src/dspg17/sphadke/04_ohloh_projects_analysis.R
In uva-bi-sdad/oss: Measuring the Cost and Value of Open Source Software
####################################
#### OpenHub projects: analysis ####
####################################
# This code runs profiling code and produces descriptives for project tables
# Assign the file of interest to "data" to have the code run without changing names
#### Created by: sphadke
#### Creted on: 07/06/2017
#### Last edited on: 07/07/2017
####################
#### Cleanup
####################
rm(list=ls())
gc()
set.seed(312)
####################
#### R Setup
####################
library(ggplot2)
library(gtools)
library(lubridate)
library(network)
library(reshape2)
library(scales)
library(SnowballC)
library(stringr)
library(tm)
library(wordcloud)
## Data import and rename
# random projects
load("~/git/oss/data/oss/working/openhub/randomProjects/all_random_projects_table.RData")
# relevant projects
load("~/git/oss/data/oss/working/openhub/relevantProjects/projectRelevantMaster.RData")
ran_data <- randomProjectTable
rel_data <- projectRelevantMaster
rm(randomProjectTable)
rm(projectRelevantMaster)
####################
#### Data quality check
#### And cleanup
####################
## Convert blanks and "NA"s to NAs
ran_data[ran_data == ""] <- NA
ran_data[ran_data == "\n "] <- NA
ran_data[ran_data == "NA"] <- NA
rel_data[rel_data == ""] <- NA
rel_data[rel_data == "\n "] <- NA
rel_data[rel_data == "NA"] <- NA
## Make relevant columns numeric
ran_data$user_count <- as.numeric(ran_data$user_count)
ran_data$average_rating <- as.numeric(ran_data$average_rating)
ran_data$rating_count <- as.numeric(ran_data$rating_count)
ran_data$review_count <- as.numeric(ran_data$review_count)
ran_data$twelve_month_contributor_count <- as.numeric(ran_data$twelve_month_contributor_count)
ran_data$total_contributor_count <- as.numeric(ran_data$total_contributor_count)
ran_data$twelve_month_commit_count <- as.numeric(ran_data$twelve_month_commit_count)
ran_data$total_commit_count <- as.numeric(ran_data$total_commit_count)
ran_data$total_code_lines <- as.numeric(ran_data$total_code_lines)
rel_data$user_count <- as.numeric(rel_data$user_count)
rel_data$average_rating <- as.numeric(rel_data$average_rating)
rel_data$rating_count <- as.numeric(rel_data$rating_count)
rel_data$review_count <- as.numeric(rel_data$review_count)
rel_data$twelve_month_contributor_count <- as.numeric(rel_data$twelve_month_contributor_count)
rel_data$total_contributor_count <- as.numeric(rel_data$total_contributor_count)
rel_data$twelve_month_commit_count <- as.numeric(rel_data$twelve_month_commit_count)
rel_data$total_commit_count <- as.numeric(rel_data$total_commit_count)
rel_data$total_code_lines <- as.numeric(rel_data$total_code_lines)
####
## Remove the fully empty rows
####
clean_ran_data <- ran_data[!apply(ran_data[2:33], 1, function(x){all(is.na(x))}), ]
rm(ran_data)
clean_rel_data <- rel_data[!apply(rel_data[2:33], 1, function(x){all(is.na(x))}), ]
rm(rel_data)
# ## Number of completely empty rows
# num_empty_rows <- length(data$licenses[apply(data[,2:ncol(data)], 1, function(x){all(is.na(x))}) == TRUE])
# rm(num_empty_rows)
## Check data quality
completeness <- matrix(NA, ncol(clean_data), 4)
for(i in 1:ncol(clean_data)){
x <- clean_data[,i]
completeness[i,1] <- colnames(clean_data)[i]
completeness[i,2] <- class(x)[1]
# Proportion of missing values
completeness[i,3] <- round(sum(is.na(x))*100/nrow(clean_data), digits = 2)
