rm(list = ls())
library(readxl)
library(statnet)
## 필요한 패키지를 로딩중입니다: tergm
## 필요한 패키지를 로딩중입니다: ergm
## 필요한 패키지를 로딩중입니다: network
##
## 'network' 1.18.2 (2023-12-04), part of the Statnet Project
## * 'news(package="network")' for changes since last version
## * 'citation("network")' for citation information
## * 'https://statnet.org' for help, support, and other information
##
## 'ergm' 4.6.0 (2023-12-17), part of the Statnet Project
## * 'news(package="ergm")' for changes since last version
## * 'citation("ergm")' for citation information
## * 'https://statnet.org' for help, support, and other information
## 'ergm' 4 is a major update that introduces some backwards-incompatible
## changes. Please type 'news(package="ergm")' for a list of major
## changes.
## 필요한 패키지를 로딩중입니다: networkDynamic
##
## 'networkDynamic' 0.11.4 (2023-12-10?), part of the Statnet Project
## * 'news(package="networkDynamic")' for changes since last version
## * 'citation("networkDynamic")' for citation information
## * 'https://statnet.org' for help, support, and other information
## Registered S3 method overwritten by 'tergm':
## method from
## simulate_formula.network ergm
##
## 'tergm' 4.2.0 (2023-05-30), part of the Statnet Project
## * 'news(package="tergm")' for changes since last version
## * 'citation("tergm")' for citation information
## * 'https://statnet.org' for help, support, and other information
##
## 다음의 패키지를 부착합니다: 'tergm'
## The following object is masked from 'package:ergm':
##
## snctrl
## 필요한 패키지를 로딩중입니다: ergm.count
##
## 'ergm.count' 4.1.1 (2022-05-24), part of the Statnet Project
## * 'news(package="ergm.count")' for changes since last version
## * 'citation("ergm.count")' for citation information
## * 'https://statnet.org' for help, support, and other information
## 필요한 패키지를 로딩중입니다: sna
## 필요한 패키지를 로딩중입니다: statnet.common
##
## 다음의 패키지를 부착합니다: 'statnet.common'
## The following object is masked from 'package:ergm':
##
## snctrl
## The following objects are masked from 'package:base':
##
## attr, order
## sna: Tools for Social Network Analysis
## Version 2.7-2 created on 2023-12-05.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.
## 필요한 패키지를 로딩중입니다: tsna
##
## 'statnet' 2019.6 (2019-06-13), part of the Statnet Project
## * 'news(package="statnet")' for changes since last version
## * 'citation("statnet")' for citation information
## * 'https://statnet.org' for help, support, and other information
## unable to reach CRAN
library(dplyr)
##
## 다음의 패키지를 부착합니다: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(sna)
library(network)
neogen_attributes <- read_excel("C:/Users/dlatj/Dropbox/Neogen Combined Datasets-07- USE THESE/Neogen Excel files and codebook/Neogen-Attribute Dataset-07 kds.xls")
getwd()
## [1] "D:/Spring 2024/DACSS 695N Social Network Analysis/Data"
setwd("D:/Spring 2024/DACSS 695N Social Network Analysis/Data")
getwd()
## [1] "D:/Spring 2024/DACSS 695N Social Network Analysis/Data"
lansing_matrix <-read.csv("C:/Users/dlatj/Dropbox/Neogen Combined Datasets-07- USE THESE/Neogen Ucinet Networks files/Lansing required workflow.csv")
#I can check the header (ID-merely id number vs Xid) is different
head(neogen_attributes)
