Age,Female,Male 0~10,5,6 11~20,15,14 21~30,20,22 31~40,25,24 41~50,25,28 51~60,30,26 61~70,20,18 71~80,15,16 81~90,10,12
Select option: This one you selected:Pyramid optgroup> A.Rasch model WrightMap>0. Wright Map0. Wright Map TamRasch>01.Tam Rasch01.Tam Rasch Simulateddata1>A Dichotomous simulated dataA Dichotomous simulated data Simulateddata2>A Multitomous simulated dataA Multitomous simulated data RaschjmleRSM>A Rasch JMLE for RSMA Rasch JMLE for RSM MCMCinR3>Rasch MCMC estimation in RRasch MCMC estimation in R KendallW2>***RSM/PCM estimation in R***RSM/PCM estimation in R RaschusingTAM>Rasch using TAMRasch using TAM RaschusingTAM2>MML dicho-Wright Map with TAMMML dicho-Wright Map with TAM RaschusingTAM3>JML poly-Group:Rasch Wright Map with TAMJML poly-Group:Rasch Wright Map with TAM Raschkidmap>Rasch KIDMAPRasch KIDMAP Raschcontinuous>Rasch Continuous ScaleRasch Continuous Scale Rasch model_ICC>1.Rasch model_ICC1.Rasch model_ICC Rasch model_WrightMap>2.Rasch model_WrightMap2.Rasch model_WrightMap RaschMML model>3.Rasch MML model3.Rasch MML model Rasch2PLIRT>4.2PLIRT4.2PLIRT Rasch DIF>5.Rasch DIF 5.Rasch DIF IRTLord DIF>6.IRTLord DIF 6.IRTLord DIF RaschAndersenLRTest>7.AndersenLRTest 7.AndersenLRTest Rasch ML>8.Rasch ML 8.Rasch ML Rasch MAPEAP>9.Rasch MAPEAP 9.Rasch MAPEAP RaschAdersonLRtest>10.RaschAdersonLRtest 10.RaschAdersonLRtest RaschAdersonLR>11.RaschAdersonLR 11.RaschAdersonLR RaschRSMICC>12.RaschRSMWrightMap 12.RaschRSMWrightMap Raschprepost2GLPCM>13.Raschprepost2GLPCM13.Raschprepost2GLPCM /optgroup> optgroup> B.SEM in R SEM001> B1.SEM x to y B1.SEM x to y SEM002> B2.SEM Simple B2.SEM Simple SEM003> B3.SEM Group B3.SEM Group SEM004> B4.SEM two laents B4.SEM two laents SEM005> B5.SEM add prediction B5.SEM add prediction SEM006> B6.SEM with groups B6.SEM with groups SEM007> B7.SEM2 with groups B7.SEM2 with groups SEM008> B8 .SEM2 modindices B8 .SEM2 modindices SEM009> B9 .SEM3 with groups B9 .SEM3 with groups SEM00A> BA .SEM4 monochrome with groups BA .SEM4 monochrome with groups SEM00B> BB .SEM5 anti-high pression with groups BB .SEM5 anti-high pression with groups SEM00C> BC .EFA decathlon in 2021 Tokyo 21 players BC .EFA decathlon in 2021 Tokyo 21 players SEM00D> BD .SEM decathlon in 2021 Tokyo 21 players BD .SEM decathlon in 2021 Tokyo 21 players SEM00E> BE. From t(data) BE. From t(data) SEM00E2>BE2. EFA for From t(data)BE2. EFA for From t(data) /optgroup> optgroup> B.Biliometrics ChordDiagram>14.ChordDiagram 14.ChordDiagram ChordDiagramA>14B. From chorddiagram to cluster Network14B. From chorddiagram to cluster Network Circlebarplot>15.Circlebarplot 15.Circlebarplot Circularbarplot2>15A. Circular barplots15A. Circular barplots Donut>16A. Donut plot16A. Donut plot Circleheatmap>15B. Circle Heat Map15B. Circle Heat Map Circleheatmap2>15C.Blue Circle Heat Map15C.Blue Circle Heat Map circlestackedbarplot>16.Circle Stacked Bar Plot 16.Circle Stacked Bar Plot Circlepacking>34.Circle Packing34.Circle Packing Barchart>341.Bar Chart341.Bar Chart Barchart2>342. Simple BarChart342. Simple BarChart Boxplot>34A.Box plot34A.Box plot Venndiagramthen>161.Venn Diagramthen161.Venn Diagramthen Venndiagramthen2>162.Venn Diagramthen2162.Venn Diagramthen2 Lollipopchart>342.Lollipop Chart342.Lollipop Chart Lollipopchart2>343.Lollipop with terms343.Lollipop with terms Sankey >37.Sankey 37.Sankey NapoleonRussiancampaign1812>@@@ Napoleon's Russian campaign 1812@@@ Napoleon's Russian campaign 1812 SankeyMatic>*** 37a.Text code for SankeyMatic*** 37a.Text code for SankeyMatic Sankey2>***38.Sankey2***38.Sankey2 RelationdatasetforSan>***Relation Dataset For Sankey***Relation Dataset For Sankey SNA22 >39. Network diagram 39. Network diagram SNA221 >40. Via pajek to Network40. Via pajek to Network SNAsimple>46. Via metrix to Network46. Via metrix to Network SNAsimple2 >47. Network dendro2 47. Network dendro2 SNAsimple3 >48. Clusters by dendrogram48. Clusters by dendrogram CircularDendrogram >50. Circular Dendrogram50. Circular Dendrogram Heatmap33 >51. Heatmap3351. Heatmap33 Heatdendrogram>52. Heatmap34(dendrogram)52. Heatmap34(dendrogram) Heatdendrogram3>53. Factor analysis53. Factor analysis Heatdendrogram4>54. Cluster analysis for items54. Cluster analysis for items Heatdendrogram41>55. Cluster analysis for persons55. Cluster analysis for persons Heatdendrogram4A>56. Distance analysis for rows56. Distance analysis for rows Heatdendrogram41A>57. Distance analysis for columns57. Distance analysis for columns Euclidean3 >410. Euclidean dendrogram0410. Euclidean dendrogram0 Euclidean >41. Euclidean dendrogram41. Euclidean dendrogram Euclidean2 >49. Euclidean dendrogram249. Euclidean dendrogram2 /optgroup> optgroup> C.MetaAnlysis TableuseinMA>12.Table used 12.Table used Forestplot>15.Forest Plot15.Forest Plot NMAforest>16.Forest Plot(NMA)16.Forest Plot(NMA) ForestNMA>17.Forest Plot(NMA for each)17.Forest Plot(NMA for each) blobbogram>15A.Forest Plot(easy)15A.Forest Plot(easy) MetaSMD>16.MetaSMD 16.MetaSMD MetaRR>17.MetaRR 17.MetaRR MetaOR>18.MetaOR18.MetaOR MAcorrel>35.Meta correlation35.Meta correlation MetaFunnel>19.MetaFunne 19.MetaFunne NMA2024A>1. Network chart(FLCA)1. Network chart(FLCA) NMA2024>16A Smoking cessation rates(example)16A Smoking cessation rates(example) NMA2024AG>2A.above data trasformed using pairwise()2A.above data trasformed using pairwise() NMA2024AB>2.NetworkMa Summary Frequent(Odds A)2.NetworkMa Summary Frequent(Odds A) NMA2024AE>32.NetworkMa MCMC Bayesian(LogOdds B)32.NetworkMa MCMC Bayesian(LogOdds B) NMA2024AW>33.NetworkMa MCMC-Bayes(Odds Fixed) NMA2024AF33.NetworkMa MCMC-Bayes(Odds Fixed) NMA2024AF NMA2024AW2>34.NetworkMa MCMC Bayes(Odds Random) NMA2024AFr34.NetworkMa MCMC Bayes(Odds Random) NMA2024AFr NMA2024AF>33.na.NetworkMa MCMC-Bayes(Odds Fixed) NMA2024AF33.na.NetworkMa MCMC-Bayes(Odds Fixed) NMA2024AF NMA2024AFr>34.na.NetworkMa MCMC Bayes(Odds Random) NMA2024AFr34.na.NetworkMa MCMC Bayes(Odds Random) NMA2024AFr NMAforest2>**.NetworkMa MCMC Bayes(contin.Fixed)**.NetworkMa MCMC Bayes(contin.Fixed) NMAforest3>**.NetworkMa MCMC Bayes(contin.Random)**.NetworkMa MCMC Bayes(contin.Random) NMA2024AC>3.NetworkMa MCMC Bayesian(SMD)3.NetworkMa MCMC Bayesian(SMD) Consistency>4. Consistency test(SMD)4. Consistency test(SMD) Consistency2>4. Consistency test(SMD2)4. Consistency test(SMD2) NetworkMeta>A1.NetworkMA plot(Frequentist )A1.NetworkMA plot(Frequentist ) NMA2024AW >A2.NetworkMA MCMC Bayesian(Odds) NetworkMetaACA2.NetworkMA MCMC Bayesian(Odds) NetworkMetaAC NMA2024AD>A3.NetworkMa Frequentist(Odds plot)A3.NetworkMa Frequentist(Odds plot) NetworkMeta4>A4.Fixed/Random Effect using R-nemeta(Frequentist)A4.Fixed/Random Effect using R-nemeta(Frequentist) NetworkMeta3A>A4*. To examine the wrong in A4(Log Odds)A4*. To examine the wrong in A4(Log Odds) NetworkMeta3B>A4*. To examine the wrong in A4(Odds)A4*. To examine the wrong in A4(Odds) NetworkMeta5>A5.NetworkMA RadomForest(Frequentist)A5.NetworkMA RadomForest(Frequentist) NetworkMeta2>A6.Forest plot(Oddes)A6.Forest plot(Oddes) AlphaBeta>to predict Apha & Beta in Beta distributionto predict Apha & Beta in Beta distribution /optgroup> optgroup> D.Biliometrixs Bibliometrix>Bibliometrix.org/home/Bibliometrix.org/home/ bibliometrix1>BX1.Descriptive analyticsBX1.Descriptive analytics Savelongformdata>22.Save long form data22.Save long form data co-citation2>BX2.Co-citation(Article (References))BX2.Co-citation(Article (References)) co-citation3>BX3.Co-citation(Journal (Source))BX3.Co-citation(Journal (Source)) co-citation4>BX4.Historiograph - Direct citation linkagesBX4.Historiograph - Direct citation linkages co-citation5>BX5.Co-word via keywordBX5.Co-word via keyword co-citation6>BX6.Co-word via CorresponceBX6.Co-word via Corresponce co-citation7>BX7.Thematic MapBX7.Thematic Map co-citation8>BX8.Author collaborationBX8.Author collaboration co-citation9>BX8.Unit collaborationBX8.Unit collaboration co-citation10>BX9.Country collaborationBX9.Country collaboration co-citation11>BX10.Keyword Co-wordBX10.Keyword Co-word co-citation12>BX11.Lotka LawBX11.Lotka Law co-citation13>BX12.Top-Authors