Getting started

clean up
general custom functions
necessary packages
load data

Correlation matrix

Describing networks

Relational characteristics
Role overlap
Tie maintenance
Tie maintenance vs similarity
Homogeneity

Getting started

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

clean up

rm(list = ls())
gc()

general custom functions

fpackage.check: Check if packages are installed (and install if not) in R
fsave: Function to save data with time stamp in correct directory
fload: Load R-objects under new names
fshowdf: Print objects (tibble / data.frame) nicely on screen in .Rmd.
ftheme: pretty ggplot2 theme

fpackage.check <- function(packages) {
    lapply(packages, FUN = function(x) {
        if (!require(x, character.only = TRUE)) {
            install.packages(x, dependencies = TRUE)
            library(x, character.only = TRUE)
        }
    })
}

fsave <- function(x, file, location = "./data/processed/", ...) {
    if (!dir.exists(location))
        dir.create(location)
    datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
    totalname <- paste(location, datename, file, sep = "")
    print(paste("SAVED: ", totalname, sep = ""))
    save(x, file = totalname)
}


fload <- function(fileName) {
    load(fileName)
    get(ls()[ls() != "fileName"])
}

fshowdf <- function(x, digits = 2, ...) {
    knitr::kable(x, digits = digits, "html", ...) %>%
        kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
        kableExtra::scroll_box(width = "100%", height = "300px")
}

# extrafont::font_import(paths = c('C:/Users/u244147/Downloads/Jost/', prompt = FALSE))
ftheme <- function() {

    # download font at https://fonts.google.com/specimen/Jost/
    theme_minimal(base_family = "Jost") + theme(panel.grid.minor = element_blank(), plot.title = element_text(family = "Jost",
        face = "bold"), axis.title = element_text(family = "Jost Medium"), axis.title.x = element_text(hjust = 0),
        axis.title.y = element_text(hjust = 1), strip.text = element_text(family = "Jost", face = "bold",
            size = rel(0.75), hjust = 0), strip.background = element_rect(fill = "grey90", color = NA),
        legend.position = "bottom")
}

necessary packages

tidyverse
knitr: generating tables
kableExtra: manipulating tables
xtable: displaying HTML format
ggpubr
GenBinomApps: compute Clopper-Pearson confidence interval
reshape2
ggVennDiagram

packages = c("knitr", "kableExtra", "xtable", "tidyverse", "GenBinomApps", "reshape2", "ggcorrplot",
    "ggVennDiagram")
fpackage.check(packages)

load data

Load the replicated data-sets (constructed here). To load these file, adjust the filenames in the following code so that it matches the most recent version of the .RDa file you have in your ./data/processed/ folder.

You may also obtain them by downloading: Download data_nested.RDa

# list.files('./data/processed/')

# get todays date:
today <- gsub("-", "", Sys.Date())

data <- fload(paste0("./data/processed/", today, "data_nested.RDa"))

some last wrangling:

make Y indicate tie loss instead of tie maintenance
make X reflect dissimilarity instead of similarity
standardize embeddedness in other network layers
proximity levels

data$Yloss <- ifelse(data$Y == 1, 0, 1)

data$different_gender <- ifelse(data$same_gender == 1, 0, 1)
data$different_educ <- ifelse(data$sim_educ == 1, 0, 1)

data$embed.ext/3 -> data$embed.ext

data$proximity <- factor(data$proximity, levels = c("far", "close", "roommate"))

Correlation matrix

#get variables of interest, for which we want to calculate correlations
cordf <- data[, rev(c("dif_age","different_gender","different_educ","closeness.t","multiplex", "embed", "embed.ext", "Yloss", "tie"))]

#relabel
names(cordf) <- c("Tie", "Tie loss", "Str. embed. other layers",  "Str. embed. focal layer", "Relational multiplexity", "Emotional closeness", "Different education", "Different gender", "Age difference")

#reorder
desired_order <- rev(c("Tie", "Tie loss", "Different gender", "Different education", "Age difference", "Emotional closeness",  "Relational multiplexity", "Str. embed. focal layer", "Str. embed. other layers"))
cordf <- cordf[, desired_order]

#1. correlations of all observations (regardless of tie type)
all <- cordf[,sapply(cordf, is.numeric)]
#correlation
cormat <- cor(all)
#p value
pmat <- cor_pmat(all)
#below diagonal
cormat[lower.tri(cormat)] <- NA
pmat[lower.tri(pmat)] <- NA
#melt
allc <- melt(cormat)
allp <- melt(pmat)
all <- cbind(allc, pval = allp$value)
#diagonal NA
all$value <- ifelse(all$Var1 == all$Var2, NA, all$value)
all$pval <- ifelse(all$Var1 == all$Var2, NA, all$pval)
#add label
all$label <- ifelse(!is.na(all$value), paste0(format(round(all$value,2), nsmall=2), ifelse(all$pval>0.05, "", "*")),NA)

plot_all <- all %>% 
  ggplot(aes(Var1, Var2, fill = value)) +
  geom_tile (color = 'lightgrey') +
  geom_text(aes(label = label), size = 4) +
  scale_fill_gradient2(low = "blue", mid = "white", high="red", midpoint=0, limit=c(-1,1), na.value = "lightgrey") +
  scale_x_discrete(limits = rev(levels(all$Var1))) +  #reverse x-axis
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "right", legend.direction="horizontal") + labs(x=NULL, y=NULL, title="(A) All alter-tie observations", fill = "Correlation") 

#2 now, disaggregated by tie type
# function to compute correlations for a tie type and add the necessary columns
fcompcorr <- function(tie_type) { 
  df <- cordf[cordf$Tie == tie_type, sapply(cordf, is.numeric)]
  cormat <- cor(df)
  pmat <- cor_pmat(df)
  cormat[lower.tri(cormat)] <- NA
  pmat[lower.tri(pmat)] <- NA
  cormat_melted <- melt(cormat)
  pmat_melted <- melt(pmat)
  df <- cbind(cormat_melted, pval = pmat_melted$value)
  df$value <- ifelse(df$Var1 == df$Var2, NA, df$value)
  df$pval <- ifelse(df$Var1 == df$Var2, NA, df$pval)
  df$label <- ifelse(!is.na(df$value), paste0(format(round(df$value, 2), nsmall = 2), ifelse(df$pval > 0.05, "", "*")), NA)
  df$group <- tie_type
  return(df)
}

tie_types <- c("Confidant", "Friend", "Sport", "Study")

#empty df
mycors <- data.frame()

#iterate through tie types and compute correlations
for (i in tie_types) {
  mycors <- rbind(mycors, fcompcorr(i))
}

#relabel...
mycors$group[mycors$group == "Friend"] <- "Best friend"

plot_dis <- mycors %>% 
  ggplot(aes(Var1, Var2, fill = value)) +
  geom_tile (color = 'lightgrey') +
  geom_text(aes(label = label), size = 2.5) +
  scale_fill_gradient2(low = "blue", mid = "white", high="red", midpoint=0, limit=c(-1,1), na.value = "lightgrey") +
  scale_x_discrete(limits = rev(levels(all$Var1))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "none") + labs(x=NULL, y=NULL,
                                                                                            title="(B) Disaggregated by social role", fill = "Correlation") +
  facet_grid(~group)

#now, disaggregated by combination tie type * uni vs multiplex
#keep for appendix...
fcompcorr2 <- function(tie_type) {
  df <- cordf[cordf$Tie == tie_type, sapply(cordf, is.numeric)]
  
  #uniplex below diagonal
  dfuni <- df[df$`Relational multiplexity`==0,]
  cormat1 <- cor(dfuni)
  pmat1 <- cor_pmat(dfuni)

  #multiplex above diagonal
  dfmulti <- df[df$`Relational multiplexity`>0,]
  cormat2 <- cor(dfmulti)
  pmat2 <- cor_pmat(dfmulti)
  cormat <- cormat1
  pmat <- pmat1
  
  cormat[upper.tri(cormat)] <- cormat2[upper.tri(cormat2)]
  pmat[upper.tri(pmat)] <- pmat2[upper.tri(pmat2)]
  cormat_melted <- melt(cormat)
  pmat_melted <- melt(pmat)
  df <- cbind(cormat_melted, pval = pmat_melted$value)
  df$value <- ifelse(df$Var1 == df$Var2, NA, df$value)
  df$pval <- ifelse(df$Var1 == df$Var2, NA, df$pval)
  df$label <- ifelse(!is.na(df$value), paste0(format(round(df$value, 2), nsmall = 2), ifelse(df$pval > 0.05, "", "*")), NA)
  df$group <- tie_type

  return(df)
}
  
#empty df
mycors <- data.frame()

#iterate through tie types and compute correlations
for (i in tie_types) {
  mycors <- rbind(mycors, fcompcorr2(i))
}

mycors$group[mycors$group == "Friend"] <- "Best friend"


plot_dis2 <- mycors %>% 
  ggplot(aes(Var1, Var2, fill = value)) +
  geom_tile (color = 'lightgrey') +
  geom_text(aes(label = label), size = 2.5) +
  scale_fill_gradient2(low = "blue", mid = "white", high="red", midpoint=0, limit=c(-1,1), na.value = "lightgrey") +
  scale_x_discrete(limits = rev(levels(all$Var1))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "none") + labs(x=NULL, y=NULL,
                                                                                            title="(C) Multiplex alters (above diagonal) vs. uniplex alters (below diagonal)", fill = "Correlation") +
  facet_grid(~group)

#figure <- ggpubr::ggarrange(plot_all, plot_dis, ncol=1, nrow=2)

#ggsave(figure, 
#       file = "./figures/corplot.png",
#       dpi = 320, 
#       width = 11,
#       height = 8)

plot_all

plot_dis

plot_dis2

Describing networks

Relational characteristics

at wave 1

all alters
uniplex ties

cdn <- data[data$tie == "Confidant" & data$period == "w1 -> w2", ]
bff <- data[data$tie == "Friend" & data$period == "w1 -> w2", ]
study <- data[data$tie == "Study" & data$period == "w1 -> w2", ]
csn <- data[data$tie == "Sport" & data$period == "w1 -> w2", ]

tab <- matrix(nrow = 5, ncol = 4)
rownames(tab) <- c("(Unique) alters", "Confidants", "Study partners", "Best friends", "Sports partners")

colnames(tab) <- c("n", "communication freq.", "closeness", "multiplexity $^{b}$")

tab[1, 1] <- length(unique(data$alterid[data$period == "w1 -> w2"]))
tab[2, 1] <- nrow(cdn)
tab[3, 1] <- nrow(study)
tab[4, 1] <- nrow(bff)
tab[5, 1] <- nrow(csn)

tab[1, 2] <- paste0(round(mean(data$frequency.t[which(!duplicated(data$alterid[data$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), " (", round(sd(data$frequency.t[which(!duplicated(data$alterid[data$period ==
    "w1 -> w2"]))], na.rm = TRUE), 2), ")")
tab[2, 2] <- paste0(round(mean(cdn$frequency.t, na.rm = TRUE), 2), " (", round(sd(cdn$frequency.t, na.rm = TRUE),
    2), ")")
tab[3, 2] <- paste0(round(mean(study$frequency.t, na.rm = TRUE), 2), " (", round(sd(study$frequency.t,
    na.rm = TRUE), 2), ")")
tab[4, 2] <- paste0(round(mean(bff$frequency.t, na.rm = TRUE), 2), " (", round(sd(bff$frequency.t, na.rm = TRUE),
    2), ")")
tab[5, 2] <- paste0(round(mean(csn$frequency.t, na.rm = TRUE), 2), " (", round(sd(csn$frequency.t, na.rm = TRUE),
    2), ")")

tab[1, 3] <- paste0(round(mean(data$closeness.t[which(!duplicated(data$alterid[data$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), " (", round(sd(data$closeness.t[which(!duplicated(data$alterid[data$period ==
    "w1 -> w2"]))], na.rm = TRUE), 2), ")")
tab[2, 3] <- paste0(round(mean(cdn$closeness.t, na.rm = TRUE), 2), " (", round(sd(cdn$closeness.t, na.rm = TRUE),
    2), ")")
tab[3, 3] <- paste0(round(mean(study$closeness.t, na.rm = TRUE), 2), " (", round(sd(study$closeness.t,
    na.rm = TRUE), 2), ")")
tab[4, 3] <- paste0(round(mean(bff$closeness.t, na.rm = TRUE), 2), " (", round(sd(bff$closeness.t, na.rm = TRUE),
    2), ")")
tab[5, 3] <- paste0(round(mean(csn$closeness.t, na.rm = TRUE), 2), " (", round(sd(csn$closeness.t, na.rm = TRUE),
    2), ")")

tab[1, 4] <- paste0(round(mean(data$multiplex[which(!duplicated(data$alterid[data$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), " (", round(sd(data$multiplex[which(!duplicated(data$alterid[data$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), ")")
tab[2, 4] <- paste0(round(mean(cdn$multiplex, na.rm = TRUE), 2), " (", round(sd(cdn$multiplex, na.rm = TRUE),
    2), ")")
tab[3, 4] <- paste0(round(mean(study$multiplex, na.rm = TRUE), 2), " (", round(sd(study$multiplex, na.rm = TRUE),
    2), ")")
tab[4, 4] <- paste0(round(mean(bff$multiplex, na.rm = TRUE), 2), " (", round(sd(bff$multiplex, na.rm = TRUE),
    2), ")")
tab[5, 4] <- paste0(round(mean(csn$multiplex, na.rm = TRUE), 2), " (", round(sd(csn$multiplex, na.rm = TRUE),
    2), ")")

knitr::kable(tab, digits = 2, "html", caption = "Relational characteristics across relational dimensions at t1 $^{a}$") %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
    kableExtra::add_footnote(c("Means and standard deviations in parentheses.", "To achieve a meaningful intercept for our multivariate analyses, we subtracted multiplexity by one. In this context, multiplexity represents the average number of extra relational dimensions for each type of relationship."),
        notation = "alphabet")