# How many unique values to the variable?
completeness[i,4] <- length(unique(x))
# summary <- summary(x)
# if(vals <= 10){
# tab <- table(x)
# print(tab)
# }
#print(c(name, class, miss, vals))
}
# View(completeness)
####
#### Plot of languages
####
# ran_language_freq <- as.data.frame(table(clean_ran_data$main_language))
# ran_lang_to_plot <- head(ran_language_freq[order(ran_language_freq$Freq, decreasing= T),], n = 10)
# ran_lang_to_plot <- clean_ran_data[(clean_ran_data$main_language %in% ran_lang_to_plot$Var1) == TRUE, 'main_language']
# ran_lang_to_plot <- as.data.frame(ran_lang_to_plot)
#
#
# random_lang <- ggplot(data = ran_lang_to_plot, aes(x = ran_lang_to_plot, fill = ran_lang_to_plot)) +
# geom_bar() +
# theme_minimal() +
# theme(legend.position = "none") +
# ggtitle("Ten main languages: Random projects") +
# labs(y = "Frequency", x = "") +
# theme(plot.title = element_text(hjust = 0.5)) +
# theme(text=element_text(size=20),
# axis.text=element_text(size=16),
# axis.title=element_text(size=20))
#
# png(filename = "./output/openhub/graphics/poster/random_main_lang.png",
# units = "in", width = 8, height = 15,
# res = 72, bg = "transparent")
# random_lang
# dev.off()
## Barplot of main languages
rel_language_freq <- as.data.frame(table(clean_rel_data$main_language))
rel_lang_to_plot <- head(rel_language_freq[order(rel_language_freq$Freq, decreasing= T),], n = 10)
rel_lang_to_plot <- clean_rel_data[(clean_rel_data$main_language %in% rel_lang_to_plot$Var1) == TRUE, 'main_language']
rel_lang_to_plot <- as.data.frame(rel_lang_to_plot)
rel_lang <- ggplot(data = rel_lang_to_plot, aes(x = rel_lang_to_plot, fill = rel_lang_to_plot)) +
geom_bar() +
scale_fill_brewer(palette = "Spectral") +
theme_minimal() +
theme(legend.position = "none") +
ggtitle("Ten main languages: Relevant projects") +
labs(y = "Frequency", x = "") +
theme(axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
title = element_text(size = 24),
axis.title = element_text(size = 20)) +
theme(plot.title = element_text(hjust = 0.5))
png(filename = "./output/openhub/graphics/poster/rel_main_lang.png",
units = "in", width = 8, height = 15,
res = 72, bg = "transparent")
rel_lang
dev.off()
# Separating out the factoid columns
clean_data$age <- NA
clean_data$team_size <- NA
clean_data$activity <- NA
clean_data$comments <- NA
for(i in 1:nrow(clean_data)){
if(length(grep("Age", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$age[i] <- unlist(str_split(grep("Age", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Age"))[2]
}
if(length(grep("Team", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$team_size[i] <- unlist(str_split(grep("Team", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Size"))[2]
}
if(length(grep("Activity", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$activity[i] <- unlist(str_split(grep("Activity", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Activity"))[2]
}
if(length(grep("Comments", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$comments[i] <- unlist(str_split(grep("Comments", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Comments"))[2]
}
print(i)
}
table(clean_data$age)
table(clean_data$team_size)
table(clean_data$activity)
table(clean_data$comments)
clean_data$age <- as.factor(clean_data$age)
clean_data$activity <- as.factor(clean_data$activity)
clean_data$team_size <- as.factor(clean_data$team_size)
clean_data$comments <- as.factor(clean_data$comments)
