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If and else
 The simplest form of flow control is conditional execution using if.
 if takes a logical value (more precisely, a logical vector of length one) and
executes the next statement only
 if that value is TRUE:
 if(TRUE) message("It was true!")
 ## It was true!
 if(FALSE) message("It wasn't true!")
 Missing values aren’t allowed to be passed to if; doing so throws an error: if(NA)
message("Who knows if it was true?")
 ## Error: missing value where TRUE/FALSE needed Where you may have a missing
value, you should test for it using is.na:
 if(is.na(NA)) message("The value is missing!")
 ## The value is missing!

 a<-3
 b<-4
 if(a<b)
 message("B is greater")

 a <- 33
b <- 33
if (b > a) {
print("b is greater than a")
} else if (a == b) {
print ("a and b are equal")
}
 a <- 200
b <- 33
if (b > a) {
print("b is greater than a")
} else if (a == b) {
print("a and b are equal")
} else {
print("a is greater than b")
}

 x <- 41
 if (x > 10) {
 print("Above ten")
 if (x > 20) {
 print("and also above 20!")
 } else {
 print("but not above 20.")
 }
 } else {
 print("below 10.")
 }
 [1] "Above ten"
[1] "and also above 20!"

 x<-5
 if(is.nan(x))
 {
 message("x is missing")
 } else if(is.infinite(x))
 {
 message("x is infinite")
 } else if(x > 0)
 {
 message("x is positive")
 } else if(x < 0)
 {
 message("x is negative")
 } else
 {
 message("x is zero")
 }

Loop
 There are three kinds of loops in R:
 Repeat
 While, and for
 they can still come in handy for repeatedly executing code
 Repeat:
 i<-0
 repeat
 {
 print(i)
 i<-i+1
 if(i>=3)
 break
 }

While
 i<-1
 While (i<=4)
 {
 i<-i+1
 print(i)
 }
 Output:
 2
 3
 4
 5

For loop
 for(i in seq(1,10,2))
 { print(i) }
 i<-1
 for(j in 1:3)
 {
 i<-i+1
 print(i)
 }
 Output:
 2
 3
 4

Functions
 A function is a block of code which only runs when
it is called.
 You can pass data, known as parameters, into a
function.
 A function can return data as a result.

Creating and Calling Function in R
 In order to understand functions better, let’s take a look
at what they consist of.
 Typing the name of a function shows you the code that
runs when you call it.
 The terms "parameter" and "argument" can be used for the
same thing: information that are passed into a function.
 From a function's perspective:
 A parameter is the variable listed inside the parentheses
in the function definition.
 An argument is the value that is sent to the function when
it is called.

Example
 Sample<-function(a,b,c)
 {
 print(a)
print(b)
print(c)
print(a+b+c)
}
Sample(2,3,4)

Passing Functions to and from Other
Functions
 Functions can be used just like other variable
types, so we can pass them as arguments to other
functions, and return them from functions.
 One common example of a function that takes
another function as an argument is do.call.
 do.call(function(x, y) x + y, list(1:5, 5:1))
 ## [1] 6 6 6 6 6

do.call()
#create three data frames
df1 <- data.frame(team=c('A', 'B', 'C'), points=c(22, 27, 38))
df2 <- data.frame(team=c('D', 'E', 'F'), points=c(22, 14, 20))
df3 <- data.frame(team=c('G', 'H', 'I'), points=c(11, 15, 18))
#place three data frames into list
df_list <- list(df1, df2, df3)
#row bind together all three data frames
do.call(rbind, df_list)

Variable Scope
 A variable’s scope is the set of places from which you can see the variable.
For example, when you define a variable inside a function, the rest of the
statements in that function will have access to that variable.
 In R subfunctions will also have access to that variable.
 In this next example, the function f takes a variable x and passes it to the
function g. f also defines a variable y, which is within the scope of g, since g
is a sub‐ function of f.

 So, even though y isn’t defined inside g, the example works:
 f <- function(x)
 {
 y <- 1
 g <- function(x)
 {
 (x + y) / 2 #y is used, but is not a formal argument of g }
 g(x)
 }
 f(sqrt(5)) #It works! y is magically found in the environment of f
 ## [1] 1.618

String Manipulation
 String manipulation basically refers to the process of
handling and analyzing strings.
 It involves various operations concerned with
modification and parsing of strings to use and change its
data.
 Paste:
 str <- paste(c(1:3), "4", sep = ":")
 print (str)
 ## "1:4" "2:4" "3:4"
 Concatenation:
 # Concatenation using cat() function
 str <- cat("learn", "code", "tech", sep = ":")
 print (str)
## learn:code:tech

Loading and Packages
 R is not limited to the code provided by the R Core Team.
It is very much a community effort, and
 there are thousands of add-on packages available to
extend it.
 The majority of R packages are currently installed in an
online repository called CRAN (the Comprehensive R
Archive Network1)
 which is maintained by the R Core Team. Installing and
using these add-on packages is an important part of the R
experience

Loading Packages
 To load a package that is already installed on your
machine, you call the library function
 We can load it with the library function:
 library(lattice)
 the functions provided by lattice. For example,
displays a fancy dot plot of the famous Immer’s barley
dataset:
dotplot(
variety ~ yield | site,
data = barley,
groups = year
)

Scatter Plot
 A "scatter plot" is a type of plot used to display the relationship between two
numerical variables, and plots one dot for each observation.
 It needs two vectors of same length, one for the x-axis (horizontal) and one
for the y-axis (vertical):
 Example
 x <- c(5,7,8,7,2,2,9,4,11,12,9,6)
y <- c(99,86,87,88,111,103,87,94,78,77,85,86)
plot(x, y)

P<- ggplot(mtcars,aes(wt,mpg) )
p+geom_point()

P<- ggplot(mtcars,aes(wt,mpg) )
p+geom_line(color=blue)

Box_plot()
ggplot(data = mpg, aes(x = drv, y = hwy,
colour = class)) +
geom_boxplot()

Geom_bar()
g <- ggplot(mpg, aes(class))
# Number of cars in each class:
g + geom_bar()

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