## # A tibble: 6 × 40
## ID Division Building Dept Status Fulltime Gender Race Ageyrs Tenureyrs
## <dbl> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 9 LEXINGTON CREATIVEDR "\"D… ACTIVE 1 MALE Black 45 5.916667…
## 2 10 LEXINGTON CREATIVEDR "\"I… ACTIVE 1 FEMALE White 20 0.666667…
## 3 16 LEXINGTON NANDINO "\"S… ACTIVE 1 FEMALE White 47 0.416667…
## 4 20 LEXINGTON NANDINO "\"D… ACTIVE 1 FEMALE White 37 9.916667…
## 5 21 LEXINGTON NANDINO "\"A… ACTIVE 1 MALE White 31 10.083333
## 6 24 LEXINGTON NANDINO "\"S… ACTIVE 1 MALE White 30 1.333333…
## # ℹ 30 more variables: Education <chr>, jobsat <dbl>, occ <dbl>, oca <dbl>,
## # oc <dbl>, wkinclu <dbl>, flextimeh <chr>, pos <dbl>, procjust <dbl>,
## # ito <dbl>, pied <dbl>, satreward <chr>, satpay <dbl>, satbenefit <dbl>,
## # distjust <chr>, satsuper <dbl>, satsocreward <dbl>, sreffort <dbl>,
## # jobsearch <chr>, trustsuper <dbl>, trustcowker <dbl>, interperjust <dbl>,
## # srocbi <dbl>, srocbo <chr>, paspos <chr>, pasneg <chr>, superocbi <chr>,
## # superocbo <chr>, superefforts <chr>, superperformance <chr>
head(lansing_matrix)
## id X375 X376 X379 X384 X385 X391 X398 X400 X401 X402 X403 X405 X409 X410
## 1 375 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 376 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 379 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 384 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 385 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 391 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X411 X413 X414 X418 X419 X429 X430 X433 X442 X444 X447 X450 X456 X459 X462
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X463 X467 X470 X471 X478 X481 X487 X488 X494 X497 X498 X499 X500 X502 X504
## 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
## X505 X509 X514 X515 X519 X521 X526 X528 X531 X537 X540 X541 X547 X548 X550
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X551 X553 X554 X556 X559 X562 X571 X572 X574 X575 X577 X583 X586 X588 X595
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X599 X602 X610 X612 X613 X618 X619 X620 X626 X629 X630 X635 X636 X638 X639
## 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## X651 X654 X656 X659 X663 X670 X671 X672 X675 X676 X678 X685 X693 X707 X714
## 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X715 X717 X720 X726 X728 X730 X734 X737 X738 X746 X751 X758 X759 X763 X764
## 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X779 X782 X788 X790 X794 X796 X800 X801 X805 X807 X810 X812 X816 X818 X820
## 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## X821 X824 X831 X832 X840 X844 X846 X849 X850 X852 X853 X857 X859 X860 X862
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## X867 X872 X873 X874 X875
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
#Sort only Lansing Division
lansing_attributes <- neogen_attributes %>%
filter(Division == "Lansing")
head(lansing_attributes)
## # A tibble: 6 × 40
## ID Division Building Dept Status Fulltime Gender Race Ageyrs Tenureyrs
## <dbl> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 375 Lansing DOAN "\"Sale… ACTIVE 1 MALE White 56 6.25
## 2 376 Lansing HERBERT "\"Diag… ACTIVE 1 FEMALE White 49 4
## 3 379 Lansing HERBERT "\"Diag… ACTIVE 1 MALE White 46 0.920000…
## 4 384 Lansing HERBERT "\"Diag… ACTIVE 1 MALE Black 31 0.419999…
## 5 385 Lansing DOAN "\"Gene… ACTIVE 1 FEMALE White 33 5.080000…
## 6 391 Lansing MILLER "\"Acco… ACTIVE 1 FEMALE White 55 10.08
## # ℹ 30 more variables: Education <chr>, jobsat <dbl>, occ <dbl>, oca <dbl>,
## # oc <dbl>, wkinclu <dbl>, flextimeh <chr>, pos <dbl>, procjust <dbl>,
## # ito <dbl>, pied <dbl>, satreward <chr>, satpay <dbl>, satbenefit <dbl>,
## # distjust <chr>, satsuper <dbl>, satsocreward <dbl>, sreffort <dbl>,
## # jobsearch <chr>, trustsuper <dbl>, trustcowker <dbl>, interperjust <dbl>,
## # srocbi <dbl>, srocbo <chr>, paspos <chr>, pasneg <chr>, superocbi <chr>,
## # superocbo <chr>, superefforts <chr>, superperformance <chr>
#remove X in id number
rownames(lansing_matrix) <- as.numeric(sub("X", "", rownames(lansing_matrix)))
