Replication package-Descriptives

---

Intro

This website is a replication package for the paper “Twitter and divides in the Dutch Parliament: Social and Political Segregation in the following, @-mentions and retweets networks” by Tolsma and Spierings ((submitted)).

It contains R code to replicate all Tables/Figures/Appendix in the manuscript.

To copy the code click the button in the upper right corner of the code-chunks.

Use the top menu to navigate to the section of interest.

The source code of this website can be found on Github

Questions can be addressed to Jochem Tolsma.

---

Packages

# install if necessary
if (!require("tidyverse", character.only = TRUE)) {
    install.packages("tidyverse", dependencies = TRUE)
}
if (!require("dplyr", character.only = TRUE)) {
    install.packages("dplyr", dependencies = TRUE)
}
if (!require("foreign", character.only = TRUE)) {
    install.packages("foreign", dependencies = TRUE)
}
if (!require("igraph", character.only = TRUE)) {
    install.packages("igraph", dependencies = TRUE)
}
if (!require("knitr", character.only = TRUE)) {
    install.packages("knitr", dependencies = TRUE)
}
if (!require("kableExtra", character.only = TRUE)) {
    install.packages("kableExtra", dependencies = TRUE)
}
if (!require("RSiena", character.only = TRUE)) {
    install.packages("RSiena", dependencies = TRUE)
}
if (!require("netseg", character.only = TRUE)) {
    install.packages("netseg", dependencies = TRUE)
}



# load packages.
library(tidyverse)
library(dplyr)
library(foreign)
library(igraph)
library(knitr)
library(kableExtra)
library(RSiena)
library(netseg)

---

Custom segregation functions

Define custom functions to calculate network-segregation:

• fdensity: density of network
  
• fdensityintra: density within subgroups
  
• fscolnet: Coleman’s Homophily Index at the network-level, see (( Bojanowski and Corten 2014))
  
• fhomomat: make a matrix indicating wheter nodes are similar
  
• fndyads: count number of dyads
  
• fndayds2: count number of within subgroup dyads

fdensity <- function(x) {
    # x is your nomination network should be relationship, divided by all possible off diagonal
    # ties.  make sure diagonal cells are NA
    diag(x) <- NA
    x[x == 10] <- NA
    sum(x == 1, na.rm = T)/(sum(x == 1 | x == 0, na.rm = T))
}

fdensityintra <- function(x, A) {
    # A is matrix indicating whether nodes constituting dyad have same characteristics
    diag(x) <- NA
    x[x == 10] <- NA
    diag(A) <- NA
    sum(x == 1 & A == 1, na.rm = T)/(sum((x == 1 | x == 0) & A == 1, na.rm = T))
}

fdensityinter <- function(x, A) {
    # A is matrix indicating whether nodes constituting dyad have same characteristics
    diag(x) <- NA
    x[x == 10] <- NA
    diag(A) <- NA
    sum(x == 1 & A != 1, na.rm = T)/(sum((x == 1 | x == 0) & A != 1, na.rm = T))
}

fhomomat <- function(x) {
    xmat <- matrix(x, nrow = length(x), ncol = length(x))
    xmatt <- t(xmat)
    xhomo <- xmat == xmatt
    return(xhomo)
}

fndyads <- function(x) {
    diag(x) <- NA
    x[x == 10] <- NA
    (sum((x == 1 | x == 0), na.rm = T))
}

fndyads2 <- function(x, A) {
    diag(x) <- NA
    x[x == 10] <- NA
    diag(A) <- NA
    (sum((x == 1 | x == 0) & A == 1, na.rm = T))
}


fscolnet <- function(network, ccovar) {
    # Calculate coleman on network level:
    # https://reader.elsevier.com/reader/sd/pii/S0378873314000239?token=A42F99FF6E2B750436DD2CB0DB7B1F41BDEC16052A45683C02644DAF88215A3379636B2AA197B65941D6373E9E2EE413

    fhomomat <- function(x) {
        xmat <- matrix(x, nrow = length(x), ncol = length(x))
        xmatt <- t(xmat)
        xhomo <- xmat == xmatt
        return(xhomo)
    }

    fsumintra <- function(x, A) {
        # A is matrix indicating whether nodes constituting dyad have same characteristics
        diag(x) <- NA
        x[x == 10] <- NA
        diag(A) <- NA
        sum(x == 1 & A == 1, na.rm = T)
    }

    # expecation w*=sum_g sum_i (ni((ng-1)/(N-1)))
    network[network == 10] <- NA
    ni <- rowSums(network, na.rm = T)
    ng <- NA
    for (i in 1:length(ccovar)) {
        ng[i] <- table(ccovar)[rownames(table(ccovar)) == ccovar[i]]
    }
    N <- length(ccovar)
    wexp <- sum(ni * ((ng - 1)/(N - 1)), na.rm = T)

    # wgg1 how many intragroup ties
    w <- fsumintra(network, fhomomat(ccovar))

    Scol_net <- ifelse(w >= wexp, (w - wexp)/(sum(ni, na.rm = T) - wexp), (w - wexp)/wexp)
    return(Scol_net)

}

---

Load data objects

Data objects:

• key: information on all politicians on election list
  
• twitter
  • keyf: information on all 147 MPs with twitter handle
    
  • mydata: RSiena object with all kind of goodies inside
    
  • seats: seating coordinates of HoP (used for plotting)

# STAP 1: read in data
key <- read.spss("data-processed\\key moederbestand 20171114.sav", use.value.labels = T, to.data.frame = T)


load("data-processed\\twitter_20190919.RData")
# str(twitter_20190919,1)
keyf <- twitter_20190919[[1]]
mydata <- twitter_20190919[[2]]
seats <- twitter_20190919[[3]]

---

Summary of dataset used.

