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@aakansh9·14 public vizzes
This visualization breaks down a personal macOS setup into a hierarchical checklist, categorizing configuration notes, applications, and developer tools. It presents the data as a tree structure built from a Markdown source file, with each branch and leaf representing a category or individual item. The rendering uses a custom treemap layout to allocate rectangular areas proportional to the size of each subcategory, while interactive hover states reveal the full item names. The code relies on D3.js for hierarchy computation and SVG rendering, enabling users to explore the organization of the setup at a glance.
This example documents a practical workflow for containerizing data science environments with Docker, covering local macOS setups and remote Amazon EC2 instances. It shows shell commands for pulling Kaggle’s Python and R images, running Jupyter or RStudio servers with memory and CPU limits, and installing tools like tmux, htop, and pyspark inside containers. For EC2, it details mounting EBS volumes, configuring security groups, and enabling GPU access via nvidia-docker, including driver installation and clock optimization for K80 cards. The source is a markdown note with command snippets, not an interactive visualization.
This example visualizes tmux configuration and usage through two code files: a `.tmux.conf` file and a Markdown cheatsheet. The configuration file demonstrates custom key bindings that remap the prefix key, pane splitting, and status bar styling. The cheatsheet organizes tmux commands into a Markdown table comparing default and custom bindings. The visualization pairs raw configuration syntax with structured documentation, showing how terminal multiplexer settings and shortcuts are documented and customized.
This visualization presents a reference table of useful Unix commands, listing syntax, options, and use cases in a structured markdown format. It also includes a second section covering system directories like `/bin`, `/dev`, and `/etc` with brief descriptions. The data appears to be sourced from a gist by aakansh9, rendered as plain text tables without any JavaScript libraries or interactive APIs—the display relies solely on static HTML and CSS for styling. The example focuses on clear, readable documentation rather than dynamic or graphical representation.
This visualization, “Brew Notes,” presents a structured overview of the Homebrew package manager through a Markdown-formatted reference sheet. It displays terminology, command syntax with options, and installation paths, organizing the information into clear tables and lists. The rendering relies on standard Markdown parsing rather than a custom JavaScript library, with no interactive or dynamic data visualization elements. The content is sourced from a plain-text gist authored by aakansh9, and the layout is achieved through basic HTML and CSS generated by the Markdown processor.
This example demonstrates how to engineer and transform date, time, categorical, and numerical features for predictive modeling. The R script defines date-based variables such as day of week, week of year, and quarter, alongside time bins for 24-hour periods, and incorporates open calendar data like national holidays and seasons. It applies categorical encodings via hashing with FeatureHashing's `hashed.model.matrix` using a 14-bit hash, one-hot, target, orthogonal polynomial, and deviation methods. Numerical transformations include Box-Cox and binning, while general manipulations support column-wise or combinatorial diff, sum, prod, and custom formula operations across data frames.
This visualization presents Docker command syntax as an interactive reference, organizing the gist’s structured notes into a force-directed network. Using d3.forceSimulation, it positions nodes for commands, options, and objects, with links encoding the relationships between root commands, their flags, and use cases. The layout clusters commands by category—containers, general, and images—while tooltips reveal full syntax and descriptions from the markdown source. The rendering relies on D3’s SVG and simulation APIs to create a browsable graph of the CLI’s command hierarchy.
This visualization presents a markdown-based reference guide for Prezto, a configuration framework for zsh, organized as an annotated document rather than a chart. It shows the framework’s config file locations, module terminology, and a table of prompt-related commands with their uses, alongside a link to prompt theme examples. The rendering relies on standard web technologies: HTML and CSS for layout and styling, with no JavaScript libraries or data-driven APIs involved. The source is a plain-text markdown file, and the display mirrors its structure directly, using tables and code blocks for clarity.
This visualization presents a curated reference card for youtube-dl, a command-line tool for downloading videos and audio. It shows the tool’s core commands and options in two grouped tables, distinguishing general playback controls from file-handling and audio-conversion flags. The layout uses plain Markdown tables to organize the API syntax, with option names and their descriptions aligned for quick scanning. The example concludes with a composite command that combines `--batch-file`, `--audio-format mp3`, and `--embed-thumbnail` to demonstrate a complete workflow for downloading music with album art.
This R script implements custom cross-validation and training functions for XGBoost, targeting a 20-column regression problem from the SPHERE challenge on safe aging. It trains 20 separate gradient-boosted tree models simultaneously, one per target column, using `xgb.DMatrix` and the internal `xgb.iter.update` API to update all boosters in lockstep. The cross-validation function computes a weighted Brier score across all targets, normalizes predictions per row, and supports early stopping. The code relies on the `xgboost` and `dplyr` packages, with prediction output as a data frame.
This example presents a step-by-step shell and R script for configuring a data-science environment on an Ubuntu 14.04 EC2 instance, focusing on installing R, RStudio Server, and the xgboost package. It shows the setup commands for security group settings, user permissions, and system updates, along with package installations via `apt-get`, `gdebi`, and R’s `install.packages` from the drat repository. The code also includes dependencies like `libcurl4-openssl-dev`, `libxml2-dev`, and tools such as `mono-complete` and `ffmpeg`. The description highlights the practical workflow rather than a visualization, emphasizing the underlying infrastructure for data analysis.
This R script provides a collection of feature-exploration functions for data science competitions, demonstrating common preprocessing tasks. It shows how to remove duplicate and highly correlated columns, one-hot encode categorical variables, and apply deviation encoding for high-cardinality factors. The code uses the `caret` package's `findCorrelation` for correlation-based feature removal, `model.matrix` for dummy encoding, and `doMC` for parallel correlation computation via `bigcorPar`. It also leverages `dplyr` for grouped summarization in deviation encoding, with the `ff` package for large correlation matrices.
This example shows a custom cross-validation function for XGBoost models on time-series data, using explicitly defined training and test index lists rather than random folds. The R code implements `xgb.ts.cv`, which extends the standard `xgb.cv` API by accepting separate `traininds` and `testinds` parameters, enabling chronological splits. It internally uses `xgb.ts.cv.mkfold` to create fold-specific boosters and watchlists, then iterates through boosting rounds with `xgb.iter.update` and `xgb.iter.eval`, aggregating results via `xgb.cv.aggcv`. The function supports early stopping, custom metrics, and prediction output, mirroring the original xgboost package’s cross-validation workflow.
This example demonstrates two custom evaluation metrics for the xgboost package in R: MAPE (mean absolute percentage error) and RMSEE (root mean square exponential error). It defines two R functions, `MAPE` and `RMSEE`, each of which takes prediction values and a training data object, then extracts labels via `getinfo` to compute the respective error metric. The code shows how to integrate custom metrics into xgboost’s training loop by returning a list with the metric name and computed value, enabling users to monitor these alternatives during model fitting.