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echo "Version: 1.0

RestoreWorkspace: No
SaveWorkspace: No
AlwaysSaveHistory: No

EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 4
Encoding: UTF-8

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# required packages

require(dplyr)
require(dygraphs)
require(readxl)
require(stringr)
require(lubridate)
require(xts)

# reading in the data

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#################################################

# un-parameterised

require(dplyr)

df = iris[,sapply(X = iris
                , function(X) is.numeric(X))]

df.cluster = df %>% kmeans(x = .

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# required package
library(VennDiagram)

# plot venn
draw.pairwise.venn(area1 =  6, area2 = 6,  cross.area = 3, 
                   category = c('x', 'y'), 
                   fill = c('darkred', 'darkgreen'),
                   alpha = rep(0.3, 2),
                   scaled = FALSE)

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# creating set x
x = set([1,2,3,4,5,6])

# creating set y
y = set([4,5,6,7,8,9])

# x UNION y
x.union(y)

{1, 2, 3, 4, 5, 6, 7, 8, 9}

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sqlSave(channel,
  dat,
  tablename = NULL,
  append = FALSE,
  rownames = TRUE,
  colnames = FALSE,
  verbose = FALSE,
  safer = TRUE,
  addPK = FALSE,
  fast = TRUE,

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sqlSave(channel
, dat
, tablename = NULL
, append = FALSE
, rownames = TRUE
, colnames = FALSE
, verbose = FALSE
, safer = TRUE
, addPK = FALSE
, fast = TRUE

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# required libraries
library(dplyr)

# loading the iris data set
df.iris = iris

# examining column classes
sapply(X = df.iris, function(X) class(X))

# the 'base' way

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guess_weight = function(posts){

  # reading in the guesses
  guesses = readLines(posts)

  # Match the guesses in kilograms and store result in vector
  kgmatch = regmatches(guesses,regexpr('[1-9]+\\.?[0-9]* *(k|K) *(g|G)(s|S)?',guesses))

  # Changing to numeric and storing in a vector
  kgnumerics = as.numeric(regmatches(kgmatch,regexpr('[1-9]+\\.?[0-9]*',kgmatch)))

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# creating an R data frame
sim_data = data.frame(X = rnorm(n = 10),
                      Y = rnorm(n = 10),
                      Z = rnorm(n = 10))

# select column j
sim_data[,1]

# ...or by name
sim_data['X']