Data Visualization

Make complex, interactive heatmaps. iheatmapr includes a modular system for iteratively building up complex heatmaps, as well as the iheatmap() function for making relatively standard heatmaps.

1. Gershanov, S., Toledano, H., Michowiz, S., Barinfeld, O., Pinhasov, A., Goldenberg-Cohen, N., & Salmon-Divon, M. (2018). MicroRNA&ndash,mRNA expression profiles associated with medulloblastoma subgroup 4. Cancer Management and Research, Volume 10, 339–352. https://doi.org/10.2147/cmar.s156709
2. Ruiz, J. L., Tena, J. J., Bancells, C., Cortés, A., Gómez-Skarmeta, J. L., & Gómez-Díaz, E. (2018). Characterization of the accessible genome in the human malaria parasite Plasmodium falciparum. Nucleic Acids Research. https://doi.org/10.1093/nar/gky643
3. Ott, C. J., Federation, A. J., Schwartz, L. S., Kasar, S., Klitgaard, J. L., Lenci, R., … Bradner, J. E. (2018). Enhancer Architecture and Essential Core Regulatory Circuitry of Chronic Lymphocytic Leukemia. Cancer Cell. https://doi.org/10.1016/j.ccell.2018.11.001
4. Kim, K. W., Allen, D. W., Briese, T., Couper, J. J., Barry, S. C., … Colman, P. G. (2019). Distinct gut virome profile of pregnant women with type 1 diabetes in the ENDIA study. Open Forum Infectious Diseases. https://doi.org/10.1093/ofid/ofz025
5. Reyes, A. L. P., Silva, T. C., Coetzee, S. G., Plummer, J. T., Davis, B. D., Chen, S., … Jones, M. R. (2019). GENAVi: a shiny web application for gene expression normalization, analysis and visualization. BMC Genomics, 20(1). https://doi.org/10.1186/s12864-019-6073-7
6. Kim, K. W., Allen, D. W., Briese, T., Couper, J. J., Barry, S. C., … Colman, P. G. (2020). Higher frequency of vertebrate‐infecting viruses in the gut of infants born to mothers with type 1 diabetes. Pediatric Diabetes, 21(2), 271–279. https://doi.org/10.1111/pedi.12952
7. Meng, S., Zhan, S., Dou, W., & Ge, W. (2019). The interactome and proteomic responses of ALKBH7 in cell lines by in-depth proteomics analysis. Proteome Science, 17(1). https://doi.org/10.1186/s12953-019-0156-x
8. Shi, L., Tian, H., Wang, P., Li, L., Zhang, Z., Zhang, J., & Zhao, Y. (2020). Spaceflight and simulated microgravity suppresses macrophage development via altered RAS/ERK/NFκB and metabolic pathways. Cellular & Molecular Immunology. https://doi.org/10.1038/s41423-019-0346-6
9. Caseys, C., Gongjun Shi, Nicole Soltis, Raoni Gwinner, Jason Corwin, Susanna Atwell, Daniel Kliebenstein. 2020. Quantitative interactions drive Botrytis cinerea disease outcome across the plant kingdom. bioRxiv preprint 507491; https://doi.org/10.1101/507491
10. Wang, Y., Zhang, X., Song, Q., Hou, Y., Liu, J., Sun, Y., & Wang, P. (2020). Characterization of the chromatin accessibility in an Alzheimer’s disease (AD) mouse model. Alzheimer’s Research & Therapy, 12(1). https://doi.org/10.1186/s13195-020-00598-2

Create interactive web graphics from ggplot2 graphs and/or a custom interface to the (MIT-licensed) JavaScript library plotly.js inspired by the grammar of graphics.

