Search Results for "sourceforge.net/projects/winpython/files/winpython_3.8/3.8.10.0/winpython64-3.8.10.0.7z"
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Showing 23 open source projects for "sourceforge.net/projects/winpython/files/winpython_3.8/3.8.10.0/winpython64-3.8.10.0.7z"
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Agentic AI SRE built for Engineering and DevOps teams.NeuBird AI's agentic AI SRE delivers autonomous incident resolution, helping team cut MTTR up to 90% and reclaim engineering hours lost to troubleshooting.
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Readr
Read flat files (csv, tsv, fwf) into R
readr is an R package that provides a fast and friendly way to read rectangular data, such as CSV and TSV files. Part of the Tidyverse, it simplifies data import and parsing tasks in R. -
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reticulate
R Interface to Python
...It lets you call Python modules, classes, and functions from within R, automatically translating between R and Python data structures. Useful for combining Python tooling with R projects, data analysis, and RMarkdown reports. -
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renv
renv: Project environments for R
renv is an R dependency management toolkit that enables project-level library isolation and reproducibility. It tracks package versions in a lockfile and can restore exact library states across machines or over time, making R projects portable and consistent. -
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gm
R Package for Music Score and Audio Generation
Create music easily, and show musical scores and audio files in R Markdown documents, R Jupyter Notebooks and RStudio. -
Free Website Monitoring Service | UptimeRobotWith the Free Plan, you can monitor up to 50 URLs, check for a website's content (using the keyword monitor), ping your server or monitor your ports in 5-minute intervals. You can create a status page to showcase your uptime. SMS or Call alerts can be bought anytime.
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RStudio Cheatsheets
Curated collection of official cheat sheets for data science tools
...Each cheat sheet is a single (or double) page PDF that condenses important syntax, functions, workflows, and best practices into a visually organized format ideal for quick reference. 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. ... -
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workflowr
Organize your project into a research website
workflowr is an R package that helps researchers organize, version, and share their data science projects in a reproducible and transparent manner. It combines R Markdown, Git, and a structured file system to create a research website that tracks analysis, results, and code changes over time. It’s ideal for academic and collaborative research workflows. -
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lintr
Static Code Analysis for R
lintr is a static code analysis tool for R that identifies syntax errors, style inconsistencies, and other potential issues in R scripts and packages. It supports customizable lint rules and integrates with many editors to provide realtime feedback and enforce coding standards (e.g., tidyverse style). -
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targets
Function-oriented Make-like declarative workflows for R
...It’s something like GNU Make for R, but more integrated. Skipping computation for up-to-date targets so that unchanged parts of the workflow are not recomputed. Targets can represent files or R objects, and tracking file changes etc is incorporated. -
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NYC Taxi Data
Import public NYC taxi and for-hire vehicle (Uber, Lyft)
...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 is often used as a benchmark dataset and example for teaching, benchmarking, and demonstration purposes in the data science and urban analytics communities. -
Composable, Open Source Payments PlatformJuspay's Payments Orchestration Platform offers a comprehensive product suite for businesses, including open-source payment orchestration, global payouts, seamless authentication, payment tokenization, fraud & risk management, end-to-end reconciliation, unified payment analytics & more. The company’s offerings also include end-to-end white label payment gateway solutions & real-time payments infrastructure for banks. These solutions help businesses achieve superior conversion rates, reduce fraud, optimize costs, and deliver seamless customer experiences at scale.
