Bibliometric analysis 方法
文献计量分析主要采用是对大量文献抓取进行趋势或研究动向判断,可借助R的bibliometrix包及VOSviewer软件进行可视化.
Vosviewer可分析国家间的合作,研究分布热力图及通过对关键词的分析来分析研究热点.
bibliometrix 包可实现相似的功能.
pacman::p_load(bibliometrix,ggplot2)
D <- readFiles("-2018abstract.bib")
M <- convert2df(D,dbsource = "scopus",format = "bibtex")
results <- biblioAnalysis(M, sep = ";")
options(width=100) # 以Scopus数据为例,进行导入
S <- summary(object = results, k = 20, pause = FALSE)
plot(x = results, k = 20, pause = FALSE)
# 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 = 20, Title = "Country Collaboration", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.7,cluster="none")
根据该包可自动输出各类统计图,可参考bibliometrix
以下为调整后显示历年趋势的代码
library(readr)
trend <- read_csv("09-18countries.csv")
ggplot(data=trend, mapping=aes(x=year, y=Number, group=Countries)) +
geom_line(aes(color=Countries),size=1)+
scale_color_discrete(breaks=c("United States","Japan","China","South Korea","Italy"))+
geom_point(aes(color=Countries))+
xlab("Year") + ylab("Articles")+
scale_y_continuous(limits = c(0,550), breaks=seq(0, 550, 50)) +
scale_x_continuous(breaks=seq(2009, 2018, 1))
publishyear <- read_csv("publishyear.csv")
ggplot(data=publishyear, mapping=aes(x=Year, y=Articles)) +
geom_line()+
geom_point(aes(color=Articles))+
scale_color_gradient(low="lightblue", high="darkblue")+
xlab("Year") + ylab("Articles")+
scale_y_continuous(limits = c(0,1750), breaks=c(0, 250,500,750, 1000,1250,1500,1750)) +
scale_x_continuous(limits = c(1935,2018),breaks=c(1935,1940,1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,
1995,2000,2005,2010,2015,2018))
效果如下
