├── .gitignore ├── Producción_sub_areas.Rmd ├── README.md ├── ToS_R.Rmd ├── ToS_old_algorithm_DS.R ├── f8417957-a805-489d-96e1-fc63ed9036e1.txt ├── functions.R ├── merging_wos_scopus.R ├── network_metrics.R ├── production_sub_areas.R ├── readFiles.R ├── scopus_graph.R ├── scopus_notebook.Rmd ├── scripts ├── scopus.R └── scopus_functions.R ├── sub_areas.R ├── tos.R ├── tos_scopus.R └── tos_wos.R /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | .Rapp.history 4 | 5 | # Session Data files 6 | .RData 7 | 8 | # Example code in package build process 9 | *-Ex.R 10 | 11 | # Output files from R CMD build 12 | /*.tar.gz 13 | 14 | # Output files from R CMD check 15 | /*.Rcheck/ 16 | 17 | # RStudio files 18 | .Rproj.user/ 19 | 20 | # produced vignettes 21 | vignettes/*.html 22 | vignettes/*.pdf 23 | 24 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 25 | .httr-oauth 26 | 27 | # knitr and R markdown default cache directories 28 | /*_cache/ 29 | /cache/ 30 | 31 | # Temporary files created by R markdown 32 | *.utf8.md 33 | *.knit.md 34 | 35 | # Shiny token, see https://shiny.rstudio.com/articles/shinyapps.html 36 | rsconnect/ 37 | 38 | # rcloud files 39 | *.Rproj 40 | 41 | *.txt 42 | *.csv -------------------------------------------------------------------------------- /Producción_sub_areas.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Cluster" 3 | output: html_document 4 | --- 5 | ```{r} 6 | library(igraph) 7 | library(tidyverse) 8 | library(tidyr) 9 | library(bibliometrix) 10 | library(ggplot2) 11 | ``` 12 | 13 | Importando grafo. 14 | ```{r} 15 | graph <- read_graph("data/graph_tos_scopus (1).graphml", format = "graphml") 16 | ``` 17 | 18 | Cargando subarea (comunidades) a grafo. 19 | ```{r} 20 | sub_areas <- cluster_louvain(as.undirected(graph), weights = NULL) 21 | graph_1 <- 22 | graph %>% 23 | set_vertex_attr(name = "sub_area", 24 | value = membership(sub_areas)) 25 | ``` 26 | 27 | Recolectando datos (fecha y longitud de comunidades). 28 | ```{r} 29 | df_graph <- 30 | data.frame(vertices = V(graph_1)$name, 31 | sub_area = V(graph_1)$sub_area, 32 | stringsAsFactors = TRUE) %>% 33 | arrange(desc(sub_area)) 34 | df_graph$year <- str_extract(df_graph$vertices, "[0-9]+") 35 | df_graph <- na.omit(df_graph) 36 | 37 | sizes_sub_areas <- 38 | data.frame(sizes(sub_areas)) %>% 39 | arrange(desc(Freq)) 40 | ``` 41 | 42 | Producción por años. 43 | ```{r} 44 | all_data <- 45 | data.frame(Year = c(min(df_graph$year):max(df_graph$year)), 46 | stringsAsFactors = FALSE) 47 | names_all_data <- list("Year") 48 | 49 | for (i in 1:length(unique(df_graph$sub_area))){ 50 | frecuency_sub_area <- 51 | df_graph %>% 52 | filter(sub_area == i) %>% 53 | count(year) 54 | names(frecuency_sub_area) <- c("Año", "Frecuencia") 55 | frecuency_sub_area$comunidades <- paste("sub_area", as.character(i), sep = "") 56 | 57 | frec_sub_area <- ifelse(all_data$Year %in% frecuency_sub_area$Año, 58 | frecuency_sub_area$Frecuencia, 0) 59 | all_data <- cbind(all_data, frec_sub_area) 60 | 61 | names_all_data <- append(names_all_data, 62 | paste("production_sub_area", as.character(i), sep = "")) 63 | } 64 | names(all_data) <- names_all_data 65 | ``` 66 | 67 | Graficas de los clusters principales. 68 | ```{r} 69 | para_graficar <- all_data %>% 70 | select(Year, production_sub_area12, production_sub_area10, 71 | production_sub_area9, production_sub_area4) %>% 72 | gather(key="sub_areas", value="frecuencia", 2:5) 73 | 74 | ggplot(para_graficar, aes(x=Year, y=frecuencia, color=sub_areas)) + 75 | xlim(c(1940, 2020)) + 76 | geom_point(size=0.6) + 77 | geom_line() + 78 | labs(title = "Producción sub_areas", 79 | x = "Years", 80 | y = "Frecuencia") 81 | ``` 82 | 83 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # r-tos 2 | 3 | This repository contains enough code to create a Tree of Science. You just need to create an ISI Web of Knowledge seed, create a main.R file and run by using R. If you use RStudio Cloud, please follow [this video](https://www.youtube.com/watch?v=cdrhWCLPypU). 4 | 5 | ## Steps 6 | 7 | 1. Create an ISI file. You could follow [this video](https://www.youtube.com/watch?v=EDjzs7xkzH4) for that. 8 | 9 | 2. Create a `main.R` file with the following code: 10 | 11 | ```r 12 | source('tos.R') 13 | filename <- "savedrecs.txt" 14 | tos_tree <- tos(filename) 15 | write.csv(tos_tree, file = "output.csv") 16 | ``` 17 | 18 | where **savedrecs.txt** is ISI filename. You could change if you have several ISI files. 19 | 20 | 3. Run the `main.R` file. If you use Rscript, run this command in the a terminal inside that folder: 21 | 22 | ```bash 23 | Rscript main.R 24 | ``` 25 | You should get something like this: 26 | 27 | ``` 28 | Converting your isi collection into a bibliographic dataframe 29 | 30 | Articles extracted 15 31 | Done! 32 | ``` 33 | -------------------------------------------------------------------------------- /ToS_R.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "ToS_R" 3 | output: html_notebook 4 | --- 5 | 6 | En esta parte se cargan las librerias (No modifique nada. Solo corra el código) 7 | 8 | ```{r} 9 | library(stringr) 10 | library(stringdist) 11 | library(bibliometrix) 12 | library(igraph) 13 | library(bibliometrix) 14 | library(tidyverse) 15 | library(roadoi) # titles 16 | library(fulltext) # Abstract 17 | library(tm) 18 | library(SnowballC) 19 | library(wordcloud) 20 | library(cluster) 21 | library(tidyverse) 22 | ``` 23 | 24 | En esta parte se cargan los datos, se procesan y visualizan 25 | OJO. Aqui si modifique el nombre de su archivo txt donde dice: "su_archivo.txt" 26 | 27 | ```{r} 28 | source("ToS_old_algorithm_DS.R") 29 | arbol <- tos_wos(c("su_archivo.txt")) 30 | ``` 31 | 32 | Para visualizar y guardar el archivo del árbol. 33 | OJO. Modifique donde dice su_arbol y ponga un nombre a su arbol 34 | 35 | ```{r} 36 | View(arbol$tos) 37 | write.csv(arbol$tos, file = "su_arbol.csv", row.names = F) 38 | ``` 39 | 40 | Para guardar el archivo de la red 41 | OJO. Modifique donde dice su_red y ponga un nombre a su red 42 | ```{r} 43 | write.graph(arbol$graph, file = "su_red.graphml", format = "graphml") 44 | ``` 45 | 46 | Finalmente, si esta herramienta le fue de utilidad le agradecemos citar los siguientes artículos. 47 | Con esto promueve la investigación y dice mucho de su etica como investigador: 48 | 49 | Zuluaga Martha, Robledo Sebastian, Osorio-Zuluaga German A, Yathe Laura, Gonzalez Diana, Taborda Gonzalo. Metabolomics and pesticides: systematic literature review using graph theory for analysis of references. Nova [Internet]. 2016 June; 14( 25 ): 121-138. Available from: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S1794-24702016000100010&lng=en. 50 | 51 | Landinez, D., Robledo, S., & Montoya, D. (2019). Executive function performance in patients with obesity: A systematic review. Psychologia, 13(2), 121–134. 52 | 53 | Buitrago, S., Duque, P. L., & Robledo, S. (2020). Branding Corporativo: una revisión bibliográfica. ECONÓMICAS CUC, 41(1). 54 | 55 | # Perspectives from Gephi 56 | 57 | Cargando los datos 58 | 59 | ```{r} 60 | df_clusters_1 <- 61 | read_csv("data/Cluster 1_Ramirez.csv") %>% 62 | select(v_name) %>% 63 | rename(ID_WOS = "v_name" ) 64 | 65 | df_clusters_2 <- 66 | read_csv("data/Cluster_2_Toro.csv") %>% 67 | select(v_name) %>% 68 | rename(ID_WOS = "v_name" ) 69 | 70 | df_clusters_3 <- 71 | read_csv("data/Cluster_3_Toro.csv") %>% 72 | select(v_name)%>% 73 | rename(ID_WOS = "v_name") 74 | ``` 75 | 76 | Registra el correo 77 | 78 | ```{r} 79 | email <- "myemail@myu.com" 80 | ``` 81 | 82 | ### Cluster 1 83 | 84 | ```{r} 85 | raw_data_1 <- 86 | df_clusters_1 %>% 87 | rename(id = "ID_WOS") %>% 88 | mutate(id = str_to_lower(id)) %>% 89 | dplyr::filter(grepl(".*doi", id)) 90 | 91 | raw_data_1$doi <- sub(".*doi", "", raw_data_1$id) 92 | 93 | 94 | raw_data_1 <- 95 | raw_data_1 %>% 96 | mutate(doi = str_trim(doi)) 97 | 98 | df <- data.frame(titulo = as.character(), 99 | stringsAsFactors = FALSE) 100 | for (i in raw_data_1$doi) { 101 | row = try(oadoi_fetch(dois = i, 102 | email = email), 103 | TRUE) 104 | if(isTRUE(class(row)=="try-error")) {next} else { 105 | df_new = data.frame(titulo = row$title, 106 | stringsAsFactors = FALSE) 107 | } 108 | 109 | df = rbind(df_new, df) 110 | } 111 | jeopCorpus <- Corpus(VectorSource(df$titulo %>% na.omit())) 112 | 113 | paperCorp <- jeopCorpus 114 | paperCorp <- tm_map(paperCorp, removePunctuation) 115 | paperCorp <- tm_map(paperCorp, removeNumbers) 116 | # added tolower 117 | paperCorp <- tm_map(paperCorp, content_transformer(tolower)) 118 | paperCorp <- tm_map(paperCorp, removeWords, stopwords("english")) 119 | # moved stripWhitespace 120 | 121 | paperCorp <- tm_map(paperCorp, stripWhitespace) 122 | paperCorp <- tm_map(paperCorp, stemDocument) 123 | 124 | paperCorp_1 <- tm_map(paperCorp, removeWords, c("viral", 125 | "market")) 126 | 127 | nube1 <- wordcloud(paperCorp_1, min.freq = 1, 128 | max.words=50, random.order=FALSE, rot.per=0.35, 129 | colors=brewer.pal(8, "Dark2")) 130 | ``` 131 | 132 | ## Cluster 2 133 | 134 | ```{r} 135 | raw_data_2 <- 136 | df_clusters_2 %>% 137 | rename(id = "ID_WOS") %>% 138 | mutate(id = str_to_lower(id)) 139 | 140 | raw_data_1_2 <- 141 | raw_data_2 %>% 142 | dplyr::filter(grepl(".*doi", id)) 143 | 144 | raw_data_1_2$doi <- sub(".