Welcome 
This is the FreesearchR data analysis tool, a free tool for basic data evaluation and analysis. If you need more advanced tools, start with FreesearchR and then you'll probably be better off using R or similar directly.
With this tool you can:
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Import data from a spreadsheet/file on your machine, directly from a REDCap server, try it with sample data or access data directly if run in R locally
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Prepare data for analysis by filtering data, modifying variables or create new variables
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Evaluate data using descriptive analyses methods and inspect cross-correlations as well as missing observations
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Visualise data by creating simple, clean plots for overview and quick insights
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Create simple regression models for even more advanced data analyses
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Download results as a report, get the modified data set and save the code for learning and to reproduce the results later
The full project documentation is here where you'll find detailed descriptions of the app and link to the source code! If you want to share feedback, please follow this link to a simple survey.
Choose your data source
Careful with sensitive data
The FreesearchR app only stores data for analyses, but please only use with sensitive data when running locally. Read more here .
REDCap server
Please fill in server address (URI) and API token, then press 'Connect'.
Data import parameters
Please specify data to download, then press 'Import'.
Select variables for final import
Exclude incomplete variables:
Manual selection:
After importing, hit "Start" or navigate to the desired tab.
Overview and filtering
Below is a short summary table, on the right you can click to visualise data classes or browse data and create different data filters.
Filter data types
Read more on how data types are defined.Filter observations
Filter on observation level
Subset, rename and convert variables
Below, are several options for simple data manipulation like update variables by renaming, creating new labels (for nicer tables in the report) and changing variable classes (numeric, factor/categorical etc.).
There are more advanced options to modify factor/categorical variables as well as create new factor from a continous variable or new variables with R code. At the bottom you can restore the original data.
Please note that data modifications are applied before any filtering.
Advanced data manipulation
Below options allow more advanced varaible manipulations.
Reorder the levels of factor/categorical variables.
Create factor/categorical variable from a continous variable (number/date/time).
Create a new variable/column based on an R-expression.
Compare modified data to original
Raw print of the original vs the modified data.
Reset to original imported dataset. Careful! There is no un-doing.
Report
Choose your favourite output file format for further work, and download, when the analyses are done.Download report
Data
Choose your favourite output data format to download the modified data.Download data
Code snippets
Below are the code bits used to create the final data set and the main analyses.
This can be used as a starting point for learning to code and for reproducibility.