Gist bae6758b4e3645b55f022e6432f29458

100%

Gist bae6758b4e3645b55f022e6432f29458

✓ Published0🌍 Public

Last edited May 22, 2017

Created on May 22, 2017

This visualization demonstrates the interaction between R and PowerShell, showing how a system call from an R environment triggers a PowerShell script that manipulates data. The code uses the `system2` function to execute a PowerShell command with a specified file path, passing arguments to run `coolScript.ps1`. The example highlights the practical integration of two scripting environments, where R serves as the orchestrator for external process execution. The rendering relies on the command-line interface rather than graphical libraries, focusing on the workflow of invoking and managing external scripts from within an R session. The data flow is implicit in the script's execution, emphasizing interoperability over visual output.

AI-generated description

Similar vizzes

Loading thumbnail…

R.Execute example without the use of R.Execute() in Power Query [behind the scenes this gets executed bin\x64\Rscript.exe" --encoding=UTF-8 RScriptWrapper.R"]

This example demonstrates how to execute an R script from within Power Query without explicitly invoking the `R.Execute` function, instead relying on the `AdoDotNet.DataSource` with the `R.Provider` to run a native query. The code passes a string containing an `output` variable that captures the result of `system2("whoami", stdout=TRUE)`, which retrieves the current username. The resulting table is then converted from binary R data using `RData.FromBinary`. This approach showcases the underlying use of `Rscript.exe` via the provider, highlighting an alternative integration path for R workflows in Power Query.

HHugoberry

66% match

Loading thumbnail…

Gist 39225

This visualization presents a server-stack installation script, not a typical data-driven graphic. It maps the procedural flow of an unattended Bash deployment of Ruby Enterprise and the Passenger Apache module, tracing each command and conditional branch as a node in a directed graph. The code uses a custom layout to arrange the script’s logic, with nodes representing commands like `sudo apt-get install` and `gem install`, while edges show the sequence and decision points (e.g., `if [ -e $REEF ]`). The rendering approach highlights the script’s structure, with color coding for installation steps versus error-handling exits, and the data source is the literal Bash code itself, parsed to extract control flow.

DDylanFM

63% match

Loading thumbnail…

Power Query script to get msmdsrv.exe port number via R

This example shows how to retrieve the port number of the msmdsrv.exe process by combining R scripts with Power Query’s M language. It first runs a system command via `system2` to capture the task list and netstat output, then parses the results using `read.fwf` with fixed-width columns. The M code invokes the R script through `R.Execute`, formats the resulting tables with `Table.TransformColumns` and `Table.PromoteHeaders`, and finally joins the process ID from the task list with the matching netstat entry to extract the local address port. The visualization relies on text-based data processing rather than graphical libraries.

HHugoberry

62% match

Loading thumbnail…

Gist 182083

This example visualizes the output of a shell script that forcibly reinstalls Ruby Version Manager (RVM), capturing the terminal session as a log. The data is a sequence of command prompts and system responses, shown as text. The visualization would likely render this as a timeline or list of terminal output, highlighting the progression of commands, the confirmation prompt, and the cascade of permission errors during the uninstall. It emphasizes the iterative nature of the process and the repeated permission failures, turning a mundane system log into a visual narrative about software management and error handling.

DDylanFM

62% match

Loading thumbnail…

Gist f5904c4a8279840f341742d30c5d9a9e

This example demonstrates a PowerShell script that processes a text file by replacing caret characters with semicolons and writes the result to a new file. It shows a batch text-transformation workflow using the `Get-Content` cmdlet to read input in chunks of 1000 lines, a custom filter function `replace-chars` that applies the `-replace` operator, and `Add-Content` to append the processed lines to the output file. The script first checks if the output file exists and clears it to ensure a fresh write. The approach highlights pipeline-based file handling and character encoding preservation without additional libraries.

HHugoberry

61% match

Loading thumbnail…

Gist 8bb4970e2700b40a3dce1d785654d907

This visualization uses a force-directed graph to map the relationships between identifiers extracted from a Power Query M codebase. The data originates from a gist by Hugoberry, showing dependencies among functions, modules, and error-handling patterns. The rendering relies on D3.js’s `forceSimulation` with custom link and node positioning, while each node’s label is drawn using SVG text. The code specifically highlights groupings like `TableAction.*`, `ValueAction.*`, and `Pattern.*`, revealing hierarchical clusters of related API calls. The graph’s layout is animated via D3’s tick handler, and the visible node sizes suggest a metric like degree centrality, though the exact encoding is not explicit in the snippet.

HHugoberry

60% match

Loading thumbnail…

My PowerShell profile. To edit yours type `code $profile`

This example visualizes a developer’s PowerShell profile, showing how aliases and functions streamline common workflows. It renders the profile’s source code as text, highlighting the use of posh-git for prompt customization and the `Set-Alias` and `function` keywords to map commands like `yarn build` to `b`. The code also references `$GitPromptSettings` to adjust the prompt’s prefix and suffix, and sets environment variables like `$HOME`. The visualization is a static code snippet, with no external libraries or data sources beyond the profile’s own content.

HHerbCaudill

60% match

Loading thumbnail…

Run certain gists in R from GithubGist

This example shows how to run a series of R scripts hosted as GitHub Gists, each generating a clustering module for a Shiny application. It demonstrates the use of the `devtools` package’s `source_gist` function to fetch and execute code directly from specific gist URLs, with filenames specified for each dataset. The code processes specific time-point Rds files, all time points, and full datasets, including renamed and merged clustering variants. The visualization highlights a reproducible workflow for sourcing remote R code, relying on the gist API and local file handling rather than any graphical or interactive rendering.

FFloWuenne

60% match