Adrienne Traxler & Jesper Bruun 3/28/2020
This document is part of the supplemental material for the paper: sample(Traxler, A. & Bruun, J.) (202x) Classifying Passing and Failing xxx . JOURNAL. DOI.
This markdown takes you through the R-script, “loadAllNetworks.r”. The purpose of the script is use the igraph package to load and prepare the networks on which we will perform our analyses. Moreover, the script attaches attributes to the network, such as gender, grades and fci-pre test scores. The pre-test scores includes NAs from students who did not answer them. We employ different strategies for dealing with this. We load networks for each week separately, and we also add networks per week. In adding networks per week, we create accumulated networks; an accumulated network for week 2 is obtained by adding links from week 2 to week 1. The accumulated network for week 3 then adds links from week 3 to the accumulated network of week 2 and so on.
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
Apart from the networks, the data set includes a number of attributes. We start by loading these.
###Loading node attributes###
##These will be attached to networks as node attributes later###
attributes<-read.csv("data/SNA_ESERA2013.csv")
FCI_PRE<-c(17,NA,NA,28,26,18,NA,15,26,13,30,27,23,20,27,24,24,15,24,19,9,5,14,28,16,29,NA,
19,NA,27,NA,9,17,12,NA,29,29,NA,24,8,NA,15,18,NA,21,NA,12,28,18,16,NA,NA,NA,27,
24,23,NA,13,16,26,10,23,25,22,NA,29,6,10,NA,23,NA,NA,29,21,26,NA,24,17,15,NA,20,
23,25,27,NA,11,15,NA,29,NA,12,20,10,20,9,17,26,22,NA,NA,23,24,NA,18,7,25,16,19,
23,27,29,23,22,NA,7,17,NA,24,25,6,26,NA,19,21,14,23,28,13,NA,24,19,19,10,NA,24,
NA,26,12,27,18,29,12,27,23,28,11,14,NA,23,NA,16,21,26,8,20,NA,NA,19,10,15,29,NA,
6,18,NA,8,20,26,8,NA,25,18,20,8,NA,15,16,NA,26,26,21,29,NA,11,NA,24,24)
SOG<-c(6,NA,9,20,7,0,11,NA,NA,11,24,NA,NA,20,10,9,6,14,14,17,NA,17,10,NA,NA,22,NA,NA,22,8,
24,11,6,NA,NA,24,24,NA,20,14,NA,14,-1,NA,16,NA,NA,24,NA,6,NA,NA,NA,22,22,22,NA,-3,11,
NA,NA,-3,7,NA,NA,22,NA,4,NA,20,NA,NA,NA,12,14,NA,14,NA,14,NA,17,NA,14,17,NA,6,0,9,NA,
22,9,14,14,NA,-3,NA,24,2,NA,NA,14,8,19,NA,NA,11,17,20,24,24,6,NA,10,NA,NA,
11,4,20,NA,9,17,2,22,2,22,14,11,6,NA,NA,2,NA,9,0,0,0,22,NA,0,19,17,NA,22,11,14,4,8,NA,
NA,NA,17,9,20,NA,9,NA,NA,NA,4,NA,17,NA,NA,17,NA,12,11,20,19,NA,9,NA,11,2,NA,9,17,NA,9,
14,14,24,NA,0,14,24,NA)Now, we preprocess some of the attributes. The FCI-data has a number of NAs, and R will as a standard exclude these from calculations. An NA means that the student did not fill out the FCI pre-test. However, this filling out was done in lab-classes of ~30 students and – We employ three strategies to impute different meanings to these NAs. The first strategy is to impute 0 for each NA. The second is to impute a random score chosen from the scores from students who achieved the same grade as the student in question. The third strategy is part of a categorisation-strategy. All NAs put in one category, while scores are put in three different categories based on Halloun & Hestenes (1995).
#New FCI_PRE attribute, where NAs are set to zero.
