Most recently, Daniel Liden was the Director of Data Analytics at the Guinn Center for Policy Priorities in Las Vegas, Nevada. In this role, he was responsible for conducting data-driven policy analysis and for supporting ongoing research and data analysis at the Guinn Center.
Background This short write up is in pursuit of a personal goal to put more of my work and thoughts to paper. I’m sure the topic will still be useful to others, but it’s not yet fully developed. It’s a sketch of a work in progress. Last August, I took the fast.ai Practical Deep Learning for Coders online course (and worked through the accompanying book). The course taught the fundamentals of deep learning using the fastai Python library, which offers a higher-level API (and a vast range of useful features and tools) for PyTorch.
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Background For the past several months, I have been working on an analysis on the effects of urban heat on vulnerable populations, particularly during a public health crisis. For some background, I currently live in Las Vegas, where summer heat can exceed 110 degrees. Last summer included a 45-day-long streak of temperatures over 100 degrees. Urban heat is not distributed evenly within cities. Features such as parks or ponds can lead to cooler temperatures in some areas, while areas without foliage or with dark surfaces such as roads and buildings lead to warmer temperatures.
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Introduction Below is the output of an iPython notebook covering the process of transforming images for deep learning applications in the fastai library. In particular, it shows how to use the many transformations in the Albumentations library within a fastai DataBlock. A Brief Note I’ve made a number of these small guides, but haven’t posted them here. I may do so in the future. In general, I want to be better about putting materials I generate here on my site in some format.
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Note 2: Now that the 2020 Census has concluded, the embedded Shiny app has been shut down. I’ve left the code used to embed the app on the site, and I’ve linked to a blog post with screenshots of the app itself. But the app itself is no longer linked and no longer embedded here. Note: This post was updated on 2020-07-10 with a new version of the Shiny app. I recently developed an R Shiny app for tracking the daily 2020 census numbers in Nevada.
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Contents Table of Contents Contents Introduction Base R Figure in Base R with Default Options High-Resolution Figure with Incorrect Figure Component Dimensions in Base R High-Resolution Figure with Correct Font Sizes in Base R High-resolution figures in ggplot Figure in ggplot2 with default options Figure in ggplot2 with higher resolution Formatted, high-resolution ggplot2 figure Introduction In this post, I go over the process for exporting high-resolution graphics of the desired size with consistent layouts and font sizes.
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