Have you ever completed the development of a data product with the feeling that you may have done a mistake or not have used the optimal way to clean or process your data?

As an analyst, if using “point & click” interface, “rewinding” all the steps at an advanced stage of the development of your product can be extremely painful and lengthy. Errare humanum est sed perseverare... If you want to learn from your mistake rather than to suffer from them, then analysis reproducibility is what you need...

In this session, we will introduce you to the basics of analysis reproducibility and explain you what elements you need to watch for when you kick start your analysis so that you can always rewind and improve any products you have already spent time on. We will also show you how you can learn from analysis done in a reproducible way done by other colleagues.

We will also show you through practical examples how to implement a fully reproducible data analysis workflow applied to a Household Survey dataset using R statistical Language: from initial data exploration to joint interpretation till the creation of data stories.

Last, we hope that this session will motivate you to join the vibrant R users community in UNHCR and soon become an R champion. In order to make the most of the session, we would advise you to install the following open source environment:

R - https://cran.r-project.org/bin/windows/base/ 
Rstudio Free version: https://www.rstudio.com/products/rstudio/download/ 
Create an account on Github - https://github.com/join?  and install Github desktop https://desktop.github.com/ 

You may also start installing UNHCR Packages – following the instruction in their respective documentation published on Github:

Use UNHCR Open data  - https://unhcr.github.io/unhcrdatapackage/docs/  
API to connect to internal data source - https://unhcr-web.github.io/hcrdata/docs/ 
Perform High Frequency Check https://unhcr.github.io/HighFrequencyChecks/docs/ 
Process data crunching for survey dataset - https://unhcr.github.io/koboloadeR/docs/  
Use UNHCR graphical template- https://unhcr-web.github.io/unhcRstyle/docs/ 

Last, you may also take advantage of going through one or more of the R learning content on Learn & Connect: Achieve your potential: UNHCR (csod.com) and see some practical tutorial on https://humanitarian-user-group.github.io/

The best way to start and learn is to have a concrete project! If you have one and need mentoring, we can liaise after the session.

https://towardsdatascience.com/data-science-explained-with-cooking-1a801731d749

https://towardsdatascience.com/5-reasons-why-data-science-is-like-cooking-daa506b4166a

Without good ingredients, you can’t cook a good dish. Most time and effort are spent on cleaning and preparing the ingredients.
Different tools and techniques are needed for different recipes. Cooking is both a science and an art.
You can’t become a great cook overnight.

simple data visualization for decision making or complex patterns interpretation for knowledge building

Ethical principles into algorithmic design - would apply as well when designing household vulnerability scoring formula to inform humanitarian targeting - https://www.hum-dseg.org/sites/default/files/2020-10/Framework%20for%20the%20ethical%20use.pdf

https://blog.jonudell.net/2012/01/09/another-way-to-think-about-geeks-and-repetitive-tasks/

https://speakerdeck.com/player/078592d490ae459790af7e77695fafcf?title=false&skipResize=true

A comparison of packages for Automated Exploratory Data Analysis https://arxiv.org/pdf/1904.02101.pdf https://github.com/mstaniak/autoEDA-resources

One of the most significant disadvantages of PowerBI is that it is read-only. As a user, you cannot use charts/tables to make decisions and save them in a database directly. Also, PowerBI doesn’t have an accessible source code. You can only edit fields in WYSIWYG mode, which makes PowerBI easy to start but difficult to maintain. Having no source code makes it nearly impossible to have proper version control, automatically test logic, or collaborate on large projects. https://appsilon.com/powerbi-vs-r-shiny/

Pros of Power BI - Cross-filtering

Pros of RStudio -

• Visual editor for R Markdown documents
• In-line code execution using blocks
• Sophisticated statistical packages
• Supports Rcpp, python and SQL
• Can be themed
• In-line graphing support
• Latex support

What is RStudio? An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages.

You can use R script with Power BI in several ways. The key ones include:

• Data source – expand the range of available data sources, provide data processing logic at the import stage
• Processing script – enrich data processing capabilities and engineering features
• Visualization – extend the range of visualizations available in R and implement charts
• As a part of Power BI visualization – gain the ability to build R-based visualizations and package them into ready-to-put boxes directly into Power BI. https://www.predicagroup.com/blog/visualizing-data-r-script-power-bi/

https://www.ictworks.org/wp-content/uploads/2021/02/usaid-guide-artificial-intelligence.pdf https://bluemoondigital.co/our-blog/5-elements-storytelling-data/ Check the analysis repo: http://analysis.unhcr.org

See https://github.com/unhcr-americas/ageingonthemove/blob/main/README.md

https://distill.pub/2020/communicating-with-interactive-articles/#applications-tab Research Dissemination Conducting novel research requires deep understanding and expertise in a specific area. Once achieved, researchers continue contributing new knowledge for future researchers to use and build upon. Over time, this consistent addition of new knowledge can build up, contributing to what some have called research debt. Not everyone is an expert in every field, and it can be easy to lose perspective and forget the bigger picture. Yet research should be understood by many. Interactive articles can be used to distill the latest progress in various research fields and make their methods and results accessible and understandable to a broader audience. Opportunities

Engage and excite broader audience with latest research progress
Remove research debt, onboard new researchers
Make faster and clearer research progress 

Challenges

No clear incentive structure for researchers
Little funding for bespoke research dissemination and communication
Not seen as a legitimate research contribution (e.g., to the field, or one's career)