This is an advanced overview of the programming language R showing the concepts and paradigms used there.
Target audience: R programmers who want to see the big picture and software engineers who have a broad experience in other technologies and want to grasp how R is designed.
R is a free statistical programming language and software environment used for statistical analysis and graphics. It was originally based on S, a programming language developed at Bell Labs in the 1970s for statistical analysis. R can be used for data manipulation, calculation, and graphical displays. It includes functions for topics like linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, and graphical techniques.
The presentation is a brief case study of R Programming Language. In this, we discussed the scope of R, Uses of R, Advantages and Disadvantages of the R programming Language.
R is a statistical and graphical programming language that is easy to use and powerful. It is free and exists on many platforms. The number of R users has been growing substantially each year as it has become the lingua franca of data science. R allows for data import, analysis, and visualization through functions, packages, and libraries. Common data types in R include vectors, matrices, data frames, factors, and lists which allow for storing and manipulating different types of data.
Workshop presentation hands on r programmingNimrita Koul
This document provides an overview of the R programming language. It discusses that R is an environment for statistical computing and graphics. It includes conditionals, loops, user defined functions, and input/output facilities. The document describes how to download and install R and RStudio. It also covers key R features such as objects, classes, vectors, matrices, lists, functions, packages, graphics, and input/output.
This short text will get you up to speed in no time on creating visualizations using R's ggplot2 package. It was developed as part of a training to those who had no prior experience in R and had limited knowledge on general programming concepts. It's a must have initial guide for those exploring the field of Data Science
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
R is a free statistical programming language and software environment used for statistical analysis and graphics. It was originally based on S, a programming language developed at Bell Labs in the 1970s for statistical analysis. R can be used for data manipulation, calculation, and graphical displays. It includes functions for topics like linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, and graphical techniques.
The presentation is a brief case study of R Programming Language. In this, we discussed the scope of R, Uses of R, Advantages and Disadvantages of the R programming Language.
R is a statistical and graphical programming language that is easy to use and powerful. It is free and exists on many platforms. The number of R users has been growing substantially each year as it has become the lingua franca of data science. R allows for data import, analysis, and visualization through functions, packages, and libraries. Common data types in R include vectors, matrices, data frames, factors, and lists which allow for storing and manipulating different types of data.
Workshop presentation hands on r programmingNimrita Koul
This document provides an overview of the R programming language. It discusses that R is an environment for statistical computing and graphics. It includes conditionals, loops, user defined functions, and input/output facilities. The document describes how to download and install R and RStudio. It also covers key R features such as objects, classes, vectors, matrices, lists, functions, packages, graphics, and input/output.
This short text will get you up to speed in no time on creating visualizations using R's ggplot2 package. It was developed as part of a training to those who had no prior experience in R and had limited knowledge on general programming concepts. It's a must have initial guide for those exploring the field of Data Science
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
R is a programming language and software environment for statistical analysis and graphics. It is widely used among statisticians and data scientists. R was created by Ross Ihaka and Robert Gentleman in the early 1990s and is currently developed by the R Core Team. Key features of R include its use as a programming language, effective data handling and storage, graphical display capabilities, and large collection of statistical and machine learning packages. R is open source, has a large user community, and is often used for statistical analysis, data mining, and creating statistical graphics.
R is a statistical computing and graphics language used for statistical development and data analysis. It provides effective data handling, storage, and reporting with a wide range of statistical and graphical techniques. Some key features of R include its open source nature, large community support through CRAN, and ability to handle complex mathematical formulas and statistical tests easily. However, R also has a steep learning curve and some available packages can be buggy. Common R objects include vectors, lists, matrices, arrays, factors, and data frames to store and manipulate tabular data.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
It is one of the Best Presentation on the topic "R Programming" having interesting Slides consisting of Amazing Images & Very Useful Information. It also have Transitions & Animation which makes the Presentation more Interesting & Attractive.
Created By - Abhishek Pratap Singh (Aps)
A short tutorial on R, basically for a starter who wants to do data mining especially text data mining.
