The document discusses variable scope in R, explaining how variables defined in functions can be accessed by sub-functions. It covers string manipulation techniques, including concatenation and string formatting, as well as the use of R packages for enhanced functionality. Additionally, it touches on different types of plots like scatter plots, box plots, and bar charts in R, illustrating how to create visual representations of data.

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()