Working with Markdown files in Visual Studio Code is simple, straightforward, and fun. Besides VS Code's basic editing, there are a number of Markdown specific features that will help you be more productive.
Jupyter 노트북에 수학 공식을 작성하고 싶습니다. 라텍스는 마크 다운으로 만드는 간단한 방법 인 것 같습니다. 나는 여러 링크를 겪었지만 모두 그들에게. This article describes only a subset of the features the Go extension provides. See the extension's documentation for the full, up-to-date list of supported features. IntelliSense features are provided by the Go language server, gopls, maintained by the Go team. You can configure the behavior of gopls using the gopls settings. A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company.
Markdown extensions
In addition to the functionality VS Code provides out of the box, you can install an extension for greater functionality.
Tip: Click on an extension tile above to read the description and reviews to decide which extension is best for you. See more in the Marketplace.
Markdown preview
VS Code supports Markdown files out of the box. You just start writing Markdown text, save the file with the .md extension and then you can toggle the visualization of the editor between the code and the preview of the Markdown file; obviously, you can also open an existing Markdown file and start working with it. To switch between views, press ⇧⌘V (Windows, Linux Ctrl+Shift+V) in the editor. You can view the preview side-by-side (⌘K V (Windows, Linux Ctrl+K V)) with the file you are editing and see changes reflected in real-time as you edit.
Here is an example with a very simple file.
Tip: You can also right-click on the editor Tab and select Open Preview (⇧⌘V (Windows, Linux Ctrl+Shift+V)) or use the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) to run the Markdown: Open Preview to the Side command (⌘K V (Windows, Linux Ctrl+K V)).
Dynamic previews and preview locking
By default, Markdown previews automatically update to preview the currently active Markdown file:
You can lock a Markdown preview using the Markdown: Toggle Preview Locking command to keep it locked to its current Markdown document. Locked previews are indicated by [Preview] in the title:
Editor and preview synchronization
VS Code automatically synchronizes the Markdown editor and the preview panes. Scroll the Markdown preview and the editor is scrolled to match the preview's viewport. Scroll the Markdown editor and the preview is scrolled to match its viewport:
You can disable scroll synchronization using the markdown.preview.scrollPreviewWithEditor and markdown.preview.scrollEditorWithPreviewsettings.
The currently selected line in the editor is indicated in the Markdown preview by a light gray bar in the left margin:
Additionally, double clicking an element in the Markdown preview will automatically open the editor for the file and scroll to the line nearest the clicked element.
Outline view
The Outline view is a separate section in the bottom of the File Explorer. When expanded, it will show the symbol tree of the currently active editor. For Markdown files, the symbol tree is the Markdown file's header hierarchy.
The Outline view is a great way to review your document's header structure and outline.
Extending the Markdown preview
Extensions can contribute custom styles and scripts to the Markdown preview to change its appearance and add new functionality. Here's a set of example extensions that customize the preview:
Using your own CSS
You can also use your own CSS in the Markdown preview with the 'markdown.styles': []setting. This lists URLs for style sheets to load in the Markdown preview. These stylesheets can either be https URLs, or relative paths to local files in the current workspace.
For example, to load a stylesheet called Style.css at the root of your current workspace, use File > Preferences > Settings to bring up the workspace settings.json file and make this update:
Keep trailing whitespace in order to create line breaks
To create hard line breaks, Markdown requires two or more spaces at the end of a line. Depending on your user or workspace settings, VS Code may be configured to remove trailing whitespace. In order to keep trailing whitespace in Markdown files only, you can add these lines to your settings.json:
Markdown preview security
For security reasons, VS Code restricts the content displayed in the Markdown preview. This includes disabling script execution and only allowing resources to be loaded over https.
When the Markdown preview blocks content on a page, an alert popup is shown in the top right corner of the preview window:
You can change what content is allowed in the Markdown preview by clicking on this popup or running the Markdown: Change preview security settings command in any Markdown file:
The Markdown preview security settings apply to all files in the workspace.
Here are the details about each of these security levels:
Strict
This is the default setting. Only loads trusted content and disables script execution. Blocks http images.