*doi", "", raw_data_1_2$id) 145 | 146 | 147 | raw_data_1_2 <- 148 | raw_data_1_2 %>% 149 | mutate(doi = str_trim(doi)) 150 | 151 | df_2 <- data.frame(titulo = as.character(), 152 | stringsAsFactors = FALSE) 153 | for (i in raw_data_1_2$doi) { 154 | row = try(oadoi_fetch(dois = i, 155 | email = email), 156 | TRUE) 157 | if(isTRUE(class(row)=="try-error")) {next} else { 158 | df_new = data.frame(titulo = row$title, 159 | stringsAsFactors = FALSE) 160 | } 161 | 162 | df_2 = rbind(df_new, df_2) 163 | } 164 | jeopCorpus_2 <- Corpus(VectorSource(df_2$titulo %>% na.omit())) 165 | 166 | paperCorp_2 <- jeopCorpus_2 167 | paperCorp_2 <- tm_map(paperCorp_2, removePunctuation) 168 | paperCorp_2 <- tm_map(paperCorp_2, removeNumbers) 169 | # added tolower 170 | paperCorp_2 <- tm_map(paperCorp_2, content_transformer(tolower)) 171 | paperCorp_2 <- tm_map(paperCorp_2, removeWords, stopwords("english")) 172 | # moved stripWhitespace 173 | 174 | paperCorp_2 <- tm_map(paperCorp_2, stripWhitespace) 175 | paperCorp_2 <- tm_map(paperCorp_2, stemDocument) 176 | 177 | paperCorp_2 <- tm_map(paperCorp_2, removeWords, c("viral", 178 | "market")) 179 | 180 | nube2 <- wordcloud(paperCorp_2, min.freq = 1, 181 | max.words=50, random.order=FALSE, rot.per=0.35, 182 | colors=brewer.pal(8, "Dark2")) 183 | ``` 184 | 185 | ## Cluster 2 186 | 187 | ```{r} 188 | raw_data_3 <- 189 | df_clusters_3 %>% 190 | rename(id = "ID_WOS") %>% 191 | mutate(id = str_to_lower(id)) 192 | 193 | raw_data_1_3 <- 194 | raw_data_3 %>% 195 | dplyr::filter(grepl(".*doi", id)) 196 | 197 | raw_data_1_3$doi <- sub(".*doi", "", raw_data_1_3$id) 198 | 199 | 200 | raw_data_1_3 <- 201 | raw_data_1_3 %>% 202 | mutate(doi = str_trim(doi)) 203 | 204 | df_3 <- data.frame(titulo = as.character(), 205 | stringsAsFactors = FALSE) 206 | for (i in raw_data_1_3$doi) { 207 | row = try(oadoi_fetch(dois = i, 208 | email = email), 209 | TRUE) 210 | if(isTRUE(class(row)=="try-error")) {next} else { 211 | df_new = data.frame(titulo = row$title, 212 | stringsAsFactors = FALSE) 213 | } 214 | 215 | df_3 = rbind(df_new, df_3) 216 | } 217 | 218 | jeopCorpus_3 <- Corpus(VectorSource(df_3$titulo %>% na.omit())) 219 | 220 | paperCorp_3 <- jeopCorpus_3 221 | paperCorp_3 <- tm_map(paperCorp_3, removePunctuation) 222 | paperCorp_3 <- tm_map(paperCorp_3, removeNumbers) 223 | # added tolower 224 | paperCorp_3 <- tm_map(paperCorp_3, content_transformer(tolower)) 225 | paperCorp_3 <- tm_map(paperCorp_3, removeWords, stopwords("english")) 226 | # moved stripWhitespace 227 | 228 | paperCorp_3 <- tm_map(paperCorp_3, stripWhitespace) 229 | paperCorp_3 <- tm_map(paperCorp_3, stemDocument) 230 | 231 | paperCorp_3 <- tm_map(paperCorp_3, removeWords, c("viral", 232 | "market")) 233 | 234 | nube3 <- wordcloud(paperCorp_3, min.freq = 1, 235 | max.words=50, random.order=FALSE, rot.per=0.35, 236 | colors=brewer.pal(8, "Dark2")) 237 | ``` 238 | 239 | -------------------------------------------------------------------------------- /ToS_old_algorithm_DS.R: -------------------------------------------------------------------------------- 1 | if (!require(bibliometrix)) { 2 | install.packages("bibliometrix") 3 | } 4 | 5 | if (!require(igraph)) { 6 | install.packages("igraph") 7 | } 8 | 9 | if (!require(stringr)) { 10 | install.packages("stringr") 11 | } 12 | 13 | if (!require(stringdist)) { 14 | install.packages("stringdist") 15 | } 16 | 17 | if (!require(tidyverse)) { 18 | install.packages("tidyverse") 19 | } 20 | 21 | tos_wos <- function(file) { 22 | 23 | library(stringr) 24 | library(stringdist) 25 | library(bibliometrix) 26 | library(igraph) 27 | library(tidyverse) 28 | 29 | source("readFiles.R") 30 | 31 | data_wos <- readISI(file) 32 | 33 | data_wos$ID_WOS <- rownames(data_wos) 34 | 35 | data_wos$ID_WOS <- ifelse(!is.na(data_wos$VL), 36 | paste(data_wos$ID_WOS, 37 | data_wos$VL, 38 | sep = ", V"), 39 | data_wos$ID_WOS) 40 | 41 | data_wos$ID_WOS <- ifelse(!is.na(data_wos$BP), 42 | paste(data_wos$ID_WOS, 43 | data_wos$BP, 44 | sep = ", P"), 45 | data_wos$ID_WOS) 46 | 47 | data_wos$ID_WOS <- ifelse(!is.na(data_wos$DI), 48 | paste(data_wos$ID_WOS, 49 | data_wos$DI, 50 | sep = ", DOI "), 51 | data_wos$ID_WOS) 52 | 53 | edgelist <- 54 | as_tibble(data_wos) %>% 55 | mutate(cited_references = CR) %>% 56 | separate_rows(CR, sep = ";") %>% 57 | filter(!grepl(pattern = "^[0-9].*", 58 | CR)) %>% 59 | select(ID_WOS, CR) %>% 60 | filter(CR != "" & is.na(CR) == FALSE) %>% 61 | mutate(ID_WOS = str_to_upper(ID_WOS), 62 | CR = str_to_upper(CR)) %>% 63 | unique() 64 | 65 | graph <- 66 | graph.data.frame(edgelist) %>% 67 | simplify() 68 | 69 | graph_1 <- delete.vertices(graph, 70 | which(degree(graph, mode = "in") == 1 & 71 | degree(graph, mode = "out") == 0)) 72 | giant.component <- function(graph) { 73 | cl <- clusters(graph) 74 | induced.subgraph(graph, which(cl$membership == which.max(cl$csize))) 75 | } 76 | 77 | graph_2 <- giant.component(graph_1) 78 | 79 | network.metrics <- tibble( 80 | id = V(graph_2)$name, 81 | indegree = degree(graph_2, mode = "in"), 82 | outdegree = degree(graph_2, mode = "out"), 83 | bet = betweenness(graph_2) 84 | ) 85 | 86 | seminals <- network.metrics[network.metrics$outdegree == 0, 87 | c("id","indegree")] 88 | seminals <- head(seminals[with(seminals, order(-indegree)),],10) 89 | 90 | structurals <- network.metrics[network.metrics$bet > 0, 91 | c("id", "bet")] 92 | structurals <- head(structurals[with(structurals, order(-bet)),],10) 93 | 94 | current <- network.metrics[network.metrics$indegree == 0, 95 | c("id","outdegree")] 96 | current <- head(current[with(current, order(-outdegree)),], 60) 97 | 98 | if(sum(network.metrics$bet) == 0 ) { 99 | stop("Your ToS does not have trunk, check the search out")} else { 100 | 101 | 102 | seminals$ToS <- "Raiz" 103 | seminals$order <- 1:length(seminals$id) 104 | structurals$ToS <- "Tronco" 105 | structurals$order <- 1:length(structurals$id) 106 | current$ToS <- "Hojas" 107 | current$order <- 1:length(current$id) 108 | 109 | tos <- rbind(seminals[,c(1,3,4)], structurals[,c(1,3,4)], current[,c(1,3,4)]) 110 | } 111 | 112 | # tos.1 <- merge(tos, wom.raw.1, by.x = "id", by.y = "SR", all.x = TRUE) 113 | 114 | # tos.2 <- tos.1[,c("id", "ToS","AU", "TI", "DI")] 115 | 116 | list(df = data_wos, 117 | graph = graph_2, 118 | net_metrics = network.metrics, 119 | tos = tos) 120 | 121 | } 122 | 123 | -------------------------------------------------------------------------------- /f8417957-a805-489d-96e1-fc63ed9036e1.txt: -------------------------------------------------------------------------------- 1 | FN Thomson Reuters Web of Science™ 2 | VR 1.0 3 | PT J 4 | AU Mueller, MB 5 | Lovell, GP 6 | AF Mueller, Marcus B. 7 | Lovell, Geoff P. 8 | TI Theoretical Constituents of Relatedness Need Satisfaction in Senior 9 | Executives 10 | SO HUMAN RESOURCE DEVELOPMENT QUARTERLY 11 | LA English 12 | DT Article 13 | DE senior executives; motivation; need satisfaction; performance; 14 | relatedness; risk management; human resource development 15 | ID SELF-DETERMINATION THEORY; CORPORATE SOCIAL-RESPONSIBILITY; INTRINSIC 16 | MOTIVATION; PSYCHOLOGICAL NEEDS; FIRM PERFORMANCE; GROUNDED THEORY; 17 | UPPER ECHELONS; EXCLUSION; MANAGEMENT; LEADERSHIP 18 | AB This qualitative research interviewed 22 C-level executives to explore the theoretical constituents of relatedness need satisfaction, suggested by literature as a predictor of individuals' motivation, behavior, and well-being, for the first time in a sample of senior executives. Joint activity, time, continuity, and common concern were identified as components of how senior executives conceptualized relatedness need satisfaction. This novel research investigated relative importance of the theoretical constituents and senior executive participants ranked common concern as the most important constituent indicator of their relatedness need satisfaction. The results are compared to Baumeister and Leary's (1995) seminal conceptualization of relatedness need satisfaction which was based on a meta-analytical approach of nonexecutive sample studies. This research has both applied and theoretical implications. Our new theory of the constituents of relatedness need satisfaction in senior executives further provides an evidence basis for the design of efficacious practical organizational applications, such as raising senior executives' effectiveness through executive development programs focusing on common concern, joint activity, time, and continuity experiences. 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Q. 154 | PD SUM 155 | PY 2015 156 | VL 26 157 | IS 2 158 | BP 209 159 | EP 229 160 | DI 10.1002/hrdq.21205 161 | PG 21 162 | WC Industrial Relations & Labor; Psychology, Applied; Management 163 | SC Business & Economics; Psychology 164 | GA CL0GC 165 | UT WOS:000356617800005 166 | ER 167 | 168 | PT J 169 | AU Tangaraja, G 170 | Rasdi, RM 171 | Ismail, M 172 | Abu Samah, B 173 | AF Tangaraja, Gangeswari 174 | Rasdi, Roziah Mohd 175 | Ismail, Maimunah 176 | Abu Samah, Bahaman 177 | TI Fostering knowledge sharing behaviour among public sector managers: a 178 | proposed model for the Malaysian public service 179 | SO JOURNAL OF KNOWLEDGE MANAGEMENT 180 | LA English 181 | DT Article 182 | DE Public sector; Knowledge management; Knowledge sharing; Managers; 183 | Knowledge collecting; Knowledge donating 184 | ID ORGANIZATIONAL COMMITMENT; INNOVATION CAPABILITY; INTRINSIC MOTIVATION; 185 | CITIZENSHIP BEHAVIOR; VIRTUAL COMMUNITIES; TRUST; CREATION; ANTECEDENTS; 186 | PERFORMANCE; INTENTIONS 187 | AB Purpose - This paper aims to propose a conceptual model of knowledge sharing behaviour among Malaysian public sector managers. 188 | Design/methodology/approach - An extensive literature review method was used to identify and analyse relevant literature in order to propose a knowledge sharing model. 189 | Findings - The authors identified three potential predictor groups of knowledge sharing behaviour among Malaysian public sector managers. The groups are intrinsic motivational factors, extrinsic motivational factors and organisational socialisation factors. The paper proposes organisational commitment as the mediating variable between the identified predictors and knowledge sharing behaviour (knowledge donating and knowledge collecting). 190 | Research limitations/implications - The paper offers a number of propositions, which leads to a knowledge sharing model. Future research should validate and examine the predictive power of the proposed model. 