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Back in the late nineties, when statisticians apparently decided coding was cool, R emerged as a sort of open-source rebellion against the expensive, proprietary statistical packages dominating the landscape. It promised a flexible environment for data analysis, visualization, and, let’s be honest, a frankly alarming degree of customizability. The reality? It’s a bit like building a car from spare parts – incredibly powerful if you really know what you’re doing, but prone to unexpected breakdowns and requiring constant tinkering.

Compared to Python, which has become the darling of the machine learning crowd, R remains stubbornly focused on statistical computing. While Python strives for general-purpose dominance, R happily wallows in its niche, beloved by academics, biostatisticians, and anyone who enjoys wrestling with package dependencies. It’s still surprisingly relevant, powering much of the statistical rigor behind everything from clinical trials to financial modeling. Though one suspects many users are still running code written on floppy disks.

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analytics engineer

Operational Excellence Associate/Assistant Manager - Regional Logistics @ shopee

SG | 2025-12-26
Shopee's Regional Operational Excellence role promises real, if stubborn, impact on cost, speed, and waste across the logistics mile by mile. It leans into data - large datasets and root-cause... read more »
Data Analytics, SQL, Python, R, KPI, Management
analytics engineer

Junior Data Management Specialist @ amarisconsulting

SG | 2025-12-25
Amaris Consulting seeks a data-focused professional to help enhance investment and research data strategies within a global team. The role's differentiator is the opportunity to influence... read more »
AI/ML, Computer Science, Data Management, Data Analytics, BI, DataViz, Tableau, Power BI, DWH, SQL, Python, R, LLM, GenAI, Agile/Scrum