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
Exploring Health Expenditure using State-level data
Health policy in the United States is complicated, and several forms of healthcare coverage exist, including both federal goverment-led healthcare policy, and private insurance company. 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 the health expenditure, including healthcare coverage and healthcare spending, across the United States.
The data for this demonstration come from Henry J Kaiser Family Foundation (KFF).
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.
We use the R package
library(readr) for data import in this tutorial.
Two R package
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
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
join(), adding columns using
summaries of columns using
summarise(), and grouping operations using
For exploratory analysis, we use data visulization for exploratory
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
geom_boxplot(), and how to facet by two variables using
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.
The libraries used in this study are
ggrepel. In order
to run this code please ensure you have these packages installed
readr), data wrangling (
dplyr) , and data visualization (