annuallyBX12.Top-Authors annually co-citation14>BX13.CouplingBX13.Coupling /optgroup> optgroup> Cluster analysis in Network Authornetwork>No cluster in networkNo cluster in network Authornetwork2>Taipei MRT systemTaipei MRT system TwocolumnFLCA>2 columns with Leader & Folower using FLCA2 columns with Leader & Folower using FLCA FLCAto6columns>@@@ csv for FLCA to 6 columns@@@ csv for FLCA to 6 columns Authorcorrence9C53>***FLCA type.Highlight 3 clusters***FLCA type.Highlight 3 clusters Authorcorrence9C52>Propagation.Highlight 2 clustersPropagation.Highlight 2 clusters Authorcorrence9C5>Betweenness.Highlight clustersBetweenness.Highlight clusters Authorcorrence9C54>cluster_fast_greedy.Highlight clusterscluster_fast_greedy.Highlight clusters Authorcorrence9C55>cluster_infomap.Highlight clusterscluster_infomap.Highlight clusters Authorcorrence9C56>cluster_leading_eigen.Highlight clusterscluster_leading_eigen.Highlight clusters Authorcorrence9C57>cluster_louvain.Highlight clusterscluster_louvain.Highlight clusters Authorcorrence9C58>cluster_optimal.Highlight clusterscluster_optimal.Highlight clusters Authorcorrence9C59>cluster_spinglass(network)cluster_spinglass(network) Authorcorrence9C5A>cluster_walktrap(network)cluster_walktrap(network) Authorcorrence9C5B>components(network)components(network) Authorcorrence9C5C>TO kwnow FLCA in R (network)TO kwnow FLCA in R (network) /optgroup> optgroup> DDSPP Model in Bibliometrics Countcomputedindata>1.0 Top 10 keywords extracted in CSV1.0 Top 10 keywords extracted in CSV Alluviealtrend>@@@1.1 Alluvial Trend @@@1.1 Alluvial Trend Alluviealtrend2>1.12 Stacked Alluvial Trend 1.12 Stacked Alluvial Trend Inflectionpoint23>1. 2-axes IP with 1st Diff cumulative 1. 2-axes IP with 1st Diff cumulative Inflectionpoint232>2.1. Boxplot for one variable 2.1. Boxplot for one variable Inflectionpoint24>2. HotSpot(CiteSpace)2. HotSpot(CiteSpace) Heatmap3345>H18. Add value to Heatmap by vertex and yearsH18. Add value to Heatmap by vertex and years Timelineview>P46.D7:To gain dataframe of HotSpot(CiteSpace)P46.D7:To gain dataframe of HotSpot(CiteSpace) Timelineview3>P48.Eg. Combined HotSpot & HeatmapP48.Eg. Combined HotSpot & Heatmap Inflectionpoint24A>Z41.Multiply Line ChartZ41.Multiply Line Chart Inflectionpoint24W>Z42.Multiply Line Chart with IPZ42.Multiply Line Chart with IP Authorcorrence9C7>D34(Layout).4-Quadrant Thematic Map D34(Layout).4-Quadrant Thematic Map Authorcorrence9C8>D35(Layout).Kano diagram for Thematic Map D35(Layout).Kano diagram for Thematic Map Authorcorrence9C83>D39(Sankey).row...Sankey BlocksD39(Sankey).row...Sankey Blocks Sankey>37.Sankey(Sankey).column.no link in cluster37.Sankey(Sankey).column.no link in cluster ERSankey>D40(Saneky)ER exampleD40(Saneky)ER example Sankeymatic3>D39(Sankey).column..SankeymaticD39(Sankey).column..Sankeymatic SankeyMatic>*** 37a.Text code for SankeyMatic*** 37a.Text code for SankeyMatic Sankey3>D43(Alluvial).Dual-map viewD43(Alluvial).Dual-map view Sankey4>D431(Alluvial).Dual-map view2(Pyramid)D431(Alluvial).Dual-map view2(Pyramid) Alluvial>433(Alluvial by survival case on the Titanic433(Alluvial by survival case on the Titanic Authorcorrence9C61>D331.Time Line View D331.Time Line View PDFchinesecharacter>P49.PDF for Chinese characters in RP49.PDF for Chinese characters in R PDFextracttext>P49B PDF to extract textP49B PDF to extract text Inflectionpoint242>3. 4Q radar trend in R3. 4Q radar trend in R Inflectionpoint243>D4. Chord plot by row with corrD4. Chord plot by row with corr Inflectionpoint244>D5. Chord plot by column with corrD5. Chord plot by column with corr Scatterbyyear2>D12. 4-Quadrant Radar Plot in RD12. 4-Quadrant Radar Plot in R Scatterbyyear23>D13. 4-Scatters Plots in RD13. 4-Scatters Plots in R Scatterbyyear24>D14.95%CI 4-Scatters Plots in RD14.95%CI 4-Scatters Plots in R Pyramid>D15.Pyramid PlotD15.Pyramid Plot Multilinebar2>D15.Multi-Line Bar2 ChartD15.Multi-Line Bar2 Chart MAuthorcorrence>D21.Author Collaborations with NetworkD21.Author Collaborations with Network Authorcorrence3>D22 D2:*Chord(cluster & few links) to #14 D22 D2:*Chord(cluster & few links) to #14 Authorcorrence31>D23.D3:*Dendrogram to #47D23.D3:*Dendrogram to #47 Authorcorrence4>D24.CircleBar. to #15 CircleBarD24.CircleBar. to #15 CircleBar Authorcorrence5>D25.*CirclePacking. to #34 CirclePacking D25.*CirclePacking. to #34 CirclePacking Authorcorrence6>D26.*HeatDendro. to #52 HeatDendro D26.*HeatDendro. to #52 HeatDendro Authorcorrence9A4>D211.3-column to 6 then Layout SelectedD211.3-column to 6 then Layout Selected Authorcorrence9C1>D27(Layout). Common Network cluster D27(Layout). Common Network cluster Authorcorrence9C>***D28(Layout). Spread down by cluster ***D28(Layout). Spread down by cluster Authorcorrence9C2>D29(Layout). Grid by clusterD29(Layout). Grid by cluster Authorcorrence9CB>D2B(Layout). layout_as_starD2B(Layout). layout_as_star Authorcorrence9CC>D2C(Layout). c(1, vcount(network):2)D2C(Layout). c(1, vcount(network):2) Authorcorrence9C3>D30(Layout). Circle by clusterD30(Layout). Circle by cluster Authorcorrence9C4>D31(Layout).DenddroPlot by clusterD31(Layout).DenddroPlot by cluster Authorcorrence9C5>Betweenness.Highlight clustersBetweenness.Highlight clusters Authorcorrence9C52>Propagation.Highlight 2 clustersPropagation.Highlight 2 clusters Authorcorrence9C53>FLCA type.Highlight 3 clustersFLCA type.Highlight 3 clusters Authorcorrence9C6>D33(Layout).horizontal Cluster View D33(Layout).horizontal Cluster View Authorcorrence9C61>D331.Time Line View D331.Time Line View Authorcorrence9C62>D332.Time Zone View D332.Time Zone View Authorcorrence9C7>D34(Layout).4-Quadrant Thematic Map D34(Layout).4-Quadrant Thematic Map Authorcorrence9C7A>Hightlight.4-Quadrant Thematic Map Hightlight.4-Quadrant Thematic Map Authorcorrence9C8>D35(Layout).Kano diagram for Thematic MapD35(Layout).Kano diagram for Thematic Map SimpleIPA-KanoDiagarm>D35A @@@ Simple Kano Diagarm D35A @@@ Simple Kano Diagarm SimpleIPA-KanoDiagarm95CI>D35A95CI @@@ Simple Kano Diagarm D35A95CI @@@ Simple Kano Diagarm Authorcorrence9C9>D36(Layout).Unique Thematic Map for Keyword Cluster HeadD36(Layout).Unique Thematic Map for Keyword Cluster Head Authorcorrence9CA>D37(Layout).Dual Map ViewD37(Layout).Dual Map View Authorcorrence9CA1>D38(Layout).all Dual Map ViewD38(Layout).all Dual Map View Authorcorrence9C84>D38(Layout).all Sankey ViewD38(Layout).all Sankey View Authorcorrence9C83>D39(Sankey)....Sankey BlocksD39(Sankey)....Sankey Blocks Circularpacking>01.2 Circular packing including clusters01.2 Circular packing including clusters Circularpacking2>02.3 Circular packing separating clusters 02.3 Circular packing separating clusters Circularpacking3>03.... Circular packing 3 layers 03.... Circular packing 3 layers Circularpacking4>04.... Circular packing 4 layers04.... Circular packing 4 layers Donuttree>05 Donut tree 05 Donut tree Impactbarplot2>S32. Timeline View for Theme with linksS32. Timeline View for Theme with links TwomodeyearA34>S33.CIDA Focus by Theme S33.CIDA Focus by Theme TwomodeyearA3>P41.TimeZone View(Themes) P41.TimeZone View(Themes) Timezone>P42.95%CI Control linesP42.95%CI Control lines CI95scatter>P43. Spline 95%CI Control lines in RP43. Spline 95%CI Control lines in R CI95scatter2>P44.Linear 95%CI Control lines in RP44.Linear 95%CI Control lines in R CI95scatter3>P45.Density Heatmap with counts by x, yP45.Density Heatmap with counts by x, y Heatdendrogram2>P45.Loess 95%CI Control lines in RP45.Loess 95%CI Control lines in R CI95trend>p.45 Trend with 95%CI in Rp.45 Trend with 95%CI in R Ridgeline>H16. Ridgeline distribution of counts over yrsH16. Ridgeline distribution of counts over yrs Ridgeline2>H17. Ridgeline distribution of counts over yrsH17. Ridgeline distribution of counts over yrs Ridgeline3>H18. Ridgeline distribution of counts over yrsH18. Ridgeline distribution of counts over yrs Ridgeline5>H181. Ridgelin one color of counts over yrsH181. Ridgelin one color of counts over yrs Ridgeline4>H19. violine scatter spots over valuesH19. violine scatter spots over values Ridgeline6>H21. violin