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BES-MIRE  September 2019

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Subject:

Course on Data Manipulation and Visualization in R – scholarships available

From:

"soledad.esteban" <[log in to unmask]>

Reply-To:

soledad.esteban

Date:

Thu, 19 Sep 2019 12:42:27 +0200

Content-Type:

text/plain

Parts/Attachments:

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text/plain (41 lines)

Dear colleagues,
 
Transmitting Science course DATA MANIPULATION, ANALYSIS, AND VISUALIZATION IN R USING THE TIDYVERSE has some scholarships available. You can apply here (free course attendance) here: https://www.transmittingscience.org/funding/scholarships-application/ 
Deadline: October 15th.
 
Course dates: November 4th-8th, 2019.

Instructors: Dr. Ashton Drew (KDV Decision Analysis LLC United States of America) and Dr. Sheila Saia (United States Forest Service Center for Integrated Forest Science, United States of America)

Place:  Sabadell, Barcelona (Spain)

Registration and more info:  https://www.transmittingscience.org/courses/statistics-and-bioinformatics/data-manipulation-analysis-visualization-in-r-using-tidyverse/

Course Overview

This intermediate course exposes students to the breadth of resources available in the R tidyverse to build their fluency and confidence when working in R.  The tidyverse is a collection of libraries and functions in R sharing an underlying design (philosophy, grammar, and data structures) that aims to help users create efficient, "tidy" code.  Many of the libraries address the common data wrangling tasks (read, select, filter, gather, spread, modify, recode, summarize) that are the precursors to both data analysis and visualization.  Other libraries address specific tasks (e.g., ggplot2 to create graphs; purr to create functions; broom to tabulate model results) or specific data types (e.g., sf for spatial data, tibbletime for temporal data).

A mix of lectures and exercises will walk students through a series of increasingly complex data manipulation and visualization tasks.  These exercises will emphasize the underlying principles and grammar of the tidyverse to help students transfer what they learn in the examples to their own research coding projects.

Import tabular, spatial, and temporal data (readr, readxl, haven, sf, tibbletime)
Manage data structure (dplyr, tidyr, magrittr, forcats, lubridate, stringr)
Introduction to functional programming (purr)
Visualize data and model results (broom, ggplot2)

Please feel free to distribute this information between your colleagues if you consider it appropriate.



Soledad De Esteban-Trivigno
Institut Català de Paleontologia Miquel Crusafont (ICP)

Campus de la Universitat Autònoma de Barcelona
Cerdanyola del Vallès (Barcelona). Spain
www.icp.cat
   

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