Tags: R

Teaching Materials (21-40 of 42)

  1. DataCamp

    05 Nov 2018 | Teaching Materials

    DataCamp: The Easiest Way to Learn Data Science Online

    https://qubeshub.org/publications/913/?v=1

  2. Computational Biology using R

    30 Oct 2018 | Teaching Materials | Contributor(s):

    By Hong Qin

    University of Tennessee Chattanooga

    This is an introductory level course on computational biology.

    https://qubeshub.org/publications/907/?v=1

  3. BIO103R: Bio 103 and 104 Labs in R at URI

    29 Oct 2018 | Teaching Materials | Contributor(s):

    By Rachel Schwartz1, Linda Forrester1

    University of Rhode Island

    GitHub repository for Bio 103 and 104 Labs in R at University of Rhode Island

    https://qubeshub.org/publications/886/?v=1

  4. BIO 263 Ecological Data Analysis

    29 Oct 2018 | Teaching Materials | Contributor(s):

    By Rachel Schwartz1, Linda Forrester1

    University of Rhode Island

    Course materials for BIO 263 Ecological Data Analysis

    https://qubeshub.org/publications/885/?v=1

  5. BIO 439/539: Big Data Analysis

    29 Oct 2018 | Teaching Materials | Contributor(s):

    By Rachel Schwartz1, Linda Forrester1

    University of Rhode Island

    Course materials for BIO 439/539: Big Data Analysis

    https://qubeshub.org/publications/884/?v=1

  6. BIO 181G: The information age

    29 Oct 2018 | Teaching Materials | Contributor(s):

    By Rachel Schwartz1, Linda Forrester1

    University of Rhode Island

    Course materials for BIO181G: The information age

    https://qubeshub.org/publications/883/?v=1

  7. Data Analysis for the Life Sciences

    23 Oct 2018 | Teaching Materials | Contributor(s):

    By Rafael A Irizarry1, Michael I Love1

    Harvard University

    An online stats book written completely in R

    https://qubeshub.org/publications/841/?v=1

  8. R Reference Card

    23 Oct 2018 | Teaching Materials | Contributor(s):

    By Tom Short

    EPRI PEAC

    Summary of reference info for using R

    https://qubeshub.org/publications/840/?v=1

  9. Rstudio cheatsheets

    22 Oct 2018 | Teaching Materials

    Cheatsheets are quickstart guides to using some of the most popular packages and features of Rstudio.

    https://qubeshub.org/publications/839/?v=1

  10. A Quick Guide to Teaching R Programming to Computational Biology Students

    22 Oct 2018 | Teaching Materials | Contributor(s):

    By Stephen Elgen

    University of Cambridge

    An introduction from an experienced educator in teaching R to computational biology graduate students.

    https://qubeshub.org/publications/838/?v=1

  11. Ecology and Epidemiology in R

    22 Oct 2018 | Teaching Materials | Contributor(s):

    By Paul Esker

    Kansas State University

    Ecology and epidemiology in R: Modeling dispersal gradients.

    https://qubeshub.org/publications/837/?v=1

  12. A Very Basic Tutorial for Performing Linear Mixed Effects Analyses: Tutorial 2

    20 Oct 2018 | Teaching Materials | Contributor(s):

    By Bodo Winter

    University of California, Merced

    The second of two tutorials that introduce you to linear and linear mixed models. This tutorial serves as a quick boot camp to jump-start your own analyses with linear mixed effects models.

    https://qubeshub.org/publications/833/?v=1

  13. Linear Models and Linear Mixed Effects in R: Tutorial 1

    20 Oct 2018 | Teaching Materials | Contributor(s):

    By Bodo Winter

    University of California, Merced

    The first of two tutorials that introduce you to linear and linear mixed models.

    https://qubeshub.org/publications/832/?v=1

  14. R for biologists

    18 Oct 2018 | Teaching Materials | Contributor(s):

    By Marco Martinez

    University of Tennessee - Knoxville

    An introductory guide to data analysis in R for life sciences researchers.

    https://qubeshub.org/publications/815/?v=1

  15. Cookbook for R

    11 Sep 2018 | Teaching Materials | Contributor(s):

    By Winston Chang

    The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data.

    https://qubeshub.org/publications/780/?v=1

  16. IPMpack: an R package for Integral Projection Models

    11 Sep 2018 | Teaching Materials | Contributor(s):

    By C. Jessica E. Metcalf1, Sean M. McMahon2, Roberto Salguero-Gómez3, Eelke Jongejans4, Cory Merow5

    1. University of Oxford 2. Smithsonian Tropical Research Institute 3. University of Queensland 4. Radbound University Nijmegen 5. STRI and University of Connecticut

    IPMpack is an R package (R Development Core Team 2013) that allows users to build and analyse Integral Projection Models.

    https://qubeshub.org/publications/779/?v=1

  17. Phenotypic plasticity and predation

    29 Aug 2018 | Teaching Materials | Contributor(s):

    By Jeremy M Wojdak1, Justin Touchon2

    1. Radford University 2. Vassar College

    Students predict changes to tadpole morphology and coloration after considering characteristics of the predator species and the prey themselves then test their own hypotheses (typically with...

    https://qubeshub.org/publications/545/?v=2

  18. Phenotypic plasticity and predation

    24 Aug 2018 | Teaching Materials | Contributor(s):

    By Jeremy M Wojdak1, Justin Touchon2

    1. Radford University 2. Vassar College

    Students predict changes to tadpole morphology and coloration after considering characteristics of the predator species and the prey themselves then test their own hypotheses (typically with...

    https://qubeshub.org/publications/545/?v=1

  19. Teaching R with Swirl

    19 Jun 2018 | Teaching Materials | Contributor(s):

    By Paige Parry

    George Fox University

    Session presentation on using Swirl, an interactive platform for learning and teaching R in the R console

    https://qubeshub.org/publications/664/?v=1

  20. Painting turtles: an introduction to species distribution modeling in R

    12 Jun 2018 | Teaching Materials | Contributor(s):

    By Anna Carter

    Iowa State University

    This module uses R software to introduce students to the process of accessing databases and building species distribution models based on occurrence records.

    https://qubeshub.org/publications/378/?v=1