no scatter spots over valuesH21. violin no scatter spots over values Ridgeline9>H25. True violin over valuesH25. True violin over values Ridgeline7>H22. Box plot burst spots over yrsH22. Box plot burst spots over yrs Ridgeline8>H23. Points-errorbars over yrsH23. Points-errorbars over yrs Timelineview2>P47.Eg. Combined HotSpot & Ridge P47.Eg. Combined HotSpot & Ridge Heatmap334>H17. Heatmap by vertex and yearsH17. Heatmap by vertex and years Impactbarplot>P51. Impact bar plot by year or by themeP51. Impact bar plot by year or by theme Worldmap>H02.A Choropleth mapH02.A Choropleth map Worldmap3>H04.B World map with BubblesH04.B World map with Bubbles Worldmap4>H05.C World map with Bubbles and curvesH05.C World map with Bubbles and curves /optgroup> optgroup> E.General graph gallery Scalefillgradient>92. Scale fill gradient92. Scale fill gradient Pyramid>25.Pyramid25.Pyramid Pyramid2>41.Pyramid241.Pyramid2 Pyramid22>42.Pyramid342.Pyramid3 Pyramid6>43.Pyramid643.Pyramid6 Survial >26..Survial analysis26..Survial analysis TwomodeRelation>27.Two mode Relation27.Two mode Relation TwomodeRadar>28.Radar Plot28.Radar Plot TwomodeRadar1>281.Radar Plot_1281.Radar Plot_1 bibarlinechart>29.Bibar line chart29.Bibar line chart bStackbar>30.Stack Bar chart30.Stack Bar chart Multilinechart>31.Multi-Line chart31.Multi-Line chart Multilinebar>32.Multi-Line Bar Chart32.Multi-Line Bar Chart Multilinebar2>33.Multi-Line Bar2 Chart33.Multi-Line Bar2 Chart Wordcloud>36.Wordcloud36.Wordcloud Wordcloud2>43.Wordcloud243.Wordcloud2 Wordcloud3>432.Wordcloud3432.Wordcloud3 Spiderplot>44.Spider plot44.Spider plot Twowayanova>45.Plot for Two Way Anova45.Plot for Two Way Anova KendallW>Kendall Coefficient of Concordance (W)Kendall Coefficient of Concordance (W) /optgroup> optgroup> F.Rstatistics Rcourse1>101.RS1.Rstatistics101.RS1.Rstatistics effectsize>102.Compute effect size102.Compute effect size LogisticRegression>103.Logistic Regression103.Logistic Regression LogisticRegression2>105.LR for prostate cancer105.LR for prostate cancer DensityinR>106.Prob. with sapply(in, function)106.Prob. with sapply(in, function) MCMCinR>107.Metropolis-Hastings MCMC in R107.Metropolis-Hastings MCMC in R MCMCinR2>108.Metropolis-Hastings MCMC in R108.Metropolis-Hastings MCMC in R Fligner-Killeentest>Fligner-Killeen TessFligner-Killeen Tess IQR>Calculate IQR 1st & 3nd quartilesCalculate IQR 1st & 3nd quartiles APInamenationality>API for Names to NationalityAPI for Names to Nationality /optgroup> optgroup> G.Pajek software & Google Maps bibliometricschien>Chien. Biliometrics in PubmedChien. Biliometrics in Pubmed Bibliometriccom>https://bibliometric.com/ https://bibliometric.com/ bibliometricschien2>Guideline in Visualizations Guideline in Visualizations TNASNA>EG2 Social network analysis(MP4) EG2 Social network analysis(MP4) Simplematrix2>EG31 matrix to Network to REG31 matrix to Network to R Simplematrix>EG32 Transforming matrix to #59AEG32 Transforming matrix to #59A MAuthorcorrence>Start[here]. 2-column(couple) dataStart[here]. 2-column(couple) data MAuthorcorrence2>Start[here]2. 2-column(couple) Excluding ego-self Start[here]2. 2-column(couple) Excluding ego-self Authorcorrence7A>60A7A.D1:3-column to 6-column data60A7A.D1:3-column to 6-column data Authorcorrence7A2>EEEEE: 3-column to 6-column(main)EEEEE: 3-column to 6-column(main) Authorcorrence9A>60A7B.D1:3 to 4 for Network in SNA60A7B.D1:3 to 4 for Network in SNA Authorcorrence9C>***60A7C.D1:6 to 4 for Network in SNA***60A7C.D1:6 to 4 for Network in SNA Network7A12>60A7B1.D1:4 to Network for SNA60A7B1.D1:4 to Network for SNA probleminSNA2>Hit:Notice of the Google MapsHit:Notice of the Google Maps SNA221 >40. D1: Network chart(example)40. D1: Network chart(example) SNA221A>40A.D1: To gain Pajek format(.paj)40A.D1: To gain Pajek format(.paj) probleminSNA>Hit: Drawbacks above(many link, no cluster)Hit: Drawbacks above(many link, no cluster) AuthorcorrenceA>59 D2:Random cluster with size #46 59 D2:Random cluster with size #46 AuthorcorrenceB>59A D2:Enhancement: cluster then to #46 59A D2:Enhancement: cluster then to #46 Authorcorrence2>59B D2: No cluster to #14 Chord 59B D2: No cluster to #14 Chord SNA221A1>60A1 D2: Cluster to #14 Chord 60A1 D2: Cluster to #14 Chord Authorcorrence3>60A3 D2:*Chord(cluster & few links) to #14 60A3 D2:*Chord(cluster & few links) to #14 Authorcorrence31>60A31.D3:*Dendrogram to #47 60A31.D3:*Dendrogram to #47 Timelineview>80A31.D7:HotSpot(CiteSpace) 80A31.D7:HotSpot(CiteSpace) Authorcorrence4>60A4.*CircleBar. to #15 CircleBar 60A4.*CircleBar. to #15 CircleBar Authorcorrence5>60A5.*CirclePacking. to #34 CirclePacking 60A5.*CirclePacking. to #34 CirclePacking Authorcorrence6>60A6.D4:*HeatDendro. to #52 HeatDendro60A6.D4:*HeatDendro. to #52 HeatDendro Authorcorrence32>60A7.D55:*Network: next to #4660A7.D55:*Network: next to #46 Authorcorrence7>60A7.D5:*Network: next to #40A60A7.D5:*Network: next to #40A SNA221A>Hit: To Pajek by 60A7 then to 40AHit: To Pajek by 60A7 then to 40A Authorcorrence9>Hit: Coordinate in Pajek & Google MapsHit: Coordinate in Pajek & Google Maps Googlemap>60A9.D6:Google Maps(Pajek in 40A)60A9.D6:Google Maps(Pajek in 40A) Authorcorrence8>60A8: *Details about cluster process60A8: *Details about cluster process Authorcorrence88>60A88: *Details without self connections60A88: *Details without self connections Authorcorrence7A>60A7A.D5:Data from 60A8 to pajek for Google Maps60A7A.D5:Data from 60A8 to pajek for Google Maps Network7A1>60AA2.D5:nodes & relations in Network60AA2.D5:nodes & relations in Network Network7A12>60AA3.D5:nodes & relations Group in Network Group Network蝬脰楝60AA3.D5:nodes & relations Group in Network Group Network蝬脰楝 Authorcorrence7A1>60AA1.D5:3-column data to Pajek for Google Maps60AA1.D5:3-column data to Pajek for Google Maps Googlemap3>60AA3.D5:Pajek data to EdgebundleR**Network 60AA3.D5:Pajek data to EdgebundleR**Network Googlemap2>60AA.D7:Google Maps(from #60A7A:clustering)60AA.D7:Google Maps(from #60A7A:clustering) Authorcorrence8A>62A. Solving k in CLC algorithm 62A. Solving k in CLC algorithm Metaanalysis>End.D7: Exercise and submissionEnd.D7: Exercise and submission MeSHterm>EG01. Loop to MeSH terms or Keywords #59AEG01. Loop to MeSH terms or Keywords #59A MeSHterm2>EG012. Excluding self to MeSH terms or Keywords #59AEG012. Excluding self to MeSH terms or Keywords #59A BetweennessSNA>BTW01. Betweenness SNABTW01. Betweenness SNA Betweennesschord>BTW02. Betweenness chordBTW02. Betweenness chord /optgroup> optgroup>Two mode data Twomodeyear>FG2. Two mode data for keywords on chordFG2. Two mode data for keywords on chord TwomodeyearA>FG2A. Two mode data for keywords on HeatmapFG2A. Two mode data for keywords on Heatmap TwomodeyearA1>FG2B. Two mode data for *Details about cluster processFG2B. Two mode data for *Details about cluster process TwomodeyearA2>FG2C. Two mode data for Box PlotFG2C. Two mode data for Box Plot TwomodeyearA3>FG2D. ***:Two mode data of year in columnsFG2D. ***:Two mode data of year in columns Impactbarplot>IBPA1. Impact bar plot by year or by themeIBPA1. Impact bar plot by year or by theme Impactbarplot2>IBPA2. Impact bar plot by year with linksIBPA2. Impact bar plot by year with links KeywordanalysisABC>KW01 Article related to keyword analysisKW01 Article related to keyword analysis Timezone>KW02 Forest plotKW02 Forest plot /optgroup> optgroup> H.World Maps(Choropleth maps) Worldmap2>H01.A blank world map H01.A blank world map Worldmap>H02.A Choropleth mapH02.A Choropleth map WorldmapA>Heatmap.A Choropleth mapHeatmap.A Choropleth map WorldmapB>Heatmap.B Choropleth mapHeatmap.B Choropleth map USmap>US Map(choropleth)US Map(choropleth) Chinamap>China Map(choropleth)China Map(choropleth) Taiwanmap>Taiwan Map(choropleth)Taiwan Map(choropleth) Barbyyear>H03. Bar Chart by stacked yearH03. Bar Chart by stacked year Linebyyear>H04. Line Chart by yearH04. Line Chart by year Scatterbyyear>H05. Scatter Chart by yearH05. Scatter Chart by year Worldmapconnect>H06. World Map with EdgesH06. World Map with Edges Combinedworldmap>Ex. Combine H02 & H06Ex. Combine