Psychomotricity
Andre Santos
03/03/2020
Análise Bibliométrica
Esse é um R Markdown documento sobre análise bibliométrica da Psicomotricidade, retirada da base de dados Scopus.
Install and load package if require:
if(!require("install.load")) {
install.packages("install.load")
library(install.load)
}## Loading required package: install.loadinstall_load("dplyr","factoextra", "FactoMineR", "ggplot2", "igraph", "Matrix", "rscopus",
"SnowballC", "stringr", "bibliometrix")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa##
## Attaching package: 'igraph'## The following objects are masked from 'package:dplyr':
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## as_data_frame, groups, union## The following objects are masked from 'package:stats':
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## decompose, spectrum## The following object is masked from 'package:base':
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## union## To cite bibliometrix in publications, please use:
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## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
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## http:\\www.bibliometrix.org
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##
## To start with the shiny web-interface, please digit:
## biblioshiny()# Base de dados
D <- readFiles("C:/Users/andre/Downloads/psychomotricity.bib") # carregar base
M <- convert2df(D, dbsource = "scopus", format = "bibtex") # converter base##
## Converting your scopus collection into a bibliographic dataframe
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## Articles extracted 100
## Articles extracted 200
## Articles extracted 300
## Articles extracted 348
## Done!
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##
## Generating affiliation field tag AU_UN from C1: Done!Análise bibliométrica
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## Main Information about data
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## Documents 348
## Sources (Journals, Books, etc.) 220
## Keywords Plus (ID) 1383
## Author's Keywords (DE) 891
## Period 1950 - 2020
## Average citations per documents 4.296
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## Authors 993
## Author Appearances 1121
## Authors of single-authored documents 69
## Authors of multi-authored documents 924
## Single-authored documents 77
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## Documents per Author 0.35
## Authors per Document 2.85
## Co-Authors per Documents 3.22
## Collaboration Index 3.41
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## Document types
## ARTICLE 271
## BOOK 4
## BOOK CHAPTER 12
## CONFERENCE PAPER 16
## CONFERENCE REVIEW 1
## EDITORIAL 6
## NOTE 1
## REVIEW 29
## SHORT SURVEY 8
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## Annual Scientific Production
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## Year Articles
## 1950 1
## 1952 1
## 1959 2
## 1968 1
## 1972 1
## 1973 3
## 1974 2
## 1975 2
## 1977 8
## 1978 2
## 1979 2
## 1980 1
## 1981 1
## 1982 2
## 1984 1
## 1985 1
## 1986 2
## 1988 1
## 1989 2
## 1990 1
## 1991 3
## 1992 2
## 1993 2
## 1994 3
## 1996 3
## 1997 2
## 1999 1
## 2000 2
## 2001 3
## 2002 3
## 2003 1
## 2004 2
## 2005 1
## 2006 5
## 2007 12
## 2008 12
## 2009 14
## 2010 13
## 2011 12
## 2012 11
## 2013 20
## 2014 16
## 2015 13
## 2016 18
## 2017 23
## 2018 48
## 2019 55
## 2020 11
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## Annual Percentage Growth Rate 3.484913
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## Most Productive Authors
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## Authors Articles Authors Articles Fractionalized