Related codes and data will be found at the following lnik: http://textanalytics.in/wm/R%20tutorial%20(DATA2014).zip
Basic tutorial for R programming. this video contains lot of information about r programming like
agenda
history
SOFTWARE PARADIGM
R interface
advantages of r
drawbacks of r
R is a programming language and software environment for statistical analysis, graphics representation and reporting. Are You Interested to Learning R Programming in Best Institute Join Besant Technologies in Bangalore.
R is a programming language and software environment for statistical analysis, graphics, and reporting. It was created at the University of Auckland for statistical analysis and graphics. R provides tools for data analysis including statistical functions, data handling, graphical display, and importing/exporting data from other programs like Excel. Some key data types in R are vectors, lists, matrices, and data frames. R can also perform regression analysis and create graphical outputs like pie charts, bar plots, and histograms.
R is a programming language and environment for statistical analysis and graphics. It has many built-in statistical and graphical techniques. R can be installed from CRAN and runs on Windows, MacOS, and UNIX systems. The basic R interface is the console, but RStudio provides an integrated development environment. In RStudio, you can write scripts, see outputs and plots, and access help and packages. Packages extend R's functionality through additional functions and data. Common data types in R include numeric, integer, character, factor, and logical. Vectors are the basic data structure, but R also supports matrices, arrays, data frames and lists.
R is a programming language for statistical analysis and graphics. It is an open-source language developed by statisticians to allow for easy statistical analysis and visualization of data. The document provides an overview of R, discussing its origins, functionality, uses in data science, and popular packages and IDEs used with R. Examples are given of basic R syntax for vectors, matrices, data frames, plotting, and applying functions to data.
The document provides an outline of topics covered in R including introduction, data types, data analysis techniques like regression and ANOVA, resources for R, probability distributions, programming concepts like loops and functions, and data manipulation techniques. R is a programming language and software environment for statistical analysis that allows data manipulation, calculation, and graphical visualization. Key features of R include its programming language, high-level functions for statistics and graphics, and ability to extend functionality through packages.
R is a programming language developed as an alternative for S at AT&T Bell Laboratories. It excels at statistical computation and graphic visualization. R is free, open source, and available across platforms. It has over 3,000 packages on CRAN that extend its functionality. R has a steep learning curve and working with large datasets is limited by RAM size. Major companies use R in business.
This document discusses applications of functional programming languages. It analyzes algorithms, data mining, game programming, and data visualization in functional languages like Haskell, Erlang, and Clojure. For algorithms, it compares merge sort implementations in Python and Haskell. For data mining, it examines examples in Python, Haskell, and Scala and finds that functional programming is not widely used in practice. Game programming in Haskell is discussed through examples of optimizing a game for performance. Data visualization challenges in functional languages involve representing tree and graph structures. In conclusion, while functional programming fits some applications well, it has not seen broad real-world adoption likely due to its restrictions compared to mainstream languages.
This document provides an introduction and overview of R programming for statistics. It discusses how to run R sessions and functions, basic math operations and data types in R like vectors, data frames, and matrices. It also covers statistical and graphical features of R, programming features like functions, and gives examples of built-in and user-defined functions.
1.3 introduction to R language, importing dataset in r, data exploration in rSimple Research
Introduction to R language, How to install R and R studio
How to import dataset in R
How to explore data in R
www.simpleresearch.net
info@simpleresearch.net
The presentation discusses about the following topics:
DBMS Architecture
Relational Algebra Review
Relational calculus
Relational calculus building blocks
Tuple relational calculus
Tuple relational calculus Formulas
This document provides an overview of the Lisp programming language. It discusses key features of Lisp including its invention in 1958, machine independence, dynamic updating, and wide data types. The document also covers Lisp syntax, data types, variables, constants, operators, decision making, arrays, loops, text editors, and common uses of Lisp like Emacs. Overall, the document serves as a high-level introduction to the concepts and capabilities of the Lisp programming language.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It was closely tied to early AI research and used in systems like SHRDLU. Lisp allows programs to treat code as data through homoiconicity and features like lambdas, conses, and list processing functions make it good for symbolic and functional programming.
