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SOCNET  January 2016

SOCNET January 2016

Subject:

#Sunbelt2016 workshop: Introduction to ego-network analysis with R

From:

Raffaele Vacca <[log in to unmask]>

Reply-To:

Raffaele Vacca <[log in to unmask]>

Date:

Wed, 6 Jan 2016 12:40:40 -0500

Content-Type:

text/plain

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text/plain (108 lines)

*****  To join INSNA, visit http://www.insna.org  *****

Dear All,

A reminder that regular registration for #Sunbelt2016 is now open, 
including workshop registration, at 
http://insna.org/sunbelt2016/registration/. The deadline for workshop 
registration is March 7th, 2016.

This is a workshop that I'm teaching for the 4th time at a Sunbelt/EUSN 
conference. Please don't hesitate to forward this email or contact me if 
you'd like more info.

(Apologies for cross-posting.)


*Introduction to ego-network analysis with R*
=============================================

*Session Time:* Tuesday, April 5, 2016 - 3pm
*Workshop Length:* 2 sessions (6 hours)
*Attendance Limit:* 30
*Instructor:*  Raffaele Vacca, University of Florida | [log in to unmask] | 
www.raffaelevacca.com
*Workshop webpage:* www.raffaelevacca.com/teaching/workshops/ego-network-r/
*How to register:* http://insna.org/sunbelt2016/registration/

*Prerequisites:*
None.

*Topics:*
-- Short introduction to ego-network research and data.
-- Data structures for ego-networks in R: vectors, data frames and lists.
-- Network objects in R.
-- The split-apply-combine strategy: creating ego-level summary variables.
-- Looping over multiple ego-networks (for, while, repeat loops).
-- Writing your own R functions.
-- Applying your functions to multiple ego-networks: The “apply” family 
of functions (apply, lapply, sapply, etc.).
-- The plyr package for easier split-apply-combining.

*Technology requirements:*
A laptop with RStudio installed. More details will be emailed to 
participants before the workshop.

*Background:*
This workshop offers an introduction to ego-network analysis with R, 
presenting essential facilities available in R to store and manipulate 
ego-network data, to visualize ego-networks, and to perform 
compositional and structural analysis on large collections of ego-networks.
The central idea behind ego-network analysis is that the people (alters) 
that an individual (ego) knows, and the way that these people interact 
with each other, affect outcomes in that individual’s life such as 
mental wellbeing, smoking behavior, or assimilation to a foreign 
culture. A typical ego-network study involves identifying a sample of 
focal individuals (the egos), and collecting a network of personal 
contacts (the alters) from each. Ego is asked about characteristics of 
each alter, characteristics of each ego-alter relation, and 
characteristics of alter-alter relations. This information is then 
frequently aggregated into ego-level variables that summarize 
ego-network characteristics, which can subsequently be linked to other 
ego attributes and outcomes.
Typical ego-network analysis requires handling dozens or hundreds of 
datasets, each representing one ego-network with ego attributes, alter 
characteristics, and alter-alter ties. The analysis involves running the 
same set of operations on each dataset, e.g. to extract compositional 
and structural summary variables on each ego-network; and joining the 
resulting metrics into a single dataset, together with other ego-level 
or alter-level variables. This has been called the split-apply-combine 
process in data analysis, in which raw data are split into pieces (in 
this case, each piece representing one ego), the same analysis is 
applied on each piece, and results are then combined together into a 
single dataset.
Handling the split-apply-combine process in traditional point-and-click 
software for statistical analysis is inefficient. Pointing and clicking 
is repetitive, boring and prone to errors. It typically does not allow 
users to run the same set of operations on many objects in batch, 
without the user’s intervention. Perhaps more importantly, pointing and 
clicking makes research not reproducible. R overcomes these limitations 
and opens up a whole different way of doing ego-network analysis. It 
eliminates pointing-and-clicking entirely, and allows users to write 
reproducible scripts that batch analyze hundreds or thousands of 
ego-networks simultaneously in few seconds.
This workshop will use real-world ego-network data, in combination with 
the main R packages for network analysis (igraph and statnet). The 
workshop can be taken as an introduction to the workshop “Simplifying 
ego-centered network analysis in R with egonetR” by Till Krenz and 
Andreas Herz. Students interested in a general introduction to social 
network analysis with R should also consider taking the workshop “Using 
R and igraph for Social Network Analysis" by Michal Bojanowski.

-- 
* Raffaele Vacca *
Research Assistant Professor
College of Nursing ∙ Clinical and Translational Science Institute ∙ 
Bureau of Economic and Business Research
University of Florida

✎ Ayers Medical Plaza, 720 SW 2nd Ave #150 ∙ Gainesville ∙ FL 32601
✆ +1 (352) 392-2908 x221 ∙ +1 (352) 273-6010
☞ www.raffaelevacca.com
✉ [log in to unmask]

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
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