Closed kylebaron closed 8 months ago
Add on to what was merged in #77. This creates continuous versus categorical plots with similar syntax.
col-label
eta_covariate()
library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(pmplots) #> Loading required package: ggplot2 data <- pmplots_data_obs() id <- dplyr::distinct(data, ID, .keep_all = TRUE) conts <- c("AAG//AAG (IU/ml)", "SCR//Creatinine (mg/dL)", "AST//AST (IU/ml)", "AGE//Age (years)") cats <- c("RF//Renal function", "CPc//Child-Pugh", "STUDYc//Study") a <- cont_cat_panel(id, cats, conts, tag_levels = "A") a[[1]]
a[[2]]
b <- cont_cat_panel(id, cats, conts, transpose = TRUE, byrow = FALSE) b[[1]]
b[[2]]
l <- cont_cat_panel_list(id, cats, conts, transpose = TRUE) with(l$RF, (AAG+SCR)/AST, tag_levels = "a")
Created on 2024-01-30 with reprex v2.0.2
Thanks, @kyleam
Summary
Add on to what was merged in #77. This creates continuous versus categorical plots with similar syntax.
Housekeeping
col-label
inputs; includes testeta_covariate()
, which is the most similar function with respect to inputs and outputsExamples
Created on 2024-01-30 with reprex v2.0.2