Resources

GitHub - teunbrand/ggplot_tricks: Here, I collect some tricks I've learned about the {ggplot2} R package

A collection of tricks and tips for using the ggplot2 R package.

GitHub - teunbrand/ggplot_tricks: Here, I collect some tricks I've learned about the {ggplot2} R package

A collection of tricks and tips for using the ggplot2 R package.

googletraffic

The 'googletraffic' package facilitates creating georeferenced traffic data using Google Maps Javascript API. It allows for the creation of rasters representing traffic conditions with pixel values indicating varying levels of traffic. The package can produce traffic data around specific points or polygons and requires a Google API key. It's useful for merging traffic data with other spatial datasets. The package includes functions for installation, configuration, and visualization of traffic rasters. Alternatives like Mapbox are mentioned, with differences in data format and coverage outlined.

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Greenland ice thickness

This resource provides a tutorial on using R to visualize the thickness of Greenland's ice, based on data from Bamber (2001). The data is formatted as a fixed width ASCII file and requires wrangling to be processed with R packages such as terra, readr, dplyr, and tidyr. After cleaning and projecting the data into a suitable format, the resource guides creating both raw and interactive polar stereographic maps. The tutorial includes R code snippets, methods for arranging and visualizing the data with ggplot2 and leaflet, and a discussion on the relevance of ice thickness in Arctic studies.

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Happy 18th birthday ggplot2!

This content celebrates the 18th birthday of the data visualization package ggplot2, created by Hadley Wickham. It illustrates the significance and widespread appreciation of ggplot2 within the data science community through comments and reactions from various users. Comments highlight how ggplot2 has revolutionized the creation of data plots in R, and the playful puns acknowledge the package's 'maturity' with references to drinking age and bar charts. The community expresses gratitude towards Wickham and his team for their contributions to the R ecosystem.

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Hillshade effects

Dr. Dominic Royé's blog post on hillshade effects explains creating relief maps in R with shadow effects to generate visual depth. He uses several R packages, including 'sf' for vector data, 'elevatr' for elevation API access, 'terra' for raster manipulation, 'whitebox' for geospatial analysis, and 'ggplot2' extensions for scales and color blending. The tutorial covers installing packages, importing and filtering lake boundaries, and manipulating Digital Elevation Models (DEMs) for Switzerland, with reproducible R code snippets.

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Homelessness and Rents in Canada

This content is a comprehensive R code walkthrough for analyzing homelessness and rent data in Canada. It uses multiple R libraries, including the tidyverse for data wrangling, can census for accessing census data, and patchwork for visualizing data. Important steps include data import, cleaning, and transforming with functions like mutate, filter, and summarize. Quantile calculations for rents and adjustments for CPI are shown to assess real rents over time. It highlights metros like Vancouver and Toronto, using colors to represent different years. The code indicates a rich, data-driven analysis and visualization process focusing on socio-economic issues of homelessness and rents.

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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.

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How many cars are there in Madison?

Harald Kliems analyzes car ownership in Madison using the American Community Survey data to examine trends over time. His study reveals periods of growth and stagnation in car numbers. By plotting the data, Harald provides a visual representation of these trends in comparison to Seattle. The analysis also includes looking at households in Madison to contextualize the car ownership data, calculating cars per 100 households to understand the implications of city growth on transportation. The article uses R code snippets to demonstrate data extraction, transformation, and visualization.

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How to create CUSTOM Callout Boxes in Quarto - YouTube

This video tutorial demonstrates how to create custom callout boxes in Quarto, a document authoring system in R. Quarto allows you to customize your callout boxes to make your documents visually appealing and informative.

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How to fill maps with density gradients with R, {ggplot2}, and {sf} | Andrew Heiss

The blog post by Andrew Heiss explains how to address overplotting in geographic maps created with R, {ggplot2}, and {sf}. A student's project sparked the investigation into making maps more readable by filling areas with a gradient based on event count, rather than using many individual points. The post assumes familiarity with R and the {tidyverse}, and touches on various strategies for fixing overplotting, including transparency, point resizing, and density contour plotting. The author provides code examples, discusses the unique challenges of mapping, and offers solutions for visually coherent geographic data representation.

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How to fill maps with density gradients with R, {ggplot2}, and {sf} | Andrew Heiss

Learn how to fill maps with density gradients using R, ggplot2, and sf.

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