191 | Practical implications - Upon model validation, the paper could offer practical interventions for human resource development (HRD) practitioners to assist organisations towards fostering knowledge sharing behaviour. The paper highlights the importance of employee's organisational commitment in order to engage in organizational-related behaviours such as knowledge sharing. 192 | Originality/value - The paper used a new approach in theorising knowledge sharing behaviour by integrating the General Workplace Commitment Model, Self-Determination Theory and Social Capital Theory. 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Manag. 299 | PY 2015 300 | VL 19 301 | IS 1 302 | SI SI 303 | BP 121 304 | EP 140 305 | DI 10.1108/JKM-11-2014-0449 306 | PG 20 307 | WC Information Science & Library Science; Management 308 | SC Information Science & Library Science; Business & Economics 309 | GA CC7YE 310 | UT WOS:000350583900010 311 | ER 312 | 313 | PT J 314 | AU Buzea, C 315 | AF Buzea, Carmen 316 | TI Equity Theory Constructs in a Romanian Cultural Context 317 | SO HUMAN RESOURCE DEVELOPMENT QUARTERLY 318 | LA English 319 | DT Article 320 | DE equity; Romania; employee-employer exchange; reward; work behavior 321 | ID DISTRIBUTIVE JUSTICE; REWARD-ALLOCATION; PERCEPTIONS; PAY; DIRECTIONS; 322 | JUDGMENTS; INEQUITY; FACES 323 | AB This study explores how Romanian employees understand and describe equity theory constructs. We conducted 87 interviews to identify employee perceptions in relation to: (a) contribution that is worth rewarding, (b) expected rewards in return for contribution, (c) referent person selected for comparison in order to evaluate the fairness of employee-employer exchange, and (d) the dominant strategy to reduce inequity. Results show that Romanian employees expect to be rewarded first for their personal features and second for their work behavior. The nonfinancial rewards, mainly appreciation and recognition, have similar importance as financial reward. When they evaluate the exchange with the organization, Romanian employees choose not to compare with others or to compare with their own performance standards. The preferred strategy to reduce inequity is to cognitively distort inputs and outcomes, by lowering the value of work effort and maximizing the occupational status. 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Q. 394 | PD WIN 395 | PY 2014 396 | VL 25 397 | IS 4 398 | BP 421 399 | EP 439 400 | DI 10.1002/hrdq.21184 401 | PG 19 402 | WC Industrial Relations & Labor; Psychology, Applied; Management 403 | SC Business & Economics; Psychology 404 | GA AW5YR 405 | UT WOS:000346347400003 406 | ER 407 | 408 | PT J 409 | AU Totawar, AK 410 | Nambudiri, R 411 | AF Totawar, Abhishek Kumar 412 | Nambudiri, Ranjeet 413 | TI Mood and Self-Efficacy: The Moderation of Hedonic and Utilitarian 414 | Motivation 415 | SO HUMAN RESOURCE DEVELOPMENT REVIEW 416 | LA English 417 | DT Article 418 | DE emotion in organizations; motivation; self-efficacy; work motivation; 419 | organizational behavior 420 | ID POSITIVE AFFECT; INTRINSIC MOTIVATION; SERVICE PERFORMANCE; EMOTION 421 | REGULATION; TASK-PERFORMANCE; MEMORY; PROBABILITY; JUDGMENTS; BEHAVIOR; 422 | BELIEFS 423 | AB The relationship between an individuals' mood and perceived self-efficacy (SE) has been of fundamental interest for organizational researchers. However, the causality of this relationship has not been agreed upon in existing research. While one set of studies propose that mood influences SE the contrasting view contends that this relationship is not significant. The article presents a conceptual model on the relationship between mood and SE, positioning hedonic and utilitarian motivation as moderators of this relationship. The literature concerning these constructs is reviewed and linkages between them are examined. A four-quadrant framework delineating the impact of hedonic and utilitarian motivation on the relationship between mood and SE is proposed. 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G., 1998, PSYCHOL LIFE 525 | NR 97 526 | TC 0 527 | Z9 0 528 | U1 5 529 | U2 14 530 | PU SAGE PUBLICATIONS INC 531 | PI THOUSAND OAKS 532 | PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA 533 | SN 1534-4843 534 | EI 1552-6712 535 | J9 HUM RESOUR DEV REV 536 | JI Hum. Resour. Dev. Rev. 537 | PD SEP 538 | PY 2014 539 | VL 13 540 | IS 3 541 | BP 314 542 | EP 335 543 | DI 10.1177/1534484313492330 544 | PG 22 545 | WC Management 546 | SC Business & Economics 547 | GA AW9JM 548 | UT WOS:000346573200004 549 | ER 550 | 551 | PT J 552 | AU Lyons, ST 553 | Ng, ES 554 | Schweitzer, L 555 | AF Lyons, Sean T. 556 | Ng, Eddy S. 557 | Schweitzer, Linda 558 | TI Changing Demographics and the Shifting Nature of Careers: Implications 559 | for Research and Human Resource Development 560 | SO HUMAN RESOURCE DEVELOPMENT REVIEW 561 | LA English 562 | DT Article 563 | DE shifting careers; gender; generations; immigration; socioeconomic status 564 | ID WORK-FAMILY CONFLICT; GENERATIONAL-DIFFERENCES; UNITED-STATES; VALUES; 565 | SUCCESS; COMMITMENT; CANADA; WOMEN; MODEL; EXPECTATIONS 566 | AB Research on human resource development is needed to empirically document the changing nature of careers among successive generational cohorts of workers. This article offers a conceptual framework for examining shifting careers, which accounts for important demographic shifts occurring in the labor force. Our research framework provides a comprehensive set of career-related variables (including career expectations, experience, and outcomes) for investigation. We illustrate the need to consider confounding influences of other demographic shifts in the study of changing careers, using the Canadian context as an illustrative example. We further argue that generational differences may be confounded with gender, immigration, and socioeconomic effects, and therefore, all three effects need to be considered simultaneously with generation. 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Rev. 757 | PD JUN 758 | PY 2014 759 | VL 13 760 | IS 2 761 | BP 181 762 | EP 206 763 | DI 10.1177/1534484314524201 764 | PG 26 765 | WC Management 766 | SC Business & Economics 767 | GA AW9JL 768 | UT WOS:000346573100004 769 | ER 770 | 771 | PT J 772 | AU Mayer, AP 773 | Grenier, RS 774 | Warhol, L 775 | Donaldson, M 776 | AF Mayer, Anysia P. 777 | Grenier, Robin S. 778 | Warhol, Larisa 779 | Donaldson, Morgaen 780 | TI Making a Change: The Role of External Coaches in School-Based 781 | Communities of Practice 782 | SO HUMAN RESOURCE DEVELOPMENT QUARTERLY 783 | LA English 784 | DT Article 785 | ID POLICY IMPLEMENTATION; MANAGEMENT; PERFORMANCE; SUPPORT; TEAMS 786 | AB External coaches, provided by intermediary organizations, are being utilized to support and develop principals, teachers, and schools and bring about substantive change through communities of practice (CoPs). These coaches provide an external perspective and are integral to the reform process. HRD recognizes the value of coaching in organizational improvement and change, yet understanding the use of external coaches in CoP is lacking in the literature. This article seeks to address this gap by presenting the results from a qualitative case study exploring the roles and experiences of three coaches, in seven elementary-middle schools implementing the Together Initiative (TI) school reform. Researchers' fieldwork consisted of informal site visits and 155 formal interviews with stakeholders involved in the Together Initiative, a reform aimed at improving urban public schools. 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Resour. Dev. Q. 891 | PD SEP 892 | PY 2013 893 | VL 24 894 | IS 3 895 | BP 337 896 | EP 363 897 | DI 10.1002/hrdq.21164 898 | PG 27 899 | WC Industrial Relations & Labor; Psychology, Applied; Management 900 | SC Business & Economics; Psychology 901 | GA 226BY 902 | UT WOS:000325010500004 903 | ER 904 | 905 | PT J 906 | AU Ning, J 907 | Jing, RT 908 | AF Ning, Jing 909 | Jing, Runtian 910 | TI Commitment to Change: Its Role in the Relationship Between Expectation 911 | of Change Outcome and Emotional Exhaustion 912 | SO HUMAN RESOURCE DEVELOPMENT QUARTERLY 913 | LA English 914 | DT Article 915 | ID ORGANIZATIONAL-CHANGE; EMPLOYEE COMMITMENT; 3-COMPONENT MODEL; 916 | ANTECEDENTS; SURVIVORS; JUSTICE; CONSEQUENCES; PERSPECTIVE; STRATEGIES; 917 | MANAGEMENT 918 | AB Successful implementation of organizational changes greatly depends on committed employees. It is crucial for managers, leaders, and HRD professionals to understand the antecedents and outcomes of commitment to change. The purpose of this study is to investigate the relationships among expectation of change outcome at the individual level, commitment to change, and emotional exhaustion. Four hundred and sixty-three usable surveys were returned from managers and employees from three companies in the Chinese telecom industry that were undergoing large-scale organizational changes. Results of correlation analysis demonstrated that expectation of change outcome was positively correlated with affective and normative commitments to change and negatively correlated with continuance commitment to change. Affective and normative commitments to change were both negatively correlated with emotional exhaustion, while continuance commitment to change was positively correlated with emotional exhaustion. Structural equation modeling analysis indicated that a full mediation model fitted best to the data. The relationships between the two dimensions of expectation of change outcome and emotional exhaustion were fully mediated by affective and normative commitments to change. 