FCI_PRE_0<-FCI_PRE
FCI_PRE_0[is.na(FCI_PRE_0)]<-0
#New FCI_PRE attribute where NAs are replaced by a sample
FCI_PRE_S<-FCI_PRE
FCI_PRE_S[is.na(FCI_PRE) & attributes$Course.Grade==-3]<-sample(FCI_PRE[!is.na(FCI_PRE)
& attributes$Course.Grade==-3],1)
FCI_PRE_S[is.na(FCI_PRE)& attributes$Course.Grade==0]<-sample(FCI_PRE[!is.na(FCI_PRE)
& attributes$Course.Grade==0],8)
FCI_PRE_S[is.na(FCI_PRE)& attributes$Course.Grade==2]<-sample(FCI_PRE[!is.na(FCI_PRE)
& attributes$Course.Grade==2],5)
FCI_PRE_S[is.na(FCI_PRE)& attributes$Course.Grade==4]<-sample(FCI_PRE[!is.na(FCI_PRE)
& attributes$Course.Grade==4],1)
FCI_PRE_S[is.na(FCI_PRE)& attributes$Course.Grade==7]<-sample(FCI_PRE[!is.na(FCI_PRE)
& attributes$Course.Grade==7],3)
FCI_PRE_S[is.na(FCI_PRE)& attributes$Course.Grade==10]<-sample(FCI_PRE[!is.na(FCI_PRE)
& attributes$Course.Grade==10],1)
attributes$Course.Grade[attributes$Course.Grade==100]<-NA #R counts NA in the above...
#New FCI_PRE attribute, where we make four classes. Three based on score and 1 based on NAs.
#Classification based on
#Halloun & Hestenes 1995 (Interpreting the Force Concept Inventory):
#http://modeling.asu.edu/R&E/InterFCI.pdf
#NA is Class 1. Below 60% correct (17 or less) is Class 2, "below entry level"
#(see p. 6 in H&H). Between 60% and 85% (17-25) is "entry level". Above 85% (26) is expert.
FCI_PRE_C<-vector()
FCI_PRE_C[is.na(FCI_PRE)]<-1
FCI_PRE_C[FCI_PRE<18]<-2
FCI_PRE_C[FCI_PRE>=18 & FCI_PRE<=25]<-3
FCI_PRE_C[FCI_PRE>25]<-4Next, since we will be analysing passing and failing the course, we create a new attribute based on the course grade. The grade 2 is the minimum passing grade. We make a distinction between passing (grades 2, 4, 7, 10, 12) vs failing (-3, 0) and just passing vs just failing (2 vs. 0).
PASS<-vector(length = 187)
PASS[attributes$Course.Grade<2]<-0
PASS[attributes$Course.Grade>=2]<-1
JUSTPASS<-vector(length=187)
JUSTPASS<-NA
JUSTPASS[attributes$Course.Grade==0]<-0
JUSTPASS[attributes$Course.Grade==2]<-1We append the new attributes to the attributes data frame.
attributes$fci_pre<-FCI_PRE
attributes$fci_pre_0<-FCI_PRE_0
attributes$fci_pre_s<-FCI_PRE_S
attributes$fci_pre_c<-FCI_PRE_C
attributes$sog<-SOG
attributes$pass<-PASS
attributes$justpass<-JUSTPASSNetworks are loaded in subfolders of the data/networks folder. Is in Bruun & Brewe (2013), we use networks in which links signify communication about problems solving (PS), conceptual discussions (CD), and in-class social interactions (ICS). Here we load and display the networks for each of the weeks for which we have data. Networks are loaded into arrays per type.
# Import PS weekly networks
dirs <- list.files("data/networks/")
files <- c("week36-37physStandQ1.net","AnonymousWeek38physStandQ1.net",
"week39physStandQ1.net","week40physStandQ1.net","week42physStandQ1.net",
"week43physStandQ1.net","week44physQ1Standardized.net")
paths <- paste("data/networks",dirs,files,sep="/")
weeksPS <- lapply(paths,read.graph,format="pajek")
names(weeksPS) <- c("week36-37","week38","week39","week40","week42","week43","week44")
weeksPS## $`week36-37`
## IGRAPH 4d3aa23 DNW- 187 269 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from 4d3aa23 (vertex names):
## [1] Person33 ->Person50 Person33 ->Person121 Person160->Person94
## [4] Person160->Person178 Person95 ->Person25 Person95 ->Person42
## [7] Person28 ->Person8 Person28 ->Person56 Person28 ->Person75
## [10] Person28 ->Person84 Person28 ->Person92 Person28 ->Person107
## [13] Person28 ->Person133 Person28 ->Person164 Person28 ->Person169
## [16] Person133->Person13 Person133->Person22 Person133->Person28
## [19] Person133->Person56 Person133->Person64 Person133->Person84
## [22] Person133->Person85 Person133->Person107 Person133->Person126
## + ... omitted several edges
##
## $week38
## IGRAPH d033d68 DNW- 187 971 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from d033d68 (vertex names):
## [1] Person33->Person101 Person33->Person131 Person40->Person124
## [4] Person40->Person167 Person13->Person11 Person13->Person109
## [7] Person13->Person110 Person13->Person113 Person13->Person127
## [10] Person13->Person135 Person13->Person153 Person13->Person171
## [13] Person79->Person10 