Bibliometrics (17): The Biblioshiny App For Non-Coders | Bibliometrix R Package
Hello everyone so those who are doing bibliometric analysis using bibliometric package and those who are scared of coding. For you we have a good news the bibliometrics developers that have come up with a new package new tool called big blue shiny. Where you don't have to do any coding only very passive only a couple of passing codes which you can just copy from here. Ok so let me show you how it works ok so first. I am on my Arduino. If you don't know how our works or how do you still are. Please see our video on that ok. We already have a video on how to install packages and how digital are properly so have a look on that we start working with bibliometrics. You first have to install the nemetrix package in our and which i have already done so. I will just load the package which is a library command. Ok so if you don't know if you don't if you have not installed the package yet so you just remove this hash okay and then run the install package come on but. I have downloaded it yet. I have installed it so I'll just load the package. And then we give this comment below shani and then brackets and then it will load the big new shiny package Shantanu or maybe. It's good to send a shiny app so come on tightly run this code and no shiny comes on my wave explorer ok so it looks like this ok so here you have some basic information above the big new shiny thing about the people shiny too and I recommend everyone to read this article. When you when you work with in the metrics package here you will find all the detail of each of the tools that are used is in this app. Ok so please go through this article have a look on it ok anyway so when we have this weekend simply load our data file and if you do not know how to load data file please see our videos on that. We already have videos on how to load data from SEO web science and also from you scoopers so we cover Buddha both two largest data size data so we already covered the two largest databases so have a look on our videos how to extract data from these databases.
So for now. I'm going to use the AIA site web science data which is called wave of knowledge as well. And here you see you have the genomics. Koopa so you can use this type of data and forward although you have to use plain text and when you use school bus that's when you have to use the beep text okay so I'm just going to browse a text file that I have extracted from web of knowledge which is stored in my laptop so I'll just load it here so I have like 207 articles here okay so the loading is complete now. I can see the data here briefly okay so I have about two hundred and seven articles. If you want to save in different formats you can here. We only have the excellent supported okay but I'm not going to save anything I will just proceed with the analyzes with this blue shiny tool. Okay you will have this filter option here so you can filter data based on publication years based on citations based on source and all these things but I'm going to use all of it okay. So for the data side first we have some aggregate information about we have like 207 articles published in 104 journals. Okay and we have like 400 460 key word plus then. Auto skew orders 644 for data ranges from 1992 to 2009 teen presentations per document. Total number of authors are fifteen hundred and thirteen so these 207 articles are written by fifteen hundred and thirteen authors. And then we have other appearances single other documents multi author documents. Yeah so we have all these informations here and if you want to learn more about this information please read read this article so there in this article. The developers explained it very clearly and very nicely how everything works okay. Okay and we can also see and we'll scientific production so for this topic that I have chosen an data using here you see the publication's in the recent recent years are are the peak in 2018 so the peak and number of publication actually kind of increased exponentially.
Okay from source you can see most relevant sources which are the journals so this is based on number of documents as you can see it here you can also instead of the if you were to save the plot you can just right click and copy image okay or save images you can use these two options you can also see it in table form okay so we see the number the most number of articles for this topic was published in Transportation Research Party Journal. Okay and yeah we see the other journals we can copy it from here you can copy it in. CSV in Excel and PDF or print it. Okay so this is the relevant sources based on number of articles but we can also see sources based on their citations and impact which here we are calling 18 decks okay. 18x normally means that for instance this journal here transport there 18 decks is more or less 25 something like that 24 this means that they have about 24 articles which were cited 24 times. Okay we can see it in the table as well detail about the journals and all these things okay so then we have this bad force law also if you just do a quick. Google search you will find these that it's actually about the exponentially diminishing pattern of searching for references in science journal premier. Maybe means that these are the journals where the citations are increasing exponentially and here it decreases exponentially for the journals in the later groups. I hope that's what it means but I'm not sure so I have a look at it. Also we have this source dynamics where we can see the journals we the number of articles published in journals. So you see like this journal is doing great here. The number of obligations on this topic is Greg's potentially in these journals and some journal in other journals it seems to be decreasing a little bit. Yeah here yeah. It seems to be decreasing in other journals but in these channels in sync so these things are interesting to see. Actually what's happening in the field okay. Then we have authors so we can see most relevant authors authors based on number of documents you can again switch between plot and table okay.
