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: cl...@ecy.wa.gov
Air Quality Modeler INTERNET: cl...@math.utah.edu
Department of Ecology VOICE: (360) 407-6815
PO Box 47600 FAX: (360) 407-7534
Olympia, WA 98504-7600
USPS: PO Box 47600, Olympia, WA 98504-7600
Parcels: 300 Desmond Drive, Lacey, WA 98503-1274
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@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@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@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.
______________________________________________
R-help@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.
