feature: Adds imputation and improves missing data summary #29
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Closes #28 **IMPORTANT** Currently there is a [bug in the stable release of Quarto](quarto-dev/quarto-cli#10196) which prevents rendering of the missing data figures. It is fixed in development version [`v1.6.1`](https://github.com/quarto-dev/quarto-cli/releases/tag/v1.6.1) (currently available as pre-release, so if things don't render upgrade to this version). + Uses the [mice](https://amices.org/mice/index.html) package to summarise missing data graphically and undertake three different methods of multiple imputation. Functions are defined to aid with the plotting of imputed data for comparison to the original dataset. Notes on tasks that could be done to augment this such as tabulation. This is via the `sections/_interpolation.qmd` file. Includes citation for the mice R package. + Moves data dictionary to Appendix. + Tidies up tables adding missing captions and removing `print()` + Moves tables to [panel-tabset](https://quarto.org/docs/interactive/layout.html#tabset-panel) as document was getting long and cluttered. This makes it shorter and easier to navigate. Used for plots that summarise imputation. + Introduces caching to the document so that computationally expensive sections of code are not re-run on every render. + Some house keeping wrapping lines to 120 characters. + Moves summary of missing data patterns to `sections/_missing.qmd`. + Removes `dark_theme_minimal()` from plot of final lasso. + Tidies up `sections/_logistic.qmd` to explicitly use `family = binomial(link = "logit")` (**NB** Previous work ensured the `train` data frame is used in all logistic regression rather the raw `df` which includes individuals with missing `final_pathology`).
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Closes #28
IMPORTANT Currently there is a bug in the stable release of Quarto which prevents rendering of the missing data figures. It is fixed in development version
v1.6.1(currently available as pre-release, so if things don't render upgrade to this version).sections/_interpolation.qmdfile. Includes citation for the mice R package.print()sections/_missing.qmd.dark_theme_minimal()from plot of final lasso.sections/_logistic.qmdto explicitly usefamily = binomial(link = "logit")(NB Previous work ensured thetraindata frame is used in all logistic regression rather the rawdfwhich includes individuals with missingfinal_pathology).