We can see authors productive production over time so yeah here we have the year so we can see the production of authors when they published and how many they published. Okay time here. We have the number of articles and total citations per year. We can see long kazlaw again. I don't really know much about this as long. But it shows kind of distribution of scientific production so here for instance when we look here it means that more maturity of the scientific production in this topic came from all the thirst with less than less than 2.5 articles or like less than maybe 2 less than 2 articles so most of the people in this field they have written one or two articles ok and there are very few who wrote more than 5 articles in these topics. This topic. Okay and then again you can see authors h-index again. You can go for table you can see the most relevant affiliation. We see that you deafed done that and you left university from netherlands. That's the top one in this. Pick with a with a lot of articles okay and then for change my CD Brussels that's also doing good with who know Nina Missy D from China okay and we can go to corresponding alters country. Okay so here we can also see we see that the most our articles are coming from USA and as a female singer author participating country and MC P means multiple author articles. Okay so here we see from us and many people are writing single of articles but UK. Many are collaborating. Okay again you can see this information. In tables countries we can see production like this so we see production most of the productions are coming from USA and maybe some from China so the darker the more article and you can see the same information in table. You see you say okay. We have most cited countries so while USA was the top one in number of publications but number in number of citations today. Netherland is the top one okay and then we have this United Kingdom that we can move to documents global sided documents.
Which means that total citation of a and we have local citations which means that the articles which received the most number of citation among in this 2007 articles and then we have most local cited references. These are the documents and these are the references which articles which articles were cited most among the references of the 207 articles. Okay references to the screw roughy here we can see so. This is the number of cited references. This is the cited reference. I also actually desperates like here. You see the deviations within the past five years here we seems to be having some deviations okay and we have most frequent words so we see logisitics model and for management optimization. Most because word we have we can make a word cloud this takes another words or sounding it we can change the number of words in our word cloud we can look into different things like currency square root. I think currency is fine shapes. We can change the shapes we can change things you can play with it so if we want to do something you play a bit and see which one feels the best with your topic and your title here also you can choose between different fields and other things. Okay here you can make a tree map here. We see that this word is the most used and algorithm is the list used may be in this case so these things are also nice. See what dynamics the growth of words okay so here. In general we see ambition is growing predation is growing cost and network is growing. Lourdes statistics is declining. Okay policy is declining declining. That's all from there. Then we can move to conceptual structure we can see co-occurrence Network which is so here. We see that there are about that's it's based on keyword paths and you can choose between authors keyword title and abstract and you can play with different styles and layout but you choose the author then it will give you the best fitted one can go for ovation normalization.
These things are very nicely explained in the in the paper published by the developers of this tool so have looked here a bit here with number of levels little size and all these things okay so normally. I see that they're about two clusters to conceptual structures here. It can change when we increase the number of articles or here. Actually if we increase the number of notes you see here now we have some more clusters change. Okay so yeah you have to play with it a bit to get the best ones we did. Fourier you're already go okay. You can't go for facto analysis here again you can find the conceptual structure in your field yeah. I see two clusters here again because I can maybe reason to like 40 or something like that the number of terms and see what happens. Yeah so you have to play a bit to get a very good one and change the structure change so we have three options here on scaling. Isis can change between authors keyword titles and abstract here. This map like this this each of this dots. They represent one article and you can see it here okay. So this is the cluster. You see the articles here which articles from this information. You can find the article and discuss about them in your paper and this red one is another cluster and article surrounding. It is okay and the same thing you can. It can be based on most cited papers and most contributing papers again. You will get most relevant papers for that clusters here and for these clusters here. Okay we can also have a genetic map here which normally should give you the team senior in your articles and in your research so this is how it looks if I go to the table and I see but I have four teams one two three and four so one team is about location and operations you know you know I have not yet see how it works with each other we have nice we can see detail here please have a look again. I am referring to the article. Then we can look into the console intellectual structure based on co-citation okay so if this is consultation.
