---
esco_uri: http://data.europa.eu/esco/skill/51586df8-1c46-4b47-8583-773cb63bf00b
esco_label: "R"
relation: optional
onet_soc: 15-1252.00
source: stackexchange
confidence: medium
qa_count: 4
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# R — Software Engineer

For a Software Engineer, 'Competence R' – encompassing software development techniques including analysis, algorithms, coding, testing, and compilation *specifically within the R programming language* – isn’t about being a statistician first. It's about leveraging R as a tool *within* larger systems. This often manifests in tasks like building data pipelines for feature engineering in machine learning models (using packages like `dplyr`, `data.table`), creating automated reporting dashboards (with `shiny` or `RMarkdown`), or prototyping statistical analyses before implementing them in more scalable languages like Python or Java.  You'll frequently see R used for exploratory data analysis (EDA) and initial model validation, then the *results* integrated into production systems.

## Praxiswissen (Stack Exchange, CC-BY-SA 4.0)

**The user is building a regression model and needs appropriate methods to assess correlations between categorical variables (both two-way and one categorical with one continuous), as well as alternatives to VIF for multi-level categorical predictors.**

When examining relationships between categorical variables, the Chi-Squared test of independence determines if they are associated. A significant p-value suggests a correlation, which can be quantified using Cramér's V. For relating a categorical variable to a continuous one, ANOVA is appropriate; it compares variance *within* groups defined by the categorical variable to variance *between* those groups to detect dependence. These tests assess association rather than linear correlation like Pearson’s coefficient.

*Source: [How to get correlation between two categorical variable and a categorical variab](https://datascience.stackexchange.com/q/893) — answer by Alexey Grigorev, CC-BY-SA 4.0*

**The questioner is learning machine learning and currently uses R, but sees many examples using Python and wants to know which language is preferred in academic and industry settings.**

While both languages are capable for the initial 'model building' phase of a machine learning project, Python excels at deployment into production systems due to its status as a general-purpose programming language already used in many organizations’ infrastructure. R remains strong in statistical analysis and visualization, particularly within academia, but often requires integration with other languages for full lifecycle implementation. Ultimately, the choice depends on where the model will be *used* – Python is favored when seamless production integration is key.

*Source: [Python vs R for machine learning](https://datascience.stackexchange.com/q/326) — answer by binga, CC-BY-SA 4.0*

**The user questions whether the R programming language is appropriate for handling 'Big Data' datasets (around 5TB in size), given its typical in-memory processing.**

While traditionally limited by memory constraints, R *can* be used with Big Data through integration with distributed computing frameworks like Hadoop. The key is to avoid loading the entire dataset into RAM at once; instead, leverage tools like RHadoop to process data in chunks across a cluster of machines. This approach allows R's analytical capabilities to be applied to datasets exceeding single-machine memory limits.

*Source: [Is the R language suitable for Big Data](https://datascience.stackexchange.com/q/41) — answer by MCP_infiltrator, CC-BY-SA 4.0*

**The user, accustomed to robust IDEs from other programming languages, is curious about alternatives to RStudio for R development, specifically focusing on features beyond basic coding like debugging and deployment.**

While RStudio remains a popular choice, several alternative IDEs cater to different needs and workflows. These range from browser-based options like Radiant and JupyterLab offering collaboration and reproducibility, to extensions for existing IDEs like Visual Studio (RTVS) or Atom (Rbox). The best option depends on the user's preferred environment and whether they prioritize features like visual data analysis (Rattle), flexible layouts (RIDE), or integration with other tools.

*Source: [IDE alternatives for R programming (RStudio, IntelliJ IDEA, Eclipse, Visual Stud](https://datascience.stackexchange.com/q/5345) — answer by karupakalas, CC-BY-SA 4.0*

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