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Q. 989 | PD WIN 990 | PY 2012 991 | VL 23 992 | IS 4 993 | BP 461 994 | EP 485 995 | DI 10.1002/hrdq.21149 996 | PG 25 997 | WC Industrial Relations & Labor; Psychology, Applied; Management 998 | SC Business & Economics; Psychology 999 | GA 057GE 1000 | UT WOS:000312550200003 1001 | ER 1002 | 1003 | PT J 1004 | AU Kulkarni, M 1005 | AF Kulkarni, Mukta 1006 | TI Contextual Factors and Help Seeking Behaviors of People With 1007 | Disabilities 1008 | SO HUMAN RESOURCE DEVELOPMENT REVIEW 1009 | LA English 1010 | DT Article 1011 | DE disability; help seeking; work unit norms; coworker perceptions 1012 | AB The objective of this article is to provide a framework of contextual factors that affect help seeking expectancies and behaviors of people with disabilities. 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Rev. 1118 | PD MAR 1119 | PY 2012 1120 | VL 11 1121 | IS 1 1122 | BP 77 1123 | EP 96 1124 | DI 10.1177/1534484311416488 1125 | PG 20 1126 | WC Management 1127 | SC Business & Economics 1128 | GA V35RZ 1129 | UT WOS:000209167200005 1130 | ER 1131 | 1132 | PT J 1133 | AU Tabassi, AA 1134 | Ramli, M 1135 | Abu Bakar, AH 1136 | AF Tabassi, Amin Akhavan 1137 | Ramli, Mahyuddin 1138 | Abu Bakar, Abu Hassan 1139 | TI Effects of training and motivation practices on teamwork improvement and 1140 | task efficiency: The case of construction firms 1141 | SO INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT 1142 | LA English 1143 | DT Article 1144 | DE Training; Motivation; Teamwork improvement; Task efficiency; 1145 | Construction 1146 | ID HUMAN-RESOURCES; SELF-EFFICACY; PERFORMANCE; ENVIRONMENT; ASSIGNMENT; 1147 | MANAGEMENT; EMPLOYEES; TRAINEES; MEDIATOR 1148 | AB A well-organised human resource development programme is a critical strategy for construction companies, as in the coming years, human capital will increasingly play a significant role in organisational success. Based on a combination of the literature appraisal and questionnaire survey, the study explores the correlation of HRD strategies in employee training and motivation practices with teamwork improvement and task efficiency in construction projects. The research analysis was completed on a sample population of 107 individuals within nominated construction firms in Iran. The analysis methods in this research were mainly descriptive and regression-based analyses and the type of investigation was a co-relational study. The research found the relationship of training and motivation practices with teamwork improvement and task efficiency in the respondents' companies by exploring the two generated research models. (C) 2011 Elsevier Ltd. PMA and IPMA. 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Manag. 1243 | PD FEB 1244 | PY 2012 1245 | VL 30 1246 | IS 2 1247 | BP 213 1248 | EP 224 1249 | DI 10.1016/j.ijproman.2011.05.009 1250 | PG 12 1251 | WC Management 1252 | SC Business & Economics 1253 | GA 890HY 1254 | UT WOS:000300136900007 1255 | ER 1256 | 1257 | PT J 1258 | AU Tabassi, AA 1259 | Ramli, M 1260 | Abu Bakar, AH 1261 | AF Tabassi, Amin Akhavan 1262 | Ramli, Mahyuddin 1263 | Abu Bakar, Abu Hassan 1264 | TI Training, motivation and teamwork improvement: The case of construction 1265 | firms 1266 | SO AFRICAN JOURNAL OF BUSINESS MANAGEMENT 1267 | LA English 1268 | DT Article 1269 | DE Human resource development; training; motivation; teamwork improvement; 1270 | construction 1271 | ID MODERN LEARNING-METHODS; HUMAN-RESOURCES; SELF-EFFICACY; ENVIRONMENT; 1272 | EMPLOYEES; REALITY 1273 | AB A powerful human resource development system is a critical strategy for every construction companies, as in the coming years, human capital plays a significant role in order to have a successful organization. Based on a combination of literature research and questionnaire surveys, the study explores the effect of training and motivation in HRD practices on teamwork improvement in construction firms. The research was conducted by sending 50 sets of questionnaires to the nominated contractor firms in Mashhad, Iran. The analysis methods in this research were mainly descriptive and regression analysis and the type of investigation was co-relational study. The research found that the percentage of skilled and unskilled labour in the construction companies, some barriers and solutions of training and motivating workforces and the relationship between training and motivation practices in teamwork improvement. 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G., 2001, USING MULTIVARIATE S 1329 | Tabassi AA, 2009, INT J PROJ MANAG, V27, P471, DOI 10.1016/j.ijproman.2008.08.002 1330 | Tabassi AA, 2011, INT J PROJ MANAG, DOI [10.1016/j.ijproman.05.009, DOI 10.1016/J.IJPROMAN.05.009] 1331 | Tai WT, 2006, PERS REV, V35, P51, DOI 10.1108/0048348480610636786 1332 | Wang MK, 2011, EXPERT SYST APPL, V38, P3777, DOI 10.1016/j.eswa.2010.09.038 1333 | Wexley K.N, 1991, DEV TRAINING HUMAN R 1334 | WILD A, 2002, ENG CONSTRUCTION ARC, V9, P345, DOI 10.1046/j.1365-232X.2002.00250.x 1335 | Winch G.M., 1998, CONSTRUCTION MANAGEM, V16, P531, DOI DOI 10.1080/014461998372079 1336 | TANNENBAUM SI, 1992, ANNU REV PSYCHOL, V43, P399, DOI 10.1146/annurev.ps.43.020192.002151 1337 | NR 57 1338 | TC 0 1339 | Z9 1 1340 | U1 1 1341 | U2 9 1342 | PU ACADEMIC JOURNALS 1343 | PI VICTORIA ISLAND 1344 | PA P O BOX 5170-00200 NAIROBI, VICTORIA ISLAND, LAGOS 73023, NIGERIA 1345 | SN 1993-8233 1346 | J9 AFR J BUS MANAGE 1347 | JI Afr. J. Bus. Manag. 1348 | PD JUL 18 1349 | PY 2011 1350 | VL 5 1351 | IS 14 1352 | BP 5627 1353 | EP 5636 1354 | PG 10 1355 | WC Business; Management 1356 | SC Business & Economics 1357 | GA 821CY 1358 | UT WOS:000294953600025 1359 | ER 1360 | 1361 | PT J 1362 | AU Khan, MA 1363 | Kashif-Ur-Rehman 1364 | Ijaz-Ur-Rehman 1365 | Safwan, N 1366 | Ahmad, A 1367 | AF Khan, Muhammad Asif 1368 | Kashif-Ur-Rehman 1369 | Ijaz-Ur-Rehman 1370 | Safwan, Nadeem 1371 | Ahmad, Ashfaq 1372 | TI Modeling link between internal service quality in human resources 1373 | management and employees retention: A case of Pakistani privatized and 1374 | public sector banks 1375 | SO AFRICAN JOURNAL OF BUSINESS MANAGEMENT 1376 | LA English 1377 | DT Article 1378 | DE Internal service quality in HRM; employee job satisfaction; employee 1379 | retention 1380 | AB The purpose of this research study is to establish link between perceived human resources internal service quality practices with employee retentions in mediating environment of employee job satisfaction. This study was conducted in twin cities of Rawalpindi and Islamabad. For the analyses of research work, the data has been collected about the employees of public and privatized banks' employees. A total of 550 copies of the questionnaire were distributed to different bankers of selected areas and 400 copies of the same were collected back. The study finds that employee selection, employee training and development, work design, job definition employee rewards and compensation report high, positive and significant dimensionality to internal service quality in human resource management. The study further finds that internal service quality in human resource has positive and significant effect on employee job satisfaction and employee job satisfaction has positive and significant effect on employee retention. Based on findings of the study that the human resource development departments should review and enhance the motivation, training, and retention of good employees and that employee should support the concept of the ISQ. 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J. Bus. Manag. 1437 | PD FEB 4 1438 | PY 2011 1439 | VL 5 1440 | IS 3 1441 | BP 949 1442 | EP 959 1443 | PG 11 1444 | WC Business; Management 1445 | SC Business & Economics 1446 | GA 765DE 1447 | UT WOS:000290682800032 1448 | ER 1449 | 1450 | PT J 1451 | AU Kalargyrou, V 1452 | Woods, RH 1453 | AF Kalargyrou, Valentini 1454 | Woods, Robert H. 1455 | TI Wanted: training competencies for the twenty-first century 1456 | SO INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT 1457 | LA English 1458 | DT Article 1459 | DE Human resource development; Hospitality education; Training; Hospitality 1460 | management; Competences 1461 | AB Purpose - The purpose of this research paper is to describe the results and implications of the training professionals' competencies in the hospitality industry in the USA. 1462 | Design/methodology/approach - A qualitative implicit method, in-depth interviews using pictures were conducted with ten training professionals, where metaphors were widely used and links between concepts were identified. The qualitative software Atlas ti was used in creating a consensus map: a display showing the required competencies of training professionals in hospitality. 1463 | Findings - The training competencies that resulted from the current study are imperative for the effectiveness of training and development such as teamwork, inspiration-motivation, creativity, mentoring, keeping current, proactiveness, active listening, staying healthy, training measurement, consistency, and love and passion for the profession. 1464 | Research limitations/implications - In-depth interviews using pictures is an implicit research technique that attempts to reveal the unconscious thoughts and opinions of training professionals. The study took place in Las Vegas, Nevada, a unique tourist destination, and the interviewees were employed by large hotel-casino corporations. Future studies need to research different sizes of hospitality institutions and different tourist destinations. 1465 | Practical implications - Human resources management can use the outcome of the study as a guide for compiling competency-based job analyses for training positions, and consequently recruit and appraise training professionals based on these job specifications. Moreover, the outcome of the paper can be proven beneficial not only for hospitality executives, and future and current training professionals, but also for educators since training and teaching show-case many similarities. 1466 | Social implications - Improved and efficient training will result in lower employee turnover since employees will rate higher in job satisfaction and consequently that will result in better customer service and returning business for the company; a win-win situation for all involved stakeholders in a hospitality company. 