1. Rothman, A. M. K., Arnold, N. D., Chang, W., Watson, O., Swift, A. J., Condliffe, R., … Lawrie, A. (2015). Pulmonary Artery Denervation Reduces Pulmonary Artery Pressure and Induces Histological Changes in an Acute Porcine Model of Pulmonary Hypertension. Circulation: Cardiovascular Interventions, 8(11), e002569–e002569. https://doi.org/10.1161/circinterventions.115.002569
2. Doyle, J. M., Merovitch, N., Wyeth, R. C., Stoyek, M. R., Schmidt, M., Wilfart, F., … Croll, R. P. (2017). A simple automated system for appetitive conditioning of zebrafish in their home tanks. Behavioural Brain Research, 317, 444–452. https://doi.org/10.1016/j.bbr.2016.09.044
3. Hertler, B., Buitrago, M. M., Luft, A. R., & Hosp, J. A. (2016). Temporal course of gene expression during motor memory formation in primary motor cortex of rats. Neurobiology of Learning and Memory, 136, 105–115. https://doi.org/10.1016/j.nlm.2016.09.018
4. Meyer, A. G. (2016). Analysis of infection biomarkers within a Bayesian framework reveals their role in pneumococcal pneumonia diagnosis in HIV patients. https://doi.org/10.1101/070144
5. Walker, Kyle. (in press). tigris: An R Package to Access and Work with Geographic Data from the US Census Bureau. https://journal.r-project.org/archive/accepted/walker.pdf
6. Rastrojo, A., García-Hernández, R., Vargas, P., Camacho, E., Corvo, L., Imamura, H., … Requena, J. M. (2018). Genomic and transcriptomic alterations in Leishmania donovani lines experimentally resistant to antileishmanial drugs. International Journal for Parasitology: Drugs and Drug Resistance. https://doi.org/10.1016/j.ijpddr.2018.04.002
7. Tang, Y. (2018). autoplotly: An R package for automatic generation of interactive visualizations for statistical results. Journal of Open Source Software, 3(24), 657. https://doi.org/10.21105/joss.00657
8. Rastrojo, A., García-Hernández, R., Vargas, P., Camacho, E., Corvo, L., Imamura, H., … Requena, J. M. (2018). Genomic and transcriptomic alterations in Leishmania donovani lines experimentally resistant to antileishmanial drugs. International Journal for Parasitology: Drugs and Drug Resistance, 8(2), 246–264. https://doi.org/10.1016/j.ijpddr.2018.04.002
9. Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., … Butterworth, A. S. (2018). Genomic atlas of the human plasma proteome. Nature, 558(7708), 73–79. https://doi.org/10.1038/s41586-018-0175-2
10. Hsu, Lawrence. 2018. Linking Traditional Chinese Medicinal Herbs to Cancer Related Pathways. Scholar Archive. 4054. https://digitalcommons.ohsu.edu/etd/4054
11. Krogsgaard, L. R., Andersen, L. O. ‘Brien, Johannesen, T. B., Engsbro, A. L., Stensvold, C. R., Nielsen, H. V., & Bytzer, P. (2018). Characteristics of the bacterial microbiome in association with common intestinal parasites in irritable bowel syndrome. Clinical and Translational Gastroenterology, 9(6). https://doi.org/10.1038/s41424-018-0027-2
12. Sanford, T., Gadzinski, A. J., Gaither, T., Osterberg, E. C., Murphy, G. P., Carroll, P. R., & Breyer, B. N. (2018). Effect of Oscillation on Perineal Pressure in Cyclists: Implications for Micro-Trauma. Sexual Medicine. https://doi.org/10.1016/j.esxm.2018.05.002
13. Koc, A., Henriksson, T., & Chawade, A. (2018). Specalyzer—an interactive online tool to analyze spectral reflectance measurements. PeerJ, 6, e5031. https://doi.org/10.7717/peerj.5031
14. Devlin, J. C., Battaglia, T., Blaser, M. J., & Ruggles, K. V. (2018). WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-4870-z
