup notebook.
Can I switch between R, Julia and Python?
Yes. Each notebook runs on one kernel, chosen in the launcher or from the Kernel menu. You can change it at any time. An R analysis, a Julia model and a Python script can sit side by side in the same file browser, on one server behind one password.
How is Jupyter Data Science different from JupyterLab?
It is the same JupyterLab with the R and Julia kernels added to Python, so its image is much larger and the console allows about twenty minutes to deploy it. If you only write Python, JupyterLab hosting is the lighter template, with numpy, pandas and scikit-learn installed.
Why might a new Jupyter Data Science wait for approval?
On an organisation that has never topped up, a platform admin approves each new Jupyter Data Science before it starts. Its name stays reserved meanwhile; an email brings the decision. After a top-up it starts at once. Crypto mining is banned on every server. To withdraw a request, delete the service.
Can I put Jupyter Data Science on my own domain?
Yes. The template first answers on a generated HTTPS address. Once it is running, add a domain you own in the service's Settings and create the DNS record shown there. When that record resolves, your R and Julia notebooks open on your own domain as well.
R and Julia in Jupyter. Nothing to install first.
- R, Julia and Python kernels, registered before you sign in
- The tidyverse and scikit-learn, ready to load
- A European 8 GB server, about $23 a month
About Selfhost.dev
Selfhost.dev is a managed self hosting platform for developers and small teams. Open source apps and databases on dedicated servers you control, billed by the hour, in six locations across three continents.
Best for: R and Julia notebooks beside the databases they read, plain JupyterLab for Python and managed PostgreSQL, MySQL and Redis.