Relational characteristics across relational dimensions at t1
	n	communication freq.	closeness	multiplexity
(Unique) alters	3104	5.83 (1.17)	3.1 (0.89)	0.59 (0.8)
Confidants	1120	6.32 (0.97)	3.64 (0.64)	1.3 (0.79)
Study partners	961	5.84 (1.27)	2.83 (0.95)	0.83 (0.98)
Best friends	1925	5.97 (1.03)	3.44 (0.65)	0.89 (0.85)
Sports partners	933	6.05 (1.06)	3.07 (0.92)	0.98 (1.02)
^a Means and standard deviations in parentheses.
^b To achieve a meaningful intercept for our multivariate analyses, we subtracted multiplexity by one. In this context, multiplexity represents the average number of extra relational dimensions for each type of relationship.

cdn <- data[data$tie == "Confidant" & data$period == "w1 -> w2" & data$multiplex == 0, ]
bff <- data[data$tie == "Friend" & data$period == "w1 -> w2" & data$multiplex == 0, ]
study <- data[data$tie == "Study" & data$period == "w1 -> w2" & data$multiplex == 0, ]
csn <- data[data$tie == "Sport" & data$period == "w1 -> w2" & data$multiplex == 0, ]
dataa <- data[data$multiplex == 0, ]

tab <- matrix(nrow = 5, ncol = 4)
rownames(tab) <- c("(Unique) alters", "Confidants", "Study partners", "Best friends", "Sports partners")

colnames(tab) <- c("n", "communication freq.", "closeness", "multiplexity $^{b}$")

tab[1, 1] <- length(unique(dataa$alterid[dataa$period == "w1 -> w2"]))
tab[2, 1] <- nrow(cdn)
tab[3, 1] <- nrow(study)
tab[4, 1] <- nrow(bff)
tab[5, 1] <- nrow(csn)

tab[1, 2] <- paste0(round(mean(dataa$frequency.t[which(!duplicated(dataa$alterid[dataa$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), " (", round(sd(dataa$frequency.t[which(!duplicated(dataa$alterid[dataa$period ==
    "w1 -> w2"]))], na.rm = TRUE), 2), ")")
tab[2, 2] <- paste0(round(mean(cdn$frequency.t, na.rm = TRUE), 2), " (", round(sd(cdn$frequency.t, na.rm = TRUE),
    2), ")")
tab[3, 2] <- paste0(round(mean(study$frequency.t, na.rm = TRUE), 2), " (", round(sd(study$frequency.t,
    na.rm = TRUE), 2), ")")
tab[4, 2] <- paste0(round(mean(bff$frequency.t, na.rm = TRUE), 2), " (", round(sd(bff$frequency.t, na.rm = TRUE),
    2), ")")
tab[5, 2] <- paste0(round(mean(csn$frequency.t, na.rm = TRUE), 2), " (", round(sd(csn$frequency.t, na.rm = TRUE),
    2), ")")

tab[1, 3] <- paste0(round(mean(dataa$closeness.t[which(!duplicated(dataa$alterid[data$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), " (", round(sd(dataa$closeness.t[which(!duplicated(dataa$alterid[data$period ==
    "w1 -> w2"]))], na.rm = TRUE), 2), ")")
tab[2, 3] <- paste0(round(mean(cdn$closeness.t, na.rm = TRUE), 2), " (", round(sd(cdn$closeness.t, na.rm = TRUE),
    2), ")")
tab[3, 3] <- paste0(round(mean(study$closeness.t, na.rm = TRUE), 2), " (", round(sd(study$closeness.t,
    na.rm = TRUE), 2), ")")
tab[4, 3] <- paste0(round(mean(bff$closeness.t, na.rm = TRUE), 2), " (", round(sd(bff$closeness.t, na.rm = TRUE),
    2), ")")
tab[5, 3] <- paste0(round(mean(csn$closeness.t, na.rm = TRUE), 2), " (", round(sd(csn$closeness.t, na.rm = TRUE),
    2), ")")

tab[1, 4] <- paste0(round(mean(dataa$multiplex[which(!duplicated(dataa$alterid[data$period == "w1 -> w2"]))],
    na.rm = TRUE), 2), " (", round(sd(dataa$multiplex[which(!duplicated(dataa$alterid[data$period ==
    "w1 -> w2"]))], na.rm = TRUE), 2), ")")
tab[2, 4] <- paste0(round(mean(cdn$multiplex, na.rm = TRUE), 2), " (", round(sd(cdn$multiplex, na.rm = TRUE),
    2), ")")
tab[3, 4] <- paste0(round(mean(study$multiplex, na.rm = TRUE), 2), " (", round(sd(study$multiplex, na.rm = TRUE),
    2), ")")
tab[4, 4] <- paste0(round(mean(bff$multiplex, na.rm = TRUE), 2), " (", round(sd(bff$multiplex, na.rm = TRUE),
    2), ")")
tab[5, 4] <- paste0(round(mean(csn$multiplex, na.rm = TRUE), 2), " (", round(sd(csn$multiplex, na.rm = TRUE),
    2), ")")

knitr::kable(tab, digits = 2, "html", caption = "Relational characteristics of uniplex ties, across relational dimensions at t1 $^{a}$") %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
    kableExtra::add_footnote(c("Means and standard deviations in parentheses.", "To achieve a meaningful intercept for our multivariate analyses, we subtracted multiplexity by one. In this context, multiplexity represents the average number of extra relational dimensions for each type of relationship."),
        notation = "alphabet")

Relational characteristics of uniplex ties, across relational dimensions at t1
	n	communication freq.	closeness	multiplexity
(Unique) alters	1809	5.49 (1.23)	2.72 (0.89)	0 (0)
Confidants	165	5.94 (1.44)	3.22 (1.02)	0 (0)
Study partners	489	5.36 (1.41)	2.25 (0.82)	0 (0)
Best friends	731	5.45 (1.07)	3.19 (0.68)	0 (0)
Sports partners	424	5.52 (1.17)	2.39 (0.81)	0 (0)
^a Means and standard deviations in parentheses.
^b To achieve a meaningful intercept for our multivariate analyses, we subtracted multiplexity by one. In this context, multiplexity represents the average number of extra relational dimensions for each type of relationship.

x1 <- list(
  'Best friend' = data$alterid[data$bff == 1 & data$period == "w1 -> w2"],
  'Confidant' = data$alterid[data$cdn == 1 & data$period == "w1 -> w2"],
  
  'Sports partner' = data$alterid[data$csn == 1 & data$period == "w1 -> w2"],
  'Study partner' = data$alterid[data$study == 1 & data$period == "w1 -> w2"]
)


(venn <- ggVennDiagram(x1, label = "percent", label_size = 3, label_percent_digit = 1, set_size = 3, show_intersect = FALSE) + 
    scale_fill_distiller(palette = "OrRd", direction = 1) +
    theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
    scale_x_continuous(expand = expansion(mult = .2)) +
    labs(fill = "Alter count"))

#ggsave(venn, filename = "./figures/venn1.png")

#?ggVennDiagram

x2 <- list(`Best Friend` = data$alterid[data$bff == 1 & data$period == "w2 -> w3"], Confidant = data$alterid[data$cdn ==
    1 & data$period == "w2 -> w3"], `Sports partner` = data$alterid[data$csn == 1 & data$period == "w2 -> w3"],
    `Study partner` = data$alterid[data$study == 1 & data$period == "w2 -> w3"])


(venn <- ggVennDiagram(x2, label = "percent", label_size = 3, label_percent_digit = 1, set_size = 3,
    show_intersect = FALSE) + scale_fill_distiller(palette = "OrRd", direction = 1) + theme(legend.position = "right",
    plot.title = element_text(hjust = 0.5)) + scale_x_continuous(expand = expansion(mult = 0.2)) + labs(fill = "Alter count"))

Tie maintenance

# make table
tab <- matrix(nrow = 5, ncol = 2)
rownames(tab) <- c("All (unique) ties", "Confidants", "Best friends", "Study partners", "Sports partners")
colnames(tab) <- c("n", "Relisted")


bff <- data[data$tie == "Friend", ]
cdn <- data[data$tie == "Confidant", ]
study <- data[data$tie == "Study", ]
csn <- data[data$tie == "Sport", ]

tab[1, 1] <- nrow(data)
tab[2, 1] <- nrow(cdn)
tab[3, 1] <- nrow(bff)
tab[4, 1] <- nrow(study)
tab[5, 1] <- nrow(csn)

tab[1, 2] <- round(prop.table(table(data$Y))[[2]], 2)
tab[2, 2] <- round(prop.table(table(cdn$Y))[[2]], 2)
tab[3, 2] <- round(prop.table(table(bff$Y))[[2]], 2)
tab[4, 2] <- round(prop.table(table(study$Y))[[2]], 2)
tab[5, 2] <- round(prop.table(table(csn$Y))[[2]], 2)

options(knitr.kable.NA = "")

knitr::kable(tab, digits = 2, "html", caption = "Maintenance of multiple social ties over time among 513 students") %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))

Maintenance of multiple social ties over time among 513 students
	n	Relisted
All (unique) ties	7924	0.58
Confidants	1843	0.68
Best friends	2981	0.69
Study partners	1616	0.38
Sports partners	1484	0.43

Tie maintenance vs similarity

# 1. similarities vs tie loss, per dimension
plotdata1 <- data[, c("duration", "same_gender", "Y", "tie")]
plotdata1$same <- ifelse(plotdata1$same_gender == 1, 1, 0)
plotdata1$dimension <- "Gender"

plotdata2 <- data[, c("duration", "sim_educ", "Y", "tie")]
plotdata2$same <- ifelse(plotdata2$sim_educ == 1, 1, 0)
plotdata2$dimension <- "Education"

plotdata3 <- data[, c("duration", "dif_age", "Y", "tie")]
plotdata3$same <- ifelse(plotdata3$dif_age < 4, 1, 0)
plotdata3$dimension <- "Age"

plotdata1 <- plotdata1[, -2]
plotdata2 <- plotdata2[, -2]
plotdata3 <- plotdata3[, -2]
plotdata <- rbind(plotdata1, plotdata2, plotdata3)
plotdata$same <- factor(plotdata$same)

# calculate tie loss rate for similarity dimensions
means <- aggregate(Y ~ same + dimension, data = plotdata, FUN = "mean")
sd <- aggregate(Y ~ same + dimension, data = plotdata, FUN = "sd")
n <- aggregate(Y ~ same + dimension, data = plotdata, FUN = "sum")
means <- cbind(means, sd = sd$Y, n = n$Y)

# calculate a confidence interval around this proportion, usingthe Clopper-Pearson Confidence
# Interval
means$LL <- NA
means$UL <- NA

for (i in 1:nrow(means)) {
    cp.ci <- GenBinomApps::clopper.pearson.ci(k = round(means$Y[i] * means$n[i]), n = means$n[i], alpha = 0.05,
        CI = "two.sided")
    means$LL[i] <- cp.ci$Lower.limit
    means$UL[i] <- cp.ci$Upper.limit
}


plot1 <- ggplot(means, aes(x = same, y = Y, fill = as.factor(same))) + facet_wrap(~dimension) + geom_bar(stat = "identity",
    position = "dodge", width = 0.75) + geom_errorbar(aes(ymin = LL, ymax = UL), position = position_dodge(width = 0.5),
    width = 0.25) + labs(x = NULL, y = "Prop. maintained", fill = "Similarity", title = "(A) Maintenance of alter-ties of self-similar vs. dissimilar alters",
    font = "Jost") + scale_y_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1)) + theme_bw() + theme(axis.text.x = element_blank(),
    legend.position = "bottom")

