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Introduction to The DiscovR Workshop
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We follow The Carpentries Code of Conduct.
Our fundamental goal is to become more comfortable exploring and working with data.
Our workshop goal is to write a sharable and reproducible report.
This lesson content is targeted to absolute beginners with no data science or coding experience.
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Getting Started with R
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R is a free programming language used by many for reproducible data analysis.
Functions allow you to perform complex tasks.
Objects allow you to store information.
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R for Plotting
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Use read_csv() to read tabular data in R.
Geometries are the visual elements drawn on data visualizations (lines, points, etc.), and aesthetics are the visual properties of those geometries that are assigned to variables in the data (color, position, etc.).
Use ggplot() and geoms to create data visualizations, and save them using ggsave().
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R for Data Cleaning
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Package loading is an important first step in preparing an R environment.
Assessing data source and structure is an important first step in analysis.
There are many useful functions in the tidyverse packages that can aid in data analysis.
Preparing data for analysis can take significant effort and planning.
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R for Data Analysis
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Writing Reports with R Markdown
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R Markdown is a useful way to create a report that integrates text, code, and figures.
Options such as include and echo determine what parts of an R code chunk are included in the R Markdown report.
R Markdown can render HTML, PDF, and Microsoft Word outputs.
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Conclusion
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Using R regularly is the best way to improve your skills.
When it comes to trying to figure out how to code something, and debugging, Internet searching is your best friend.
Don’t be afraid to reach out to others and ask for help.
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