CEWIT Hand-on workshop.
Link to materials - https://languagevariationsuite.wordpress.com/2020/01/31/faculty-accelerator-crash-course-rmarkdown-with-r-introduction/amp/
The R language is a project designed to create a free, open source language which can be used as a replacement for the S-PLUS language, originally developed as the S language at AT&T Bell Labs, and currently marketed by Insightful Corporation of Seattle, Washington. R is an open source implementation of S, and differs from S-plus largely in its command-line only format.
Topics Covered:
1.Introduction to R
2.Installing R
3.Why Learn R
4.The R Console
5.Basic Arithmetic and Objects
6.Program Example
7.Programming with Big Data in R
8.Big Data Strategies in R
9.Applications of R Programming
10.Companies Using R
11.What R is not so good at
12.Conclusion
CEWIT Hand-on workshop.
Link to materials - https://languagevariationsuite.wordpress.com/2020/01/31/faculty-accelerator-crash-course-rmarkdown-with-r-introduction/amp/
The R language is a project designed to create a free, open source language which can be used as a replacement for the S-PLUS language, originally developed as the S language at AT&T Bell Labs, and currently marketed by Insightful Corporation of Seattle, Washington. R is an open source implementation of S, and differs from S-plus largely in its command-line only format.
Topics Covered:
1.Introduction to R
2.Installing R
3.Why Learn R
4.The R Console
5.Basic Arithmetic and Objects
6.Program Example
7.Programming with Big Data in R
8.Big Data Strategies in R
9.Applications of R Programming
10.Companies Using R
11.What R is not so good at
12.Conclusion
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
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
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.
A presentation on the history, design, and use of R. The talk will focus on companies that use and support R, use cases, where it is going, competitors, advantages and disadvantages, and resources to learn more about R. Speaker Bio
Joseph Kambourakis has been the Lead Data Science Instructor at EMC for over two years. He has taught in eight countries and been interviewed by Japanese and Saudi Arabian media about his expertise in Data Science. He holds a Bachelors in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA from Bentley University with a concentration in Business Analytics.
Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time
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
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)
stackconf 2020 | Let’s Debug Django like pro by Sayantika BanikNETWAYS
Writing clean code, with good logic is important.
But how about debugging the same like a pro and build pretty graphs?
“Debug like a pro just like you code”
Print statements though easy and powerful don’t help in understanding the behavioral aspects. In order to perform functionalities like visualizing the error/warning rates, we need an advanced debugging tool.
Through my talk, I aim to introduce debugging libraries like “logger”. Logger can be incorporated with Django with a couple of lines of code, which not only helps us understand the errors, but also the possible areas of improvement.
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
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
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.
A presentation on the history, design, and use of R. The talk will focus on companies that use and support R, use cases, where it is going, competitors, advantages and disadvantages, and resources to learn more about R. Speaker Bio
Joseph Kambourakis has been the Lead Data Science Instructor at EMC for over two years. He has taught in eight countries and been interviewed by Japanese and Saudi Arabian media about his expertise in Data Science. He holds a Bachelors in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA from Bentley University with a concentration in Business Analytics.
Very brief introduction to R software that I have presented at UNISZA. No R codes and No Statistical Contents. Basically for those who just heard about R software for the first time
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
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)
stackconf 2020 | Let’s Debug Django like pro by Sayantika BanikNETWAYS
Writing clean code, with good logic is important.
But how about debugging the same like a pro and build pretty graphs?
“Debug like a pro just like you code”
Print statements though easy and powerful don’t help in understanding the behavioral aspects. In order to perform functionalities like visualizing the error/warning rates, we need an advanced debugging tool.
Through my talk, I aim to introduce debugging libraries like “logger”. Logger can be incorporated with Django with a couple of lines of code, which not only helps us understand the errors, but also the possible areas of improvement.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
Presentation slides from WordCamp Toronto 2014 talk.
This talk is about creating a multilingual WordPress site using WordPress multisite. The talk will cover: the basics of setting up multisite, some plugins to make it easier to create a multilingual site, pros & cons of using multisite for multilingual sites, and some tips and tricks to help with your sites.
A comprehensive introduction to handling date and time data in R. Get an introduction to date and time manipulation in R. Learn to create, transform, extract and operate on date/time objects.
Learn the grammar of data manipulation using dplyr. You will work through a case study to explore the dplyr verbs such as filter, select, mutate, arrange, summarize, group_by etc.
Learn to write readable code with pipes using the magrittr package. You will learn about the forward operator (%>%), exposition operator (%$%) and the assignment operator (%<>%).
tibbles are an alternative for dataframes. You will learn how tibbles are different from dataframes, why you should use them, how to create and modify them.
Learn how to install & update R packages from CRAN, GitHub, Bioconductor etc. You wlll also learn to install specific versions of a package from CRAN or GitHub.
A brief introduction to the R ecosystem for absolute beginners. You will learn about the history and capabilities of R as a modern language for data science.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
In this tutorial, we learn to create dynamic documents using R Markdown. It enables us to create beautiful reports and presentations that are fully reproducible.
In this tutorial, we learn to create univariate bar plots using the Graphics package in R. We also learn to modify graphical parameters associated with the bar plot.
In this tutorial, we explore the most basic data structure in R, the vector. We cover everything from creating vectors to subsetting them in different ways.
In this tutorial, we learn to create variables in R. Followed by that, we explore the different data types including numeric, integer, character, logical and date/time.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
5. r-squared
Slide 5
What is R?
www.r-squared.in/rprogramming
● R is a free open source programming language available on every major platform.
● It is a dynamically typed interpreted language.
● R is very popular in the fields of statistics, machine learning, visualization and data
manipulation.
● It is supported by a package system, CRAN, where you can find packages related to
wide range of fields.
● Awesome support community.
6. r-squared
Slide 6
Install R: Download
www.r-squared.in/rprogramming
Visit the homepage of the R project and click on download R.
7. r-squared
Slide 7
Install R: Select Mirror
www.r-squared.in/rprogramming
Select the appropriate mirror based on your geographical location.
8. r-squared
Slide 8
Install R: Download Installer
www.r-squared.in/rprogramming
Download the installer based on your operating system.
11. r-squared
Slide 11
Install R: Installation Language
www.r-squared.in/rprogramming
Double click on the installer to select the installation language.
12. r-squared
Slide 12
Install R: Setup Wizard
www.r-squared.in/rprogramming
Click Next on the Setup Wizard to begin the installation
14. r-squared
Slide 14
Install R: Installation Folder
www.r-squared.in/rprogramming
● Select the folder where R should be installed.
● C:/Program Files is the default location.
26. r-squared
Slide 26
Connect With Us
www.r-squared.in/rprogramming
Visit r-squared for tutorials
on:
● R Programming
● Business Analytics
● Data Visualization
● Web Applications
● Package Development
● Git & GitHub