# 2. dissagregate by relationship duration

# calculate tie loss rate for similarity dimensions means <- aggregate(Y ~ same + dimension +
# duration, data = plotdata, FUN = 'mean') sd <- aggregate(Y ~ same + dimension + duration, data =
# plotdata, FUN = 'sd') n <- aggregate(Y ~ same + dimension + duration, data =plotdata, FUN =
# 'sum') means <- cbind(means, sd=sd$Y, n=n$Y)

# calculate a confidence interval around this proportion, usingthe Clopper-Pearson Confidence
# Interval means$LL <- NA means$UL <- NA

# for (i in 1:nrow(means)) { cp.ci <- GenBinomApps::clopper.pearson.ci( k = round(means$Y[i] *
# means$n[i]), n = means$n[i], alpha = 0.05, CI='two.sided' ) means$LL[i] <- cp.ci$Lower.limit
# means$UL[i] <- cp.ci$Upper.limit }

# duration_names <- c( `0` = 'Under 1 year', `2` = '1-3 years', `6` = '4-8 years', `12` = '9-15
# years', `15` = 'Over 15 years' )

# plot2 <- ggplot(means, aes(x = dimension, y = Y, fill = as.factor(same))) + facet_wrap(~
# duration, labeller = as_labeller(duration_names), nrow = 1) + geom_bar(stat = 'identity',
# position = 'dodge', width = 0.7) + geom_errorbar(aes(ymin = LL, ymax = UL), position =
# position_dodge(width = 0.75), #width=0.25) + labs (x = NULL, y = 'Prop. maintained', fill =
# 'Similarity', title = '(B) Disaggregated by: #tie duration', font = 'Jost') +
# scale_y_continuous(breaks = seq(0, 1, .25), limits = c(0,1)) + theme(axis.text.x =
# element_text(angle = 33, hjust = 1))

# this is more informative. to show convergence effects plot2 <- ggplot(means, aes(x =
# as.factor(duration), y = Y, fill = as.factor(same))) + facet_wrap(~ dimension, nrow = 1) +
# geom_bar(stat = 'identity', position = 'dodge', width = .7) + geom_errorbar(aes(ymin = LL, ymax =
# UL), position = position_dodge(width = 0.75), #width=0.25) + labs (x = NULL, y = 'Prop.
# maintained', fill = 'Similarity', title = '(B) Convergence of #tie maintenance rates with similar
# vs. dissimilar alters over time',font = 'Jost') + scale_y_continuous(breaks = seq(0, 1, .25),
# limits = c(0,1)) + scale_x_discrete(labels = c( '<1', '1-3', '4-8', '9-15', '>15')) +
# theme(axis.text.x = element_text(angle = 33, hjust = 1), legend.position = 'none')

# per relationship type

# calculate tie loss rate for similarity dimensions
means <- aggregate(Y ~ same + dimension + tie, data = plotdata, FUN = "mean")
sd <- aggregate(Y ~ same + dimension + tie, data = plotdata, FUN = "sd")
n <- aggregate(Y ~ same + dimension + tie, data = plotdata, FUN = "sum")
means <- cbind(means, sd = sd$Y, n = n$Y)

# calculate a confidence interval around this proportion, usingthe Clopper-Pearson Confidence
# Interval
means$LL <- NA
means$UL <- NA

for (i in 1:nrow(means)) {
    cp.ci <- GenBinomApps::clopper.pearson.ci(k = round(means$Y[i] * means$n[i]), n = means$n[i], alpha = 0.05,
        CI = "two.sided")
    means$LL[i] <- cp.ci$Lower.limit
    means$UL[i] <- cp.ci$Upper.limit
}

means$tie[means$tie == "Friend"] <- "Best friend"

plot3 <- ggplot(means, aes(x = tie, y = Y, fill = as.factor(same))) + facet_wrap(~dimension, nrow = 1) +
    geom_bar(stat = "identity", position = "dodge", width = 0.7) + geom_errorbar(aes(ymin = LL, ymax = UL),
    position = position_dodge(width = 0.75), width = 0.25) + labs(x = NULL, y = "Prop. maintained", fill = "Similarity",
    title = "(B) Differences between social roles", font = "Jost") + scale_y_continuous(breaks = seq(0,
    1, 0.25), limits = c(0, 1)) + theme_bw() + theme(axis.text.x = element_text(angle = 33, hjust = 1),
    legend.position = "none")

(figure <- ggpubr::ggarrange(plot1, plot3, ncol = 1, nrow = 2))

# ggsave(figure, filename = './figures/desfig.png')

Confidants with a different gender than ego are more, rather than less stable!

Our post-hoc hypothesis is that this may be due to women generally providing more social support than men (Wellman and Wortley 1989), making them valuable confidants and thus less likely to be dissolved, resulting in a negative different-gender effect on confidant loss among men.

# prop.table(table(data$different_gender)) #74% of ties are same-gender

# let's see how this differs across the ties, and further disaggregated by the genders:
tab <- matrix(c(mean(data$same_gender[data$tie == "Confidant" & data$ego_female == 1]), mean(data$same_gender[data$tie ==
    "Confidant" & data$ego_female == 0]), mean(data$same_gender[data$tie == "Friend" & data$ego_female ==
    1]), mean(data$same_gender[data$tie == "Friend" & data$ego_female == 0]), mean(data$same_gender[data$tie ==
    "Study" & data$ego_female == 1]), mean(data$same_gender[data$tie == "Study" & data$ego_female ==
    0]), mean(data$same_gender[data$tie == "Sport" & data$ego_female == 1]), mean(data$same_gender[data$tie ==
    "Sport" & data$ego_female == 0])), nrow = 4, byrow = TRUE)

rownames(tab) <- c("Confidant", "Best friends", "Study partner", "Sports partner")
colnames(tab) <- c("Female", "Male")

knitr::kable(tab, digits = 2, "html", caption = "Proportion of same gender ties, across different tie types, disaggregated by gender") %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))

Proportion of same gender ties, across different tie types, disaggregated by gender
	Female	Male
Confidant	0.73	0.55
Best friends	0.79	0.72
Study partner	0.77	0.50
Sports partner	0.73	0.75

We observe that men tend to have more cross-gender confiding ties (73%) than do women (55%).

# subset relevant columns
plotdata <- data[, c("same_gender", "Y", "tie", "ego_female")]

# filter on confidants
plotdata <- plotdata[plotdata$tie == "Confidant", ]

# calculate tie maintenance rate
means <- aggregate(Y ~ same_gender + ego_female, data = plotdata, FUN = "mean")
sd <- aggregate(Y ~ same_gender + ego_female, data = plotdata, FUN = "sd")
n <- aggregate(Y ~ same_gender + ego_female, data = plotdata, FUN = "sum")

# bind
means <- cbind(means, sd = sd$Y, n = n$Y)
means$ego_female <- ifelse(means$ego_female == 1, "Women", "Men")

# calculate a confidence interval around this proportion, usingthe Clopper-Pearson Confidence
# Interval
means$LL <- NA
means$UL <- NA

for (i in 1:nrow(means)) {
    cp.ci <- GenBinomApps::clopper.pearson.ci(k = round(means$Y[i] * means$n[i]), n = means$n[i], alpha = 0.05,
        CI = "two.sided")
    means$LL[i] <- cp.ci$Lower.limit
    means$UL[i] <- cp.ci$Upper.limit
}

ggplot(means, aes(x = same_gender, y = Y, fill = as.factor(same_gender))) + geom_bar(stat = "identity",
    position = "dodge", width = 0.75) + facet_wrap(~ego_female) + geom_errorbar(aes(ymin = LL, ymax = UL),
    position = position_dodge(width = 0.5), width = 0.25) + labs(x = NULL, y = "Prop. maintained", fill = "Similarity",
    title = "Maintenance of same-gender vs. different gender confidants,\namong men vs. women", font = "Jost") +
    scale_y_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1)) + theme(axis.text.x = element_blank())

But both men (although not significantly) and women maintain same-gender confidants more often than they maintain different-gender confidants…

Homogeneity

EI index for dichotomous variables (same gender, same education)
Average similarity score for continuous variable (age difference)

tab <- matrix(nrow = 3, ncol = 3)
rownames(tab) <- c("Gender", "Education", "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period == "w2 -> w3"]))) {

    # get network observed at wave 1
    netw1 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w1 -> w2",
        ]

    # get unique alters
    netw1u <- netw1[which(!duplicated(netw1$alterid)), ]

    # EI index for gender and education
    simgender <- length(which(netw1u$same_gender == 1))
    difgender <- length(which(netw1u$different_gender == 1))
    simeduc <- length(which(netw1u$sim_educ == 1))
    difeduc <- length(which(netw1u$different_educ == 1))
    n_alter <- nrow(netw1u)

    # calculate EI
    eigender1[i] <- (simgender - difgender)/n_alter
    eieduc1[i] <- (simeduc - difeduc)/n_alter

    # and average similarity for age
    ego_age <- netw1u$ego_age[1]  #ego age
    alters_age <- as.numeric(netw1u$alter_age)  #alters' age
    min <- 16
    max <- 45
    rv <- max - min  #range
    avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # now repeat for wave 2
    netw2 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w2 -> w3",
        ]

    # get unique alters
    netw2u <- netw2[which(!duplicated(netw2$alterid)), ]

    simgender <- length(which(netw2u$same_gender == 1))
    difgender <- length(which(netw2u$different_gender == 1))
    simeduc <- length(which(netw2u$sim_educ == 1))
    difeduc <- length(which(netw2u$different_educ == 1))
    n_alter <- nrow(netw2u)

    eigender2[i] <- (simgender - difgender)/n_alter
    eieduc2[i] <- (simeduc - difeduc)/n_alter

    alters_age <- as.numeric(netw2u$alter_age)  #alters' age
    avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # difference
    eigenderd[i] <- eigender1[i] - eigender2[i]
    eieducd[i] <- eieduc1[i] - eieduc2[i]
    avsimaged[i] <- avsimage1[i] - avsimage2[i]

}


tab[1, 1] <- paste0(format(round(mean(eigender1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 2] <- paste0(format(round(mean(eigender2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 3] <- paste0(format(round(mean(eigenderd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigenderd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[2, 1] <- paste0(format(round(mean(eieduc1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 2] <- paste0(format(round(mean(eieduc2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 3] <- paste0(format(round(mean(eieducd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieducd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[3, 1] <- paste0(format(round(mean(avsimage1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 2] <- paste0(format(round(mean(avsimage2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 3] <- paste0(format(round(mean(avsimaged, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimaged,
    na.rm = TRUE), 2), nsmall = 2), ")")

fshowdf(tab, caption = "Homogeneity in ego-networks of 281 students who participated in waves 1 and 2")

Homogeneity in ego-networks of 281 students who participated in waves 1 and 2
	Homogeneity W1	Homogeneity W2	Δ W1-W2
Gender	0.53 (0.46)	0.44 (0.48)	0.09 (0.33)
Education	0.38 (0.63)	0.02 (0.56)	0.36 (0.46)
Age	0.94 (0.04)	0.94 (0.05)	0.00 (0.04)

tab <- matrix(nrow = 3, ncol = 3)
rownames(tab) <- c("Gender", "Education", "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period == "w2 -> w3"]))) {

    # get network observed at wave 1
    netw1 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w1 -> w2",
        ]

    # get unique alters
    netw1u <- netw1[which(!duplicated(netw1$alterid)), ]

    # filter on tie type
    netw1u <- netw1u[netw1u$tie == "Confidant", ]

    # EI index for gender and education
    simgender <- length(which(netw1u$same_gender == 1))
    difgender <- length(which(netw1u$different_gender == 1))
    simeduc <- length(which(netw1u$sim_educ == 1))
    difeduc <- length(which(netw1u$different_educ == 1))
    n_alter <- nrow(netw1u)

    # calculate EI
    eigender1[i] <- (simgender - difgender)/n_alter
    eieduc1[i] <- (simeduc - difeduc)/n_alter

    # and average similarity for age
    ego_age <- netw1u$ego_age[1]  #ego age
    alters_age <- as.numeric(netw1u$alter_age)  #alters' age
    min <- 16
    max <- 45
    rv <- max - min  #range
    avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # now repeat for wave 2
    netw2 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w2 -> w3",
        ]

    # get unique alters
    netw2u <- netw2[which(!duplicated(netw2$alterid)), ]

    # filter on tie type
    netw2u <- netw2u[netw2u$tie == "Confidant", ]

    simgender <- length(which(netw2u$same_gender == 1))
    difgender <- length(which(netw2u$different_gender == 1))
    simeduc <- length(which(netw2u$sim_educ == 1))
    difeduc <- length(which(netw2u$different_educ == 1))
    n_alter <- nrow(netw2u)

    eigender2[i] <- (simgender - difgender)/n_alter
    eieduc2[i] <- (simeduc - difeduc)/n_alter

    alters_age <- as.numeric(netw2u$alter_age)  #alters' age
    avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # difference
    eigenderd[i] <- eigender1[i] - eigender2[i]
    eieducd[i] <- eieduc1[i] - eieduc2[i]
    avsimaged[i] <- avsimage1[i] - avsimage2[i]