Network here I see. There are three clusters and these articles. They are one one topic one team they are and these articles are either. These are another team your again. You can increase the number of articles and maybe we'll see more teams now maybe less teams okay. Yeah we have now four teams and it's it's very nice and chilly as you can see here okay and you can see here detail which of articles are on which team okay you can again play here with different things. All these options you can you can see the co-citation of authors instead of papers so which authors are Co citing. Yeah this also sometimes gives us some nice formation about the individual structure of research field can also do it for sources the citation among the journals with two journals are cited together by other journals. I think these informations are very nice. Actually yeah and it can really help you find the course. Also we have the histogram where it maps maps the articles based on the year of publication and also it shows with the arrow who's citing. Okay so these articles cited this one. This article is cited by these ones. And this one's okay you can again. L so they here you can. You can have two different algorithms here and you can change the number of notes these are the piece which means number of articles. Okay no size you can also make it a smaller big and then then we have this social structure where we have the collaboration network so here for instance we can see which authors are writing papers together. These guys are writing. The other of these guys are running together. You can also see it for institutions which institutions are collaborating together. So you see as much university and you've seen it basically. They are collaborating together then here. I know open and may you Vienna there collaborating together in this topic. Okay so we can see that we can also see it four countries.
Okay so here you see you know that. Kingdom is cooperating in this countries Serbia with peace. I think this is a fantastic tool in the research field and then we done. We ate the last thing. We have world map collaboration. So this collaboration thing we can show it in the world map we can increase the numbers so here maybe if we increase it that will see more connections okay so at least there. If there is at least one cooperation between countries it will show up but if we increase it now there should be at least two collaboration between countries and then it will show up in the map. Again you can increase the age size please decrease play with it and show how JEWS and you see the collaboration information you can click on the table so like for now when we have aged a minimum age 2 then this will not show up okay because so only the two and more than two will show up in the map. Okay that's all so again thank you for watching this video if you find it useful. Please subscribe to our channel to support us and like come on that shear and this. I think this is a fantastic tool for those. Who don't like the code. Okay normally with our and the geometrics we have two core but with a shiny. It's great for those who don't love the corridor find a difficulty code okay and you can find please. I'm telling it again. Please read this article so that you get to know the detail about all these functions. Thank you for watching this video and thank you for today. Please like share comment and subscribe.
So for now. I'm going to use the AIA site web science data which is called wave of knowledge as well. And here you see you have the genomics. Koopa so you can use this type of data and forward although you have to use plain text and when you use school bus that's when you have to use the beep text okay so I'm just going to browse a text file that I have extracted from web of knowledge which is stored in my laptop so I'll just load it here so I have like 207 articles here okay so the loading is complete now. I can see the data here briefly okay so I have about two hundred and seven articles. If you want to save in different formats you can here. We only have the excellent supported okay but I'm not going to save anything I will just proceed with the analyzes with this blue shiny tool. Okay you will have this filter option here so you can filter data based on publication years based on citations based on source and all these things but I'm going to use all of it okay. So for the data side first we have some aggregate information about we have like 207 articles published in 104 journals. Okay and we have like 400 460 key word plus then. Auto skew orders 644 for data ranges from 1992 to 2009 teen presentations per document. Total number of authors are fifteen hundred and thirteen so these 207 articles are written by fifteen hundred and thirteen authors. And then we have other appearances single other documents multi author documents. Yeah so we have all these informations here and if you want to learn more about this information please read read this article so there in this article. The developers explained it very clearly and very nicely how everything works okay. Okay and we can also see and we'll scientific production so for this topic that I have chosen an data using here you see the publication's in the recent recent years are are the peak in 2018 so the peak and number of publication actually kind of increased exponentially.