####
#### See if things are correlated
####
# cor(clean_data[, c('average_rating', 'user_count', 'rating_count', 'review_count', 'twelve_month_contributor_count', 'total_contributor_count', 'total_commit_count', 'total_code_lines', 'twelve_month_commit_count')])
####
#### Time plot of development
####
# ggplot(clean_data, aes(created_at, total_commit_count)) +
# geom_point() +
# scale_x_datetime(date_labels = "%Y-%b") #+ xlab("") + ylab("Daily Views")
#
# ggplot(clean_data, aes(created_at, total_contributor_count)) +
# geom_line() +
# scale_x_datetime(date_labels = "%Y-%b")
####
#### Wordclouds
####
## Main development language
language_corpus <- Corpus(VectorSource(clean_data$main_language))
language_dtm <- TermDocumentMatrix(language_corpus)
dps_m <- as.matrix(language_dtm)
dps_m <- sort(rowSums(dps_m),decreasing=TRUE)
dps_m <- data.frame(word = names(dps_m),freq = dps_m)
# png(filename="/wordcloud_dps.png",
# units="in",
# width=10,
# height=10,
# #pointsize=12,
# res=72
# )
ggplot(data = clean_data, aes(x = main_language)) +
geom_bar()
wordcloud(dps_m$word, dps_m$freq, min.freq = 1, random.order = FALSE, colors=brewer.pal(8, "Dark2"))
# dev.off()
## Top 3 languages
all_languages <- c(unlist(str_split(clean_data$languages, ";")))
all_languages <- all_languages[str_detect(all_languages, "other") == FALSE]
language_corpus <- Corpus(VectorSource(all_languages))
language_corpus <- tm_map(language_corpus, removeNumbers)
language_corpus <- tm_map(language_corpus, removeWords, c("other", "Other"))
language_dtm <- TermDocumentMatrix(language_corpus)
dps_m <- as.matrix(language_dtm)
dps_m <- sort(rowSums(dps_m),decreasing=TRUE)
dps_m <- data.frame(word = names(dps_m),freq = dps_m)
wordcloud(dps_m$word, dps_m$freq, min.freq = 1, random.order = FALSE, colors=brewer.pal(8, "Dark2"))
####
## Later
####
## Description
dps_chrg_corpus <- Corpus(VectorSource(clean_data$description))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, PlainTextDocument)
dps_chrg_corpus <- tm_map(dps_chrg_corpus, content_transformer(tolower))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removeWords, stopwords('english'))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removeWords, c("will","set","functions", "data", "package", "based", "can", "provides", "set", "used", "project", "using", "contains", "function"))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removeNumbers)
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removePunctuation)
dps_chrg_corpus <- tm_map(dps_chrg_corpus, stemDocument)
dps_temp_stemmed <- data.frame(text = sapply(dps_chrg_corpus, as.character), stringsAsFactors = FALSE)
oh_random_proj_desc_dtm <- TermDocumentMatrix(dps_chrg_corpus)
save(oh_random_proj_desc_dtm, file = "~/git/oss/data/oss/working/openhub/randomProjects/oh_random_proj_desc_dtm.RData")
dps_m <- as.matrix(dps_dtm)
dps_m <- sort(rowSums(dps_m),decreasing=TRUE)
dps_m <- data.frame(word = names(dps_m),freq = dps_m)
wordcloud(dps_m$word, dps_m$freq, min.freq = 20, random.order = FALSE, colors=brewer.pal(8, "Dark2"))
####
#### Network of tags
####
tags_df <- na.omit(clean_data$tags)
tag_edgelist <- matrix(ncol = 2)
for (i in 1:nrow(clean_data)){
num_tags <- length(unlist(str_split(tags_df[i], pattern = ";")))
if(num_tags > 1) {
val_tags <- unlist(str_split(tags_df[i], pattern = ";"))
combinations <- combinations(n = num_tags, r = 2, val_tags, repeats.allowed = FALSE, set = TRUE)
tag_edgelist <- rbind(tag_edgelist, combinations)
}
print(i)
}
tag_edgelist <- na.omit(tag_edgelist)
tag_net <- network(as.data.frame(tag_edgelist), directed = FALSE, matrix.type = "edgelist")