## 1 SUH YT 7 VAIVRE-DOURET L 4.50
## 2 VAIVRE-DOURET L 6 SUH YT 3.33
## 3 VENETSANOU F 5 LATOUR AM 3.00
## 4 ALBARET JM 4 VENETSANOU F 2.33
## 5 KAMBAS A 4 BLETON JP 2.00
## 6 MAANO C 4 FEUILLERAT B 2.00
## 7 ORTEGA FZ 4 KAMBAS A 2.00
## 8 DETREZ S 3 NA NA 2.00
## 9 DI CATALDO C 3 SOULAYROL R 2.00
## 10 JURADO PJ 3 ALBARET JM 1.78
##
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## Top manuscripts per citations
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## Paper TC TCperYear
## 1 SOUNDY A, 2014, ARCH PSYCHIATR NURS 92 13.14
## 2 VANCAMPFORT D, 2010, PSYCHIATRY RES 91 8.27
## 3 VENETSANOU F, 2010, EARLY CHILD EDUC J 85 7.73
## 4 HIETANEN M, 1986, ACTA NEUROL SCAND 80 2.29
## 5 BARANEK GT, 2008, PHYS OCCUP THER PEDIATR 76 5.85
## 6 GLOSSER G, 1977, INT J NEUROSCI 66 1.50
## 7 BANKI CM, 1977, J NEUROCHEM 66 1.50
## 8 RHRICHT F, 2009, BODY MOV DANCE PSYCHOTHER 64 5.33
## 9 SOUNDY A, 2013, INT J THER REHABIL 44 5.50
## 10 PRAT G, 2008, ADDICT BEHAV 38 2.92
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## Corresponding Author's Countries
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## Country Articles Freq SCP MCP MCP_Ratio
## 1 FRANCE 80 0.3320 79 1 0.0125
## 2 SPAIN 37 0.1535 33 4 0.1081
## 3 ITALY 16 0.0664 15 1 0.0625
## 4 BRAZIL 13 0.0539 13 0 0.0000
## 5 KOREA 12 0.0498 12 0 0.0000
## 6 BELGIUM 9 0.0373 5 4 0.4444
## 7 GREECE 9 0.0373 8 1 0.1111
## 8 CANADA 7 0.0290 5 2 0.2857
## 9 MEXICO 7 0.0290 7 0 0.0000
## 10 PORTUGAL 6 0.0249 3 3 0.5000
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## SCP: Single Country Publications
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## MCP: Multiple Country Publications
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## Total Citations per Country
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## Country Total Citations Average Article Citations
## 1 FRANCE 170 2.125
## 2 ITALY 126 7.875
## 3 BELGIUM 124 13.778
## 4 UNITED KINGDOM 114 38.000
## 5 GREECE 98 10.889
## 6 USA 85 17.000
## 7 SPAIN 83 2.243
## 8 FINLAND 80 80.000
## 9 HUNGARY 66 66.000
## 10 BRAZIL 42 3.231
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## Most Relevant Sources
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## Sources Articles
## 1 NEUROPSYCHIATRIE DE L'ENFANCE ET DE L'ADOLESCENCE 19
## 2 ANAE - APPROCHE NEUROPSYCHOLOGIQUE DES APPRENTISSAGES CHEZ L'ENFANT 15
## 3 JOURNAL OF SPORT AND HEALTH RESEARCH 12
## 4 RETOS 11
## 5 ENFANCES ET PSY 6
## 6 SOINS PEDIATRIE/PUERICULTURE 6
## 7 BODY MOVEMENT AND DANCE IN PSYCHOTHERAPY 5
## 8 MOTRICIDADE 5
## 9 RESEARCH IN DEVELOPMENTAL DISABILITIES 5
## 10 EARLY CHILD DEVELOPMENT AND CARE 4
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## Most Relevant Keywords
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## Author Keywords (DE) Articles Keywords-Plus (ID) Articles
## 1 PSYCHOMOTRICITY 80 HUMAN 172
## 2 PHYSICAL EDUCATION 15 CHILD 120
## 3 PHYSICAL ACTIVITY 14 FEMALE 111
## 4 CHILDREN 13 MALE 110
## 5 BODY 12 ARTICLE 101
## 6 MOTOR SKILLS 10 HUMANS 61
## 7 REHABILITATION 8 PSYCHOMOTOR PERFORMANCE 38
## 8 HEALTH 7 AGED 37
## 9 ANXIETY 6 ADULT 36
## 10 ASSESSMENT 6 ADOLESCENT 35
Análise das referências citadas
M$CR[1]## [1] NACR1 <- citations(M, field = "article", sep = ";") # To obtain the most frequent cited manuscripts
cbind(CR1$Cited[1:10])## [,1]
## ABDELAZIZ, H.A., FROM CONTENT ENGAGEMENT TO COGNITIVE ENGAGEMENT: TOWARD AN IMMERSIVE WEB-BASED LEARNING MODEL TO DEVELOP SELF-QUESTIONING AND SELF-STUDY SKILLS (2013) INTERNATIONAL JOURNAL OF TECHNOLOGY DIFFUSION, 4 (1), PP. 16-32 3
## ACHA, V., HARGISS, K.M., HOWARD, C., THE RELATIONSHIP BETWEEN EMOTIONAL INTELLIGENCE OF A LEADER AND EMPLOYEE MOTIVATION TO JOB PERFORMANCE (2013) INTERNATIONAL JOURNAL OF STRATEGIC INFORMATION TECHNOLOGY AND APPLICATIONS, 4 (4), PP. 80-103 3
## AGRAWAL, P.R., DIGITAL INFORMATION MANAGEMENT: PRESERVING TOMORROW'S MEMORY (2014) CLOUD COMPUTING AND VIRTUALIZATION TECHNOLOGIES IN LIBRARIES, PP. 