1467 | Originality/value - Both hospitality executives and future and current training professionals should find these research results most interesting as they reveal the necessary competencies that will enable them to best train and develop hospitality employees. 1468 | C1 [Kalargyrou, Valentini; Woods, Robert H.] Univ Nevada, Hotel Management Dept, William Harrah Coll Hotel Adm, Las Vegas, NV 89154 USA. 1469 | RP Kalargyrou, V (reprint author), Univ Nevada, Hotel Management Dept, William Harrah Coll Hotel Adm, Las Vegas, NV 89154 USA. 1470 | EM valentini.kalargyrou@unh.edu 1471 | CR Tracey JB, 2008, CORNELL HOSP Q, V49, P12, DOI 10.1177/0010880407310191 1472 | BERTA D, 2006, NATIONS RESTAURANT N, V40, P33 1473 | BOON J, 2001, P HRD RES PRACT ACR, P309 1474 | Boyatzis R. 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Manag. 1526 | PY 2011 1527 | VL 23 1528 | IS 3 1529 | BP 361 1530 | EP 376 1531 | DI 10.1108/09596111111122532 1532 | PG 16 1533 | WC Hospitality, Leisure, Sport & Tourism; Management 1534 | SC Social Sciences - Other Topics; Business & Economics 1535 | GA 767UD 1536 | UT WOS:000290882700007 1537 | ER 1538 | 1539 | PT J 1540 | AU Joo, BK 1541 | Jeung, CW 1542 | Yoon, HJ 1543 | AF Joo, Baek-Kyoo (Brian) 1544 | Jeung, Chang-Wook 1545 | Yoon, Hea Jun 1546 | TI Investigating the Influences of Core Self-Evaluations, Job Autonomy, and 1547 | Intrinsic Motivation on In-Role Job Performance 1548 | SO HUMAN RESOURCE DEVELOPMENT QUARTERLY 1549 | LA English 1550 | DT Article 1551 | ID ORGANIZATIONAL CITIZENSHIP; EMPLOYEE CREATIVITY; LIFE SATISFACTION; 1552 | DECISION-MAKING; WORK; PERSONALITY; MODEL; METAANALYSIS; PREDICTORS; 1553 | BEHAVIORS 1554 | AB This study investigates the effects of core self-evaluations, job autonomy, and intrinsic motivation on employees' perceptions of their in-role job performance, based on a cross-sectional survey of 283 employees in a Fortune Global 100 company in Korea. 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The reliability of 360 degree feedback on the capacity to develop personal qualities has been investigated. This study shows to what extent the number of raters is related to an increasing reliability and an enhancement of correlation between supervisor and peer ratings. Ten raters are needed to reach a satisfying reliability level of 0.7 for the rating of the capacity to develop personal qualities, while six raters are needed for a reliability level of 0.7 with regard to the rating of motivation to develop these qualities. The use of two or three peer raters, as is common in the daily HRM/HRD practice, results in low reliability levels and in low agreement between supervisor and peer ratings. These results imply that 360 degree feedback is more useful in a personal growth system than in an administrative system, where the outcomes of the feedback are considered to be objective representations of work behaviour. 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Relat. 1907 | PD JUL 1908 | PY 2007 1909 | VL 60 1910 | IS 7 1911 | BP 987 1912 | EP 1018 1913 | DI 10.1177/0018726707081155 1914 | PG 32 1915 | WC Management; Social Sciences, Interdisciplinary 1916 | SC Business & Economics; Social Sciences - Other Topics 1917 | GA 202FG 1918 | UT WOS:000248886800001 1919 | ER 1920 | 1921 | EF -------------------------------------------------------------------------------- /functions.R: -------------------------------------------------------------------------------- 1 | library("tidyverse") 2 | library("bibliometrix") 3 | library("igraph") 4 | 5 | 6 | read_isi_file <- function (isi_file) { 7 | text <-readFiles(isi_file) 8 | tos_dataframe <- convert2df(text, dbsource = "isi", format = "plaintext") 9 | tos_dataframe$IDWOS <-rownames(tos_dataframe) 10 | return(tos_dataframe) 11 | } 12 | 13 | 14 | split_references <- function (tos_dataframe, separator) { 15 | dataframe_splitted <- tos_dataframe %>% separate_rows(CR, sep = separator) 16 | return(dataframe_splitted) 17 | } 18 | 19 | 20 | format_dataframe <- function (tos_dataframe) { 21 | dataframe_formated <- tos_dataframe %>% mutate( 22 | IDWOS2 = paste(IDWOS, sep = ", ", 23 | paste("V", sep = "", VL), 24 | paste("P", sep = "", BP), 25 | paste("DOI ", sep = "", DI) 26 | ) 27 | ) 28 | return(dataframe_formated) 29 | } 30 | 31 | 32 | graph_compute <- function (tos_dataframe) { 33 | edges <- tos_dataframe[ 34 | !is.na(tos_dataframe$CR), 35 | c("IDWOS2", "CR") 36 | ] 37 | tos_graph <-graph.data.frame(edges, directed = TRUE) 38 | return(tos_graph) 39 | } 40 | 41 | 42 | clean_graph <- function (tos_graph) { 43 | tos_cleaned = delete.vertices( 44 | tos_graph, 45 | which(degree(tos_graph, mode = "in") == 1 & 46 | degree(tos_graph, mode = "out") == 0 47 | ) 48 | ) 49 | 50 | return(tos_cleaned) 51 | 52 | } 53 | 54 | 55 | tos_labels <- function(tos_graph) { 56 | network.metrics <- data.frame( 57 | id = V(tos_graph)$name, 58 | indegree = degree(tos_graph, mode = "in"), 59 | outdegree = degree(tos_graph, mode = "out"), 60 | bet = betweenness(tos_graph), 61 | stringsAsFactors = FALSE 62 | ) 63 | roots <- 64 | network.metrics %>% 65 | filter(outdegree == 0) %>% 66 | arrange(desc(indegree)) %>% 67 | mutate(tos = "raiz") %>% 68 | select(-indegree, 69 | -outdegree, 70 | -bet) 71 | trunk <- 72 | network.metrics %>% 73 | arrange(desc(bet)) %>% 74 | mutate(tos = "tronco") %>% 75 | select(-indegree, 76 | -outdegree, 77 | -bet) 78 | leaves <- 79 | network.metrics %>% 80 | filter(indegree == 0) %>% 81 | arrange(desc(indegree)) %>% 82 | mutate(tos = "hoja") %>% 83 | select(-indegree, 84 | -outdegree, 85 | -bet) 86 | tos_structure <- rbind(roots, 87 | trunk, 88 | leaves) 89 | 90 | return(tos_structure) 91 | } 92 | -------------------------------------------------------------------------------- /merging_wos_scopus.R: -------------------------------------------------------------------------------- 1 | wos_scopus <- function(tos_wos, tos_scopus) { 2 | 3 | edgelist_tos_wos <- 4 | tos_wos %>% 5 | select(ID_TOS, 6 | CR_NESTED) %>% 7 | unnest(CR_NESTED) %>% 8 | mutate(TARGET = sub("^(\\S*\\s+\\S+\\s+\\S+).*", # removing strings after second comma 9 | "\\1", 10 | CR)) %>% 11 | select(-CR) %>% 12 | rename(SOURCE = "ID_TOS") 13 | 14 | edgelist_tos_wos_year <- 15 | edgelist_tos_wos %>% 16 | left_join(tos_wos %>% 17 | select(ID_TOS, 18 | PY), 19 | by = c("SOURCE" = "ID_TOS")) 20 | 21 | edgelist_tos_scopus_year <- 22 | as_tibble(biblio_scopus) %>% 23 | mutate(ID_TOS = str_extract(SR, ".*,")) %>% 24 | separate_rows(CR, sep = "; ") %>% 25 | select(ID_TOS, 26 | CR, 27 | PY) %>% 28 | mutate(lastname = sub("\\., .*", "", CR), 29 | lastname = sub(",", "", lastname), 30 | lastname = sub("\\.", "", lastname),# extracting lastnames, 31 | year = str_extract(CR, "\\(([0-9]{4})\\)"), 32 | year = str_remove_all(year, "\\(|\\)")) %>% 33 | filter(!grepl(pattern = "[():[:digit:]]", lastname), 34 | str_length(year) == 4) %>% 35 | mutate(CR = paste0(lastname, ", ", year, ",")) %>% 36 | select(ID_TOS, CR, PY) %>% 37 | rename(SOURCE = "ID_TOS", 38 | TARGET = "CR") 39 | 40 | edgelist_tos <- 41 | bind_rows(edgelist_tos_wos_year, 42 | edgelist_tos_scopus_year) %>% 43 | distinct() %>% 44 | na.omit() 45 | 46 | graph_tos <- 47 | graph_from_data_frame(edgelist_tos, 48 | directed = TRUE) %>% 49 | simplify() 50 | 51 | giant.component <- function(graph) { 52 | cl <- clusters(graph) 53 | induced.subgraph(graph, which(cl$membership == which.max(cl$csize))) 54 | } 55 | 56 | graph_wos_scopus <- 57 | delete.vertices(graph_tos, 58 | which(degree(graph_tos, mode = "in") == 1 & 59 | degree(graph_tos, mode = "out") == 0)) %>% 60 | giant.component() 61 | 62 | graph_wos_scopus 63 | 64 | } -------------------------------------------------------------------------------- /network_metrics.R: -------------------------------------------------------------------------------- 1 | network.metrics <- function(graph) { 2 | 3 | network.metrics <- data.frame( 4 | id = V(graph)$name, 5 | indegree = degree(graph, mode = "in"), 6 | outdegree = degree(graph, mode = "out"), 7 | bet = betweenness(graph), 8 | subarea = V(graph)$sub_area, 9 | stringsAsFactors = FALSE 10 | ) 11 | 12 | network.metrics 13 | 14 | } -------------------------------------------------------------------------------- /production_sub_areas.R: -------------------------------------------------------------------------------- 1 | production_sub_area <- function(graph){ 2 | 3 | source('sub_areas.R') 4 | graph_1 <- modularity(graph) 5 | 6 | df_graph <- 7 | data.frame(vertices = V(graph_1)$name, 8 | sub_area = V(graph_1)$sub_area, 9 | stringsAsFactors = TRUE) %>% 10 | arrange(desc(sub_area)) 11 | df_graph$year <- str_extract(df_graph$vertices, "[0-9]+") 12 | df_graph <- na.omit(df_graph) 13 | 14 | 15 | all_data <- 16 | data.frame(Year = c(min(unique(df_graph$year)): max(unique(df_graph$year))), 17 | stringsAsFactors = FALSE) 18 | names_all_data <- list("Year") 19 | 20 | for (i in 1:length(unique(df_graph$sub_area))){ 21 | frecuency_sub_area <- 22 | df_graph %>% 23 | filter(sub_area == i) %>% 24 | count(year) 25 | names(frecuency_sub_area) <- c("Año", "Frecuencia") 26 | 27 | frec_sub_area <- ifelse(all_data$Year %in% frecuency_sub_area$Año, 28 | frecuency_sub_area$Frecuencia, 0) 29 | all_data <- cbind(all_data, frec_sub_area) 30 | 31 | names_all_data <- append(names_all_data, 32 | paste("production_sub_area", as.character(i), sep = "")) 33 | } 34 | names(all_data) <- names_all_data 35 | 36 | all_data_largo <- all_data %>% 37 | gather(key="sub_areas", value="frecuencia", 2:(length(unique(df_graph$sub_area))+1)) 38 | 39 | len_comunities <- df_graph %>% group_by(sub_area) %>% tally() 40 | 41 | list(production = all_data_largo, 42 | graph = graph_1, 43 | length_comunities = len_comunities) 44 | } -------------------------------------------------------------------------------- /readFiles.R: -------------------------------------------------------------------------------- 1 | readISI <- function(file) { 2 | 3 | M <- convert2df(file, dbsource = "isi", format = "plaintext") 4 | 5 | } 6 | 7 | readScopus <- function(file) { 8 | 9 | M <- convert2df(file, dbsource = "scopus", format = "bibtex") 10 | M 11 | } -------------------------------------------------------------------------------- /scopus_graph.R: -------------------------------------------------------------------------------- 1 | tos_scopus <- function(file) { 2 | 3 | source("readFiles.R") 4 | 5 | giant.component <- function(graph) { 6 | cl <- clusters(graph) 7 | induced.subgraph(graph, which(cl$membership == which.max(cl$csize))) 8 | } 9 | 10 | data_raw_scopus <- 11 | readScopus(file) %>% 12 | separate_rows(CR, 13 | sep = "; ") 14 | 15 | data_raw_edgelist_scopus <- 16 | data_raw_scopus %>% 17 | mutate(lastname = sub("\\., .