This document provides an introduction to the Julia programming language and demonstrates its use for statistical computing and analysis. It summarizes Julia's capabilities for technical computing and compares its performance to R, Python, and other languages. Examples showing Julia's speed advantages include bootstrap analysis of correlation coefficients and MCMC sampling for Bayesian logistic regression. Overall, Julia is shown to be significantly faster than R and other options for many statistical tasks.
This document provides an overview of the R programming language. It describes R as a functional programming language for statistical computing and graphics that is open source and has over 6000 packages. Key features of R discussed include matrix calculation, data visualization, statistical analysis, machine learning, and data manipulation. The document also covers using R Studio as an IDE, reading and writing different data types, programming features like flow control and functions, and examples of correlation, regression, and plotting in R.
This document provides an introduction to R, including what R is, how it compares to other statistical software packages, its advantages and disadvantages, how to install R, and options for R editors and graphical user interfaces (GUIs). It discusses R as a language for statistical computing and graphics, compares it to packages like SAS, Stata, and SPSS in terms of cost, usage mode, and prevalence. It outlines some of R's advantages like being free and open-source software with an active user community contributing packages, and some disadvantages like the learning curve and lack of a standard GUI.
R is a programming language and software environment for statistical analysis and graphics. It is widely used among statisticians and data scientists. R was created by Ross Ihaka and Robert Gentleman in the early 1990s and is currently developed by the R Core Team. Key features of R include its use as a programming language, effective data handling and storage, graphical display capabilities, and large collection of statistical and machine learning packages. R is open source, has a large user community, and is often used for statistical analysis, data mining, and creating statistical graphics.
R is a statistical computing and graphics language used for statistical development and data analysis. It provides effective data handling, storage, and reporting with a wide range of statistical and graphical techniques. Some key features of R include its open source nature, large community support through CRAN, and ability to handle complex mathematical formulas and statistical tests easily. However, R also has a steep learning curve and some available packages can be buggy. Common R objects include vectors, lists, matrices, arrays, factors, and data frames to store and manipulate tabular data.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
It is one of the Best Presentation on the topic "R Programming" having interesting Slides consisting of Amazing Images & Very Useful Information. It also have Transitions & Animation which makes the Presentation more Interesting & Attractive.
Created By - Abhishek Pratap Singh (Aps)
A short tutorial on R, basically for a starter who wants to do data mining especially text data mining.
Related codes and data will be found at the following lnik: http://textanalytics.in/wm/R%20tutorial%20(DATA2014).zip
Basic tutorial for R programming. this video contains lot of information about r programming like
agenda
history
SOFTWARE PARADIGM
R interface
advantages of r
drawbacks of r
R is a programming language and software environment for statistical analysis, graphics representation and reporting. Are You Interested to Learning R Programming in Best Institute Join Besant Technologies in Bangalore.
R is a programming language and software environment for statistical analysis, graphics, and reporting. It was created at the University of Auckland for statistical analysis and graphics. R provides tools for data analysis including statistical functions, data handling, graphical display, and importing/exporting data from other programs like Excel. Some key data types in R are vectors, lists, matrices, and data frames. R can also perform regression analysis and create graphical outputs like pie charts, bar plots, and histograms.
R is a programming language and environment for statistical analysis and graphics. It has many built-in statistical and graphical techniques. R can be installed from CRAN and runs on Windows, MacOS, and UNIX systems. The basic R interface is the console, but RStudio provides an integrated development environment. In RStudio, you can write scripts, see outputs and plots, and access help and packages. Packages extend R's functionality through additional functions and data. Common data types in R include numeric, integer, character, factor, and logical. Vectors are the basic data structure, but R also supports matrices, arrays, data frames and lists.
R is a programming language for statistical analysis and graphics. It is an open-source language developed by statisticians to allow for easy statistical analysis and visualization of data. The document provides an overview of R, discussing its origins, functionality, uses in data science, and popular packages and IDEs used with R. Examples are given of basic R syntax for vectors, matrices, data frames, plotting, and applying functions to data.
The document provides an outline of topics covered in R including introduction, data types, data analysis techniques like regression and ANOVA, resources for R, probability distributions, programming concepts like loops and functions, and data manipulation techniques. R is a programming language and software environment for statistical analysis that allows data manipulation, calculation, and graphical visualization. Key features of R include its programming language, high-level functions for statistics and graphics, and ability to extend functionality through packages.