# select all MPs (we need to filter the correct EGOhandle)
data_mp <- key %>%
    filter(Elected20170315 == "yes, verkozen" & !(EGOhandle %in% c(10401, 10403, 13102, 15102, 106101,
        41400))) %>%
    select("Naam", "Partij", "Geslacht", "GebJaar", "EthMinZ", "PlekLijst", "Kamerlid20161123", "ZetelSegment20170315",
        "ZetelRij20170315", "ZetelKolom20170315")

# not necessary: 'Twitterhandle' 'Elected20170315'

seats$ZetelSegment20170315 <- seats$segments
seats$ZetelRij20170315 <- seats$rows
seats$ZetelKolom20170315 <- seats$columns

data_mp <- inner_join(data_mp, seats) %>%
    select("Naam", "Partij", "Geslacht", "GebJaar", "EthMinZ", "PlekLijst", "Kamerlid20161123", "ZetelSegment20170315",
        "ZetelRij20170315", "ZetelKolom20170315", "X", "Y")


colnames(data_mp) <- c("Name", "Party", "Sex", "Birth Year", "Visible Minority", "List Position", "Incumbency status",
    "Seating Segment", "Seating Row", "Seating Column", "X-coord.", "Y-coord")

data_mp$Sex <- as.character(data_mp$Sex)
data_mp$Sex[data_mp$Sex == "vrouw"] <- "female"
data_mp$Sex[data_mp$Sex == "man"] <- "male"


sumdat <- kbl(data_mp, booktabs = TRUE, digits = 2, caption = "Dataset summary", align = "c") %>%
    kable_classic(full_width = F, html_font = "Cambria") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

scroll_box(sumdat, width = "100%", height = "500px")

