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# dumping textual data
# stores data and its meta data too in a file.
# no need to read to read meta data back, so tables can be created
# back quickly. Only downside is space efficency

# dput & dget command generates the R code and can read back. Also 
# dput can work on single R object

# create a data frame
y <- data.frame(a=1, b="abc")

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# reading a small or moderately sized CSV data file 
csvdata <- read.csv("data/sample.csv")
tabdata <- read.table("data/sample.csv")

#---------------------------------------------------------------------
# reading larger dataset

# read only an initial data set
initdata <- read.table("data/sample.csv", nrows=100, sep=",", header=T)

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# names in R
#------------------------------------------------------------
# Vectors can have names
v1 <- 1:3

# print names for v1 this shall return null
names(v1)

# assigning names to values
names(v1) <- c("a", "b", "c")

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# matrix in R
m <- matrix(nrow=2, ncol=3)

# get the dimensions
dim(m)
attributes(m)

# matrix are filled column wise
n <- matrix(1:6, nrow=2, ncol=3)

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x <- c(1,2,NA,3)
is.na(x)
is.nan(x)

y <- c(1,2,NaN,3)
is.na(y)
is.nan(y)

z <- c(1,2,NaN,NA)
is.na(z)

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# factor for categorical data can be ordered or unordered
# eg. gender (M, F), size (S, M, L, XL, XXL)
# (M, F) or (S, M, L, XL, XXL) are called levels

x <- factor(c("S", "M", "L", "XL", "XXL", "S", "XL", "XXL"))

# represent the class of x which is factor
class(x)

# strips out the class and shows the factors represents as integer

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# C stands for CONCAT and helps in creating vector
x <- c(1,2,3)
y1 <- c(T, F, T, F)
y2 <- c(TRUE, FALSE)

# sequence of numbers
z <- 1:100

# complex numbers
a <- c(1+2i, 3+4i)

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public class BizService {
  private SqlSessionFactory sqlSessionFactory;
  /**
	 * Initalize the 
	 *
	 * @param config the config
	 */
	private void init(QueryEngineConfiguration config) {
		InputStream inputStream;

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CREATE TABLE BOOK (
  ID SERIAL PRIMARY KEY, 
  NAME VARCHAR (50) UNIQUE NOT NULL, 
  TITLE VARCHAR (100), 
  DESCRIPTION VARCHAR (200)
);

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package learn.scala.polymorphism

trait List[T] {
	def isEmpty: Boolean
	def head: T
	def tail: List[T]
}

class Nil[T] extends List[T] {
  def isEmpty: Boolean = true