R is a programming language developed as an alternative for S at AT&T Bell Laboratories. It excels at statistical computation and graphic visualization. R is free, open source, and available across platforms. It has over 3,000 packages on CRAN that extend its functionality. R has a steep learning curve and working with large datasets is limited by RAM size. Major companies use R in business.
This document discusses applications of functional programming languages. It analyzes algorithms, data mining, game programming, and data visualization in functional languages like Haskell, Erlang, and Clojure. For algorithms, it compares merge sort implementations in Python and Haskell. For data mining, it examines examples in Python, Haskell, and Scala and finds that functional programming is not widely used in practice. Game programming in Haskell is discussed through examples of optimizing a game for performance. Data visualization challenges in functional languages involve representing tree and graph structures. In conclusion, while functional programming fits some applications well, it has not seen broad real-world adoption likely due to its restrictions compared to mainstream languages.
This document provides an introduction and overview of R programming for statistics. It discusses how to run R sessions and functions, basic math operations and data types in R like vectors, data frames, and matrices. It also covers statistical and graphical features of R, programming features like functions, and gives examples of built-in and user-defined functions.
1.3 introduction to R language, importing dataset in r, data exploration in rSimple Research
Introduction to R language, How to install R and R studio
How to import dataset in R
How to explore data in R
www.simpleresearch.net
info@simpleresearch.net
The presentation discusses about the following topics:
DBMS Architecture
Relational Algebra Review
Relational calculus
Relational calculus building blocks
Tuple relational calculus
Tuple relational calculus Formulas
This document provides an overview of the Lisp programming language. It discusses key features of Lisp including its invention in 1958, machine independence, dynamic updating, and wide data types. The document also covers Lisp syntax, data types, variables, constants, operators, decision making, arrays, loops, text editors, and common uses of Lisp like Emacs. Overall, the document serves as a high-level introduction to the concepts and capabilities of the Lisp programming language.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It was closely tied to early AI research and used in systems like SHRDLU. Lisp allows programs to treat code as data through homoiconicity and features like lambdas, conses, and list processing functions make it good for symbolic and functional programming.
This document provides an introduction to the Julia programming language and demonstrates its use for statistical computing and analysis. It summarizes Julia's capabilities for technical computing and compares its performance to R, Python, and other languages. Examples showing Julia's speed advantages include bootstrap analysis of correlation coefficients and MCMC sampling for Bayesian logistic regression. Overall, Julia is shown to be significantly faster than R and other options for many statistical tasks.
This document provides an overview of the R programming language. It describes R as a functional programming language for statistical computing and graphics that is open source and has over 6000 packages. Key features of R discussed include matrix calculation, data visualization, statistical analysis, machine learning, and data manipulation. The document also covers using R Studio as an IDE, reading and writing different data types, programming features like flow control and functions, and examples of correlation, regression, and plotting in R.
This document provides an introduction to R, including what R is, how it compares to other statistical software packages, its advantages and disadvantages, how to install R, and options for R editors and graphical user interfaces (GUIs). It discusses R as a language for statistical computing and graphics, compares it to packages like SAS, Stata, and SPSS in terms of cost, usage mode, and prevalence. It outlines some of R's advantages like being free and open-source software with an active user community contributing packages, and some disadvantages like the learning curve and lack of a standard GUI.
Why R? A Brief Introduction to the Open Source Statistics PlatformSyracuse University
This document discusses the statistical programming language R. It describes R as an open source platform for statistics, data management, and graphics. It notes that R comprises a core program plus thousands of add-in packages. It then compares R to other popular statistical software packages and notes that R is more popular and used by more analysts. Finally, it highlights some advantages of R, including its emphasis on reproducibility through coding data transformations.
The document discusses the evolution of computer programming languages from the earliest languages like Fortran and ALGOL in the 1950s to more modern languages like JavaScript, Python, and C#. It notes that in the evolution of computer languages, older species/languages do not necessarily die out, branches can converge, and mutations are not purely random. The document asks questions about the reader's favorite or current language and programming in general.
A description of how technology has changed the face of Biology, specially in the fields of genetics, proteomics, and evolution.