15. Václav Brázda, Jiri Lysek, Martin Bartas, and Miroslav Fojta. 2018. Complex analyses of short inverted repeats in all sequenced chloroplast DNAs. BioMed Research International. https://www.hindawi.com/journals/bmri/aip/1097018/
16. Fontaine, A., Lequime, S., Moltini-Conclois, I., Jiolle, D., Leparc-Goffart, I., Reiner, R. C., & Lambrechts, L. (2018). Epidemiological significance of dengue virus genetic variation in mosquito infection dynamics. PLOS Pathogens, 14(7), e1007187. https://doi.org/10.1371/journal.ppat.1007187
17. Lawrence, T. N., & Bhalla, R. S. (2018). Spatially explicit action research for coastal fisheries management. PLOS ONE, 13(7), e0199841. https://doi.org/10.1371/journal.pone.0199841
18. Zhang, Y., Oates, L. G., Serate, J., Xie, D., Pohlmann, E., Bukhman, Y. V., … Ong, R. G. (2018). Diverse lignocellulosic feedstocks can achieve high field-scale ethanol yields while providing flexibility for the biorefinery and landscape-level environmental benefits. GCB Bioenergy. https://doi.org/10.1111/gcbb.12533
19. Wang, C., Moya, L., Clements, J. A., Nelson, C. C., & Batra, J. (2018). Mining human cancer datasets for kallikrein expression in cancer: the “KLK-CANMAP” Shiny web tool. Biological Chemistry, 0(0). https://doi.org/10.1515/hsz-2017-0322
20. Locard-Paulet, M., Parra, J., Albigot, R., Mouton-Barbosa, E., Bardi, L., Burlet-Schiltz, O., & Marcoux, J. (2018). VisioProt-MS: interactive 2D maps from intact protein mass spectrometry. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty680
21. Horvatić, A., Guillemin, N., Kaab, H., McKeegan, D., O’Reilly, E., Bain, M., … Eckersall, P. D. (2018). Quantitative proteomics using tandem mass tags in relation to the acute phase protein response in chicken challenged with Escherichia coli lipopolysaccharide endotoxin. Journal of Proteomics. https://doi.org/10.1016/j.jprot.2018.08.009
22. Bharanidharan, R., Arokiyaraj, S., Kim, E. B., Lee, C. H., Woo, Y. W., Na, Y., … Kim, K. H. (2018). Ruminal methane emissions, metabolic, and microbial profile of Holstein steers fed forage and concentrate, separately or as a total mixed ration. PLOS ONE, 13(8), e0202446. https://doi.org/10.1371/journal.pone.0202446
23. Schieffer, K. M., Kline, B. P., Harris, L. R., Deiling, S., Koltun, W. A., & Yochum, G. S. (2018). A Differential Host Response to Viral Infection Defines a Subset of Earlier-Onset Diverticulitis Patients. J Gastrointestin Liver Dis, 27(3), 249-255. https://doi.org/10.15403/jgld.2014.1121.273.sch
24. Longuespée, R., Kriegsmann, K., Cremer, M., Zgorzelski, C., Casadonte, R., Kazdal, D., … Kriegsmann, M. (2018). In MALDI mass spectrometry imaging on formalin-fixed paraffin-embedded tissue specimen section thickness significantly influences m/z peak intensity. PROTEOMICS - Clinical Applications, 1800074. https://doi.org/10.1002/prca.20180007
25. Tong, M., Deng, Z., Yang, M., Xu, C., Zhang, X., Zhang, Q., … Liu, Q. (2018). Transcriptomic but not genomic variability confers phenotype of breast cancer stem cells. Cancer Communications, 38(1). https://doi.org/10.1186/s40880-018-0326-8
26. Denecker, T., & Lelandais, G. (2018). Empowering the detection of ChIP-seq “basic peaks” (bPeaks) in small eukaryotic genomes with a web user-interactive interface. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3802-y
27. Wylie, K. M., Blankenship, S. A., Tuuli, M. G., Macones, G. A., & Stout, M. J. (2018). Evaluation of patient- versus provider-collected vaginal swabs for microbiome analysis during pregnancy. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3809-4