It is strongly recommended that you keep Strict security enabled unless you have a very good reason to change it AND you trust all markdown files in the workspace.
Allow insecure content
Keeps scripts disabled but allows content to be loaded over http.
Disable
Disables additional security in the preview window. This allows script execution and also allows content to be loaded over http.
Snippets for Markdown
There are several built-in Markdown snippets included in VS Code - press ⌃Space (Windows, Linux Ctrl+Space) (Trigger Suggest) and you get a context specific list of suggestions.
Tip: You can add in your own User Defined Snippets for Markdown. Take a look at User Defined Snippets to find out how.
Compiling Markdown into HTML
VS Code integrates with Markdown compilers through the integrated task runner. We can use this to compile .md files into .html files. Let's walk through compiling a simple Markdown document.
Step 1: Install a Markdown compiler
For this walkthrough, we use the popular Node.js module, markdown-it.
Note: There are many Markdown compilers to choose from beyond markdown-it. Pick the one that best suits your needs and environment.
Step 2: Create a simple MD file
Open VS Code on an empty folder and create a sample.md file.
Note: You can open a folder with VS Code by either selecting the folder with File > Open Folder or navigating to the folder and typing 'code .' at the command line.
Place the following source code in that file:
Step 3: Create tasks.json
The next step is to set up the task configuration file tasks.json. To do this, run Terminal > Configure Tasks and click Create tasks.json file from templates. VS Code then presents a list of possible tasks.json templates to choose from. Select Others since we want to run an external command.
This generates a tasks.json file in your workspace .vscode folder with the following content:
To use markdown-it to compile the Markdown file, change the contents as follows:
Tip: While the sample is there to help with common configuration settings, IntelliSense is available for the tasks.json file as well to help you along. Use ⌃Space (Windows, Linux Ctrl+Space) to see the available settings.
Step 4: Run the Build Task
Since in more complex environments there can be more than one build task we prompt you to pick the task to execute after pressing ⇧⌘B (Windows, Linux Ctrl+Shift+B) (Run Build Task). In addition, we allow you to scan the output for compile problems. Since we only want to convert the Markdown file to HTML select Never scan the build output from the presented list.
At this point, you should see an additional file show up in the file list sample.html.
If you want to make the Compile Markdown task the default build task to run execute Configure Default Build Task from the global Terminal menu and select Compile Markdown from the presented list. The final tasks.json file will then look like this:
Automating Markdown compilation
Let's take things a little further and automate Markdown compilation with VS Code. We can do so with the same task runner integration as before, but with a few modifications.
Step 1: Install Gulp and some plug-ins
We use Gulp to create a task that automates Markdown compilation. We also use the gulp-markdown plug-in to make things a little easier.
We need to install gulp both globally (-g switch) and locally:
Note: gulp-markdown-it is a Gulp plug-in for the markdown-it module we were using before. There are many other Gulp Markdown plug-ins you can use, as well as plug-ins for Grunt.
You can test that your gulp installation was successful by typing gulp -v. You should see a version displayed for both the global (CLI) and local installations.
Step 2: Create a simple Gulp task
Open VS Code on the same folder from before (contains sample.md and tasks.json under the .vscode folder), and create gulpfile.js at the root.
Place the following source code in that file:
What is happening here?
- We are watching for changes to any Markdown file in our workspace, i.e. the current folder open in VS Code.
- We take the set of Markdown files that have changed, and run them through our Markdown compiler, i.e.
gulp-markdown-it. - We now have a set of HTML files, each named respectively after their original Markdown file. We then put these files in the same directory.
Step 3: Run the gulp default Task
To complete the tasks integration with VS Code, we will need to modify the task configuration from before to run the default Gulp task we just created. You can either delete the tasks.json file or empty it only keeping the 'version': '2.0.0' property. Now execute Run Task from the global Terminal menu. Observe that you are presented with a picker listing the tasks defined in the gulp file. Select gulp: default to start the task. We allow you to scan the output for compile problems. Since we only want to convert the Markdown file to HTML select Never scan the build output from the presented list. At this point, if you create and/or modify other Markdown files, you see the respective HTML files generated and/or changes reflected on save. You can also enable Auto Save to make things even more streamlined.