colnames(lansing_matrix) <- as.numeric(sub("X", "", colnames(lansing_matrix)))
## Warning: 강제형변환에 의해 생성된 NA 입니다
print(lansing_matrix)
## NA 375 376 379 384 385 391 398 400 401 402 403 405 409 410 411 413 414 418
## 1 375 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 376 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 379 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 384 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 385 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 391 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 398 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 8 400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 401 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 10 402 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 11 403 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 12 405 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 13 409 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 14 410 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 15 411 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 16 413 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 414 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0
## 18 418 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 19 419 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 20 429 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 430 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 433 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 442 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 444 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 447 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 26 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 27 456 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 459 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 29 462 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 30 463 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 31 467 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 470 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 471 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0
## 34 478 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 36 487 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 37 488 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0
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## 153 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 154 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 873 874 875
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 1
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 1 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## 51 0 0 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 0
## 60 0 0 0
## 61 0 0 0
## 62 1 0 0
## 63 0 0 0
## 64 1 0 0
## 65 0 0 0
## 66 0 0 0
## 67 0 0 0
## 68 0 0 0
## 69 0 0 1
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 0 0
## 77 0 0 0
## 78 0 0 1
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 0
## 84 1 0 0
## 85 0 0 0
## 86 0 0 0
## 87 0 1 0
## 88 0 0 1
## 89 0 0 1
## 90 0 0 0
## 91 0 0 0
## 92 0 0 0
## 93 0 1 0
## 94 0 0 0
## 95 0 0 0
## 96 0 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 0 0 0
## 101 0 0 0
## 102 0 0 0
## 103 0 0 0
## 104 0 0 1
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 0 0
## 109 0 0 0
## 110 0 0 0
## 111 0 0 0
## 112 0 0 0
## 113 0 0 0
## 114 0 0 0
## 115 0 0 0
## 116 1 0 0
## 117 0 0 0
## 118 0 0 0
## 119 0 0 0
## 120 0 0 0
## 121 0 0 0
## 122 0 0 0
## 123 0 0 0
## 124 0 0 0
## 125 0 0 0
## 126 0 0 0
## 127 0 0 0
## 128 0 0 0
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 0 0
