# Data Science: Data Visualization

**URL:** <https://stemaway.com/t/data-science-data-visualization/61>\
**Category:** Technical Resources\
**Created:** [August 29, 2019, 3:44pm UTC](https://stemaway.com/t/data-science-data-visualization/61 "2019-08-29T15:44:19Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![anoori](https://d2nat51eddc6b4.cloudfront.net/user_avatar/stemaway.com/anoori/32/275_2.png) [@anoori](https://stemaway.com/u/anoori)\
**Post date:** [August 29, 2019, 3:44pm UTC](https://stemaway.com/t/data-science-data-visualization/61/1 "2019-08-29T15:44:20Z")

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# Module 1: Drug Poisoning Mortality by State

### Ayush Noori | EduSTEM Data Science
  
Welcome to Module 1 of EduSTEM Data Science! This course is designed for motivated middle school and high school students who are curious to learn about diverse contemporary challenges through the lens of data science.

> Module 1 will give you a hands-on introduction to **data visualization** in R. We will use the popular `ggplot2` package to create an animated bar plot representation of the drug poisoning epidemic over time in the United States. Please refer to the background reading of this module to learn more about unintentional drug overdose and the opioid epidemic.

This module will use data from National Center of Health Statistics, available [here](https://catalog.data.gov/dataset/drug-poisoning-mortality-by-state-united-states).

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10).

Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes

- X40–X44 (unintentional), 
- X60–X64 (suicide), 
- X85 (homicide), or 
-