@eagleoutice/flowr/documentation/wiki-setup.js

Static Dataflow Analyzer and Program Slicer for the R Programming Language

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.WikiSetup = void 0;
const doc_maker_1 = require("./wiki-mk/doc-maker");
const doc_files_1 = require("./doc-util/doc-files");
/**
 * https://github.com/flowr-analysis/flowr/wiki/Setup
 */
class WikiSetup extends doc_maker_1.DocMaker {
    constructor() {
        super('wiki/Setup.md', module.filename, 'setup instructions');
    }
    text({ ctx }) {
        return `
There are several ways to use _flowR_.
You can download and build it from source, download the accompanying [docker image](${doc_files_1.FlowrDockerRef}), 
or use its [Visual Studio Code extension](${doc_files_1.FlowrVsCode}) and [RStudio Addin](${doc_files_1.FlowrRStudioAddin}).

- [🗒️ Using the Visual Studio Code Extension](#-using-the-visual-studio-code-extension)
- [🗒️ Using the RStudio Addin](#-using-the-rstudio-addin)
- [🐳️ Using the Docker Image](#-using-the-docker-image)
- [⚒️ Building From Scratch](#-building-from-scratch)
- [📜 Developing for _flowR_](#-developing-for-flowr)


If you want to develop for _flowR_, you want to see how to [build from scratch](#-building-from-scratch) and have a 
look at the [Contributing Guidelines](https://github.com/flowr-analysis/flowr/tree/main//.github/CONTRIBUTING.md).

## 🗒️ Using the Visual Studio Code Extension

The easiest way to use _flowR_ is to install the [Visual Studio Code extension](${doc_files_1.FlowrVsCode}).
The extension directly includes a version of _flowR_ that can be used by default, so you can start analyzing code right away (given that you have R installed on your system).
Please check out the marketplace entry for more information.
This extension works with Positron as well (in general, it is available on [open-vsx](${doc_files_1.FlowrPositron}) as well).

## 🗒️ Using the RStudio Addin

You can also use _flowR_ as an RStudio Addin. Please check out the [RStudio Addin repository](${doc_files_1.FlowrRStudioAddin}) for more information on how to get started!

## 🐳️ Using the Docker Image

You can get the image from [docker hub](${doc_files_1.FlowrDockerRef}) by running:

\`\`\`shell
docker pull eagleoutice/flowr
\`\`\`

Afterward, you can test if the installation was successful by running the following (currently, there is no helper script for that):

\`\`\`shell
docker run -it --rm eagleoutice/flowr
\`\`\`

This should drop you into _flowR_'s read-evaluate-print loop.
Enter ${ctx.replCmd('help')} to receive more information and ${ctx.replCmd('quit')} to leave.
Please remember that you have to link external directories to make them available within the running container.

To start flowr as a server, you can run:

\`\`\`shell
docker run -it --rm -p1042:1042 eagleoutice/flowr --server
\`\`\`

For more information, see the ${ctx.linkPage('wiki/Interface')} wiki page.

## ⚒️ Building From Scratch

To use _flowR_, you may need [_R_](https://www.r-project.org/) installed and on your path
(this only affects the [\`r-shell\` engine][r-shell], which is _not_ the default).

### Installing R
Although there are several ways to do so, there is nothing wrong with installing&nbsp;R with the help of your favorite package manager or directly from the [website](https://cloud.r-project.org/).<a href="#note1" id="note1ref"><sup>&lt;1&gt;</sup></a>
For 🪟&nbsp;Windows, see [here](https://www.hanss.info/sebastian/post/rtools-path/) for an explanation on how to add [_R_](https://www.r-project.org/) to your path variable.
Again, you only require this for the [\`r-shell\` engine][r-shell].

### Installing Node.js

Furthermore, you need the [node package manager](https://www.npmjs.com/) (for Linux, we recommend using [nvm](https://github.com/nvm-sh/nvm)).
To work, we currently rely on node versions starting from \`22.x\`.

### Installing _flowR_

After cloning the repository,<a href="#note2" id="note2ref"><sup>&lt;2&gt;</sup></a> you can install the dependencies with:

\`\`\`shell
npm i
\`\`\`

After that, you should be fine! You may test _flowR_'s command-line interface by running the following from the \`cli\` directory:

\`\`\`shell
npm run slicer -- --criterion "12@product" test/testfiles/example.R
\`\`\`

The output should look similar to this:

\`\`\`R
product <- 1
N <- 10
for(i in 1:(N-1)) product <- product * i
cat("Product:", product, "\\n")
\`\`\`

At the time of writing this, there is currently no page for frequently encountered errors. So just [message me](mailto:florian.sihler@uni-ulm.de) in case of problems.

## 📜 Developing for _flowR_

If you want to develop for _flowR_, explore the wiki.
For details on _how_ to contribute, please refer to the [CONTRIBUTING.md](https://github.com/flowr-analysis/flowr/blob/main/.github/CONTRIBUTING.md) in the repository.

-----
<a id="note1" href="#note1ref">&lt;1&gt;</a>: Currently, _flowR_ is only tested with R versions \`4.x\` and \`3.6.x\`.

<a id="note2" href="#note2ref">&lt;2&gt;</a>: We use
[git-lfs](https://git-lfs.com/) to store larger files, especially for the wiki pages. So if you want to work on these parts, make sure to have it set-up (see the [CONTRIBUTING.md](https://github.com/flowr-analysis/flowr/blob/main/.github/CONTRIBUTING.md) in the repository for more information).

[r-shell]: (https://github.com/flowr-analysis/flowr/wiki/Engines)
    `.trim();
    }
}
exports.WikiSetup = WikiSetup;
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