View(RL_stacked_mapped)
library(readxl)
RL_stacked_unmapped <- read_excel("Documents/00 University of Bath/Year 2/Role_Evo/Paper 1 - cluster and classication/Data/ALL_CLASSIFIED/Aug_analysis/RL_stacked_unmapped.xlsx")
View(RL_stacked_unmapped)
#stacked percentage line/area graph for RL over time
#the RL tables just re order the role names
#and the time names to make plot clearer
RL_table2 = table(RL_stacked_mapped$Role)
RL_levels2 = names(RL_table2)[order(RL_table2)]
RL_stacked_mapped$Role = factor(RL_stacked_mapped$Role, levels = c("Leader", "Collaborator", "Contributor"))
RL_table4 = table(RL_stacked_mapped$Time)
RL_levels4 = names(RL_table4)[order(RL_table4)]
RL_stacked_mapped$Time = factor(RL_stacked_mapped$Time, levels = c("-24 months to -18 months", "-18 months to -12 months", "-12 months to -6 months", "-6 months to Data Collection"))
#stacked plot
s1 = ggplot(RL_stacked_mapped, aes(x=RL_stacked_mapped$Time, y=RL_stacked_mapped$Freq, group=RL_stacked_mapped$Role, fill=RL_stacked_mapped$Role)) +
geom_area(position="fill") + scale_fill_brewer(palette="Purples") +  scale_y_continuous(labels = scales::percent) +
theme_tufte(base_size=15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + guides(fill=guide_legend(title=NULL)) +
ylab("") + xlab("")
s1
#reordering variables for unmapped
RL_table3 = table(RL_stacked_unmapped$Role)
RL_levels3 = names(RL_table3)[order(RL_table3)]
RL_stacked_unmapped$Role = factor(RL_stacked_unmapped$Role, levels = c("Elite", "Popular", "Supporter", "Information Provider", "Conversationalist", "Low Volume Supporter", "Taciturn", "Newbie/Questioner"))
RL_table5 = table(RL_stacked_unmapped$Time)
RL_levels5 = names(RL_table5)[order(RL_table5)]
RL_stacked_unmapped$Time = factor(RL_stacked_unmapped$Time, levels = c("-24 months to -18 months", "-18 months to -12 months", "-12 months to -6 months", "-6 months to Data Collection"))
#stacked plot
s2 = ggplot(RL_stacked_unmapped, aes(x=RL_stacked_unmapped$Time, y=RL_stacked_unmapped$Freq, group=RL_stacked_unmapped$Role, fill=RL_stacked_unmapped$Role)) +
geom_area(position="fill") +  scale_fill_brewer(palette="Greens") +  scale_y_continuous(labels = scales::percent) +
theme_tufte(base_size=15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + guides(fill=guide_legend(title=NULL)) +
ylab("") + xlab("")
plot_grid(s1, s2, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "blue") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "purple") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "purple") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "green") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "orange") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "steelblue") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "orange") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", colour = "red", fill = "steelblue") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", colour = "red", fill = "yellow") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "yellow") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "blue") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#reordered variable roles
RL_table = table(RL_60_clus_count_mapped$Role)
RL_levels = names(RL_table)[order(RL_table)]
RL_60_clus_count_mapped$Role = factor(RL_60_clus_count_mapped$Role, levels = c("Contributor", "Collaborator", "Leader"))
p5 = ggplot(RL_60_clus_count_mapped, aes(x = RL_60_clus_count_mapped$Role, y = RL_60_clus_count_mapped$Frequency)) +
geom_bar(stat = "identity", fill = "blue") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
IA_table = table(IA_counts_mapped$Role)
IA_levels = names(IA_table)[order(IA_table)]
IA_counts_mapped$Role = factor(IA_counts_mapped$Role, levels = c("Contributor", "Collaborator", "Leader"))
p6 =ggplot(IA_counts_mapped, aes(x = IA_counts_mapped$Role, y = IA_counts_mapped$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p5, p6, labels = c('A', 'B'))
p6
p6 =ggplot(IA_counts_mapped, aes(x = IA_counts_mapped$Role, y = IA_counts_mapped$Frequency)) +
geom_bar(stat = "identity", fill = "steelblue") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p5, p6, labels = c('A', 'B'))
p6 =ggplot(IA_counts_mapped, aes(x = IA_counts_mapped$Role, y = IA_counts_mapped$Frequency)) +
