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1. 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.

2. 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

3. 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

4. Packages and Visualization

5. 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

6. 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 )

7. 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)

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

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

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

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