*", "", CR), 18 | lastname = sub(",", "", lastname), 19 | lastname = sub("\\.", "", lastname),# extracting lastnames 20 | year = str_extract(CR, "[0-9]+")) %>% # extracting year 21 | filter(!grepl(pattern = "[():[:digit:]]", lastname), 22 | str_length(year) == 4) %>% 23 | mutate(id_scopus = paste0(lastname, ", ", year, ","), 24 | SR = str_trim(SR)) %>% 25 | select(SR, id_scopus) 26 | 27 | graph_raw <- 28 | graph.data.frame(data_raw_edgelist_scopus, 29 | directed = TRUE) %>% 30 | simplify() 31 | 32 | graph <- 33 | delete.vertices(graph_raw, 34 | which(degree(graph_raw, mode = "in") == 1 & 35 | degree(graph_raw, mode = "out") == 0)) %>% 36 | giant.component() 37 | 38 | network.metrics <- tibble( 39 | id = V(graph)$name, 40 | indegree = degree(graph, mode = "in"), 41 | outdegree = degree(graph, mode = "out"), 42 | bet = betweenness(graph)) 43 | 44 | 45 | 46 | } 47 | -------------------------------------------------------------------------------- /scopus_notebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Scopus cienciometría" 3 | output: html_notebook 4 | --- 5 | 6 | # Creating environment 7 | 8 | ```{r message=FALSE, warning=FALSE} 9 | source("scripts/scopus.R") 10 | ``` 11 | 12 | # Getting data 13 | 14 | En esta parte se carga el archivo 15 | 16 | ```{r message=FALSE, warning=FALSE} 17 | paper <- tos_scopus(file.choose()) 18 | ``` 19 | 20 | ## Importance 21 | 22 | In this part, you will suport your research topic through a scientometrics analysis. Bibliometrix package helps us with it, please cite it in your paper 23 | 24 | Aria, M. & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007 25 | 26 | ### Anual scientific production 27 | 28 | ```{r} 29 | paper$pccion_anual$anual_pccion 30 | ``` 31 | 32 | 33 | ### Most productive autors 34 | 35 | ```{r warning=FALSE} 36 | paper$pccion_anual$author_pccion %>% gt() 37 | ``` 38 | 39 | 40 | ### Most popular journals 41 | 42 | This part shows the most important journals according to their production in your research 43 | 44 | ```{r} 45 | paper$pccion_anual$journals_pccion %>% gt() 46 | ``` 47 | 48 | 49 | ## Evolution Tree of Science 50 | 51 | This part shows the papers through the analogy of a tree 52 | 53 | ```{r warning=TRUE} 54 | paper$tos %>% gt() 55 | ``` 56 | 57 | ## Subareas of your topic 58 | 59 | This figure shows the amount of papers for each cluster. It will help you to select the subareas. 60 | 61 | ```{r warning=FALSE} 62 | paper$subareas$tipping_poing 63 | ``` 64 | 65 | The graph with the subareas selected 66 | 67 | ```{r} 68 | plot(paper$subareas$graph_subareas_network, 69 | vertex.color=V(paper$subareas$graph_subareas_network)$color, 70 | vertex.label = "", 71 | layout = layout.fruchterman.reingold) 72 | ``` 73 | 74 | 75 | this is the table of subarea 1 76 | 77 | ```{r warning=FALSE} 78 | paper$subareas$subarea_1 %>% gt() 79 | ``` 80 | 81 | This is the table of subarea 2 82 | 83 | ```{r} 84 | paper$subareas$subarea_2 %>% gt() 85 | ``` 86 | 87 | This is the table of subarea 3 88 | ```{r} 89 | paper$subareas$subarea_3 %>% gt() 90 | ``` 91 | 92 | Do you want to know what is the topic of each subarea? Below you will find wordclouds to help you whit it. 93 | 94 | This is your wordcloud 1 95 | 96 | ```{r warning=FALSE} 97 | paperCorp_1 <- tm_map(paper$wordclouds$wordcloud_1, 98 | removeWords, 99 | c("scientometr")) 100 | wordcloud(paperCorp_1, 101 | min.freq = 1, 102 | max.words=50, 103 | random.order=FALSE, 104 | rot.per=0.35, 105 | colors=brewer.pal(8, 106 | "Dark2")) 107 | ``` 108 | 109 | This is your wordcloud 2 110 | 111 | ```{r warning=FALSE} 112 | paperCorp_2 <- tm_map(paper$wordclouds$wordcloud_2, 113 | removeWords, 114 | c("scientometr")) 115 | wordcloud(paperCorp_2, 116 | min.freq = 1, 117 | max.words=50, 118 | random.order=FALSE, 119 | rot.per=0.35, 120 | colors=brewer.pal(8, 121 | "Dark2")) 122 | ``` 123 | 124 | This is your wordcloud 3 125 | 126 | ```{r message=FALSE, warning=FALSE} 127 | paperCorp_3 <- tm_map(paper$wordclouds$wordcloud_3, 128 | removeWords, 129 | c("scientometr")) 130 | wordcloud(paperCorp_3, 131 | min.freq = 1, 132 | max.words=50, 133 | random.order=FALSE, 134 | rot.per=0.35, 135 | colors=brewer.pal(8, 136 | "Dark2")) 137 | ``` 138 | 139 | # Export all data 140 | 141 | ```{r} 142 | write_csv(paper$pccion_anual$annual_pccion_data, "annual_pccion_data.csv") 143 | write_csv(paper$pccion_anual$author_pccion, "author_pccion.csv") 144 | write_csv(paper$pccion_anual$journals_pccion, "journals_pccion.csv") 145 | write_csv(paper$tos, "tos.csv") 146 | write.graph(paper$network, "graph.graphml", "graphml") 147 | write.graph(paper$subareas$graph_subareas_network, "graph_subareas.graphml", "graphml") 148 | write_csv(paper$subareas$subareas_all, "subareas.csv") 149 | ``` 150 | 151 | 152 | 153 | -------------------------------------------------------------------------------- /scripts/scopus.R: -------------------------------------------------------------------------------- 1 | source("scripts/scopus_functions.R") 2 | 3 | tos_scopus <- function(fileinput) { 4 | # Read scopus bitex file 5 | scopus_dataframe <- read_scopus_file(fileinput) 6 | # Create a dataframe 7 | cited_references <- cited_references_df(scopus_dataframe) 8 | # Create graph object 9 | graph <- graph_scopus(cited_references) 10 | 11 | # Create the edgelist 12 | # edge_list <- edge_list_scopus(scopus_dataframe) 13 | # ToS labels 14 | # titles <- titles_scopus(scopus_dataframe, edge_list) 15 | # Create graph 16 | # graph <- graph_scopus(edge_list) 17 | 18 | # Create ToS table 19 | tos_structure <- tos_labels(graph, cited_references, scopus_dataframe) 20 | # Calculate subareas 21 | graph_subareas <- sub_area(graph, cited_references, scopus_dataframe) 22 | # Importance of the topic 23 | importance <- importance_bibliometrix(scopus_dataframe) 24 | # Word clouds 25 | wordclouds_all <- wordclouds(graph_subareas$subarea_1, 26 | graph_subareas$subarea_2, 27 | graph_subareas$subarea_3) 28 | 29 | list(scopus = scopus_dataframe, 30 | network = graph, 31 | tos = tos_structure, 32 | subareas = graph_subareas, 33 | pccion_anual = importance, 34 | wordclouds = wordclouds_all) 35 | 36 | } -------------------------------------------------------------------------------- /scripts/scopus_functions.R: -------------------------------------------------------------------------------- 1 | if (!require(bibliometrix)) { 2 | install.packages("bibliometrix") 3 | } 4 | 5 | if (!require(tidyverse)) { 6 | install.packages("tidyverse") 7 | } 8 | 9 | if (!require(igraph)) { 10 | install.packages("igraph") 11 | } 12 | 13 | if (!require(lubridate)) { 14 | install.packages("lubridate") 15 | } 16 | 17 | if (!require(tm)) { 18 | install.packages("tm") 19 | } 20 | 21 | if (!require(wordcloud)) { 22 | install.packages("wordcloud") 23 | } 24 | 25 | if (!require(gt)) { 26 | install.packages("gt") 27 | } 28 | 29 | if (!require(rebus)) { 30 | install.packages("rebus") 31 | } 32 | 33 | # Read scopus bibtex 34 | read_scopus_file <- function (scopus_file) { 35 | scopus_dataframe <- 36 | convert2df(scopus_file, 37 | dbsource = "scopus", 38 | format = "bibtex") %>% 39 | as_tibble() %>% 40 | mutate(SR_TOS = str_extract(SR, one_or_more(WRD) %R% 41 | SPC %R% one_or_more(WRD) %R% 42 | "," %R% SPC %R% 43 | one_or_more(DGT) %R% ","), 44 | SR_TOS = str_c(SR_TOS, " ", SO)) 45 | 46 | return(scopus_dataframe) 47 | } 48 | 49 | # Create a df with CRs 50 | 51 | cited_references_df <- function(scopus_dataframe) { 52 | 53 | pattern_authors <- 54 | SPC %R% 55 | one_or_more(WRD) %R% 56 | SPC %R% 57 | one_or_more(or(WRD, ANY_CHAR)) 58 | 59 | pattern_titles <- 60 | OPEN_PAREN %R% 61 | repeated(DGT, 4) %R% 62 | CLOSE_PAREN %R% 63 | one_or_more(or(WRD,ANY_CHAR)) 64 | 65 | pattern_year <- 66 | OPEN_PAREN %R% 67 | repeated(DGT, 4) %R% 68 | CLOSE_PAREN 69 | 70 | pattern_journal <- 71 | one_or_more(or(WRD,SPC)) 72 | 73 | pattern_volume <- 74 | one_or_more(or(WRD, SPC)) 75 | 76 | pattern_pages <- 77 | "PP. " %R% 78 | one_or_more(or(DGT, ANY_CHAR)) 79 | 80 | cited_references <- 81 | scopus_dataframe %>% 82 | separate_rows(CR, sep = "; ") %>% 83 | select(SR_TOS, 84 | CR) %>% 85 | mutate(CR_AUTHOR = str_remove(CR, pattern_authors), 86 | CR_TITLE_1 = str_extract(CR, pattern_authors), 87 | CR_TITLE = str_remove(CR_TITLE_1, pattern_titles), 88 | CR_TITLE = str_trim(CR_TITLE), 89 | CR_YEAR_1 <- str_extract(CR_TITLE_1, pattern_titles), 90 | CR_YEAR = str_extract(CR_YEAR_1, repeated(DGT, 4)), 91 | CR_JOURNAL_1 = str_remove(CR_YEAR_1, pattern_year), 92 | CR_JOURNAL = str_extract(CR_JOURNAL_1, pattern_journal), 93 | CR_JOURNAL = str_trim(CR_JOURNAL), 94 | CR_VOLUME_1 = str_remove(CR_JOURNAL_1, pattern_journal), 95 | CR_VOLUME = str_extract(CR_VOLUME_1, pattern_volume), 96 | CR_PAGES = str_extract(CR_VOLUME_1, pattern_pages), 97 | CR_PAGES = str_remove(CR_PAGES, "PP. ")) %>% 98 | select(SR_TOS, 99 | CR, 100 | CR_AUTHOR, 101 | CR_TITLE, 102 | CR_YEAR, 103 | CR_JOURNAL, 104 | CR_VOLUME, 105 | CR_PAGES) %>% 106 | mutate(lastname = sub("\\., .