Dataset summary

Name	Party	Sex	Birth Year	Visible Minority	List Position	Incumbency status	Seating Segment	Seating Row	Seating Column	X-coord.	Y-coord
Agema, Fleur	PVV	female	1976	0	2	1	1	1	2	7.9	-2.9
Amhaouch, Mustafa	CDA	male	1970	1	15	1	2	3	1	9.5	1.3
Arib, Khadija	PvdA	female	1960	1	2	1	6	6	3	-16.1	-0.8
Ark, Tamara	VVD	female	1974	0	4	1	3	2	1	3.9	3.5
Azmani, Malik	VVD	male	1976	1	10	1	3	3	2	3.7	5.5
Beertema, Harm	PVV	male	1952	0	10	1	1	4	1	13.6	-4.5
Belhaj, Salima	D66	female	1979	1	14	1	4	4	3	-3.3	7.4
Bergkamp, Vera	D66	female	1971	0	6	1	4	3	4	-4.6	5.0
Bisschop, Roelof	SGP	male	1956	0	3	1	3	4	3	3.6	7.4
Bosma, Martin	PVV	male	1964	0	6	1	1	2	1	10.0	-4.4
Bosman, Andre	VVD	male	1965	0	24	1	3	4	5	1.2	7.8
ten Broeke, Han	VVD	male	1969	0	9	1	3	2	2	2.6	4.0
Bruins Slot, Hanke	CDA	female	1977	0	9	1	2	3	3	7.8	2.9
Van Dijck, Tony	PVV	male	1963	0	8	1	1	3	3	11.3	-1.9
Van Dijk, Jasper	SP	male	1971	0	14	1	6	6	6	-16.9	-4.4
Dijkgraaf, Elbert	SGP	male	1970	0	2	1	3	4	2	4.7	7.0
Dijkstra, Pia	D66	female	1954	0	4	1	4	2	1	-1.1	4.2
Dijkstra, Remco	VVD	male	1972	0	27	1	3	5	3	4.0	9.1
Dik-Faber, Carla	CU	female	1971	0	4	1	2	5	4	9.9	6.1
Duisenberg, Pieter	VVD	male	1967	0	15	1	4	5	1	-1.2	9.6
Fritsma, Sietse	PVV	male	1972	0	5	1	1	3	1	11.8	-4.5
Geurts, Jaco	CDA	male	1970	0	10	1	1	4	4	12.8	-1.0
De Graaf, Machiel	PVV	male	1969	0	11	1	1	4	2	13.5	-3.2
Grashoff, Rik	GroenLinks	male	1961	0	5	1	5	3	1	-6.8	3.8
Graus, Dion	PVV	male	1967	0	12	1	1	4	3	13.2	-2.1
Van Haersma Buma, Sybrand	CDA	male	1965	0	1	1	2	1	2	5.3	0.5
Harbers, Mark	VVD	male	1969	0	8	1	3	2	3	1.3	4.2
Heerma, Pieter	CDA	male	1977	0	7	1	2	2	1	7.9	0.3
Helder, Lilian	PVV	female	1973	0	9	1	1	3	4	10.8	-0.9
Van Helvert, Martijn	CDA	male	1978	0	16	1	1	4	5	12.5	0.0
Keijzer, Mona	CDA	female	1968	0	2	1	2	1	1	6.4	-0.4
Klaver, jesse	GroenLinks	male	1986	1	1	1	5	1	1	-5.1	0.5
Knops, Raymond	CDA	male	1971	0	6	1	2	2	2	7.1	1.4
Kooiman, Nine	SP	female	1980	0	12	1	6	6	4	-16.4	-1.9
Koolmees, Wouter	D66	male	1977	0	3	1	4	2	3	-3.7	3.5
Krol, Henk	50Plus	male	1950	0	1	1	5	5	5	-12.4	3.1
Kuiken, Attje	PvdA	female	1977	0	6	1	6	3	2	-11.0	-1.9
Kuzu, Tunahan	DENK	male	1981	1	1	1	5	6	2	-10.3	7.5
Leijten, Renske	SP	female	1979	0	2	1	6	1	2	-8.0	-4.3
Lodders, Helma	VVD	female	1968	0	12	1	3	3	1	4.8	5.0
Madlener, Barry	PVV	male	1969	0	7	1	1	3	2	11.6	-3.2
Van Meenen, Paul	D66	male	1956	0	8	1	4	4	5	-5.5	6.5
Mulder, Agnes	CDA	female	1973	0	13	1	1	5	5	14.0	0.8
Nijboer, Henk	PvdA	male	1983	0	7	1	6	5	2	-14.3	-0.4
Nijkerken-de Haan, Chantal	VVD	female	1973	0	30	1	4	6	2	-2.3	11.0
Van Nispen, Michiel	SP	male	1982	0	7	1	6	3	4	-11.5	-4.5
Omtzigt, Pieter	CDA	male	1974	0	4	1	2	2	3	6.1	2.1
Van Oosten, Foort	VVD	male	1977	0	32	1	3	5	2	5.4	8.6
Ozturk, Selcuk	DENK	male	1972	1	3	1	5	6	4	-12.2	5.8
Pechtold, Alexander	D66	male	1965	0	1	1	4	1	2	-2.6	2.1
Van Raak, Ronald	SP	male	1969	0	4	1	6	2	2	-9.5	-3.1
Roemer, Emile	SP	male	1962	0	1	1	6	1	1	-7.7	-2.9
Rog, Michel	CDA	male	1973	0	14	1	2	5	2	11.8	4.1
Ronnes, Erik	CDA	male	1967	0	17	1	2	6	1	14.1	3.8
De Roon, Raymond	PVV	male	1952	0	13	1	1	5	1	15.4	-4.4
Rutte, Arno	VVD	male	1972	0	20	1	3	5	5	1.3	9.6
Schouten, Carola	CU	female	1977	0	2	1	2	4	4	8.8	4.7
Segers, Gert-Jan	CU	male	1969	0	1	1	2	4	5	7.9	5.4
Sjoerdsma, Sjoerd	D66	male	1981	0	11	1	4	3	1	-1.0	6.0
Van der Staaij, Kees	SGP	male	1968	0	1	1	3	4	1	5.7	6.5
Tellegen, Ockje	VVD	female	1974	0	21	1	3	5	4	2.6	9.3
Thieme, Marianne	PvdDieren	female	1972	0	1	1	5	4	2	-8.6	4.7
Van Toorenburg, Madeleine	CDA	female	1986	0	5	1	2	6	2	13.3	4.8
Van Veldhoven, Stientje	D66	female	1973	0	2	1	4	1	1	-1.3	2.4
Verhoeven, Kees	D66	male	1976	0	7	1	4	2	2	-2.4	4.0
Visser, Barbara	VVD	female	1977	0	7	1	3	4	4	2.3	7.7
Voordewind, Joel	CU	male	1965	0	3	1	2	5	5	8.8	6.8
Voortman, Linda	GroenLinks	female	1979	0	4	1	5	2	2	-6.9	1.4
De Vries, Aukje	VVD	female	1964	0	18	1	4	6	3	-3.6	10.8
Wassenberg, Frank	PvdDieren	male	1966	0	4	0	5	5	2	-9.6	6.1
Van Weyenberg, Steven	D66	male	1973	0	10	1	4	3	3	-3.6	5.5
Wilders, Geert	PVV	male	1963	0	1	1	1	1	1	8.2	-4.3
Van t’Wout, Bas	VVD	male	1979	0	13	1	4	5	2	-2.4	9.3
Ziengs, Erik	VVD	male	1960	0	23	1	4	4	1	-1.1	7.8
Zijlstra, Halbe	VVD	male	1969	0	3	1	3	1	2	1.4	2.4
Rutte, Mark	VVD	male	1967	0	1	0	3	1	1	3.0	2.1
Ploumen, Lilianne	PvdA	female	1962	0	10	0	6	6	2	-15.7	0.6
Hennis-Plasschaert, Jeanine	VVD	female	1973	0	2	0	3	3	4	1.3	6.0
Dijsselbloem, Jeroen	PvdA	male	1966	0	3	0	6	4	2	-12.6	-1.0
Asscher, Lodewijk	PvdA	male	1974	0	1	0	6	3	1	-10.6	-0.9
Dijksma, Sharon	PvdA	female	1971	0	4	0	6	4	3	-13.0	-2.1
Dekker, Sander	VVD	male	1975	0	6	0	3	5	1	6.5	8.2
Dijkhoff, Klaas	VVD	male	1981	0	5	0	3	3	3	2.4	5.9
Thierry Baudet	FvD	male	1983	0	1	0	1	6	6	15.4	1.6
Eppo Bruins	CU	male	1969	0	5	1	2	5	3	10.9	5.1
LILIAN MARIJNISSEN	SP	female	1985	0	3	0	6	2	1	-9.0	-1.8
SADET KARABULUT	SP	female	1975	1	5	1	6	2	3	-9.8	-4.4
SANDRA BECKERMAN	SP	female	1983	0	6	0	6	3	3	-11.4	-3.2
PETER KWINT	SP	male	1984	0	8	0	6	4	4	-13.2	-3.2
BART VAN KENT	SP	male	1983	0	9	0	6	4	5	-13.3	-4.5
CEM LACIN	SP	male	1986	1	10	0	6	5	4	-15.0	-3.2
FRANK FUTSELAAR	SP	male	1979	0	11	0	6	5	5	-15.1	-4.4
MAARTEN HIJINK	SP	male	1983	0	13	0	6	6	5	-16.6	-3.2
Ingrid van Engelshoven	D66	female	1966	0	5	0	4	3	2	-2.3	5.9
Jan Paternotte	D66	male	1984	0	9	0	4	5	3	-3.7	9.1
Rob Jetten	D66	male	1987	0	12	0	4	5	4	-5.1	8.6
Jessica van Eijs	D66	female	1981	0	13	0	4	4	4	-4.5	7.0
Maarten Groothuizen	D66	male	1976	0	15	0	4	6	5	-5.9	10.1
Rens Raemakers	D66	male	1991	0	17	0	4	6	4	-4.8	10.5
Achraf Bouali	D66	male	1974	1	16	0	4	5	5	-6.3	8.2
Antje Diertens	D66	female	1958	0	18	0	4	6	6	-7.2	9.6
Tjeerd de Groot	D66	male	1968	0	19	0	5	6	1	-9.4	8.3
René Peters	CDA	male	1975	0	3	0	2	5	1	12.7	3.1
Harry van der Molen	CDA	male	1980	0	8	0	2	4	3	9.6	4.0
Anne Kuik	CDA	female	1987	0	11	0	2	4	1	11.1	2.2
Chris van Dam	CDA	male	1963	0	12	0	2	3	4	6.8	3.8
Joba van den Berg-Jansen	CDA	female	1958	0	18	0	2	3	2	8.7	2.2
Maurits von Martels	CDA	male	1960	0	44	0	2	4	2	10.4	3.0
Dennis Wiersma	VVD	male	1986	0	11	0	4	6	1	-1.0	11.2
Bente Becker	VVD	female	1985	0	14	0	3	6	6	1.3	11.2
Sophie Hermans	VVD	female	1981	0	16	0	3	6	5	2.6	11.0
Anne Mulder	VVD	male	1969	0	17	1	4	4	2	-2.2	7.7
Dilan Yesilgöz-Zegerius	VVD	female	1977	1	19	0	3	6	4	3.7	10.8
Daniel Koerhuis	VVD	male	1981	0	22	0	3	6	3	5.0	10.5
Albert van den Bosch	VVD	male	1955	0	25	0	3	6	2	6.3	10.1
Zohair el Yassini	VVD	male	1979	1	26	0	3	6	1	7.3	9.6
Martin Wörsdörfer	VVD	male	1972	0	28	0	2	6	6	9.5	8.3
Arne Weverling	VVD	male	1974	0	29	0	2	6	5	10.6	7.5
Sven Koopmans	VVD	male	1973	0	33	0	2	6	4	11.6	6.8
Jan Middendorp	VVD	male	1975	0	34	0	2	6	3	12.5	5.8
Léonie Sazias	50Plus	female	1957	0	2	0	5	5	4	-11.5	4.1
Martin van Rooijen	50Plus	male	1942	0	3	0	5	6	5	-13.1	4.8
Corrie van Brenk	50Plus	female	1960	0	4	0	5	6	6	-13.9	3.8
Esther Ouwehand	PvdDieren	female	1976	0	2	1	5	4	1	-7.7	5.4
Kathalijne Buitenweg	GroenLinks	female	1970	0	2	0	5	1	2	-6.1	-0.4
Tom van der Lee	GroenLinks	male	1964	0	3	0	5	2	3	-7.7	0.3
Corinne Ellemeet	GroenLinks	female	1976	0	7	0	5	3	3	-8.6	2.2
Zihni Özdil	GroenLinks	male	1981	1	8	0	5	3	4	-9.3	1.3
Bart Snels	GroenLinks	male	1966	0	9	0	5	2	1	-5.9	2.1
Suzanne Kröger	GroenLinks	female	1977	0	11	0	5	4	4	-10.4	3.0
Bram van Oijk	GroenLinks	male	1954	0	10	0	5	4	5	-11.0	2.2
Nevin Özütok	GroenLinks	female	1960	1	12	0	5	4	3	-9.5	4.0
Lisa Westerveld	GroenLinks	female	1981	0	14	0	6	5	1	-13.8	0.8
Isabelle Diks	GroenLinks	female	1965	0	19	0	6	6	1	-15.2	1.6
Liesbeth van Tongeren	GroenLinks	female	1958	0	6	1	5	3	2	-7.7	2.9
Lammert van Raan	PvdDieren	male	1962	0	3	0	5	5	1	-8.6	6.8
Femke Merel Arissen	PvdDieren	female	1983	0	5	0	5	5	3	-10.7	5.1
Farid Azarkan	DENK	male	1971	1	2	0	5	6	3	-11.3	6.8
Gijs van Dijk	PvdA	male	1980	0	5	0	6	4	1	-12.2	0.0
Kirsten van den Hul	PvdA	female	1976	0	8	0	6	5	3	-14.6	-1.7
Gerbrands, Karen	PVV	female	1967	0	16	0	1	5	4	14.4	-0.4
Theo Hiddema	FvD	male	1944	0	2	0	1	6	5	15.9	0.6
Vicky Maeijer	PVV	female	1986	0	3	0	1	2	3	9.3	-1.8
Gidi Markuszower	PVV	male	1977	1	4	0	1	2	2	9.8	-3.1
Danai van Weerdenburg	PVV	female	1976	0	14	0	1	5	2	15.2	-3.2
Edgar Mulder	PVV	male	1961	0	15	0	1	5	3	14.9	-1.7
Léon de Jong	PVV	male	1982	0	17	0	1	6	1	17.1	-4.4
Gabriëlle Popken	PVV	female	1983	0	18	0	1	6	2	16.9	-3.2
Alexander Kops	PVV	male	1984	0	19	0	1	6	3	16.7	-1.9
Roy van Aalst	PVV	male	1983	0	20	0	1	6	4	16.3	-0.8

