.. -*- coding: utf-8 -*-

===

 R

===

.. contents::

   :local:

Inspecting objects

==================

Info about object dimensions::

  length(c(1,2,3))

  dim(matrix(1:6, 2, 3))

  ncol(matrix(1:6, 2, 3))

  nrow(matrix(1:6, 2, 3))

Brief info about any object::

  typeof(str)

  class(str)

  unclass(str)

  str(c(1, 2))

  str(summary)

Column names of datasets::

  names(...)

  names(list(colA=1, colB=2))

Column/row names of matrixes::

  colnames(matrix(...))

  rownames(matrix(...))

List objects in global context: ``ls()``.

Objext size in memory: ``object.site(1:2)``

Interactive session

===================

Controlling output precision::

  options(digits=3)

List of all options::

  str(options())

Debugging

=========

To mark function for debugging call::

  debug(fun, text = "", condition = NULL)

  debugonce(fun, text = "", condition = NULL)

To return function to normal execution::

  undebug(fun)

  isdebugged(fun)

You can under to debug mode in any piece of code by calling ``browser``.

``traceback`` prints out the function call stack after an error occurs; does

nothing if there's no error.

``trace`` allows you to insert debugging code into a function a specific places.

``recover`` allows you to modify the error behavior so that you can browse the

function call stack.

Profiling

=========

How long execution of expression takes (in low sec/milisec resolution)::

  system.time(expr, gcFirst = TRUE)

  unix.time(expr, gcFirst = TRUE)

``Rprof`` function enable global profiling. ``summaryRprof`` function decrypt

profiling data::

  Rprof()       ## start profiling

  Rprof(NULL)   ## suspend profiling

  Rprof(append = TRUE)  ## resume profiling

  Rprof(NULL)   ## end profiling

  summaryRprof() ## investigate profiling report

Generating random numbers

=========================

For each distribution there are exists corresponding generation function, named

with prefix ``r``::

  rnorm(n, mean = 0, sd = 1)

  rt(n, df, ncp)

  rbinom(n, size, prob)

  rpois(n, lambda)

  runif(n, min = 0, max = 1)

  rexp

  rchisq

  rgamma

In order to generate predictable sequences use::

  set.seed(seed, kind = NULL, normal.kind = NULL)

Sampling from array::

  sample(x, size, replace = FALSE, prob = NULL)

  sample.int(n, size = n, replace = FALSE, prob = NULL)

  sample(1:10, 10)  ## permutation!!

  sample(1:10, 100, replace=TRUE)

Looping over data

=================

``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

==============

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)

Renaming columns

================

::

  names(d)[names(d)=="beta"] <- "two"

  names(d)[2] <- "two"

  library(plyr)

  newd <- rename(d, c("beta"="two", "gamma"="three"))

Removing names for raws and columns

===================================

::

  rownames(dt) <- NULL

  colnames(dt) <- NULL

Filtering raws and columns

==========================

::

  TODO

Droping raws and columns

========================

Drop column from data frame by number::

  dfnew <- df[-1]         # first

  dfnew <- df[-ncol(df)]  # last

  dfnew <- df[-c(1, 3:4, 7)]  # range

Drop column from data frame by name::

  newdf <- df[ , !(names(df) %in% c("lat", "long"))]

  df <- data.frame( a = 1:10, b = 2:11, c = 3:12 )

  df <- subset(df, select = c(a,c))

  df <- subset(df, select = -c(a,c))