Person79->Person56 Person79->Person88
## [16] Person79->Person106 Person79->Person111 Person79->Person123
## [19] Person79->Person153 Person79->Person164 Person79->Person168
## [22] Person79->Person180 Person56->Person10 Person56->Person22
## + ... omitted several edges
##
## $week39
## IGRAPH d4543d6 DNW- 187 703 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from d4543d6 (vertex names):
## [1] Person172->Person94 Person172->Person115 Person172->Person149
## [4] Person172->Person187 Person85 ->Person160 Person24 ->Person137
## [7] Person24 ->Person154 Person24 ->Person159 Person24 ->Person187
## [10] Person56 ->Person66 Person56 ->Person153 Person56 ->Person164
## [13] Person146->Person108 Person33 ->Person50 Person33 ->Person101
## [16] Person33 ->Person108 Person33 ->Person131 Person33 ->Person158
## [19] Person38 ->Person4 Person38 ->Person5 Person38 ->Person66
## [22] Person38 ->Person92 Person38 ->Person121 Person38 ->Person131
## + ... omitted several edges
##
## $week40
## IGRAPH 540c257 DNW- 187 656 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from 540c257 (vertex names):
## [1] Person50 ->Person78 Person50 ->Person106 Person50 ->Person123
## [4] Person50 ->Person131 Person50 ->Person167 Person40 ->Person32
## [7] Person40 ->Person50 Person40 ->Person108 Person40 ->Person125
## [10] Person40 ->Person147 Person40 ->Person167 Person146->Person33
## [13] Person32 ->Person40 Person32 ->Person54 Person32 ->Person102
## [16] Person32 ->Person108 Person32 ->Person118 Person32 ->Person167
## [19] Person180->Person4 Person180->Person15 Person180->Person66
## [22] Person180->Person79 Person180->Person88 Person180->Person123
## + ... omitted several edges
##
## $week42
## IGRAPH 80e2e05 DNW- 187 335 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from 80e2e05 (vertex names):
## [1] Person34 ->Person9 Person34 ->Person99 Person34 ->Person119
## [4] Person121->Person4 Person121->Person58 Person121->Person66
## [7] Person121->Person86 Person121->Person93 Person121->Person101
## [10] Person121->Person131 Person121->Person134 Person121->Person139
## [13] Person146->Person86 Person146->Person158 Person146->Person180
## [16] Person33 ->Person32 Person33 ->Person101 Person33 ->Person121
## [19] Person33 ->Person124 Person33 ->Person131 Person33 ->Person178
## [22] Person40 ->Person5 Person40 ->Person18 Person40 ->Person19
## + ... omitted several edges
##
## $week43
## IGRAPH 063ce3d DNW- 187 420 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from 063ce3d (vertex names):
## [1] Person121->Person4 Person121->Person58 Person121->Person63
## [4] Person121->Person66 Person121->Person93 Person121->Person95
## [7] Person121->Person101 Person121->Person131 Person121->Person134
## [10] Person121->Person139 Person33 ->Person32 Person33 ->Person63
## [13] Person33 ->Person101 Person33 ->Person121 Person33 ->Person124
## [16] Person33 ->Person131 Person33 ->Person178 Person50 ->Person40
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## [22] Person50 ->Person131 Person4 ->Person5 Person4 ->Person66
## + ... omitted several edges
##
## $week44
## IGRAPH e4e9753 DNW- 187 437 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from e4e9753 (vertex names):
## [1] Person158->Person5 Person158->Person63 Person158->Person78
## [4] Person158->Person101 Person158->Person131 Person158->Person168
## [7] Person121->Person4 Person121->Person33 Person121->Person66
## [10] Person121->Person101 Person121->Person131 Person33 ->Person4
## [13] Person33 ->Person63 Person33 ->Person66 Person33 ->Person101
## [16] Person33 ->Person121 Person33 ->Person131 Person33 ->Person178
## [19] Person56 ->Person22 Person56 ->Person118 Person56 ->Person133
## [22] Person56 ->Person153 Person56 ->Person164 Person56 ->Person180
## + ... omitted several edges
# Import networks from their various directories
dirs <- list.files("data/networks/")
files <- c("week36-37physStandQ2.net","AnonymousWeek38physStandQ2.net",
"week39physStandQ2.net","week40physStandQ2.net","week42physStandQ2.net",
"week43physStandQ2.net","week44physQ2Standardized.net")
paths <- paste("data/networks",dirs,files,sep="/")
weeksCD <- lapply(paths,read.graph,format="pajek")