H02 & H06 /optgroup> optgroup> W.Time series TimesABC0>0.Simple example0.Simple example TimesABC>1.Age example1.Age example TimesABC2>2.Birthday example2.Birthday example TimesABC3>3.Souvenir example3.Souvenir example TimesABC4>4.Smoth data example4.Smoth data example TimesABC5>5.Decompose:seasonal, trend and irregular component5.Decompose:seasonal, trend and irregular component TimesABC21>6.Seasonally adjusting6.Seasonally adjusting TimesABC22>7.Forecases7.Forecases Inflectionpoint>8. Inflection points on trend8. Inflection points on trend Inflectionpoint2>9. IP with 1st Diff cumulative9. IP with 1st Diff cumulative /optgroup> optgroup> Z.Animition GDPnation>GDP for nationsGDP for nations Baranimation>Bar animationBar animation Baranimation2>Box plot anaimationBox plot anaimation GDPnation2>Line animationLine animation GDPnation3>Multipy GDP for nationsMultipy GDP for nations GDPworldtop10>GDP world top 10 bar chartGDP world top 10 bar chart taiwancity>Taiwan health data annuallyTaiwan health data annually Imagelayers>Combined images with layersCombined images with layers Imagelayers2>Animation imagesAnimation images Image1177>1177design series1177design series Image11772>1177design with background1177design with background Image11773>1177design with fade-in fade-out1177design with fade-in fade-out Image11774>1177design with composite 2 images1177design with composite 2 images Image11775>1177design with annotation on images1177design with annotation on images Image11776>1177design add image in position1177design add image in position Imagetotext>Image to text (OCR)Image to text (OCR) Pngtotiffiles>Convert Png to tif filesConvert Png to tif files ExtractPDFpages>Extract PDF pagesExtract PDF pages ExtractPDFpages2>Extract & Combine them into an single PDFExtract & Combine them into an single PDF /optgroup> optgroup>Metropolis-Hastings algorithm MCMCnorm>MCMC norm distribution(exclude lower 100)MCMC norm distribution(exclude lower 100) MCMCMachine>MCMC CMC Machine Learning on Metropolis-HastingsMCMC CMC Machine Learning on Metropolis-Hastings SpatialStatistics>Spatial Statistics Spatial Statistics /optgroup> optgroup>AI classification NaiveBayes>Naive Bayes ClassificationNaive Bayes Classification CNNmodel>CNN modelCNN model ANNmodel>ANN modelANN model Logisticmodel>Logistic modelLogistic model RandomForest>randomForest modelrandomForest model Gmoser> Gradient Boosting Machines Gradient Boosting Machines Decisiontree>Decision TreeDecision Tree KNNmodel>KNN modelKNN model SVMmodel>SVM modelSVM model LDAmodel>Linear Discriminant Analysis (LDA)Linear Discriminant Analysis (LDA) /optgroup> optgroup>PI related statistics PIcomputed> Compute PI Compute PI /optgroup> optgroup>GEO analysis Vacanoplot5>1.Vacano plot Rasch1.Vacano plot Rasch Vacanoplot3>2 Vacano plot without Fit regression2 Vacano plot without Fit regression Vacanoplot4>3 Vacano plot with Fit regression3 Vacano plot with Fit regression GEOexpression>4 GEO Differential Gene Analysis4 GEO Differential Gene Analysis GEOexpression2>5 GEO Differential Gene Analysis5 GEO Differential Gene Analysis HeatmapGEO>6A Simple Heatmap GEO6A Simple Heatmap GEO HeatmapGEO2024Chinese>6B Heatmap GEO 2024 Chinese6B Heatmap GEO 2024 Chinese HeatmapGEO2024Chinese2>6C Heatmap GEO 2024 Chinese6C Heatmap GEO 2024 Chinese HeatmapGEOexpression>6C Heat Map for GEO Differential Gene Analysis6C Heat Map for GEO Differential Gene Analysis HeatmapGEOexpression2>7 Heat Map for GEO Differential Gene Analysis7 Heat Map for GEO Differential Gene Analysis WGCNAIdentifiesKeyModules>8 WGCNA Identifies Key Modules8 WGCNA Identifies Key Modules KEGGpathwayenrichmentdotplot>9 KEGG pathway enrichment dot plot9 KEGG pathway enrichment dot plot Networkvitalincenter>10 Network vital in center10 Network vital in center CheckGenesinvolved>11 Check Genes involved in GEO11 Check Genes involved in GEO Checktwogseidentical>12Check Two GSE identical12Check Two GSE identical Checktwogsewithgenes>13 Check GSE with Genes13 Check GSE with Genes GEO2RGSE79973>14 GEO2R GSE7997314 GEO2R GSE79973 GEOitemkmean>14A GEO item kmean Cluster14A GEO item kmean Cluster GEOfeatureheatmap>14B GEO Feature Heatmap14B GEO Feature Heatmap GEOfeatureFLCA>14C GEO Feature FLCA14C GEO Feature FLCA GEOsampleFLCA>14D GEO Sample FLCA14D GEO Sample FLCA VacanoGEO2RGSE79973>15 Vacano GSE7997315 Vacano GSE79973 Densityforhealthabnormal>16 Density for health & abnormal16 Density for health & abnormal DensityforGSE79973>17 ROC Density for GSE7997317 ROC Density for GSE79973 DensityforGSE6919>18 ROC Density for GSE691918 ROC Density for GSE6919 GEOssaveforFLCA>21 GEOs saved for FLCA21 GEOs saved for FLCA GEOsimulateddata>22A GEO Simulated Data22A GEO Simulated Data 2024Chinese>22B GEO Probe from GSE or data in Chinese2024 Entrance22B GEO Probe from GSE or data in Chinese2024 Entrance GEOgenefromprobeGSE79973>22C GEO gene from probe GSE7997322C GEO gene from probe GSE79973 HeatdendrogramCC>22D Chord for cluster analysis22D Chord for cluster analysis /optgroup> optgroup>PI related statistics PIcomputed> Compute PI Compute PI /optgroup> optgroup>100 amazing visuals GPTprompt>0. ChatGPT with Prompts to R0. ChatGPT with Prompts to R GPTnR2>00. PDF: Instruction to R platform00. PDF: Instruction to R platform InputwithTable> ====Begin to start========== ====Begin to start========== Clusterfor3column>1.3-column to clusters1.3-column to clusters GooglemapinR>2. Google Map in R(dashboard)2. Google Map in R(dashboard) 4quadrantplot>21.4-Quadrant Plot on internet21.4-Quadrant Plot on internet Temporalbarchart>3.Temporal Bar Chart3.Temporal Bar Chart Temporalbarchart2>4 Trend for Temporal Bar Chart4 Trend for Temporal Bar Chart Inflectionpoint24X>4-1.Table Temporal Bar Chart with trend4-1.Table Temporal Bar Chart with trend Inflectionpoint24Y>4-2.Table Trend & Cluster Temporal Bar Chart with trend4-2.Table Trend & Cluster Temporal Bar Chart with trend Temporalbarchart3>5 Trend & Cluster for Temporal Bar Chart5 Trend & Cluster for Temporal Bar Chart InputwithTable> ====Similarity By Distance ========== ====Similarity By Distance ========== Heatdendrogram4A>6. By Distance Cluster analysis for persons6. By Distance Cluster analysis for persons Heatdendrogram41A>7.By Distance Cluster for items7.By Distance Cluster for items Heatdendrogram41Aslope>6A. Slope By Distance for items6A. Slope By Distance for items Heatdendrogram41Abarchart>6-2.Bar Chart Distance for items6-2.Bar Chart Distance for items Heatdendrogram4Aslope>7A. Slope By Distance for persons 7A. Slope By Distance for persons Heatdendrogram4Abarchart>7B.Bar Chart By Distance for persons7B.Bar Chart By Distance for persons InputwithTable> ====Similarity By correlation========== ====Similarity By correlation========== Heatdendrogram4>8. By corr. Cluster analysis for items 8. By corr. Cluster analysis for items Heatdendrogram41>9.By corr. Cluster analysis for persons9.By corr. Cluster analysis for persons Heatdendrogram4slope>8A. corr. Slope Graph for items 8A. corr. Slope Graph for items Heatdendrogram41slope>9A.corr.Slope Graph for persons9A.corr.Slope Graph for persons Heatdendrogram41slopeABC>@@@9ABC.Corr & Dist.Slope Graph for persons@@@9ABC.Corr & Dist.Slope Graph for persons Heatdendrogram41slopeA>9AB.Outfit corr.Slope Graph for persons9AB.Outfit corr.Slope Graph for persons Heatdendrogram4barchart>8B. corr. Bar Chart for items 8B. corr. Bar Chart for items Heatdendrogram41barchart>9B. corr.Bar Chart for persons9B. corr.Bar Chart for persons InputwithTable> ====Input with Table format below========== ====Input with Table format below========== TableuseinMA>10.Table input useful for the dataframe10.Table input useful for the dataframe Tableusedforpractice>11.(Slope Graph) Table used for practice11.