plot(tag_net, displaylabels = F,
#label = get.vertex.attribute(contact.net, "female"),
vertex.cex = 1)#, vertex.col = c("Blue", "Red"))
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####################################
#### OpenHub projects: analysis ####
####################################
# This code runs profiling code and produces descriptives for project tables
# Assign the file of interest to "data" to have the code run without changing names
#### Created by: sphadke
#### Creted on: 07/06/2017
#### Last edited on: 07/07/2017
####################
#### Cleanup
####################
rm(list=ls())
gc()
set.seed(312)
####################
#### R Setup
####################
library(ggplot2)
library(gtools)
library(lubridate)
library(network)
library(reshape2)
library(scales)
library(SnowballC)
library(stringr)
library(tm)
library(wordcloud)
## Data import and rename
# random projects
load("~/git/oss/data/oss/working/openhub/randomProjects/all_random_projects_table.RData")
# relevant projects
load("~/git/oss/data/oss/working/openhub/relevantProjects/projectRelevantMaster.RData")
ran_data <- randomProjectTable
rel_data <- projectRelevantMaster
rm(randomProjectTable)
rm(projectRelevantMaster)
####################
#### Data quality check
#### And cleanup
####################
## Convert blanks and "NA"s to NAs
ran_data[ran_data == ""] <- NA
ran_data[ran_data == "\n "] <- NA
ran_data[ran_data == "NA"] <- NA
rel_data[rel_data == ""] <- NA
rel_data[rel_data == "\n "] <- NA
rel_data[rel_data == "NA"] <- NA
## Make relevant columns numeric
ran_data$user_count <- as.numeric(ran_data$user_count)
ran_data$average_rating <- as.numeric(ran_data$average_rating)
ran_data$rating_count <- as.numeric(ran_data$rating_count)
ran_data$review_count <- as.numeric(ran_data$review_count)
ran_data$twelve_month_contributor_count <- as.numeric(ran_data$twelve_month_contributor_count)
ran_data$total_contributor_count <- as.numeric(ran_data$total_contributor_count)
ran_data$twelve_month_commit_count <- as.numeric(ran_data$twelve_month_commit_count)
ran_data$total_commit_count <- as.numeric(ran_data$total_commit_count)
ran_data$total_code_lines <- as.numeric(ran_data$total_code_lines)
rel_data$user_count <- as.numeric(rel_data$user_count)
rel_data$average_rating <- as.numeric(rel_data$average_rating)
rel_data$rating_count <- as.numeric(rel_data$rating_count)
rel_data$review_count <- as.numeric(rel_data$review_count)
rel_data$twelve_month_contributor_count <- as.numeric(rel_data$twelve_month_contributor_count)
rel_data$total_contributor_count <- as.numeric(rel_data$total_contributor_count)
rel_data$twelve_month_commit_count <- as.numeric(rel_data$twelve_month_commit_count)
rel_data$total_commit_count <- as.numeric(rel_data$total_commit_count)
rel_data$total_code_lines <- as.numeric(rel_data$total_code_lines)
####
## Remove the fully empty rows
####
clean_ran_data <- ran_data[!apply(ran_data[2:33], 1, function(x){all(is.na(x))}), ]
rm(ran_data)
clean_rel_data <- rel_data[!apply(rel_data[2:33], 1, function(x){all(is.na(x))}), ]
rm(rel_data)
# ## Number of completely empty rows
# num_empty_rows <- length(data$licenses[apply(data[,2:ncol(data)], 1, function(x){all(is.na(x))}) == TRUE])
# rm(num_empty_rows)
## Check data quality
completeness <- matrix(NA, ncol(clean_data), 4)
for(i in 1:ncol(clean_data)){
x <- clean_data[,i]
completeness[i,1] <- colnames(clean_data)[i]
completeness[i,2] <- class(x)[1]
# Proportion of missing values
completeness[i,3] <- round(sum(is.na(x))*100/nrow(clean_data), digits = 2)
