######################################################################
# Initial examples of how to use R                                   #
# This comes from my book: Analysis of Categorical Data with R       #
######################################################################


######################################################################
#Section 1

   2+2
   pnorm(1.96)
   (2-3)/6
   2^2
   sin(pi/2)
   log(1)
 
   save<-2+2
   save
  
   ls()
   objects()
  

######################################################################
#Section 2

  x <- c(1,2,3,4,5)
  sd2 <- function(numbers) {
    sqrt(var(numbers))
  }
  sd2(x)
  
  sd2 <- function(numbers) {
    cat("Print the data \n", numbers, "\n")
    sqrt(var(numbers))
  } 
  save <- sd2(x)
  save 



  #Another exmaple of calculating the standrd deviation where all of the function's code is on one line.
  #  The semicolon is used to separate cat() and sqrt() function calls. This symbol is used
  #  to signal the end of a complete line of code (rarely is there a need for it).
  sd3 <- function(numbers) { cat("Print the data \n", numbers, "\n");  sqrt(var(numbers))  }
  sd3(x)


######################################################################
#Section 3

  pnorm(1.96) 
  pnorm(q = 1.96) 
  pnorm(1.96, 0, 1) 
  pnorm(q = 1.96, mean = 0, sd = 1)  


######################################################################
#Section 4

  pnorm(q = c(-1.96, 1.96))

  x <- c(3.68, -3.63, 0.80, 3.03, -9.86, -8.66, -2.38, 8.94, 0.52, 1.25)
  y <- c(0.55, 1.65, 0.98, -0.07, -0.01, -0.31, -0.34, -1.38, -1.32, 0.53)
  x
  y

  x + y  # Elementwise addition
  x * y  # Elementwise multiplication

  mean(x)
  x - mean(x) # Each element of x has the mean of x subtracted
  x * 2  # Each element of x is multiplied by 2

  # Other languages may require the following to add x and y
  x[1]  # First element of x
  y[1]  # First element of y
  x[1] + y[1]
  x[2] + y[2]
  x[8] + y[8]






























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