diff -r a260027dfa5b -r 998d6c646f20 r.rst
--- a/r.rst	Sat Mar 05 22:43:02 2016 +0200
+++ b/r.rst	Sat Mar 05 22:50:41 2016 +0200
@@ -38,6 +38,13 @@
 
 Objext size in memory: ``object.site(1:2)``
 
+Interactive session
+===================
+
+Controlling output precision::
+
+  options(digits=3)
+
 Debugging
 =========
 
@@ -108,11 +115,50 @@
 Looping over data
 =================
 
-``lapply`` iterate over data and return list of function application::
+``lapply`` iterate over data and return list with result of function
+application::
 
   lapply(1:5, function(x) x^2)
   lapply(matrix(rnorm(20*10),20,10), mean)
 
+Usually you don't need a list but a vector. ``sapply`` works like ``lapply`` but
+also try to convert result to matrix or vector is dimantions and elvement types
+permit this::
+
+  lapply(list(1:5), mean)
+  [[1]]
+  [1] 3
+  sapply(list(1:5), mean)
+  [1] 3
+
+``apply`` works on specific dimension of data so useful to work with matrixes
+and data frames::
+
+  apply(matrix(1:6, 2, 3), 1, min)
+  [1] 1 2
+
+  apply(matrix(1:6, 2, 3), 2, max)
+  [1] 2 4 6
+
+  apply(array(rnorm(2*2*10), c(2, 2, 10)), c(1, 2), mean)
+             [,1]       [,2]
+  [1,] -0.2733804  0.3154234
+  [2,]  0.1830982 -0.5889010
+
+``colSums``, ``rowSums``, ``colMeans``, ``rowMeans`` is defined as optimized
+equivalent for::
+
+  rowSums = apply(x, 1, sum)
+  colSums = apply(x, 2, sum)
+  rowMeans = apply(x, 1, mean)
+  colMeans = apply(x, 2, mean)
+
+``split`` partitioning data on factor (analog of SQL ``group by``)::
+
+  data<-data.frame(rnorm(10),rbinom(10,1,prob=.7))
+  sdata<-split(data[,1],data[,2])
+  lapply(sdata,mean)
+
 Exploring data
 ==============
 
@@ -148,3 +194,10 @@
   hist(rpois(100,10))
   hist(rpois(100,10),breaks=20)
 
+Renaming columns
+================
+::
+
+  names(d)[names(d)=="beta"] <- "two"
+  names(d)[2] <- "two"
+