names(weeksCD) <- c("week36-37","week38","week39","week40","week42","week43","week44")
weeksCD## $`week36-37`
## IGRAPH 61b5118 DNW- 187 269 --
## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from 61b5118 (vertex names):
## [1] Person33 ->Person50 Person33 ->Person121 Person160->Person94
## [4] Person160->Person178 Person95 ->Person25 Person95 ->Person42
## [7] Person28 ->Person8 Person28 ->Person56 Person28 ->Person75
## [10] Person28 ->Person84 Person28 ->Person92 Person28 ->Person107
## [13] Person28 ->Person133 Person28 ->Person164 Person28 ->Person169
## [16] Person133->Person13 Person133->Person22 Person133->Person28
## [19] Person133->Person56 Person133->Person64 Person133->Person84
## [22] Person133->Person85 Person133->Person107 Person133->Person126
## + ... omitted several edges
##
## $week38
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## + attr: id (v/c), name (v/c), weight (e/n)
## + edges from b622eaf (vertex names):
## [1] Person33->Person101 Person33->Person131 Person40->Person124
## [4] Person40->Person167 Person13->Person11 Person13->Person109
## [7] Person13->Person110 Person13->Person113 Person13->Person127
## [10] Person13->Person135 Person13->Person153 Person13->Person171
## [13] Person79->Person10 Person79->Person56 Person79->Person88
## [16] Person79->Person106 Person79->Person111 Person79->Person123
## [19] Person79->Person153 Person79->Person164 Person79->Person168
## [22] Person79->Person180 Person56->Person10 Person56->Person22
## + ... omitted several edges
##
## $week39
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## [1] Person172->Person94 Person172->Person115 Person172->Person149
## [4] Person172->Person187 Person85 ->Person160 Person24 ->Person137
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## [10] Person56 ->Person66 Person56 ->Person153 Person56 ->Person164
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## [19] Person38 ->Person4 Person38 ->Person5 Person38 ->Person66
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## + ... omitted several edges
##
## $week40
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## + ... omitted several edges
##
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## [22] Person40 ->Person5 Person40 ->Person18 Person40 ->Person19
## + ... omitted several edges
##
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## + ... omitted several edges
##
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## [10] Person33 ->Person121 Person33 ->Person131 Person56 ->Person118
## [13] Person56 ->Person133 Person56 ->Person153 Person56 ->Person164
## [16] Person56 ->Person169 Person56 ->Person180 Person50 ->Person158
## [19] Person4 ->Person5 Person4 ->Person66 Person4 ->Person121
## [22] Person40 ->Person54 Person40 ->Person74 Person40 ->Person108
## + ... omitted several edges
dirs <- list.files("data/networks/")
files <- c("week36-37socStandQ1.net","AnonymousWeek38SocStandQ1.net",
"week39socStandQ1.net","week40socStandQ1.net","week42socStandQ1.net",
"week43socStandQ1.net","week44socQ1Standardized.net")
paths <- paste("data/networks",dirs,files,sep="/")
weeksICS <- lapply(paths,read.graph,format="pajek")
names(weeksICS) <- c("week36-37","week38","week39","week40","week42","week43","week44")
weeksICS## $`week36-37`
## IGRAPH 7215bb1 DNW- 187 328 --
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## [1] Person95 ->Person4 Person95 ->Person5 Person95 ->Person20
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## [10] Person95 ->Person104 Person95 ->Person111 Person95 ->Person121
## [13] Person95 ->Person137 Person95 ->Person166 Person95 ->Person185
## [16] Person95 ->Person186 Person107->Person6 Person107->Person20
## [19] Person107->Person26 Person107->Person48 Person107->Person49
## [22] Person107->Person56 Person76 ->Person69 Person133->Person22
## + ... omitted several edges
##
## $week38
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## [10] Person86 ->Person185 Person123->Person15 Person123->Person17
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## [16] Person4 ->Person5 Person4 ->Person38 Person4 ->Person66
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## + ... omitted several edges
