Learning

Apache Pig  is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop  sub-project ). Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties:    Ease of programming.  It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand

We look at some of the ways R can display information graphically. This is a basic introduction to some of the basic plotting commands. It is assumed that you know how to enter data or read data files which is covered in the first chapter, and it is assumed that you are familiar with the different data types.       In each of the topics that follow it is assumed that two different data sets,   w1.dat   and   trees91.csv   have been read and defined using the same variables as in the first chapter. Both of these data sets come from the study discussed on the web site given in the first chapter. We assume that they are read using “read.csv” into variables   w1   and   tree : >   w1  <-  read.csv ( file = "w1.dat" , sep = "," , head = TRUE ) >  names ( w1 ) [1] "vals" >   tree  <-  read.csv ( file = "trees91.csv" , sep = "," , head = TRUE ) >  names ( tree )  [1] "C"      "N&quo