class: center, middle, inverse, title-slide .title[ # Exploring past literature: <br>A bibliometric approach ] .subtitle[ ## R confeRence 2022 ] .date[ ### November27, 2022 ] --- name: about-me layout: false class: about-me-slide, inverse, middle, center ## About me <div> <style type="text/css">.xaringan-extra-logo { width: 60px; height: 70px; z-index: 0; background-image: url(figs/R_MY.jpg); background-size: contain; background-repeat: no-repeat; position: absolute; top:1em;right:1em; } </style> <script>(function () { let tries = 0 function addLogo () { if (typeof slideshow === 'undefined') { tries += 1 if (tries < 10) { setTimeout(addLogo, 100) } } else { document.querySelectorAll('.remark-slide-content:not(.title-slide):not(.inverse):not(.hide_logo)') .forEach(function (slide) { const logo = document.createElement('div') logo.classList = 'xaringan-extra-logo' logo.href = null slide.appendChild(logo) }) } } document.addEventListener('DOMContentLoaded', addLogo) })()</script> </div> <img style="border-radius: 50%;" src="figs/tengku_hanis.jpg" width="150px"/> ### Tengku Muhammad Hanis Mokhtar #### MBBCh, MSc (Medical Statistics), <br>PhD Student (Public Health Epidemiology) .fade[Universiti Sains Malaysia<br>Kelantan, Malaysia] [<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> tengkuhanis.netlify.app](https://tengkuhanis.netlify.app/) [<svg aria-label="Twitter" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><title>Twitter</title><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 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8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> @tengku-hanis](https://github.com/tengku-hanis)<br> [<svg aria-label="LinkedIn" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><title>LinkedIn</title><path d="M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z"/></svg> @Tengku Muhammad Hanis](https://www.linkedin.com/in/tengku-muhammad-hanis-9a7222144/) --- ## Outline ### - A bit about bibliometrics ### - Demo in R (not live demo) ### - Recap - brief step to do a bibliometric analysis --- ## What is biliometrics? - Basically, an analysis of bibliographic information - What we can do with bibliometric analysis: - Evaluate research progress - Quantitatively summarise research output - "Mapping" research contributions by author, institution, etc - Explore research trends - When to do a bibliometric? - Too many literature - The scope (research question) is broad - A lot of papers available on [Google Scholar (>45K papers)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=allintitle%3A+bibliometrics+OR+bibliometric&btnG=) --- ## Bibliographic information <img src="figs/paper-both.png" width="76%" style="display: block; margin: auto;" /> --- ## Type of analysis <img src="figs/type_analysis.png" width="90%" style="display: block; margin: auto;" /> --- class: middle, center background-color: white ## Bibliometrics using bibliometrix --- ## Data - Data was downloaded from the [Scopus](https://www.scopus.com/search/form.uri?display=basic&clear=t&origin=searchadvanced&txGid=f676aa0f046baff36a28dd0a11c7427e#basic) through USM's access - Search terms used: _**(TITLE("covid-19") OR TITLE("covid 19") OR TITLE("2019-nCov") OR TITLE("SARS-CoV-2") OR TITLE("2019 novel coronavirus") OR TITLE("coronavirus disease 2019") OR TITLE("coronavirus disease-19")) AND (TITLE-ABS-KEY(Malaysia))**_ - The search result was further filtered into: 1. Article 2. Review 3. Conference paper 4. Data paper - Final data - 1,265 papers --- ## Load packages ```r library(tidyverse) library(bibliometrix) ``` ## Read data Data should downloaded in a BibTex format for Scopus database ```r dat <- convert2df("data/covid_msia.bib", dbsource = "scopus", format = "bibtex") ``` ## Descriptive ```r res <- biblioAnalysis(dat) summary(res, k = 10) ``` `k` reflects the number of results to be displayed. Since the result is too long, the output is available [here](https://tengku-hanis.github.io/add_rconf2022/). --- ## Basic plots ```r plot(res, k = 10) ``` .panelset.sideways[ .panel[.panel-name[**Plot 1**] <img src="index_files/figure-html/basic plots2-1.png" width="80%" style="display: block; margin: auto;" /> ] .panel[.panel-name[**Plot 2**] <img src="index_files/figure-html/basic plots3-1.png" width="80%" style="display: block; margin: auto;" /> ] .panel[.panel-name[**Plot 3**] <img src="index_files/figure-html/basic plots4-1.png" width="80%" style="display: block; margin: auto;" /> ] .panel[.panel-name[**Plot 4**] <img src="index_files/figure-html/basic plots5-1.png" width="80%" style="display: block; margin: auto;" /> ] .panel[.panel-name[**Plot 5**] <img src="index_files/figure-html/basic plots6-1.png" width="80%" style="display: block; margin: auto;" /> ] ] --- ## Collaborations ### Institutions ```r # Create network MT <- metaTagExtraction(dat, Field = "AU_CO", sep = ";") country_collab <- biblioNetwork(MT, analysis = "collaboration", network = "universities") # Plot network set.seed(123) int_collab <- networkPlot(country_collab, n = 20, cluster = "louvain", Title = "Institutions collaboration", type = "circle", size.cex = T) ``` <img src="index_files/figure-html/inst_collab1-1.png" width="30%" style="display: block; margin: auto;" /> --- Let's see only 2 institutions for each 3 clusters as suggested by the louvain algorithm. ```r int_collab$cluster_res %>% group_by(cluster) %>% slice(1:2) ``` ``` ## # A tibble: 6 × 5 ## # Groups: cluster [3] ## vertex cluster btw_centrality clos_centrality pagera…¹ ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 universiti kebangsaan malaysia 1 15.3 0.0256 0.0658 ## 2 universiti teknologi mara 1 10.2 0.0244 0.0573 ## 3 university of malaya 2 23.5 0.0256 0.0721 ## 4 universiti sains malaysia 2 54.2 0.0278 0.0643 ## 5 universiti malaysia terengganu 3 2.11 0.0238 0.0477 ## 6 monash university malaysia 3 15.4 0.025 0.0497 ## # … with abbreviated variable name ¹​pagerank_centrality ``` The top 5 most active institutions based on the institutions collaboration plot. ```r int_collab$nodeDegree %>% slice(1:5) %>% remove_rownames() ``` ``` ## node degree ## 1 universiti sains malaysia 1.0000000 ## 2 universiti kebangsaan malaysia 0.9946043 ## 3 university of malaya 0.9262590 ## 4 universiti teknologi mara 0.6402878 ## 5 universiti putra malaysia 0.5359712 ``` --- ### Countries ```r # Create network country_collab <- biblioNetwork(MT, analysis = "collaboration", network = "countries") # Plot network set.seed(123) country_collab <- networkPlot(country_collab, n = 30, cluster = "louvain", Title = "Countries collaboration", type = "circle", size.cex = T) ``` <img src="index_files/figure-html/country_collab1-1.png" width="45%" style="display: block; margin: auto;" /> --- Let's see only 3 countries for each 2 clusters as suggested by the louvain algorithm. ```r country_collab$cluster_res %>% group_by(cluster) %>% slice(1:3) ``` ``` ## # A tibble: 6 × 5 ## # Groups: cluster [2] ## vertex cluster btw_centrality clos_centrality pagerank_centrality ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 malaysia 1 36.6 0.0244 0.0329 ## 2 singapore 1 29.7 0.0233 0.0327 ## 3 australia 1 27.2 0.0233 0.0327 ## 4 china 2 21.2 0.0222 0.0374 ## 5 indonesia 2 9.34 0.0204 0.0370 ## 6 united kingdom 2 21.2 0.0222 0.0374 ``` The top 5 most active countries based on the countries collaboration plot. ```r country_collab$nodeDegree %>% slice(1:5) %>% remove_rownames() ``` ``` ## node degree ## 1 malaysia 1.0000000 ## 2 united kingdom 0.1579826 ## 3 china 0.1469606 ## 4 indonesia 0.1456246 ## 5 usa 0.1319305 ``` --- ## Lotka's law Lotka's law describe the scientific productivity in certain research area. ```r L <- lotka(res) L$p.value ``` ``` ## [1] 0.01455341 ``` Kolmogorov-Smirnov two sample test showed that there is a significant difference between the observed and theoretical distribution. We can further plot the two distributions. ```r # Theoretical distribution with Beta = 2 Theoretical <- 10^(log10(L$C) - 2 * log10(L$AuthorProd[, 1])) # Data wrangling for plotting ldata <- L$AuthorProd %>% bind_cols(theory = Theoretical) %>% pivot_longer(cols = c(Freq, theory), names_to = "distribution", values_to = "val_distr") %>% mutate(distribution = as.factor(distribution), distribution = fct_recode(distribution, Observed = "Freq", Theoretical = "theory")) ``` --- ```r ggplot(ldata, aes(N.Articles, val_distr, color = distribution)) + geom_line(linewidth = 1) + labs(color = "Distribution:") + ylab("Frequency of authors") + xlab("Number of articles") + theme_minimal() + theme(legend.position = "top") + annotate("text", label = paste0("P-value = ", round(L$p.value, 3)), x = 12, y = 0.1, size = 4) ``` <img src="index_files/figure-html/lotka3 hidden-1.png" width="40%" style="display: block; margin: auto;" /> Lotka's law plot indicated that the observed distribution is significantly lower than the expected distribution. --- ## Trending keywords ```r trend_kw <- fieldByYear(dat, field = "DE", timespan = c(2020, 2022), min.freq = 3, n.items = 6, graph = FALSE, labelsize = 3) trend_kw$graph + theme_bw() ``` <img src="index_files/figure-html/trending keywords-1.png" width="40%" style="display: block; margin: auto;" /> --- ## Biblioshiny <img src="figs/biblioshiny.gif" width="60%" style="display: block; margin: auto;" /> --- ## Steps for bibliometric analysis 1. Define scope, objectives and selection criteria 2. Specify techniques (based on objectives) 3. Comes up with search terms - always check with the databases whether the terms valid or not 4. Data searching on databases: - SCOPUS, WOS, Digital Science Dimensions, PubMed, Lens or Cochrane 5. Review downloaded data/abstracts if needed (brief review) 6. Run bibliometric analysis and report finding --- ## References - Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070 - Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007 - Hood, W.W., Wilson, C.S. The Literature of Bibliometrics, Scientometrics, and Informetrics. Scientometrics 52, 291 (2001). https://doi.org/10.1023/A:1017919924342 --- class: center, middle <img src="figs/logo2.png" width="60%" /> # Thanks! .center[.footnote[<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> Material: https://tinyurl.com/biblio-rconference2022]]