# How many unique values to the variable?
completeness[i,4] <- length(unique(x))
# summary <- summary(x)
# if(vals <= 10){
# tab <- table(x)
# print(tab)
# }
#print(c(name, class, miss, vals))
}
# View(completeness)
####
#### Plot of languages
####
# ran_language_freq <- as.data.frame(table(clean_ran_data$main_language))
# ran_lang_to_plot <- head(ran_language_freq[order(ran_language_freq$Freq, decreasing= T),], n = 10)
# ran_lang_to_plot <- clean_ran_data[(clean_ran_data$main_language %in% ran_lang_to_plot$Var1) == TRUE, 'main_language']
# ran_lang_to_plot <- as.data.frame(ran_lang_to_plot)
#
#
# random_lang <- ggplot(data = ran_lang_to_plot, aes(x = ran_lang_to_plot, fill = ran_lang_to_plot)) +
# geom_bar() +
# theme_minimal() +
# theme(legend.position = "none") +
# ggtitle("Ten main languages: Random projects") +
# labs(y = "Frequency", x = "") +
# theme(plot.title = element_text(hjust = 0.5)) +
# theme(text=element_text(size=20),
# axis.text=element_text(size=16),
# axis.title=element_text(size=20))
#
# png(filename = "./output/openhub/graphics/poster/random_main_lang.png",
# units = "in", width = 8, height = 15,
# res = 72, bg = "transparent")
# random_lang
# dev.off()
## Barplot of main languages
rel_language_freq <- as.data.frame(table(clean_rel_data$main_language))
rel_lang_to_plot <- head(rel_language_freq[order(rel_language_freq$Freq, decreasing= T),], n = 10)
rel_lang_to_plot <- clean_rel_data[(clean_rel_data$main_language %in% rel_lang_to_plot$Var1) == TRUE, 'main_language']
rel_lang_to_plot <- as.data.frame(rel_lang_to_plot)
rel_lang <- ggplot(data = rel_lang_to_plot, aes(x = rel_lang_to_plot, fill = rel_lang_to_plot)) +
geom_bar() +
scale_fill_brewer(palette = "Spectral") +
theme_minimal() +
theme(legend.position = "none") +
ggtitle("Ten main languages: Relevant projects") +
labs(y = "Frequency", x = "") +
theme(axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
title = element_text(size = 24),
axis.title = element_text(size = 20)) +
theme(plot.title = element_text(hjust = 0.5))
png(filename = "./output/openhub/graphics/poster/rel_main_lang.png",
units = "in", width = 8, height = 15,
res = 72, bg = "transparent")
rel_lang
dev.off()
# Separating out the factoid columns
clean_data$age <- NA
clean_data$team_size <- NA
clean_data$activity <- NA
clean_data$comments <- NA
for(i in 1:nrow(clean_data)){
if(length(grep("Age", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$age[i] <- unlist(str_split(grep("Age", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Age"))[2]
}
if(length(grep("Team", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$team_size[i] <- unlist(str_split(grep("Team", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Size"))[2]
}
if(length(grep("Activity", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$activity[i] <- unlist(str_split(grep("Activity", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Activity"))[2]
}
if(length(grep("Comments", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE)) > 0){
clean_data$comments[i] <- unlist(str_split(grep("Comments", unlist(str_split(clean_data$factoids[i], ";")), value = TRUE), "Comments"))[2]
}
print(i)
}
table(clean_data$age)
table(clean_data$team_size)
table(clean_data$activity)
table(clean_data$comments)
clean_data$age <- as.factor(clean_data$age)
clean_data$activity <- as.factor(clean_data$activity)
clean_data$team_size <- as.factor(clean_data$team_size)
clean_data$comments <- as.factor(clean_data$comments)
