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translations and vignettes
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Andreas Gammelgaard Damsbo 2025-09-24 11:33:01 +02:00
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11
vignettes/FreesearchR.Rmd
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@ -8,8 +8,11 @@ vignette: >
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,eval = FALSE)
knitr::opts_chunk$set(echo = TRUE,eval = TRUE)
library(FreesearchR)
i18n_path <- system.file("translations", package = "FreesearchR")
i18n <- shiny.i18n::Translator$new(translation_csvs_path = i18n_path)
i18n$set_translation_language("en")
```
# Getting started with ***FreesearchR***
@ -20,7 +23,7 @@ Below is a simple walk-trough and basic descriptions on the different features o
The easiest way to get started is to launch [the onlie version of the app (click this link)](https://app.freesearchr.org/). Please be aware not to upload sensitive data in this version as data security can not be guaranteed in this online environment. The app can easily be run from *R* on your own computer by running the code below ([read more on running locally here](https://agdamsbo.github.io/FreesearchR/#run-locally-on-your-own-machine)):
```{r}
```{r eval = FALSE}
require("pak")
pak::pak("agdamsbo/FreesearchR")
library(FreesearchR)
@ -89,7 +92,7 @@ There are a number of plotting options to visualise different aspects of the dat
Below are the available plot types listed.
```{r echo = FALSE, eval = TRUE}
```{r echo = FALSE}
c("continuous", "dichotomous", "categorical") |>
lapply(\(.x){
dplyr::bind_cols(
@ -118,7 +121,7 @@ This section is only intended for very simple explorative analyses and as a proo
Below are the available regression types listed.
```{r echo = FALSE, eval = TRUE}
```{r echo = FALSE}
c("continuous", "dichotomous", "categorical") |>
lapply(\(.x){
dplyr::bind_cols(

6
vignettes/data-types.Rmd
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@ -10,6 +10,7 @@ vignette: >
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
eval = TRUE,
comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
@ -17,13 +18,16 @@ options(rmarkdown.html_vignette.check_title = FALSE)
```{r setup}
library(FreesearchR)
i18n_path <- system.file("translations", package = "FreesearchR")
i18n <- shiny.i18n::Translator$new(translation_csvs_path = i18n_path)
i18n$set_translation_language("en")
```
## A clinical data class
Traditionally in *R*, data is identified by classes, like numeric, integer, double, logical, factor etc. These classes can be a little confusing from a clinical or operational standpoint. In the ***FreesearchR*** app, these classes has been simplified and modified to the following data types, that are assigned on a prioritised order like the following:
```{r echo = FALSE, eval = TRUE}
```{r echo = FALSE}
data_types() |> purrr::imap(\(.x,.i){
dplyr::bind_cols("type"=.i,.x,.name_repair = "unique_quiet")
}) |> dplyr::bind_rows() |>

6
vignettes/visuals.Rmd
View file

@ -10,6 +10,7 @@ vignette: >
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
eval = TRUE,
comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
@ -17,6 +18,9 @@ options(rmarkdown.html_vignette.check_title = FALSE)
```{r setup}
library(FreesearchR)
i18n_path <- system.file("translations", package = "FreesearchR")
i18n <- shiny.i18n::Translator$new(translation_csvs_path = i18n_path)
i18n$set_translation_language("en")
```
## Basic visualisations
@ -36,7 +40,7 @@ If you want to go further, have a look at these sites with suggestions and sampl
Below are the available plot types listed.
```{r echo = FALSE, eval = TRUE}
```{r echo = FALSE}
c("continuous", "dichotomous", "categorical") |>
lapply(\(.x){
dplyr::bind_cols(