geom_bar(stat = "identity", fill = "red") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p5, p6, labels = c('A', 'B'))
p5 = ggplot(RL_60_clus_count_mapped, aes(x = RL_60_clus_count_mapped$Role, y = RL_60_clus_count_mapped$Frequency)) +
geom_bar(stat = "identity", fill = "blue2") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p6 =ggplot(IA_counts_mapped, aes(x = IA_counts_mapped$Role, y = IA_counts_mapped$Frequency)) +
geom_bar(stat = "identity", fill = "firebrick2") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p5, p6, labels = c('A', 'B'))
#bar plots for cluster frequencies for RL and IA
#go into aug analysis folder for RL stuff, and then the IA folder in there for IA data
p3 = ggplot(RL_60_clus_count, aes(x = RL_60_clus_count$Cluster, y = RL_60_clus_count$Frequency)) +
geom_bar(stat = "identity", fill = "blue2") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("Frequency")
p4 = ggplot(IA_counts, aes(x = IA_counts$Cluster, y = IA_counts$Frequency)) +
geom_bar(stat = "identity", fill = "firebrick2") +
theme_tufte(base_size = 15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) +
xlab("") +
ylab("")
plot_grid(p3, p4, labels = c('A', 'B'))
#stacked plot
s1 = ggplot(RL_stacked_mapped, aes(x=RL_stacked_mapped$Time, y=RL_stacked_mapped$Freq, group=RL_stacked_mapped$Role, fill=RL_stacked_mapped$Role)) +
geom_area(position="fill") + scale_fill_brewer(palette="PuRd") +  scale_y_continuous(labels = scales::percent) +
theme_tufte(base_size=15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + guides(fill=guide_legend(title=NULL)) +
ylab("") + xlab("")
#stacked plot
s2 = ggplot(RL_stacked_unmapped, aes(x=RL_stacked_unmapped$Time, y=RL_stacked_unmapped$Freq, group=RL_stacked_unmapped$Role, fill=RL_stacked_unmapped$Role)) +
geom_area(position="fill") +  scale_fill_brewer(palette="GnBu") +  scale_y_continuous(labels = scales::percent) +
theme_tufte(base_size=15) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + guides(fill=guide_legend(title=NULL)) +
ylab("") + xlab("")
plot_grid(s1, s2, labels = c('A', 'B'))
library(readr)
install.packages("digest")
library(digest)
setwd("~/Desktop/Data_roles_RL")
library(readr)
RRL_6_0_clustered_arff <- read_csv("RRL_6_0_clustered.arff.csv")
View(RRL_6_0_clustered_arff)
library(plyr)
library(dplyr)
communityA_6_0 = select(RRL_6_0_clustered_arff,
!"Wekacluster",
!"Cluster")
communityA_6_0 = select(RRL_6_0_clustered_arff,
-"Wekacluster",
-"Cluster")
communityA_6_0 = select(RRL_6_0_clustered_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercURLs",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
communityA_6_0 = select(RRL_6_0_clustered_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
#onto hashing scripts
communityA_6_0 %>%
mutate(digest(id, algo = "md5", serialize = F))
View(communityA_6_0)
#removing clustered varaibles
communityA_6_0 = select(RRL_6_0_clustered_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
#onto hashing scripts
digest(communityA_6_0$id, algo = "md5", serialize = TRUE)
View(communityA_6_0)
#removing clustered varaibles
communityA_6_0 = select(RRL_6_0_clustered_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
as.factor(communityA_6_0$id)
is.factor(communityA_6_0$id)
md5("foo")
#onto hashing scripts
install.packages("libcrypto")
#onto hashing scripts
communityA_6_0 %>%
rowwise() %>%
do(data.frame(., hash = digest(.)))
View(communityA_6_0)
#removing clustered varaibles
communityA_6_0 = select(RRL_6_0_clustered_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
View(communityA_6_0)
#onto hashing scripts
communityA_6_0$id = sapply(communityA_6_0$id, digest)
View(communityA_6_0)
write.csv(communityA_6_0, file = "CommunityA_6_0")
write.csv(communityA_6_0, file = "CommunityA_6_0.csv")
library(readr)
rl12_6_arff <- read_csv("rl12_6.arff.csv")
View(rl12_6_arff)
communityA_12_6 = select(rl12_6_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
communityA_12_6$id = sapply(communityA_12_6$id, digest)
View(communityA_12_6)
write.csv(communityA_6_0, file = "CommunityA_12_6.csv")
library(readr)
rl18_12_WITH_IDS <- read_csv("rl18_12_WITH_IDS.csv")
View(rl18_12_WITH_IDS)
library(stringr)
rl18_12 = rl18_12_WITH_IDS %>% as_tibble() %>% mutate(
"id" = str_sub(rl18_12_WITH_IDS$ID, 1, str_length(rl18_12_WITH_IDS$ID)-1)
)
View(rl18_12)
