{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data2vis-automatic-generation-of-data","title":"Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks","arxiv_id":"1804.03126","date":"2018-04-09","proceeding":null,"authors":["Victor Dibia","Çağatay Demiralp"],"abstract":"Rapidly creating effective visualizations using expressive grammars is\nchallenging for users who have limited time and limited skills in statistics\nand data visualization. Even high-level, dedicated visualization tools often\nrequire users to manually select among data attributes, decide which\ntransformations to apply, and specify mappings between visual encoding\nvariables and raw or transformed attributes.\n  In this paper we introduce Data2Vis, a neural translation model for\nautomatically generating visualizations from given datasets. We formulate\nvisualization generation as a sequence to sequence translation problem where\ndata specifications are mapped to visualization specifications in a declarative\nlanguage (Vega-Lite). To this end, we train a multilayered attention-based\nrecurrent neural network (RNN) with long short-term memory (LSTM) units on a\ncorpus of visualization specifications.\n  Qualitative results show that our model learns the vocabulary and syntax for\na valid visualization specification, appropriate transformations (count, bins,\nmean) and how to use common data selection patterns that occur within data\nvisualizations. Data2Vis generates visualizations that are comparable to\nmanually-created visualizations in a fraction of the time, with potential to\nlearn more complex visualization strategies at scale.","url_abs":"http://arxiv.org/abs/1804.03126v3","url_pdf":"http://arxiv.org/pdf/1804.03126v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data2vis-automatic-generation-of-data","repo_url":"https://github.com/victordibia/data2vis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"data2vis-automatic-generation-of-data","repo_url":"https://github.com/devonds/automated_data_vis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.03126"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/victordibia/data2vis","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/devonds/automated_data_vis","reach":{"status":"unanswered"}}],"summary":{"ran_violates":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c9592bad043afa08","entry":"ensure_str","repo":"victordibia/data2vis","repo_kind":"official","path":"webserver.py","file_url":"https://github.com/victordibia/data2vis/blob/HEAD/webserver.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c9592bad043afa08"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}