## 133 0 0 0
## 134 1 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 0 0 1
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 0 0
## 143 0 0 1
## 144 0 0 0
## 145 0 0 0
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
## 149 0 0 0
## 150 0 0 0
## 151 0 0 0
## 152 0 0 0
## 153 0 0 0
## 154 0 0 0
#creat edgelist
rownames(lansing_matrix) <- lansing_matrix[,1]
lansing_matrix <- lansing_matrix[,-1]
indices <- which(lansing_matrix == 1, arr.ind = TRUE)
edge_list <- data.frame(
from = rownames(lansing_matrix)[indices[, 1]],
to = colnames(lansing_matrix)[indices[, 2]]
)
print(edge_list)
## from to
## 1 459 375
## 2 471 375
## 3 575 375
## 4 612 375
## 5 635 375
## 6 651 375
## 7 728 375
## 8 779 375
## 9 790 375
## 10 805 375
## 11 832 375
## 12 671 376
## 13 478 379
## 14 519 379
## 15 531 384
## 16 541 384
## 17 738 384
## 18 459 385
## 19 488 391
## 20 499 391
## 21 630 391
## 22 730 391
## 23 857 391
## 24 730 398
## 25 758 398
## 26 398 400
## 27 414 400
## 28 471 400
## 29 571 400
## 30 583 400
## 31 639 400
## 32 820 400
## 33 844 400
## 34 873 400
## 35 554 401
## 36 618 401
## 37 758 401
## 38 763 401
## 39 821 401
## 40 638 402
## 41 714 402
## 42 832 402
## 43 875 402
## 44 531 403
## 45 541 403
## 46 572 403
## 47 572 405
## 48 588 405
## 49 663 405
## 50 414 409
## 51 488 409
## 52 498 409
## 53 630 409
## 54 715 409
## 55 411 410
## 56 410 411
## 57 463 413
## 58 488 413
## 59 409 414
## 60 463 414
## 61 471 414
## 62 487 414
## 63 498 414
## 64 521 414
## 65 610 414
## 66 630 414
## 67 715 414
## 68 790 414
## 69 846 414
## 70 402 418
## 71 462 418
## 72 572 418
## 73 639 418
## 74 654 418
## 75 805 418
## 76 824 418
## 77 846 418
## 78 504 419
## 79 550 419
## 80 554 419
## 81 629 419
## 82 636 419
## 83 758 419
## 84 807 419
## 85 413 429
## 86 414 429
## 87 463 429
## 88 488 429
## 89 630 429
## 90 442 430
## 91 586 430
## 92 782 430
## 93 547 433
## 94 659 433
## 95 456 442
## 96 586 442
## 97 459 444
## 98 471 444
## 99 528 444
## 100 541 444
## 101 571 444
## 102 610 444
## 103 790 444
## 104 800 444
## 105 540 447
## 106 548 447
## 107 556 447
## 108 670 447
## 109 758 447
## 110 820 447
## 111 419 450
## 112 514 450
## 113 629 450
## 114 873 450
## 115 442 456
## 116 586 456
## 117 375 459
## 118 651 459
## 119 676 459
## 120 728 459
## 121 790 459
## 122 832 459
## 123 654 462
## 124 764 462
## 125 779 462
## 126 800 462
## 127 820 462
## 128 840 462
## 129 413 463
## 130 429 463
## 131 487 463
## 132 413 467
## 133 414 467
## 134 429 467
## 135 463 467
## 136 487 467
## 137 488 467
## 138 654 470
## 139 824 470
## 140 375 471
## 141 402 471
## 142 414 471
## 143 444 471
## 144 521 471
## 145 541 471
## 146 571 471
## 147 574 471
## 148 583 471
## 149 602 471
## 150 714 471
## 151 832 471
## 152 462 478
## 153 505 478
## 154 519 478
## 155 537 478
## 156 547 478
## 157 562 478
## 158 572 478
## 159 656 478
## 160 659 478
## 161 671 478
## 162 874 478
## 163 588 481
## 164 675 481
## 165 413 487
## 166 414 487
## 167 429 487
## 168 463 487
## 169 467 487
## 170 471 487
## 171 499 487
## 172 521 487
## 173 540 487
## 174 556 487
## 175 630 487
## 176 670 487
## 177 824 487
## 178 857 487
## 179 391 488
## 180 414 488
## 181 463 488
## 182 487 488
## 183 499 488
## 184 556 488
## 185 630 488
## 186 857 488
## 187 403 494
## 188 410 494
## 189 411 494
## 190 400 497
## 191 459 497
## 192 471 497
## 193 818 497
## 194 840 497
## 195 844 497
## 196 488 498
## 197 630 498
## 198 391 499
## 199 463 499
## 200 488 499
## 201 541 499
## 202 574 499
## 203 630 499
## 204 717 499
## 205 