}

tab[1, 1] <- paste0(format(round(mean(eigender1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 2] <- paste0(format(round(mean(eigender2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 3] <- paste0(format(round(mean(eigenderd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigenderd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[2, 1] <- paste0(format(round(mean(eieduc1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 2] <- paste0(format(round(mean(eieduc2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 3] <- paste0(format(round(mean(eieducd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieducd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[3, 1] <- paste0(format(round(mean(avsimage1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 2] <- paste0(format(round(mean(avsimage2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 3] <- paste0(format(round(mean(avsimaged, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimaged,
    na.rm = TRUE), 2), nsmall = 2), ")")

fshowdf(tab, caption = "Homogeneity in confiding network layer of 281 students who participated in waves 1 and 2")

Homogeneity in confiding network layer of 281 students who participated in waves 1 and 2
	Homogeneity W1	Homogeneity W2	Δ W1-W2
Gender	0.32 (0.74)	0.31 (0.72)	0.02 (0.58)
Education	0.36 (0.77)	0.28 (0.74)	0.09 (0.56)
Age	0.94 (0.09)	0.94 (0.08)	0.00 (0.07)

tab <- matrix(nrow = 3, ncol = 3)
rownames(tab) <- c("Gender", "Education", "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period == "w2 -> w3"]))) {

    # get network observed at wave 1
    netw1 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w1 -> w2",
        ]

    # get unique alters
    netw1u <- netw1[which(!duplicated(netw1$alterid)), ]

    # filter on tie type
    netw1u <- netw1u[netw1u$tie == "Friend", ]

    # EI index for gender and education
    simgender <- length(which(netw1u$same_gender == 1))
    difgender <- length(which(netw1u$different_gender == 1))
    simeduc <- length(which(netw1u$sim_educ == 1))
    difeduc <- length(which(netw1u$different_educ == 1))
    n_alter <- nrow(netw1u)

    # calculate EI
    eigender1[i] <- (simgender - difgender)/n_alter
    eieduc1[i] <- (simeduc - difeduc)/n_alter

    # and average similarity for age
    ego_age <- netw1u$ego_age[1]  #ego age
    alters_age <- as.numeric(netw1u$alter_age)  #alters' age
    min <- 16
    max <- 45
    rv <- max - min  #range
    avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # now repeat for wave 2
    netw2 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w2 -> w3",
        ]

    # get unique alters
    netw2u <- netw2[which(!duplicated(netw2$alterid)), ]

    # filter on tie type
    netw2u <- netw2u[netw2u$tie == "Friend", ]

    simgender <- length(which(netw2u$same_gender == 1))
    difgender <- length(which(netw2u$different_gender == 1))
    simeduc <- length(which(netw2u$sim_educ == 1))
    difeduc <- length(which(netw2u$different_educ == 1))
    n_alter <- nrow(netw2u)

    eigender2[i] <- (simgender - difgender)/n_alter
    eieduc2[i] <- (simeduc - difeduc)/n_alter

    alters_age <- as.numeric(netw2u$alter_age)  #alters' age
    avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # difference
    eigenderd[i] <- eigender1[i] - eigender2[i]
    eieducd[i] <- eieduc1[i] - eieduc2[i]
    avsimaged[i] <- avsimage1[i] - avsimage2[i]

}

tab[1, 1] <- paste0(format(round(mean(eigender1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 2] <- paste0(format(round(mean(eigender2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 3] <- paste0(format(round(mean(eigenderd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigenderd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[2, 1] <- paste0(format(round(mean(eieduc1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 2] <- paste0(format(round(mean(eieduc2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 3] <- paste0(format(round(mean(eieducd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieducd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[3, 1] <- paste0(format(round(mean(avsimage1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 2] <- paste0(format(round(mean(avsimage2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 3] <- paste0(format(round(mean(avsimaged, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimaged,
    na.rm = TRUE), 2), nsmall = 2), ")")

fshowdf(tab, caption = "Homogeneity in friendship network layer of 281 students who participated in waves 1 and 2")

Homogeneity in friendship network layer of 281 students who participated in waves 1 and 2
	Homogeneity W1	Homogeneity W2	Δ W1-W2
Gender	0.69 (0.57)	0.60 (0.68)	0.05 (0.57)
Education	0.31 (0.82)	0.07 (0.81)	0.22 (0.82)
Age	0.95 (0.04)	0.95 (0.04)	0.00 (0.04)

tab <- matrix(nrow = 3, ncol = 3)
rownames(tab) <- c("Gender", "Education", "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period == "w2 -> w3"]))) {

    # get network observed at wave 1
    netw1 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w1 -> w2",
        ]

    # get unique alters
    netw1u <- netw1[which(!duplicated(netw1$alterid)), ]

    # filter on tie type
    netw1u <- netw1u[netw1u$tie == "Sport", ]

    # EI index for gender and education
    simgender <- length(which(netw1u$same_gender == 1))
    difgender <- length(which(netw1u$different_gender == 1))
    simeduc <- length(which(netw1u$sim_educ == 1))
    difeduc <- length(which(netw1u$different_educ == 1))
    n_alter <- nrow(netw1u)

    # calculate EI
    eigender1[i] <- (simgender - difgender)/n_alter
    eieduc1[i] <- (simeduc - difeduc)/n_alter

    # and average similarity for age
    ego_age <- netw1u$ego_age[1]  #ego age
    alters_age <- as.numeric(netw1u$alter_age)  #alters' age
    min <- 16
    max <- 45
    rv <- max - min  #range
    avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # now repeat for wave 2
    netw2 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w2 -> w3",
        ]

    # get unique alters
    netw2u <- netw2[which(!duplicated(netw2$alterid)), ]

    # filter on tie type
    netw2u <- netw2u[netw2u$tie == "Sport", ]

    simgender <- length(which(netw2u$same_gender == 1))
    difgender <- length(which(netw2u$different_gender == 1))
    simeduc <- length(which(netw2u$sim_educ == 1))
    difeduc <- length(which(netw2u$different_educ == 1))
    n_alter <- nrow(netw2u)

    eigender2[i] <- (simgender - difgender)/n_alter
    eieduc2[i] <- (simeduc - difeduc)/n_alter

    alters_age <- as.numeric(netw2u$alter_age)  #alters' age
    avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # difference
    eigenderd[i] <- eigender1[i] - eigender2[i]
    eieducd[i] <- eieduc1[i] - eieduc2[i]
    avsimaged[i] <- avsimage1[i] - avsimage2[i]

}

tab[1, 1] <- paste0(format(round(mean(eigender1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 2] <- paste0(format(round(mean(eigender2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 3] <- paste0(format(round(mean(eigenderd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigenderd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[2, 1] <- paste0(format(round(mean(eieduc1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 2] <- paste0(format(round(mean(eieduc2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 3] <- paste0(format(round(mean(eieducd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieducd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[3, 1] <- paste0(format(round(mean(avsimage1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 2] <- paste0(format(round(mean(avsimage2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 3] <- paste0(format(round(mean(avsimaged, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimaged,
    na.rm = TRUE), 2), nsmall = 2), ")")

fshowdf(tab, caption = "Homogeneity in sporting network layer of 281 students who participated in waves 1 and 2")

Homogeneity in sporting network layer of 281 students who participated in waves 1 and 2
	Homogeneity W1	Homogeneity W2	Δ W1-W2
Gender	0.60 (0.73)	0.38 (0.87)	0.16 (0.93)
Education	0.29 (0.85)	-0.35 (0.82)	0.41 (1.01)
Age	0.92 (0.09)	0.92 (0.07)	-0.01 (0.10)

tab <- matrix(nrow = 3, ncol = 3)
rownames(tab) <- c("Gender", "Education", "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period == "w2 -> w3"]))) {

    # get network observed at wave 1
    netw1 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w1 -> w2",
        ]

    # get unique alters
    netw1u <- netw1[which(!duplicated(netw1$alterid)), ]

    # filter on tie type
    netw1u <- netw1u[netw1u$tie == "Study", ]

    # EI index for gender and education
    simgender <- length(which(netw1u$same_gender == 1))
    difgender <- length(which(netw1u$different_gender == 1))
    simeduc <- length(which(netw1u$sim_educ == 1))
    difeduc <- length(which(netw1u$different_educ == 1))
    n_alter <- nrow(netw1u)

    # calculate EI
    eigender1[i] <- (simgender - difgender)/n_alter
    eieduc1[i] <- (simeduc - difeduc)/n_alter

    # and average similarity for age
    ego_age <- netw1u$ego_age[1]  #ego age
    alters_age <- as.numeric(netw1u$alter_age)  #alters' age
    min <- 16
    max <- 45
    rv <- max - min  #range
    avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # now repeat for wave 2
    netw2 <- data[data$ego == unique(data$ego[data$period == "w2 -> w3"])[i] & data$period == "w2 -> w3",
        ]

    # get unique alters
    netw2u <- netw2[which(!duplicated(netw2$alterid)), ]

    # filter on tie type
    netw2u <- netw2u[netw2u$tie == "Study", ]

    simgender <- length(which(netw2u$same_gender == 1))
    difgender <- length(which(netw2u$different_gender == 1))
    simeduc <- length(which(netw2u$sim_educ == 1))
    difeduc <- length(which(netw2u$different_educ == 1))
    n_alter <- nrow(netw2u)

    eigender2[i] <- (simgender - difgender)/n_alter
    eieduc2[i] <- (simeduc - difeduc)/n_alter

    alters_age <- as.numeric(netw2u$alter_age)  #alters' age
    avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv)  #average similarity score

    # difference
    eigenderd[i] <- eigender1[i] - eigender2[i]
    eieducd[i] <- eieduc1[i] - eieduc2[i]
    avsimaged[i] <- avsimage1[i] - avsimage2[i]

}

tab[1, 1] <- paste0(format(round(mean(eigender1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 2] <- paste0(format(round(mean(eigender2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigender2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[1, 3] <- paste0(format(round(mean(eigenderd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eigenderd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[2, 1] <- paste0(format(round(mean(eieduc1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 2] <- paste0(format(round(mean(eieduc2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieduc2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[2, 3] <- paste0(format(round(mean(eieducd, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(eieducd,
    na.rm = TRUE), 2), nsmall = 2), ")")

tab[3, 1] <- paste0(format(round(mean(avsimage1, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage1,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 2] <- paste0(format(round(mean(avsimage2, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimage2,
    na.rm = TRUE), 2), nsmall = 2), ")")
tab[3, 3] <- paste0(format(round(mean(avsimaged, na.rm = TRUE), 2), nsmall = 2), " (", format(round(sd(avsimaged,
    na.rm = TRUE), 2), nsmall = 2), ")")

fshowdf(tab, caption = "Homogeneity in study network layer of 281 students who participated in waves 1 and 2")

Homogeneity in study network layer of 281 students who participated in waves 1 and 2
	Homogeneity W1	Homogeneity W2	Δ W1-W2
Gender	0.55 (0.71)	0.44 (0.78)	0.10 (0.81)
Education	0.74 (0.65)	-0.33 (0.88)	0.76 (0.93)
Age	0.95 (0.06)	0.95 (0.04)	0.00 (0.06)

Wellman, Barry, and Scot Wortley. 1989. “Brothers’ Keepers: Situating Kinship Relations in Broader Networks of Social Support.” Sociological Perspectives 32 (3): 273–306.

---
title: "Descriptives"
bibliography: references.bib
link-citations: true
date: "Last compiled on `r format(Sys.time(), '%B, %Y')`"
output: 
  html_document:
    css: tweaks.css
    toc:  true
    toc_float: true
    number_sections: false
    toc_depth: 2
    code_folding: show
    code_download: yes
---

```{r, globalsettings, echo=FALSE, warning=FALSE, results='hide', message=FALSE}
library(knitr)
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE)
opts_chunk$set(tidy.opts=list(width.cutoff=100),tidy=TRUE, warning = FALSE, message = FALSE,comment = "#>", cache=TRUE, class.source=c("test"), class.output=c("test3"))
options(width = 100)
rgl::setupKnitr()
options(knitr.kable.NA='')

colorize <- function(x, color) {sprintf("<span style='color: %s;'>%s</span>", color, x) }
```


```{r klippy, echo=FALSE, include=TRUE}
klippy::klippy(position = c('top', 'right'))
#klippy::klippy(color = 'darkred')
#klippy::klippy(tooltip_message = 'Click to copy', tooltip_success = 'Done')
```



---  
  
# Getting started

To copy the code, click the button in the upper right corner of the code-chunks.