(Slope Graph) Table used for practice PolarAreaChart>12.Polar Area Chart12.Polar Area Chart Calendarheatmap>13. Calendar Heatmap13. Calendar Heatmap StreamgraphABC>14.Stream graph14.Stream graph WaterfallABC>15. Water Fall plot15. Water Fall plot AlluvialABC>16. Alluvial diagram 16. Alluvial diagram CircularBarplot2>17.Circular Barplot17.Circular Barplot SankeyABC>18. Sankey Diagram18. Sankey Diagram TreemapABC>19.Tree Map(proportion) 19.Tree Map(proportion) Sumburst>20. Sunburst chart20. Sunburst chart Screeplot>21. Scree plot with eigenvalues21. Scree plot with eigenvalues Youdenplot>22. Youden Plot22. Youden Plot Corrmatrix>23. Correlation matrix plot23. Correlation matrix plot Heatmap3346>24. Heat Map24. Heat Map RidgelineB>25. Ridgeline Plot with group25. Ridgeline Plot with group Densityplot>26.Density Plot with Gradient Fill 26.Density Plot with Gradient Fill RidgelineA>27. Box plot with group27. Box plot with group CI95scatter4>28. Scatter Plot with Regression Line by x, y28. Scatter Plot with Regression Line by x, y Scatterbyyear24>29.95%CI 4-Scatters Plots in R29.95%CI 4-Scatters Plots in R Scatterbyyear23>30. 4-Scatters Plots in R 30. 4-Scatters Plots in R Scatterbyyear2>31. 4-Quadrant Radar Plot in R31. 4-Quadrant Radar Plot in R Ridgeline>32. Ridgeline distribution of counts over yrs32. Ridgeline distribution of counts over yrs Ridgeline2>33. Ridgeline distribution of counts over yrs33. Ridgeline distribution of counts over yrs Ridgeline3>34. Ridgeline distribution of counts over yrs34. Ridgeline distribution of counts over yrs Ridgeline4>35. violine scatter spots over values35. violine scatter spots over values Ridgeline5>36. Ridgelin one color of counts over yrs36. Ridgelin one color of counts over yrs Ridgeline6>37. violin no scatter spots over values37. violin no scatter spots over values Ridgeline9>38. True violin over values38. True violin over values Ridgeline7>39. Box plot burst spots over yrs39. Box plot burst spots over yrs Ridgeline8>40. Points-errorbars over yrs40. Points-errorbars over yrs CalendarHeatmap>41. Calendar Heatmap41. Calendar Heatmap 5yearsurvival2>***42. Slope graph: Timeline view of spaces BW elements***42. Slope graph: Timeline view of spaces BW elements 5yearsurvival>43. Slope graph: Hotspots for Timeline view of spaces BW elements43. Slope graph: Hotspots for Timeline view of spaces BW elements 5yearsurvival3>43-1.Slope graph: Cluster Hotspots for Timeline view of spaces BW elements43-1.Slope graph: Cluster Hotspots for Timeline view of spaces BW elements Datadistribution>44. Data Distribution of Box plot44. Data Distribution of Box plot Datadistribution2>45. Data Distribution of Z-score45. Data Distribution of Z-score Twotimepointcomparison>46. Two time point comparison46. Two time point comparison DivergingLollipopChart>47. Diverging Lollipop Chart47. Diverging Lollipop Chart Itemdifficulty>47A. Item Difficulty47A. Item Difficulty DivergingLollipopChart2>48. Above Average plot48. Above Average plot Correlogram>49. Correlogram correlation49. Correlogram correlation Twodendrograms>50.Two dendrograms in comparison50.Two dendrograms in comparison InputwithTable> ====Bibliometrics========== ====Bibliometrics========== Sankey4>51. Dual-map overlay(Biblio)51. Dual-map overlay(Biblio) DualPyramid>51A. Dual-map overlay(Pyramid)51A. Dual-map overlay(Pyramid) Twoaxesforarticles>51A.Two Axes for Articles51A.Two Axes for Articles Twoaxesarticlecitation>@@@51a Two Axes Article & Citation@@@51a Two Axes Article & Citation Performanceanalysis>51B.Performance Analysis51B.Performance Analysis Pointchart>51C.Point Chart for lowest $ highest 51C.Point Chart for lowest $ highest DiagrammeR>51D.Flow chart via DiagrammeR51D.Flow chart via DiagrammeR Authorcorrence9C61>52.Time Line View(bibliometric)52.Time Line View(bibliometric) TwomodeyearA3>53.TimeZone View(Themes)(bibliometric)53.TimeZone View(Themes)(bibliometric) Authorcorrence9C62>54.Time Zone View(bibliometric)54.Time Zone View(bibliometric) Alluvial>55.(Alluvial by survival case on the Titanic55.(Alluvial by survival case on the Titanic Sankey3>56.(Alluvial).Dual-map view56.(Alluvial).Dual-map view ERSankey>57.(Saneky)ER example57.(Saneky)ER example Sankey>58.Sankey(Sankey).column.no link in cluster58.Sankey(Sankey).column.no link in cluster Authornetwork>59.No cluster in network59.No cluster in network Authornetwork2>60. Taipei MRT system60. Taipei MRT system TwocolumnFLCA>61. 2 columns with Leader & Folower using FLCA61. 2 columns with Leader & Folower using FLCA Authorcorrence9C53>62. FLCA type.Highlight 3 clusters62. FLCA type.Highlight 3 clusters Alluviealtrend>@@@63. Alluvial Trend by time @@@63. Alluvial Trend by time Alluviealtrend3>63-1. Alluvial Trend with IP by time63-1. Alluvial Trend with IP by time Alluviealtrend32>63-2. Stacked Alluvial Trend with IP by time63-2. Stacked Alluvial Trend with IP by time Alluviealtrend33>63-3. Stacked Area Chart with Trend and IP by time63-3. Stacked Area Chart with Trend and IP by time Alluviealtrend34>63-4. Grouped Barchart by column63-4. Grouped Barchart by column Alluviealtrend35>63-5. Stacked Grouped Barchart by column63-5. Stacked Grouped Barchart by column Alluviealtrend36>63-6. Stacked Grouped Barchart by row63-6. Stacked Grouped Barchart by row Alluviealtrend37>63-7. Percentage Stacked Grouped Barchart by row63-7. Percentage Stacked Grouped Barchart by row Alluviealtrend2>64. Stacked Alluvial Trend by time64. Stacked Alluvial Trend by time Inflectionpoint23>65. 2-axes IP with 1st Diff cumulative65. 2-axes IP with 1st Diff cumulative ReadcsvforIP>652 Search IP and Trend Type in csv File 652 Search IP and Trend Type in csv File Inflectionpoint24W>66.Multiply Line Chart with IP66.Multiply Line Chart with IP SlopeChart>@@@66A. (Pre/Post tests)Line Chart with values@@@66A. (Pre/Post tests)Line Chart with values Slope>66B. (Pre/Post tests)Slope Plot with values66B. (Pre/Post tests)Slope Plot with values Inflectionpoint24A>67..Multiply Line Chart67..Multiply Line Chart Inflectionpoint24>68. HotSpot(CiteSpace)68. HotSpot(CiteSpace) Heatmap3345>69. Add value to Heatmap by vertex and years69. Add value to Heatmap by vertex and years Inflectionpoint232>70. Boxplot for one variable 70. Boxplot for one variable Authorcorrence9C7>71.(Layout).4-Quadrant Thematic Map71.(Layout).4-Quadrant Thematic Map Authorcorrence9C8>72.(Layout).Kano diagram for Thematic Map 72.(Layout).Kano diagram for Thematic Map Sankeymatic3>73. (Sankey).column. sankeymatic.com/73. (Sankey).column. sankeymatic.com/ Taiwanmap>74.Taiwan Map(choropleth)74.Taiwan Map(choropleth) GDPworldtop10>75. GDP world top 10 bar chart75. GDP world top 10 bar chart Chinamap>76. China Map(choropleth)76. China Map(choropleth) USmap>77. US Map(choropleth)77. US Map(choropleth) WorldmapGreenchoropleth>@@@Lowere:.Green choropleth Map @@@Lowere:.Green choropleth Map Worldmap>78B. World Choropleth map78B. World Choropleth map Worldmapconnect>79. World Map with Edges79. World Map with Edges Combinedworldmap>80. Combine H02world & H06edge80. Combine H02world & H06edge Worldmapforcountrycollaborations>@@@Upper: Worldmap for country collaborations@@@Upper: Worldmap for country collaborations WorldmapGreenchoroplethcombined>@@@Lowere: Combined Green Worldmap Choropleth@@@Lowere: Combined Green Worldmap Choropleth InputwithTable> ====Common statistical Visuals========== ====Common statistical Visuals========== Scatterbyyear>81. Scatter Chart by year81. Scatter Chart by year Top10keywordsfrequency>***810 Top10 keywords Frequency***810 Top10 keywords Frequency Top10valueyearscatterplot>811.Top10 value year trend line chart811.Top10 value year trend line chart Top10valueyearscatterplot2>812.Top10 value year scatter for Names 812.Top10 value year scatter for Names Top10valueyearscatterplot3>***813.Top10 value year scatter for Names ***813.Top10 value year scatter for Names Piechart>82. Pie Chart82. Pie Chart Wafflechart>82.1Waffle Chart82.1Waffle Chart Multilinebar2>83. Multi-Line Bar2 Chart83. Multi-Line Bar2 Chart Multilinechart>84.Multi-Line chart84.Multi-Line chart bibarlinechart>85.Bibar line chart85.Bibar line chart TwomodeRadar1>86.Single-Radar Plot_186.Single-Radar Plot_1 TwomodeRadar>87.Multi-Radar Plot87.Multi-Radar Plot TwomodeRelation>88.Two mode Relation(single to multi pair)88.Two mode Relation(single to multi pair) Survial>89.Survial analysis89.Survial analysis Pyramid6>90. Stacked bar Pyramid90. Stacked bar Pyramid Pyramid>91.Pyramid(Simple)91.Pyramid(Simple) Readtable>91A.Read Table for dataframe91A.Read Table for dataframe KendallW>92. Kendall (W) judge in rows92. Kendall (W) judge in rows KendallW2>921. Kendall (W) judge in cplumn921. Kendall (W) judge in cplumn Twowayanova>93.Plot for Two Way Anova93.Plot for Two Way Anova Wordcloud3>941.Wordcloud3941.Wordcloud3 Wordcloud2>94.Wordcloud294.Wordcloud2 Wordcloud>95.Wordcloud95.Wordcloud Authorcorrence4>96.CircleBar. to #15 CircleBar96.CircleBar. to #15 CircleBar Authorcorrence3>97.