Welcome

This is the FreesearchR data analysis tool. We intend FreesearchR to be a free tool for easy 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.

Here is a brief summary of the functions:

Import data from a spreadsheet/file on your machine, direct export from a REDCap server, sample data or data from a your local environment if run locally.
Data inspection and modification like modifying variables or creating new (categorical from numeric or time data, or completely new variables from the data)
Evaluate data using descriptive analyses methods and inspect cross-correlations
Create and export simple, clean plots for data overview and insights
Create regression simple models for even more advanced data analyses
- Linear, dichotomous or ordinal logistic regression will be used depending on specified outcome variable
- Plot regression analysis coefficients
- Evaluate model assumptions
Export results
- Descriptive and regression analyses results for MS Word or LibreOffice
- Modified data with preserved metadata
- Code to recreate all steps locally

The full project documentation is here where you'll find detailed description 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

File upload REDCap server export Local or sample data

Upload a file from your device, get data directly from REDCap or select a sample data set for testing from the app.

Upload a file:

Browse...

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No file selected: You can import .csv, .tsv, .txt, .xls, .xlsx, .rds, .ods, .dta files

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

Web address

Format should be either 'https://redcap.your.institution/' or 'https://your.institution/redcap/'

API token

The token is a string of 32 numbers and letters.

Please fill in server address (URI) and API token, then press 'Connect'.

Data import parameters

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Select fields/variables to import and click the funnel to apply optional filters

Please specify data to download, then press 'Import'.

Select a data.frame:

Select an environment in which to search:

No data selected! Use a data.frame from your environment or from the environment of a package.

Select variables for final import

Exclude incomplete variables:

Only include variables missing less observations than the specified percentage.

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.

Select variables to keep (if none selected, all are kept), rename variables and labels, and convert variable type/class in the table above. Apply changes by clicking the button below.

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.

Original data:

Modified data:

Reset to original imported dataset. Careful! There is no un-doing.

Characteristics

Only factor/categorical variables are available for stratification. Go back to the 'Data' tab to reclass a variable if it's not on the list.

Compare strata?

No Yes

Option to perform statistical comparisons between strata in baseline table.

Correlations

To avoid evaluating the correlation of the outcome variable, this can be excluded from the plot or select 'none'.

Correlation cut-off

Set the cut-off for considered 'highly correlated'.

Missings

To consider if daata is missing by random, choose the outcome/dependent variable, if it has any missings to evaluate if there is a significant difference across other variables depending on missing data or not.

Creating plot

Only non-text variables are available for plotting. Go the "Data" to reclass data to plot.

Adjust settings, then press "Plot".

Download

Plot height (mm)

Plot width (mm)

File format

Download plot

Regression

Specify covariables

Yes No

If none are selected, all are included.

Press 'Analyse' to create the regression model and after changing parameters.

Show p-value

Yes No

Coefficient plot

Plot height (mm)

Plot width (mm)

File format

Download plot

Checks

Report

Choose your favourite output file format for further work, and download, when the analyses are done.

Output format

Download report

Data

Choose your favourite output data format to download the modified data.

Data format

No metadata is saved when exporting to csv.

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.