R/hr_plot.R

In ewenharrison/summarizer: Summarizer

hr.plot = function(df, dependent, explanatory, dependent_label = NULL, factorlist=NULL, coxfit=NULL,
									 breaks=NULL, column_space=c(-0.5, 0, 0.5), ...){
	require(ggplot2)
	require(scales)
	# Generate or format factorlist object
	if(is.null(factorlist)){
		factorlist = summary.factorlist(df, dependent, explanatory, glm.id=TRUE)
	}

	# Specify breaks if provided
	if(is.null(breaks)){
		breaks = pretty_breaks()
	}

	# Extract totals (this is CPH specific due to how summary.factorlist works)
	factorlist$Total = as.numeric(str_extract(as.character(factorlist$all), "^[:digit:]*"))
	factorlist$all = NULL

	# Generate or format glm
	if(is.null(coxfit)){
		coxfit = coxphmulti(df, dependent, explanatory)
	}
	df_fit_c = fit2df(coxfit, condense = TRUE, estimate.suffix = " (multivariable)", ...)
	df_fit = fit2df(coxfit, condense = FALSE, ...)

	# Merge
	df.out = summarizer.merge(factorlist, df_fit_c)
	df.out = summarizer.merge(df.out, df_fit, ref.symbol = "1.0")

	# Fill in total for continuous variables (NA by default)
	df.out$Total[df.out$levels == "Mean (SD)" | df.out$levels == "Median (IQR)"] = dim(df)[1]

	# Remove unwanted lines, where there are more variables in model than wish to display.
	# Note merge function in summarizer merge is now `all` rather than `all.x` as wish to preserve interactions
	# These not named in factorlist, creating this problem. Interactions don't show on plot.
	if (any(
		is.na(df.out$label)
	)
	){
		remove_rows = which(is.na(df.out$label)) # This row doesn't work when is.na == FALSE, hence if()
		df.out = df.out[-remove_rows,]
	} else {
		df.out
	}

	# Fix order
	df.out$levels = as.character(df.out$levels)
	df.out$glm.id = factor(df.out$glm.id, levels = df.out$glm.id[order(-df.out$index)])

	# Plot
	g1 = ggplot(df.out, aes(x = as.numeric(HR), xmin = as.numeric(L95), xmax  = as.numeric(U95),
													y = glm.id))+
		geom_point(aes(size = Total), shape=22, fill="darkblue")+
		geom_errorbarh(height=0.2) +
		geom_vline(xintercept = 1, linetype = "longdash", colour = "black")+
		scale_x_continuous(name="Hazard ratio (95% CI, log scale)", trans="log10", breaks= breaks)+
		theme_classic(14)+
		theme(axis.title.x = element_text(),
					axis.title.y = element_blank(),
					axis.text.y = element_blank(),
					axis.line.y = element_blank(),
					axis.ticks.y = element_blank(),
					legend.position="none")

	t1 = ggplot(df.out, aes(x = as.numeric(HR), y = glm.id))+
		annotate("text", x = column_space[1], y =  df.out$glm.id, label=df.out[,2], hjust=0, size=5)+
		annotate("text", x = column_space[2], y =  df.out$glm.id, label=df.out[,3], hjust=1, size=5)+
		annotate("text", x = column_space[3], y =  df.out$glm.id, label=df.out[,6], hjust=1, size=5)+
		theme_classic(14)+
		theme(axis.title.x = element_text(colour = "white"),
					axis.text.x = element_text(colour = "white"),
					axis.title.y = element_blank(),
					axis.text.y = element_blank(),
					axis.ticks.y = element_blank(),
					line = element_blank())

	if (is.null(dependent_label)){
		title = paste0(dependent, ": ", "(HR, 95% CI, p-value)")
	} else {
		title = paste0(dependent_label, ": ", "(HR, 95% CI, p-value)")
	}

	gridExtra::grid.arrange(t1, g1, ncol=2, widths = c(3,2),
							 top=grid::textGrob(title, x=0.02, y=0.2, gp=grid::gpar(fontsize=18), just="left"))
}