R help - Sep 2015 - Visualization of people's interactions by participation to parties

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 hand.

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 
people.

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,

Hendrik

Hendrik,

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.

Jean

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
> hand.
>
> 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
> people.
>
> 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,
>
> Hendrik
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

Hendrik,

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

Clint Bowman			INTERNET:	clint at ecy.wa.gov
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On Fri, 4 Sep 2015, Adams, Jean wrote:
> Hendrik,
>
> 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.
>
> Jean
>
> 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
>> hand.
>>
>> 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
>> people.
>>
>> 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,
>>
>> Hendrik
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>