Okay from source you can see most relevant sources which are the journals so this is based on number of documents as you can see it here you can also instead of the if you were to save the plot you can just right click and copy image okay or save images you can use these two options you can also see it in table form okay so we see the number the most number of articles for this topic was published in Transportation Research Party Journal. Okay and yeah we see the other journals we can copy it from here you can copy it in. CSV in Excel and PDF or print it. Okay so this is the relevant sources based on number of articles but we can also see sources based on their citations and impact which here we are calling 18 decks okay. 18x normally means that for instance this journal here transport there 18 decks is more or less 25 something like that 24 this means that they have about 24 articles which were cited 24 times. Okay we can see it in the table as well detail about the journals and all these things okay so then we have this bad force law also if you just do a quick. Google search you will find these that it's actually about the exponentially diminishing pattern of searching for references in science journal premier. Maybe means that these are the journals where the citations are increasing exponentially and here it decreases exponentially for the journals in the later groups. I hope that's what it means but I'm not sure so I have a look at it. Also we have this source dynamics where we can see the journals we the number of articles published in journals. So you see like this journal is doing great here. The number of obligations on this topic is Greg's potentially in these journals and some journal in other journals it seems to be decreasing a little bit. Yeah here yeah. It seems to be decreasing in other journals but in these channels in sync so these things are interesting to see. Actually what's happening in the field okay. Then we have authors so we can see most relevant authors authors based on number of documents you can again switch between plot and table okay.
We can see authors productive production over time so yeah here we have the year so we can see the production of authors when they published and how many they published. Okay time here. We have the number of articles and total citations per year. We can see long kazlaw again. I don't really know much about this as long. But it shows kind of distribution of scientific production so here for instance when we look here it means that more maturity of the scientific production in this topic came from all the thirst with less than less than 2.5 articles or like less than maybe 2 less than 2 articles so most of the people in this field they have written one or two articles ok and there are very few who wrote more than 5 articles in these topics. This topic. Okay and then again you can see authors h-index again. You can go for table you can see the most relevant affiliation. We see that you deafed done that and you left university from netherlands. That's the top one in this. Pick with a with a lot of articles okay and then for change my CD Brussels that's also doing good with who know Nina Missy D from China okay and we can go to corresponding alters country. Okay so here we can also see we see that the most our articles are coming from USA and as a female singer author participating country and MC P means multiple author articles. Okay so here we see from us and many people are writing single of articles but UK. Many are collaborating. Okay again you can see this information. In tables countries we can see production like this so we see production most of the productions are coming from USA and maybe some from China so the darker the more article and you can see the same information in table. You see you say okay. We have most cited countries so while USA was the top one in number of publications but number in number of citations today. Netherland is the top one okay and then we have this United Kingdom that we can move to documents global sided documents.
Which means that total citation of a and we have local citations which means that the articles which received the most number of citation among in this 2007 articles and then we have most local cited references. These are the documents and these are the references which articles which articles were cited most among the references of the 207 articles. Okay references to the screw roughy here we can see so. This is the number of cited references. This is the cited reference. I also actually desperates like here. You see the deviations within the past five years here we seems to be having some deviations okay and we have most frequent words so we see logisitics model and for management optimization. Most because word we have we can make a word cloud this takes another words or sounding it we can change the number of words in our word cloud we can look into different things like currency square root. I think currency is fine shapes. We can change the shapes we can change things you can play with it so if we want to do something you play a bit and see which one feels the best with your topic and your title here also you can choose between different fields and other things. Okay here you can make a tree map here. We see that this word is the most used and algorithm is the list used may be in this case so these things are also nice. See what dynamics the growth of words okay so here. In general we see ambition is growing predation is growing cost and network is growing. Lourdes statistics is declining. Okay policy is declining declining. That's all from there. Then we can move to conceptual structure we can see co-occurrence Network which is so here. We see that there are about that's it's based on keyword paths and you can choose between authors keyword title and abstract and you can play with different styles and layout but you choose the author then it will give you the best fitted one can go for ovation normalization.
