knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of medicareAnalytics is to simplify the data processing and modeling for medicare research projects. This project is based on the medicare analytic file data structures, and used a lot variable names specifically only for the medicare analytic file.
Since most of the medicare projects are using the same cohort filters and models, this package can help the analysts and reseach fellows to conduct analyses.
You can install the development version of medicareAnalytics from GitHub with:
# install.packages("devtools") devtools::install_github("UMCSTaR/medicareAnalytics")
library(medicareAnalytics) library(tidyverse) ## basic example code # load medicare dt medicare = data.table::fread("/Volumes/George_Surgeon_Projects/standardized_medicare_data_using_R/analysis_ready_data/ecs_primary_surgeon_medicare2018.csv", nrows = 5000) multiple_procedures_table(medicare, project_sepcific_procedure_tax = "e_proc_grp_lbl")
# load medicare dt medicare = data.table::fread("/Volumes/George_Surgeon_Projects/standardized_medicare_data_using_R/analysis_ready_data/ecs_primary_surgeon_medicare2018.csv", nrows = 5000) remove_nonUS_trained(medicare) %>% select(-contains("id"), -contains("dt")) %>% # remove sensitive data head()
medicare %>% apply_all_filters(add_variables = "val_yr_practice") %>% select(-contains("id"), -contains("dt")) %>% # remove sensitive data glimpse()
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