See the repository for this function: https://github.com/DrNickRedfern/loessggplot

loessggplot.R

loessggplot <- function(x, low = 0.1, high = 0.9, step = 0.01, title = "", ticks = 0.1){ 
  
  n <- length(x)
  t <- cumsum(x); t <- 100*t/max(t)  # normalize the running time to percent
  
  # Create an empty data fame to store the results of the for loop
  df <- data.frame() 
  
  # Loop over the sequence of spans for the loess smoothers and store the result
  for(s in seq(low, high, step)) { 
    fit <- loess(x ~ t, span = s, degree = 2)$fitted 
    sn <- as.numeric(rep(s, n)) 
    df_a <- data.frame(cbind(sn, t, fit)) 
    df <- rbind(df,df_a) 
  } 
  
  # Plot the data
  ggplot() + 
    geom_vline(xintercept = seq(25, 75, 25), colour="black", linetype = "longdash") +  # reference lines
    geom_line(data = df, aes(x = t, y = fit, group = sn, colour = sn)) +  # loess smoothers
    scale_x_continuous(name = "\nRunning time (%)", limits = c(0, 100),
                       breaks = seq(0, 100, 25), labels = seq(0, 100, 25)) +
    labs(title = title, y  = "Fitted values (s)\n") +
    scale_colour_viridis_c(name = "Span", breaks = seq(low, high, ticks)) +
    guides(colour = guide_colourbar(barwidth = 20, barheight = 1, title.position = "top")) +
    theme_classic() +
    theme(legend.position = "bottom",
          plot.title = element_text(face = "bold"))
     
}