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Writing Clean Code in R Workshop

Author(s): Max Joseph1, Leah Wasser

Earth Lab - University of Colorado, Boulder

1444 total view(s), 332 download(s)

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Summary:
When working with data, you often spend the most amount of time cleaning your data. Learn how to write more efficient code using the tidyverse in R.

Licensed under CC Attribution-NonCommercial-NoDerivatives 4.0 International according to these terms

Version 1.0 - published on 15 Oct 2019

Keywords

  1. data analysis
  2. data types
  3. data visualization
  4. data science
  5. earth science
  6. R programming language
  7. remote sensing
  8. Geographic Information System (GIS)
  9. data science education
  10. computer programming
  11. wildfire
  12. earth analytics

Alignments

  1. Resource Type
  2. Audience Level
  3. Activity Length
  4. Open Science and Education Practices Ontology
Contents:
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1,444

The above numbers reflect non-unique views of the page, which may include multiple views in the same day by the same user.

Total Downloads

332

The above numbers reflect non-unique user access to the linked content, which includes but is not limited to primary access button clicks, bundle downloads, and supporting documents.

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