#onto hashing scripts
communityA_6_0$id = sapply(communityA_6_0$id, digest)
rl18_12 = rl18_12_WITH_IDS %>% as_tibble() %>% mutate(
"id" = str_sub(rl18_12_WITH_IDS$ID, 1, str_length(rl18_12_WITH_IDS$ID)-1)
)
View(rl18_12)
View(rl18_12_WITH_IDS)
library(stringr)
library(dplyr)
rl18_12 = rl18_12_WITH_IDS %>% as_tibble() %>% mutate(
"id" = str_sub(rl18_12_WITH_IDS$ID, 1, str_length(rl18_12_WITH_IDS$ID)-1)
)
View(rl18_12)
library(readr)
rl18_12_WITH_IDS <- read_csv("rl18_12_WITH_IDS.csv")
View(rl18_12_WITH_IDS)
View(rl18_12_WITH_IDS)
rl18_12 = rl18_12_WITH_IDS %>% as_tibble() %>% mutate(
"ID" = str_sub(rl18_12_WITH_IDS$ID, 1, str_length(rl18_12_WITH_IDS$ID)-1)
)
View(rl18_12)
communityA_18_12 = select(rl18_12,
"ID",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
View(communityA_18_12)
communityA_18_12$id = sapply(communityA_18_12$id, digest)
communityA_18_12$ID = sapply(communityA_18_12$ID, digest)
View(communityA_18_12)
write.csv(communityA_18_12, file = "CommunityA_18_12")
write.csv(communityA_18_12, file = "CommunityA_18_12.csv")
library(readr)
rl24_18_arff <- read_csv("rl24_18.arff.csv")
View(rl24_18_arff)
communityA_24_18 = select(rl24_18_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
communityA_24_18$id = sapply(communityA_24_18$id, digest)
View(communityA_18_12)
write.csv(communityA_24_18, file = "CommunityA_24_18")
View(communityA_24_18)
write.csv(communityA_24_18, file = "CommunityA_24_18.csv")
library(readr)
IIA_6_0 <- read_csv("IIA_6_0.csv")
View(IIA_6_0)
communityB = select(IIA_6_0,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
communityB$id = sapply(communityB$id, digest)
View(IIA_6_0)
library(readr)
IIA_6_0 <- read_csv("IIA_6_0.csv")
View(IIA_6_0)
communityB = select(IIA_6_0,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
communityB$id = sapply(communityB$id, digest)
View(communityB)
write.csv(communityB, file = "communityB.csv")
library(readr)
CommunityA_6_0 <- read_csv("CommunityA_6_0.csv")
View(CommunityA_6_0)
library(readr)
CommunityA_12_6 <- read_csv("CommunityA_12_6.csv")
View(CommunityA_12_6)
test = merge.data.frame(CommunityA_6_0, CommunityA_12_6, by = c("id"))
View(test)
library(readr)
rl12_6_arff <- read_csv("orig/rl12_6.arff.csv")
View(rl12_6_arff)
communityA_12_6 = select(rl12_6_arff,
"id",
"InDegree",
"OutDegree",
"TotalPosts",
"MeanWC",
"ThankRate",
"PercQues",
"PercUrls",
"MeanPostsPerThread",
"InitiationRatio",
"MeanPostsPerSubForum",
"PercBiNeighbours")
communityA_12_6$id = sapply(communityA_12_6$id, digest)
write.csv(communityA_12_6, file = "CommunityA_12_6.csv")
write.csv(communityA_12_6, file = "CommunityAA_12_6.csv")
library(readr)
CommunityAA_12_6 <- read_csv("CommunityAA_12_6.csv")
View(CommunityAA_12_6)
library(readr)
CommunityA_12_6 <- read_csv("CommunityA_12_6.csv")
View(CommunityA_12_6)
library(readr)
dataset <- read_csv(NULL)
View(dataset)
library(readr)
RL_6_0 <- read_csv("~/Desktop/Role_Evo_Data/USE THIS ONLY FOR DATA with_ID/Updated_InOut Deg/NO_DUPS/Final_Datasets/RL_6_0.csv")
View(RL_6_0)
library(digest)
library(plyr)
library(dplyr)
#imported each dataset and then do this:
RL_6_0$id = sapply(RL_6_0$id, digest)
View(RL_6_0)
setwd("~/Desktop/Role_Evo_Data/USE THIS ONLY FOR DATA with_ID/Updated_InOut Deg/NO_DUPS/Final_Datasets/Data_Shared")
write.csv(RL_6_0, file = "CommunityA_6_0.csv")
library(readr)
RL_12_6 <- read_csv("~/Desktop/Role_Evo_Data/USE THIS ONLY FOR DATA with_ID/Updated_InOut Deg/NO_DUPS/Final_Datasets/RL_12_6.csv")
View(RL_12_6)
RL_12_6$id = sapply(RL_12_6$id, digest)
View(RL_12_6)
write.csv(RL_12_6, file = "CommunityA_12_6.csv")
library(readr)
dataset <- read_csv(NULL)
View(dataset)
library(readr)
RL_18_12 <- read_csv("~/Desktop/Role_Evo_Data/USE THIS ONLY FOR DATA with_ID/Updated_InOut Deg/NO_DUPS/Final_Datasets/RL_18_12.csv")
View(RL_18_12)
RL_18_12$ID = sapply(RL_18_12$ID, digest)
View(RL_18_12)
write.csv(RL_18_12, file = "CommunityA_18_12.csv")
library(readr)
dataset <- read_csv(NULL)
View(dataset)
library(readr)
RL_24_18 <- read_csv("~/Desktop/Role_Evo_Data/USE THIS ONLY FOR DATA with_ID/Updated_InOut Deg/NO_DUPS/Final_Datasets/RL_24_18.csv")
View(RL_24_18)
RL_24_18$id = sapply(RL_24_18$id, digest)
View(RL_24_18)
write.csv(RL_24_18, file = "CommunityA_24_18.csv")
please = merge(RL_24_18, RL_6_0, by = "id")
View(please)
