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When & Where
Date: 
Mon, July 6, 2020 - 9:00 AM to 12:00 PM
Location: 
Remote (Zoom Link Below)
Description
Type: 
Zoom Link
To obtain the Zoom link for this workshop please click the link below:
 
After re-registering on the Zoom website, you will receive a confirmation email containing information about joining the meeting.
If you already have and use a Zoom account, please sign into it first, before trying to access the D-Lab workshop.
 
If you have questions or problems with Zoom, please email: dlab-frontdesk@berkeley.edu
 
Overview
Geospatial data are an important component data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. 
 

Geospatial Data in R, part 2: Geoprocessing and analysis

Part two of this three-part workshop series will dive deeper into data driven mapping in R, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. Note, this workshop focuses on vector spatial data.

Knowledge Requirements: Basic knowledge of geospatial data is expected. R experience equivalent to the D-Lab R Fundamentals workshop series is required to follow along with the tutorial. Knowledge of ggplot helpful. 

Technology Requirements: Bring a laptop with R, RStudio and the following R packages installed: sp, rgdal, rgeos, ggplot2, ggmap, leaflet, RColorBrewer, classInt, and tmap.

 
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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