22-35. , S. DHAMDHERE (ED.), HERSHEY, PA: IGI GLOBAL 3
## AKRAM, H.A., MAHMOOD, A., PREDICTING PERSONALITY TRAITS, GENDER AND PSYCHOPATH BEHAVIOR OF TWITTER USERS (2014) INTERNATIONAL JOURNAL OF TECHNOLOGY DIFFUSION, 5 (2), PP. 1-14 3
## AKYOL, Z., METACOGNITIVE DEVELOPMENT WITHIN THE COMMUNITY OF INQUIRY (2013) EDUCATIONAL COMMUNITIES OF INQUIRY: THEORETICAL FRAMEWORK, RESEARCH AND PRACTICE, PP. 30-44. , Z. AKYOL & D. GARRISON (EDS.), HERSHEY, PA: IGI GLOBAL 3
## ALLY, M., DESIGNING MOBILE LEARNING FOR THE USER (2012) USER INTERFACE DESIGN FOR VIRTUAL ENVIRONMENTS: CHALLENGES AND ADVANCES, PP. 226-235. , B. KHAN (ED.), HERSHEY, PA: IGI GLOBAL 3
## ALMEIDA, L., MENEZES, P., DIAS, J., AUGMENTED REALITY FRAMEWORK FOR THE SOCIALIZATION BETWEEN ELDERLY PEOPLE (2013) HANDBOOK OF RESEARCH ON ICTS FOR HUMAN-CENTERED HEALTHCARE AND SOCIAL CARE SERVICES, PP. 430-448. , M. CRUZ-CUNHA, I. MIRANDA, & P. GONALVES (EDS.), HERSHEY, PA: IGI GLOBAL 3
## ALONSO, E., MONDRAGN, E., COMPUTATIONAL MODELS OF LEARNING AND BEYOND: SYMMETRIES OF ASSOCIATIVE LEARNING (2011) COMPUTATIONAL NEUROSCIENCE FOR ADVANCING ARTIFICIAL INTELLIGENCE: MODELS, METHODS AND APPLICATIONS, PP. 316-332. , E. ALONSO & E. MONDRAGN (EDS.), HERSHEY, PA: IGI GLOBAL 3
## ARORA, A.S., RAISINGHANI, M.S., LESEANE, R., THOMPSON, L., PERSONALITY SCALES AND LEARNING STYLES: PEDAGOGY FOR CREATING AN ADAPTIVE WEB-BASED LEARNING SYSTEM (2013) CURRICULUM, LEARNING, AND TEACHING ADVANCEMENTS IN ONLINE EDUCATION, PP. 161-182. , M. RAISINGHANI (ED.), HERSHEY, PA: IGI GLOBAL 3
## ASTON, J., DATABASE NARRATIVE, SPATIAL MONTAGE, AND THE CULTURAL TRANSMISSION OF MEMORY: AN ANTHROPOLOGICAL PERSPECTIVE (2013) DIGITAL MEDIA AND TECHNOLOGIES FOR VIRTUAL ARTISTIC SPACES, PP. 150-158. , D. HARRISON (ED.), HERSHEY, PA: IGI GLOBAL 3CR2 <- citations(M, field = "author", sep = ";") # To obtain the most frequent cited first authors
cbind(CR2$Cited[1:10])## [,1]
## CAROTENUTO M 76
## ESPOSITO M 68
## KAMBAS A 64
## VENETSANOU F 50
## ZIMMER R 43
## WANG Y 38
## TOUS J M 37
## LIUTSKO L 36
## SIMONS J 32
## BARNETT L M 31VENETSANOU F’s impact indices:
indices$H## Author h_index g_index m_index TC NP PY_start
## 1 VENETSANOU F 3 5 0.2727273 104 5 2010VENETSANOU F’s citations
indices$CitationList## [[1]]
## Authors Journal Year TotalCitation
## 1 KARACHLE N;DANIA A;VENETSANOU SCIENCE OF GYMNASTICS JOURNAL 2017 2
## 3 VENETSANOU F;KAMBAS A EARLY CHILDHOOD EDUCATION JOUR 2017 3
## 5 VENETSANOU F;KAMBAS A SAGE OPEN 2016 4
## 4 VENETSANOU F;KAMBAS A PEDIATRIC EXERCISE SCIENCE 2017 10
## 2 VENETSANOU F;KAMBAS A EARLY CHILDHOOD EDUCATION JOUR 2010 85Lptkas law
# Lotkas Law coefficient estimation
L <- lotka(results)
# Author Productivity. Empirical Distribution
L$AuthorProd## N.Articles N.Authors Freq
## 1 1 897 0.903323263
## 2 2 77 0.077542800
## 3 3 12 0.012084592
## 4 4 4 0.004028197
## 5 5 1 0.001007049
## 6 6 1 0.001007049
## 7 7 1 0.001007049lokta_table <- matrix(c(L$Beta, L$C, L$R2, L$p.value), ncol = 1, byrow = TRUE)
colnames(lokta_table) <- "Estimation"
rownames(lokta_table) <- c("Beta: ", "Constant: ", "Goodness of fit: ", "P-value: ")
lokta_table <- as.table(lokta_table)
print(lokta_table)## Estimation
## Beta: 3.77514112
## Constant: 0.85368328
## Goodness of fit: 0.97835669
## P-value: 0.05623007Compare the two distributions using plot function:
# Observed distribution
Observed=L$AuthorProd[,3]
# Theoretical distribution with Beta = 2
Theoretical=10^(log10(L$C)-2*log10(L$AuthorProd[,1]))Scientific Production
plot(L$AuthorProd[,1],Theoretical,type="l",col="red",ylim=c(0, 1), xlab="Articles",ylab="Freq. of Authors",main="Scientific Productivity")
lines(L$AuthorProd[,1],Observed,col="blue")
legend(x="topright",c("Theoretical (B=2)","Observed"),col=c("red","blue"),lty = c(1,1,1),cex=0.6,bty="n")
Coupling
# Bibliographic coupling
NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "authors", sep = ";")
