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Andreas Gammelgaard Damsbo 2025-04-22 13:57:59 +02:00
parent b1c44a75ef
commit 2249ba06db
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11 changed files with 54 additions and 8 deletions

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@ -61,6 +61,7 @@ export(index_embed)
export(is_any_class)
export(is_consecutive)
export(is_datetime)
export(is_identical_to_previous)
export(is_valid_redcap_url)
export(is_valid_token)
export(launch_FreesearchR)

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@ -39,6 +39,7 @@ plot_box <- function(data, pri, sec, ter = NULL) {
#' @export
#'
#' @examples
#' mtcars |> plot_box_single("mpg")
#' mtcars |> plot_box_single("mpg","cyl")
plot_box_single <- function(data, pri, sec=NULL, seed = 2103) {
set.seed(seed)

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@ -38,3 +38,13 @@ launch_FreesearchR()
## Code of Conduct
Please note that the ***FreesearchR*** project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
## Acknowledgements
Like any other project, this project was never possible without the great work of others. These are some of the sources and packages I have used:
- The ***FreesearchR*** app is build with [Shiny](https://shiny.posit.co/) and based on (*R*)[https://www.r-project.org/].
- [gtsummary](https://www.danieldsjoberg.com/gtsummary/): superb and flexible way to create publication-ready analytical and summary tables.
- [dreamRs](https://github.com/dreamRs): maintainers of a broad selection of great extensions and tools for [Shiny](https://shiny.posit.co/).

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@ -13,8 +13,8 @@ template:
navbar:
bg: primary
structure:
left: [intro, reference, roadmap, q_a, news]
right: [search, github]
left: [intro, reference, articles, roadmap, q_a, news]
right: [search, github, lightswitch]
components:
roadmap:
text: Roadmap

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@ -5,6 +5,6 @@ account: agdamsbo
server: shinyapps.io
hostUrl: https://api.shinyapps.io/v1
appId: 13611288
bundleId: 10119038
bundleId: 10156735
url: https://agdamsbo.shinyapps.io/freesearcheR/
version: 1

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@ -162,7 +162,7 @@ ui_elements <- list(
shiny::uiOutput(outputId = "column_filter"),
shiny::helpText("Variable ", tags$a(
"data type",
href = "https://agdamsbo.github.io/FreesearchR/articles/FreesearchR.html",
href = "https://agdamsbo.github.io/FreesearchR/articles/data-types.html",
target = "_blank",
rel = "noopener noreferrer"
), " filtering."),

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@ -0,0 +1,24 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{is_identical_to_previous}
\alias{is_identical_to_previous}
\title{Test if element is identical to the previous}
\usage{
is_identical_to_previous(data, no.name = TRUE)
}
\arguments{
\item{data}{data. vector, data.frame or list}
\item{no.name}{logical to remove names attribute before testing}
}
\value{
logical vector
}
\description{
Test if element is identical to the previous
}
\examples{
c(1, 1, 2, 3, 3, 2, 4, 4) |> is_identical_to_previous()
mtcars[c(1, 1, 2, 3, 3, 2, 4, 4)] |> is_identical_to_previous()
list(1, 1, list(2), "A", "a", "a") |> is_identical_to_previous()
}

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@ -16,11 +16,15 @@ data of same class as input
Remove empty/NA attributes
}
\examples{
ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |> dplyr::bind_cols()
ds <- mtcars |>
lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |>
dplyr::bind_cols()
ds |>
remove_empty_attr() |>
str()
mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |> remove_empty_attr() |>
mtcars |>
lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |>
remove_empty_attr() |>
str()
}

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@ -21,6 +21,6 @@ Easily subset by data type function
}
\examples{
default_parsing(mtcars) |> subset_types("ordinal")
default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal", "categorical"))
default_parsing(mtcars) |> subset_types(c("dichotomous", "categorical"))
#' default_parsing(mtcars) |> subset_types("factor",class)
}

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@ -67,6 +67,12 @@ This is the panel to get a good overview of your data, check data is classed and
### Summary
Here, the data variables can be inspected with a simple visualisation and a few key measures. Also, data filtering is available at two levels:
- Data type filtering allows to filter by variable [data type]()
- Observations level filtering allow to filter data by variable
### Modify