Resources

Creating template files with R | Nicola Rennie

Learn how to create template files in R to automate repetitive tasks.

Creating typewriter-styled maps in {ggplot2}

This blog post by Nicola Rennie details how to create a typewriter-styled map of Scotland using the {ggplot2} package in R. The process involves gathering elevation data from a shapefile and using the {elevatr} package for accessing the elevation API. Selecting a suitable typewriter font with {sysfonts} and {showtext}, Rennie demonstrates how to represent different elevation levels with various letters in a monospace font. The final output replicates the appearance of a map created with a typewriter, reminiscent of RJ Andrews' handcrafted map of California using over 2,500 keystrokes.

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Creating typewriter-styled maps in {ggplot2} | Nicola Rennie

Creating typewriter-styled maps in ggplot2. This blog post explains the process of gathering elevation data, selecting a suitable typewriter font, and coding up a map.

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Creating your personal website using Quarto

This document by Sam Shanny-Csik offers an introductory guide to creating personal websites using Quarto. It covers the basics of Quarto as a multipurpose publishing system built on Pandoc and how it integrates with languages like R, Python, and Julia for dynamic content creation. The tutorial explains the similarities and differences between RMarkdown and Quarto Markdown files, provides tips for code chunk execution, and guides readers through deploying their Quarto website using GitHub Pages. Additionally, it encourages exploring further resources on Quarto and the possibility of using other R-based tools or HTML templates for website creation.

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Data Cleaning Flipbook

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

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Data cleaning for data sharing | Crystal Lewis

Data cleaning for data sharing by Crystal Lewis in tutorials February 14, 2023.

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Data Humans Podcast

Libby Heeren is a self-professed Data Human on a mission to speak candidly about the day-to-day work of data professionals and tear down the veil of mystery that hangs over the world of data jobs. Find her at datahumans.club

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Data Pipelines with {targets}

This content introduces the 'targets' R package, designed to assist in creating reproducible and efficient data pipelines. 'targets' tracks each component of an analytical pipeline, updating steps only when changes occur and avoiding redundant computations. It facilitates clean, function-oriented code that significantly reduces frustration and time spent on re-running analyses due to errors or alterations in the code. The post includes a simple analysis example using the 'palmerpenguins' dataset, demonstrating how 'targets' can streamline the workflow. The analogy to The Eye of Sauron exemplifies its vigilant tracking capability.

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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.

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Data Science Resources

Data Science Resources is a carefully selected list of free tools and references for data science, maintained by Nicola Rennie. The repository allows community contributions; individuals can propose additions or modifications to the resource list by filing an issue or editing the 'resources.csv' file on GitHub, followed by submitting a pull request. This open-source approach ensures the collection remains up-to-date and comprehensive, benefiting data scientists at various levels of expertise looking for reliable references and tools.

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Data Tips and Tricks - Creating Population Pyramid Plots in R with ggplot2

In this tutorial, Steve guides us through creating population pyramid plots in R using the ggplot2 library. Ideal for visualizing demographic data, these plots compare population distribution across age groups and genders or different time periods. The post includes a step-by-step guide, beginning with installing ggplot2, to loading libraries and preparing data. It covers generating a basic bar chart for one gender and extending it to combine both genders, thereby constructing the desired population pyramid plot. Readers will learn how to manipulate plot aesthetics for visual clarity and symmetry in demographic presentations.

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Data Visualization

Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data

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