Open Source R Information Analysis Software

R Information Analysis Software

View 2551 business solutions

Browse free open source R Information Analysis Software and projects below. Use the toggles on the left to filter open source R Information Analysis Software by OS, license, language, programming language, and project status.

  • Accounting Software for Small Businesses | Xero Icon
    Accounting Software for Small Businesses | Xero

    Save 90% for 6 months on Xero's award-winning accounting and online bookkeeping platform for businesses of all sizes and stages of growth.

    Xero offers a robust ecosystem of connected apps and integrations with banks and financial institutions, enabling small businesses to access a wide range of solutions within Xero's open platform to streamline operations and manage finances. Additionally, accounting and bookkeeping firms benefit from efficient compliance tools, advanced practice management software, and a cloud-based unified accounting ledger for all clients, centralized in one place.
    Get 90% off for 6 months
  • Enterprise and Small Business CRM Solution | Clear C2 C2CRM Icon
    Enterprise and Small Business CRM Solution | Clear C2 C2CRM

    Voted Best CRM System with Top Ranked Customer Support. CRM Management includes Sales, Marketing, Relationship Management, and Help Desk.

    C2CRM consists of four modules that integrate to provide a comprehensive CRM solution: Relationship Management, Sales Automation, Marketing Automation, and Customer Service. Only buy what each user needs.
    Learn More
  • 1
    LabPlot

    LabPlot

    Data Visualization and Analysis

    LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.
    Downloads: 21 This Week
    Last Update:
    See Project
  • 2
    Reproducible-research

    Reproducible-research

    A Reproducible Data Analysis Workflow with R Markdown, Git, Make, etc.

    In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next