These things are very nicely explained in the in the paper published by the developers of this tool so have looked here a bit here with number of levels little size and all these things okay so normally. I see that they're about two clusters to conceptual structures here. It can change when we increase the number of articles or here. Actually if we increase the number of notes you see here now we have some more clusters change. Okay so yeah you have to play with it a bit to get the best ones we did. Fourier you're already go okay. You can't go for facto analysis here again you can find the conceptual structure in your field yeah. I see two clusters here again because I can maybe reason to like 40 or something like that the number of terms and see what happens. Yeah so you have to play a bit to get a very good one and change the structure change so we have three options here on scaling. Isis can change between authors keyword titles and abstract here. This map like this this each of this dots. They represent one article and you can see it here okay. So this is the cluster. You see the articles here which articles from this information. You can find the article and discuss about them in your paper and this red one is another cluster and article surrounding. It is okay and the same thing you can. It can be based on most cited papers and most contributing papers again. You will get most relevant papers for that clusters here and for these clusters here. Okay we can also have a genetic map here which normally should give you the team senior in your articles and in your research so this is how it looks if I go to the table and I see but I have four teams one two three and four so one team is about location and operations you know you know I have not yet see how it works with each other we have nice we can see detail here please have a look again. I am referring to the article. Then we can look into the console intellectual structure based on co-citation okay so if this is consultation.
Network here I see. There are three clusters and these articles. They are one one topic one team they are and these articles are either. These are another team your again. You can increase the number of articles and maybe we'll see more teams now maybe less teams okay. Yeah we have now four teams and it's it's very nice and chilly as you can see here okay and you can see here detail which of articles are on which team okay you can again play here with different things. All these options you can you can see the co-citation of authors instead of papers so which authors are Co citing. Yeah this also sometimes gives us some nice formation about the individual structure of research field can also do it for sources the citation among the journals with two journals are cited together by other journals. I think these informations are very nice. Actually yeah and it can really help you find the course. Also we have the histogram where it maps maps the articles based on the year of publication and also it shows with the arrow who's citing. Okay so these articles cited this one. This article is cited by these ones. And this one's okay you can again. L so they here you can. You can have two different algorithms here and you can change the number of notes these are the piece which means number of articles. Okay no size you can also make it a smaller big and then then we have this social structure where we have the collaboration network so here for instance we can see which authors are writing papers together. These guys are writing. The other of these guys are running together. You can also see it for institutions which institutions are collaborating together. So you see as much university and you've seen it basically. They are collaborating together then here. I know open and may you Vienna there collaborating together in this topic. Okay so we can see that we can also see it four countries.
Okay so here you see you know that. Kingdom is cooperating in this countries Serbia with peace. I think this is a fantastic tool in the research field and then we done. We ate the last thing. We have world map collaboration. So this collaboration thing we can show it in the world map we can increase the numbers so here maybe if we increase it that will see more connections okay so at least there. If there is at least one cooperation between countries it will show up but if we increase it now there should be at least two collaboration between countries and then it will show up in the map. Again you can increase the age size please decrease play with it and show how JEWS and you see the collaboration information you can click on the table so like for now when we have aged a minimum age 2 then this will not show up okay because so only the two and more than two will show up in the map. Okay that's all so again thank you for watching this video if you find it useful. Please subscribe to our channel to support us and like come on that shear and this. I think this is a fantastic tool for those. Who don't like the code. Okay normally with our and the geometrics we have two core but with a shiny. It's great for those who don't love the corridor find a difficulty code okay and you can find please. I'm telling it again. Please read this article so that you get to know the detail about all these functions. Thank you for watching this video and thank you for today. Please like share comment and subscribe.