Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
## ---- eval = FALSE------------------------------------------------------------
# system.file("pccc_references/Categories_of_CCCv2_and_Corresponding_ICD.docx", package = "pccc")
## ---- message = FALSE---------------------------------------------------------
library(pccc)
library(dplyr)
## -----------------------------------------------------------------------------
dat <- data.frame(ids = c("A", "B", "C"),
dxs = c("4251", "425.1", "425.1"),
procs = c("37.51", "3751", "37.51"))
dat
ccc(dat,
id = ids,
dx_cols = dxs,
pc_cols = procs,
icdv = 9)
## ----eval = FALSE-------------------------------------------------------------
# pccc_icd9_dataset <- generate_sample(
# v = 9,
# n_rows = 10000,
# d_cols = 10,
# p_cols = 10,
# g_cols = 10
# )
#
# save(pccc_icd9_dataset, file="pccc_icd9_dataset.rda")
## -----------------------------------------------------------------------------
library(dplyr)
library(pccc)
ccc_result <-
ccc(pccc::pccc_icd9_dataset[, c(1:21)], # get id, dx, and pc columns
id = id,
dx_cols = dplyr::starts_with("dx"),
pc_cols = dplyr::starts_with("pc"),
icdv = 09)
# review results
head(ccc_result)
# view number of patients with each CCC
sum_results <- dplyr::summarize_at(ccc_result, vars(-id), sum) %>% print.data.frame
# view percent of total population with each CCC
dplyr::summarize_at(ccc_result, vars(-id), mean) %>% print.data.frame
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