If you want to make the gulp: default task the default build task executed when pressing ⇧⌘B (Windows, Linux Ctrl+Shift+B) run Configure Default Build Task from the global Terminal menu and select gulp: default from the presented list. The final tasks.json file will then look like this:
Step 4: Terminate the gulp default Task
The gulp: default task runs in the background and watches for file changes to Markdown files. If you want to stop the task, you can use the Terminate Task from the global Terminal menu.
Next steps
Read on to find out about:
- CSS, SCSS, and Less - Want to edit your CSS? VS Code has great support for CSS, SCSS, and Less editing.
Common questions
Is there spell checking?
Not installed with VS Code but there are spell checking extensions. Check the VS Code Marketplace to look for useful extensions to help with your workflow.
Does VS Code support GitHub Flavored Markdown?
No, VS Code targets the CommonMark Markdown specification using the markdown-it library. GitHub is moving toward the CommonMark specification which you can read about in this update.
In the walkthrough above, I didn't find the Configure Task command in the Command Palette?
You may have opened a file in VS Code rather than a folder. You can open a folder by either selecting the folder with File > Open Folder or navigating to the folder and typing 'code .' at the command line.
When working on data science problems, you might want to set up an interactive environment to work and share your code for a project with others. You can easily set this up with a notebook.
In other cases, you’ll just want to communicate about the workflow and the results that you have gathered for the analysis of your data science problem. For a transparent and reproducible report, a notebook can also come in handy.
That's right; notebooks are perfect for situations where you want to combine plain text with rich text elements such as graphics, calculations, etc.
R And The Jupyter Notebook
Contrary to what you might think, Jupyter doesn’t limit you to working solely with Python: the notebook application is language agnostic, which means that you can also work with other languages.
There are two general ways to get started on using R with Jupyter: by using a kernel or by setting up an R environment that has all the essential tools to get started on doing data science.
Running R in Jupyter With The R Kernel
As described above, the first way to run R is by using a kernel. If you want to have a complete list of all the available kernels in Jupyter, go here.
To work with R, you’ll need to load the IRKernel and activate it to get started on working with R in the notebook environment.
First, you'll need to install some packages. Make sure that you don't do this in your RStudio console, but in a regular R terminal, otherwise you'll get an error like this:
This command will prompt you to type in a number to select a CRAN mirror to install the necessary packages. Enter a number and the installation will continue.
Then, you still need to make the R kernel visible for Jupyter:
Now open up the notebook application with jupyter notebook. You'll see R appearing in the list of kernels when you create a new notebook.
Using An R Essentials Environment In Jupyter
The second option to quickly work with R is to install the R essentials in your current environment:
These 'essentials' include the packages dplyr, shiny, ggplot2, tidyr, caret, and nnet. If you don't want to install the essentials in your current environment, you can use the following command to create a new environment just for the R essentials:
Now open up the notebook application to start working with R.
You might wonder what you need to do if you want to install additional packages to elaborate your data science project. After all, these packages might be enough to get you started, but you might need other tools.
Well, you can either build a Conda R package by running, for example:
Or you can install the package from inside of R via install.packages() or devtools::install_github (to install packages from GitHub). You just have to make sure to add the new package to the correct R library used by Jupyter:
If you want to know more about kernels or about running R in a Docker environment, check out this page.
Adding Some R Magic To Jupyter
A huge advantage of working with notebooks is that they provide you with an interactive environment. That interactivity comes mainly from the so-called 'magic commands'.
These commands allow you to switch from Python to command line instructions or to write code in another language such as R, Julia, Scala, …
To switch from Python to R, you first need to download the following package:
After that, you can get started with R, or you can easily switch from Python to R in your data analysis with the %R magic command.
Let's demonstrate how the R magic works with a small example:
If you want more details about Jupyter, on how to set up a notebook, where to download the application, how you can run the notebook application (via Docker, pip install or with the Anaconda distribution) or other details, check out our Definitive Guide.
