seanhelvey / IntroToPythonForRUsers

Collab with RLadiesSB

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R-Ladies SB x Central Coast Python Collab - Data Wrangling and Viz

Meetup Details

  • Event Description: Mark you calendars for a first-of-its-kind R-Ladies SB x Central Coast Python introductory workshop on December 15th! This hybrid event with online and in-person attendees will be an opportunity to celebrate two unique communities by coming together and analyzing one dataset. You may choose to follow along on our screens or download the required software and packages to code along on your own machines. If you do not already have R and Python installed, please be sure to do so before the event on December 15th (see Software Installation Instructions below). We will be available to help troubleshoot any errors the week before.

  • Event Date: December 15, 2021 at 17:30-19:00PT, office hours 1 week before 12/07 from 5:30-6:30pm via Zoom: https://ucsb.zoom.us/j/81620992244?pwd=d2ZQTCtwZVZvU3Q4M25FN1hIM1Fxdz09.

  • Event Location: Zoom link available upon RSVP and optional in-person attendance at National Center for Ecological Analysis & Synthesis (NCEAS) 1021 Anacapa St. Paid parking is available nearby at city lot #8. The first 75min are free to park and then it’s $1.50 per hour after that. Street parking at the crossroads is also an option, especially after 6pm.

  • RSVP: R-Ladies SB Meetup Event (Python allies can RSVP at Central Coast Python Meetup Event)

Software Installation Instructions

Install R & RStudio and the tidyverse (expected install/test time: ~10-15 minutes)

  1. If you have not yet installed R and/or RStudio (you will need both), follow the steps outlined here
  2. We will be using the {tidyverse} (a collection of packages designed for data science, particularly useful for data wrangling and visualization). If you do not yet have the {tidyverse} installed, run the following line of code in your RStudio Console: install.packages("tidyverse")
  3. We encourage you to test that your installations are working as expected. Follow these instructions to ensure a smooth workshop ahead!

Install Python & Anaconda (expected install/test time: ~10-15 minutes)

  1. Install Conda following these instructions. If you aren't sure whether to choose Miniconda or Anaconda, we would reccommend Miniconda.
  2. Run jupyter notebook to start coding in the browser.

** Alternatives to Conda ** For R users who are new to Python, we are reccommending Conda above for this Meetup, so that everyone is on the same page. If you would prefer to use pip though, please feel free to explore independently or use this great repo from Lars to get started. There are also great online tools like Colab which you can use to get up and running in a browser.

Install git/GitHub (expected install time: ~5 minutes)

  1. If you are not already using git/GitHub for version control, we recommend you get set up by following the steps outlined here. Doing so will allow you to clone (i.e. copy to your computer) the GitHub repositories containing all the data and scripts that you need to follow along with us (steps for cloning a GitHub repository can be found below).

Clone workshop repositories to your computer

Data

We will be using ugly holiday sweater data crowdsourced from R-Ladies (and friends) in November/December 2020. If you would like to contribute your own ugly holiday sweater info to this dataset, please fill out this Google Form! See a summary of the data attributes here:

  • sweater: entry number
  • hs_tf: Do you have a holiday sweater? (Yes/No/NA)
  • sparkly: is it sparkly? (Yes/No/NA)
  • noise: does it make noise? (Yes/No/NA)
  • lights: does it light up? (Yes/No/NA)
  • objects: does it have anything attached to it? (Yes/No/NA)
  • colors: What colors does it have?
  • image_tf: Does it have an image on it? (Yes/No/NA)
  • image_desc: User-provided image description

Objective

We will use both R's tidyverse packages and Python's XYZ libraries to demonstrate how you might wrangle the ulgy sweater data and create this plot:

Rplot

About

Collab with RLadiesSB


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Language:Jupyter Notebook 99.8%Language:Python 0.1%Language:R 0.1%