It includes a brief history, examples of usage, and a look into the future.
R originated in the 1970s at Bell Labs and has since evolved significantly. It is an open-source programming language used widely for statistical analysis and graphics. While powerful, R has some drawbacks like poor performance for large datasets and a steep learning curve. However, its key advantages including being free, having a large community of users, and extensive libraries have made it a popular tool, especially for academic research.
This document provides an overview of bioinformatics and related topics across 7 parts:
Part I introduces bioinformatics and its areas including genomics, proteomics, computational biology, and databases.
Part II discusses the history of bioinformatics from Darwin's theory of evolution to the human genome project.
Part III focuses on the human genome project, its goals of identifying genes and sequencing DNA, and its benefits like improved medicine.
Part IV explains how the internet plays an important role in bioinformatics for retrieving biological information and resources like databases, tools, and software.
Part V describes different types of biological databases including primary, secondary, and composite databases that combine different sources.
Part VI discusses knowledge discovery
Organic farming is a system of agriculture that uses natural and biodegradable inputs while avoiding synthetic fertilizers. The main principles of organic farming are health for the soil, plants, animals, humans and the planet; ecology in agriculture based on living ecological systems and cycles; and fairness and care for the common environment and life opportunities. Organic farming helps conserve the environment by using inputs that don't leave toxic residues, promoting biodiversity, and encouraging recycling of biodegradable materials.
This document discusses organic farming methods and their advantages over conventional farming. Organic farming prohibits synthetic pesticides, fertilizers, genetic engineering, sewage sludge, and food irradiation. It relies on crop diversity, pest control, livestock, and plant nutrition to farm sustainably. Organic farming can reduce production costs by 25% while eliminating chemicals and increasing yields within 5 years. It produces food free from harmful additives and may reduce health risks like heart disease and cancer. Organic farming also benefits the environment by building soil, reducing water pollution, decreasing energy use and greenhouse gases, and sequestering carbon in the soil.
The document provides an overview of key components of Linux including processor management using processes and scheduling, file management using a hierarchical directory structure and permissions, memory management using virtual memory and paging, device management through device drivers and identifiers, and the command line interface for navigating the file system and running commands. It describes processes like fork() and exec() for managing processes, and concepts like virtual addressing and page tables for memory management. Device management in Linux treats all devices as files that are accessed through device drivers defined by a major and minor number.
An Interactive Introduction To R (Programming Language For Statistics)Dataspora
This is an interactive introduction to R.
R is an open source language for statistical computing, data analysis, and graphical visualization.
While most commonly used within academia, in fields such as computational biology and applied statistics, it is gaining currency in industry as well – both Facebook and Google use R within their firms.
Extending and customizing ibm spss statistics with python, r, and .net (2)Armand Ruis
This document discusses extending and customizing IBM SPSS Statistics using R, Python, and .NET. It provides four examples of how programmability can be used: 1) Automating repetitive work by applying syntax files to multiple data files; 2) Integrating programs and scripting to modify output table formatting; 3) Adding a quantile regression procedure from the R programming language; and 4) Adding a TURF (Total Unduplicated Reach and Frequency) analysis procedure using Python. Programmability allows users to tap existing statistical modules in R and Python to extend IBM SPSS Statistics' capabilities.
The document discusses functional programming in R. It begins by explaining the differences between object-oriented/imperative and functional programming metaphysics. Functional programming treats things as fixed values undergoing processes over time rather than objects with state and behavior. The document then covers elements of functional programming like pure functions, recursion, and immutability. It explains how R is a strongly functional language and highlights features like vectorized functions and higher-order functions. It provides an example of a functional programming style bootstrap function to sample linear models.
This document provides an overview of the R programming language and environment. It discusses why R is useful, outlines its interface and workspace, describes how to access help and tutorials, install packages, and input/output data. The interactive nature of R is highlighted, where results from one function can be used as input for another.
following is work on Advance Python part 1 Functional Programming in Python
for code and more details plz do visit
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for more free study material and Projects follow on
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#python #datascience #programming #machinelearning #github #deeplearning #coding #developer #projects #work #developers #linkedin #google #amazonindia#IBM
This document discusses various techniques for integrating R with other programming languages and ecosystems. It begins by asking what to do after building a model in R, such as rewriting the code, integrating R with other languages, or implementing business logic directly in R. The document then covers options for integrating R at the command line, library level, and for calling R from other languages like C++. It also discusses using R for web applications via techniques like rApache, shiny, and openCPU. In the end it argues that R can be effectively used as part of an application infrastructure along with software engineering languages.