28. Johnson, E. C. B., Dammer, E. B., Duong, D. M., Yin, L., Thambisetty, M., Troncoso, J. C., … Seyfried, N. T. (2018). Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Molecular Neurodegeneration, 13(1). https://doi.org/10.1186/s13024-018-0282-4
29. Haddaway, N. R., & Westgate, M. J. (2018). Predicting the time needed for environmental systematic reviews and systematic maps. Conservation Biology. https://doi.org/10.1111/cobi.1323
30. Kollar, B., Shubin, A., Borges, T. J., Tasigiorgos, S., Win, T. S., Lian, C. G., … Riella, L. V. (2018). Increased levels of circulating MMP3 correlate with severe rejection in face transplantation. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-33272-7
31. Rahman, R., Ung, P. M.-U., & Schlessinger, A. (2018). KinaMetrix: a web resource to investigate kinase conformations and inhibitor space. Nucleic Acids Research. https://doi.org/10.1093/nar/gky916
32. Horvatić, A., Guillemin, N., Kaab, H., McKeegan, D., O’Reilly, E., Bain, M., … Eckersall, P. D. (2018). Integrated dataset on acute phase protein response in chicken challenged with Escherichia coli lipopolysaccharide endotoxin. Data in Brief. https://doi.org/10.1016/j.dib.2018.09.103
33. Barra, M., Labberton, A. S., Faiz, K. W., Lindstrøm, J. C., Rønning, O. M., Viana, J., … Rand, K. (2018). Stroke incidence in the young: evidence from a Norwegian register study. Journal of Neurology. https://doi.org/10.1007/s00415-018-9102-6
34. Orwoll, E. S., Fino, N. F., Gill, T. M., Cauley, J. A., Strotmeyer, E. S., … Ensrud, K. E. (2018). The relationships between physical performance, activity levels and falls in older men. The Journals of Gerontology: Series A. https://doi.org/10.1093/gerona/gly248
35. Lynd, A., Oruni, A., van’t Hof, A. E., Morgan, J. C., Naego, L. B., Pipini, D., … Weetman, D. (2018). Insecticide resistance in Anopheles gambiae from the northern Democratic Republic of Congo, with extreme knockdown resistance (kdr) mutation frequencies revealed by a new diagnostic assay. Malaria Journal, 17(1). https://doi.org/10.1186/s12936-018-2561-5
36. Soul, J., Hardingham, T., Boot-Handford, R., & Schwartz, J. M. (2018). SkeletalVis: An exploration and meta-analysis data portal of cross-species skeletal transcriptomics data. Bioinformatics. https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/bty947/26770069/bty947.pdf
37. Kline, B. P., Schieffer, K. M., Choi, C. S., Connelly, T., Chen, J., Harris, L., … Koltun, W. A. (2018). Multifocal Versus Conventional Unifocal Diverticulitis: A Comparison of Clinical and Transcriptomic Characteristics. Digestive Diseases and Sciences. https://doi.org/10.1007/s10620-018-5403-y
38. Rehbach, F., Stork, J., & Bartz-Beielstein, T. (2018). Bridging Theory and Practice Through Modular Graphical User Interfaces. Journal of Multimedia Processing and Technologies, 9(4), 134. https://doi.org/10.6025/jmpt/2018/9/4/134-140
39. Singer, R. A., Love, K. J., & Page, L. M. (2018). A survey of digitized data from U.S. fish collections in the iDigBio data aggregator. PLOS ONE, 13(12), e0207636. https://doi.org/10.1371/journal.pone.0207636
40. Duan, J., Wei Shi, Nathaniel K Jue, Zongliang Jiang, Lynn Kuo, Rachel O’Neill, Eckhard Wolf, Hong Dong, Xinbao Zheng, Jingbo Chen, Xiuchun (Cindy) Tian. 2018. Dosage Compensation of the X Chromosomes in Bovine Germline Early Embryos and Somatic Tissues. Genome Biology and Evolution. https://academic.oup.com/gbe/advance-article/doi/10.1093/gbe/evy270/5253178
