Introduction

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Introductions

• Nick Nicholas.Gotelli@uvm.edu
• George George.Ni@uvm.edu

“The Workflow”

• get data
  • hand entry
  • machine generated
  • scraped from Web
• wrangle data
  • transform and format for analysis
• analyze data
  • statistical analysis
  • simulation modeling, forecasting
• graph data
  • exploratory graphics
  • publication quality graphics
• write paper
  • tools for speeding up writing process

Traditional and Modern Computational Tools

• Traditional

  • Word/Excel/Powerpoint
  • SAS/SPSS/JMP for stats
  • Sigmaplot for graphics

• Commercial

• Proprietary

• Expensive, availability of updates

• Code not available to modify, improve

• File formats usually not compatable

• Poor tools for documentation, transparency

• Modern

  • /Markdown/Typora/LaTeX/Overleaf (instead of Word)
  • plain-text editors (instead of Excel)
  • Beamer slides package in LaTeX (instead of Powerpoint)
  • R (instead of stats and graphics packages)

• Plain text files

  • always can be opened and read by humans
  • requires special plain-text editor
  • illustrate BBedit editor

• Advantages of Modern

  • free, open source
  • files always usable and readable
  • huge network of expert users (Wikipedia style)
  • less vulnerable to hackers and malware

• Learning to code

  • deeper understanding of problem
  • hypothesis testing
  • life long habit of learning and staying abreast of field

• Three kinds of students

  • complete beginners (no chance for bad habits)
  • currently use R (R user, but not a programmer)
  • experienced programmer (still useful for refresher of basics, new tools)

• Prerequisites

  • Familiarity with files/folders basic operations clicking, dragging, creating, deleting files
  • Familiarity with unholy trio (Word, Excel, Powerpoint)
  • Familiarity with basic statistics (mean, variance, p-value, regression, anova)
  • Familiarity with basic probability (normal, uniform)

Course Structure

Lectures

• bring fully charged computer
• code line by line with me
• help your neighbor
• help me

Labs

• do homeworks intended for in class work
• post work to your GitHub portfolio
• 5 - 8 hours of work a week

Grading

• portfolio
• participation
• presentation

Course content

• Computational tools
  • plain-text editors (use for all files and for writing)
  • markdown (simple word processing)
  • LaTeX (for formatting equations)
  • regular expressions (search and find on steroids)
  • git and GitHub (for organizing and storing data and scripts)
  • web page construction and maintenance (using markdown and GitHub)
  • shell commands (for using GitHub and other programs)
  • learning new tools (doing new things in R)
• Programming Foundations in R
  • Basic coding in R studio (scripts, sourcing, console, data structures, functions)
  • Foundational Programming Methods (annotating code, for loops, control structures, ifelse, and much more)
  • Data Files (creating and annotating your scripts and data files for safe storage and archival use)
• Advanced Topics
  • Graphics (creating publication quality graphics with ggplot2)
  • Basic experimental designs (ANOVA, regression, logistic regression, contingency table)
  • Data simulation (normal, uniform, poisson, beta, gamma distributions)
  • Advanced git commands
  • Advanced shell commands
  • Manuscript production in LaTeX
  • Slide presentations in LaTeX

Non-computational tools

• A good notebook
• Touch typing