diffnetobjects (the core of netdiffuseR).
diffnetobjects is not the only way to use netdiffuseR. Most of the functions can also be used with matrices and arrays.
library(netdiffuseR) knitr::opts_chunk$set(comment = '#')
The issue is how to read it into R and handle it altogether.
Before start, we recommend the user to take a look at the Data input functions included in the
utils package (see
?read.table), to the functions included in the
foreign package (useful to read from Stata, SPSS, etc.), and to the
read_excel function in the
readxl package[^excelfiles] for reading excel files into R.
[^excelfiles]: While there are other candidates as the
openxlsx package, the
readxl package has the nice feature of correctly processing the encoding of the excel files. This is specially important if you are dealing with non ASCII or UTF-8 datasets.
edgelist_to_adjmatsupports both weights and spells.
For this example we will use the
fakeEdgelist datasets. The later was been generated using the
fakesurvey dataset, which holds survey information retrieved from 10 different individuals in two different groups. Ties in the
fakeEdgelist dataset are valued, and its value coincides with the number of nominatios that each individual in the survey did to each other.
# Loading the datasets data("fakesurvey") data("fakeEdgelist")
Taking a look at
id column and
alter columns the user can tell that the laters have been generated by adding up
head(fakesurvey[,c("id", "group")]) head(fakeEdgelist)
We will use this information later on to verify the way the data is sorted in the resulting
To use the
as_diffnet function we need at least two objects: a dynamic graph represented as either an array or a list of adjacency matrices, each of size $n\times n$, which in our case will be $10\times 10$, and an integer vector of size $n=10$ which holds each vertex's time of adoption. Lets start by generating the dynamic graph using th
# Coercing the edgelist to an adjacency matrix adjmat <- edgelist_to_adjmat( edgelist = fakeEdgelist[,1:2], # Should be a two column matrix/data.frame w = fakeEdgelist$value, # An optional vector with weights undirected = FALSE, # In this case, the edgelist is directed t = 5) # We use this option to make 5 replicas of it
As the function warns, there is an edge that had incomplete information, and further was not used to create the adjacency matrix, the edge 11. If we take a look at that edge, we will see that indeed it had incomplete information on the weight attribute:
In order to address this, if we want to keep the vertex 202, an isolated vertex in the data, we need to fill that value up so that when creating the diffnet object we won't have any problem having more attributes or times of adoption that vertices in the graph.
# Filling the empty data, and checking the outcome fakeEdgelist[11,"value"] <- 1 fakeEdgelist[11,,drop=FALSE] # Coercing the edgelist to an adjacency matrix (again) adjmat <- edgelist_to_adjmat( edgelist = fakeEdgelist[,1:2], # Should be a two column matrix/data.frame w = fakeEdgelist$value, # An optional vector with weights undirected = FALSE, # In this case, the edgelist is directed keep.isolates = TRUE, # NOTICE THIS NEW ARGUMENT! t = 5) # We use this option to make 5 replicas of it
As expected, there is no warning. Furthermore, we have told the function that in case of having isolated vertices to keep them, as is in the case of the edge #11 which has the vertex 202. Since we asked the function to create 5 copies of the adjacency matrix, we have a list of length 5 with adjacency matrices. Lets take a look at the first element of this list:
As you can see, the
edgelist_to_adjmat function kept the vertices labels and included them as dimnames in the matrix.[^sparsemat] Now that our adjacency matrix has the number of elements that we expected, which actually coincides with the number of rows in the
fakesurvey dataset, we can create a
[^sparsemat]: Another thing to tell, the matrices stored in
adjmat are of class
dgCMatrix from the
Matrix package, these are Column Compressed Stored sparse matrices and allows saving memory in matrices with many zeros. netdiffuseR routines are based in this class of matrices. Furthermore, to have an idea of how much memory sparse matrices save, while a square matrix of size $5e4\times 5e4$ would need close to 18GB of memory using a regular R
dgCMatrix of the same size takes around 6MB.
