ca-coast-poll

California coastal poll analysis and introduction to R for Coastal and Marine Management class at CSUCI 2019-11.

Objectives 2019-11-06

1. Become acquainted with programming basics and reproducible research.
2. Make your own R Markdown (aka Rmarkdown, Rmd) document.
3. Learn some R basics by working on inflammation.Rmd. And time permitting...
4. Make interactive plots using plotly::ggplot().
5. Perform some quality control (QC) on the MASTER DATA-Opinion Poll F2019 - Google Sheets by reviewing answers having responses > 1: ca-coast-poll/poll_log.csv at master · bbest/ca-coast-poll.
6. Have fun with data science :) ...

Resources

• slides: Data Science for Marine Conservation
• video: What is R Markdown? (1 minute)
• poll.Rmd: poll analysis
  • MASTER DATA-Opinion Poll F2019 - Google Sheets
  • poll.html
• inflammation.Rmd: Software Carpentry lesson
  • inflammation.html
  • Programming with R: Analyzing Patient Data

Instructions

1. Launch Rstudio in your web browser by clicking on the following:
2. Make your first Rmd. In RStudio, File > New File > R Markdown... Name the file first and Save, which will get saved as file.Rmd. Click on the Knit.
3. Open inflammation.Rmd and click cursor line by line on R code and execute by entering Control + Enter keys.

Further Resources

1. R for Data Science: the "bible" book free online
2. DataCamp.com: learn R, Python & Data Science Online with badges (some free, most paid)

Old Resources

These resources were created when previously looking at this opinion poll 2019-02:

• calcoastpoll: R package
  • Analyze Poll Data: vignette
• calcoastpoll-tutorial: bookdown

Binder Setup

Here's how the necessary files were created in this repository for the "Launch Rstudio Binder" link to work:

library(holepunch)
write_compendium_description(
  package     = "ca-coast-poll", 
  description = "California coastal poll analysis and introduction to R")
# to write a description, with dependencies. Be sure to fill in placeholder text

write_dockerfile(maintainer = "Ben Best") 
# To write a Dockerfile. It will automatically pick the date of the last 
# modified file, match it to that version of R and add it here. You can 
# override this by passing r_date to some arbitrary date
# (but one for which a R version exists).

generate_badge() # This generates a badge for your readme.

# ----------------------------------------------
# At this time 🙌 push the code to GitHub 🙌
# ----------------------------------------------

# And click on the badge or use the function below to get the build 
# ready ahead of time.
build_binder()