r tracé de densité - aire de remplissage sous courbe

Question

J'ai écrit un code pour tracer des données de densité pour les variations d'un test A/B. Je voudrais améliorer le visuel en ombrant (avec le remplissage étant légèrement transparent) la zone en dessous de chaque courbe. J'utilise actuellement matplot, mais comprendre ggplot pourrait être une meilleure option.

Des idées? Merci.

# Setup data frame - these are results from an A/B experiment 
conv_data = data.frame(
      VarNames = c("Variation 1", "Variation 2", "Variation 3") # Set variation names 
      ,NumSuccess = c(1,90,899) # Set number of successes/conversions 
      ,NumTrials = c(10,100,1070) # Set number of trials 
      ) 
conv_data$NumFailures = conv_data$NumTrials - conv_data$NumSuccess # Set number of failures [no conversions] 
num_var = NROW(conv_data) # Set total number of variations 
plot_col = rainbow(num_var) # Set plot colors 

get_density_data <- function(n_var, s, f) { 
    x = seq(0,1,length.out=100) # 0.01,0.02,0.03...1 
    dens_data = matrix(data = NA, nrow=length(x), ncol=(n_var+1)) 
    dens_data[,1] = x 

    # set density data 
    for(j in 1:n_var) { 
     # +1 to s[], f[] to ensure uniform prior 
     dens_data[,j+1] = dbeta(x, s[j]+1, f[j]+1) 
    } 
    return(dens_data) 
} 

density_data = get_density_data(num_var, conv_data$NumSuccess, conv_data$NumFailures) 

matplot(density_data[,1]*100, density_data[,-1], type = "l", lty = 1, col = plot_col, ylab = "Probability Density", xlab = "Conversion Rate %", yaxt = "n") 
legend("topleft", col=plot_col, legend = conv_data$VarNames, lwd = 1) 

Ce produit le tracé suivant:

Answer 1

# Setup data frame - these are results from an A/B experiment 
conv_data = data.frame(
    VarNames = c("Variation 1", "Variation 2", "Variation 3") # Set variation names 
    ,NumSuccess = c(1,90,899) # Set number of successes/conversions 
    ,NumTrials = c(10,100,1070) # Set number of trials 
) 
conv_data$NumFailures = conv_data$NumTrials - conv_data$NumSuccess # Set number of failures [no conversions] 
num_var = NROW(conv_data) # Set total number of variations 
plot_col = rainbow(num_var) # Set plot colors 

get_density_data <- function(n_var, s, f) { 
    x = seq(0,1,length.out=100) # 0.01,0.02,0.03...1 
    dens_data = matrix(data = NA, nrow=length(x), ncol=(n_var+1)) 
    dens_data[,1] = x 

    # set density data 
    for(j in 1:n_var) { 
    # +1 to s[], f[] to ensure uniform prior 
    dens_data[,j+1] = dbeta(x, s[j]+1, f[j]+1) 
    } 
    return(dens_data) 
} 

density_data = get_density_data(num_var, conv_data$NumSuccess, conv_data$NumFailures) 

matplot(density_data[,1]*100, density_data[,-1], type = "l", 
     lty = 1, col = plot_col, ylab = "Probability Density", 
     xlab = "Conversion Rate %", yaxt = "n") 
legend("topleft", col=plot_col, legend = conv_data$VarNames, lwd = 1) 

## and add this part 
for (ii in seq_along(plot_col)) 
    polygon(c(density_data[, 1] * 100, rev(density_data[, 1] * 100)), 
      c(density_data[, ii + 1], rep(0, nrow(density_data))), 
      col = adjustcolor(plot_col[ii], alpha.f = .25)) 

Answer 2

a été en mesure de répondre propre question:

df = as.data.frame(t(conversion_data)) 
dfs = stack(df) 
ggplot(dfs, aes(x=values)) + geom_density(aes(group=ind, colour=ind, fill=ind), alpha=0.3)