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

The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.

Context

Datacamp.com is one of the most popular websites to learn Data Science from. It has courses & tutorials in both R & Python and has courses for different verticals & industries.

Content

The data was scraped from datacamp keeping in mind the need to find the list of tutorials that datacamp offers across various topics.

Acknowledgements

This dataset was based on datacamp's tutorial to scrap web pages using rvest

Inspiration

If you are looking to find tutorials in a particular topic, you can find from this dataset rather than scrolling through the pages all day long. Hope this is useful for everyone.

Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms’ life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.

We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.

We show that rou...

The data if part of the tutorial supplement to the paper "A Primer on Acoustic Analysis for Landscape Ecologists" by Villanueva-Rivera et al. featured in the Landscape Ecology special issue entitled "Soundscape Ecology" (vol. 26, pages 1233-1246, doi: 10.1007/s10980-011-9636-9). Accordingly, the exercises in the tutorial are meant to be undertaken while reading the article.

Primer_Tutorial_1.3.1.pdf - pdf of the tutorial, version 1.3.1 (24june2014) Exercise1.zip - Files for exercise 1 Exercise2.zip - Files for exercise 2 The following zip files contain 1-minute versions of the files for exercise 3 (the original files were 15 minutes long). Each site was divided in 4 files: Ag1_1min_[number].zip - Files from the Ag1 site Ag2_1min_[number].zip - Files from the Ag2 site FNRFarm_1min_[number].zip - Files from the FNR Farm site Martell_1min_[number].zip - Files from the Martell site McCormick_1min_[number].zip - Files from the McCormick site PurdueWildlife_1min_[number].zip - Files from the Purdue Wildlife site Ross_1min_[number].zip - Files from the Ross site

This dataset was revised on 26Jun2014 to correct the date of the Tutorial v 1.3.1.

This tutorial will teach you the implicit background knowledge that informs every piece of R code.

Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data. The tutorial is published in a blog for The Node (https://thenode.biologists.com/converting-excellent-spreadsheets-tidy-data/education/)

Video on normalizing microbiome data from the Research Experiences in Microbiomes Network

Context: Modelling Volatility is an advanced technique in financial econometrics, with several applications for academic research. Objective: In this tutorial paper we will address the topic of volatility modeling in R. We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive example of a real-world application of volatility modelling. Methods: we use a GARCH model to predict how much time it will take, after the latest crisis, for the Ibovespa index to reach its historical peak once again. The empirical data covers the period between years 2000 and 2020, including the 2009 financial crisis and the current 2020’s episode of the COVID-19 pandemia. Conclusion: we find that, according to our GARCH model, Ibovespa is more likely than not to reach its peak once again in one year and four months from June 2020. All data and R code used to produce this tutorial are freely available on the internet and all results can be easily replicated.

ABSTRACT Context: this document is designed to be along with those that are in the first edition of the new section of the Journal of Contemporary Administration (RAC): the tutorial-articles section. Objective: the purpose is to present the new section and discuss relevant topics of tutorial-articles. Method: I divide the document into three main parts. First, I provide a summary of the state of the art in open data and open code at the current date that, jointly, create the context for tutorial-articles. Second, I provide some guidance to the future of the section on tutorial-articles, providing a structure and some insights that can be developed in the future. Third, I offer a short R script to show examples of open data that, I believe, can be used in the future in tutorial-articles, but also in innovative empirical studies. Conclusion: finally, I provide a short description of the first tutorial-articles accepted for publication in this current RAC’s edition.

The deposit contains a dataset created for the paper, 'Many Models in R: A Tutorial'. ncds.Rds is an R format synthetic dataset created with the synthpop dataset in R using data from the National Child Development Study (NCDS), a birth cohort of individuals born in a single week of March 1958 in Britain. The dataset contains data on fourteen biomarkers collected at the age 46/47 sweep of the survey, four measures of cognitive ability from age 11 and 16, and three covariates, sex, body mass index at age 11 and father's social class. The data is only intended to be used in the tutorial - it is not to be used for drawing statistical inferences.

This project contains data used in the paper, "Many Models in R: A Tutorial". The data are a simplified, synthetic and imputed version of the National Child Development Study. There are variables for 14 biomarkers from the age 46/47 biomedical survey, 4 measures of cognitive ability from tests at ages 11 and 16, and 3 covariates (sex, father's socioeconomic class and BMI at age 11).

R tutorial associated to the paper: An overview of computational tools for preparing, constructing and using resistance surfaces in connectivity research.Unzip the archive, and follow the instruction in the file "script_how_to_create_resistance_surfaces.r"

Please see published paper.

Data for tutorial at https://privefl.github.io/bigsnpr-extdoc/ and course at https://privefl.github.io/statgen-course/.This data contains a zip with PLINK .bed/.bim/.fam files and a phenotype file. This was previously available at https://www.mtholyoke.edu/courses/afoulkes/Data/statsTeachR/. Described in https://doi.org/10.1002/sim.6605.Also a subset of the data from https://doi.org/10.6084/m9.figshare.16858534 to be used with the tutorial data above.Also GWAS summary statistics for testosterone levels in females and 1000 genomes European data, subsetted around two loci.And GWAS summary statistics for CAD computed from the UK Biobank.

Dataset for the tutorial of the R package "supeRbaits" (https://github.com/BelenJM/supeRbaits)

File List suppl-1.pdf (MD5: d238efc445a572b0a83024eb52886bc1)Description

   The file suppl-1.pdf is a tutorial showing how the fourth-corner and RLQ analyses can be performed using the ade4 package for R. The data set presented in the paper is used to illustrate the methods.

Retrieving soil raster data from POLARIS using the XPolaris R-package.

Nowadays, there is a growing tendency to use Python and R in the analytics world for physical/statistical modeling and data visualization. As scientists, analysts, or statisticians, we oftentimes choose the tool that allows us to perform the task in the quickest and most accurate way possible. For some, that means Python. For others, that means R. For many, that means a combination of the two. However, it may take considerable time to switch between these two languages, passing data and models through .csv files or database systems. There's a solution that allows researchers to quickly and easily interface R and Python together in one single Jupyter Notebook. Here we provide a Jupyter Notebook that serves as a tutorial showing how to interface R and Python together in a Jupyter Notebook on CUAHSI JupyterHub. This tutorial walks you through the installation of rpy2 library and shows simple examples illustrating this interface.

This code was written to inform the findings presented in the following manuscript: "Using Gaussian process emulation to improve efficiency of computationally intensive multidisease models: A tutorial with adaptable R code" This code can be adapted by future users of the emulator.

This dataset contains the code-file in R markdown format to implement the mathematical model (modified ALCC) proposed by Correndo et al. (2017), https://doi.org/10.1071/CP16444