####
#### See if things are correlated
####
# cor(clean_data[, c('average_rating', 'user_count', 'rating_count', 'review_count', 'twelve_month_contributor_count', 'total_contributor_count', 'total_commit_count', 'total_code_lines', 'twelve_month_commit_count')])
####
#### Time plot of development
####
# ggplot(clean_data, aes(created_at, total_commit_count)) +
# geom_point() +
# scale_x_datetime(date_labels = "%Y-%b") #+ xlab("") + ylab("Daily Views")
#
# ggplot(clean_data, aes(created_at, total_contributor_count)) +
# geom_line() +
# scale_x_datetime(date_labels = "%Y-%b")
####
#### Wordclouds
####
## Main development language
language_corpus <- Corpus(VectorSource(clean_data$main_language))
language_dtm <- TermDocumentMatrix(language_corpus)
dps_m <- as.matrix(language_dtm)
dps_m <- sort(rowSums(dps_m),decreasing=TRUE)
dps_m <- data.frame(word = names(dps_m),freq = dps_m)
# png(filename="/wordcloud_dps.png",
# units="in",
# width=10,
# height=10,
# #pointsize=12,
# res=72
# )
ggplot(data = clean_data, aes(x = main_language)) +
geom_bar()
wordcloud(dps_m$word, dps_m$freq, min.freq = 1, random.order = FALSE, colors=brewer.pal(8, "Dark2"))
# dev.off()
## Top 3 languages
all_languages <- c(unlist(str_split(clean_data$languages, ";")))
all_languages <- all_languages[str_detect(all_languages, "other") == FALSE]
language_corpus <- Corpus(VectorSource(all_languages))
language_corpus <- tm_map(language_corpus, removeNumbers)
language_corpus <- tm_map(language_corpus, removeWords, c("other", "Other"))
language_dtm <- TermDocumentMatrix(language_corpus)
dps_m <- as.matrix(language_dtm)
dps_m <- sort(rowSums(dps_m),decreasing=TRUE)
dps_m <- data.frame(word = names(dps_m),freq = dps_m)
wordcloud(dps_m$word, dps_m$freq, min.freq = 1, random.order = FALSE, colors=brewer.pal(8, "Dark2"))
####
## Later
####
## Description
dps_chrg_corpus <- Corpus(VectorSource(clean_data$description))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, PlainTextDocument)
dps_chrg_corpus <- tm_map(dps_chrg_corpus, content_transformer(tolower))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removeWords, stopwords('english'))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removeWords, c("will","set","functions", "data", "package", "based", "can", "provides", "set", "used", "project", "using", "contains", "function"))
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removeNumbers)
dps_chrg_corpus <- tm_map(dps_chrg_corpus, removePunctuation)
dps_chrg_corpus <- tm_map(dps_chrg_corpus, stemDocument)
dps_temp_stemmed <- data.frame(text = sapply(dps_chrg_corpus, as.character), stringsAsFactors = FALSE)
oh_random_proj_desc_dtm <- TermDocumentMatrix(dps_chrg_corpus)
save(oh_random_proj_desc_dtm, file = "~/git/oss/data/oss/working/openhub/randomProjects/oh_random_proj_desc_dtm.RData")
dps_m <- as.matrix(dps_dtm)
dps_m <- sort(rowSums(dps_m),decreasing=TRUE)
dps_m <- data.frame(word = names(dps_m),freq = dps_m)
wordcloud(dps_m$word, dps_m$freq, min.freq = 20, random.order = FALSE, colors=brewer.pal(8, "Dark2"))
####
#### Network of tags
####
tags_df <- na.omit(clean_data$tags)
tag_edgelist <- matrix(ncol = 2)
for (i in 1:nrow(clean_data)){
num_tags <- length(unlist(str_split(tags_df[i], pattern = ";")))
if(num_tags > 1) {
val_tags <- unlist(str_split(tags_df[i], pattern = ";"))
combinations <- combinations(n = num_tags, r = 2, val_tags, repeats.allowed = FALSE, set = TRUE)
tag_edgelist <- rbind(tag_edgelist, combinations)
}
print(i)
}
tag_edgelist <- na.omit(tag_edgelist)
tag_net <- network(as.data.frame(tag_edgelist), directed = FALSE, matrix.type = "edgelist")
plot(tag_net, displaylabels = F,
#label = get.vertex.attribute(contact.net, "female"),
vertex.cex = 1)#, vertex.col = c("Blue", "Red"))
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