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R Color Palettes
Comprehensive list of color palettes available in R
...The goal is to provide designers, data scientists, and R users with aesthetically pleasing, perceptually consistent color schemes that work well for plots, maps, and graphics. The repo contains static files listing palette definitions (e.g. hex codes, named hues), sample visualizations showing how each palette performs under different contexts (categorical, sequential, diverging), and helper functions/scripts to import or use the palettes in R. The author also documents palette provenance and usage guidance (contrast, readability, colorblind friendliness). ... -
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reprex
Render bits of R code for sharing, e.g., on GitHub or StackOverflow
reprex is an R package (from the tidyverse / Posit ecosystem) that helps users make reproducible examples (reprexes) of R code: self-contained, shareable, minimal examples capturing an issue or showing desired behavior. It formats code and its output nicely (often using Markdown or syntax appropriate to posting on forums, GitHub, StackOverflow etc.), handles dependencies, session info, etc. The goal is to make debugging, asking for help, or demonstrating code easier through rigorous... -
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pkgdown
Generate static html documentation for an R package
pkgdown is an R package (by the r-lib group) whose purpose is to generate static websites (HTML) for R packages, automatically converting a package’s help files, vignettes, README, NEWS, etc., into a documentation website. It helps package authors share their documentation online with minimal friction. It supports custom templates, themes, and configuration. pkgdown 2.0.0 includes an upgrade from Bootstrap 3 to Bootstrap 5, which is accompanied by a whole bunch of minor UI improvements. ... -
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data.table
Extends base R’s data for high-performance data manipulation
data.table is an R package that extends base R’s data.frame for high-performance data manipulation. It offers concise syntax, blazing speed, and memory-efficient operations. It supports fast file reading/writing, joins, grouping, reshaping, and updates by reference. It is heavily used in large data workflows, big data in R, production pipelines, etc. Extremely efficient grouping/aggregation/summarization; can handle very large datasets (hundreds of millions to billions of rows) in memory (if... -
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box
Write reusable, composable and modular R code
...Such modules can be stored in a central module search path (configured via options('box.path')) analogous to the R package library, or locally in individual projects. Let’s assume the module we just defined is stored in a file hello_world.r inside a directory mod, which is inside the module search path. -
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ggthemes
Additional themes, scales, and geoms for ggplot2
ggthemes is an R package that provides extra themes, scales, and geoms for ggplot2. It supplements the default ggplot2 offerings by allowing users to apply special themes (e.g., inspired by classic publications, external visualization styles), additional scale functions, and specialized geoms or color scales. It is often used to make ggplot2 plots adhere to aesthetic styles from famous news outlets, scientific journals, or presentation decks. Additional color scales and palettes for discrete... -
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activAnalyzer is a Shiny app that has been developed to analyze daily physical behavior data recorded at the hip in adults using an ActiGraph accelerometer (.agd file from a GT3X, GT3X+, wGT3X+ or wGT3X-BT device). Once analysis is completed, the app allows exporting results to .csv files and generating a report of the measurement (in either an .html format or a .pdf format). All the configured inputs relevant for interpreting the results are recorded in the report. Be sure that the inputs that are configured when generating the report correspond to the analysis that was actually performed (in other words, avoid modifying the inputs after generating satisfactory results). ...
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Data Analysis for the Life Sciences
Rmd source files for the HarvardX series PH525x
This repository holds the R Markdown (.Rmd) source files for the PH525x / HarvardX course series (Data Analysis for the Life Sciences / Genomics) managed by GenomicsClass. It functions as the canonical source for course lab exercises, lecture modules, and reading materials in reproducible format. Students and learners use these R Markdown files to follow along, knit notebooks, run code samples, and complete the lab-based assignments. -
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osm4scala
Reading OpenStreetMap Pbf files.
Scala and polyglot Spark library (Scala, PySpark, SparkSQL, ... ) focused on reading OpenStreetMap Pbf files. -
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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
...It contains course materials such as R scripts, notebooks, and worked examples aligned with McElreath’s textbook. The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. ... -
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R4DS (R for Data Science)
R for data science: a book
...It covers the workflow from importing data, tidying, transforming, visualizing, modelling, communicating results, and programming in R. The repository contains the source files (Quarto / RMarkdown), example datasets, visualizations, exercises, and all content needed to build the book. Includes many example datasets, diagrams, code samples, and “hands-on” exercises. Comprehensive coverage of data-science workflow: data import, cleaning, transformation, exploration, modelling etc. Includes topics beyond basics: relational data (joins), date/time, strings, working with missing values, visualizing data, etc. -
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Open Intro Statistics
An open-source textbook written at the college level
...Each chapter's content is in one of the eight chapter folders that start with "ch_". Within each folder, there is a "figures" folder and a "TeX" folder. The TeX folder contains the text files that are used to typeset the chapters in the textbook. In many cases, R code is supplied with figures to regenerate the figure. It will often be necessary to install the "openintro" R package that is available from GitHub (https://github.com/OpenIntroOrg) if you would like to regenerate a figure. Other packages may also occasionally be required. -
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. 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,... -
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Data Science Specialization
Course materials for the Data Science Specialization on Coursera
...It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.
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