## clean up

```{r, clean, results='hide'}
rm(list=ls())
gc()
```

<br>

## general custom functions

- `fpackage.check`: Check if packages are installed (and install if not) in R
- `fsave`: Function to save data with time stamp in correct directory
- `fload`: Load R-objects under new names
- `fshowdf`: Print objects (`tibble` / `data.frame`) nicely on screen in `.Rmd`.
- `ftheme`: pretty ggplot2 theme


```{r, functions}
fpackage.check <- function(packages) {
    lapply(packages, FUN = function(x) {
        if (!require(x, character.only = TRUE)) {
            install.packages(x, dependencies = TRUE)
            library(x, character.only = TRUE)
        }
    })
}

fsave <- function(x, file, location = "./data/processed/", ...) {
    if (!dir.exists(location))
        dir.create(location)
    datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
    totalname <- paste(location, datename, file, sep = "")
    print(paste("SAVED: ", totalname, sep = ""))
    save(x, file = totalname)
}


fload <- function(fileName) {
    load(fileName)
    get(ls()[ls() != "fileName"])
}

fshowdf <- function(x, digits = 2, ...) {
    knitr::kable(x, digits = digits, "html", ...) %>%
        kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
        kableExtra::scroll_box(width = "100%", height = "300px")
}

#extrafont::font_import(paths = c("C:/Users/u244147/Downloads/Jost/", prompt = FALSE))
ftheme <- function() {
  
  #download font at https://fonts.google.com/specimen/Jost/
  theme_minimal(base_family = "Jost") +
    theme(panel.grid.minor = element_blank(),
          plot.title = element_text(family = "Jost", face = "bold"),
          axis.title = element_text(family = "Jost Medium"),
          axis.title.x = element_text(hjust = 0),
          axis.title.y = element_text(hjust = 1),
          strip.text = element_text(family = "Jost", face = "bold",
                                    size = rel(0.75), hjust = 0),
          strip.background = element_rect(fill = "grey90", color = NA),
          legend.position = "bottom")
}

```

```{r fonts, echo=FALSE, warning=FALSE, results='hide'}
# import font JOST
#extrafont::font_import(pattern = "Jost")
extrafont::loadfonts(device="win")

# Set default theme and font stuff
theme_set(ftheme())
update_geom_defaults("text", list(family = "Jost", fontface = "plain"))
update_geom_defaults("label", list(family = "Jost", fontface = "plain"))
```

<br>

## necessary packages

- `tidyverse`
- `knitr`: generating tables
- `kableExtra`: manipulating tables
- `xtable`: displaying HTML format
- `ggpubr`
- `GenBinomApps`: compute Clopper-Pearson confidence interval
- `reshape2`
- `ggVennDiagram`


```{r, packages, results='hide', message=FALSE, warning=FALSE}
packages = c("knitr", "kableExtra", "xtable", "tidyverse", "GenBinomApps", "reshape2", "ggcorrplot", "ggVennDiagram")
fpackage.check(packages)
```

<br>

## load data

Load the replicated data-sets (constructed [here](https://netchange.netlify.app/prep.html)). To load these file, adjust the filenames in the following code so that it matches the most recent version of the `.RDa` file you have in your `./data/processed/` folder.

You may also obtain them by downloading: `r xfun::embed_file("./data_shared/data_nested.RDa")`

```{r,data}
#list.files("./data/processed/")

#get todays date:
today <- gsub("-", "", Sys.Date())

data <- fload(paste0("./data/processed/", today, "data_nested.RDa"))
```


<br>

some last wrangling:

- make Y indicate tie **loss** instead of tie **maintenance**
- make X reflect **dissimilarity** instead of **similarity**
- standardize embeddedness in other network layers
- proximity levels

```{r, wrangle}
data$Yloss <- ifelse(data$Y==1, 0, 1)

data$different_gender <- ifelse(data$same_gender==1, 0, 1)
data$different_educ <- ifelse(data$sim_educ==1, 0, 1)

data$embed.ext/3 -> data$embed.ext

data$proximity <- factor(data$proximity, levels = c("far","close","roommate"))

```


<br>


# Correlation matrix

```{r, correlation, message=FALSE, warning=FALSE, fig.width=11, fig.height=4, class.source = 'fold-hide'}
#get variables of interest, for which we want to calculate correlations
cordf <- data[, rev(c("dif_age","different_gender","different_educ","closeness.t","multiplex", "embed", "embed.ext", "Yloss", "tie"))]

#relabel
names(cordf) <- c("Tie", "Tie loss", "Str. embed. other layers",  "Str. embed. focal layer", "Relational multiplexity", "Emotional closeness", "Different education", "Different gender", "Age difference")

#reorder
desired_order <- rev(c("Tie", "Tie loss", "Different gender", "Different education", "Age difference", "Emotional closeness",  "Relational multiplexity", "Str. embed. focal layer", "Str. embed. other layers"))
cordf <- cordf[, desired_order]

#1. correlations of all observations (regardless of tie type)
all <- cordf[,sapply(cordf, is.numeric)]
#correlation
cormat <- cor(all)
#p value
pmat <- cor_pmat(all)
#below diagonal
cormat[lower.tri(cormat)] <- NA
pmat[lower.tri(pmat)] <- NA
#melt
allc <- melt(cormat)
allp <- melt(pmat)
all <- cbind(allc, pval = allp$value)
#diagonal NA
all$value <- ifelse(all$Var1 == all$Var2, NA, all$value)
all$pval <- ifelse(all$Var1 == all$Var2, NA, all$pval)
#add label
all$label <- ifelse(!is.na(all$value), paste0(format(round(all$value,2), nsmall=2), ifelse(all$pval>0.05, "", "*")),NA)

plot_all <- all %>% 
  ggplot(aes(Var1, Var2, fill = value)) +
  geom_tile (color = 'lightgrey') +
  geom_text(aes(label = label), size = 4) +
  scale_fill_gradient2(low = "blue", mid = "white", high="red", midpoint=0, limit=c(-1,1), na.value = "lightgrey") +
  scale_x_discrete(limits = rev(levels(all$Var1))) +  #reverse x-axis
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "right", legend.direction="horizontal") + labs(x=NULL, y=NULL, title="(A) All alter-tie observations", fill = "Correlation") 

#2 now, disaggregated by tie type
# function to compute correlations for a tie type and add the necessary columns
fcompcorr <- function(tie_type) { 
  df <- cordf[cordf$Tie == tie_type, sapply(cordf, is.numeric)]
  cormat <- cor(df)
  pmat <- cor_pmat(df)
  cormat[lower.tri(cormat)] <- NA
  pmat[lower.tri(pmat)] <- NA
  cormat_melted <- melt(cormat)
  pmat_melted <- melt(pmat)
  df <- cbind(cormat_melted, pval = pmat_melted$value)
  df$value <- ifelse(df$Var1 == df$Var2, NA, df$value)
  df$pval <- ifelse(df$Var1 == df$Var2, NA, df$pval)
  df$label <- ifelse(!is.na(df$value), paste0(format(round(df$value, 2), nsmall = 2), ifelse(df$pval > 0.05, "", "*")), NA)
  df$group <- tie_type
  return(df)
}

tie_types <- c("Confidant", "Friend", "Sport", "Study")

#empty df
mycors <- data.frame()

#iterate through tie types and compute correlations
for (i in tie_types) {
  mycors <- rbind(mycors, fcompcorr(i))
}

#relabel...
mycors$group[mycors$group == "Friend"] <- "Best friend"

plot_dis <- mycors %>% 
  ggplot(aes(Var1, Var2, fill = value)) +
  geom_tile (color = 'lightgrey') +
  geom_text(aes(label = label), size = 2.5) +
  scale_fill_gradient2(low = "blue", mid = "white", high="red", midpoint=0, limit=c(-1,1), na.value = "lightgrey") +
  scale_x_discrete(limits = rev(levels(all$Var1))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "none") + labs(x=NULL, y=NULL,
                                                                                            title="(B) Disaggregated by social role", fill = "Correlation") +
  facet_grid(~group)

#now, disaggregated by combination tie type * uni vs multiplex
#keep for appendix...
fcompcorr2 <- function(tie_type) {
  df <- cordf[cordf$Tie == tie_type, sapply(cordf, is.numeric)]
  
  #uniplex below diagonal
  dfuni <- df[df$`Relational multiplexity`==0,]
  cormat1 <- cor(dfuni)
  pmat1 <- cor_pmat(dfuni)

  #multiplex above diagonal
  dfmulti <- df[df$`Relational multiplexity`>0,]
  cormat2 <- cor(dfmulti)
  pmat2 <- cor_pmat(dfmulti)
  cormat <- cormat1
  pmat <- pmat1
  
  cormat[upper.tri(cormat)] <- cormat2[upper.tri(cormat2)]
  pmat[upper.tri(pmat)] <- pmat2[upper.tri(pmat2)]
  cormat_melted <- melt(cormat)
  pmat_melted <- melt(pmat)
  df <- cbind(cormat_melted, pval = pmat_melted$value)
  df$value <- ifelse(df$Var1 == df$Var2, NA, df$value)
  df$pval <- ifelse(df$Var1 == df$Var2, NA, df$pval)
  df$label <- ifelse(!is.na(df$value), paste0(format(round(df$value, 2), nsmall = 2), ifelse(df$pval > 0.05, "", "*")), NA)
  df$group <- tie_type

  return(df)
}
  
#empty df
mycors <- data.frame()

#iterate through tie types and compute correlations
for (i in tie_types) {
  mycors <- rbind(mycors, fcompcorr2(i))
}

mycors$group[mycors$group == "Friend"] <- "Best friend"


plot_dis2 <- mycors %>% 
  ggplot(aes(Var1, Var2, fill = value)) +
  geom_tile (color = 'lightgrey') +
  geom_text(aes(label = label), size = 2.5) +
  scale_fill_gradient2(low = "blue", mid = "white", high="red", midpoint=0, limit=c(-1,1), na.value = "lightgrey") +
  scale_x_discrete(limits = rev(levels(all$Var1))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "none") + labs(x=NULL, y=NULL,
                                                                                            title="(C) Multiplex alters (above diagonal) vs. uniplex alters (below diagonal)", fill = "Correlation") +
  facet_grid(~group)

#figure <- ggpubr::ggarrange(plot_all, plot_dis, ncol=1, nrow=2)

#ggsave(figure, 
#       file = "./figures/corplot.png",
#       dpi = 320, 
#       width = 11,
#       height = 8)

plot_all
plot_dis
plot_dis2

```



----

<br>

# Describing networks 


<!---

## network size

at wave 1

```{r, class.source = 'fold-hide'}
# total netsize (no. of unique alters, non-kin)
ns1 <- NA
# size of each egonet
cdn1 <- NA
stu1 <- NA
bff1 <- NA
csn1 <- NA

for (i in unique(data$ego[data$period=="w1 -> w2"])) {
  
  ns1[i] <- length(unique(data$alterid[data$ego==i & data$period == "w1 -> w2"]))
  cdn1[i] <- length(unique(data$alterid[data$ego == i & data$period == "w1 -> w2" & data$tie == "Confidant"]))
  stu1[i] <- length(unique(data$alterid[data$ego == i & data$period == "w1 -> w2" & data$tie == "Study"])) 
  csn1[i] <- length(unique(data$alterid[data$ego == i & data$period == "w1 -> w2" & data$tie == "Sport"])) 
  bff1[i] <- length(unique(data$alterid[data$ego == i & data$period == "w1 -> w2" & data$tie == "Friend"]))   
}

ns1 <- psych::describe(ns1)
stu1 <- psych::describe(stu1)
cdn1 <- psych::describe(cdn1)
csn1 <- psych::describe(csn1)
bff1 <- psych::describe(bff1)



#make table
tab <- matrix(nrow=5, ncol=5)
rownames(tab) <- c("total network size (# unique non-kin alters)", 
                   "# confidants", 
                   "# study partners", 
                   "# best friends" 
                   ,"# sports partners")

colnames(tab) <- c("n", "mean", "sd", "min", "max")

tab[1,] <- unlist(ns1)[c(2,3,4,8,9)]
tab[2,] <- unlist(cdn1)[c(2,3,4,8,9)]
tab[3,] <- unlist(stu1)[c(2,3,4,8,9)]
tab[4,] <- unlist(bff1)[c(2,3,4,8,9)]
tab[5,] <- unlist(csn1)[c(2,3,4,8,9)]

fshowdf(tab,caption="Table 1. Egonet size (non-kin relations) at t1 among a sample of 513 students")
```