41. Shen, Z., & Spruit, M. (2019). A Systematic Review of Open Source Clinical Software on GitHub for Improving Software Reuse in Smart Healthcare. Applied Sciences, 9(1), 150. https://www.mdpi.com/2076-3417/9/1/150/pdf
42. Łącki, M. K., Lermyte, F., Miasojedow, B., Startek, M. P., Sobott, F., Valkenborg, D., & Gambin, A. (2019). masstodon: A tool for assigning peaks and modeling electron transfer reactions in top-down mass spectrometry. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.8b01479
43. SHANG, D., & GHRIGA, M. (2018). EXPLORING SOCIAL MEDIA ANALYTICS ON COMMUNITY DEVELOPMENT PRACTICES. Journal of Information Technology Management, 29(4), 39. http://jitm.ubalt.edu/XXIX-4/article3.pdf
44. Waltz, F., Nguyen, T.-T., Arrivé, M., Bochler, A., Chicher, J., Hammann, P., … Giegé, P. (2019). Small is big in Arabidopsis mitochondrial ribosome. Nature Plants, 5(1), 106–117. https://doi.org/10.1038/s41477-018-0339-y
45. Hofmann, A., Cross, M., Karow, M. A., Straub, J. H., Clemen, C. S., & Eichinger, L. (2019). A convenient tool for bivariate data analysis and bar graph plotting with R. Biochemistry and Molecular Biology Education. https://doi.org/10.1002/bmb.21205
46. Sellgren, C. M., Gracias, J., Jungholm, O., Perlis, R. H., Engberg, G., Schwieler, L., … Erhardt, S. (2019). Peripheral and central levels of kynurenic acid in bipolar disorder subjects and healthy controls. Translational Psychiatry, 9(1). https://doi.org/10.1038/s41398-019-0378-9
47. Jovanović, G., Romanić, S. H., Stojić, A., Klinčić, D., Sarić, M. M., Letinić, J. G., & Popović, A. (2019). Introducing of modeling techniques in the research of POPs in breast milk – A pilot study. Ecotoxicology and Environmental Safety, 172, 341–347. https://doi.org/10.1016/j.ecoenv.2019.01.087
48. Kay, S., Graves, A., Palma, J. H. N., Moreno, G., Roces-Díaz, J. V., Aviron, S., … Herzog, F. (2019). Agroforestry is paying off – Economic evaluation of ecosystem services in European landscapes with and without agroforestry systems. Ecosystem Services, 36, 100896. https://doi.org/10.1016/j.ecoser.2019.100896
49. Martins, J., Magalhaes, C., Vieira, V., Rocha, M., & Osorio, N. S. (2019). HABIT - a webserver for interactive T cell neoepitope discovery. https://doi.org/10.1101/535716
50. Nadal-Ribelles, M., Islam, S., Wei, W., Latorre, P., Nguyen, M., de Nadal, E., … Steinmetz, L. M. (2019). Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations. Nature Microbiology. https://doi.org/10.1038/s41564-018-0346-9
51. Joish, V. N., Shah, S., Tierce, J. C., Patel, D., McKee, C., Lapuerta, P., & Zacks, J. (2019). Serotonin levels and 1-year mortality in patients with neuroendocrine tumors: a systematic review and meta-analysis. Future Oncology https://doi.org/10.2217/fon-2018-0960
52. Wheeler, D. L., Scott, J., Dung, J. K. S., & Johnson, D. A. (2019). Evidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodes. PLOS ONE, 14(2), e0211508. https://doi.org/10.1371/journal.pone.0211508
53. Aiello, M., Terenzi, D., Furlanis, G., Catalan, M., Manganotti, P., Eleopra, R., … Rumiati, R. I. (2019). Deep brain stimulation of the subthalamic nucleus and the temporal discounting of primary and secondary rewards. Journal of Neurology. https://doi.org/10.1007/s00415-019-09240-0
54. Michalak, W., Tsiamis, V., Schwämmle, V., & Rogowska-Wrzesińska, A. (2019). ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets. https://doi.org/10.1101/573774
