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When & Where
Date: 
Wed, September 23, 2020 - 2:00 PM to 5:00 PM
Fri, September 25, 2020 - 2:00 PM to 5:00 PM
Location: 
Remote (Zoom information forthcoming)
Description
Type: 
*NOTE: Due to limited resources and staff, we are only able to offer workshops to UC Berkeley affiliates, partners (LBL, UCSF), and invited guests. We respectfully ask you not to register if you are not affliliated with UCB, LBL or UCSF.*
 
Overview
The Advanced Data Wrangling Workshop aims to help students to learn powerful data wrangling tools and techniques in R to wrangle data with less pain and more fun. The workshop will show how R can make your data wrangling process faster, more reliable, and interpretable. The workshop focuses on introducing new package developments in the tidyverse, particularly dplyr 1.0.0, and it has something new and exciting even for experienced R users. The workshop will first examine how to reshape and manipulate data (Part 1) and discuss how to summarise data using the tidyverse packages (Part 2). 
 
Prerequisites: R-Fundamentals parts 1-4 or equivalent knowledge + familiarity with the tidyverse. Make sure your R version is greater than or equal to 3.9. 
 
Make sure to install the tidyverse before the start of the workshop by following these instructions: https://tidyverse.tidyverse.org/
 
Check that your dplyr package is up-to-date by typing packageVersion("dplyr"). If the current installed version is less than 1.0.0, then update it by typing update.packages("dplyr"). You may need to restart R to make it work.
Keyword: 
Primary Tool: 
R
Details
Training Host: 
Format Detail: 
Remote, hands-on, interactive
Participant Technology Requirement: 
Laptop, Internet connection, Zoom account

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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