--- 

<br>

-->


## Relational characteristics {.tabset .tabset-fade} 

at wave 1

### all alters

```{r, class.source = 'fold-hide'}
cdn <- data[data$tie == "Confidant" & data$period == "w1 -> w2",]
bff <- data[data$tie == "Friend" & data$period == "w1 -> w2",]
study <- data[data$tie == "Study" & data$period == "w1 -> w2",]
csn <- data[data$tie == "Sport" & data$period == "w1 -> w2",]

tab <- matrix(nrow=5, ncol=4)
rownames(tab) <- c("(Unique) alters", 
                   "Confidants", 
                    "Study partners",
                   "Best friends", 
                   "Sports partners")

colnames(tab) <- c("n", "communication freq.", "closeness", "multiplexity $^{b}$")

tab[1,1] <- length(unique(data$alterid[data$period=="w1 -> w2"]))
tab[2,1] <- nrow(cdn)
tab[3,1] <- nrow(study)
tab[4,1] <- nrow(bff)
tab[5,1] <- nrow(csn)

tab[1,2] <- paste0(round(mean(data$frequency.t[which(!duplicated(data$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), " (", round(sd(data$frequency.t[which(!duplicated(data$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), ")")
tab[2,2] <- paste0(round(mean(cdn$frequency.t,na.rm=TRUE),2), " (", round(sd(cdn$frequency.t,na.rm=TRUE),2), ")")
tab[3,2] <- paste0(round(mean(study$frequency.t,na.rm=TRUE),2), " (", round(sd(study$frequency.t,na.rm=TRUE),2), ")")
tab[4,2] <- paste0(round(mean(bff$frequency.t,na.rm=TRUE),2), " (", round(sd(bff$frequency.t,na.rm=TRUE),2), ")")
tab[5,2] <- paste0(round(mean(csn$frequency.t,na.rm=TRUE),2), " (", round(sd(csn$frequency.t,na.rm=TRUE),2), ")")

tab[1,3] <-  paste0(round(mean(data$closeness.t[which(!duplicated(data$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), " (", round(sd(data$closeness.t[which(!duplicated(data$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), ")")
tab[2,3] <- paste0(round(mean(cdn$closeness.t,na.rm=TRUE),2), " (", round(sd(cdn$closeness.t,na.rm=TRUE),2), ")")
tab[3,3] <- paste0(round(mean(study$closeness.t,na.rm=TRUE),2), " (", round(sd(study$closeness.t,na.rm=TRUE),2), ")")
tab[4,3] <- paste0(round(mean(bff$closeness.t,na.rm=TRUE),2), " (", round(sd(bff$closeness.t,na.rm=TRUE),2), ")")
tab[5,3] <- paste0(round(mean(csn$closeness.t,na.rm=TRUE),2), " (", round(sd(csn$closeness.t,na.rm=TRUE),2), ")")

tab[1,4] <- paste0(round(mean(data$multiplex[which(!duplicated(data$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), " (", round(sd(data$multiplex[which(!duplicated(data$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), ")")
tab[2,4] <- paste0(round(mean(cdn$multiplex,na.rm=TRUE),2), " (", round(sd(cdn$multiplex,na.rm=TRUE),2), ")")
tab[3,4] <- paste0(round(mean(study$multiplex,na.rm=TRUE),2), " (", round(sd(study$multiplex,na.rm=TRUE),2), ")")
tab[4,4] <- paste0(round(mean(bff$multiplex,na.rm=TRUE),2), " (", round(sd(bff$multiplex,na.rm=TRUE),2), ")")
tab[5,4] <- paste0(round(mean(csn$multiplex,na.rm=TRUE),2), " (", round(sd(csn$multiplex,na.rm=TRUE),2), ")")

knitr::kable(tab, digits=2, "html", caption="Relational characteristics across relational dimensions at t1 $^{a}$") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kableExtra::add_footnote(c("Means and standard deviations in parentheses.", "To achieve a meaningful intercept for our multivariate analyses, we subtracted multiplexity by one. In this context, multiplexity represents the average number of extra relational dimensions for each type of relationship."), notation="alphabet")
```

<br> 

### uniplex ties

```{r, class.source = 'fold-hide'}
cdn <- data[data$tie == "Confidant" & data$period == "w1 -> w2" & data$multiplex == 0,]
bff <- data[data$tie == "Friend" & data$period == "w1 -> w2" & data$multiplex == 0,]
study <- data[data$tie == "Study" & data$period == "w1 -> w2" & data$multiplex == 0,]
csn <- data[data$tie == "Sport" & data$period == "w1 -> w2" & data$multiplex == 0,]
dataa <- data[data$multiplex==0,]

tab <- matrix(nrow=5, ncol=4)
rownames(tab) <- c("(Unique) alters", 
                   "Confidants", 
                    "Study partners",
                   "Best friends", 
                   "Sports partners")

colnames(tab) <- c("n", "communication freq.", "closeness", "multiplexity $^{b}$")

tab[1,1] <- length(unique(dataa$alterid[dataa$period=="w1 -> w2"]))
tab[2,1] <- nrow(cdn)
tab[3,1] <- nrow(study)
tab[4,1] <- nrow(bff)
tab[5,1] <- nrow(csn)

tab[1,2] <- paste0(round(mean(dataa$frequency.t[which(!duplicated(dataa$alterid[dataa$period =="w1 -> w2"]))], na.rm=TRUE),2), " (", round(sd(dataa$frequency.t[which(!duplicated(dataa$alterid[dataa$period =="w1 -> w2"]))], na.rm=TRUE),2), ")")
tab[2,2] <- paste0(round(mean(cdn$frequency.t,na.rm=TRUE),2), " (", round(sd(cdn$frequency.t,na.rm=TRUE),2), ")")
tab[3,2] <- paste0(round(mean(study$frequency.t,na.rm=TRUE),2), " (", round(sd(study$frequency.t,na.rm=TRUE),2), ")")
tab[4,2] <- paste0(round(mean(bff$frequency.t,na.rm=TRUE),2), " (", round(sd(bff$frequency.t,na.rm=TRUE),2), ")")
tab[5,2] <- paste0(round(mean(csn$frequency.t,na.rm=TRUE),2), " (", round(sd(csn$frequency.t,na.rm=TRUE),2), ")")

tab[1,3] <-  paste0(round(mean(dataa$closeness.t[which(!duplicated(dataa$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), " (", round(sd(dataa$closeness.t[which(!duplicated(dataa$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), ")")
tab[2,3] <- paste0(round(mean(cdn$closeness.t,na.rm=TRUE),2), " (", round(sd(cdn$closeness.t,na.rm=TRUE),2), ")")
tab[3,3] <- paste0(round(mean(study$closeness.t,na.rm=TRUE),2), " (", round(sd(study$closeness.t,na.rm=TRUE),2), ")")
tab[4,3] <- paste0(round(mean(bff$closeness.t,na.rm=TRUE),2), " (", round(sd(bff$closeness.t,na.rm=TRUE),2), ")")
tab[5,3] <- paste0(round(mean(csn$closeness.t,na.rm=TRUE),2), " (", round(sd(csn$closeness.t,na.rm=TRUE),2), ")")

tab[1,4] <- paste0(round(mean(dataa$multiplex[which(!duplicated(dataa$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), " (", round(sd(dataa$multiplex[which(!duplicated(dataa$alterid[data$period =="w1 -> w2"]))], na.rm=TRUE),2), ")")
tab[2,4] <- paste0(round(mean(cdn$multiplex,na.rm=TRUE),2), " (", round(sd(cdn$multiplex,na.rm=TRUE),2), ")")
tab[3,4] <- paste0(round(mean(study$multiplex,na.rm=TRUE),2), " (", round(sd(study$multiplex,na.rm=TRUE),2), ")")
tab[4,4] <- paste0(round(mean(bff$multiplex,na.rm=TRUE),2), " (", round(sd(bff$multiplex,na.rm=TRUE),2), ")")
tab[5,4] <- paste0(round(mean(csn$multiplex,na.rm=TRUE),2), " (", round(sd(csn$multiplex,na.rm=TRUE),2), ")")

knitr::kable(tab, digits=2, "html", caption="Relational characteristics of uniplex ties, across relational dimensions at t1 $^{a}$") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kableExtra::add_footnote(c("Means and standard deviations in parentheses.", "To achieve a meaningful intercept for our multivariate analyses, we subtracted multiplexity by one. In this context, multiplexity represents the average number of extra relational dimensions for each type of relationship."), notation="alphabet")
```


## {.unlisted .unnumbered}

---

<br>


## Role overlap {.tabset .tabset-fade}

### wave 1

```{r, class.source = 'fold-hide'}
x1 <- list(
  'Best friend' = data$alterid[data$bff == 1 & data$period == "w1 -> w2"],
  'Confidant' = data$alterid[data$cdn == 1 & data$period == "w1 -> w2"],
  
  'Sports partner' = data$alterid[data$csn == 1 & data$period == "w1 -> w2"],
  'Study partner' = data$alterid[data$study == 1 & data$period == "w1 -> w2"]
)


(venn <- ggVennDiagram(x1, label = "percent", label_size = 3, label_percent_digit = 1, set_size = 3, show_intersect = FALSE) + 
    scale_fill_distiller(palette = "OrRd", direction = 1) +
    theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
    scale_x_continuous(expand = expansion(mult = .2)) +
    labs(fill = "Alter count"))

#ggsave(venn, filename = "./figures/venn1.png")

#?ggVennDiagram
```

### wave 2

```{r, class.source = 'fold-hide'}
x2 <- list(
   'Best Friend' = data$alterid[data$bff == 1 & data$period == "w2 -> w3"],
     'Confidant' = data$alterid[data$cdn == 1 & data$period == "w2 -> w3"],
  'Sports partner' = data$alterid[data$csn == 1 & data$period == "w2 -> w3"],
  'Study partner' = data$alterid[data$study == 1 & data$period == "w2 -> w3"]
)


(venn <- ggVennDiagram(x2, label = "percent", label_size = 3, label_percent_digit = 1, set_size = 3, show_intersect = FALSE) + 
    scale_fill_distiller(palette = "OrRd", direction = 1) +
    theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
    scale_x_continuous(expand = expansion(mult = .2)) +
    labs(fill = "Alter count"))

```

## {.unlisted .unnumbered}


-----

<br> 


## Tie maintenance


```{r, class.source = 'fold-hide'}
#make table
tab <- matrix(nrow=5, ncol=2)
rownames(tab) <- c("All (unique) ties",
                   "Confidants",
                   "Best friends",
                   "Study partners",
                   "Sports partners"
                   )
colnames(tab) <- c("n", "Relisted")


bff <- data[data$tie == "Friend",]
cdn <- data[data$tie == "Confidant",]
study <- data[data$tie == "Study",]
csn <- data[data$tie == "Sport",]

tab[1,1] <- nrow(data)
tab[2,1] <- nrow(cdn)
tab[3,1] <- nrow(bff)
tab[4,1] <- nrow(study)
tab[5,1] <- nrow(csn)

tab[1,2] <- round(prop.table(table(data$Y))[[2]],2)
tab[2,2] <- round(prop.table(table(cdn$Y))[[2]],2)
tab[3,2] <- round(prop.table(table(bff$Y))[[2]],2)
tab[4,2] <- round(prop.table(table(study$Y))[[2]],2)
tab[5,2] <- round(prop.table(table(csn$Y))[[2]],2)

options(knitr.kable.NA='')

knitr::kable(tab, digits=2, "html", caption="Maintenance of multiple social ties over time among 513 students") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))
```

