Getting started with FreesearchR
Below is a simple walk-trough and basic descriptions on the different features of the FreesearchR app.
Launching
The easiest way to get started is to launch the onlie version of the app (click this link). 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):
require("pak")
pak::pak("agdamsbo/FreesearchR")
library(FreesearchR)
FreesearchR::launch_FreesearchR()As a small note, a standalone Windows app version is on the drawing board as well, but no time frame is currently available.
Get started
Once in the app, get started by loading your data. You have three options available for importing data: file upload, REDCap server export and local or sample data.
After choosing a data source nad importing data, you can preview the basic data structure and missing observations, set a threshold to filter data by completeness and further manually specify variables to include for analyses.
File upload
Several data file formats are supported for easy import (csv, txt, xls(x), ods, rds, dta). If importing workbooks (xls(x) or ods), you are prompted to specify sheet(s) to import. If choosing multiple sheets, these are automatically merged by common variable(s), so please make sure that key/ID variables are correctly named identically.
REDCap server export
Export data directly from a REDCap server. You need to first generate an API-token (see these instruction) in REDCap. Make sure you have the necessary rights to do so.
Please don’t store the API-key on your device unless encrypted or in a keyring, as this may compromise data safety. Log in to your REDCap server and retrieve the token when needed.
Type the correct web address of your REDCap server.
The module will validate the information and you can click “Connect”.
This will unfold options to preview your data dictionary (the main database metadata), choose fields/variables to download as well as filtering options.
Local or sample data
When opening the online hosted app, you can load some sample data to try out the app. When running the app locally from R on your own computer, you will find all data frames loaded in your environment here. This extends the possible uses of this app to allow for quick and easy data insights and code generation.
Prepare
This is the panel to prepare data for evaluation and analyses and get a good overview of your data, check data is classed and formatted correctly, perform simple modifications and filter data.
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
Evaluate
This panel allows for basic data evaluation.
Visuals
There are a number of plotting options to visualise different aspects of the data.
Below are the available plot types listed.
| Data type | Plot type | Description |
|---|---|---|
| continuous | Violin plot | A modern alternative to the classic boxplot to visualise data distribution |
| continuous | Scatter plot | A classic way of showing the association between to variables |
| continuous | Box plot | A classic way to plot data distribution by groups |
| dichotomous | Stacked horizontal bars | A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars |
| dichotomous | Violin plot | A modern alternative to the classic boxplot to visualise data distribution |
| dichotomous | Sankey plot | A way of visualising change between groups |
| dichotomous | Box plot | A classic way to plot data distribution by groups |
| dichotomous | Euler diagram | Generate area-proportional Euler diagrams to display set relationships |
| categorical | Stacked horizontal bars | A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars |
| categorical | Violin plot | A modern alternative to the classic boxplot to visualise data distribution |
| categorical | Sankey plot | A way of visualising change between groups |
| categorical | Box plot | A classic way to plot data distribution by groups |
| categorical | Euler diagram | Generate area-proportional Euler diagrams to display set relationships |
Export the plots directly from the sidebar with easily adjusted plot dimensions for your next publication.
Also copy the code to generate the plot in your own R-environment and fine tune all the small details.
Regression
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.
| Data type | Regression model | Function | Study design |
|---|---|---|---|
| continuous | Linear regression model | stats::lm | cross-sectional |
| dichotomous | Logistic regression model | stats::glm | cross-sectional |
| categorical | Ordinal logistic regression model | MASS::polr | cross-sectional |
Table
Generate simple regression models and get the results in a nice table. This will also be included in the exported report.
This will generate a combined table with both univariate regression model results for each included variable and a multivariate model with all variables included for explorative analyses.