---

Appendix A.

# select all MPs (we need to filter the correct EGOhandle)
data_mp <- key %>%
    filter(Elected20170315 == "yes, verkozen" & !(EGOhandle %in% c(10401, 10403, 13102, 15102, 106101,
        41400)))

dg <- data_mp %>%
    group_by(Geslacht) %>%
    summarize(count = n(), freq = count/150)

de <- data_mp %>%
    group_by(EthMinZ) %>%
    summarize(count = n(), freq = count/150)

da <- data_mp %>%
    mutate(variable = "AGE") %>%
    summarize(age = first(variable), mean = mean(2017 - GebJaar, na.rm = T), sd = sd(2017 - GebJaar,
        na.rm = T))

dp <- data_mp %>%
    group_by(Partij) %>%
    summarize(count = n(), freq = count/150) %>%
    arrange(desc(count))

# by party
dgp <- data_mp %>%
    group_by(Partij) %>%
    add_tally() %>%
    group_by(Partij, Geslacht) %>%
    summarize(count = n(), freq = count/first(n), first = first(n)) %>%
    arrange(desc(first)) %>%
    select("Partij", "Geslacht", "count", "freq")

dep <- data_mp %>%
    group_by(Partij) %>%
    add_tally() %>%
    group_by(Partij, EthMinZ) %>%
    summarize(count = n(), freq = count/first(n), first = first(n)) %>%
    arrange(desc(first)) %>%
    select("Partij", "EthMinZ", "count", "freq")

dap <- data_mp %>%
    mutate(variable = "AGE") %>%
    group_by(Partij) %>%
    add_tally() %>%
    summarize(age = first(variable), mean = mean(2017 - GebJaar, na.rm = T), sd = sd(2017 - GebJaar,
        na.rm = T), first = first(n)) %>%
    arrange(desc(first)) %>%
    select("Partij", "age", "mean", "sd")

dg <- dg %>%
    add_column(grouping = "NO", .before = "Geslacht")
da <- da %>%
    add_column(grouping = "NO", .before = "age")
de <- de %>%
    add_column(grouping = "NO", .before = "EthMinZ")
dp <- dp %>%
    add_column(grouping = "NO", .before = "Partij")


# dgp dap dep

# write_csv(dg, 'table.csv') write_csv(da, 'table.csv', append = TRUE) write_csv(de, 'table.csv',
# append = TRUE) write_csv(dp, 'table.csv', append = TRUE) write_csv(dgp, 'table.csv', append =
# TRUE) write_csv(dap, 'table.csv', append = TRUE) write_csv(dep, 'table.csv', append = TRUE)

test <- read_csv("table.csv")

colnames(test) <- c("grouping", "social dimension", "N / mean", "prop. / SD")
test$grouping[test$grouping == "NO"] <- "HoP"
test[test == "man"] <- "male"
test[test == "vrouw"] <- "female"
test[test == "0"] <- "no visible minority"
test[, 2][test[, 2] == "1"] <- "visible minority"


AppendixA <- kbl(test, booktabs = TRUE, digits = 2, caption = "Appendix A. Composition of parliament and political parties in the Netherlands (2017)",
    align = "c") %>%
    kable_classic(full_width = F, html_font = "Cambria") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

scroll_box(AppendixA, width = "100%", height = "500px")

