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@ajing·5 public vizzes
This example presents a shell script that removes all exited Docker containers. It shows the command sequence for identifying stopped containers and cleaning them up in one step. The script uses the Docker command-line API with `docker ps --filter status=exited -q` to list container IDs in quiet mode, then passes those IDs to `docker rm` for deletion. The rendering approach is purely textual, displaying the essential command and its immediate purpose in a straightforward, copy-paste format.
This example demonstrates a JupyterLab customization that inserts the R pipe operator `%>%` into a notebook cell with a single keyboard shortcut. It shows how the `notebook:replace-selection` command can be bound to the `Ctrl Accel M` key combination, replacing the current selection with the specified text. The configuration uses a JSON shortcuts file that targets the `.jp-Notebook` selector, leveraging JupyterLab’s built-in command system. This approach simplifies repetitive typing for R users by providing a direct, accelerator-based alternative to manual entry.
This example shows a step-by-step shell script for installing and configuring Phusion Passenger with Apache on Ubuntu, including commands to enable the passenger module and restart the web server. It demonstrates the use of `apt-get`, `a2enmod`, `apache2ctl`, and `passenger-config` to validate the installation and check memory statistics. The script also includes instructions for enabling a specific site configuration and disabling the default one, ending with a service restart. The visualization is a plain-text terminal command sequence, not a graphical chart.
This example presents a shell script that installs a curated collection of R packages for a new R version, organized by workflow category. It covers data manipulation with `dplyr`, `tidyr`, `lubridate`, and `purrr`; reading data via `readr` and `rvest`; plotting with `ggplot2`, `ggmap`, and `viridis`; database connections through `RODBC`, `RJDBC`, and `RMySQL`; and modeling with `mgcv`, `lme4`, and `caret`. The script also includes commented-out spatial packages (`sp`, `maptools`) and instructions for ROracle installation, showing a practical setup routine.
A tree visualization uses a force-directed layout to display hierarchical data, with nodes sized by their aggregated child sizes and colored by group. The tree is animated, and users can click nodes to expand or collapse their children, while drag-and-drop and zoom behaviors allow manual repositioning and scaling. The code relies on D3 v2 APIs including `d3.layout.force`, `d3.layout.tree`, `d3.behavior.drag`, and `d3.behavior.zoom`, with data loaded from an inline JSON object.