*Chord(cluster & few links) to #1497.*Chord(cluster & few links) to #14 Authorcorrence31>98.*Dendrogram to #4798.*Dendrogram to #47 Circularpacking3>99.*Circular Packing fom Nested Data Frame99.*Circular Packing fom Nested Data Frame Authorcorrence6>100..*HeatDendro. to #52 HeatDendro 100..*HeatDendro. to #52 HeatDendro WrightMap>101. Wright Map101. Wright Map MCMCinR3>102.Rasch MCMC estimation in R102.Rasch MCMC estimation in R InputwithTable> ====Rasch analysis========== ====Rasch analysis========== RaschusingTAM2>103.MML dicho-Wright Map with TAM103.MML dicho-Wright Map with TAM RaschusingTAM3>104.JML poly-Group:Rasch Wright Map with TAM104.JML poly-Group:Rasch Wright Map with TAM Raschkidmap>105.Rasch KIDMAP105.Rasch KIDMAP Rasch model_ICC>106.Rasch model_ICC106.Rasch model_ICC Rasch model_WrightMap>107.Rasch model_WrightMap107.Rasch model_WrightMap RaschMML model>108.Rasch MML model108.Rasch MML model Rasch DIF>109.Rasch DIF 109.Rasch DIF IRTLord DIF>110.IRTLord DIF 110.IRTLord DIF RaschAndersenLRTest>111.AndersenLRTest 111.AndersenLRTest Rasch MAPEAP>112.Rasch MAPEAP112.Rasch MAPEAP RaschAdersonLRtest>113.RaschAdersonLRtest113.RaschAdersonLRtest RaschRSMICC>114.RaschRSMWrightMap 114.RaschRSMWrightMap InputwithTable> ====Amazing Visuals========== ====Amazing Visuals========== ChordDiagram>115.ChordDiagram 115.ChordDiagram Circlebarplot>116.Circlebarplot116.Circlebarplot Circularbarplot>117. Circular barplots117. Circular barplots Donut>118. Donut plot118. Donut plot Circleheatmap>119. Circle Heat Map119. Circle Heat Map Circleheatmap2>120.Blue Circle Heat Map120.Blue Circle Heat Map circlestackedbarplot>121.Circle Stacked Bar Plot 121.Circle Stacked Bar Plot Barchart>122.Bar Chart horizontally122.Bar Chart horizontally Barchart2>123. Bar Chart vertically123. Bar Chart vertically Boxplot>124. Box plot124. Box plot Venndiagramthen>125.Venn Diagramthen125.Venn Diagramthen Lollipopchart>126.Lollipop Chart126.Lollipop Chart Lollipopchart2>127.Lollipop with terms127.Lollipop with terms Linechartforsalary>371 Line Chart For Salary371 Line Chart For Salary Dotchartforsalary>372 Dot Chart For Salary372 Dot Chart For Salary IRTparameter1>373 IRT parameter1373 IRT parameter1 IRTparameter2>374 IRT parameter2374 IRT parameter2 IRTmodelwithc>375 IRT model with c Da375 IRT model with c Da SankeyMatic>*** 37a.Text code for SankeyMatic*** 37a.Text code for SankeyMatic Primaryedgelink> 37b.Primary Edge Linked only 37b.Primary Edge Linked only DDIfordrugdruginteraction>@@@Leader on relation DDI for Drug-drug-interaction@@@Leader on relation DDI for Drug-drug-interaction DDIfordrugdruginteractionarrow> Above FLCA for directed arrow DDI Above FLCA for directed arrow DDI DDIfordrugdruginteractionchord>@@@ Chord diagram Above FLCA @@@ Chord diagram Above FLCA DDIfordrugdruginteractionB2>edge for leader by value with count on Sankeyedge for leader by value with count on Sankey DDIfordrugdruginteractionB>@@@ Leader on count DDI for Drug-drug-interaction@@@ Leader on count DDI for Drug-drug-interaction Widetolongerdataset>Wide To Longer DataSetWide To Longer DataSet Relationdatato6columns>***Relation Data To 6 columns***Relation Data To 6 columns Sankey2>***128.Sankey2***128.Sankey2 RelationdatasetforSan>***Relation Dataset For Sankey***Relation Dataset For Sankey InputwithTable> ====Network & Cluster analysis========== ====Network & Cluster analysis========== SNAsimple>129. Via metrix to Network129. Via metrix to Network SNAsimple2>130. Network dendro2130. Network dendro2 SNAsimple3>131. Clusters by dendrogram131. Clusters by dendrogram CircularDendrogram>132. Dendrogram Tree132. Dendrogram Tree CircularDendrogram2>133. Dendrogram Circle133. Dendrogram Circle Heatmap33>134. Heatmap33134. Heatmap33 Heatdendrogram>135. Heatmap34(dendrogram)135. Heatmap34(dendrogram) Euclidean>136. Euclidean dendrogram136. Euclidean dendrogram Euclidean2 >137. Euclidean dendrogram2137. Euclidean dendrogram2 InputwithTable> ====Meta analysis========== ====Meta analysis========== Forestplot>138.Forest Plot138.Forest Plot NMAforest>139.Forest Plot(NMA)139.Forest Plot(NMA) ForestNMA>140.Forest Plot(NMA for each)140.Forest Plot(NMA for each) blobbogram>141.Forest Plot(easy)141.Forest Plot(easy) MetaSMD>142.MetaSMD 142.MetaSMD MetaRR>143.MetaRR 143.MetaRR MetaOR>144.MetaOR144.MetaOR MetaFunnel>145.MetaFunnel 145.MetaFunnel InputwithTable> ====Other visuals========== ====Other visuals========== Decisiontree>146.Decision Tree146.Decision Tree GBMmodel>147 Gradient Boosting Machines147 Gradient Boosting Machines ANNmodel>148. ANN model148. ANN model Circlepacking>149.Circle Packing149.Circle Packing Treemap>150. Tree map150. Tree map Treemap2>151. Tree map(subgroup)151. Tree map(subgroup) Gantt>152. Gantt Plot152. Gantt Plot Flowchart>153. Flow Chart153. Flow Chart Infographics>154. Infographics154. Infographics Vocanoplot>155. Vocano plot155. Vocano plot Vocanoplot2>155A. Vocano plot155A. Vocano plot c>156. KEGG pathway enrichment plot156. KEGG pathway enrichment plot protein-proteininteraction(PPI)>157. Protein-Protein interaction (PPI)157. Protein-Protein interaction (PPI) RaschcontinuousRSMCompare>Rasch Continuous & RSM in Comparison Rasch Continuous & RSM in Comparison Changefilenamesinafolder>166. Change File Names In A Folder166. Change File Names In A Folder Widetolongdatasetformetrixin3columns>@@@ Wide to long dataset for metrix in 3 columns@@@ Wide to long dataset for metrix in 3 columns /optgroup>
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0. Wright Map
01.Tam Rasch
A Dichotomous simulated data
A Multitomous simulated data
A Rasch JMLE for RSM
Rasch MCMC estimation in R
***RSM/PCM estimation in R
Rasch using TAM
MML dicho-Wright Map with TAM
JML poly-Group:Rasch Wright Map with TAM
Rasch KIDMAP
Rasch Continuous Scale
1.Rasch model_ICC
2.Rasch model_WrightMap
3.Rasch MML model
4.2PLIRT
5.Rasch DIF
6.IRTLord DIF
7.AndersenLRTest
8.Rasch ML
9.Rasch MAPEAP
10.RaschAdersonLRtest
11.RaschAdersonLR
12.RaschRSMWrightMap
13.Raschprepost2GLPCM
B1.SEM x to y
B2.SEM Simple
B3.SEM Group
B4.SEM two laents
B5.SEM add prediction
B6.SEM with groups
B7.SEM2 with groups
B8 .SEM2 modindices
B9 .SEM3 with groups
BA .SEM4 monochrome with groups
BB .SEM5 anti-high pression with groups
BC .EFA decathlon in 2021 Tokyo 21 players
BD .SEM decathlon in 2021 Tokyo 21 players
BE. From t(data)
BE2. EFA for From t(data)
14.ChordDiagram
14B. From chorddiagram to cluster Network
15.Circlebarplot
15A. Circular barplots
16A. Donut plot
15B. Circle Heat Map
15C.Blue Circle Heat Map
16.Circle Stacked Bar Plot
34.Circle Packing
341.Bar Chart
342. Simple BarChart
34A.Box plot
161.Venn Diagramthen
162.Venn Diagramthen2
342.Lollipop Chart
343.Lollipop with terms
37.Sankey
@@@ Napoleon's Russian campaign 1812
*** 37a.Text code for SankeyMatic
***38.Sankey2
***Relation Dataset For Sankey
39. Network diagram
40. Via pajek to Network
46. Via metrix to Network
47. Network dendro2
48. Clusters by dendrogram
50. Circular Dendrogram
51. Heatmap33
52. Heatmap34(dendrogram)
53. Factor analysis
54. Cluster analysis for items
55. Cluster analysis for persons
56. Distance analysis for rows
57. Distance analysis for columns
410. Euclidean dendrogram0
41. Euclidean dendrogram
49. Euclidean dendrogram2
12.Table used
15.Forest Plot
16.Forest Plot(NMA)
17.Forest Plot(NMA for each)
15A.Forest Plot(easy)
16.MetaSMD
17.MetaRR
18.MetaOR
35.Meta correlation
19.MetaFunne
1. Network chart(FLCA)
16A Smoking cessation rates(example)
2A.above data trasformed using pairwise()