730 499
## 206 857 499
## 207 873 499
## 208 414 500
## 209 459 500
## 210 528 500
## 211 676 500
## 212 505 502
## 213 572 502
## 214 840 502
## 215 550 504
## 216 554 504
## 217 629 504
## 218 636 504
## 219 478 505
## 220 481 505
## 221 502 505
## 222 547 505
## 223 656 505
## 224 671 505
## 225 874 505
## 226 820 509
## 227 419 514
## 228 629 514
## 229 678 515
## 230 379 519
## 231 478 519
## 232 801 519
## 233 541 521
## 234 575 521
## 235 602 521
## 236 717 521
## 237 720 521
## 238 779 521
## 239 846 521
## 240 540 526
## 241 556 526
## 242 670 526
## 243 414 528
## 244 500 528
## 245 583 528
## 246 676 528
## 247 384 531
## 248 541 531
## 249 738 531
## 250 810 531
## 251 478 537
## 252 505 537
## 253 519 537
## 254 547 537
## 255 659 537
## 256 414 540
## 257 447 540
## 258 548 540
## 259 556 540
## 260 630 540
## 261 670 540
## 262 685 540
## 263 384 541
## 264 531 541
## 265 571 541
## 266 588 541
## 267 602 541
## 268 675 541
## 269 738 541
## 270 810 541
## 271 840 541
## 272 562 547
## 273 659 547
## 274 874 547
## 275 540 548
## 276 556 548
## 277 618 548
## 278 670 548
## 279 685 548
## 280 758 548
## 281 419 550
## 282 504 550
## 283 554 550
## 284 577 550
## 285 629 550
## 286 635 550
## 287 758 550
## 288 779 550
## 289 800 550
## 290 807 550
## 291 850 550
## 292 873 550
## 293 403 551
## 294 405 551
## 295 411 551
## 296 481 551
## 297 494 551
## 298 505 551
## 299 541 551
## 300 572 551
## 301 588 551
## 302 620 551
## 303 675 551
## 304 763 551
## 305 796 551
## 306 442 553
## 307 540 553
## 308 574 553
## 309 586 553
## 310 693 553
## 311 734 553
## 312 746 553
## 313 758 553
## 314 818 553
## 315 862 553
## 316 504 554
## 317 550 554
## 318 626 554
## 319 629 554
## 320 636 554
## 321 758 554
## 322 821 554
## 323 540 556
## 324 548 556
## 325 670 556
## 326 685 556
## 327 554 559
## 328 629 559
## 329 758 559
## 330 820 559
## 331 873 559
## 332 659 562
## 333 874 562
## 334 414 571
## 335 444 571
## 336 471 571
## 337 521 571
## 338 541 571
## 339 602 571
## 340 612 571
## 341 714 571
## 342 790 571
## 343 820 571
## 344 620 572
## 345 763 572
## 346 403 574
## 347 471 574
## 348 531 574
## 349 554 574
## 350 577 574
## 351 610 574
## 352 618 574
## 353 738 574
## 354 759 574
## 355 779 574
## 356 790 574
## 357 812 574
## 358 821 574
## 359 867 574
## 360 471 575
## 361 676 575
## 362 779 575
## 363 419 577
## 364 751 577
## 365 759 577
## 366 763 577
## 367 800 577
## 368 812 577
## 369 867 577
## 370 414 583
## 371 471 583
## 372 498 583
## 373 500 583
## 374 528 583
## 375 571 583
## 376 714 583
## 377 790 583
## 378 430 586
## 379 442 586
## 380 456 586
## 381 405 588
## 382 671 588
## 383 414 602
## 384 471 602
## 385 498 602
## 386 521 602
## 387 531 602
## 388 541 602
## 389 571 602
## 390 717 602
## 391 720 602
## 392 790 602
## 393 846 602
## 394 414 610
## 395 419 610
## 396 444 610
## 397 550 610
## 398 730 610
## 399 779 610
## 400 800 610
## 401 816 610
## 402 853 610
## 403 375 612
## 404 402 612
## 405 414 612
## 406 528 612
## 407 575 612
## 408 610 612
## 409 639 612
## 410 676 612
## 411 714 612
## 412 779 612
## 413 850 612
## 414 875 612
## 415 401 618
## 416 481 618
## 417 504 618
## 418 554 618
## 419 629 618
## 420 758 618
## 421 821 618
## 422 852 618
## 423 654 619
## 424 758 619
## 425 764 619
## 426 779 619
## 427 820 619
## 428 873 619
## 429 659 620
## 430 763 620
## 431 402 626
## 432 554 626
## 433 559 626
## 434 629 626
## 435 758 626
## 436 820 626
## 437 873 626
## 438 515 629
## 439 618 629
## 440 626 629
## 441 678 629
## 442 391 630
## 443 413 630
## 444 414 630
## 445 429 630
## 446 463 630
## 447 