R vs. Python in Power BI:
R is best suited for:
Statistical Modeling: R has comprehensive libraries, such as ggplot2, caret, and randomForest, that help users perform sophisticated statistical analysis and modeling.
Data Visualization: R can be very specific with its statistical plots, like box plots and density plots, using high-quality visualizations through the library ggplot2.
Research/Academic Use: In academic environments, R is more favorable for statistical analysis and visualization as it contains highly extensive libraries and support.
Data Wrangling/Transformation: Python offers good libraries, including Pandas and Numpy, that can be used in data wrangling and transformation.
Machine Learning: It offers libraries such as scikit-learn, TensorFlow, and Keras to build more advanced machine learning models.
Custom Visualizations: The library is supported for doing interactive and customized visualizations, like matplotlib, seaborn, and plotly.
R: Suitable for statistical analysis, though it might be less flexible when used as a general-purpose programming and for machine learning.
Python: Python is more versatile for a range of tasks but requires more effort to achieve complex statistical analysis than R.
Use Cases:
Select R for heavy statistical analysis or customized statistical visualizations.
Select Python for general-purpose data manipulation, machine learning, and interactive visualizations.