# plot authors' similarity (first 20 authors), using salton similarity index
net <- networkPlot(NetMatrix, normalize = "salton", weighted=NULL, n = 100, Title = "Authors' Coupling", type = "fruchterman", size=5,size.cex=T,remove.multiple=TRUE,labelsize=0.8,label.n=10,label.cex=F)
Co-citation
# Bibliographic co-citation
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ". ")
net <- networkPlot(NetMatrix, n = 20, type = "kamada", Title = "co-citation", labelsize = 1.0)
Collaboration
# Bibliographic collaboration
# authors' collaboration network:
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net <- networkPlot(NetMatrix, n = 20, type = "kamada", Title = "Author collaboration", labelsize = 1.0)
Country collaboration
# Create a country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# Plot the network
net <- networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = 'circle', size = TRUE, remove.multiple = FALSE, labelsize = 0.8)
Keyword co-occurrences
# Create keyword co-occurrencies network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net <- networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)
Co-word analysis:
# Conceptual Structure using keywords
CS <- conceptualStructure(M,field="DE_TM", minDegree = 5, k.max = 5, stemming = FALSE, labelsize = 9)
CS <- conceptualStructure(M,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)
Table: Author’s productivity per year
# AuthorProdOverTime, fig.height=6, fig.width=8
topAU <- authorProdOverTime(M, k = 10, graph = TRUE)
Table: Auhtor’s documents list
# AuthorProdOverTime, fig.height=6, fig.width=8
head(topAU$dfAU)## Author year freq TC TCpY
## 1 ALBARET JM 2014 1 2 0.2857143
## 2 ALBARET JM 2015 1 1 0.1666667
## 3 ALBARET JM 2017 1 0 0.0000000
## 4 ALBARET JM 2018 1 0 0.0000000
## 5 DETREZ S 2012 1 0 0.0000000
## 6 DETREZ S 2013 1 0 0.0000000#head(topAU$dfPapersAU)Bipartite network
A <- cocMatrix(M, Field = "SO", sep = ";")
sort(Matrix::colSums(A), decreasing = TRUE)[1:5]## NEUROPSYCHIATRIE DE L'ENFANCE ET DE L'ADOLESCENCE
## 19
## ANAE - APPROCHE NEUROPSYCHOLOGIQUE DES APPRENTISSAGES CHEZ L'ENFANT
## 15
## JOURNAL OF SPORT AND HEALTH RESEARCH
## 12
## RETOS
## 11
## SOINS PEDIATRIE/PUERICULTURE
## 6M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")NetMatrix
# similarity, fig.height=9, fig.width=9, warning=FALSE
NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "authors", sep = ";")
net=networkPlot(NetMatrix, normalize = "salton", weighted=NULL, n = 100, Title = "Authors' Coupling", type = "fruchterman", size=5,size.cex=T,remove.multiple=TRUE,labelsize=0.8,label.n=10,label.cex=F)
Netmatrix
# An example of a classical keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
netstat <- networkStat(NetMatrix)
names(netstat$network)## [1] "networkSize" "networkDensity" "networkTransitivity" "networkDiameter"
## [5] "networkDegreeDist" "networkCentrDegree" "networkCentrCloseness" "networkCentrEigen"
## [9] "networkCentrbetweenness" "NetworkAverPathLeng"names(netstat$vertex)## NULLsummary(netstat, k=10)##
##
## Main statistics about the network
##
## Size 1387
## Density 0.033
## Transitivity 0.253
## Diameter 5
## Degree Centralization 0.833
## Average path length 2.109
## Country collaboration
# Create a country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.7,cluster="none")
Co-citation network, fig.height=7, fig.width=7, warning=FALSE
# Create a co-citation network
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, n = 30, Title = "Co-Citation Network", type = "fruchterman", size=T, remove.multiple=FALSE, labelsize=0.7,edgesize = 5)
—-Keyword c-occurrences
# Create keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
# Plot the network
net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "fruchterman", size=T,edgesize = 5,labelsize=0.7)