467 630
## 448 487 630
## 449 488 630
## 450 498 630
## 451 499 630
## 452 556 630
## 453 857 630
## 454 375 635
## 455 459 635
## 456 651 635
## 457 790 635
## 458 832 635
## 459 419 636
## 460 504 636
## 461 547 636
## 462 550 636
## 463 554 636
## 464 562 636
## 465 629 636
## 466 758 636
## 467 807 636
## 468 402 638
## 469 613 638
## 470 639 638
## 471 832 638
## 472 402 639
## 473 638 639
## 474 714 639
## 475 832 639
## 476 875 639
## 477 375 651
## 478 459 651
## 479 635 651
## 480 790 651
## 481 832 651
## 482 418 654
## 483 462 654
## 484 470 654
## 485 619 654
## 486 764 654
## 487 779 654
## 488 796 654
## 489 820 654
## 490 846 654
## 491 376 656
## 492 419 656
## 493 478 656
## 494 481 656
## 495 505 656
## 496 519 656
## 497 531 656
## 498 541 656
## 499 551 656
## 500 572 656
## 501 574 656
## 502 577 656
## 503 671 656
## 504 675 656
## 505 763 656
## 506 779 656
## 507 796 656
## 508 862 656
## 509 478 659
## 510 519 659
## 511 537 659
## 512 547 659
## 513 447 670
## 514 462 670
## 515 526 670
## 516 540 670
## 517 548 670
## 518 556 670
## 519 758 670
## 520 376 671
## 521 502 671
## 522 531 671
## 523 572 671
## 524 656 671
## 525 675 671
## 526 707 671
## 527 758 672
## 528 481 675
## 529 588 675
## 530 414 676
## 531 459 676
## 532 500 676
## 533 528 676
## 534 583 676
## 535 779 676
## 536 800 676
## 537 832 676
## 538 515 678
## 539 629 678
## 540 447 685
## 541 540 685
## 542 548 685
## 543 556 685
## 544 572 685
## 545 670 685
## 546 820 685
## 547 734 693
## 548 862 693
## 549 547 707
## 550 414 714
## 551 471 714
## 552 528 714
## 553 531 714
## 554 541 714
## 555 571 714
## 556 583 714
## 557 638 714
## 558 639 714
## 559 790 714
## 560 414 715
## 561 488 715
## 562 630 715
## 563 414 717
## 564 521 717
## 565 541 717
## 566 602 717
## 567 779 717
## 568 521 720
## 569 602 720
## 570 717 720
## 571 730 720
## 572 846 720
## 573 379 726
## 574 478 726
## 575 505 726
## 576 519 726
## 577 537 726
## 578 801 726
## 579 375 728
## 580 459 728
## 581 651 728
## 582 790 728
## 583 832 728
## 584 398 730
## 585 528 730
## 586 758 730
## 587 846 730
## 588 850 730
## 589 873 730
## 590 442 734
## 591 586 734
## 592 693 734
## 593 862 734
## 594 405 737
## 595 502 737
## 596 620 737
## 597 763 737
## 598 419 738
## 599 444 738
## 600 531 738
## 601 541 738
## 602 571 738
## 603 574 738
## 604 577 738
## 605 602 738
## 606 759 738
## 607 788 738
## 608 790 738
## 609 821 738
## 610 831 738
## 611 873 738
## 612 481 746
## 613 556 746
## 614 618 746
## 615 796 746
## 616 818 746
## 617 840 746
## 618 577 751
## 619 763 751
## 620 402 758
## 621 450 758
## 622 548 758
## 623 550 758
## 624 554 758
## 625 559 758
## 626 626 758
## 627 629 758
## 628 820 758
## 629 821 758
## 630 850 758
## 631 873 758
## 632 376 759
## 633 419 759
## 634 577 759
## 635 763 759
## 636 812 759
## 637 867 759
## 638 401 763
## 639 541 763
## 640 554 763
## 641 572 763
## 642 577 763
## 643 620 763
## 644 751 763
## 645 759 763
## 646 800 763
## 647 821 763
## 648 419 764
## 649 654 764
## 650 779 764
## 651 375 779
## 652 462 779
## 653 500 779
## 654 654 779
## 655 676 779
## 656 717 779
## 657 764 779
## 658 800 779
## 659 805 779
## 660 824 779
## 661 846 779
## 662 430 782
## 663 442 782
## 664 586 782
## 665 541 788
## 666 602 788
## 667 738 788
## 668 867 788
## 669 375 790
## 670 459 790
## 671 471 790
## 672 571 790
## 673 574 790
## 674 651 790
## 675 714 790
## 676 805 790
## 677 820 790
## 678 821 790
## 679 850 790
## 680 414 794
## 681 521 794
## 682 541 794
## 683 602 794
## 684 717 794
## 685 444 800
## 686 462 800
## 687 577 800
## 688 610 800
## 