Visualizing Emergency Room Visits

Eugene Joh has a great blog post showing how to parse ICD-9 codes using regular expressions and then visualize the results as a treemap:

It looks like there is a header/title at [1], numeric grouping  at [2] “1.\tINFECTIOUS AND PARASITIC DISEASES”,  subgrouping by ICD-9 code ranges, at [3] “Intestinal infectious diseases (001-009)” and then 3-digit ICD-9 codes followed by a specific diagnosis, at [10] “007\tOther protozoal intestinal diseases”. At the end we want to produce three separate data frames that we’ll categorize as:

  1. Groups: the title which contains the general diagnosis grouping

  2. Subgroups: the range of ICD-9 codes that contain a certain diagnosis subgroup

  3. Classification: the specific 3-digit ICD-9 code that corresponds with a diagnosis

It’s a beefy article full of insight.

Related Posts

Spark And H2O

Avkash Chauhan shows how to use sparklyr and rsparkling to tie Spark together with the H2O library in R: In order to work with Spark H2O using rsparkling and sparklyr in R, you must first ensure that you have both sparklyr and rsparkling installed. Once you’ve done that, you can check out the working script, the […]

Read More

Power BI Supports Interactive R Visuals

David Smith reports on a great update to Power BI: The above chart was created with the plotly package, but you can also use htmlwidgets or any other R package that creates interactive graphics. The only restriction is that the output must be HTML, which can then be embedded into the Power BI dashboard or […]

Read More