--- a/r.rst	Sun Feb 28 01:00:06 2016 +0200
+++ b/r.rst	Sun Feb 28 14:10:07 2016 +0200
@@ -19,17 +19,24 @@
 Brief info about any object::
 
   typeof(str)
+  class(str)
+  unclass(str)
   str(c(1, 2))
   str(summary)
 
-Brief info about vectors and matrixes::
+Column names of datasets::
+
+  names(...)
+  names(list(colA=1, colB=2))
+
+Column/row names of matrixes::
 
-  summary(1:8)
-  summary(matrix(1:20, 4, 5))
+  colnames(matrix(...))
+  rownames(matrix(...))
 
-Brief info on datasets and matrixes::
+List objects in global context: ``ls()``.
 
-  names(list(colA=1, colB=2))
+Objext size in memory: ``object.site(1:2)``
 
 Debugging
 =========
@@ -82,6 +89,9 @@
   rbinom(n, size, prob)
   rpois(n, lambda)
   runif(n, min = 0, max = 1)
+  rexp
+  rchisq
+  rgamma
 
 In order to generate predictable sequences use::
 
@@ -94,3 +104,47 @@
 
   sample(1:10, 10)  ## permutation!!
   sample(1:10, 100, replace=TRUE)
+
+Looping over data
+=================
+
+``lapply`` iterate over data and return list of function application::
+
+  lapply(1:5, function(x) x^2)
+  lapply(matrix(rnorm(20*10),20,10), mean)
+
+Exploring data
+==============
+
+Check `Inspecting objects`_ section.
+
+Investigating unique values::
+
+  sapply(data, unique)
+  sapply(data$col, unique)
+  sapply(data[,c("col1","col2")], unique)
+  sapply(data[,5:10], unique)
+
+  table(data$col)
+
+  tapply(data$what, data$by, unique)
+  tapply(data$what, data$by, summary)
+  tapply(data$what, data$by, range)
+  tapply(data$what, data$by, mean)
+  tapply(data$what, data$by, sd)
+
+Brief info about vectors and matrixes::
+
+  summary(1:8)
+  summary(matrix(1:20, 4, 5))
+
+Simple plots::
+
+  i<-1:100
+  x<-i/10
+  y<-x^2
+  plot(x,y)
+
+  hist(rpois(100,10))
+  hist(rpois(100,10),breaks=20)
+