minimalR

These are instructional materials to help people learn to use R. The materials are built around visualizing microbiome-related data and files generated by mothur. You can download the files that go with these materials from the raw_data GitHub repository. If you would like to contribute to the project or flag any issues that you think need to be addressed, please feel free to file a pull request or start an issue on the minimalR GitHub repository. The overall philosophy is to give learners the minimum that they need to get going and to teach them the material in a way that most R users learned the material (i.e. not from a book but by hacking their way to success).

  1. Installing software
    • R
    • RStudio
    • tidyverse
  2. Introduction to minimalR
    • Philosophy
    • Why R
    • Set up our minimalR project
  3. Introduction to R via Scatter Plots
    • Determine when a scatter plot is an appropriate data visualization tool
    • Manipulate plotting symbols and colors to plot metadata
    • Adapt existing code to achieve a goal
    • Install R packages and libraries
  4. Data Frames
    • Read in data from various file formats
    • Write data out to various file formats
    • Clean up data frames
    • Data types in R
    • Value of keeping raw data, raw
    • Importance of how things are named
    • Summarizing data with bar plots
    • Factors
  5. Combining and Exploring Data Frames
    • Merging data frames
    • Selecting columns from data frames
    • Selecting rows from data frames
    • Connecting steps in data processing with pipes
  6. Working with data in data frames
    • Aggregating and summarizing data by a categorical variable
    • Adding columns to data frames
    • Creating customized functions
    • The importance of keeping code DRY
  7. Comparing distributions of continuous data across categorical variables
    • Problems with bar plots
    • Error bars
    • Positioning with jitter and dodging
    • Strip charts
    • Box plots
    • Violin plots
  8. Line plots
    • Scripting
    • Tidy data
    • Altering text
    • Making line plots
    • Annotating figures with lines
  9. Statistical analyses
    • Transforming data to make them normally distributed
    • T-test and Analysis of Variance
    • Wilcoxon and Kruskal-Wallis tests
    • Correlations and regression
    • Overlaying regression on scatter plots
    • Chi-squared tests and visualization
  10. Working with taxonomic data
    • String manipulations
    • Regular expressions
    • Representing taxonomic data
    • Identifying significantly different taxa

Pat Schloss © 2021