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metadat's Introduction

Hi there!

Work Details

  • I am associate professor of methodology and statistics in the Department of Psychiatry and Neuropsychology at Maastricht University in the Netherlands
  • I do research on the statistical methods for meta-analysis and the analysis of multilevel and longitudinal data, with particular emphasis on computational statistics and software development

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alistairmcnairsenior avatar christianroever avatar daniel1noble avatar drmattg avatar ekothe avatar guido-s avatar kylehamilton avatar olivroy avatar rcalinjageman avatar robbievanaert avatar thomased avatar wviechtb avatar

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metadat's Issues

todo list

todo

  • create todo
  • naming scheme
    • dat.authoryear
    • dat.authoryearb.1
    • dat.authoryear.corr
    • dat.authoryear.phylo
  • boilerplate function for doc generation
  • wrapper function to unite data cleaning + doc generation
  • port over existing datasets from metafor
  • document/describe the workflow (raw -> clean data) and add contributor guidelines
  • CI
  • tests (see #8)
  • license
  • cran
  • manuscript?

Warning on build with dat.ho2012 unknown macro '\italic

When installing metadat the system throws a warning about the \italic I'll submit a patch in a second that fixes this.

==> Rcmd.exe INSTALL --no-multiarch --with-keep.source metadat

  • installing to library 'C:/Users/Kyle Hamilton/Documents/R/win-library/3.6'
  • installing source package 'metadat' ...
    ** using staged installation
    ** R
    ** data
    *** moving datasets to lazyload DB
    ** byte-compile and prepare package for lazy loading
    ** help
    Warning: C:/Users/Kyle Hamilton/Documents/GitHub/metadat/man/dat.ho2012.Rd:10: unknown macro '\italic'
    Warning: C:/Users/Kyle Hamilton/Documents/GitHub/metadat/man/dat.ho2012.Rd:11: unknown macro '\italic'
    Warning: C:/Users/Kyle Hamilton/Documents/GitHub/metadat/man/dat.ho2012.Rd:15: unknown macro '\italic'

Code under 'Examples' in the help files

Hi all,

I would like to start an open/transparent discussion about the following issue:

At the end of the help files (in the Examples section), there is often at times quite extensive code to illustrate some ways of analyzing the data or reproducing the results from the paper from which the data were taken. Essentially, this was a result of me moving the datasets that were originally part of the metafor package to metadat and hence copying the help files from one package to the other. As a result, the majority of help files illustrate the analysis of the data using the metafor package (given that about 3/4 of the datasets that are currently in metadat were contributed by me). The question has come up whether it should be possible to contribute code to illustrate the analysis of datasets using other packages (of course this would apply to any dataset, not just those that were contributed by me).

My approach so far has been this: The person who spends the time to extract, document, and contribute a dataset to the package (which often can take considerable amounts of time) is also the person who gets to write the Examples section (can think of this as a reward). This is why I have also never touched the Examples section on any of the datasets that were contributed to the package by other people. But given that many datasets were contributed by me, this might come across as me 'gate keeping' what packages are used to illustrate the analyses. Hence, I would like to solicit feedback on this issue in an open/transparent manner.

A few additional thoughts:

  • Additional example code is certainly useful for those who would like to see different packages/approaches for analyzing the same data.
  • If additional code can be contributed, who gets to decide what code is actually added?
  • What if somebody just wants to add 'boiler plate' analysis code to every single dataset?
  • Also, how would the code section be structured? For example, whose code is shown first? What if there is disagreement as to the appropriate way(s) of analyzing the data?
  • Is there a limit to the extent of the code? Is there a danger that the Examples section might become confusing/messy if there is too much code/output there?
  • Analysis code is wrapped in \dontrun{} because the package would already be rejected by CRAN due to excessively long run times on some of the examples. However, for creating the pkgdown docs (https://wviechtb.github.io/metadat/), I build the docs with pkgdown::build_site(run_dont_run = TRUE), which is why the actual results are shown there. What if code breaks? Who is then responsible for fixing things? As maintainer (and the person who currently builds these docs), it would then at least be my responsibility to email people about their code.
  • Right now, it takes about 15-20 minutes to build these docs. Can this start to get out of hand if a lot more code is added? Should there be a limit on the run times?
  • If we decide to stick to the 'the contributor gets to write the Examples section' approach: I see lots of datasets in other packages that are not part of metadat. Why not move those datasets over in which case one could balance out which packages are emphasized in the Examples section?
  • Another possibility: No code at all in the Examples section (but everybody is of course free to put code to illustrate the analysis of datasets on their own website or some other repository).

Hope to hear other people's thoughts on this!

Datasets from `to_be_added` that need to be added to the package

These are the datasets in the to_be_added dir that need to be added back to the package:

  • dat.barone2019.Rd
  • dat.cleasby2012.Rd
  • dat.english2016.Rd
  • dat.griffith2006.Rd
  • dat.ho2012.Rd
  • dat.nakagawa2015.Rd
  • dat.reed2001.Rd
  • dat.sala2019.Rd
  • dat.sorokowski2019.Rd
  • dat.tamim2011.Rd
  • dat.valstad2017.Rd
  • dat.weaver2018.Rd
  • dat.xia2008.Rd

Tests (via testthat)

In #5, @kylehamilton suggested to add tests, so that we can do automated testing via testthat. Although I am all for testing, I am not sure we should do this in the context of this package. Instead, I think tests of other packages should be done by those packages themselves. So, I removed the tests that were originally added in #5, but I wanted to open up the discussion here in case there are different opinions.

new dataset to be added McCann etal 2016

Dear Wolfgang,

As this is the first time I contribute to your package, I raise an issue here first to ask to check my contribution to your package.
https://github.com/abannachbrown/metadat

Our dataset is preclinical meta-analysis on a treatment for stroke, tested in animal models.

If you are happy, I can make a pull request for your review/approval.

Best wishes, Alex

Use of \concept or \keyword in documentation

Currently, in the documentation we are using \concept to describe what the datasets look like. A lot of the tags are for the academic fields of publication such as education or medicine. Do we want this to be how we organize the types of datasets or do we want to use \keyword to describe these datasets? The Writing R Extensions manual says "each \concept entry should give a single index term", for now, I went back into the documentation and removed any case where I used two words in a \concept field but there are a few others currently there. I just wanted to wait until I got direction to make sure I didn't break something.

https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Indices

Add dat.barone2019

This meta-analysis has a nice example of publication bias see funnel plot from paper. Authors have included data and code on OSF so adding to metadat should be pretty straight forward. I'll try to make a pull request with the data sometime this week. But just in case the semester gets me I wanted to make sure there was a record of it here.

Citation
Barone, P., Corradi, G., & Gomila, A. (2019). Infants’ performance in spontaneous-response false belief tasks: A review and meta-analysis. Infant Behavior and Development, 57, 101350. https://doi.org/10.1016/j.infbeh.2019.101350

OSF
https://osf.io/re8uj/?view_only=d2605771dd664831a104318db9ff7aa9

image
Fig. 3

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