Resources
This area is specifically curated to support your learning journey in data science using R and Python programming. Here, we have compiled a comprehensive set of learning materials, datasets and tools designed to enhance your understanding and skills.
eBooks
| Name | Description | Author | Categories |
|---|---|---|---|
| ggpubr | Create ggplot2 based publication ready plots. Add p-values and significance levels to ggplots. | R, Data Visualization | |
| factoextra | Extract and visualize the results of multivariate data analyses including PCA, CA, MCA, MFA, etc. | R, Dimension Reduction, Data Visualization | |
| survminer | Provides functions to easily create publication-ready survival curves with a ‘number at risk’ table and censoring plot. Additional functions enable plotting adjusted Cox model curves and visually assessing Cox model assumptions. | R, Survival Analysis | |
| ggcorrplot |
Simplifies visualizing a correlation matrix with ggplot2, offers matrix reordering, displays significance levels on the correlogram, and includes a function to compute correlation p-values.
|
R, Data Visualization, Correlation | |
| rstatix | A framework for basic statistical tests in R, with results automatically formatted into tidy data frames for easy visualization. | R, Data Analysis, Statistics | |
| datarium | A data bank for statistical analysis and visualization, featuring datasets on topics like categorical data, regression, means comparisons, ANOVA, and ANCOVA. | R, Datasets | |
| mall | Enables row-wise LLM predictions on a data frame column. Compatible with both R and Python. Supports sentiment analysis, text summarization, text classification, information extraction, translation, binary verification of text content, and custom prompts. | Machine Learning, R, Python |
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