##
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## [10] Person38 ->Person66 Person38 ->Person121 Person56 ->Person10
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## [16] Person56 ->Person92 Person56 ->Person107 Person56 ->Person109
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## [22] Person56 ->Person133 Person56 ->Person153 Person56 ->Person164
## + ... omitted several edges
##
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## [13] Person180->Person10 Person180->Person22 Person180->Person56
## [16] Person180->Person79 Person180->Person88 Person180->Person111
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## + ... omitted several edges
##
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## [10] Person86 ->Person103 Person86 ->Person145 Person86 ->Person164
## [13] Person86 ->Person166 Person86 ->Person185 Person86 ->Person186
## [16] Person100->Person43 Person100->Person104 Person21 ->Person61
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## + ... omitted several edges
##
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## [10] Person146->Person33 Person146->Person121 Person146->Person158
## [13] Person4 ->Person66 Person4 ->Person95 Person4 ->Person121
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## + ... omitted several edges
##
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## [16] Person56 ->Person92 Person56 ->Person107 Person56 ->Person118
## [19] Person56 ->Person127 Person56 ->Person133 Person56 ->Person153
## [22] Person56 ->Person164 Person56 ->Person173 Person50 ->Person78
## + ... omitted several edges
To facilitate our joint analyses of the networks, we make sure all relevant links have weight 1 and that all links that echo connections in a different network layer are deleted for each type of network for each week.
###PS###
#In one network, links were given the weight "NA".
lapply(weeksPS,function(x) table(E(x)$weight,useNA="ifany"))## $`week36-37`
##
## 0 1
## 104 165
##
## $week38
##
## 0 NaN
## 420 551
##
## $week39
##
## 0 1
## 248 455
##
## $week40
##
## 0 1
## 202 454
##
## $week42
##
## 0 1
## 135 200
##
## $week43
##
## 0 1
## 138 282
##
## $week44
##
## 1
## 437
E(weeksPS$week38)$weight[is.na(E(weeksPS$week38)$weight)] <- 1
# Remove zero-weight edges -- these appear when links in another layer but not in this layer
gzero <- lapply(weeksPS,function(x) x-E(x)[weight==0])
lapply(gzero,function(x) sum(is.multiple(x)))## $`week36-37`
## [1] 2
##
## $week38
## [1] 6
##
## $week39
## [1] 2
##
## $week40
## [1] 9
##
## $week42
## [1] 1
##
## $week43
## [1] 5
##
## $week44
## [1] 28
graphsPS <- lapply(gzero,simplify,edge.attr.comb="first")
#Evidence of cleansing
lapply(graphsPS,function(x) table(E(x)$weight,useNA="ifany"))## $`week36-37`
##
## 1
## 162
##
## $week38
##
## 1
## 541
##
## $week39
##
## 1
## 450
##
## $week40
##
## 1
## 442
##
## $week42
##
## 1
## 197
##
## $week43
##
## 1
## 277
##
## $week44
##
## 1
## 407
##CD###
#In one network, links were given the weight "NA".
lapply(weeksCD,function(x) table(E(x)$weight,useNA="ifany"))## $`week36-37`
##
## 0 1
## 201 68
##
## $week38
##
## 0 NaN
## 698 273
##
## $week39
##
## 0 1
## 461 242
##
## $week40
##
## 0 1
## 457 199
##
## $week42
##
## 0 1
## 196 139
##
## $week43
##
## 0 1
## 235 185
##
## $week44
##
## 1
## 296
E(weeksCD$week38)$weight[is.na(E(weeksCD$week38)$weight)] <- 1
# Remove zero-weight edges -- these appear when links in another layer but not in this layer
gzero <- lapply(weeksCD,function(x) x-E(x)[weight==0])
lapply(gzero,function(x) sum(is.multiple(x)))## $`week36-37`
## [1] 0
##
## $week38
## [1] 4
##
## $week39
## [1] 1
##
## $week40
## [1] 1
##
## $week42
## [1] 3
##
## $week43
## [1] 0
##
## $week44
## [1] 42
graphsCD <- lapply(gzero,simplify,edge.attr.comb="first")
#Evidence of cleansing
lapply(graphsCD,function(x) table(E(x)$weight,useNA="ifany"))## $`week36-37`
##
## 1
## 68
##
## $week38
##
## 1
## 266
##
## $week39
##
## 1
## 238
##
## $week40
##
## 1
## 197
##
## $week42
##
## 1
## 134
##
## $week43
##
## 1
## 185
##
## $week44
##
## 1
## 252
###ICS###
#In one network, links were given the weight "NA" and in another no weight was given.
