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diff --git a/r/fitdistrplus-example.R b/r/fitdistrplus-example.R
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+n <- 50
+m <- 50
+set.seed(1)
+mu <- -0.4
+sig <- 0.12
+x <- matrix(data=rlnorm(n*m, mu, sig), nrow=m)
+
+library(fitdistrplus)
+## Fit a log-normal distribution to the 50 random data set
+f <- apply(x, 2,  fitdist, "lnorm")
+
+## Plot the results 
+for(i in 1:n)
+plot(f[[i]])
+
+## Save plot in an animated GIF-file
+library(animation)
+saveGIF({for(i in 1:n) plot(f[[i]])})
+
+apply((sapply(f, "[[", "estimate")),1, summary)
+#         meanlog   sdlog
+# Min.    -0.4347 0.09876
+# 1st Qu. -0.4140 0.11480
+# Median  -0.4010 0.12110
+# Mean    -0.4011 0.12270
+# 3rd Qu. -0.3899 0.12950
+# Max.    -0.3522 0.14780
+
+
+## How much variance can we expect in the mean and std?
+## Expeted mean
+ExpectedMean <- function(mu, sig) exp(mu+ sig^2/2)
+## Expected std
+ExpectedStd <- function(mu, sig) sqrt((exp(sig^2)-1)*exp(2*mu + sig^2))
+
+summary(apply(sapply(f, "[[", "estimate"), 2, function(x) ExpectedMean(x[1], x[2])))
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+# 0.6529  0.6665  0.6747  0.6748  0.6819  0.7087 
+summary(apply(sapply(f, "[[", "estimate"), 2, function(x) ExpectedStd(x[1], x[2])))
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+# 0.06604 0.07880 0.08212 0.08316 0.08794 0.10170 
+
+## Let's look at the goodness of fit statistics to get an
+## idea how much variance we can expect there:
+gof.ln <- lapply(f, gofstat)
+gof.test <- lapply(gof.ln, function(x) data.frame(x[c("chisqpvalue", "cvm", "ad", "ks")]))
+apply(do.call("rbind", gof.test), 2, summary)
+#         chisqpvalue     cvm     ad      ks
+# Min.      0.0002673 0.02117 0.1537 0.05438
+# 1st Qu.   0.1394000 0.03755 0.2708 0.07488
+# Median    0.3578000 0.04841 0.3216 0.08054
+# Mean      0.3814000 0.05489 0.3564 0.08431
+# 3rd Qu.   0.6409000 0.06913 0.4358 0.09436
+# Max.      0.9245000 0.13220 0.7395 0.12570
+\ No newline at end of file