# 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)#  7n[pos_max]#  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[])            // Output  1

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

Example:
diffTypeList[] <- 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
{"ft_lang_label":"__label__en","ft_lang_prob":0.7253125,"math_prob":0.8978631,"size":2782,"snap":"2023-14-2023-23","text_gpt3_token_len":675,"char_repetition_ratio":0.12455004,"word_repetition_ratio":0.05790646,"special_character_ratio":0.2426312,"punctuation_ratio":0.10721649,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99584097,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-06-05T13:21:08Z\",\"WARC-Record-ID\":\"<urn:uuid:b39c36b2-62af-42ff-b07a-8d7b21d73f49>\",\"Content-Length\":\"99110\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:e7450b30-2a53-40f3-9f52-5d753f33f021>\",\"WARC-Concurrent-To\":\"<urn:uuid:1995587c-61ac-4581-92d4-f6299f7f0b2e>\",\"WARC-IP-Address\":\"192.0.78.132\",\"WARC-Target-URI\":\"https://abhyas.co.in/category/introduction-to-r-programming/\",\"WARC-Payload-Digest\":\"sha1:UWSPSORHHXRPQY255JIFDT7K4GURP4ZX\",\"WARC-Block-Digest\":\"sha1:FRRZS5TRIUHXRPV2PEO4YEOFUGKRXVTV\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224652116.60_warc_CC-MAIN-20230605121635-20230605151635-00735.warc.gz\"}"}