lapply(weeksICS,function(x) table(E(x)$weight,useNA="ifany"))## $`week36-37`
##
## 0 1
## 138 190
##
## $week38
##
## 0 NaN
## 214 416
##
## $week39
##
## 0 1
## 201 355
##
## $week40
##
## 0 1
## 227 423
##
## $week42
##
## 0 1
## 142 86
##
## $week43
##
## 0 1
## 73 264
##
## $week44
## < table of extent 0 >
# Add weight 1 to edges to weeksICS 38 and 44
E(weeksICS$week38)$weight[is.na(E(weeksICS$week38)$weight)] <- 1
weeksICS$week44 <- set.edge.attribute(weeksICS$week44, "weight", value=1)
# Remove zero-weight edges
gzero <- lapply(weeksICS,function(x) x-E(x)[weight==0])
lapply(gzero,function(x) sum(is.multiple(x)))## $`week36-37`
## [1] 3
##
## $week38
## [1] 1
##
## $week39
## [1] 2
##
## $week40
## [1] 5
##
## $week42
## [1] 0
##
## $week43
## [1] 0
##
## $week44
## [1] 13
graphsICS <- lapply(gzero,simplify,edge.attr.comb="first") # defaults to remove-loops=TRUE
#Evidence of cleansing
lapply(graphsICS,function(x) table(E(x)$weight,useNA="ifany"))## $`week36-37`
##
## 1
## 187
##
## $week38
##
## 1
## 413
##
## $week39
##
## 1
## 350
##
## $week40
##
## 1
## 417
##
## $week42
##
## 1
## 84
##
## $week43
##
## 1
## 264
##
## $week44
##
## 1
## 411
###MAKING ACCUMULATED NETWORKS####
accWeekNets<-function(graphlist,attributes){
n<-length(graphlist)
accNets<-list()
accNets[[1]]<-graphlist[[1]]
for(i in 2:n){
accNets[[i]]<-graph_from_adjacency_matrix(as_adj(accNets[[i-1]],attr="weight") +
as_adj(graphlist[[i]],attr="weight"),weighted=T)
}
for(i in 1:n){
V(accNets[[i]])$id<-V(accNets[[i]])$name
V(accNets[[i]])$grade<-attributes$Course.Grade
V(accNets[[i]])$gender<-attributes$Gender
V(accNets[[i]])$age<-attributes$Age
V(accNets[[i]])$cohort<-attributes$Cohort
V(accNets[[i]])$sog<-attributes$sog
V(accNets[[i]])$fci_pre<-attributes$fci_pre
V(accNets[[i]])$fci_pre_0<-attributes$fci_pre_0
V(accNets[[i]])$fci_pre_s<-attributes$fci_pre_s
V(accNets[[i]])$fci_pre_c<-attributes$fci_pre_c
V(accNets[[i]])$pass<-attributes$pass
V(accNets[[i]])$justpass<-attributes$justpass
}
return(accNets)
}
accPS<-accWeekNets(graphsPS,attributes)
accCD<-accWeekNets(graphsCD,attributes)
accICS<-accWeekNets(graphsICS,attributes)###REMOVE NODES THAT THAT REPRESENT TEACHERS
accPS<-lapply(accPS,delete.vertices,is.na(attributes$Course.Grade))
accCD<-lapply(accCD,delete.vertices,is.na(attributes$Course.Grade))
accICS<-lapply(accICS,delete.vertices,is.na(attributes$Course.Grade))
### NON-PARTICIPATING STUDENTS?###
biggraph<-graph_from_adjacency_matrix(as_adj(accPS[[7]])+
as_adj(accCD[[7]])+as_adj(accICS[[7]]),weighted = T)
which(degree(biggraph)==0) ## Person12 Person35 Person148
## 12 33 134
#There are three isolates (these have a degree of at least 1 in other SINs,
#just not PS, CD, and ICS)
accPS<-lapply(accPS,delete.vertices,degree(biggraph)==0)
accCD<-lapply(accCD,delete.vertices,degree(biggraph)==0)
accICS<-lapply(accICS,delete.vertices,degree(biggraph)==0)
### Plots