2013.11.14 Big Data Workshop Adam Ralph - 2nd set of slidesNUI Galway
Adam Ralph from the Irish Centre for High End Computing presented this Introduction to Basic R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
R is a programming language and free software environment for statistical analysis and graphics. It is widely used among statisticians and data scientists for developing statistical software and data analysis. Some key facts about R:
- It was created in the 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland.
- R can be used for statistical computing, machine learning, graphical display, and other tasks related to data analysis.
- It runs on Windows, Linux, and MacOS operating systems. Code written in R is cross-platform.
- R has a large collection of statistical and graphical techniques built-in, and users can extend its capabilities by downloading additional packages.
- Major
R is a programming language and software environment for statistical analysis and graphics. It was created by Ross Ihaka and Robert Gentleman in the early 1990s at the University of Auckland, New Zealand. Some key points:
- R can be used for statistical computing, machine learning, and data analysis. It is widely used among statisticians and data scientists.
- It runs on Windows, Mac OS, and Linux. The source code is published under the GNU GPL license.
- Popular companies like Facebook, Google, Microsoft, Uber and Airbnb use R for data analysis, machine learning, and statistical computing.
- R has a variety of data structures like vectors, matrices, arrays, lists
Active Reports is a .NET reporting tool used to develop reports in Windows and ASP.NET applications. It supports C# and VB.NET and can export to PDF, Excel, Word, RTF, HTML, text, XML, and TIFF formats. Reports are built using layout sections like report header, detail, and footer. Controls and properties are used to design the report. Data is connected using data sources. Scripting or coding can be added for additional functionality. Active Reports offers more dynamic scripting compared to Crystal Reports but has fewer financial and date functions and limitations like border height issues in details sections.
Introduction about programming language , types of programming language , execution process of programs , algorithms and flowcharts in programming . The slides describes the introductory information about basic structure of programming language focusing on Python.
R is a programming language and software environment for statistical analysis, graphics, and statistical computing. It provides functions for data manipulation, calculation, and graphical display. Some key features of R include its programming language, effective data handling and storage, object-oriented and functional programming capabilities, and suite of operators for calculations on vectors, matrices, and arrays. R also provides a large collection of statistical tools for data analysis and graphical functions for data display. It is freely available under GNU license and runs on Linux, Windows, and Mac operating systems.
This document summarizes Language Integrated Query (LINQ) and its capabilities in Microsoft technologies. It discusses how LINQ brings query capabilities to programming languages like VB and C#, and what data sources LINQ can query, including SQL, XML, objects, and entities. It also provides an overview of LINQ's implementation in .NET Framework 3.5 and future versions, allowing querying of data from SQL Server, Oracle, and other sources.
R Shiny is a web application framework for R that allows users to create interactive web apps. Shiny apps have two components: a user interface script that controls layout and appearance, and a server script that contains the code needed to build the app. No knowledge of HTML, JavaScript or CSS is required to use Shiny, though knowing some of these languages can make apps more interesting. Getting started requires installing the Shiny package and creating two R scripts, ui.R and server.R, that define the user interface and server logic. Widgets like sliders and text inputs can be added to collect user input and interact with analyses. Shiny makes it easy to share live and explorable analyses by integrating R with web pages
The document discusses programming design and paradigms in C++. It covers the problem solving process which includes analyzing the problem, designing an algorithm, writing code, compiling and linking, and executing the program. It also discusses programming paradigms like structured, imperative, object-oriented, and functional programming. Specifically, it contrasts structured programming which uses functions and global data, with object-oriented programming which defines classes with data and methods and allows for data abstraction, encapsulation, polymorphism, and inheritance. Classes contain objects which package data and methods together.