689 779 800
## 690 853 800
## 691 379 801
## 692 478 801
## 693 519 801
## 694 375 805
## 695 418 805
## 696 462 805
## 697 471 805
## 698 571 805
## 699 575 805
## 700 612 805
## 701 654 805
## 702 779 805
## 703 790 805
## 704 800 805
## 705 824 805
## 706 846 805
## 707 504 807
## 708 550 807
## 709 636 807
## 710 531 810
## 711 541 810
## 712 738 810
## 713 790 810
## 714 502 812
## 715 577 812
## 716 444 816
## 717 610 816
## 718 853 816
## 719 413 818
## 720 463 818
## 721 487 818
## 722 497 818
## 723 553 818
## 724 556 818
## 725 630 818
## 726 670 818
## 727 734 818
## 728 824 818
## 729 862 818
## 730 559 820
## 731 758 820
## 732 790 820
## 733 824 820
## 734 850 820
## 735 873 820
## 736 401 821
## 737 541 821
## 738 554 821
## 739 574 821
## 740 618 821
## 741 759 821
## 742 763 821
## 743 790 821
## 744 812 821
## 745 873 821
## 746 462 824
## 747 619 824
## 748 654 824
## 749 764 824
## 750 779 824
## 751 820 824
## 752 846 824
## 753 403 831
## 754 505 831
## 755 531 831
## 756 572 831
## 757 574 831
## 758 671 831
## 759 717 831
## 760 471 832
## 761 442 840
## 762 586 840
## 763 400 844
## 764 414 844
## 765 800 844
## 766 873 844
## 767 521 846
## 768 541 846
## 769 602 846
## 770 717 846
## 771 403 849
## 772 410 849
## 773 411 849
## 774 402 850
## 775 554 850
## 776 559 850
## 777 635 850
## 778 639 850
## 779 654 850
## 780 758 850
## 781 790 850
## 782 820 850
## 783 821 850
## 784 824 850
## 785 873 850
## 786 618 852
## 787 629 852
## 788 821 852
## 789 444 853
## 790 550 853
## 791 610 853
## 792 800 853
## 793 816 853
## 794 391 857
## 795 463 857
## 796 488 857
## 797 499 857
## 798 630 857
## 799 547 860
## 800 872 860
## 801 586 862
## 802 693 862
## 803 734 862
## 804 758 862
## 805 444 867
## 806 550 867
## 807 574 867
## 808 577 867
## 809 602 867
## 810 610 867
## 811 676 867
## 812 759 867
## 813 763 867
## 814 779 867
## 815 790 867
## 816 812 867
## 817 816 867
## 818 821 867
## 819 547 872
## 820 874 872
## 821 554 873
## 822 559 873
## 823 629 873
## 824 758 873
## 825 820 873
## 826 505 874
## 827 636 874
## 828 659 874
## 829 402 875
## 830 575 875
## 831 612 875
## 832 638 875
## 833 639 875
## 834 714 875
## 835 832 875
## 836 850 875
lansing_net <- network(edge_list, directed = TRUE, matrix.type = 'edgelist')
network.vertex.names(lansing_net) <- sort(unique(c(as.numeric(edge_list$from), as.numeric(edge_list$to))))
print(lansing_net)
## Network attributes:
## vertices = 151
## directed = TRUE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges= 836
## missing edges= 0
## non-missing edges= 836
##
## Vertex attribute names:
## vertex.names
##
## No edge attributes
print(network.size(lansing_net))
## [1] 151
#plot 1. basic
par(mar = c(0, 0, 0, 0)) # remove borders from the picture
gplot(lansing_net, # gplot()
mode = "random",) # locate nodes in a circle
gplot(lansing_net, # gplot()
mode = "fruchtermanreingold",) # locate nodes in a circle
gplot(lansing_net,
gmode = "digraph", # type of network: undirected (anyway we symmetrized and are focusing only on the reciprocated ties); "digraph" for directed, "twomode" for twomode
mode = "fruchtermanreingold", # how the nodes are positioned
vertex.cex = .8, # the size of the node shapes
displaylabels = TRUE, # to add/remove the node labels
label.pos = 1, # to position the labels below the node shapes (1: below, 2: left, 3: above, 4: right, 5: over the node)
label.cex = .8, # to decrease the size of the node labels
edge.col = "grey70", # to make the color of the ties
displayisolates=FALSE # I removed the isolates. There were lots of isolates isolates, so it looked really messy.