2.NetworkMa Summary Frequent(Odds A)
32.NetworkMa MCMC Bayesian(LogOdds B)
33.NetworkMa MCMC-Bayes(Odds Fixed) NMA2024AF
34.NetworkMa MCMC Bayes(Odds Random) NMA2024AFr
33.na.NetworkMa MCMC-Bayes(Odds Fixed) NMA2024AF
34.na.NetworkMa MCMC Bayes(Odds Random) NMA2024AFr
**.NetworkMa MCMC Bayes(contin.Fixed)
**.NetworkMa MCMC Bayes(contin.Random)
3.NetworkMa MCMC Bayesian(SMD)
4. Consistency test(SMD)
4. Consistency test(SMD2)
A1.NetworkMA plot(Frequentist )
A2.NetworkMA MCMC Bayesian(Odds) NetworkMetaAC
A3.NetworkMa Frequentist(Odds plot)
A4.Fixed/Random Effect using R-nemeta(Frequentist)
A4*. To examine the wrong in A4(Log Odds)
A4*. To examine the wrong in A4(Odds)
A5.NetworkMA RadomForest(Frequentist)
A6.Forest plot(Oddes)
to predict Apha & Beta in Beta distribution
Bibliometrix.org/home/
BX1.Descriptive analytics
22.Save long form data
BX2.Co-citation(Article (References))
BX3.Co-citation(Journal (Source))
BX4.Historiograph - Direct citation linkages
BX5.Co-word via keyword
BX6.Co-word via Corresponce
BX7.Thematic Map
BX8.Author collaboration
BX8.Unit collaboration
BX9.Country collaboration
BX10.Keyword Co-word
BX11.Lotka Law
BX12.Top-Authors annually
BX13.Coupling
No cluster in network
Taipei MRT system
2 columns with Leader & Folower using FLCA
@@@ csv for FLCA to 6 columns
***FLCA type.Highlight 3 clusters
Propagation.Highlight 2 clusters
Betweenness.Highlight clusters
cluster_fast_greedy.Highlight clusters
cluster_infomap.Highlight clusters
cluster_leading_eigen.Highlight clusters
cluster_louvain.Highlight clusters
cluster_optimal.Highlight clusters
cluster_spinglass(network)
cluster_walktrap(network)
components(network)
TO kwnow FLCA in R (network)
1.0 Top 10 keywords extracted in CSV
@@@1.1 Alluvial Trend
1.12 Stacked Alluvial Trend
1. 2-axes IP with 1st Diff cumulative
2.1. Boxplot for one variable
2. HotSpot(CiteSpace)
H18. Add value to Heatmap by vertex and years
P46.D7:To gain dataframe of HotSpot(CiteSpace)
P48.Eg. Combined HotSpot & Heatmap
Z41.Multiply Line Chart
Z42.Multiply Line Chart with IP
D34(Layout).4-Quadrant Thematic Map
D35(Layout).Kano diagram for Thematic Map
D39(Sankey).row...Sankey Blocks
37.Sankey(Sankey).column.no link in cluster
D40(Saneky)ER example
D39(Sankey).column..Sankeymatic
D43(Alluvial).Dual-map view
D431(Alluvial).Dual-map view2(Pyramid)
433(Alluvial by survival case on the Titanic
D331.Time Line View
P49.PDF for Chinese characters in R
P49B PDF to extract text
3. 4Q radar trend in R
D4. Chord plot by row with corr
D5. Chord plot by column with corr
D12. 4-Quadrant Radar Plot in R
D13. 4-Scatters Plots in R
D14.95%CI 4-Scatters Plots in R
D15.Pyramid Plot
D15.Multi-Line Bar2 Chart
D21.Author Collaborations with Network
D22 D2:*Chord(cluster & few links) to #14
D23.D3:*Dendrogram to #47
D24.CircleBar. to #15 CircleBar
D25.*CirclePacking. to #34 CirclePacking
D26.*HeatDendro. to #52 HeatDendro
D211.3-column to 6 then Layout Selected
D27(Layout). Common Network cluster
***D28(Layout). Spread down by cluster
D29(Layout). Grid by cluster
D2B(Layout). layout_as_star
D2C(Layout). c(1, vcount(network):2)
D30(Layout). Circle by cluster
D31(Layout).DenddroPlot by cluster
FLCA type.Highlight 3 clusters
D33(Layout).horizontal Cluster View
D332.Time Zone View
Hightlight.4-Quadrant Thematic Map
D35A @@@ Simple Kano Diagarm
D35A95CI @@@ Simple Kano Diagarm
D36(Layout).Unique Thematic Map for Keyword Cluster Head
D37(Layout).Dual Map View
D38(Layout).all Dual Map View
D38(Layout).all Sankey View
D39(Sankey)....Sankey Blocks
01.2 Circular packing including clusters
02.3 Circular packing separating clusters
03.... Circular packing 3 layers
04.... Circular packing 4 layers
05 Donut tree
S32. Timeline View for Theme with links
S33.CIDA Focus by Theme
P41.TimeZone View(Themes)
P42.95%CI Control lines
P43. Spline 95%CI Control lines in R
P44.Linear 95%CI Control lines in R
P45.Density Heatmap with counts by x, y
P45.Loess 95%CI Control lines in R
p.45 Trend with 95%CI in R
H16. Ridgeline distribution of counts over yrs
H17. Ridgeline distribution of counts over yrs
H18. Ridgeline distribution of counts over yrs
H181. Ridgelin one color of counts over yrs
H19. violine scatter spots over values
H21. violin no scatter spots over values
H25. True violin over values
H22. Box plot burst spots over yrs
H23. Points-errorbars over yrs
P47.Eg. Combined HotSpot & Ridge
H17. Heatmap by vertex and years
P51. Impact bar plot by year or by theme
H02.A Choropleth map
H04.B World map with Bubbles
H05.C World map with Bubbles and curves
92. Scale fill gradient
25.Pyramid
41.Pyramid2
42.Pyramid3
43.Pyramid6
26..Survial analysis
27.Two mode Relation
28.Radar Plot
281.Radar Plot_1
29.Bibar line chart
30.Stack Bar chart
31.Multi-Line chart
32.Multi-Line Bar Chart
33.Multi-Line Bar2 Chart
36.Wordcloud
43.Wordcloud2
432.Wordcloud3
44.Spider plot
45.Plot for Two Way Anova
Kendall Coefficient of Concordance (W)
101.RS1.Rstatistics
102.Compute effect size
103.Logistic Regression
105.LR for prostate cancer
106.Prob. with sapply(in, function)
107.Metropolis-Hastings MCMC in R
108.Metropolis-Hastings MCMC in R
Fligner-Killeen Tess
Calculate IQR 1st & 3nd quartiles
API for Names to Nationality
Chien. Biliometrics in Pubmed
https://bibliometric.com/
Guideline in Visualizations
EG2 Social network analysis(MP4)
EG31 matrix to Network to R
EG32 Transforming matrix to #59A
Start[here]. 2-column(couple) data
Start[here]2. 2-column(couple) Excluding ego-self
60A7A.D1:3-column to 6-column data
EEEEE: 3-column to 6-column(main)
60A7B.D1:3 to 4 for Network in SNA
***60A7C.D1:6 to 4 for Network in SNA
60A7B1.D1:4 to Network for SNA
Hit:Notice of the Google Maps
40. D1: Network chart(example)
40A.D1: To gain Pajek format(.paj)
Hit: Drawbacks above(many link, no cluster)
59 D2:Random cluster with size #46
59A D2:Enhancement: cluster then to #46
59B D2: No cluster to #14 Chord
60A1 D2: Cluster to #14 Chord
60A3 D2:*Chord(cluster & few links) to #14
60A31.D3:*Dendrogram to #47
80A31.D7:HotSpot(CiteSpace)
60A4.*CircleBar. to #15 CircleBar
60A5.*CirclePacking. to #34 CirclePacking
60A6.D4:*HeatDendro. to #52 HeatDendro
60A7.D55:*Network: next to #46
60A7.D5:*Network: next to #40A
Hit: To Pajek by 60A7 then to 40A
Hit: Coordinate in Pajek & Google Maps
60A9.D6:Google Maps(Pajek in 40A)
60A8: *Details about cluster process
60A88: *Details without self connections
60A7A.D5:Data from 60A8 to pajek for Google Maps
60AA2.D5:nodes & relations in Network
60AA3.D5:nodes & relations Group in Network Group Network蝬脰楝
60AA1.D5:3-column data to Pajek for Google Maps
60AA3.D5:Pajek data to EdgebundleR**Network
60AA.D7:Google Maps(from #60A7A:clustering)
62A. Solving k in CLC algorithm
End.D7: Exercise and submission
EG01. Loop to MeSH terms or Keywords #59A
EG012. Excluding self to MeSH terms or Keywords #59A
BTW01. Betweenness SNA
BTW02. Betweenness chord
FG2. Two mode data for keywords on chord
FG2A. Two mode data for keywords on Heatmap
FG2B. Two mode data for *Details about cluster process
FG2C. Two mode data for Box Plot
FG2D. ***:Two mode data of year in columns
IBPA1. Impact bar plot by year or by theme
IBPA2. Impact bar plot by year with links
KW01 Article related to keyword analysis
KW02 Forest plot
H01.A blank world map
Heatmap.A Choropleth map
Heatmap.B Choropleth map
US Map(choropleth)
China Map(choropleth)
Taiwan Map(choropleth)
H03. Bar Chart by stacked year
H04. Line Chart by year
H05. Scatter Chart by year
H06. World Map with Edges
Ex. Combine H02 & H06
0.Simple example
1.Age example
2.Birthday example
3.Souvenir example
4.Smoth data example
5.Decompose:seasonal, trend and irregular component
6.Seasonally adjusting
7.Forecases
8. Inflection points on trend
9. IP with 1st Diff cumulative
GDP for nations
Bar animation
Box plot anaimation
Line animation
Multipy GDP for nations
GDP world top 10 bar chart
Taiwan health data annually
Combined images with layers
Animation images
1177design series
1177design with background
1177design with fade-in fade-out
1177design with composite 2 images
1177design with annotation on images
1177design add image in position
