Resources

Data Cleaning Flipbook

A flipbook with examples of data cleaning using R and the tidyverse package

Data Science for the Biomedical Sciences

Data Science for the Biomedical Sciences is a book that provides an introduction to data science concepts and tools specifically tailored for the biomedical sciences. It covers topics such as spreadsheets, R and RStudio, data loading, descriptive calculations, data cleaning, visualization, analysis, working with multiple datasets, APIs, functions, survival analysis, machine learning, and more.

How (and Why) I came to Use R for Data Analysis and Evaluation

Alberto Espinoza recounts his journey with R for data analysis and evaluation, marking his 10-year experience since first encountering R during his graduate assistantship. Initially clueless about R, he was tasked with assisting and leading statistics labs using R. Despite early challenges and a steep learning curve, he recognized R's power over software like SPSS or Excel. His continued use of R spanned graduate projects, market research, data preparation for Tableau, and Survey Monkey analysis. Espinoza outlines R's advantages: reproducibility, efficiency, clarity, and an extensive package ecosystem, underlining R's significance in his professional growth.

Jonathan Kitt - #TidyTuesday 2023 - Week 31

This is a tutorial on how to participate in the #TidyTuesday weekly challenge, organized by the R4DS Online Learning Community. The tutorial covers loading packages, downloading the dataset, cleaning the data, and creating visualizations.

Pivoting tidily

This post discusses the new pivot_longer() and pivot_wider() functions from the tidyr package in R. It demonstrates how these functions can facilitate common data processing steps and reduce the need for extensive data wrangling. The post uses an example from a Plant Physiology Lab course to illustrate the use of these functions.

R-Bootcamp

A free online course about the basics of the tidyverse

RStudio Cloud Primer: Tidy Your Data

Posit Cloud is a cloud-based platform that provides data storage and analysis tools for the R programming language.

Stat545

This is the table of contents for the STAT 545 resource, which covers various topics related to R programming.

tidy data

Tidy data for efficiency, reproducibility, and collaboration

This illustrated series discusses the power of tidy data for efficiency, reproducibility, and collaboration in data science. It emphasizes the importance of organizing data in a structured and standardized format, which enables the use of existing tools, facilitates collaboration, and enhances reproducibility. The series provides examples and resources for working with tidy data and highlights its benefits in data analysis and research.

Tidy Data Vignette

Tidy data is a concept in data analysis that involves structuring datasets to facilitate analysis. The tidy data standard provides a standardized way to organize data values within a dataset. This resource is a vignette that explains the principles and importance of tidy data and provides examples in R using the tidyr package.

Tidy Flowchart Generator

The Tidy Flowchart Generator, or the 'flowchart' package, is an R package designed for drawing participant flow diagrams directly from a dataframe, employing the tidyverse syntax. It offers a suite of functions that utilize the pipe operator to generate flowcharts conveniently and flexibly from dataframes. The package is accessible through CRAN and can be installed traditionally or via the development version on GitHub. The process of creating a flowchart with this tool is demonstrated through a GIF example on its homepage, showcasing its usefulness in drafting flow diagrams for clinical trials or similar studies.