diff --git a/R/calibration_by_factor.R b/R/calibration_by_factor.R
index 72e0147..7dc3282 100644
--- a/R/calibration_by_factor.R
+++ b/R/calibration_by_factor.R
@@ -13,7 +13,7 @@
 #' @import data.table
 #'
 #' @param data data.table object containing data to be plotted. (data table)
-#' @param output_file filepath to which we write plot. (character)
+#' @param output_file filepath to which we write plot (if NA or '', plot will not save). (character)
 #' @param outcome_col_name column name of outcome for which we want the mean (by factor and risk quantile) plotted on y-axis. (character)
 #' @param quantile_col_name column name of risk quantile to go on x-axis (could be something like y_hat_percentile). (character)
 #' @param cluster_by_col_name column name of grouping for which we want to cluster standard errors (typically something like empi). (character)
@@ -27,7 +27,8 @@
 #'                      vector of hex color codes matching the number of levels in plot_by_col_name. (character)
 #' @param ymin minimum of y-axis. (numeric)
 #' @param make_footnote whether or not to include a footnote on the plot (boolean)
-#' @param return_plot whether or not to return created plot (boolean)'
+#' @param include_legend whether or not to include a legend on the plot (boolean)
+#' @param return_plot whether or not to return created plot (boolean)
 #'
 #' @return g optionally returns created plot
 #'
@@ -55,8 +56,12 @@ plot_calibration_by_risk_quantile_by_factor <- function(
 	SE_style = 'ribbon',
 	ymin = 0,
 	make_footnote = TRUE,
+	include_legend = TRUE,
 	return_plot = FALSE) {
 
+	# set legend to appear at bottom of plot, or not appear at all
+	legend_position <- ifelse(include_legend == TRUE, 'bottom', 'none')
+
 	# compute means
 	data[, mean_obs_outcome := mean(get(outcome_col_name)), by = c(quantile_col_name, plot_by_col_name)]
 
@@ -102,6 +107,8 @@ plot_calibration_by_risk_quantile_by_factor <- function(
 	n_breaks <- ifelse(uniqueN(data[, get(quantile_col_name)]) < 5,
 											uniqueN(data[, get(quantile_col_name)]), 5)
 
+	plot_dt[, (quantile_col_name) := as.numeric(get(quantile_col_name))] # convert quantile column to numeric (if not already) for better plotting
+
 	# create calibration plot - with LINES for SE
 	if(!SE_line | (SE_line & SE_style == 'line')) {
 		calibration_plot <- ggplot(data = plot_dt, aes(y = mean_obs_outcome, x = get(quantile_col_name), color = factor(get(plot_by_col_name)))) +
@@ -109,7 +116,7 @@ plot_calibration_by_risk_quantile_by_factor <- function(
 						   geom_line(aes(group = factor(get(plot_by_col_name)))) +
 						   color_scale +
 						   theme_bw() +
-						   theme(legend.position = 'bottom') +
+						   theme(legend.position = legend_position) +
 						   xlab(xlabel) +
 						   ylab(ylabel) +
 	             # determine where the axes begin, make sure y axis is %
@@ -131,7 +138,7 @@ plot_calibration_by_risk_quantile_by_factor <- function(
 	}
 
 	# create calibration plot - with RIBBON for SE
-	if(SE_line == TRUE & SE_style == 'ribbon'){
+	if (SE_line == TRUE & SE_style == 'ribbon') {
 		calibration_plot <- ggplot(data = plot_dt, aes(
 			y = mean_obs_outcome,
 			x = get(quantile_col_name),
@@ -145,7 +152,7 @@ plot_calibration_by_risk_quantile_by_factor <- function(
 		    xlab(xlabel) +
 		    ylab(ylabel) +
 		    theme_bw() +
-		    theme(legend.position = 'bottom') +
+		    theme(legend.position = legend_position) +
 			scale_y_continuous(labels = scales::percent, limits = c(ymin, NA))+
 			scale_x_continuous(breaks = pretty_breaks(n = n_breaks))
 	}
@@ -157,9 +164,11 @@ plot_calibration_by_risk_quantile_by_factor <- function(
 		g <- calibration_plot
 	}
 
-	# save
-	ggsave(output_file, g)
-	if(return_plot){
+	# save output if path is not NA or ''
+	if (!is.na(output_file) & output_file != '') {
+		ggsave(output_file, g)
+	}
+	if (return_plot) {
 		return(g)
 	}
 }