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Open Source Science

https://github.com/cmcntsh/dataanalysisr

https://github.com/cmcntsh/dataanalysisr

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: cmcntsh
  • Default Branch: main
  • Size: 3.91 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

Data Analysis Using R

  • Data Analysis Courses https://utah.catalog.instructure.com/browse/ds-learn/

Introduction to R

  • Install RStudio
  • https://posit.cloud/
  • Navigating RStudio and Quarto
    • Code in console
    • Quarto document
    • render documents
    • run code chunks
    • Source and Visual studio editor
  • Objects in R
    • Projects, working directories, and defining objects/variables in R
    • create a new project
    • create a new quarto document
    • x <- 1
    • quarto document header embed-resources: true
    • Best practices for reproducibility: Workspace image
    • Don't save workspace image (start fresh each time)
    • Working with objects/variables
    • order of code execution
    • Types
    • numeric
    • character
    • logical (TRUE FALSE)
    • class()
    • Type conversions
    • as.character() as.logical() as.numeric()
    • NA objects
    • Logical operations
  • Vectors
    • Introduction to vectors
    • c()
    • Working with vectors and vectorization
    • Subsetting vectors
    • Integer sequence vectors
    • Logical subsetting
    • Vectors
  • Loading data into R
    • Introduction to data frames
    • Loading .csv data files into R
    • Loading excel files and installing packages
  • Working with dataframes using dplyr and the tidyverse
    • Subsetting data frames in "base R"
    • Extracting columns using the select() dplyr function
    • Chaining functions together with the pipe |>
    • Filtering rows using the filter() dplyr function
    • Adding/modifying columns using the mutate() dplyr function
    • Summarizing columns using the summarize() dplyr function
    • More grouped operations with the group_by()
  • Data visualization with ggplot2
    • Ggplot2 setup
    • Introction to ggplot2 with scatterplots
    • specifying aesthetics
    • creating line plots
    • Boxplots, histograms, adn layering geoms
    • Customizing ggplot2
    • Ggplot2 practice

Owner

  • Login: cmcntsh
  • Kind: user

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