Programming with R in Big Data Analytics
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R is an open source programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and tools. R was originally developed in the late 1980s and is currently maintained by an international volunteer team. The presentation introduces basic R syntax for performing calculations, storing results, creating and manipulating data vectors and matrices, and defining functions.
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Hadoop
The document provides an overview of Hadoop Distributed File System (HDFS):
1) HDFS is designed to reliably store very large files across commodity servers and is optimized for batch processing of huge datasets. It distributes data across clusters in a fault-tolerant way to handle hardware failures.
2) HDFS has a master/slave architecture with a NameNode that manages file system metadata and DataNodes that store file data blocks. Files are broken into blocks and replicated across DataNodes for reliability.
3) The NameNode tracks metadata like file locations and DataNode statuses, while DataNodes store and retrieve blocks. HDFS provides a unified namespace and facilitates reliable and high-throughput access to data.
If statements in c programming
This document discusses different types of decision making and branching statements in C programming, including if, switch, conditional operator, and goto statements. It focuses on explaining the if statement in more detail. The if statement allows for conditional execution of code blocks depending on the evaluation of a test expression. There are several types of if statements including simple if, if-else, nested if-else, and else-if ladder statements. Flowcharts and examples are provided to illustrate the syntax and logic flow for each type of if statement.
un Guided media
Unguided media, also known as wireless transmission, transmits electromagnetic waves without using a physical medium. It includes three main categories: radio waves, which transmit signals omnidirectionally and are used for applications like FM radio; microwaves, which transmit focused beams between aligned antennas and are used for terrestrial and satellite communication; and infrared, which supports short-range communication between devices in closed areas. Each type has advantages like coverage area but also disadvantages such as susceptibility to environmental interference.
Guided media Transmission Media
Introduction to Transmission Media
Types of Transmission Media
Description about Guided Media
Types of Guided media
Main Memory RAM and ROM
The document discusses different types of computer memory. It describes cache memory as very high speed memory between the CPU and main memory used to store frequently accessed data and programs. Primary/main memory is volatile semiconductor memory that holds currently running programs and data. RAM and ROM are types of main memory. RAM is read/write memory that stores data temporarily while power is on, while ROM is read-only memory that permanently stores basic input/output instructions. The document outlines characteristics and types of each memory including static RAM, dynamic RAM, programmable ROM, erasable programmable ROM, and electrically erasable programmable ROM.
Java thread life cycle
The life cycle of a thread involves several stages: being born when created, started, running, and eventually dying. A thread can be in one of five states: newborn, runnable, running, blocked, or dead. Creating a thread involves implementing the Runnable interface, instantiating a Thread object with the Runnable object, and calling the start() method.
PCSTt11 overview of java
This document provides an overview of advanced Java programming concepts covered in the unit, including object-oriented programming, data types, variables, arrays, operators, inheritance, and control statements. It defines key concepts like classes, objects, encapsulation, polymorphism, and inheritance. For data types, it covers primitive types like int, float, boolean and char, as well as arrays. Operators covered include unary, arithmetic, relational, logical, and assignment operators. The document also discusses variables, arrays, and control statements like selection, iteration, and jump statements.
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Programming with R in Big Data Analytics
- 1. Hello! Myself Archana R Assistant Professor In Dept Of CS SACWC. I am here because I love to give presentations.
- 3. INTRODUCTION TO R • R is an open source programming language and software environment for statistical computing and graphics. • The R language is widely used among statisticians and data miners for developing statistical software and data analytics tools
- 4. History of R • Modelled after S & S-plus, developed at AT&T labs in late 1980s. • R project was started by Robert Gentleman and Ross Ihaka Department of Statistics, University of Auckland (1995). • Currently maintained by R core development team – an international team of volunteer developers (since 1997).
- 5. A test run with R in Windows • Double click the R icon on the Desktop and the R Console will open. • Wait while the program loads. You observe something like this. • You can type your own program at the prompt line >.
- 6. How to use R for simple math's • > 3+5 • > 12 + 3 / 4 – 5 + 3*8 • > (12 + 3 / 4 – 5) + 3*8 • > pi * 2^3 – sqrt(4) • >factorial(4) • >log(2,10) • >log(2, base=10) • >log10(2) • >log(2)
- 7. How to store results of calculations for future use • > x = 3+5 • > x • > y = 12 + 3 / 4 – 5 + 3*8 • > y • > z = (12 + 3 / 4 – 5) + 3*8 • > z • > A <- 6 + 8 ## no space should be between < & - • > a ## Note: R is case sensitive • >A
- 8. Identifiers naming • Don't use underscores ( _ ) or hyphens ( - ) in identifiers. • The preferred form for variable names is all lower case letters and words separated with dots (variable.name) but variableName is also accepted. • Examples: avg.clicks GOOD avgClicks OK avg_Clicks BAD • Function names have initial capital letters and no dots (e.g., FunctionName).
- 9. Using C command • > data1 = c(3, 6, 9, 12, 78, 34, 5, 7, 7) ## numerical data • > data1.text = c(‘Mon’, ‘Tue’, “Wed”) ## Text data • ## Single or double quote both ok • ##copy/paste into R console may not work • > data1.text = c(data1.text, ‘Thu’, ‘Fri’)
- 10. Scan command for making data • > data3 = scan() ## data separated by Space / Press ## Press Enter key twice to exit • 1: 4 5 7 8 • 5: 2 9 4 • 8: 3 • 9: • > data3 • [1] 4 5 7 8 2 9 4 3
- 11. Concept of working directory • >getwd() • [1] "C:UsersDSamantaRDatabase" • • > setwd('D:Data AnalyticsProjectDatabase) • • > dir() ## working directory listing • • >ls() ## Workspace listing of objects • • >rm(‘object’) ## Remove an element “object”, if exist • • > rm(list = ls()) ## Cleaning
- 12. Different data items in R • Vector • Matrix • Data Frame • List
- 13. Vectors in R • >x=c(1,2,3,4,56) • >x • > x[2] • > x = c(3, 4, NA, 5) • >mean(x) • [1] NA • >mean(x, rm.NA=T) • [1] 4 • > x = c(3, 4, NULL, 5) • >mean(x) • [1] 4
- 14. More on Vectors in R • >y = c( x, c(-1,5),x) • >length(x) • >length(y) • There are useful methods to create long vectors whose elements are in arithmetic progression: • > x=1:20 • > x • If the common difference is not 1 or -1 then we can use the seq function • > y= seq(2,5,0.3) • > y • [1] 2.0 2.3 2.6 2.9 3.2 3.5 3.8 4.1 4.4 4.7 5.0 • > length(y) • [1] 11
- 15. Functions in R >g = function( x, y) (x+2*y)/3 >g(1,2) >g(2,1)
- 16. THANK YOU !