----

<br>

## Tie maintenance vs similarity 


```{r, class.source = 'fold-hide', message=FALSE, warning=FALSE}
#1. similarities vs tie loss, per dimension
plotdata1 <- data[,c("duration", "same_gender", "Y", "tie")]
plotdata1$same <- ifelse(plotdata1$same_gender==1,1,0)
plotdata1$dimension <- "Gender"

plotdata2 <- data[,c("duration", "sim_educ", "Y", "tie")]
plotdata2$same <- ifelse(plotdata2$sim_educ==1,1,0)
plotdata2$dimension <- "Education"

plotdata3 <- data[,c("duration", "dif_age", "Y", "tie")]
plotdata3$same <- ifelse(plotdata3$dif_age < 4, 1, 0)
plotdata3$dimension <- "Age"

plotdata1 <- plotdata1[,-2]
plotdata2 <- plotdata2[,-2]
plotdata3 <- plotdata3[,-2]
plotdata <- rbind(plotdata1,plotdata2,plotdata3)
plotdata$same <- factor(plotdata$same)

#calculate tie loss rate for similarity dimensions
means <- aggregate(Y ~ same + dimension, data = plotdata, FUN = "mean")
sd <- aggregate(Y ~ same + dimension, data = plotdata, FUN = "sd")
n <- aggregate(Y ~ same + dimension, data =plotdata, FUN = "sum")
means <- cbind(means, sd=sd$Y, n=n$Y)

#calculate a confidence interval around this proportion,
#usingthe Clopper-Pearson Confidence Interval  
means$LL <- NA
means$UL <- NA

for (i in 1:nrow(means)) {
  cp.ci <- GenBinomApps::clopper.pearson.ci(
    k = round(means$Y[i] * means$n[i]),
    n = means$n[i],
    alpha = 0.05,
    CI="two.sided"
  )
  means$LL[i] <- cp.ci$Lower.limit
  means$UL[i] <- cp.ci$Upper.limit
}


plot1 <- ggplot(means, aes(x = same, y = Y, fill = as.factor(same))) +
  facet_wrap(~dimension) + 
  geom_bar(stat = "identity", position = "dodge", width = .75) +
  geom_errorbar(aes(ymin = LL, ymax = UL), position = position_dodge(width = 0.5), width=0.25) + labs (x = NULL, y = "Prop. maintained", fill = "Similarity", title = "(A) Maintenance of alter-ties of self-similar vs. dissimilar alters",font = "Jost") +
  scale_y_continuous(breaks = seq(0, 1, .25), limits = c(0,1)) +
  theme_bw() +
    theme(axis.text.x = element_blank(), legend.position = "bottom")

# 2. dissagregate by relationship duration

#calculate tie loss rate for similarity dimensions
#means <- aggregate(Y ~ same + dimension + duration, data = plotdata, FUN = "mean")
#sd <- aggregate(Y ~ same + dimension + duration, data = plotdata, FUN = "sd")
#n <- aggregate(Y ~ same + dimension + duration, data =plotdata, FUN = "sum")
#means <- cbind(means, sd=sd$Y, n=n$Y)

#calculate a confidence interval around this proportion,
#usingthe Clopper-Pearson Confidence Interval  
#means$LL <- NA
#means$UL <- NA

#for (i in 1:nrow(means)) {
#  cp.ci <- GenBinomApps::clopper.pearson.ci(
#    k = round(means$Y[i] * means$n[i]),
#    n = means$n[i],
#    alpha = 0.05,
#    CI="two.sided"
#  )
#  means$LL[i] <- cp.ci$Lower.limit
#  means$UL[i] <- cp.ci$Upper.limit
#}

#duration_names <- c(
#                    `0` = "Under 1 year",
#                    `2` = "1-3 years",
#                    `6` = "4-8 years",
#                    `12` = "9-15 years",
#                    `15` = "Over 15 years"
#                    )

#plot2 <- ggplot(means, aes(x = dimension, y = Y, fill = as.factor(same))) +
#  facet_wrap(~ duration, labeller = as_labeller(duration_names), nrow = 1) +
#  geom_bar(stat = "identity", position = "dodge", width = 0.7) +
#  geom_errorbar(aes(ymin = LL, ymax = UL), position = position_dodge(width = 0.75), #width=0.25) +
#  labs (x = NULL, y = "Prop. maintained", fill = "Similarity", title = "(B) Disaggregated by: #tie duration", font = "Jost") +
#   scale_y_continuous(breaks = seq(0, 1, .25), limits = c(0,1)) +
#  theme(axis.text.x = element_text(angle = 33, hjust = 1))

#this is more informative. to show convergence effects
#plot2 <- ggplot(means, aes(x = as.factor(duration), y = Y, fill = as.factor(same))) +
#  facet_wrap(~ dimension, nrow = 1) +
#  geom_bar(stat = "identity", position = "dodge", width = .7) +
#  geom_errorbar(aes(ymin = LL, ymax = UL), position = position_dodge(width = 0.75), #width=0.25) +
#  labs (x = NULL, y = "Prop. maintained", fill = "Similarity", title = "(B) Convergence of #tie maintenance rates with similar vs. dissimilar alters over time",font = "Jost") +
#   scale_y_continuous(breaks = seq(0, 1, .25), limits = c(0,1)) +
#    scale_x_discrete(labels = c( "<1", "1-3", "4-8", "9-15", ">15")) +
#  theme(axis.text.x = element_text(angle = 33, hjust = 1), legend.position = "none")

# per relationship type

#calculate tie loss rate for similarity dimensions
means <- aggregate(Y ~ same + dimension + tie, data = plotdata, FUN = "mean")
sd <- aggregate(Y ~ same + dimension + tie, data = plotdata, FUN = "sd")
n <- aggregate(Y ~ same + dimension + tie, data =plotdata, FUN = "sum")
means <- cbind(means, sd=sd$Y, n=n$Y)

#calculate a confidence interval around this proportion,
#usingthe Clopper-Pearson Confidence Interval  
means$LL <- NA
means$UL <- NA

for (i in 1:nrow(means)) {
  cp.ci <- GenBinomApps::clopper.pearson.ci(
    k = round(means$Y[i] * means$n[i]),
    n = means$n[i],
    alpha = 0.05,
    CI="two.sided"
  )
  means$LL[i] <- cp.ci$Lower.limit
  means$UL[i] <- cp.ci$Upper.limit
}

means$tie[means$tie == "Friend"] <- "Best friend"

plot3 <- ggplot(means, aes(x = tie, y = Y, fill = as.factor(same))) +
  facet_wrap(~ dimension, nrow = 1) +
  geom_bar(stat = "identity", position = "dodge", width = 0.7) +
  geom_errorbar(aes(ymin = LL, ymax = UL), position = position_dodge(width = 0.75), width=0.25) +
  labs (x = NULL, y = "Prop. maintained", fill = "Similarity", title = "(B) Differences between social roles", font = "Jost") +
   scale_y_continuous(breaks = seq(0, 1, .25), limits = c(0,1)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 33, hjust = 1), legend.position = "none")

(figure <- ggpubr::ggarrange(plot1, plot3, ncol=1, nrow=2) )

#ggsave(figure, filename = "./figures/desfig.png")

```

<br>

Confidants with a different gender than ego are **more**, rather than less stable!

Our post-hoc hypothesis is that this may be due to women generally providing more social support than men [@wellman], making them valuable confidants and thus less likely to be dissolved, resulting in a negative different-gender effect on confidant loss among men.

```{r,  class.source = 'fold-hide'}
#prop.table(table(data$different_gender)) #74% of ties are same-gender

#let's see how this differs across the ties, and further disaggregated by the genders:
tab <- matrix(c(
  mean(data$same_gender[data$tie == "Confidant" & data$ego_female == 1]),
  mean(data$same_gender[data$tie == "Confidant" & data$ego_female == 0]),
  mean(data$same_gender[data$tie == "Friend" &  data$ego_female == 1]),
  mean(data$same_gender[data$tie == "Friend" & data$ego_female == 0]),
  mean(data$same_gender[data$tie == "Study" & data$ego_female == 1]),
  mean(data$same_gender[data$tie == "Study" & data$ego_female == 0]),
  mean(data$same_gender[data$tie == "Sport" & data$ego_female == 1]),
  mean(data$same_gender[data$tie == "Sport" & data$ego_female == 0])), 
  nrow = 4, byrow = TRUE)

rownames(tab) <- c("Confidant", "Best friends", "Study partner", "Sports partner")
colnames(tab) <- c("Female", "Male")

knitr::kable(tab, digits=2, "html", caption="Proportion of same gender ties, across different tie types, disaggregated by gender") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) 

``` 

1. We observe that men tend to have more cross-gender confiding ties (73%) than do women (55%).

<br>

```{r, class.source='fold-hide', warning=FALSE, message=FALSE}
#subset relevant columns
plotdata <- data[,c("same_gender", "Y", "tie", "ego_female")]

#filter on confidants
plotdata <- plotdata[plotdata$tie == "Confidant",]

#calculate tie maintenance rate
means <- aggregate(Y ~ same_gender + ego_female, data = plotdata, FUN = "mean")
sd <- aggregate(Y ~ same_gender + ego_female, data = plotdata, FUN = "sd")
n <- aggregate(Y ~ same_gender + ego_female, data = plotdata, FUN = "sum")

#bind
means <- cbind(means, sd=sd$Y, n=n$Y)
means$ego_female <- ifelse(means$ego_female==1, "Women", "Men")

#calculate a confidence interval around this proportion,
#usingthe Clopper-Pearson Confidence Interval  
means$LL <- NA
means$UL <- NA

for (i in 1:nrow(means)) {
  cp.ci <- GenBinomApps::clopper.pearson.ci(
    k = round(means$Y[i] * means$n[i]),
    n = means$n[i],
    alpha = 0.05,
    CI="two.sided"
  )
  means$LL[i] <- cp.ci$Lower.limit
  means$UL[i] <- cp.ci$Upper.limit
}

ggplot(means, aes(x = same_gender, y = Y, fill = as.factor(same_gender))) +
  geom_bar(stat = "identity", position = "dodge", width = .75) +
  facet_wrap(~ego_female) +
  geom_errorbar(aes(ymin = LL, ymax = UL), position = position_dodge(width = 0.5), width=0.25) +
  labs (x = NULL, y = "Prop. maintained", fill = "Similarity", title = "Maintenance of same-gender vs. different gender confidants,\namong men vs. women" ,font = "Jost") +
    scale_y_continuous(breaks = seq(0, 1, .25), limits = c(0,1)) +
    theme(axis.text.x = element_blank())
``` 

2. But both men (although not significantly) and women maintain same-gender confidants more often than they maintain different-gender confidants...


----

<br>

## Homogeneity {.tabset .tabset-fade}

- EI index for dichotomous variables (same gender, same education)
- Average similarity score for continuous variable (age difference)

### all alters

```{r, class.source = 'fold-hide' }
tab <- matrix(nrow=3, ncol=3)
rownames(tab) <- c("Gender", 
                   "Education", 
                   "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period=="w2 -> w3"]))) {

  #get network observed at wave 1
  netw1 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w1 -> w2",]
  
  #get unique alters
  netw1u <- netw1[which(!duplicated(netw1$alterid)),]
  
  #EI index for gender and education
  simgender <- length(which(netw1u$same_gender == 1))
  difgender <- length(which(netw1u$different_gender == 1))
  simeduc <- length(which(netw1u$sim_educ == 1))
  difeduc <- length(which(netw1u$different_educ == 1))
  n_alter <- nrow(netw1u)
  
  #calculate EI
  eigender1[i] <- (simgender-difgender)/n_alter
  eieduc1[i] <- (simeduc-difeduc)/n_alter
  
  #and average similarity for age
  ego_age <- netw1u$ego_age[1] #ego age
  alters_age <- as.numeric(netw1u$alter_age) #alters' age
  min <- 16
  max <- 45
  rv <- max-min #range
  avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #now repeat for wave 2
   netw2 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w2 -> w3",]
   
  #get unique alters
  netw2u <- netw2[which(!duplicated(netw2$alterid)),]

  simgender <- length(which(netw2u$same_gender == 1))
  difgender <- length(which(netw2u$different_gender == 1))
  simeduc <- length(which(netw2u$sim_educ == 1))
  difeduc <- length(which(netw2u$different_educ == 1))
  n_alter <- nrow(netw2u)
  
  eigender2[i] <- (simgender-difgender)/n_alter
  eieduc2[i] <- (simeduc-difeduc)/n_alter
  
  alters_age <- as.numeric(netw2u$alter_age) #alters' age
  avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #difference
  eigenderd[i] <- eigender1[i] - eigender2[i]
  eieducd[i] <- eieduc1[i] - eieduc2[i]
  avsimaged[i] <- avsimage1[i] - avsimage2[i]
  
}


tab[1,1] <- paste0( format(round(mean(eigender1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender1, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,2] <- paste0( format(round(mean(eigender2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender2, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,3] <- paste0( format(round(mean(eigenderd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigenderd, na.rm=TRUE), 2), nsmall=2), ")")

tab[2,1] <- paste0( format(round(mean(eieduc1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc1, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,2] <- paste0( format(round(mean(eieduc2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc2, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,3] <- paste0( format(round(mean(eieducd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieducd, na.rm=TRUE), 2), nsmall=2), ")")

tab[3,1] <- paste0( format(round(mean(avsimage1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage1, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,2] <- paste0( format(round(mean(avsimage2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage2, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,3] <- paste0( format(round(mean(avsimaged, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimaged, na.rm=TRUE), 2), nsmall=2), ")")

fshowdf(tab, caption="Homogeneity in ego-networks of 281 students who participated in waves 1 and 2")
```


