# What is Data Acquisition step in Data Analytics process step

## Data Acquisition step in Data Analytics process step

#### Data Acquisition step involves

• File Handling
• Web Scraping
• SQL Data

#### Some examples of Data Acquisition steps are :

• Server logs can be extracted from enterprise system servers to analyze and optimize application performance.

# tapply() function in R

tapply()
Apply a function to each cell of a ragged array, that is to each (non-empty) group of values given by a unique combination of the levels of certain factors.
##### Usage
``tapply(X, INDEX, FUN = NULL, …, default = NA, simplify = TRUE)``

# R function: which.max

R function: which.max

`which.max` returns the position of the element with the maximal value in a vector.

`n <- c (31, 47, 18, 9, 25, 19, 62, 32)pos_max <- which.max(n)print(pos_max)# [1] 7n[pos_max]# [1] 62`

# Summary function in R

### Summary function in R

summary() is a generic function used to produce result summaries of the results of various model fitting functions

### Example

summary(school)
summary(as.factor(school\$age))

# names() function in R

### names() function in R

names() function is used to give name to vector members.

### Syntax

names(vector_variable) = c(“names”)

### Example

names(employee role) = c(“EmpNo”,”EmpRole”)

# Modulo Operator in R ( %% operator )

### Modulo Operator in R ( %% operator )

The modulo returns the remainder of the division of the number to the left by the number on the right

5%%3

2

# List in R

## List in R

### 1. Creating List – with different types elements

diffTypeList <- list(1, “Introduction to R”, TRUE)

### 2. You can access single element double square brackets`[[]]`

Example :
print(diffTypeList[[1]])            // Output [1] 1

### 3. You can modify by accessing element using double square brackets`[[]]`

Example:
diffTypeList[[1]] <- 100

# Vector in R

### Vector is a data structure in R which is of fixed type and fixed length.

It contains elements of the same type at each index. The data types can be

### Vectors of different types

realNumericVector <- c(1, 2, 3, 4) # numeric

decimalNumericVector <- c(0.1, 0.2, 0.3, 0.4) # numeric

logiacalVector <- c(TRUE, FALSE) # logical

characterVector <- c(“a”, “b”, “c”) # character

integerVector <- 1:9 # integer

myComplexVector <- c(1+1i, 2+2i) # complex