The R Notebook
Up until recently, Jupyter seems to have been a popular solution for R users, next to notebooks such as Apache Zeppelin or Beaker.
Also, other alternatives to report results of data analyses, such as R Markdown, Knitr or Sweave, have been hugely popular in the R community.
However, this might change with the recent release of the R or R Markdown Notebook by RStudio.
You see it: the context of the R Markdown Notebook is complex, and it's worth looking into the history of reproducible research in R to understand what drove the creation and development of this notebook. Ultimately, you will also realize that this notebook is different from others.
R And The History of Reproducible Research
In his talk, J.J Allaire, confirms that the efforts in R itself for reproducible research, the efforts of Emacs to combine text code and input, the Pandoc, Markdown and knitr projects, and computational notebooks have been evolving in parallel and influencing each other for a lot of years. He confirms that all of these factors have eventually led to the creation and development of notebooks for R.
Firstly, computational notebooks have quite a history: since the late 80s, when Mathematica’s front end was released, there have been a lot of advancements. In 2001, Fernando Pérez started developing IPython, but only in 2011 the team released the 0.12 version of IPython was realized. The SageMath project began in 2004. After that, there have been many notebooks. The most notable ones for the data science community are the Beaker (2013), Jupyter (2014) and Apache Zeppelin (2015).
Then, there are also the markup languages and text editors that have influenced the creation of RStudio's notebook application, namely, Emacs, Markdown, and Pandoc. Org-mode was released in 2003. It’s an editing and organizing mode for notes, planning and authoring in the free software text editor Emacs. Six years later, Emacs org-R was there to provide support for R users. Markdown, on the other hand, was released in 2004 as a markup language that allows you to format your plain text in such a way that it can be converted to HTML or other formats. Fast forward another couple of years, and Pandoc was released. It's a writing tool and as a basis for publishing workflows.
Lastly, the efforts of the R community to make sure that research can be reproducible and transparent have also contributed to the rise of a notebook for R. 2002, Sweave was introduced in 2002 to allow the embedding of R code within LaTeX documents to generate PDF files. These pdf files combined the narrative and analysis, graphics, code, and the results of computations. Ten years later, knitr was developed to solve long-standing problems in Sweave and to combine features that were present in other add-on packages into one single package. It’s a transparent engine for dynamic report generation in R. Knitr allows any input languages and any output markup languages.
Also in 2012, R Markdown was created as a variant of Markdown that can embed R code chunks and that can be used with knitr to create reproducible web-based reports. The big advantage was and still is that it isn’t necessary anymore to use LaTex, which has a learning curve to learn and use. The syntax of R Markdown is very similar to the regular Markdown syntax but does have some tweaks to it, as you can include, for example, LaTex equations.
R Markdown Versus Computational Notebooks
R Markdown is probably one of the most popular options in the R community to report on data analyses. It's no surprise whatsoever that it is still a core component in the R Markdown Notebook.
And there are some things that R Markdown and notebooks share, such as the delivering of a reproducible workflow, the weaving of code, output, and text together in a single document, supporting interactive widgets and outputting to multiple formats. However, they differ in their emphases: R Markdown focuses on reproducible batch execution, plain text representation, version control, production output and offers the same editor and tools that you use for R scripts.
On the other hand, the traditional computational notebooks focus on outputting inline with code, caching the output across sessions, sharing code and outputting in a single file. Notebooks have an emphasis on an interactive execution model. They don’t use a plain text representation, but a structured data representation, such as JSON.
That all explains the purpose of RStudio's notebook application: it combines all the advantages of R Markdown with the good things that computational notebooks have to offer.
That's why R Markdown is a core component of the R Markdown Notebook: RStudio defines its notebook as 'an R Markdown document with chunks that can be executed independently and interactively, with output visible immediately beneath the input'.
How To Work With R Notebooks
If you’ve ever worked with Jupyter or any other computational notebook, you’ll see that the workflow is very similar. One thing that might seem very different is the fact that now you’re not working with code cells anymore by default: you’re rather working with a sort of text editor in which you indicate your code chunks with R Markdown.