)
#plot2. colored by gender
lansing_net.gender <- network(lansing_net, directed = TRUE)
lansing_net.gender %v% "gender" <- lansing_attributes$Gender
gender_colors <- ifelse(lansing_net.gender %v% "gender" == "MALE", "blue", "red")
par(mar = c(0, 0, 0, 0)) # remove borders from the picture
gplot(lansing_net.gender, # gplot()
mode = "random",) # locate nodes in a circle
gplot(lansing_net.gender,
gmode = "digraph", # type of network: undirected (anyway we symmetrized and are focusing only on the reciprocated ties); "digraph" for directed, "twomode" for twomode
mode = "random", # how the nodes are positioned
vertex.cex = .8, # the size of the node shapes
displaylabels = TRUE, # to add/remove the node labels
label.pos = 1, # to position the labels below the node shapes (1: below, 2: left, 3: above, 4: right, 5: over the node)
label.cex = .8, # to decrease the size of the node labels
edge.col = "grey70",
vertex.col = gender_colors,# to make the color of the ties
displayisolates=FALSE # I removed the isolates. There were lots of isolates isolates, so it looked really messy.
)
par(mar = c(0, 0, 0, 0)) # remove borders from the picture
gplot(lansing_net.gender,
gmode = "digraph", # type of network: undirected (anyway we symmetrized and are focusing only on the reciprocated ties); "digraph" for directed, "twomode" for twomode
mode = "fruchtermanreingold", # how the nodes are positioned
vertex.cex = .8, # the size of the node shapes
displaylabels = TRUE, # to add/remove the node labels
label.pos = 1, # to position the labels below the node shapes (1: below, 2: left, 3: above, 4: right, 5: over the node)
label.cex = .8, # to decrease the size of the node labels
edge.col = "grey70",
vertex.col = gender_colors,# to make the color of the ties
displayisolates=TRUE # I removed the isolates. There were lots of isolates isolates, so it looked really messy.
)
#plot 3. Degree centrality
degree_centrality <- degree(lansing_net.gender, gmode = "digraph", cmode = "freeman")
scaled_degree <- sqrt(degree_centrality)*0.5
par(mar = c(0, 0, 0, 0))
gplot(lansing_net.gender,
gmode = "digraph",
mode = "fruchtermanreingold",
vertex.cex = scaled_degree, # node size-> degree centrality
displaylabels = TRUE,
label.pos = 1,
label.cex = 0.8,
edge.col = "grey70",
vertex.col = gender_colors,
displayisolates = TRUE)
#in-degree and out-degree
in_degrees <- degree(lansing_net.gender, cmode = "indegree")
out_degrees <- degree(lansing_net.gender, cmode = "outdegree")
scaled_in_degrees <- sqrt(in_degrees)*0.5
scaled_out_degrees <- sqrt(out_degrees)*0.5
par(mfrow = c(1, 2)) # two graph display
par(mar = c(0, 0, 0, 0))
gplot(lansing_net.gender,
gmode = "digraph",
mode = "fruchtermanreingold",
vertex.cex = scaled_in_degrees,
displaylabels = TRUE,
label.pos = 1,
label.cex = 0.8,
edge.col = "grey70",
vertex.col = gender_colors,
displayisolates = TRUE,
main = "In-Degree Centrality")
gplot(lansing_net.gender,
gmode = "digraph",
mode = "fruchtermanreingold",
vertex.cex = scaled_out_degrees,
displaylabels = TRUE,
label.pos = 1,
label.cex = 0.8,
edge.col = "grey70",
vertex.col = gender_colors,
displayisolates = TRUE,
main = "Out-Degree Centrality")