55. Su, W., Sun, J., Shimizu, K., & Kadota, K. (2019). TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data. BMC Research Notes, 12(1). https://doi.org/10.1186/s13104-019-4179-2
56. Ravenhall, M., Campino, S., & Clark, T. G. (2019). SV-Pop: population-based structural variant analysis and visualization. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-2718-4
57. Kelly, M. J., So, J., Rogers, A. J., Gregory, G., Li, J., Zethoven, M., … Kats, L. M. (2019). Bcor loss perturbs myeloid differentiation and promotes leukaemogenesis. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-09250-6
58. Chakroborty, D., Kurppa, K. J., Paatero, I., Ojala, V. K., Koivu, M., Tamirat, M. Z., … & Elenius, K. (2019). An unbiased in vitro screen for activating epidermal growth factor receptor mutations. Journal of Biological Chemistry, jbc-RA118. http://www.jbc.org/content/early/2019/04/05/jbc.RA118.006336
59. Campbell, M. (2019). Learn RStudio IDE. https://doi.org/10.1007/978-1-4842-4511-8
60. Seyednasrollah, B., Milliman, T., & Richardson, A. D. (2019). Data extraction from digital repeat photography using xROI: An interactive framework to facilitate the process. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 132–144. https://doi.org/10.1016/j.isprsjprs.2019.04.009
61. Van Strien, M. J., Huber, S. H., Anderies, J. M., & Grêt-Regamey, A. (2019). Resilience in social-ecological systems: identifying stable and unstable equilibria with agent-based models. Ecology and Society, 24(2). https://doi.org/10.5751/es-10899-240208
62. Piccione, P. M., Baumeister, J., Salvesen, T., Grosjean, C., Flores, Y., Groelly, E., … Lothschütz, C. (2019). Solvent Selection Methods and Tool. Organic Process Research & Development, 23(5), 998–1016. https://doi.org/10.1021/acs.oprd.9b00065
63. Łagód, G., Duda, S. M., Majerek, D., Szutt, A., & Dołhańczuk-Śródka, A. (2019). Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP. Processes, 7(5), 251. https://doi.org/10.3390/pr7050251
64. Kirsch, S. A., & Böckmann, R. A. (2019). Coupling of Membrane Nanodomain Formation and Enhanced Electroporation near Phase Transition. Biophysical Journal. https://doi.org/10.1016/j.bpj.2019.04.024
65. Germon, A., Jourdan, C., Bordron, B., Robin, A., Nouvellon, Y., Chapuis-Lardy, L., … Laclau, J.-P. (2019). Consequences of clear-cutting and drought on fine root dynamics down to 17 m in coppice-managed eucalypt plantations. Forest Ecology and Management, 445, 48–59. https://doi.org/10.1016/j.foreco.2019.05.010
66. Best, B. D., & Halpin, P. N. (2019). Minimizing wildlife impacts for offshore wind energy development: Winning tradeoffs for seabirds in space and cetaceans in time. PLOS ONE, 14(5), e0215722. https://doi.org/10.1371/journal.pone.0215722
67. Ogłuszka, M., Orzechowska, M., Jędroszka, D., Witas, P., & Bednarek, A. K. (2019). Evaluate Cutpoints: Adaptable continuous data distribution system for determining survival in Kaplan-Meier estimator. Computer Methods and Programs in Biomedicine, 177, 133–139. https://doi.org/10.1016/j.cmpb.2019.05.023
68. Kortz, A. R., & Magurran, A. E. (2019). Increases in local richness (α-diversity) following invasion are offset by biotic homogenization in a biodiversity hotspot. Biology Letters, 15(5), 20190133. https://doi.org/10.1098/rsbl.2019.0133
69. Pérez-Palma, E., Gramm, M., Nürnberg, P., May, P., & Lal, D. (2019). Simple ClinVar: an interactive web server to explore and retrieve gene and disease variants aggregated in ClinVar database. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz411
