new docs

ROADMAP.md

@@ -1,55 +1,47 @@
 # Project roadmap
 
-The current state of the app is considered experimental, and a lot of things are still changing. It is, however, in a usable state, with basic functions available to the user.
+The current state of the app is considered experimental, however, from version 25.4.2, the ***FreesearchR*** app is considered functional and can be used for data evaluation and analyses.
 
-Below are some (the actual list is quite long and growing) of the planned features and improvements:
+Below are some (the actual list is quite long and growing) of the planned new features and improvements:
 
--   Additional study designs in regression models (expansion of the regression analysis functionality have been put on hold for now to focus on the more basic use-cases):
+### Implementation in real world clinical studies and projects
 
-    -   [x] Cross-sectional data analyses
+This really is the main goal of the whole project.
 
-    -   [ ] Longitudinal data analyses
+### New features:
 
-    -   [ ] Survival analysis
+-   [ ] Merge data from multiple sources (you can merge sheets from a workbook (xls or ods), but this would allow merging several files and/or REDcap data)
 
-    -   [ ] Stratified analyses
+-   [ ] Additional plot types (bar plots, *others...*)
 
--   More detailed variable browser
+-   [ ] Missingness analysis panel
 
-    -   [x] Add histograms for data distribution. 2025-01-16
+### Expanded options for regression models:
 
-    -   [x] Option to edit labels. 2025-01-16
+Expansion of the regression analysis functionality have been put on hold for now to focus on the more basic use-cases):
 
--   More output controls
+More study designs
 
-    -   [x] ~~Theming output tables~~ The "JAMA" theme is the new standard.
+-   [x] Cross-sectional data analyses
 
-    -   [x] ~~Select analyses to include in report.~~ Includes characteristics table and regression table if present. No other analyses are intended for the report as of now.
+-   [ ] Longitudinal data analyses
 
--   [x] Export modified data. 2025-01-16
+-   [ ] Survival analysis
 
--   [x] Include reproducible code for all steps (maybe not all, but most steps, and the final dataset can be exported) 2025-04-10
+Other regression models
 
--   [x] ~~Modify factor levels~~ Factor level modifications is possible through converting factors to numeric > cutting numeric with desired fixed values. 2024-12-12
+-   [ ] Stratified analyses
     
--   [x] More options for date/datetime/time grouping/factoring. Included weekday and month-only options. 2024-12-12
+-   [ ] Mixed models of repeated measures
     
--   Graphs and plots
+-   [ ] Cox regression analyses
 
-    - [x] Correlation matrix plot for data exploration 2025-2-20
+Data handling
 
-    - [x] Grotta bars for ordianl outcomes (and sankey) 2025-3-17
+-   [ ] Transforming data (transpose and pivoting)
 
-    - [x] Coefficient plotting for regression analyses (forest plot) 2025-2-20
+### Improved documentation:
 
-Documentation:
+-   [ ] Video walk-through of central functions
 
--   [ ] Complete getting started page describing all functionality.
 
--   [ ] Streamlined functions documentation
-
-New features:
-
--   [ ] Merge data from multiple sources (this would in itself be a great feature, but not of highest importance)
-
--   [ ] Additional plot types (missingness, *others...*)

vignettes/FreesearchR.Rmd

@@ -61,6 +61,16 @@ This will unfold options to preview your data dictionary (the main database meta
 
 When opening the online hosted app, this is mainly for testing purposes. When running the app locally from *R* on your own computer, you will find all data.frames in the current environment here. This extends the possible uses of this app to allow for quick and easy data insights and code generation for basic plotting to fine tune.
 
+## Data
+
+This is the panel to get a good overview of your data, check data is classed and formatted correctly, perform simple modifications and filter data. 
+
+### Summary
+
+### Modify
+
+
+
 ## Evaluate
 
 This panel allows for basic data evaluation.
@@ -80,7 +90,7 @@ 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}
-c("continuous", "dichotomous", "ordinal", "categorical") |> 
+c("continuous", "dichotomous", "categorical") |> 
   lapply(\(.x){
     dplyr::bind_cols(
       dplyr::tibble("Data type"=.x),
@@ -106,6 +116,27 @@ Also copy the code to generate the plot in your own R-environment and fine tune
 
 This section is only intended for very simple explorative analyses and as a proof-of-concept for now. If you are doing complex regression analyses you should probably just write the code yourself.
 
+Below are the available regression types listed.
+
+```{r echo = FALSE, eval = TRUE}
+c("continuous", "dichotomous", "categorical") |> 
+  lapply(\(.x){
+    dplyr::bind_cols(
+      dplyr::tibble("Data type"=.x),
+      supported_functions()|> 
+      lapply(\(.y){
+        if (.x %in% .y$out.type){
+          .y[c("descr","fun","design")]|> dplyr::bind_cols()
+        }
+})|> 
+  dplyr::bind_rows() |> 
+  setNames(c("Regression model","Function","Study design")))
+  }) |> 
+  dplyr::bind_rows() |> 
+  # toastui::datagrid(filters=TRUE,theme="striped") |> 
+  kableExtra::kable()
+```
+
 ### Table
 
 Generate simple regression models and get the results in a nice table. This will also be included in the exported report.

vignettes/data-types.Rmd

@@ -0,0 +1,33 @@
+---
+title: "Data types"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{data-types}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(FreesearchR)
+```
+
+## 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}
+data_types() |> purrr::imap(\(.x,.i){
+  dplyr::bind_cols("type"=.i,.x,.name_repair = "unique_quiet")
+  }) |> dplyr::bind_rows() |> 
+  setNames(c("Data type","Description","Data classes included")) |> 
+  kableExtra::kable()
+```
+
+Categorising data in this way makes sense when making choices on how to evaluate and analyse data. This is used throughout the ***FreesearchR*** app to simplify data handling.