Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width = "100%",
fig.width = 14,
fig.height = 10.5
# global.par = TRUE
)
library("icd")
n <- 10000
set.seed(1441)
# load the dice with more PCCC diagnostic codes
h <- c(
icd10cm2019$code,
rep(unlist(unname(icd10_map_pccc_dx)), 10)
)
dat <- data.frame(
id = n + seq(n),
icd_dx1 = sample(h, n, replace = TRUE),
icd_dx2 = sample(h, n, replace = TRUE)
)
# for builds without the ability to download or use cached data, ignore pcs
if (icd:::.exists_in_cache("icd10cm2019_pc")) {
i <- get_icd10cm2019_pc()$code
dat <- cbind(dat,
icd_pcs1 = sample(i, n, replace = TRUE),
icd_pcs2 = sample(i, n, replace = TRUE)
)
}
## ----headdat------------------------------------------------------------------
head(dat)
## ----calcpre, eval = TRUE, echo = FALSE---------------------------------------
pccc_dx <- comorbid_pccc_dx(dat)
if (icd:::.exists_in_cache("icd10cm2019_pc")) {
pccc_pcs <- icd10_comorbid_pccc_pcs(dat,
icd_name = c("icd_pcs1", "icd_pcs2")
)
res <- pccc_dx | pccc_pcs
} else {
res <- pccc_dx
}
## ----calc, eval = FALSE-------------------------------------------------------
# pccc_dx <- comorbid_pccc_dx(dat)
# pccc_pcs <- icd10_comorbid_pccc_pcs(dat,
# icd_name = c("icd_pcs1", "icd_pcs2")
# )
# res <- pccc_dx | pccc_pcs
## -----------------------------------------------------------------------------
res[295:300, ]
## ----colsumpccc---------------------------------------------------------------
colSums(res)
## ----par, include = FALSE-----------------------------------------------------
#graphics::par(mar = c(12, 4, 4, 2) + 0.1) # bottom, left, top, right
## ----pcccbarplot, echo=FALSE--------------------------------------------------
graphics::barplot(sort(colSums(res), decreasing = TRUE),
names.arg = names_pccc,
ylab = "count",
las = 2,
cex.names = 0.75
)
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