*", "", CR), 107 | lastname = sub(",", "", lastname), 108 | lastname = sub("\\.", "", lastname), 109 | CR_SO = str_c(lastname, 110 | ", ", 111 | CR_YEAR, 112 | ", ", 113 | CR_JOURNAL)) %>% 114 | select(-lastname) 115 | 116 | return(cited_references) 117 | 118 | } 119 | 120 | # Create the edgelist 121 | 122 | edge_list_scopus <- function (scopus_dataframe) { 123 | 124 | edge_list <- 125 | cited_references %>% 126 | select(SR_TOS, 127 | CR_SO) %>% 128 | na.omit() %>% 129 | unique() 130 | 131 | return(edge_list) 132 | } 133 | 134 | titles_scopus <- function (scopus_dataframe, edge_list) { 135 | titles_orig <- 136 | scopus_dataframe %>% 137 | select(SR_TOS, TI) 138 | 139 | titles <- 140 | edge_list %>% 141 | select(CR_SO, 142 | TITLE) %>% 143 | anti_join(titles_orig, 144 | by = c("CR_SO" = "SR_TOS")) %>% 145 | rename(SR_TOS = "CR_SO", 146 | TI = "TITLE") %>% 147 | bind_rows(titles_orig) 148 | 149 | return(titles) 150 | 151 | } 152 | 153 | 154 | graph_scopus <- function (cited_references) { 155 | 156 | edge_list <- 157 | cited_references %>% 158 | select(SR_TOS, 159 | CR_SO) %>% 160 | na.omit() %>% 161 | unique() 162 | 163 | graph_raw <- 164 | graph.data.frame(edge_list, 165 | directed = TRUE) %>% 166 | simplify() 167 | 168 | graph_cleaned <- 169 | delete.vertices(graph_raw, 170 | which(degree(graph_raw, 171 | mode = "in") == 1 & 172 | degree(graph_raw, 173 | mode = "out") == 0) 174 | ) 175 | 176 | giant.component <- function(graph) { 177 | cl <- clusters(graph) 178 | induced.subgraph(graph, which(cl$membership == which.max(cl$csize))) 179 | } 180 | 181 | graph_gc <- giant.component(graph_cleaned) 182 | 183 | subareas <- 184 | as.undirected(graph_gc, 185 | mode = "each") %>% 186 | cluster_louvain() 187 | 188 | graph <- 189 | graph_gc %>% 190 | set_vertex_attr(name = "sub_area", 191 | value = membership(subareas)) 192 | 193 | return(graph) 194 | } 195 | 196 | tos_labels <- function(graph, cited_references, scopus_dataframe) { 197 | 198 | network_metrics <- tibble( 199 | id = V(graph)$name, 200 | indegree = degree(graph, mode = "in"), 201 | outdegree = degree(graph, mode = "out"), 202 | bet = betweenness(graph) 203 | ) 204 | 205 | roots <- 206 | network_metrics %>% 207 | filter(outdegree == 0) %>% 208 | arrange(desc(indegree)) %>% 209 | head(10) %>% 210 | mutate(tos = "raiz", 211 | order = 1:length(tos)) %>% 212 | select(-indegree, 213 | -outdegree, 214 | -bet) 215 | 216 | trunk <- 217 | network_metrics %>% 218 | arrange(desc(bet)) %>% 219 | head(10) %>% 220 | mutate(tos = "tronco", 221 | order = 1:length(tos)) %>% 222 | select(-indegree, 223 | -outdegree, 224 | -bet) 225 | 226 | leaves <- 227 | network_metrics %>% 228 | filter(indegree == 0) %>% 229 | arrange(desc(outdegree)) %>% 230 | head(60) %>% 231 | mutate(tos = "hoja", 232 | order = 1:length(tos)) %>% 233 | select(-indegree, 234 | -outdegree, 235 | -bet) 236 | 237 | tos_structure <- 238 | bind_rows(roots, 239 | trunk, 240 | leaves) 241 | 242 | tos_scopus_df <- 243 | tos_structure %>% 244 | left_join(scopus_dataframe %>% 245 | select(SR_TOS, 246 | TI, 247 | PY, 248 | AU, 249 | SO), 250 | by = c("id" = "SR_TOS")) %>% 251 | rename(TITLE = "TI", 252 | YEAR = "PY", 253 | AUTHOR = "AU", 254 | JOURNAL = "SO") 255 | 256 | tos_cited_ref <- 257 | tos_scopus_df %>% 258 | filter(is.na(TITLE)) %>% 259 | select(id, 260 | tos, 261 | order) %>% 262 | left_join(cited_references %>% 263 | select(CR_SO, 264 | CR_TITLE, 265 | CR_YEAR, 266 | CR_AUTHOR, 267 | CR_JOURNAL), 268 | by = c("id" = "CR_SO")) %>% 269 | filter(!duplicated(id)) %>% 270 | rename(TITLE = "CR_TITLE", 271 | YEAR = "CR_YEAR", 272 | AUTHOR = "CR_AUTHOR", 273 | JOURNAL = "CR_JOURNAL") 274 | 275 | tos_structure <- 276 | rbind(tos_cited_ref, 277 | tos_scopus_df %>% 278 | filter(!is.na(TITLE))) %>% 279 | select(order, 280 | tos, 281 | TITLE, 282 | YEAR, 283 | AUTHOR, 284 | JOURNAL, 285 | id) 286 | 287 | return(tos_structure) 288 | } 289 | 290 | sub_area <- function (graph, cited_references, scopus_dataframe) { 291 | # Identify the first three clusters 292 | subareas_3 <- 293 | tibble( 294 | subarea = V(graph)$sub_area) %>% 295 | group_by(subarea) %>% 296 | count() %>% 297 | arrange(desc(n)) %>% 298 | head(3) %>% 299 | select(subarea) 300 | 301 | graph_subarea_1 <- 302 | graph %>% 303 | delete_vertices(V(graph)$sub_area != subareas_3$subarea[1]) 304 | 305 | sub_area_net_metrics_1 <- 306 | tibble( 307 | id = V(graph_subarea_1)$name, 308 | indegree = degree(graph_subarea_1, 309 | mode = "in"), 310 | outdegree = degree(graph_subarea_1, 311 | mode = "out"), 312 | bet = betweenness(graph_subarea_1) 313 | ) 314 | 315 | roots_1 <- 316 | sub_area_net_metrics_1 %>% 317 | filter(outdegree == 0) %>% 318 | arrange(desc(indegree)) %>% 319 | head(10) %>% 320 | mutate(tos = "raiz", 321 | order = 1:length(tos)) %>% 322 | select(-indegree, 323 | -outdegree, 324 | -bet) 325 | 326 | trunk_1 <- 327 | sub_area_net_metrics_1 %>% 328 | arrange(desc(bet)) %>% 329 | head(10) %>% 330 | mutate(tos = "tronco", 331 | order = 1:length(tos)) %>% 332 | select(-indegree, 333 | -outdegree, 334 | -bet) 335 | 336 | leaves_1 <- 337 | sub_area_net_metrics_1 %>% 338 | filter(indegree == 0) %>% 339 | arrange(desc(indegree)) %>% 340 | head(60) %>% 341 | mutate(tos = "hoja", 342 | order = 1:length(tos)) %>% 343 | select(-indegree, 344 | -outdegree, 345 | -bet) 346 | 347 | tos_scopus_subarea_1 <- 348 | bind_rows(roots_1, 349 | trunk_1, 350 | leaves_1) %>% 351 | left_join(scopus_dataframe %>% 352 | select(SR_TOS, 353 | TI, 354 | PY, 355 | AU, 356 | SO), 357 | by = c("id" = "SR_TOS")) %>% 358 | rename(TITLE = "TI", 359 | YEAR = "PY", 360 | AUTHOR = "AU", 361 | JOURNAL = "SO") 362 | 363 | tos_cited_ref_subarea_1 <- 364 | tos_scopus_subarea_1 %>% 365 | filter(is.na(TITLE)) %>% 366 | select(id, 367 | tos, 368 | order) %>% 369 | left_join(cited_references %>% 370 | select(CR_SO, 371 | CR_TITLE, 372 | CR_YEAR, 373 | CR_AUTHOR, 374 | CR_JOURNAL), 375 | by = c("id" = "CR_SO")) %>% 376 | filter(!duplicated(id)) %>% 377 | rename(TITLE = "CR_TITLE", 378 | YEAR = "CR_YEAR", 379 | AUTHOR = "CR_AUTHOR", 380 | JOURNAL = "CR_JOURNAL") 381 | 382 | tos_structure_1 <- 383 | rbind(tos_cited_ref_subarea_1, 384 | tos_scopus_subarea_1 %>% 385 | filter(!is.na(TITLE))) %>% 386 | select(order, 387 | tos, 388 | TITLE, 389 | YEAR, 390 | AUTHOR, 391 | JOURNAL, 392 | id) %>% 393 | mutate(subarea = 1) 394 | 395 | graph_subarea_2 <- 396 | graph %>% 397 | delete_vertices(V(graph)$sub_area != subareas_3$subarea[2]) 398 | 399 | sub_area_net_metrics_2 <- 400 | tibble( 401 | id = V(graph_subarea_2)$name, 402 | indegree = degree(graph_subarea_2, 403 | mode = "in"), 404 | outdegree = degree(graph_subarea_2, 405 | mode = "out"), 406 | bet = betweenness(graph_subarea_2) 407 | ) 408 | 409 | roots_2 <- 410 | sub_area_net_metrics_2 %>% 411 | filter(outdegree == 0) %>% 412 | arrange(desc(indegree)) %>% 413 | head(10) %>% 414 | mutate(tos = "raiz", 415 | order = 1:length(tos)) %>% 416 | select(-indegree, 417 | -outdegree, 418 | -bet) 419 | 420 | trunk_2 <- 421 | sub_area_net_metrics_2 %>% 422 | arrange(desc(bet)) %>% 423 | head(10) %>% 424 | mutate(tos = "tronco", 425 | order = 1:length(tos)) %>% 426 | select(-indegree, 427 | -outdegree, 428 | -bet) 429 | 430 | leaves_2 <- 431 | sub_area_net_metrics_2 %>% 432 | filter(indegree == 0) %>% 433 | arrange(desc(indegree)) %>% 434 | head(60) %>% 435 | mutate(tos = "hoja", 436 | order = 1:length(tos)) %>% 437 | select(-indegree, 438 | -outdegree, 439 | -bet) 440 | #### Subarea 2 #### 441 | 442 | tos_scopus_subarea_2 <- 443 | bind_rows(roots_2, 444 | trunk_2, 445 | leaves_2) %>% 446 | left_join(scopus_dataframe %>% 447 | select(SR_TOS, 448 | TI, 449 | PY, 450 | AU, 451 | SO), 452 | by = c("id" = "SR_TOS")) %>% 453 | rename(TITLE = "TI", 454 | YEAR = "PY", 455 | AUTHOR = "AU", 456 | JOURNAL = "SO") 457 | 458 | tos_cited_ref_subarea_2 <- 459 | tos_scopus_subarea_2 %>% 460 | filter(is.na(TITLE)) %>% 461 | select(id, 462 | tos, 463 | order) %>% 464 | left_join(cited_references %>% 465 | select(CR_SO, 466 | CR_TITLE, 467 | CR_YEAR, 468 | CR_AUTHOR, 469 | CR_JOURNAL), 470 | by = c("id" = "CR_SO")) %>% 471 | filter(!duplicated(id)) %>% 472 | rename(TITLE = "CR_TITLE", 473 | YEAR = "CR_YEAR", 474 | AUTHOR = "CR_AUTHOR", 475 | JOURNAL = "CR_JOURNAL") 476 | 477 | tos_structure_2 <- 478 | rbind(tos_cited_ref_subarea_2, 479 | tos_scopus_subarea_2 %>% 480 | filter(!is.na(TITLE))) %>% 481 | select(order, 482 | tos, 483 | TITLE, 484 | YEAR, 485 | AUTHOR, 486 | JOURNAL, 487 | id) %>% 488 | mutate(subarea = 2) 489 | 490 | #### Subarea 3 #### 491 | 492 | graph_subarea_3 <- 493 | graph %>% 494 | delete_vertices(V(graph)$sub_area != subareas_3$subarea[3]) 495 | 496 | sub_area_net_metrics_3 <- 497 | tibble( 498 | id = V(graph_subarea_3)$name, 499 | indegree = degree(graph_subarea_3, 500 | mode = "in"), 501 | outdegree = degree(graph_subarea_3, 502 | mode = "out"), 503 | bet = betweenness(graph_subarea_3) 504 | ) 505 | 506 | roots_3 <- 507 | sub_area_net_metrics_3 %>% 508 | filter(outdegree == 0) %>% 509 | arrange(desc(indegree)) %>% 510 | head(10) %>% 511 | mutate(tos = "raiz", 512 | order = 1:length(tos)) %>% 513 | select(-indegree, 514 | -outdegree, 515 | -bet) 516 | 517 | trunk_3 <- 518 | sub_area_net_metrics_3 %>% 519 | arrange(desc(bet)) %>% 520 | head(10) %>% 521 | mutate(tos = "tronco", 522 | order = 1:length(tos)) %>% 523 | select(-indegree, 524 | -outdegree, 525 | -bet) 526 | 527 | leaves_3 <- 528 | sub_area_net_metrics_3 %>% 529 | filter(indegree == 0) %>% 530 | arrange(desc(indegree)) %>% 531 | head(60) %>% 532 | mutate(tos = "hoja", 533 | order = 1:length(tos)) %>% 534 | select(-indegree, 535 | -outdegree, 536 | -bet) 537 | 538 | tos_scopus_subarea_3 <- 539 | bind_rows(roots_3, 540 | trunk_3, 541 | leaves_3) %>% 542 | left_join(scopus_dataframe %>% 543 | select(SR_TOS, 544 | TI, 545 | PY, 546 | AU, 547 | SO), 548 | by = c("id" = "SR_TOS")) %>% 549 | rename(TITLE = "TI", 550 | YEAR = "PY", 551 | AUTHOR = "AU", 552 | JOURNAL = "SO") 553 | 554 | tos_cited_ref_subarea_3 <- 555 | tos_scopus_subarea_3 %>% 556 | filter(is.na(TITLE)) %>% 557 | select(id, 558 | tos, 559 | order) %>% 560 | left_join(cited_references %>% 561 | select(CR_SO, 562 | CR_TITLE, 563 | CR_YEAR, 564 | CR_AUTHOR, 565 | CR_JOURNAL), 566 | by = c("id" = "CR_SO")) %>% 567 | filter(!duplicated(id)) %>% 568 | rename(TITLE = "CR_TITLE", 569 | YEAR = "CR_YEAR", 570 | AUTHOR = "CR_AUTHOR", 571 | JOURNAL = "CR_JOURNAL") 572 | 573 | tos_structure_3 <- 574 | rbind(tos_cited_ref_subarea_3, 575 | tos_scopus_subarea_3 %>% 576 | filter(!is.na(TITLE))) %>% 577 | select(order, 578 | tos, 579 | TITLE, 580 | YEAR, 581 | AUTHOR, 582 | JOURNAL, 583 | id) %>% 584 | mutate(subarea = 3) 585 | 586 | #### Plotting subgraphs #### 587 | 588 | subareas_plot <- 589 | tibble(subareas = V(graph)$sub_area) %>% 590 | group_by(subareas) %>% 591 | count() %>% 592 | arrange(desc(n)) %>% 593 | ggplot(aes(x = reorder(subareas, n), y = n)) + 594 | geom_point() + 595 | xlab("subareas") + 596 | ylab("papers") + 597 | ggtitle("Relationship of subareas by size") 598 | 599 | graph_subareas_plot <- 600 | delete.vertices(graph, 601 | which(V(graph)$sub_area != subareas_3$subarea[1] & 602 | V(graph)$sub_area != subareas_3$subarea[2] & 603 | V(graph)$sub_area != subareas_3$subarea[3])) 604 | 605 | colr = c("red", "brown", "blue") 606 | V(graph_subareas_plot)$color <- ifelse(V(graph_subareas_plot)$sub_area == subareas_3$subarea[1], "red", 607 | ifelse(V(graph_subareas_plot)$sub_area == subareas_3$subarea[2], "blue", 608 | "green")) 609 | 610 | list(subarea_1 = tos_structure_1, 611 | subarea_2 = tos_structure_2, 612 | subarea_3 = tos_structure_3, 613 | subareas_all = bind_rows(tos_structure_1, 614 | tos_structure_2, 615 | tos_structure_3), 616 | tipping_poing = subareas_plot, 617 | graph_subareas_network = graph_subareas_plot) 618 | } 619 | 620 | importance_bibliometrix <- function (scopus_dataframe) { 621 | importance_biblio <- 622 | biblioAnalysis(scopus_dataframe) 623 | 624 | annual_pccion_data <- 625 | tibble(years = importance_biblio$Years, 626 | papers = importance_biblio$nAUperPaper) %>% 627 | group_by(years) %>% 628 | summarise(papers = sum(papers)) %>% 629 | arrange(desc(years)) 630 | 631 | anual_pccion_plot <- 632 | tibble(years = importance_biblio$Years, 633 | papers = importance_biblio$nAUperPaper) %>% 634 | group_by(years) %>% 635 | summarise(papers = sum(papers)) %>% 636 | arrange(desc(years)) %>% 637 | filter(years > 2001, 638 | years < year(Sys.Date())) %>% 639 | ggplot(aes(x = years, y = papers)) + 640 | geom_line() + 641 | ggtitle("Scientific anual production") 642 | 643 | author_pccion <- 644 | as_tibble(importance_biblio$Authors) %>% 645 | head(15) %>% 646 | rename(Author = "AU", 647 | Publications = "n") %>% 648 | filter(Author != "NA NA") 649 | 650 | journals_pccion <- 651 | as_tibble(importance_biblio$Sources) %>% 652 | head(15) 653 | 654 | list(annual_pccion_data = annual_pccion_data, 655 | anual_pccion = anual_pccion_plot, 656 | author_pccion = author_pccion, 657 | journals_pccion = journals_pccion) 658 | } 659 | 660 | wordclouds <- function (subarea_1, subarea_2, subarea_3) { 661 | 662 | jeopCorpus <- Corpus(VectorSource(subarea_1$TITLE %>% na.omit())) 663 | 664 | paperCorp <- jeopCorpus 665 | paperCorp <- tm_map(paperCorp, removePunctuation) 666 | paperCorp <- tm_map(paperCorp, removeNumbers) 667 | # added tolower 668 | paperCorp <- tm_map(paperCorp, content_transformer(tolower)) 669 | paperCorp <- tm_map(paperCorp, removeWords, stopwords("english")) 670 | # moved stripWhitespace 671 | 672 | paperCorp <- tm_map(paperCorp, stripWhitespace) 673 | paperCorp <- tm_map(paperCorp, stemDocument) 674 | 675 | paperCorp_1 <- tm_map(paperCorp, removeWords, c("viral", 676 | "market")) 677 | 678 | jeopCorpus_2 <- Corpus(VectorSource(subarea_2$TITLE %>% na.omit())) 679 | 680 | paperCorp_2 <- jeopCorpus_2 681 | paperCorp_2 <- tm_map(paperCorp_2, removePunctuation) 682 | paperCorp_2 <- tm_map(paperCorp_2, removeNumbers) 683 | # added tolower 684 | paperCorp_2 <- tm_map(paperCorp_2, content_transformer(tolower)) 685 | paperCorp_2 <- tm_map(paperCorp_2, removeWords, stopwords("english")) 686 | # moved stripWhitespace 687 | 688 | paperCorp_2 <- tm_map(paperCorp_2, stripWhitespace) 689 | paperCorp_2 <- tm_map(paperCorp_2, stemDocument) 690 | 691 | paperCorp_2 <- tm_map(paperCorp_2, removeWords, c("viral", 692 | "market")) 693 | 694 | jeopCorpus_3 <- Corpus(VectorSource(subarea_3$TITLE %>% na.omit())) 695 | 696 | paperCorp_3 <- jeopCorpus_3 697 | paperCorp_3 <- tm_map(paperCorp_3, removePunctuation) 698 | paperCorp_3 <- tm_map(paperCorp_3, removeNumbers) 699 | # added tolower 700 | paperCorp_3 <- tm_map(paperCorp_3, content_transformer(tolower)) 701 | paperCorp_3 <- tm_map(paperCorp_3, removeWords, stopwords("english")) 702 | # moved stripWhitespace 703 | 704 | paperCorp_3 <- tm_map(paperCorp_3, stripWhitespace) 705 | paperCorp_3 <- tm_map(paperCorp_3, stemDocument) 706 | 707 | paperCorp_3 <- tm_map(paperCorp_3, removeWords, c("viral", 708 | "market")) 709 | 710 | 711 | list(wordcloud_1 = paperCorp_1, 712 | wordcloud_2 = paperCorp_2, 713 | wordcloud_3 = paperCorp_3) 714 | } -------------------------------------------------------------------------------- /sub_areas.R: -------------------------------------------------------------------------------- 1 | modularity <- function(graph_tos) { 2 | 3 | graph_tos_und = as.undirected(graph_tos, 4 | mode = "each") 5 | sub_areas = cluster_louvain(graph_tos_und) 6 | graph_tos_sub_areas <- 7 | graph_tos_und %>% 8 | set_vertex_attr(name = "sub_area", 9 | value = membership(sub_areas) 10 | ) 11 | graph_tos_sub_areas 12 | } 13 | -------------------------------------------------------------------------------- /tos.R: -------------------------------------------------------------------------------- 1 | source('functions.R') 2 | 3 | tos <- function(fileinput) { 4 | tos_dataframe <- read_isi_file(fileinput) 5 | tos_dataframe <- split_references(tos_dataframe, "; ") 6 | tos_dataframe <- format_dataframe(tos_dataframe) 7 | tos_graph <- graph_compute(tos_dataframe) 8 | tos_graph <- clean_graph(tos_graph) 9 | tos_structure <- tos_labels(tos_graph) 10 | 11 | return(tos_structure) 12 | } 13 | -------------------------------------------------------------------------------- /tos_scopus.R: -------------------------------------------------------------------------------- 1 | tos_scopus <- function(biblio_scopus) { 2 | 3 | tos_scopus_ref <- 4 | as_tibble(biblio_scopus) %>% 5 | mutate(ID_TOS = str_extract(SR, ".*,")) %>% 6 | separate_rows(CR, sep = "; ") %>% # the CR data is removed here, something to improve strsplit could be an option 7 | nest(data = CR) %>% 8 | rename("REFS_NESTED" = data) %>% 9 | mutate(CR = biblio_scopus$CR,) %>% 10 | select(ID_TOS, everything()) 11 | 12 | edge_list_scopus <- 13 | tos_scopus_ref %>% 14 | select(ID_TOS, REFS_NESTED) %>% 15 | unnest(REFS_NESTED) %>% 16 | mutate(lastname = sub("\\., .*", "", CR), 17 | lastname = sub(",", "", lastname), 18 | lastname = sub("\\.", "", lastname),# extracting lastnames, 19 | year = str_extract(CR, "\\(([0-9]{4})\\)"), 20 | year = str_remove_all(year, "\\(|\\)")) %>% # extracting year needs to be improved 21 | filter(!grepl(pattern = "[():[:digit:]]", lastname), 22 | str_length(year) == 4) %>% 23 | mutate(CR = paste0(lastname, ", ", year, ",")) %>% 24 | select(ID_TOS, CR) 25 | 26 | unmatched_refs_scopus <- 27 | edge_list_scopus %>% 28 | anti_join(tos_scopus_ref, 29 | by = c("CR" = "ID_TOS")) %>% 30 | select(CR) %>% 31 | set_names("ID_TOS") %>% 32 | distinct() %>% 33 | mutate(PY = str_extract(ID_TOS, 34 | ", [0-9]{4},"), 35 | PY = str_remove_all(PY, ","), 36 | PY = as.numeric(str_trim(PY))) 37 | 38 | tos_scopus_ref_cols <- 39 | tos_scopus_ref[0,] 40 | 41 | unmatched_refs_full_scopus <- 42 | complete(tos_scopus_ref_cols, 43 | unmatched_refs_scopus) 44 | 45 | tos_scopus <- 46 | tos_scopus_ref %>% 47 | bind_rows(unmatched_refs_full_scopus) %>% 48 | mutate(ID_TOS = sub("^(\\S*\\s+\\S+\\s+\\S+).*", # removing strings after second comma 49 | "\\1", 50 | ID_TOS) 51 | ) %>% 52 | select(ID_TOS, everything()) # There is a mistake here - NA and NULL (REF) 53 | 54 | tos_scopus 55 | } -------------------------------------------------------------------------------- /tos_wos.R: -------------------------------------------------------------------------------- 1 | library(bibliometrix) 2 | library(tidyverse) 3 | library(igraph) 4 | library(lubridate) 5 | library(tm) 6 | library(wordcloud) 7 | library(gt) 8 | library(rebus) 9 | 10 | tos_wos <- function(biblio_wos) { 11 | 12 | tos_wos_ref <- 13 | as_tibble(biblio_wos) %>% 14 | separate_rows(CR, sep = ";") %>% # the CR data is removed here, something to improve strsplit could be an option 15 | nest(CR_NESTED = CR) %>% 16 | mutate(CR = biblio_wos$CR) %>% 17 | select(ID_WOS, everything()) 18 | 19 | edge_list_wos <- 20 | tos_wos_ref %>% 21 | select(ID_WOS, CR_NESTED) %>% 22 | unnest(CR_NESTED) 23 | 24 | unmatched_refs_wos <- 25 | edge_list_wos %>% 26 | anti_join(tos_wos_ref, 27 | by = c("CR" = "ID_WOS")) %>% 28 | select(CR) %>% 29 | set_names("ID_WOS") %>% 30 | distinct() %>% 31 | mutate(PY = str_extract(ID_WOS, 32 | ", [0-9]{4},"), 33 | PY = str_remove_all(PY, ","), 34 | PY = as.numeric(str_trim(PY))) %>% 35 | na.omit() %>% 36 | dplyr::filter(grepl('^([a-zA-Z]+ [a-zA-Z]+, [0-9]{4}, )', 37 | ID_WOS)) %>% 38 | dplyr::filter(!grepl("")) 39 | 40 | tos_wos_ref_cols <- 41 | tos_wos_ref[0,] 42 | 43 | unmatched_refs_full_wos <- 44 | complete(tos_wos_ref_cols, 45 | unmatched_refs_wos) 46 | 47 | tos_wos <- 48 | tos_wos_ref %>% 49 | bind_rows(unmatched_refs_full_wos) %>% 50 | mutate(ID_TOS = sub("^(\\S*\\s+\\S+\\s+\\S+).*", # removing strings after second comma 51 | "\\1", 52 | ID_WOS) 53 | ) %>% 54 | select(ID_TOS, everything()) # There is a mistake here - NA and NULL (REF) 55 | 56 | tos_wos # We need to extract the year from this tibble 57 | } --------------------------------------------------------------------------------