https://github.com/opencasestudies/ocs-healthexpenditure
OCS: Examine the relationship between healthcare spending and healthcare coverage
https://github.com/opencasestudies/ocs-healthexpenditure
data-visualization exploratory-analysis healthcare healthcare-coverage
Last synced: about 2 months ago
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OCS: Examine the relationship between healthcare spending and healthcare coverage
- Host: GitHub
- URL: https://github.com/opencasestudies/ocs-healthexpenditure
- Owner: opencasestudies
- Created: 2018-10-01T18:57:01.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-07T20:03:17.000Z (over 4 years ago)
- Last Synced: 2025-04-13T15:16:41.702Z (about 2 months ago)
- Topics: data-visualization, exploratory-analysis, healthcare, healthcare-coverage
- Language: HTML
- Homepage:
- Size: 53 MB
- Stars: 7
- Watchers: 2
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
---
# OpenCaseStudies
[](https://travis-ci.com/muschellij2/ocs-healthexpenditure)
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
### License
This case study is part of the [OpenCaseStudies]() project. This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 ([CC BY-NC 3.0](https://creativecommons.org/licenses/by-nc/3.0/us/)) United States License.
### Citation
To cite this case study:
Kuo, Pei-Lun and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2019, February 14). opencasestudies/ocs-healthexpenditure: Exploring Health Expenditure using State-level data in the United States (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.2565307
[](https://zenodo.org/badge/latestdoi/151142096)
### Title
Exploring health expenditure using state-level data in the United States
Health policy in the United States is complicated, and several
forms of healthcare coverage exist, including both coverage by federal
goverment-led healthcare policy, and by private insurance companies.
Before making any inference about the relationship between
health condition and health policy, it is important for us to
have a general idea about healthcare economics in the United
States. Thus, We are interested in getting sense of healthcare
coverage and healthcare spending across the United States.
### Motivating questions
1. Is there a relationship between healthcare coverage and
healthcare spending in the United States?
2. How does the spending distribution change across geographic
regions in the Unied States?
3. Does the relationship between healthcare coverage and
healthcare spending in the United States change from 2013 to 2014?
### Data
The data for this demonstration come from
[Henry J Kaiser Family Foundation (KFF)](https://www.kff.org).
- [Health Insurance Coverage of the Total Population](https://www.kff.org/other/state-indicator/total-population/) (Includes years 2013-2016)
- [Health Care Expenditures by State of Residence (in millions)](https://www.kff.org/other/state-indicator/health-care-expenditures-by-state-of-residence-in-millions/) (Includes years 1991-2014)
For educational purposes, the data have been downloaded
and relative paths are used for this demonstration.
**Note**: If students are not familiar with relative paths,
it will be helpful to briefly introduce the idea for absolute
paths and relative paths.
We also introduce `library(datasets)` for States information.
### Learning Objetives
The skills, methods, and concepts that students will be familiar with by the end of this case study are:
**Data Science Learning Objectives:**
1. Load data from a package (`datasets`)
2. Import data from a csv (`readr`)
3. View, filter, join, and summarize data (`dplyr`)
4. Reshape data into different formats (`tidyr`)
5. Create data visualizations (`ggplot2`) with labels (`ggrepel`) and facets for different groups
#### Data Import
We use the R package `library(readr)` for data import in this tutorial.
#### Data wrangling
Two R package `library(tidyr)`, `library(dplyr)` are used for data wrangling in this tutorial.
We explain what tidy data is, and further introduce the concepts of "wide format"
and "long format." We also demonstrate how to convert from one format to the other using
`gather()` and `spread()`.
We also demonstrate some other useful functions for data wrangling, including
selecting columns using `select()`,
Selecting rows using `filter()`,
arranging or re-orderomg rows using `arrange()`,
joining two datasets using `join()`,
adding columns using `mutate()`,
creating summaries of columns using `summarize()`,
and grouping operations using `group_by()`.
#### Data exploration (exploratory analysis)
For exploratory analysis, we use data visulization for exploratory analysis. `ggplot2` is the R package
we demonstrate in this tutorial.
We explain how to create plots using `ggplot()` with basic syntax for `ggplot2`.
We also demonstrate how to create scatter plots using `geom_point()`,
how to add layers of text using `geom_text()`,
how to facet across a variable using `facet_wrap()`,
how to create boxplots using `geom_boxplot()`,
and how to facet by two variables using `facet_grid`.
#### Summary
The total healthcare expenditure is associated with
the population. To make a fair comparison,
we create "healthcare expenditure per capita."
Further, the exploratory analysis via data visualization showed
higher speding in healthcare per capita
is positively associated with higher
employer coverage proportion and is
negatively associated with the porportion
of uninsured population across the States.
### Other notes and resources
**Packages used in this case study:**
Package | Use in this case study
---------- |-------------
[datasets](https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html){target="_blank"} | to get the state data
[tibble](https://tibble.tidyverse.org/){target="_blank"} | to create tibbles (the tidyverse version of a data frame)
[readr](https://readr.tidyverse.org/){target="_blank"} | to read in the data from the csv files
[tidyr](https://tidyr.tidyverse.org/){target="_blank"} | to change the shape or format of tibbles to wide and long
[dplyr](https://dplyr.tidyverse.org/){target="_blank"} | to subset and filter the data for specific groups, to summarize the data
[ggplot2](https://ggplot2.tidyverse.org/){target="_blank"} | to create plots
[ggrepel](https://cran.r-project.org/web/packages/ggrepel/ggrepel.pdf){target="_blank"} | to add labels that do not overlap to plots
In order to run this code please ensure you have these packages installed.
### For instructors:
The objective of this tutorial is for student to get familiar with important skills in data science, including data import (`readr`), data wrangling (`dplyr`), and data visualization (`ggplot2`). This material is designed for 4.5 teaching hours. (One potential way to teach this tutorial is to divide the material into three 1.5 hour sessions. The first session focuses on data import, the second session focuses on data wrangling, and the third portion focuses on visualization.) The session starting with (*) can be made as exercise for students' practice.