# save_kable(AppendixA, 'AppendixA.html')

Appendix A. Composition of parliament and political parties in the Netherlands (2017)

grouping	social dimension	N / mean	prop. / SD
HoP	male	96.00	0.64
HoP	female	54.00	0.36
HoP	AGE	45.13	9.32
HoP	no visible minority	134.00	0.89
HoP	visible minority	16.00	0.11
HoP	VVD	33.00	0.22
HoP	PVV	20.00	0.13
HoP	CDA	19.00	0.13
HoP	D66	19.00	0.13
HoP	GroenLinks	14.00	0.09
HoP	SP	14.00	0.09
HoP	PvdA	9.00	0.06
HoP	CU	5.00	0.03
HoP	PvdDieren	5.00	0.03
HoP	50Plus	4.00	0.03
HoP	DENK	3.00	0.02
HoP	SGP	3.00	0.02
HoP	FvD	2.00	0.01
VVD	male	23.00	0.70
VVD	female	10.00	0.30
PVV	male	14.00	0.70
PVV	female	6.00	0.30
CDA	male	13.00	0.68
CDA	female	6.00	0.32
D66	male	12.00	0.63
D66	female	7.00	0.37
GroenLinks	male	6.00	0.43
GroenLinks	female	8.00	0.57
SP	male	9.00	0.64
SP	female	5.00	0.36
PvdA	male	4.00	0.44
PvdA	female	5.00	0.56
CU	male	3.00	0.60
CU	female	2.00	0.40
PvdDieren	male	2.00	0.40
PvdDieren	female	3.00	0.60
50Plus	male	2.00	0.50
50Plus	female	2.00	0.50
DENK	male	3.00	1.00
SGP	male	3.00	1.00
FvD	male	2.00	1.00
VVD	AGE	44.03	6.72
PVV	AGE	46.05	10.09
CDA	AGE	44.79	7.84
D66	AGE	43.84	10.16
GroenLinks	AGE	47.14	10.08
SP	AGE	38.36	6.99
PvdA	AGE	44.89	7.99
CU	AGE	46.80	4.38
PvdDieren	AGE	45.20	8.26
50Plus	AGE	64.75	8.02
DENK	AGE	42.33	5.51
SGP	AGE	52.33	7.57
FvD	AGE	53.50	27.58
VVD	no visible minority	30.00	0.91
VVD	visible minority	3.00	0.09
PVV	no visible minority	19.00	0.95
PVV	visible minority	1.00	0.05
CDA	no visible minority	18.00	0.95
CDA	visible minority	1.00	0.05
D66	no visible minority	17.00	0.89
D66	visible minority	2.00	0.11
GroenLinks	no visible minority	11.00	0.79
GroenLinks	visible minority	3.00	0.21
SP	no visible minority	12.00	0.86
SP	visible minority	2.00	0.14
PvdA	no visible minority	8.00	0.89
PvdA	visible minority	1.00	0.11
CU	no visible minority	5.00	1.00
PvdDieren	no visible minority	5.00	1.00
50Plus	no visible minority	4.00	1.00
DENK	visible minority	3.00	1.00
SGP	no visible minority	3.00	1.00
FvD	no visible minority	2.00	1.00

---

SEGREGATION - Descriptive statistics

# We retrieve the data from the RSiena data-object

fnet <- mydata$depvars$fnet
atmnet <- mydata$depvars$atmnet
rtnet <- mydata$depvars$rtnet

vrouw <- mydata$cCovars$vrouw
partij <- mydata$cCovars$partij
ethminz <- mydata$cCovars$ethminz
lft <- mydata$cCovars$lft

ethminz <- ethminz + attributes(ethminz)$mean
partij <- partij + attributes(partij)$mean
vrouw <- vrouw + attributes(vrouw)$mean
lft <- lft + attributes(lft)$mean

vrouwm <- fhomomat(vrouw)
partijm <- fhomomat(partij)
ethminzm <- fhomomat(ethminz)

xmat <- matrix(ethminz, nrow = length(ethminz), ncol = length(ethminz))
xmatt <- t(xmat)
minoritym <- xmat == 1 & xmatt == 1

# for age max 5 year difference / for descriptives
xmat <- matrix(lft, nrow = length(lft), ncol = length(lft))
xmatt <- t(xmat)
lftm <- (abs(xmat - xmatt) < 6)

# fndyads2(fnet[,,1], vrouwm) fndyads2(fnet[,,3], vrouwm) fndyads2(fnet[,,1], partijm)
# fndyads2(fnet[,,3], partijm) fndyads2(fnet[,,1], ethminzm) fndyads2(fnet[,,3], ethminzm)

Table 1

desmat <- matrix(NA, nrow = 10, ncol = 9)
desmat[1, 1] <- fdensity(fnet[, , 1])
desmat[1, 2] <- fdensity(fnet[, , 2])
desmat[1, 3] <- fdensity(fnet[, , 3])
desmat[2, 1] <- fdensityintra(fnet[, , 1], vrouwm)
desmat[2, 2] <- fdensityintra(fnet[, , 2], vrouwm)
desmat[2, 3] <- fdensityintra(fnet[, , 3], vrouwm)
desmat[3, 1] <- fdensityinter(fnet[, , 1], vrouwm)
desmat[3, 2] <- fdensityinter(fnet[, , 2], vrouwm)
desmat[3, 3] <- fdensityinter(fnet[, , 3], vrouwm)
desmat[4, 1] <- fdensityintra(fnet[, , 1], partijm)
desmat[4, 2] <- fdensityintra(fnet[, , 2], partijm)
desmat[4, 3] <- fdensityintra(fnet[, , 3], partijm)
desmat[5, 1] <- fdensityinter(fnet[, , 1], partijm)
desmat[5, 2] <- fdensityinter(fnet[, , 2], partijm)
desmat[5, 3] <- fdensityinter(fnet[, , 3], partijm)
desmat[6, 1] <- fdensityintra(fnet[, , 1], ethminzm)
desmat[6, 2] <- fdensityintra(fnet[, , 2], ethminzm)
desmat[6, 3] <- fdensityintra(fnet[, , 3], ethminzm)
desmat[7, 1] <- fdensityinter(fnet[, , 1], ethminzm)
desmat[7, 2] <- fdensityinter(fnet[, , 2], ethminzm)
desmat[7, 3] <- fdensityinter(fnet[, , 3], ethminzm)
desmat[8, 1] <- fdensityinter(fnet[, , 1], minoritym)
desmat[8, 2] <- fdensityinter(fnet[, , 2], minoritym)
desmat[8, 3] <- fdensityinter(fnet[, , 3], minoritym)
desmat[9, 1] <- fdensityintra(fnet[, , 1], lftm)
desmat[9, 2] <- fdensityintra(fnet[, , 2], lftm)
desmat[9, 3] <- fdensityintra(fnet[, , 3], lftm)
desmat[10, 1] <- fdensityinter(fnet[, , 1], lftm)
desmat[10, 2] <- fdensityinter(fnet[, , 2], lftm)
desmat[10, 3] <- fdensityinter(fnet[, , 3], lftm)

