Search Results for "google-visualization-python"

This part of the book teaches you how to leverage the plotly R package to create a variety of interactive graphics. There are two main ways to creating a plotly object: either by transforming a ggplot2 object (via ggplotly()) into a plotly object or by directly initializing a plotly object with plot_ly()/plot_geo()/plot_mapbox(). Both approaches have somewhat complementary strengths and weaknesses, so it can pay off to learn both approaches. Moreover, both approaches are an implementation of...

The nyc-taxi-data repository is a rich dataset and exploratory project around New York City taxi trip records. It collects and preprocesses large-scale trip datasets (fares, pickup/dropoff, timestamps, locations, passenger counts) to enable data analysis, modeling, and visualization efforts. The project includes scripts and notebooks for cleaning and filtering the raw data, memory-efficient processing for large CSV/Parquet files, and aggregation workflows (e.g. trips per hour, heatmaps of pickups/dropoffs). It also contains example analyses—spatial and temporal visualizations like maps, time-series plots, and hotspot detection—highlighting insights such as patterns of demand, peak times, and geospatial distributions. ...

...The repository contains source files (R Markdown or LaTeX) that generate the cheat sheets, version history, and metadata (title, author, description) for each. It covers topics such as data wrangling, data import, modeling, visualization, RStudio IDE shortcuts, Shiny development, and the tidyverse suite (dplyr, ggplot2, tidyr, purrr). These cheat sheets are widely used by R learners, educators, and practitioners as quick reference tools, and they often ship with RStudio by default or are linked from RStudio’s help/documentation pages. Users can also contribute new cheat sheet proposals, corrections, or translations via pull requests.

awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data.

This project contains bindings for various scripting languages to the Wheefun Options Parsing Library. It is meant to provide parity with the C implementation so .NET languages can take advantage of WFOPT. For more information, please see the main page.

Scala and polyglot Spark library (Scala, PySpark, SparkSQL, ... ) focused on reading OpenStreetMap Pbf files.

...It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. The repository also shows examples of linking R with external resources — APIs, databases, and file formats — and integrating into larger pipelines. It acts as a learning scaffold for students or beginners transitioning to more advanced data science work in R, offering a hands-on, example-driven approach. ...

This repository, owned by the user zonination (Zoni Nation), presents a data visualization and analysis project on long-term returns from broad stock market indexes, especially the S&P 500. The author gathers historical price data (adjusted for inflation and dividends) and computes growth trajectories under a “buy and hold” strategy over decades. The key insight illustrated is that over sufficiently long holding periods (e.g. 40 years), the stock market stabilizes and nearly always yields positive returns, even accounting for extreme market crashes and recessions. ...