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Extracting and Processing eBird Data

auk hex sticker

eBird is an online tool for recording bird observations. The eBird database currently contains over 500 million records of bird sightings, spanning every country and nearly every bird species, making it an extremely valuable resource for bird research and conservation. These data can be used to map the distribution and abundance of species, and assess how species’ ranges are changing over time. This dataset is available for download as a text file; however, this file is huge (over 180 GB!) and, therefore, poses some unique challenges. In particular, it isn’t possible to import and manipulate the full dataset in R. Working with these data typically requires filtering them to a smaller subset of desired observations before reading into R. This filtering is most efficiently done using AWK, a Unix utility and programming language for processing column formatted text data. The auk package acts as a front end for AWK, allowing users to filter eBird data before import into R, and provides tools to perform some important pre-processing of the data. Them name of this package comes from the happy coincidence that the command line tool AWK, upon which the package is based, is pronounced the same as auk, the family of sea birds also known as Alcids.

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A package for dimensionality reduction of large data

Comparing UMAP to other algorithms

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Motivation

Note: Recently, two new UMAP R packages have appeared. These new packages provide more features than umapr does and they are more actively developed. These packages are: umap, which provides the same Python wrapping function as umapr and also an R implementation, removing the need for the Python version to be installed. It is available on CRAN. uwot, which also provides an R implementation, removing the need for the Python version to be installed.

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rOpenSci Educators Collaborative: How Can We Develop a Community of Innovative R Educators?

tl;dr: we propose three calls to action:

  1. Share your curricular materials in the open.
  2. Participate in the rOpenSci Education profile series.
  3. Discuss with us how you want to be involved in rOpenSci Educators’ Collaborative.

In previous posts in this series, we identified challenges that individual instructors typically face when teaching science with R, and shared characteristics of effective educational resources to help address these challenges. However, the toughest challenges that educators in this area face are human, rather than technological. Our shared experiences highlight the need for a strong community of innovative R educators. However, this community is currently not well-connected or easily discoverable.

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rOpenSci Educators Collaborative: What Educational Resources Work Well and Why?

In the first post of this series, we sketched out some of the common challenges faced by educators who teach with R across scientific domains. In this post, we delve into what makes a “good” educational resource for teaching science with R.

educollab hashtags

For instructors teaching sciences with R, there are a number of open educational resources that they can reuse, tailor to their own teaching style, or use to inspire them in creating their own materials. Some examples:

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rOpenSci Educators Collaborative: What Are The Challenges When Teaching Science With R?

educollab hashtags

Educators who teach science using R tend to face common pedagogical problems, regardless of their scientific domain. Yet instructors who teach with R often feel isolated at their institutions. They may be the only ones in their departments to teach using R. Even if there are others, the culture of collaboration around teaching is generally impoverished, unlike the rich culture of collaboration around research. In this three-part series of blog posts, participants at the rOpenSci 2018 unconf briefly survey the state of teaching science with R.

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Working together to push science forward

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