desmat[1, 1 + 3] <- fdensity(atmnet[, , 1])
desmat[1, 2 + 3] <- fdensity(atmnet[, , 2])
desmat[1, 3 + 3] <- fdensity(atmnet[, , 3])
desmat[2, 1 + 3] <- fdensityintra(atmnet[, , 1], vrouwm)
desmat[2, 2 + 3] <- fdensityintra(atmnet[, , 2], vrouwm)
desmat[2, 3 + 3] <- fdensityintra(atmnet[, , 3], vrouwm)
desmat[3, 1 + 3] <- fdensityinter(atmnet[, , 1], vrouwm)
desmat[3, 2 + 3] <- fdensityinter(atmnet[, , 2], vrouwm)
desmat[3, 3 + 3] <- fdensityinter(atmnet[, , 3], vrouwm)
desmat[4, 1 + 3] <- fdensityintra(atmnet[, , 1], partijm)
desmat[4, 2 + 3] <- fdensityintra(atmnet[, , 2], partijm)
desmat[4, 3 + 3] <- fdensityintra(atmnet[, , 3], partijm)
desmat[5, 1 + 3] <- fdensityinter(atmnet[, , 1], partijm)
desmat[5, 2 + 3] <- fdensityinter(atmnet[, , 2], partijm)
desmat[5, 3 + 3] <- fdensityinter(atmnet[, , 3], partijm)
desmat[6, 1 + 3] <- fdensityintra(atmnet[, , 1], ethminzm)
desmat[6, 2 + 3] <- fdensityintra(atmnet[, , 2], ethminzm)
desmat[6, 3 + 3] <- fdensityintra(atmnet[, , 3], ethminzm)
desmat[7, 1 + 3] <- fdensityinter(atmnet[, , 1], ethminzm)
desmat[7, 2 + 3] <- fdensityinter(atmnet[, , 2], ethminzm)
desmat[7, 3 + 3] <- fdensityinter(atmnet[, , 3], ethminzm)
desmat[8, 1 + 3] <- fdensityinter(atmnet[, , 1], minoritym)
desmat[8, 2 + 3] <- fdensityinter(atmnet[, , 2], minoritym)
desmat[8, 3 + 3] <- fdensityinter(atmnet[, , 3], minoritym)
desmat[9, 1 + 3] <- fdensityintra(atmnet[, , 1], lftm)
desmat[9, 2 + 3] <- fdensityintra(atmnet[, , 2], lftm)
desmat[9, 3 + 3] <- fdensityintra(atmnet[, , 3], lftm)
desmat[10, 1 + 3] <- fdensityinter(atmnet[, , 1], lftm)
desmat[10, 2 + 3] <- fdensityinter(atmnet[, , 2], lftm)
desmat[10, 3 + 3] <- fdensityinter(atmnet[, , 3], lftm)

desmat[1, 1 + 6] <- fdensity(rtnet[, , 1])
desmat[1, 2 + 6] <- fdensity(rtnet[, , 2])
desmat[1, 3 + 6] <- fdensity(rtnet[, , 3])
desmat[2, 1 + 6] <- fdensityintra(rtnet[, , 1], vrouwm)
desmat[2, 2 + 6] <- fdensityintra(rtnet[, , 2], vrouwm)
desmat[2, 3 + 6] <- fdensityintra(rtnet[, , 3], vrouwm)
desmat[3, 1 + 6] <- fdensityinter(rtnet[, , 1], vrouwm)
desmat[3, 2 + 6] <- fdensityinter(rtnet[, , 2], vrouwm)
desmat[3, 3 + 6] <- fdensityinter(rtnet[, , 3], vrouwm)
desmat[4, 1 + 6] <- fdensityintra(rtnet[, , 1], partijm)
desmat[4, 2 + 6] <- fdensityintra(rtnet[, , 2], partijm)
desmat[4, 3 + 6] <- fdensityintra(rtnet[, , 3], partijm)
desmat[5, 1 + 6] <- fdensityinter(rtnet[, , 1], partijm)
desmat[5, 2 + 6] <- fdensityinter(rtnet[, , 2], partijm)
desmat[5, 3 + 6] <- fdensityinter(rtnet[, , 3], partijm)
desmat[6, 1 + 6] <- fdensityintra(rtnet[, , 1], ethminzm)
desmat[6, 2 + 6] <- fdensityintra(rtnet[, , 2], ethminzm)
desmat[6, 3 + 6] <- fdensityintra(rtnet[, , 3], ethminzm)
desmat[7, 1 + 6] <- fdensityinter(rtnet[, , 1], ethminzm)
desmat[7, 2 + 6] <- fdensityinter(rtnet[, , 2], ethminzm)
desmat[7, 3 + 6] <- fdensityinter(rtnet[, , 3], ethminzm)
desmat[8, 1 + 6] <- fdensityinter(rtnet[, , 1], minoritym)
desmat[8, 2 + 6] <- fdensityinter(rtnet[, , 2], minoritym)
desmat[8, 3 + 6] <- fdensityinter(rtnet[, , 3], minoritym)
desmat[9, 1 + 6] <- fdensityintra(rtnet[, , 1], lftm)
desmat[9, 2 + 6] <- fdensityintra(rtnet[, , 2], lftm)
desmat[9, 3 + 6] <- fdensityintra(rtnet[, , 3], lftm)
desmat[10, 1 + 6] <- fdensityinter(rtnet[, , 1], lftm)
desmat[10, 2 + 6] <- fdensityinter(rtnet[, , 2], lftm)
desmat[10, 3 + 6] <- fdensityinter(rtnet[, , 3], lftm)


colnames(desmat) <- c("following T1", "following T2", "following T3", "atmentions T1", "atmentions T2",
    "atmentions T3", "retweets T1", "retweets T2", "retweets T3")
rownames(desmat) <- c("total", "same sex", "different sex", "same party", "different party", "same ethnicity",
    "different ethnicity", "both minority", "same age (<6)", "different age (>5)")