How To Install And Use The R Markdown Notebook
The first requirement to use the notebook is that you have the newest version of RStudio available on your PC. Since notebooks are a new feature of RStudio, they are only available in version 1.0 or higher of RStudio. So, it’s important to check if you have a correct version installed.
If you don’t have version 1.0 or higher of RStudio, you can download the latest version here.
Then, to make a new notebook, you go to File tab, select'New File', and you'll see the option to create a new R Markdown Notebook. If RStudio prompts you to update some packages, just accept the offer and eventually a new file will appear.
Tip: double-check whether you’re working with a notebook by looking at the top of your document. The output should be html_notebook.
You’ll see that the default text that appears in the document is in R Markdown. R Markdown should feel pretty familiar to you, but if you’re not yet quite proficient, you can always check out our Reporting With R Markdown course or go through the material that is provided by RStudio.
Note that you can always use the gear icon to adjust the notebook's working space: you have the option to expand, collapse, and remove the output of your code, to change the preview options and to modify the output options.
This last option can come in handy if you want to change the syntax highlighting, apply another theme, adjust the default width and height of the figures appearing in your output, etc.
From there onwards, you can start inserting code chunks and text!
You can add code chunks in two ways: through the keyboard shortcut Ctrl + Alt + I or Cmd + Option + I, or with the insert button that you find in the toolbar.
What's great about working with these R Markdown notebooks is the fact that you can follow up on the execution of your code chunks, thanks to the little green bar that appears on the left when you're executing large code chunks or multiple code chunks at once. Also, note that there's a progress bar on the bottom.
You can see the green progress bar appearing in the gif below:
Talking about code execution: there are multiple ways in which you can execute your R code chunks.
You can run a code chunk or run the next chunk, run all code chunks below and above; but you can also choose to restart R and run all chunks or to restart and to clear the output.
Note that when you execute the notebook's code, you will also see the output appearing on your console! That might be a rather big difference for those who usually work with other computational notebooks such as Jupyter.
If there are any errors while the notebook's code chunks are being executed, the execution will stop, and there will appear a red bar alongside the code piece that produces the error.
You can suppress the halt of the execution by adding errors = TRUE in the chunk options, just like this:
Note that the error will still appear, but that the notebook's code execution won't be halted!
How To Use R Markdown Notebook’s Magic
Just like with Jupyter, you can also work interactively with your R Markdown notebooks. It works a bit differently from Jupyter, as there are no real magic commands; To work with other languages, you need to add separate Bash, Stan, Python, SQL or Rcpp chunks to the notebook.
These options might seem quite limited to you, but it's compensated in the ease with which you can easily add these types of code chunks with the toolbar's insert button.
Also working with these code chunks is easy: you can see an example of SQL chunks in this document, published by J.J Allaire. For Bash commands, you just type the command. There's no need extra characters such as ‘!’ to signal that you're working in Bash, like you would do when you would work with Jupyter.
How To Output Your R Markdown Notebooks
Before you render the final version of a notebook, you might want to preview what you have been doing. There's a handy feature that allows you to do this: you'll find it in your toolbar.
Click on the 'preview' button and the provisional version of your document will pop up on the right-hand side, in the 'Viewer' tab.
By adding some lines to the first section on top of the notebook, you can adjust your output options, like this:
To see where you can get those distributions, you can just try to knit, and the console output will give you the sites where you can download the necessary packages.
Note that this is just one of the many options that you have to export a notebook: there's also the possibility to render GitHub documents, word documents, beamer presentation, etc. These are the output options that you already had with regular R Markdown files. You can find more info here.
Tips And Tricks To Work With R Notebook
Besides the general coding practices that you should keep in mind, such as documenting your code and applying a consistent naming scheme, code grouping and name length, you can also use the following tips to make a notebook awesome for others to use and read:
- Just like with computational notebooks, it might be handy to split large code chunks or code chunks that generate more than one output into multiple chunks. This way, you will improve the general user experience and increase the transparency of a notebook.
