The growth of R and its commercial application has been synonymous with the embrace of data science techniques and data-driven thinking by organisations in industries as diverse as finance, pharmaceuticals, e-commerce and media.

In this blog, one of Data Scientists Chris McLean has considered the breadth and depth of R applications synonymous with the alphabet in preparation for all the interesting and insightful talks that will take place at EARL in September – from dating, to insurance claims, news reporting, mental health and dog welfare.

As an open-source tool, R offers users the power and flexibility to tackle the most complex of business problems with libraries that handle the full data science process from data ingestion and preparation to analysis and modelling to visualisation and dissemination. This enables organisations to build and test solutions quickly and embed them in their day-to-day activities. While they may not garner the same media attention as drones, bots or recommendation engines, many applications of R are fundamental to the way in which businesses operate.

The EARL conference enables data scientists to share examples of how they use R to help their organisations overcome specific business challenges. To get you in the mood, here is an A-to-Z guide of the ways in which R is applied commercially.

A is for Attrition.

Calculate Employee turnover can be a major challenge, particularly among those with skills in high demand. HR departments use R to identify employee’s that are likely to leave and proactively address any underlying internal problems to minimise attrition rates.

B is for Bats and Balls.

Analytics departments in sports such as football, cricket, baseball and basketball use R to analyse performance and seek ways to gain an advantage over opponents.

C is for Clinical Trials.

Shiny apps get the data from clinical trials into the hands of statisticians, clinical analysts and pharmacovigilance officers to speed up the drug development process.

D is for Demand.

Forecast future demand for goods and services based on historic sales data, which accounting for market conditions and competitor activity.

E is for E-commerce.

Analysing customer purchase and product sales history to segment products and customers that behave similarly and make data-driven decisions to improve sales KPIs.

F is for Fraud detection.

Anomaly detection and probabilistic modelling in R enables banks and finance institutions identify potentially fraudulent transactions in real-time.

G is for Genetics.

R has been at the heart of genetic analysis for decades; enabling scientists to analyse and visualise complex genomic data then publish reproducible analysis seamlessly.

H is for House price prediction.

Many realtors use models created in R to predict house prices in a given area to value properties ahead of going to market.

I is for Icelandic social swimming.

Soaking in natural thermal pools is a national pastime in Iceland so the Icelandic Government’s Data Science Team use R to gather, clean and visualise data in a Shiny app to let citizens know how crowded a specific pool is at a specific time.

J is for Journey optimisation.

With global fuel prices increasing, optimisation models in R can help identify the most efficient journeys for commercial vehicles, be they deliver vans, logistics trucks or waste disposal lorries.

K is for KPIs.

Automating Shiny dashboards and RMarkdown reports enable companies to disseminate KPIs to all areas of the business in real-time.

L is for Loyalty.

Survival analysis enables business to better understand the customer lifecycle and where to intervene to increase consumer loyalty and retention.

M is for Marketing.

Marketeers utilise R in everything from segmenting customers and predicting churn to social media benchmarking and visualising the impact of campaigns.

N is for News.

Data Journalism has grown exponentially over the last few years with outlets such as FiveThirtyEight, the New York Times and the BBC utilising R to analyse and visualise data in support of news stories.

O is for Optimised pricing.

Optimisation models in R help identify optimal pricing to maximise both revenue and demand while accounting for factors such as seasonality, competitor activity and product features.

P is for Pandemic tracking.

During the COVID-19 pandemic, many governments and health agencies turned to R as the tool of choice in monitoring the spread of the virus, developing epidemiological models and informing citizens about infection rates in their local area.

Q is for Quantitative finance.

Quantitative analysts use mathematical models in R to ensure portfolios are risk balanced, help find new trading opportunities, and evaluate asset prices.

R is for Risk modelling.

Identifying and mitigating risk is crucial for banks and lenders when offering mortgages, loans and credit cards. R is a crucial tool for developing risk models quickly and at scale.

S is for Sentiment analysis.

Understanding consumer sentiment from analysing social media posts, product reviews or online customer service chats to improve products and services.

T is for Telecoms.

Telecoms companies use R to build models that predict the likelihood of faults occurring in the network, enabling them to proactively address problems. Not only does this minimise network downtime but it also makes more efficient use of engineering resources.

U is for User experience.

A/B testing is not just about choosing the right colour for your call-to-action button, it’s about shaping and improve the entire user experience. R provides UX practitioners with the tools to quickly test, evaluate and iterate their ideas.

V is for Visualisation.

Data is beautiful and ggplot is the go-to tool for many analysts and developers to create stunning, informative and interactive visualisations that can be embedded in reports, apps and websites.

W is for Weather prediction.

Weather organisations around the world, including the National Oceanic and Atmospheric Administration (NOAA), use R to predict changes in the weather, climate and oceans for short- and long-term forecasts.

X is for Xbox.

The Xbox matchmaking service, which pairs gamers with similar skill levels in online multiplayer games such as Halo, was built in R.

Y is for Yesterday.

Mining historical texts such as newspapers, documents or even social media can help historians and investigators unearth new information.

Z is for Zombies.

Modelling the potential spread of the future zombie apocalypse, while considering various infection and population parameters helps inform the development of epidemiology models for real-life pandemics, such as COVID-19.

Do you have an interesting application of R that you are interested to share and take centre stage at EARL? The final call for abstracts is midnight 30th June - you can submit here.

*If for whatever reason you are struggling to submit by the deadline above, please do reach out to us at earl@ascent.io and we can extend your deadline accordingly.