The document discusses programming design and paradigms in C++. It describes the problem solving process as having three steps: 1) analyze the problem, 2) design an algorithm to solve it, and 3) implement the algorithm in code. It then covers programming paradigms like structured, imperative, object-oriented, and functional. Object-oriented programming uses classes that define data and methods, with objects as instances of classes that encapsulate data and functions. Structured programming also uses modules and functions but not classes and objects.
The document discusses the syllabus and objectives for the subject CS6660 - Compiler Design. It outlines 5 units that will be covered: introduction to compilers, lexical analysis, syntax analysis, syntax directed translation and runtime environment, and code optimization and code generation. Each unit defines the concepts and techniques that will be learned. The objectives are for students to understand compiler design principles, parsing techniques, different translation levels, and how to optimize code generation.
Urbanistic Equalization and Compensatory Mechanism - From Legislation to GISRui Menezes
This document describes the development of a GIS tool to automate urban planning equalization and compensation calculations that were previously done manually according to Portuguese legislation. The tool was developed by: 1) Assessing the existing manual process, 2) Designing a GIS data model, 3) Developing Python scripts to perform the necessary calculations and produce outputs, and 4) Testing the tool and making corrections. The final tool streamlines the calculation process, facilitates the work of technicians, and provides integrated spatial and non-spatial outputs, including a final report. The tool accomplishes the goals of translating the legal framework into an automated GIS workflow.
SPSS Statistics 17 completes the core programmability building blocks begun in SPSS 14. This presentation reviews the benefits and technology of programmability and shows four examples.
An introduction to PLC languages - Instruction Language (IL) , Functional Block Diagram (FBD) , Ladder Logic Diagram (LD) and Sequential Function Chart (SFC).
(Download and open with Adobe Reader to see animations)
Calling c functions from r programming unit 5Ashwini Mathur
This document discusses calling C functions from R programming language. It notes that R is slow for iterative algorithms and large datasets, so calling high-performance C functions improves R's performance. It describes three functions - .C, .Call, and .External - that provide the link between C and R. It also outlines the steps to create, compile, and dynamically load a C function for use in R: writing the C function with a void return type and no return values other than arguments, compiling the C code into a shared object file using R CMD SHLIB, and loading the shared object file into an active R session using dyn.load() to make the C functions available to call from R.
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Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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See My Other Reviews Article:
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(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
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UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
6. R programming
language:
conceptual
overview
Maxim Litvak
Introduction
Statistical
computing
Functional
programming
Dynamic
OOP
Statistical
computing -
revision I
Statistical
computing -
revision II
Statistical
computing -
revision III
Statistical
computing -
revision IV
Appendix
Functional - Basics I
ˆ Functional programming (FP) is a paradigm that
prescribes to break down the task into evaluation of
(mathematical) functions
ˆ FP is not about organizing code in subroutines (also
called functions but in dierent sense)! (this is
called procedural programming)
ˆ It's about organizing the whole programm as
function
12. R programming
language:
conceptual
overview
Maxim Litvak
Introduction
Statistical
computing
Functional
programming
Dynamic
OOP
Statistical
computing -
revision I
Statistical
computing -
revision II
Statistical
computing -
revision III
Statistical
computing -
revision IV
Appendix
Function - Referential
transparency I
ˆ Referential transparency - if an expression can be
replaced with its value without changing the
behaviour of the program (side eect)
ˆ In R it's up to the developer, she/he should be
however conscious if their code produce side eects
ˆ Assume function g returns 0 and function f returns
the only argument (f - function(x) x). Is there a
dierence between
ˆ f(0)
ˆ f(g())
26. R programming
language:
conceptual
overview
Maxim Litvak
Introduction
Statistical
computing
Functional
programming
Dynamic
OOP
Statistical
computing -
revision I
Statistical
computing -
revision II
Statistical
computing -
revision III
Statistical
computing -
revision IV
Appendix
Statistical computing -
revision
ˆ Example: given X (e.g. norm) distribution
ˆ pX is its probability function
ˆ dX is its density function
ˆ qX is its quantile function
ˆ How to abstract X?
ˆ Construct a function that takes name of the
distribution with 2 parameters as an argument (e.g.
norm, unif) and returns its quantile function
parametrized with [0;1] (hint: use eval)