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Andreas Gammelgaard Damsbo 2024-10-25 08:04:41 +02:00
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2
R/metadata.R
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@ -5,7 +5,7 @@
#' \describe{ #' \describe{
#' \item{metadata_names}{characterstrings} #' \item{metadata_names}{characterstrings}
#' } #' }
#' @seealso \url{https://www.project-redcap.org/} #' @seealso project-redcap(dot)org (currently the certificate is broken)
#' @usage data(metadata_names) #' @usage data(metadata_names)
"metadata_names" "metadata_names"

2
man/metadata_names.Rd
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@ -17,6 +17,6 @@ data(metadata_names)
Vector of REDCap metadata headers Vector of REDCap metadata headers
} }
\seealso{ \seealso{
\url{https://www.project-redcap.org/} project-redcap(dot)org (currently the certificate is broken)
} }
\keyword{datasets} \keyword{datasets}

8
vignettes/toolbox.Rmd
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@ -25,15 +25,13 @@ My own toolbox in my small workshop is a mix of some old, worn, well proven tool
I have tried to collect tools and functions from other packages that I use regularly in addition to functions that I have written myself to fill use cases, that I have not been able to find solutions to elsewhere. I have tried to collect tools and functions from other packages that I use regularly in addition to functions that I have written myself to fill use cases, that I have not been able to find solutions to elsewhere.
In documenting and testing the package, I have used [OpenAI's](https://platform.openai.com/overview) chatgpt with [gpttools](https://jameshwade.github.io/gpttools/). The chatgpt is an interesting tool, that is in no way perfect, but it helps with tedious tasks. Both `gpttools` and [`gptstudio`](https://michelnivard.github.io/gptstudio/) are interesting implementations in R and RStudio.
## CPR manipulations {#cpr-intro} ## CPR manipulations {#cpr-intro}
Note that, if handled, CPR numbers (social security numbers) should be handled with care as they a considered highly sensitive data. Note that, if handled, CPR numbers (social security numbers) should be handled with care as they a considered highly sensitive data.
The CPR number is structured as _DDMMYY-XXXX_, with the 1st _X_ designating decade of birth, the last _X_ designate binary gender (not biological sex) dependent on even/uneven as female/male, and the last for digits are used in a modulus calculation to verify the validity of the CPR number. Foreigners and unidentified persons are given temporary CPR numbers including letters. The CPR number is structured as _DDMMYY-XXXX_, with the 1st _X_ designating decade of birth, the last _X_ designate binary gender (not biological sex) dependent on even/uneven as female/male, and the last for digits are used in a modulus calculation to verify the validity of the CPR number. Foreigners and unidentified persons are given temporary CPR numbers including letters.
More information can be found on [cpr.dk](https://cpr.dk). More information can be found on [cpr.dk](https://www.cpr.dk).
Note, that all CPR numbers used in examples are publicly known or non-organic. Note, that all CPR numbers used in examples are publicly known or non-organic.
@ -48,7 +46,7 @@ trunc(age)
### cpr_check() ### cpr_check()
Checks validity of CPR numbers according to the [modulus 11 rule](https://cpr.dk/cpr-systemet/opbygning-af-cpr-nummeret). Note that due to limitations in the possible available CPR numbers, this rule [does not apply to all CPR numbers after 2007](https://cpr.dk/cpr-systemet/personnumre-uden-kontrolciffer-modulus-11-kontrol). Checks validity of CPR numbers according to the [modulus 11 rule](https://www.cpr.dk/cpr-systemet/opbygning-af-cpr-nummeret). Note that due to limitations in the possible available CPR numbers, this rule [does not apply to all CPR numbers after 2007](https://www.cpr.dk/cpr-systemet/personnumre-uden-kontrolciffer-modulus-11-kontrol).
```{r cpr_check-example} ```{r cpr_check-example}
cpr_check( cpr_check(
@ -108,7 +106,7 @@ ci_plot(
### generic_stroke() ### generic_stroke()
For learning purposes. Uses annonymized data from the [TALOS trial](https://doi.org/10.1161/STROKEAHA.117.020067) to output a Table 1 (with `gtsummary::tbl_summary()`), plotting the so-called grotta-bars based on mRS scores (with `rankinPlot::grottaBar()`) and a ordinal logistic regression model plot (with `stRoke::ci_plot()`). For learning purposes. Uses anonymized data from the [TALOS trial](https://doi.org/10.1161/STROKEAHA.117.020067) to output a Table 1 (with `gtsummary::tbl_summary()`), plotting the so-called grotta-bars based on mRS scores (with `rankinPlot::grottaBar()`) and a ordinal logistic regression model plot (with `stRoke::ci_plot()`).
```{r generic_stroke-example} ```{r generic_stroke-example}
generic_stroke(stRoke::talos, generic_stroke(stRoke::talos,