Category: Introduction to R Programming
What is Data Acquisition step in Data Analytics process step
Data Acquisition step in Data Analytics process step
Data Acquisition step is used to collect data from various sources for analysis to answer the question raised in Business Definition Step.
Data Acquisition step involves
- File Handling
- Web Scraping
- SQL Data
Some examples of Data Acquisition steps are :
- Twitter, Facebook, LinkedIn, and other social media and information sites provide streaming APIs.
- Server logs can be extracted from enterprise system servers to analyze and optimize application performance.
What are different processes involved in Data Analytics ?
Data Analytics consists of below processes
- Business Problem
- Data Acquistion
- Data Wrangling
- Explanatory Data Analysis
- Data exploration
- Conclusion or Prediction
- Communication
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.
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
Example :
5%%3
Result :
2
List in R
List in R
List is data structure in R with elements which can be of different types.
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
- Logical
- Integer
- Numeric
- Character
- Complex
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