### confidants

```{r, class.source = 'fold-hide' }
tab <- matrix(nrow=3, ncol=3)
rownames(tab) <- c("Gender", 
                   "Education", 
                   "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period=="w2 -> w3"]))) {

  #get network observed at wave 1
  netw1 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w1 -> w2",]
  
  #get unique alters
  netw1u <- netw1[which(!duplicated(netw1$alterid)),]
  
  #filter on tie type
  netw1u <- netw1u[netw1u$tie == "Confidant",]
  
  #EI index for gender and education
  simgender <- length(which(netw1u$same_gender == 1))
  difgender <- length(which(netw1u$different_gender == 1))
  simeduc <- length(which(netw1u$sim_educ == 1))
  difeduc <- length(which(netw1u$different_educ == 1))
  n_alter <- nrow(netw1u)
  
  #calculate EI
  eigender1[i] <- (simgender-difgender)/n_alter
  eieduc1[i] <- (simeduc-difeduc)/n_alter
  
  #and average similarity for age
  ego_age <- netw1u$ego_age[1] #ego age
  alters_age <- as.numeric(netw1u$alter_age) #alters' age
  min <- 16
  max <- 45
  rv <- max-min #range
  avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #now repeat for wave 2
  netw2 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w2 -> w3",]
   
  #get unique alters
  netw2u <- netw2[which(!duplicated(netw2$alterid)),]
  
  #filter on tie type
  netw2u <- netw2u[netw2u$tie == "Confidant",]

  simgender <- length(which(netw2u$same_gender == 1))
  difgender <- length(which(netw2u$different_gender == 1))
  simeduc <- length(which(netw2u$sim_educ == 1))
  difeduc <- length(which(netw2u$different_educ == 1))
  n_alter <- nrow(netw2u)
  
  eigender2[i] <- (simgender-difgender)/n_alter
  eieduc2[i] <- (simeduc-difeduc)/n_alter
  
  alters_age <- as.numeric(netw2u$alter_age) #alters' age
  avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #difference
  eigenderd[i] <- eigender1[i] - eigender2[i]
  eieducd[i] <- eieduc1[i] - eieduc2[i]
  avsimaged[i] <- avsimage1[i] - avsimage2[i]
  
}

tab[1,1] <- paste0( format(round(mean(eigender1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender1, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,2] <- paste0( format(round(mean(eigender2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender2, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,3] <- paste0( format(round(mean(eigenderd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigenderd, na.rm=TRUE), 2), nsmall=2), ")")

tab[2,1] <- paste0( format(round(mean(eieduc1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc1, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,2] <- paste0( format(round(mean(eieduc2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc2, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,3] <- paste0( format(round(mean(eieducd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieducd, na.rm=TRUE), 2), nsmall=2), ")")

tab[3,1] <- paste0( format(round(mean(avsimage1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage1, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,2] <- paste0( format(round(mean(avsimage2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage2, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,3] <- paste0( format(round(mean(avsimaged, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimaged, na.rm=TRUE), 2), nsmall=2), ")")

fshowdf(tab, caption="Homogeneity in confiding network layer of 281 students who participated in waves 1 and 2")

```


### best friends

```{r, class.source = 'fold-hide' }
tab <- matrix(nrow=3, ncol=3)
rownames(tab) <- c("Gender", 
                   "Education", 
                   "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period=="w2 -> w3"]))) {

  #get network observed at wave 1
  netw1 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w1 -> w2",]
  
  #get unique alters
  netw1u <- netw1[which(!duplicated(netw1$alterid)),]
  
  #filter on tie type
  netw1u <- netw1u[netw1u$tie == "Friend",]
  
  #EI index for gender and education
  simgender <- length(which(netw1u$same_gender == 1))
  difgender <- length(which(netw1u$different_gender == 1))
  simeduc <- length(which(netw1u$sim_educ == 1))
  difeduc <- length(which(netw1u$different_educ == 1))
  n_alter <- nrow(netw1u)
  
  #calculate EI
  eigender1[i] <- (simgender-difgender)/n_alter
  eieduc1[i] <- (simeduc-difeduc)/n_alter
  
  #and average similarity for age
  ego_age <- netw1u$ego_age[1] #ego age
  alters_age <- as.numeric(netw1u$alter_age) #alters' age
  min <- 16
  max <- 45
  rv <- max-min #range
  avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #now repeat for wave 2
  netw2 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w2 -> w3",]
   
  #get unique alters
  netw2u <- netw2[which(!duplicated(netw2$alterid)),]
  
  #filter on tie type
  netw2u <- netw2u[netw2u$tie == "Friend",]

  simgender <- length(which(netw2u$same_gender == 1))
  difgender <- length(which(netw2u$different_gender == 1))
  simeduc <- length(which(netw2u$sim_educ == 1))
  difeduc <- length(which(netw2u$different_educ == 1))
  n_alter <- nrow(netw2u)
  
  eigender2[i] <- (simgender-difgender)/n_alter
  eieduc2[i] <- (simeduc-difeduc)/n_alter
  
  alters_age <- as.numeric(netw2u$alter_age) #alters' age
  avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #difference
  eigenderd[i] <- eigender1[i] - eigender2[i]
  eieducd[i] <- eieduc1[i] - eieduc2[i]
  avsimaged[i] <- avsimage1[i] - avsimage2[i]
  
}

tab[1,1] <- paste0( format(round(mean(eigender1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender1, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,2] <- paste0( format(round(mean(eigender2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender2, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,3] <- paste0( format(round(mean(eigenderd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigenderd, na.rm=TRUE), 2), nsmall=2), ")")

tab[2,1] <- paste0( format(round(mean(eieduc1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc1, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,2] <- paste0( format(round(mean(eieduc2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc2, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,3] <- paste0( format(round(mean(eieducd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieducd, na.rm=TRUE), 2), nsmall=2), ")")

tab[3,1] <- paste0( format(round(mean(avsimage1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage1, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,2] <- paste0( format(round(mean(avsimage2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage2, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,3] <- paste0( format(round(mean(avsimaged, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimaged, na.rm=TRUE), 2), nsmall=2), ")")

fshowdf(tab, caption="Homogeneity in friendship network layer of 281 students who participated in waves 1 and 2")

```

### sports partner

```{r, class.source = 'fold-hide' }
tab <- matrix(nrow=3, ncol=3)
rownames(tab) <- c("Gender", 
                   "Education", 
                   "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period=="w2 -> w3"]))) {

  #get network observed at wave 1
  netw1 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w1 -> w2",]
  
  #get unique alters
  netw1u <- netw1[which(!duplicated(netw1$alterid)),]
  
  #filter on tie type
  netw1u <- netw1u[netw1u$tie == "Sport",]
  
  #EI index for gender and education
  simgender <- length(which(netw1u$same_gender == 1))
  difgender <- length(which(netw1u$different_gender == 1))
  simeduc <- length(which(netw1u$sim_educ == 1))
  difeduc <- length(which(netw1u$different_educ == 1))
  n_alter <- nrow(netw1u)
  
  #calculate EI
  eigender1[i] <- (simgender-difgender)/n_alter
  eieduc1[i] <- (simeduc-difeduc)/n_alter
  
  #and average similarity for age
  ego_age <- netw1u$ego_age[1] #ego age
  alters_age <- as.numeric(netw1u$alter_age) #alters' age
  min <- 16
  max <- 45
  rv <- max-min #range
  avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #now repeat for wave 2
  netw2 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w2 -> w3",]
   
  #get unique alters
  netw2u <- netw2[which(!duplicated(netw2$alterid)),]
  
  #filter on tie type
  netw2u <- netw2u[netw2u$tie == "Sport",]

  simgender <- length(which(netw2u$same_gender == 1))
  difgender <- length(which(netw2u$different_gender == 1))
  simeduc <- length(which(netw2u$sim_educ == 1))
  difeduc <- length(which(netw2u$different_educ == 1))
  n_alter <- nrow(netw2u)
  
  eigender2[i] <- (simgender-difgender)/n_alter
  eieduc2[i] <- (simeduc-difeduc)/n_alter
  
  alters_age <- as.numeric(netw2u$alter_age) #alters' age
  avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #difference
  eigenderd[i] <- eigender1[i] - eigender2[i]
  eieducd[i] <- eieduc1[i] - eieduc2[i]
  avsimaged[i] <- avsimage1[i] - avsimage2[i]
  
}

tab[1,1] <- paste0( format(round(mean(eigender1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender1, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,2] <- paste0( format(round(mean(eigender2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender2, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,3] <- paste0( format(round(mean(eigenderd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigenderd, na.rm=TRUE), 2), nsmall=2), ")")

tab[2,1] <- paste0( format(round(mean(eieduc1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc1, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,2] <- paste0( format(round(mean(eieduc2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc2, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,3] <- paste0( format(round(mean(eieducd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieducd, na.rm=TRUE), 2), nsmall=2), ")")

tab[3,1] <- paste0( format(round(mean(avsimage1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage1, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,2] <- paste0( format(round(mean(avsimage2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage2, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,3] <- paste0( format(round(mean(avsimaged, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimaged, na.rm=TRUE), 2), nsmall=2), ")")

fshowdf(tab, caption="Homogeneity in sporting network layer of 281 students who participated in waves 1 and 2")
```


### study partners

```{r, class.source = 'fold-hide' }
tab <- matrix(nrow=3, ncol=3)
rownames(tab) <- c("Gender", 
                   "Education", 
                   "Age")
colnames(tab) <- c("Homogeneity W1", "Homogeneity W2", "Δ W1-W2")

eigender1 <- NA
eieduc1 <- NA
avsimage1 <- NA
eigender2 <- NA
eieduc2 <- NA
avsimage2 <- NA
eigenderd <- NA
eieducd <- NA
avsimaged <- NA

for (i in 1:length(unique(data$ego[data$period=="w2 -> w3"]))) {

  #get network observed at wave 1
  netw1 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w1 -> w2",]
  
  #get unique alters
  netw1u <- netw1[which(!duplicated(netw1$alterid)),]
  
  #filter on tie type
  netw1u <- netw1u[netw1u$tie == "Study",]
  
  #EI index for gender and education
  simgender <- length(which(netw1u$same_gender == 1))
  difgender <- length(which(netw1u$different_gender == 1))
  simeduc <- length(which(netw1u$sim_educ == 1))
  difeduc <- length(which(netw1u$different_educ == 1))
  n_alter <- nrow(netw1u)
  
  #calculate EI
  eigender1[i] <- (simgender-difgender)/n_alter
  eieduc1[i] <- (simeduc-difeduc)/n_alter
  
  #and average similarity for age
  ego_age <- netw1u$ego_age[1] #ego age
  alters_age <- as.numeric(netw1u$alter_age) #alters' age
  min <- 16
  max <- 45
  rv <- max-min #range
  avsimage1[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #now repeat for wave 2
  netw2 <- data[data$ego == unique(data$ego[data$period=="w2 -> w3"])[i] & data$period == "w2 -> w3",]
   
  #get unique alters
  netw2u <- netw2[which(!duplicated(netw2$alterid)),]
  
  #filter on tie type
  netw2u <- netw2u[netw2u$tie == "Study",]

  simgender <- length(which(netw2u$same_gender == 1))
  difgender <- length(which(netw2u$different_gender == 1))
  simeduc <- length(which(netw2u$sim_educ == 1))
  difeduc <- length(which(netw2u$different_educ == 1))
  n_alter <- nrow(netw2u)
  
  eigender2[i] <- (simgender-difgender)/n_alter
  eieduc2[i] <- (simeduc-difeduc)/n_alter
  
  alters_age <- as.numeric(netw2u$alter_age) #alters' age
  avsimage2[i] <- mean(1 - abs(alters_age - ego_age)/rv) #average similarity score
  
  #difference
  eigenderd[i] <- eigender1[i] - eigender2[i]
  eieducd[i] <- eieduc1[i] - eieduc2[i]
  avsimaged[i] <- avsimage1[i] - avsimage2[i]
  
}

tab[1,1] <- paste0( format(round(mean(eigender1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender1, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,2] <- paste0( format(round(mean(eigender2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigender2, na.rm=TRUE), 2), nsmall=2), ")")
tab[1,3] <- paste0( format(round(mean(eigenderd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eigenderd, na.rm=TRUE), 2), nsmall=2), ")")

tab[2,1] <- paste0( format(round(mean(eieduc1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc1, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,2] <- paste0( format(round(mean(eieduc2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieduc2, na.rm=TRUE), 2), nsmall=2), ")")
tab[2,3] <- paste0( format(round(mean(eieducd, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(eieducd, na.rm=TRUE), 2), nsmall=2), ")")

tab[3,1] <- paste0( format(round(mean(avsimage1, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage1, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,2] <- paste0( format(round(mean(avsimage2, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimage2, na.rm=TRUE), 2), nsmall=2), ")")
tab[3,3] <- paste0( format(round(mean(avsimaged, na.rm=TRUE), 2), nsmall =2), " (", format(round(sd(avsimaged, na.rm=TRUE), 2), nsmall=2), ")")

fshowdf(tab, caption="Homogeneity in study network layer of 281 students who participated in waves 1 and 2")

```



---



Descriptives

Last compiled on oktober, 2024