The package can also read dynamic survey data, i.e., data that has a time variable and a time of adoption variable. The function survey_to_diffnet is used to convert survey data into a diffnet object.
data("fakesurveyDyn")fakesurveyDyn
# id toa group net1 net2 net3 age gender
# 1 1 1991 1 NA NA NA 30 M
# 2 2 1990 1 3 1 NA 35 F
# 3 3 1991 1 NA 2 NA 31 F
# 4 4 1990 1 6 5 NA 30 M
# 5 5 1991 1 4 4 3 40 F
# 6 1 1991 2 3 4 8 29 F
# 7 2 1990 2 3 NA NA 35 M
# 8 5 1990 2 10 1 NA 50 M
# 9 10 1990 2 5 1 NA 19 F
# 10 1 1991 1 NA NA NA 31 M
# 11 2 1990 1 3 1 NA 36 F
# 12 3 1991 1 NA 2 NA 32 F
# 13 4 1990 1 6 5 NA 31 M
# 14 5 1991 1 4 4 3 41 F
# 15 1 1991 2 3 4 8 30 F
# 16 2 1990 2 1 NA NA 36 M
# 17 5 1990 2 10 1 NA 51 M
# 18 10 1990 2 5 1 NA 20 F
# note time
# 1 First wave: No nominations 1990
# 2 First wave: Nothing weird 1990
# 3 First wave: Only nominates in net2 1990
# 4 First wave: Nominates someone who wasn't interview 1990
# 5 First wave: Nominates 4 two times 1990
# 6 First wave: Only nominates outsiders 1990
# 7 First wave: Isolated 1990
# 8 First wave: Nothing weird 1990
# 9 First wave: Non-adopter 1990
# 10 Second wave: No nominations 1991
# 11 Second wave: Nothing weird 1991
# 12 Second wave: Only nominates in net2 1991
# 13 Second wave: Nominates someone who wasn't interview 1991
# 14 Second wave: Nominates 4 two times 1991
# 15 Second wave: Only nominates outsiders 1991
# 16 Second wave: Now is not isolated! 1991
# 17 Second wave: Nothing weird 1991
# 18 Second wave: Non-adopter 1991
Using the rda file read.rda, read in the edgelist net_edgelist and the adjacency matrix net_list as a diffnet objects. In both cases you should use the data.frame X which has the time of adoption variable. (solution script)
Appendix
Diffusion Network Object (diffnet)
Most of the package’s functions accept different types of graphs:
Static: matrix, dgCMatrix (from the Matrix pkg),
Dynamic: list + dgCMatrix, array, diffnet
netdiffuseR has its own class of objects: diffnet, from which you get the most.
From netdiffuseR’s perspective, network data comes in three classes:
Raw R network data: Datasets with edgelist, attributes, survey data, etc.
Already R data: already read into R using igraph, statnet, etc. (igraph_to_diffnet, network_to_diffnet, etc.)
Graph files: DL, UCINET, pajek, etc. (read_pajek, read_dl, read_ucinet, etc.)
In this presentation we will show focus on 1.
What is a (diffnet) object
A diffusion network, a.k.a. diffnet object, is a list that holds the following objects:
graph: A list with \(t\)dgCMatrix matrices of size \(n\times n\),
toa: An integer vector of length \(n\),
adopt: A matrix of size \(n\times t\),
cumadopt: A matrix of size \(n\times t\),
vertex.static.attrs: A data.frame of size \(n\times k\),
vertex.dyn.attrs: A list with \(t\) dataframes of size \(n\times k\),
graph.attrs: Currently ignored…, and
meta: A list with metadata about the object.
These are created using new_diffnet (or its wrappers).
Static survey (survey_to_diffnet)
netdiffuseR can also read survey (nomination) data:
data("fakesurvey")fakesurvey
# id toa group net1 net2 net3 age gender note
# 1 1 1 1 NA NA NA 30 M No nominations
# 2 2 5 1 3 1 NA 35 F Nothing weird
# 3 3 5 1 NA 2 NA 31 F Only nominates in net2
# 4 4 3 1 6 5 NA 30 M Nominates someone who wasn't interview
# 5 5 2 1 4 4 3 40 F Nominates 4 two times
# 6 1 4 2 3 4 8 29 F Only nominates outsiders
# 7 2 3 2 3 NA NA 35 M Isolated
# 8 5 3 2 10 1 NA 50 M Nothing weird
# 9 10 NA 2 5 1 NA 19 F Non-adopter
In group one, id 4 nominates id 6, who does not show in the data, and in group two id 1 nominates 3, 4, and 8, also individuals who don’t show up in the survey.
