Big Data - Kapil Sharma (101)

Big Data consists of five types:

1. Volume

2. Velocity 

3. Variety 

4. Veracity 

5. Value  

More than 90% of big data set created in last 3+ years.

To process this huge data set of unstructured data, big data frameworks comes in picture. 

To retrieve value from processing, computing and analysis data leads to the unique value addition. 

Map/Reduce Framework Basic - Kapil Sharma

Source: Microsoft

Basic Data Science Introduction (C-01) - Kapil Sharma

Vectors: 
The vector is a very important tool in R programming. Through vectors we create matrix and data-frames. Vectors can have numeric, character and logical values. The function c() is used to create vectors in R programming.

x <- c(2,22,"xyz", -4)

Factors:
They are same like vectors but they have different meaning.
 y <- c(1,2,3,4,5,6,7)
yf<- factor(y)
yf

Lists:
They are vectors, but they consists of different data sets.
a <- c(dog = "pitbull", age = 100, color = "golden", weight = TRUE)

Matrices:
They are vectors with more than one dimensions, consists of rows and columns (ncol,nrow).
They can be rowbind (rbind()) or column bind (cbind())

# Create matrix with 4 elements:
cells <- c(3,5,16,29)
colname <- c("Jun", "Feb")
rowname <- c("Nut", "Orange")
y <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rowname, colname))

              Jun   Feb
Nut         3      5
Orange  16    29

Datasets:
It is same like matrix, but it also consists of numeric and character elements.
Location <- c("Mandi", "Manali")
Distance <- c(200, 307)
df <- data.frame(a,b)
df

Location Distance
Mandi 200
Manali 307

R Programming Tutorial, Vectors, R-06 - Kapil Sharma

R Programming Tutorial, Data and its types, R-05 - Kapil Sharma

R Programming Tutorial, Variables In RStudio, R-04 - Kapil Sharma

R Programming Tutorial, Simple Programs In RStudio, R-03 - Kapil Sharma