# desmat write.csv2(desmat, '20190829desmat.csv')

desmatkable <- kbl(desmat, booktabs = TRUE, digits = 2, caption = "Table 1.  Inter- and intra-group densities within the three Twitter layers among Dutch MPs (2017)",
    align = "c") %>%
    kable_classic(full_width = F, html_font = "Cambria") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    column_spec(column = 1, width = "3in")

desmatkable

Table 1. Inter- and intra-group densities within the three Twitter layers among Dutch MPs (2017)

	following T1	following T2	following T3	atmentions T1	atmentions T2	atmentions T3	retweets T1	retweets T2	retweets T3
total	0.25	0.28	0.28	0.05	0.04	0.01	0.05	0.03	0.03
same sex	0.26	0.29	0.29	0.05	0.04	0.01	0.05	0.03	0.03
different sex	0.24	0.27	0.27	0.04	0.03	0.01	0.05	0.03	0.03
same party	0.71	0.73	0.74	0.20	0.14	0.06	0.34	0.25	0.25
different party	0.19	0.22	0.22	0.03	0.02	0.01	0.01	0.01	0.01
same ethnicity	0.27	0.29	0.29	0.05	0.04	0.01	0.05	0.03	0.03
different ethnicity	0.21	0.24	0.24	0.05	0.03	0.01	0.04	0.03	0.03
both minority	0.25	0.28	0.28	0.05	0.03	0.01	0.05	0.03	0.03
same age (<6)	0.29	0.31	0.31	0.06	0.04	0.01	0.05	0.04	0.04
different age (>5)	0.24	0.26	0.26	0.04	0.03	0.01	0.04	0.03	0.03

A slightly different order of social dimensions (rows) as compared to manuscript but same numbers.

---

Table 2

fnet[fnet == 10] <- NA
rtnet[rtnet == 10] <- NA
atmnet[atmnet == 10] <- NA

colmat <- matrix(NA, nrow = 3, ncol = 9)

colmat[1, 1] <- fscolnet(fnet[, , 1], partij)
colmat[1, 2] <- fscolnet(fnet[, , 2], partij)
colmat[1, 3] <- fscolnet(fnet[, , 3], partij)
colmat[1, 4] <- fscolnet(atmnet[, , 1], partij)
colmat[1, 5] <- fscolnet(atmnet[, , 2], partij)
colmat[1, 6] <- fscolnet(atmnet[, , 3], partij)
colmat[1, 7] <- fscolnet(rtnet[, , 1], partij)
colmat[1, 8] <- fscolnet(rtnet[, , 2], partij)
colmat[1, 9] <- fscolnet(rtnet[, , 3], partij)

colmat[2, 1] <- fscolnet(fnet[, , 1], vrouw)
colmat[2, 2] <- fscolnet(fnet[, , 2], vrouw)
colmat[2, 3] <- fscolnet(fnet[, , 3], vrouw)
colmat[2, 4] <- fscolnet(atmnet[, , 1], vrouw)
colmat[2, 5] <- fscolnet(atmnet[, , 2], vrouw)
colmat[2, 6] <- fscolnet(atmnet[, , 3], vrouw)
colmat[2, 7] <- fscolnet(rtnet[, , 1], vrouw)
colmat[2, 8] <- fscolnet(rtnet[, , 2], vrouw)
colmat[2, 9] <- fscolnet(rtnet[, , 3], vrouw)

colmat[3, 1] <- fscolnet(fnet[, , 1], ethminz)
colmat[3, 2] <- fscolnet(fnet[, , 2], ethminz)
colmat[3, 3] <- fscolnet(fnet[, , 3], ethminz)
colmat[3, 4] <- fscolnet(atmnet[, , 1], ethminz)
colmat[3, 5] <- fscolnet(atmnet[, , 2], ethminz)
colmat[3, 6] <- fscolnet(atmnet[, , 3], ethminz)
colmat[3, 7] <- fscolnet(rtnet[, , 1], ethminz)
colmat[3, 8] <- fscolnet(rtnet[, , 2], ethminz)
colmat[3, 9] <- fscolnet(rtnet[, , 3], ethminz)

colnames(colmat) <- c("following T1", "following T2", "following T3", "atmentions T1", "atmentions T2",
    "atmentions T3", "retweets T1", "retweets T2", "retweets T3")
rownames(colmat) <- c("party", "sex", "ethnicity")
# colmat

# write.csv2(colmat, '20190829colmat.csv')

colmatkable <- kbl(colmat, booktabs = TRUE, digits = 2, caption = "Table 2. Coleman’s Homophily Index within the three Twitter layers among Dutch MPs (2017)",
    align = "c") %>%
    kable_classic(full_width = F, html_font = "Cambria") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    column_spec(column = 1, width = "3in")

colmatkable

Table 2. Coleman’s Homophily Index within the three Twitter layers among Dutch MPs (2017)

	following T1	following T2	following T3	atmentions T1	atmentions T2	atmentions T3	retweets T1	retweets T2	retweets T3
party	0.23	0.21	0.21	0.39	0.40	0.48	0.80	0.80	0.80
sex	0.05	0.04	0.04	0.07	0.03	0.05	-0.01	-0.03	-0.01
ethnicity	0.12	0.10	0.10	-0.03	0.00	-0.02	0.05	-0.01	-0.01

---

Table 3

fnet[fnet == 10] <- NA
rtnet[rtnet == 10] <- NA
atmnet[atmnet == 10] <- NA

vrouw_int <- as.integer(vrouw) + 1
partij_int <- as.integer(partij)
ethminz <- ethminz + 1

asmat <- matrix(NA, nrow = 4, ncol = 9)

gfnet1 <- graph_from_adjacency_matrix(fnet[, , 1], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)
gfnet2 <- graph_from_adjacency_matrix(fnet[, , 2], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)
gfnet3 <- graph_from_adjacency_matrix(fnet[, , 3], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)

grtnet1 <- graph_from_adjacency_matrix(rtnet[, , 1], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)
grtnet2 <- graph_from_adjacency_matrix(rtnet[, , 2], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)
grtnet3 <- graph_from_adjacency_matrix(rtnet[, , 3], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)

gatmnet1 <- graph_from_adjacency_matrix(atmnet[, , 1], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)
gatmnet2 <- graph_from_adjacency_matrix(atmnet[, , 2], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)
gatmnet3 <- graph_from_adjacency_matrix(atmnet[, , 3], mode = "directed", weighted = NULL, diag = FALSE,
    add.colnames = NA, add.rownames = NA)

asmat[1, 1] <- assortativity_nominal(gfnet1, types = partij_int, directed = TRUE)
asmat[1, 2] <- assortativity_nominal(gfnet2, types = partij_int, directed = TRUE)
asmat[1, 3] <- assortativity_nominal(gfnet3, types = partij_int, directed = TRUE)
asmat[2, 1] <- assortativity_nominal(gfnet1, types = vrouw_int, directed = TRUE)
asmat[2, 2] <- assortativity_nominal(gfnet2, types = vrouw_int, directed = TRUE)
asmat[2, 3] <- assortativity_nominal(gfnet3, types = vrouw_int, directed = TRUE)
asmat[3, 1] <- assortativity_nominal(gfnet1, types = ethminz, directed = TRUE)
asmat[3, 2] <- assortativity_nominal(gfnet2, types = ethminz, directed = TRUE)
asmat[3, 3] <- assortativity_nominal(gfnet3, types = ethminz, directed = TRUE)
asmat[4, 1] <- assortativity(gfnet1, types1 = lft, directed = TRUE)
asmat[4, 2] <- assortativity(gfnet2, types1 = lft, directed = TRUE)
asmat[4, 3] <- assortativity(gfnet3, types1 = lft, directed = TRUE)

