Characterize a cohort table.
library(knitr); library(pander); library(tibble); library(caret) devtools::load_all() knitr::opts_chunk$set(comment="#>", fig.show='hold', fig.align="center", fig.height=8, fig.width=8, message=FALSE, warning=FALSE, cache=FALSE, tidy=TRUE, rownames.print=FALSE) ggplot2::theme_set(vizR::theme_()) set.seed(123)
Cohorts are designated by a mutation and a filter, with all combinations valid. Mutations include binarization and imputation, which change the actual data values in the table. Filtering subsets the patients from a cross sectional study design to different case control study designs.
r params$mutating
. (available: r lift_vd(str_x)("none", "binary", "complete")
) r params$filtering
. (available: r str_x(hipsOpt(cohorts))
) scalars <- hipsCohort_(params$mutating, params$filtering) # After params$filtering scalars[] %<>% map_if(.p = is.factor, .f = as.character) scalars
# Filtered Scalars DF.assess_missingness(scalars) ggMissing(scalars)
# Identify categorical variables catVars_chr <- scalars %>% keep(.p = ~class(.x) %in% c("factor", "character", "logical")) %>% keep(.p = ~n_distinct(.x) < 20) %>% names() # Generate a summary table for the full dataset and each categorical grouping walk(c(character(0), catVars_chr), ~print(Kable.cohort_df(x = scalars, grp = .x)))
DATA <- scalars COL <- "sex" ALPHA <- "fx" scale_alpha_fx <- scale_alpha_manual("Fracture", values = c("TRUE"=1, "FALSE"=0.5)) # Identify continuous variables contVars_chr <- DATA %>% keep(.p = is.numeric) %>% names()
# Melt table to tee up a facet_wrap by continuous variable DATA %>% select(!!COL, !!ALPHA, one_of(contVars_chr)) %>% gather(key = "contVar", value = "contVal", one_of(contVars_chr)) %>% { ggplot(data=., aes_string(x = "contVal", alpha = ALPHA, fill = COL)) + geom_histogram(bins=15) + scale_alpha_fx + scale_fill_sex() + facet_wrap(~contVar, ncol=2, scales = "free") }
ggOddsRatios(scalars)
Codebase State: r dotfileR::gitState()
on r date()
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