{"action":"closed","issue":{"url":"https://api.github.com/repos/tompollard/tableone/issues/20","repository_url":"https://api.github.com/repos/tompollard/tableone","labels_url":"https://api.github.com/repos/tompollard/tableone/issues/20/labels{/name}","comments_url":"https://api.github.com/repos/tompollard/tableone/issues/20/comments","events_url":"https://api.github.com/repos/tompollard/tableone/issues/20/events","html_url":"https://github.com/tompollard/tableone/issues/20","id":295157087,"number":20,"title":"reviewer 2","user":{"login":"tompollard","id":822601,"avatar_url":"https://avatars2.githubusercontent.com/u/822601?v=4","gravatar_id":"","url":"https://api.github.com/users/tompollard","html_url":"https://github.com/tompollard","followers_url":"https://api.github.com/users/tompollard/followers","following_url":"https://api.github.com/users/tompollard/following{/other_user}","gists_url":"https://api.github.com/users/tompollard/gists{/gist_id}","starred_url":"https://api.github.com/users/tompollard/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tompollard/subscriptions","organizations_url":"https://api.github.com/users/tompollard/orgs","repos_url":"https://api.github.com/users/tompollard/repos","events_url":"https://api.github.com/users/tompollard/events{/privacy}","received_events_url":"https://api.github.com/users/tompollard/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-02-07T14:27:14Z","updated_at":"2018-03-28T21:57:13Z","closed_at":"2018-03-28T21:57:13Z","author_association":"OWNER","body":"> Reviewer: 2\r\n\r\n> Comments to the Author\r\nA Python package for generating summary statistics for research articles, called tableone, is presented. The package is made available on PyPI and GitHub. A Jupyter notebook demonstrates its application. \r\n\r\n> There is little novelty in this application note. The package is based on an earlier one implemented in R and seems to have essentially the same functionality (if there are differences, these should be highlighted, probably in a table). It can be useful to those who specifically prefer Python, but other than that, there does not seem to be any reason to use this package over the one implemented in R. The input to the package is a DataFrame structure, which R also implements. Several potential future improvements are listed. It would have been interesting/useful to implement these enhancements and report them, given that the note is much shorter than the allowed word count, and as it stands, does not seem to add much to the R package.\r\n\r\n> The writing is clear, but there is little substance beyond basic characteristics of Python, its packages, and usage of the package."}}
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