- Make use of the keyboard shortcuts to speed up your work. You will find most of them in the toolbar, next to the commands that you want to perform.
- Use the spellchecker in the toolbar to make sure your report's vocabulary is correct.
- Take advantage of the option to hide your code if a notebook is code-heavy. You can do this through code chunk options or in the HTML file of the notebook itself!
The R Notebook Versus The Jupyter Notebook
Besides the differences between the Jupyter and R Markdown notebooks that you have already read above, there are some more things.
Let's compare Jupyter with the R Markdown Notebook!
There are four aspects that you will find interesting to consider: notebook sharing, code execution, version control, and project management.
Notebook Sharing
The source code for an R Markdown notebook is an .Rmd file. But when you save a notebook, an .nb.html file is created alongside it. This HTML file is an associated file that includes a copy of the R Markdown source code and the generated output.
That means that you need no special viewer to see the file, while you might need it to view notebooks that were made with the Jupyter application, which are simple JSON documents, or other computational notebooks that have structured format outputs. You can publish your R Markdown notebook on any web server, GitHub or as an email attachment.
There also are APIs to render and parse R Markdown notebooks: this gives other frontend tools the ability to create notebook authoring modes for R Markdown. Or the APIs can be used to create conversion utilities to and from different notebook formats.
To share the notebooks you make in the Jupyter application, you can export the notebooks as slideshows, blogs, dashboards, etc. You can find more information in this tutorial. However, there are also the default options to generate Python scripts, HTML files, Markdown files, PDF files or reStructured Text files.
Nested List Markdown Jupyter
Code Execution
R Markdown Notebooks have options to run a code chunk or run the next chunk, run all code chunks below and above; In addition to these options, you can also choose to restart R and run all chunks or to restart and to clear the output.
These options are interesting when you’re working with R because the R Markdown Notebook allows all R code pieces to share the same environment. However, this can prove to be a huge disadvantage if you’re working with non-R code pieces, as these don’t share environments.
All in all, these code execution options add a considerable amount of flexibility for the users who have been struggling with the code execution options that Jupyter offers, even though if these are not too much different: in the Jupyter application, you have the option to run a single cell, to run cells and to run all cells. You can also choose to clear the current or all outputs. The code environment is shared between code cells.
Version control
List Markdown Jupiter Island
There have been claims that Jupyter messes up the version control of notebooks or that it's hard to use git with these notebooks. Solutions to this issue are to export the notebook as a script or to set up a filter to fix parts of the metadata that shouldn't change when you commit or to strip the run count and output.
The R Markdown notebooks seem to make this issue a bit easier to handle, as they have associated HTML files that save the output of your code and the fact that the notebook files are essentially plain text files, version control will be much easier. You can choose to only put your .Rmd file on GitHub or your other versioning system, or you can also include the .nb.html file.
Project Management
As the R Markdown Notebook is native to the RStudio development kit, the notebooks will seamlessly integrate with your R projects. Also, these notebooks support other languages, including Python, C, and SQL.
On the other hand, the Jupyter project is not native to any development kit: in that sense, it will cost some effort to integrate this notebook seamlessly with your projects. But this notebook still supports more languages and will be a more suitable companion for you if you’re looking for use Scala, Apache Toree, Julia, or another language.
Alternatives to Jupyter or R Markdown Notebooks
Apart from the notebooks that you can use as interactive data science environments which make it easy for you to share your code with colleagues, peers, and friends, there are also other alternatives to consider.
Because sometimes you don't need a notebook, but a dashboard, an interactive learning platform or a book, for example.
You have already read about options such as Sweave and Knitr in the second section. Some other options that are out there, are:
- Even though this blog post has covered R Markdown to some extent, you should know that you can do so much more with it. For example, you can build dashboards with flexdashboard.
- Or you can use Bookdown to quickly publish HTML, PDF, ePub, and Kindle books with R Markdown.
- Shiny is a tool that you can also use to create dashboards. To get started with Shiny, go to this page.
- In an educational setting, DataCamp Light might also come in handy to create interactive tutorials on your blog or website. If you want to see DataCamp light at work, go to this tutorial, for example.