[R] Visualization of people's interactions by participation to parties
clint at ecy.wa.gov
Fri Sep 4 18:41:49 CEST 2015
As a start, I'd make a matrix of zeros with the parties in rows
and all of the participants as columns and put a one for each participant
at each party. The matrix will consist of a vector for each
participant showing the parties attended. The pattern may suggest the next
step. Similarity of vectors could be an indication of interaction.
Clint Bowman INTERNET: clint at ecy.wa.gov
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On Fri, 4 Sep 2015, Adams, Jean wrote:
> It's not clear to me what kind of R help you are looking for. I suggest
> you provide more information on the data that you have and the questions
> that you want answered. Is it in an external file? Is it an R object?
> What code have you written or tried? Including example data, for example
> the output from dput(), is very helpful.
> On Wed, Sep 2, 2015 at 1:46 AM, Voxcoelestis via R-help <
> r-help at r-project.org> wrote:
>> Dear all,
>> I have a long list of parties and participants over many years and want to
>> extract network relations between people to identify groups of friends. My
>> list looks like this:
>> Party 1; date party 1; first name 1 last name 1; first name 2 last name 2;
>> first name 3 last name 3;
>> Party 2; date party 2; first name 1 last name 1; first name 3 last name 3;
>> first name 4 last name 4;
>> Party 3; date party 3; first name 3 last name 3; first name 5 last name 5;
>> Party 4; date party 4; first name 2 last name 2; first name 6 last name 6;
>> first name 3 last name 3; first name 1 last name 1;
>> Party 5; date party 5; first name 5 last name 5; first name 4 last name 4;
>> Obviously the amount and the order of names is not regular. The list is
>> far too long to count co-appearances for each person-person combination by
>> What I would like to do is first of all create a network with individual
>> persons as nodes and the co-appearances as edges and the number of
>> co-appearances as strenght of interactions clustering closesly related
>> In a second step it would be beneficial to extract information on the
>> durability of these interactions by including the time difference between
>> first and last interaction.
>> Do you have any ideas or hints how to approach this problem?
>> Thank you so much,
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