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library(dplyr)
library(tidyr)
library(ggplot2)

anscombe_transmuted <- anscombe %>% 
  gather(x,val,x1:x4) %>% 
  gather(y,val2,y1:y4) %>% 
  mutate(OX = extract_numeric(x),
         OY = extract_numeric(y)) %>%
  filter(OX == OY) %>% 

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library(ggplot2)

d <- data.frame(Kandydat = c('Komorowski','Duda','Ogórek','Korwin-Mikke',
                        'Kukiz','Jarubas','Kowalski','Palikot','Wilk',
                        'Tanajno','Braun','Grodzka','Nowicka'),
           Poparcie = c(650,1600,510,300,240,450,188,150,145,130,125,85,91))


ggplot(data = d,
       aes(x = reorder(Kandydat,-Poparcie),

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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recode <- function(df, ..., match = c("first", "last")) {
  match <- match.arg(match)

  cases <- lapply(list(...), as.case)
  if (identical(match, "last")) cases <- rev(cases)

  n <- nrow(df)
  out <- rep(NA, length(n)) # logical will be upcast as needed

  # Simple loop-y implementation

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## source: https://twitter.com/eddelbuettel/status/607675836779642880
quietLibrary <- function(x, ...) eval(substitute(suppressMessages(library(x, ...))))

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## source http://stackoverflow.com/questions/23035982/directly-creating-dummy-variable-set-in-a-sparse-matrix-in-r
fast_dummy <- function(df) {
  n <- nrow(df)
  nlevels <- sapply(df, nlevels)
  i <- rep(seq_len(n), ncol(df))
  j <- unlist(lapply(df, as.integer)) +
    rep(cumsum(c(0, head(nlevels, -1))), each = n)
  x <- 1
  mat <- sparseMatrix(i = i, j = j, x = x)
  return(mat)

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# ---------------------------------------
# Author: Andreas Alfons
#         Vienna University of Technology
# ---------------------------------------

#' Construct a matrix of binary variables for calibration
#'
#' Construct a matrix of binary variables for calibration of sample weights
#' according to known marginal population totals.
#'

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# ---------------------------------------
# Author: Andreas Alfons
#         Vienna University of Technology
# ---------------------------------------

#' Calibrate sample weights
#' 
#' Calibrate sample weights according to known marginal population totals.
#' Based on initial sample weights, the so-called \emph{g}-weights are computed
#' by generalized raking procedures.

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# improved list of objects
# source: http://stackoverflow.com/questions/1358003/tricks-to-manage-the-available-memory-in-an-r-session/4827843#4827843
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)

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## Source: https://stackoverflow.com/questions/10984556/is-there-way-to-track-progress-on-a-mclapply/26892969#26892969
library(parallel)

##------------------------------------------------------------------------------
##' Wrapper around mclapply to track progress
##' 
##' Based on http://stackoverflow.com/questions/10984556
##' 
##' @param X         a vector (atomic or list) or an expressions vector. Other
##'                  objects (including classed objects) will be coerced by