# Coercing the adjacency matrix and edgelist into a diffnet object diffnet <- as_diffnet( graph = adjmat, # Passing a dynamic graph toa = fakesurvey$toa, # This is required vertex.static.attrs = fakesurvey # Is is optional ) # Taking a look at the diffnet object diffnet
Following the previous example, instead of "manually" generating the adjacency matrix and calling the
as_diffnet function, we will use the
edgelist_to_diffnet function. The most important issue when calling this routine is to have matching ids between the edgelist and the attributes dataset. So before calling the
edgelist_to_diffnet function we need to fix the
id column in the
with function allows simplifying data management in R by allowing to reference columns in a data.frame without having to call the data.frame itself (see
# Before fakesurvey$id # Changing the id fakesurvey$id <- with(fakesurvey, group*100 + id) # After fakesurvey$id
Now that it is fixed, we can call the
diffnet2 <- edgelist_to_diffnet( edgelist = fakeEdgelist[,1:2], # Passed to edgelist_to_adjmat w = fakeEdgelist$value, # Passed to edgelist_to_adjmat dat = fakesurvey, # Data frame with -idvar- and -toavar- idvar = "id", # Name of the -idvar- in -dat- toavar = "toa", # Name of the -toavar- in -dat- keep.isolates = TRUE # Passed to edgelist_to_adjmat ) diffnet2
As a difference with the previous example, here the algorithm makes sure that the ordering of the dataset and the vertices in the adjacency matrix coincide. The previous example did gave us a correctly sorted
diffnet object, but that may not always be the case. Nevertheless, the option
id.and.per.vars allows the user providing with the names of the variables in the vertex attribute datasets that hold the ids and time period ids of each observation, so that the function sorts the data before coercing it into diffnet objects. More on this in the following examples.
fakesurvey, which holds cross section data, and
fakesurveyDyn, which holds longitudinal data.
We start by taking a look at the data
# Loading the data data("fakesurvey") fakesurvey
A couple of important remarks for this dataset. First, each individual in this dataset belongs to a different group, while this is not always the case,
survey_to_diffnet allows accounting for this through the
groupvar argument. Also, besides of having an isolated vertex, two individuals in the survey nominate people that neither weren't survey nor show in their groups:
So in group one 4 nominates id 6, who does not show in the data, and in group two 6 nominates 3, 4, and 8, also individuals who don't show up in the survey.
While for some researchers nominations of unsurveyed individuals may not be of importance, for some others might be. For such cases, the function has the option of either keeping unsurveyed individuals (so you would get a bigger adjacency matrix), or ignore them and keep only those who were surveyed. For example, if we wanted to keep unsurveyed individuals in the network we would need to set
no.unsurveyed = FALSE:
# Coercing the survey data into a diffnet object diffnet_w_unsurveyed <- survey_to_diffnet( dat = fakesurvey, # The dataset idvar = "id", # Name of the idvar (must be integer) netvars = c("net1", "net2", "net3"), # Vector of names of nomination vars toavar = "toa", # Name of the time of adoption var groupvar = "group", # Name of the group var (OPTIONAL) no.unsurveyed = FALSE # KEEP OR NOT UNSURVEYED ) diffnet_w_unsurveyed # Retrieving nodes ids nodes(diffnet_w_unsurveyed)
A network spanning 5 time periods with 13 vertices (9 surveyed individuals + 4 unsurveyed individuals). This produces a different result when compared to the case in which me use the default behavior of the function,
no.unsurveyed = TRUE:
# Coercing the survey data into a diffnet object diffnet_wo_unsurveyed <- survey_to_diffnet( dat = fakesurvey, # The dataset idvar = "id", # Name of the idvar (must be integer) netvars = c("net1", "net2", "net3"), # Vector of names of nomination vars toavar = "toa", # Name of the time of adoption var groupvar = "group" # Name of the group var (OPTIONAL) ) diffnet_wo_unsurveyed # Retrieving nodes ids nodes(diffnet_wo_unsurveyed)
Furthermore, we can compare the two diffusion networks by sustracting one from another:
difference <- diffnet_w_unsurveyed - diffnet_wo_unsurveyed difference
In this example we will use dynamic network data, this is, an edgelist with spells and dynamic attributes
# Taking a look at the data data("fakeDynEdgelist") head(fakeDynEdgelist)
Same as before, we have to make sure the ids are right
# Fixing ids fakesurveyDyn$id <- with(fakesurveyDyn, group*100 + id) # An individual who is alone fakeDynEdgelist[11,"value"] <- 1
diffnet <- edgelist_to_diffnet( edgelist = fakeDynEdgelist[,1:2], # As usual, a two column dataset w = fakeDynEdgelist$value, # Here we are using weights t0 = fakeDynEdgelist$time, # An integer vector with starting point of spell t1 = fakeDynEdgelist$time, # An integer vector with the endpoint of spell dat = fakesurveyDyn, # Attributes dataset idvar = "id", toavar = "toa", timevar = "time", keep.isolates = TRUE # Keeping isolates (if there's any) ) diffnet
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