Image to text (OCR)
Convert Png to tif files
Extract PDF pages
Extract & Combine them into an single PDF
MCMC norm distribution(exclude lower 100)
MCMC CMC Machine Learning on Metropolis-Hastings
Spatial Statistics
Naive Bayes Classification
CNN model
ANN model
Logistic model
randomForest model
Gradient Boosting Machines
Decision Tree
KNN model
SVM model
Linear Discriminant Analysis (LDA)
Compute PI
1.Vacano plot Rasch
2 Vacano plot without Fit regression
3 Vacano plot with Fit regression
4 GEO Differential Gene Analysis
5 GEO Differential Gene Analysis
6A Simple Heatmap GEO
6B Heatmap GEO 2024 Chinese
6C Heatmap GEO 2024 Chinese
6C Heat Map for GEO Differential Gene Analysis
7 Heat Map for GEO Differential Gene Analysis
8 WGCNA Identifies Key Modules
9 KEGG pathway enrichment dot plot
10 Network vital in center
11 Check Genes involved in GEO
12Check Two GSE identical
13 Check GSE with Genes
14 GEO2R GSE79973
14A GEO item kmean Cluster
14B GEO Feature Heatmap
14C GEO Feature FLCA
14D GEO Sample FLCA
15 Vacano GSE79973
16 Density for health & abnormal
17 ROC Density for GSE79973
18 ROC Density for GSE6919
21 GEOs saved for FLCA
22A GEO Simulated Data
22B GEO Probe from GSE or data in Chinese2024 Entrance
22C GEO gene from probe GSE79973
22D Chord for cluster analysis
0. ChatGPT with Prompts to R
00. PDF: Instruction to R platform
====Begin to start==========
1.3-column to clusters
2. Google Map in R(dashboard)
21.4-Quadrant Plot on internet
3.Temporal Bar Chart
4 Trend for Temporal Bar Chart
4-1.Table Temporal Bar Chart with trend
4-2.Table Trend & Cluster Temporal Bar Chart with trend
5 Trend & Cluster for Temporal Bar Chart
====Similarity By Distance ==========
6. By Distance Cluster analysis for persons
7.By Distance Cluster for items
6A. Slope By Distance for items
6-2.Bar Chart Distance for items
7A. Slope By Distance for persons
7B.Bar Chart By Distance for persons
====Similarity By correlation==========
8. By corr. Cluster analysis for items
9.By corr. Cluster analysis for persons
8A. corr. Slope Graph for items
9A.corr.Slope Graph for persons
@@@9ABC.Corr & Dist.Slope Graph for persons
9AB.Outfit corr.Slope Graph for persons
8B. corr. Bar Chart for items
9B. corr.Bar Chart for persons
====Input with Table format below==========
10.Table input useful for the dataframe
11.(Slope Graph) Table used for practice
12.Polar Area Chart
13. Calendar Heatmap
14.Stream graph
15. Water Fall plot
16. Alluvial diagram
17.Circular Barplot
18. Sankey Diagram
19.Tree Map(proportion)
20. Sunburst chart
21. Scree plot with eigenvalues
22. Youden Plot
23. Correlation matrix plot
24. Heat Map
25. Ridgeline Plot with group
26.Density Plot with Gradient Fill
27. Box plot with group
28. Scatter Plot with Regression Line by x, y
29.95%CI 4-Scatters Plots in R
30. 4-Scatters Plots in R
31. 4-Quadrant Radar Plot in R
32. Ridgeline distribution of counts over yrs
33. Ridgeline distribution of counts over yrs
34. Ridgeline distribution of counts over yrs
35. violine scatter spots over values
36. Ridgelin one color of counts over yrs
37. violin no scatter spots over values
38. True violin over values
39. Box plot burst spots over yrs
40. Points-errorbars over yrs
41. Calendar Heatmap
***42. Slope graph: Timeline view of spaces BW elements
43. Slope graph: Hotspots for Timeline view of spaces BW elements
43-1.Slope graph: Cluster Hotspots for Timeline view of spaces BW elements
44. Data Distribution of Box plot
45. Data Distribution of Z-score
46. Two time point comparison
47. Diverging Lollipop Chart
47A. Item Difficulty
48. Above Average plot
49. Correlogram correlation
50.Two dendrograms in comparison
====Bibliometrics==========
51. Dual-map overlay(Biblio)
51A. Dual-map overlay(Pyramid)
51A.Two Axes for Articles
@@@51a Two Axes Article & Citation
51B.Performance Analysis
51C.Point Chart for lowest $ highest
51D.Flow chart via DiagrammeR
52.Time Line View(bibliometric)
53.TimeZone View(Themes)(bibliometric)
54.Time Zone View(bibliometric)
55.(Alluvial by survival case on the Titanic
56.(Alluvial).Dual-map view
57.(Saneky)ER example
58.Sankey(Sankey).column.no link in cluster
59.No cluster in network
60. Taipei MRT system
61. 2 columns with Leader & Folower using FLCA
62. FLCA type.Highlight 3 clusters
@@@63. Alluvial Trend by time
63-1. Alluvial Trend with IP by time
63-2. Stacked Alluvial Trend with IP by time
63-3. Stacked Area Chart with Trend and IP by time
63-4. Grouped Barchart by column
63-5. Stacked Grouped Barchart by column
63-6. Stacked Grouped Barchart by row
63-7. Percentage Stacked Grouped Barchart by row
64. Stacked Alluvial Trend by time
65. 2-axes IP with 1st Diff cumulative
652 Search IP and Trend Type in csv File
66.Multiply Line Chart with IP
@@@66A. (Pre/Post tests)Line Chart with values
66B. (Pre/Post tests)Slope Plot with values
67..Multiply Line Chart
68. HotSpot(CiteSpace)
69. Add value to Heatmap by vertex and years
70. Boxplot for one variable
71.(Layout).4-Quadrant Thematic Map
72.(Layout).Kano diagram for Thematic Map
73. (Sankey).column. sankeymatic.com/
74.Taiwan Map(choropleth)
75. GDP world top 10 bar chart
76. China Map(choropleth)
77. US Map(choropleth)
@@@Lowere:.Green choropleth Map
78B. World Choropleth map
79. World Map with Edges
80. Combine H02world & H06edge
@@@Upper: Worldmap for country collaborations
@@@Lowere: Combined Green Worldmap Choropleth
====Common statistical Visuals==========
81. Scatter Chart by year
***810 Top10 keywords Frequency
811.Top10 value year trend line chart
812.Top10 value year scatter for Names
***813.Top10 value year scatter for Names
82. Pie Chart
82.1Waffle Chart
83. Multi-Line Bar2 Chart
84.Multi-Line chart
85.Bibar line chart
86.Single-Radar Plot_1
87.Multi-Radar Plot
88.Two mode Relation(single to multi pair)
89.Survial analysis
90. Stacked bar Pyramid
91.Pyramid(Simple)
91A.Read Table for dataframe
92. Kendall (W) judge in rows
921. Kendall (W) judge in cplumn
93.Plot for Two Way Anova
941.Wordcloud3
94.Wordcloud2
95.Wordcloud
96.CircleBar. to #15 CircleBar
97.*Chord(cluster & few links) to #14
98.*Dendrogram to #47
99.*Circular Packing fom Nested Data Frame
100..*HeatDendro. to #52 HeatDendro
101. Wright Map
102.Rasch MCMC estimation in R
====Rasch analysis==========
103.MML dicho-Wright Map with TAM
104.JML poly-Group:Rasch Wright Map with TAM
105.Rasch KIDMAP
106.Rasch model_ICC
107.Rasch model_WrightMap
108.Rasch MML model
109.Rasch DIF
110.IRTLord DIF
111.AndersenLRTest
112.Rasch MAPEAP
113.RaschAdersonLRtest
114.RaschRSMWrightMap
====Amazing Visuals==========
115.ChordDiagram
116.Circlebarplot
117. Circular barplots
118. Donut plot
119. Circle Heat Map
120.Blue Circle Heat Map
121.Circle Stacked Bar Plot
122.Bar Chart horizontally
123. Bar Chart vertically
124. Box plot
125.Venn Diagramthen
126.Lollipop Chart
127.Lollipop with terms
371 Line Chart For Salary
372 Dot Chart For Salary
373 IRT parameter1
374 IRT parameter2
375 IRT model with c Da
37b.Primary Edge Linked only
@@@Leader on relation DDI for Drug-drug-interaction
Above FLCA for directed arrow DDI
@@@ Chord diagram Above FLCA
edge for leader by value with count on Sankey
@@@ Leader on count DDI for Drug-drug-interaction
Wide To Longer DataSet
***Relation Data To 6 columns
***128.Sankey2
====Network & Cluster analysis==========
129. Via metrix to Network
130. Network dendro2
131. Clusters by dendrogram
132. Dendrogram Tree
133. Dendrogram Circle
134. Heatmap33
135. Heatmap34(dendrogram)
136. Euclidean dendrogram
137. Euclidean dendrogram2
====Meta analysis==========
138.Forest Plot
139.Forest Plot(NMA)
140.Forest Plot(NMA for each)
141.Forest Plot(easy)
142.MetaSMD
143.MetaRR
144.MetaOR
145.MetaFunnel
====Other visuals==========
146.Decision Tree
147 Gradient Boosting Machines
148. ANN model
149.Circle Packing
150. Tree map
151. Tree map(subgroup)
152. Gantt Plot
153. Flow Chart
154. Infographics
155. Vocano plot
155A. Vocano plot
156. KEGG pathway enrichment plot
157. Protein-Protein interaction (PPI)
Rasch Continuous & RSM in Comparison
166. Change File Names In A Folder
@@@ Wide to long dataset for metrix in 3 columns