d1 <-survey_to_diffnet(dat = fakesurvey, # Datasetidvar ="id", # The name of the idvarnetvars =c("net1", "net2", "net3"), # Name of the nomination variablesgroupvar ="group", # Group variable (if any)toavar ="toa"# Name of the time of adoption variable ); d1
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unknown
# # of nodes : 9 (101, 102, 103, 104, 105, 201, 202, 205, ...)
# # of time periods : 5 (1 - 5)
# Type : directed
# Num of behaviors : 1
# Final prevalence : 0.89
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
If you want to include those, you can use the option no.unsurveyed
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unknown
# # of nodes : 13 (101, 102, 103, 104, 105, 106, 201, 202, ...)
# # of time periods : 5 (1 - 5)
# Type : directed
# Num of behaviors : 1
# Final prevalence : 0.62
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
We can also check the difference
d2 - d1
# Dynamic network of class -diffnet-
# Name : Diffusion Network
# Behavior : Unknown
# # of nodes : 4 (106, 203, 204, 208)
# # of time periods : 5 (1 - 5)
# Type : directed
# Num of behaviors : 1
# Final prevalence : 0.00
# Static attributes : group, net1, net2, net3, age, gender, note (7)
# Dynamic attributes : -
rownames(d2 - d1)
# [1] "106" "203" "204" "208"
Other network formats
The package also supports working with other network formats.
Besides of .net (Pajek), and ml (UCINET), netdiffuseR can actually convert between classes: igraph, network, and networkDynamic.
data("medInnovationsDiffNet")dn_ig <-diffnet_to_igraph(medInnovationsDiffNet)# dn_ig # For some issue with lazy eval, knitr won't print thisdn_net <-diffnet_to_network(medInnovationsDiffNet)dn_net[[1]]
# Network attributes:
# vertices = 125
# directed = TRUE
# hyper = FALSE
# loops = FALSE
# multiple = FALSE
# bipartite = FALSE
# name = Medical Innovation
# behavior = Adoption of Tetracycline
# total edges= 294
# missing edges= 0
# non-missing edges= 294
#
# Vertex attribute names:
# ado adopt attend belief catbak city club coll commun ctl date detail detail2 dichot drug expect free friends here home house info journ journ2 length meet most net1_1 net1_2 net1_3 net2_1 net2_2 net2_3 net3_1 net3_2 net3_3 nojourn nonpoor office origid paadico perc position presc proage proage2 proximty recall recon reltend science social sourinfo special study tend thresh toa vertex.names young
#
# No edge attributes
# Argument base.net not specified, using first element of network.list instead
# Created net.obs.period to describe network
# Network observation period info:
# Number of observation spells: 1
# Maximal time range observed: 1 until 18
# Temporal mode: discrete
# Time unit: step
# Suggested time increment: 1
dn_ndy
# NetworkDynamic properties:
# distinct change times: 18
# maximal time range: 1 until 18
#
# Includes optional net.obs.period attribute:
# Network observation period info:
# Number of observation spells: 1
# Maximal time range observed: 1 until 18
# Temporal mode: discrete
# Time unit: step
# Suggested time increment: 1
#
# Network attributes:
# vertices = 125
# directed = TRUE
# hyper = FALSE
# loops = FALSE
# multiple = FALSE
# bipartite = FALSE
# behavior = Adoption of Tetracycline
# name = Medical Innovation
# net.obs.period: (not shown)
# total edges= 294
# missing edges= 0
# non-missing edges= 294
#
# Vertex attribute names:
# active ado adopt attend belief catbak city club coll commun ctl date detail detail2 dichot drug expect free friends here home house info journ journ2 length meet most net1_1 net1_2 net1_3 net2_1 net2_2 net2_3 net3_1 net3_2 net3_3 nojourn nonpoor office origid paadico perc position presc proage proage2 proximty recall recon reltend science social sourinfo special study tend thresh toa vertex.names young
#
# Edge attribute names:
# active
First two examples it creates a list of objects, the later actually creates a single object
networkDynamic_to_diffnet(dn_ndy, toavar ="toa")
# Dynamic network of class -diffnet-
# Name : Medical Innovation
# Behavior : Adoption of Tetracycline
# # of nodes : 125 (1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, ...)
# # of time periods : 18 (1 - 18)
# Type : directed
# Num of behaviors : 1
# Final prevalence : 1.00
# Static attributes : -
# Dynamic attributes : ado, adopt, attend, belief, catbak, city, club, co... (59)
