README.Rmd

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# bllflow - an R package for efficient, transparent data prepartion and reporting <img src="./man/figures/pbl-sticker.png" align="right" alt="" width="120" />

### Is bllflow for you?

- Do you shudder at the thought of trying to update the analyses for a previous study? (let alone imagine someone else trying to replicate your analyses?)

- Are your data and statistical models becoming more complex, challenging to perform, and challenging to report?

- Are you concerned about the [misuse](https://www.nature.com/articles/d41586-019-00857-9) of statistical findings? But not sure about prespecifying your model and reporting all results of all analyses?

- Do you work in teams that span disciplines and institutions?

We answered 'yes' to all these questions and then created _bllflow_. 

### The purpose of bllflow
**bllFlow** supports transparent, reproducible data analyses and model development. The goal is to improved science quality with quicker and more efficient data analyses. 

### What does bllflow do?
The focus of _bllflow_ is data cleaning and variable transformation -- the most time consuming and tedious analytic task -- and analyses reporting.

_bllflow_ functions and workflow build from other packages including **sjmisc**, **tableone**, **codebook**, and **Hmisc**.

There are three main features:

1) **The Model Specification Workbook (MSW)** - Start your model development with worksheets (CSV files) that contain information about the variables in your model, data cleaning and transformation steps and how to create output tables. 

1) **Functions to perform routine data cleaning and transformation tasks** - use functions with or without the Model Specification Workbook. Complex non-linear transformations and interactions are as easy to perform as simpler transformations. Functions with 'BLL' in the function name perform data cleaning and transformation using the Model Specification Workbook.  

1) **Formatted output files, tables** - results of your analyses in a consistent format following the concept of 'one document, many uses'.

At any point of your analyses you have:

- a log of data cleaning and transformed variables (how your data was cleaned and transformed).
- a codebook to facilitate data transparency and provenance.

_bllflow_ supports the use of metadata, including:

- the Data Document Initiative ([DDI](https://www.ddialliance.org)).
- Predictive Model Modelling Language for predictive algorithms ([PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html)) files for transparent algorithm reporting and deployment.  

_bllflow_ workflow and functions support reporting guidelines such as [TRIPOD](https://www.equator-network.org/2015/01/07/guidelines-for-reporting-multivariable-prediction-models-for-individual-prognosis-or-diagnosis-tripod-published/), [STROBE](http://www.equator-network.org/reporting-guidelines/strobe/), and [RECORD](http://www.equator-network.org/reporting-guidelines/record/).

## Installation
```
# If not installed, install the devtools
install.packages("devtools")

# then, install the package
devtools::install_github("Big-Life-Lab/bllFlow")
```
There are plans to submit bllFlow to CRAN once we include all seven steps of the _bllflow_ workflow. Currently on step #4.

## Contributing to the package

Please follow [this guide](CONTRIBUTING.md) if you like to contribute to the _bllflow_ package.

## Documentation

In case your documentation .Rmd file contains images you wish to include, make sure to add the path to the image in the resource_files section.
An example of that is shown below:
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