asmat[1, 7] <- assortativity_nominal(grtnet1, types = partij_int, directed = TRUE)
asmat[1, 8] <- assortativity_nominal(grtnet2, types = partij_int, directed = TRUE)
asmat[1, 9] <- assortativity_nominal(grtnet3, types = partij_int, directed = TRUE)
asmat[2, 7] <- assortativity_nominal(grtnet1, types = vrouw_int, directed = TRUE)
asmat[2, 8] <- assortativity_nominal(grtnet2, types = vrouw_int, directed = TRUE)
asmat[2, 9] <- assortativity_nominal(grtnet3, types = vrouw_int, directed = TRUE)
asmat[3, 7] <- assortativity_nominal(grtnet1, types = ethminz, directed = TRUE)
asmat[3, 8] <- assortativity_nominal(grtnet2, types = ethminz, directed = TRUE)
asmat[3, 9] <- assortativity_nominal(grtnet3, types = ethminz, directed = TRUE)
asmat[4, 7] <- assortativity(grtnet1, types1 = lft, directed = TRUE)
asmat[4, 8] <- assortativity(grtnet2, types1 = lft, directed = TRUE)
asmat[4, 9] <- assortativity(grtnet3, types1 = lft, directed = TRUE)

asmat[1, 4] <- assortativity_nominal(gatmnet1, types = partij_int, directed = TRUE)
asmat[1, 5] <- assortativity_nominal(gatmnet2, types = partij_int, directed = TRUE)
asmat[1, 6] <- assortativity_nominal(gatmnet3, types = partij_int, directed = TRUE)
asmat[2, 4] <- assortativity_nominal(gatmnet1, types = vrouw_int, directed = TRUE)
asmat[2, 5] <- assortativity_nominal(gatmnet2, types = vrouw_int, directed = TRUE)
asmat[2, 6] <- assortativity_nominal(gatmnet3, types = vrouw_int, directed = TRUE)
asmat[3, 4] <- assortativity_nominal(gatmnet1, types = ethminz, directed = TRUE)
asmat[3, 5] <- assortativity_nominal(gatmnet2, types = ethminz, directed = TRUE)
asmat[3, 6] <- assortativity_nominal(gatmnet3, types = ethminz, directed = TRUE)
asmat[4, 4] <- assortativity(gatmnet1, types1 = lft, directed = TRUE)
asmat[4, 5] <- assortativity(gatmnet2, types1 = lft, directed = TRUE)
asmat[4, 6] <- assortativity(gatmnet3, types1 = lft, directed = TRUE)

colnames(asmat) <- c("following T1", "following T2", "following T3", "atmentions T1", "atmentions T2",
    "atmentions T3", "retweets T1", "retweets T2", "retweets T3")
rownames(asmat) <- c("party", "sex", "ethnicity", "age")
# asmat write.csv2(asmat, '20190828asmat.csv')



asmatkable <- kbl(asmat, booktabs = TRUE, digits = 2, caption = "Table 3. Newman’s Assortativity Coefficient within the three Twitter layers among Dutch MPs (2017)",
    align = "c") %>%
    kable_classic(full_width = F, html_font = "Cambria") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    column_spec(column = 1, width = "3in")

asmatkable

Table 3. Newman’s Assortativity Coefficient within the three Twitter layers among Dutch MPs (2017)

	following T1	following T2	following T3	atmentions T1	atmentions T2	atmentions T3	retweets T1	retweets T2	retweets T3
party	0.22	0.20	0.20	0.39	0.39	0.47	0.82	0.83	0.83
sex	0.04	0.03	0.04	0.10	0.04	0.06	0.01	-0.01	0.02
ethnicity	0.06	0.05	0.05	0.08	0.02	-0.07	0.09	0.02	0.04
age	0.05	0.04	0.04	0.09	0.02	0.12	0.02	0.01	0.06

RR2 Coleman’s segregation per party

RR.Coleman’s Homophily Index within the three Twitter layers among Dutch MPs (2017), per political party (party size between brackets)

	following T1	following T2	following T3	atmentions T1	atmentions T2	atmentions T3	retweets T1	retweets T2	retweets T3
VVD (32)	0.34	0.33	0.33	0.86	0.82	0.87	0.41	0.35	0.41
CDA (19)	0.24	0.22	0.23	0.83	0.80	0.76	0.53	0.56	0.50
D66 (19)	0.21	0.19	0.17	0.89	0.85	0.85	0.48	0.45	0.57
PVV (18)	0.52	0.48	0.47	0.94	0.98	0.98	0.27	0.63	0.43
SP (14)	0.29	0.19	0.25	0.86	0.90	0.90	0.40	0.39	0.58
GroenLinks (14)	0.20	0.17	0.17	0.73	0.81	0.75	0.39	0.39	0.38
PvdA (9)	0.12	0.11	0.10	0.80	0.52	0.44	0.32	0.22	0.23
CU (5)	0.09	0.09	0.09	0.56	0.54	0.60	0.36	0.21	0.31
PvdDieren (5)	0.05	0.04	0.04	0.60	0.73	0.88	0.26	0.31	0.61
50Plus (4)	0.09	0.10	0.10	1.00	0.59	0.71	0.29	0.32	1.00
DENK (3)	0.06	0.06	0.06	0.66	1.00	0.39	0.26	0.19	-1.00
SGP (3)	0.01	0.01	0.01	0.37	0.26	0.57	0.28	0.32	NaN
FvD (2)	0.14	0.12	0.08	0.19	NaN	0.50	0.14	-1.00	-1.00

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References

Bojanowski, Michał, and Rense Corten. 2014. “Measuring Segregation in Social Networks.” Social Networks 39: 14–32. https://doi.org/https://doi.org/10.1016/j.socnet.2014.04.001.

Tolsma, Jochem, and Niels Spierings. (submitted). “Twitter and Divides in the Dutch Parliament: Social and Political Segregation in the Following, @-Mentions and Retweets Networks.” - - (-): –. -.

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Copyright © 2021 Jochem Tolsma