In recent years, interest in the development of predictive models and the use of machine learning libraries has grown rapidly. As part of the efficient implementation of different models, a fundamental component of this process deals with data preparation and cleaning, followed by exploration, summaries, and visualizations. Mastering modern tools for data analysis can empower students and researchers in a wide variety of fields, to better explore and understand data generated by experiments, simulations, surveys, and others. This tool provides introductory materials for exploratory data analysis using powerful tools from the tidyverse family of R packages, utilizing datasets from different STEM applications and case studies, that can be introduced as working examples for hands-on classwork activities in different courses. 

Courses across the whole curriculum for a STEM degree can benefit from either introducing students to modern tools for data analysis (e.g., statistics, physics and chemistry labs, and others), or having students use their skills in data preparation and exploration. 
 

Check this tool for an introduction to dplyr, ggplot2, and other packages from the tidyverse data science set of tools.

Jupyter notebooks (R kernel)

nanoHUB champion program 2021

Researchers should cite this work as follows:

• Rei Sanchez-Arias (2021), "tidyverse Data Science Tools for STEM Applications and Datasets," https://nanohub.org/resources/tidystem. (DOI: 10.21981/B5R8-M191).

  BibTex | EndNote

1. data science
2. Data Visualizations
3. data wrangling
4. R programming
5. tidyverse