Word Clouds

Word Clouds

Preparation for Word Clouds

corr_table_all_oa<-table(rp_single_author_all_oa$AU)
corr_table_all_oa_sorted <- corr_table_all_oa%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

corr_table_green<-table(rp_single_author_green$AU)
corr_table_green_sorted <- corr_table_green%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

corr_table_green_final<-table(rp_single_author_green_final$AU)
corr_table_green_final_sorted <- corr_table_green_final%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

corr_table_green_accepted<-table(rp_single_author_green_accepted$AU)
corr_table_green_accepted_sorted <- corr_table_green_accepted%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

corr_table_not_green<-table(rp_single_author_not_green$AU)
corr_table_not_green_sorted <- corr_table_not_green%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

Separate tables for Open Access publications

SO_table_all_oa<-table(scopus_wos_all_oa$SO)
SO_table_all_oa_sorted <- SO_table_all_oa%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

SO_table_green<-table(scopus_wos_green$SO)
SO_table_green_sorted <- SO_table_green%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

SO_table_green_final<-table(scopus_wos_green_final$SO)
SO_table_green_final_sorted <- SO_table_green_final%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

SO_table_green_accepted<-table(scopus_wos_green_accepted$SO)
SO_table_green_accepted_sorted <- SO_table_green_accepted%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

SO_table_not_green<-table(scopus_wos_not_green$SO)
SO_table_not_green_sorted <- SO_table_not_green%>%                 
  as.data.frame() %>% 
  arrange(desc(Freq))

Publications

Yarrr package seems to be best for vibrant and differing colours!

All Open Access

set.seed(1404)

DF_SO_table_all_oa_sorted <- as.data.frame(SO_table_all_oa_sorted)
wordcloud_all_oa_publications<- wordcloud(words = DF_SO_table_all_oa_sorted$Var1, freq = DF_SO_table_all_oa_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.5,0.05), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

##To fit long publication names had to make the words pretty small

Green

DF_SO_table_green_sorted <- as.data.frame(SO_table_green_sorted)
wourdcloud_green_publications<- wordcloud(words = DF_SO_table_green_sorted$Var1, freq = DF_SO_table_green_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.3,0.05), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

##To fit long publication names had to make the words pretty small

Green Final

DF_SO_table_green_final_sorted <- as.data.frame(SO_table_green_final_sorted)
wourdcloud_green_final_publications<- wordcloud(words = DF_SO_table_green_final_sorted$Var1, freq = DF_SO_table_green_final_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(0.9,0.05), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

##To fit long publication names had to make the words pretty small

Green Accepted

DF_SO_table_green_accepted_sorted <- as.data.frame(SO_table_green_accepted_sorted)
wourdcloud_green_accepted_publications<- wordcloud(words = DF_SO_table_green_accepted_sorted$Var1, freq = DF_SO_table_green_accepted_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(0.8,0.05), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

##To fit long publication names had to make the words pretty small

Not Green

DF_SO_table_not_green_sorted <- as.data.frame(SO_table_not_green_sorted)
wourdcloud_not_green_publications<- wordcloud(words = DF_SO_table_not_green_sorted$Var1, freq = DF_SO_table_not_green_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(.25,0.25), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

##To fit long publication names had to make the words pretty small

Corresponding Authors

All Open Access

df_corr_table_all_oa_sorted <- as.data.frame(corr_table_all_oa_sorted)
wourdcloud_all_oa_publications<- wordcloud(words = df_corr_table_all_oa_sorted$Var1, freq = df_corr_table_all_oa_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1, 1), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Green

df_corr_table_green_sorted <- as.data.frame(corr_table_green_sorted)
wourdcloud_green_publications<- wordcloud(words = df_corr_table_green_sorted$Var1, freq = df_corr_table_green_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1, 1), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Green Final

df_corr_table_green_final_sorted <- as.data.frame(corr_table_green_final_sorted)
wourdcloud_green_final_publications<- wordcloud(words = df_corr_table_green_final_sorted$Var1, freq = df_corr_table_green_final_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1, 1), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Green Accepted

df_corr_table_green_accepted_sorted <- as.data.frame(corr_table_green_accepted_sorted)
wourdcloud_green_publications<- wordcloud(words = df_corr_table_green_accepted_sorted$Var1, freq = df_corr_table_green_accepted_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1, 1), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Not Green

df_corr_table_not_green_sorted <- as.data.frame(corr_table_not_green_sorted)
wourdcloud_not_green_publications<- wordcloud(words = df_corr_table_not_green_sorted$Var1, freq = df_corr_table_not_green_sorted$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1, 1), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Keywords

All Open Access

all_oa_keywords<- scopus_wos_all_oa %>% separate_rows(DE, sep = "; ") %>% select(DE) 
all_oa_keywords<- all_oa_keywords %>% 
  count(DE)
all_oa_keywords<- na.omit(all_oa_keywords)
colnames(all_oa_keywords)[2]<- "Freq"
wourdcloud_just_all_oa_keywords<- wordcloud(words = all_oa_keywords$DE, freq = all_oa_keywords$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.5,0.25), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Green

green_keywords<- scopus_wos_green %>% separate_rows(DE, sep = "; ") %>% select(DE) 
green_keywords<- green_keywords %>% 
  count(DE)
green_keywords<- na.omit(green_keywords)
colnames(green_keywords)[2]<- "Freq"
wourdcloud_just_green_keywords<- wordcloud(words = green_keywords$DE, freq = green_keywords$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.5,0.25), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Green Final

green_final_keywords<- scopus_wos_green_final %>% separate_rows(DE, sep = "; ") %>% select(DE) 
green_final_keywords<- green_final_keywords %>% 
  count(DE)
green_final_keywords<- na.omit(green_final_keywords)
colnames(green_final_keywords)[2]<- "Freq"
wourdcloud_just_green_final_keywords<- wordcloud(words = green_final_keywords$DE, freq = green_final_keywords$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.5,0.25), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Green Accepted

green_accepted_keywords<- scopus_wos_green_accepted %>% separate_rows(DE, sep = "; ") %>% select(DE) 
green_accepted_keywords<- green_accepted_keywords %>% 
  count(DE)
green_accepted_keywords<- na.omit(green_accepted_keywords)
colnames(green_accepted_keywords)[2]<- "Freq"
wourdcloud_just_green_accepted_keywords<- wordcloud(words = green_accepted_keywords$DE, freq = green_accepted_keywords$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.5,0.25), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10

Not Green

not_green_keywords<- scopus_wos_not_green %>% separate_rows(DE, sep = "; ") %>% select(DE) 
not_green_keywords<- not_green_keywords %>% 
  count(DE)
not_green_keywords<- na.omit(not_green_keywords)
colnames(not_green_keywords)[2]<- "Freq"
wourdcloud_just_not_green_keywords<- wordcloud(words = not_green_keywords$DE, freq = not_green_keywords$Freq, min.freq = 1, max.words=10, random.order=FALSE, scale=c(1.5,0.25), rot.per=0.35, colors = piratepal("xmen")) ##Just the TOP 10