70. Aspillaga, E., Safi, K., Hereu, B., & Bartumeus, F. (2019). Modelling the three‐dimensional space use of aquatic animals combining topography and Eulerian telemetry data. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13232
71. Gibson, K. M., Nguyen, B. N., Neumann, L. M., Miller, M., Buss, P., Daniels, S., … Pukazhenthi, B. (2019). Gut microbiome differences between wild and captive black rhinoceros – implications for rhino health. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-43875-3
72. Wright, R. J., Gibson, M. I., & Christie-Oleza, J. A. (2019). Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome, 7(1). https://doi.org/10.1186/s40168-019-0702-x
73. Bailey, L. D., Ens, B. J., Both, C., Heg, D., Oosterbeek, K., & van de Pol, M. (2019). Habitat selection can reduce effects of extreme climatic events in a long‐lived shorebird. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13041
74. Nourbakhsh, M., Mansoor, A., Koro, K., Zhang, Q., & Minoo, P. (2019). Expression Profiling Reveals Involvement of WNT Pathway in the Malignant Progression of Sessile Serrated Adenomas. The American Journal of Pathology. https://doi.org/10.1016/j.ajpath.2019.05.009
75. Glicksberg, B. S., Oskotsky, B., Thangaraj, P. M., Giangreco, N., Badgeley, M. A., Johnson, K. W., … Butte, A. J. (2019). PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz409
76. Xia, Liu, Zhang, & Guo. (2019). GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins. Cells, 8(7), 675. https://doi.org/10.3390/cells8070675
77. Carlström, K. E., Ewing, E., Granqvist, M., Gyllenberg, A., Aeinehband, S., Enoksson, S. L., … Piehl, F. (2019). Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-11139-3
78. Dag, O., Karabulut, E., & Alpar, R. (2019). GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms—R Package and Web-Based Tool. International Journal of Computational Intelligence Systems, 12(2), 649. https://doi.org/10.2991/ijcis.d.190618.001
79. Abhilash, L., & Sheeba, V. (2019). RhythmicAlly: Your R and Shiny–Based Open-Source Ally for the Analysis of Biological Rhythms. Journal of Biological Rhythms, 074873041986247. https://doi.org/10.1177/0748730419862474
80. Van der Veer, C., Bruisten, S. M., van Houdt, R., Matser, A. A., Tachedjian, G., van de Wijgert, J. H. H. M., … van der Helm, J. J. (2019). Effects of an over-the-counter lactic-acid containing intra-vaginal douching product on the vaginal microbiota. BMC Microbiology, 19(1). https://doi.org/10.1186/s12866-019-1545-0
81. Brionne, A., Juanchich, A., & Hennequet-Antier, C. (2019). ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity. BioData Mining, 12(1). https://doi.org/10.1186/s13040-019-0204-1
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Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using ggplot2.

1. Tierney, N. (2017). visdat: Visualising Whole Data Frames. The Journal of Open Source Software, 2(16), 355. https://doi.org/10.21105/joss.00355
2. Tierney, N. J., & Cook, D. H. (2018). Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. arXiv preprint arXiv:1809.02264. https://arxiv.org/abs/1809.02264

Easily visualize and animate tabledap and griddap objects obtained via the rerddap package in a simple one-line command, using either base graphics or ggplot2 graphics. plotdap handles extracting and reshaping the data, map projections and continental outlines. Optionally the data can be animated through time using the gganmiate package.

Provide Australia-themed color palettes.

Provides community-driven color palettes.