This hands-on R course will guide users through a variety of programming functions in the open-source statistical software program, R. Topics covered include indexing, loops, conditional branching, S3 classes, and debugging. Full workshop materials available from http://projects.iq.harvard.edu/rtc/r-prog
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
A relatively short Introduction to R as presented at the Belgian Software Craftmanship meetup group.
The goal of this presentation is to give you an introduction to:
• The style of the language
• It's ecosystem
• How common things like data manipulation and visualization work
• How to use it for machine learning
• Webdevelopment and report generation in R
• Integrating R in your system
License:
Introduction To R by Samuel Bosch
To the extent possible under law, the person who associated CC0 with Introduction To R has waived all copyright and related or neighboring rights
to Introduction To R.
http://creativecommons.org/publicdomain/zero/1.0/
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
This hands-on R course will guide users through a variety of programming functions in the open-source statistical software program, R. Topics covered include indexing, loops, conditional branching, S3 classes, and debugging. Full workshop materials available from http://projects.iq.harvard.edu/rtc/r-prog
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
A relatively short Introduction to R as presented at the Belgian Software Craftmanship meetup group.
The goal of this presentation is to give you an introduction to:
• The style of the language
• It's ecosystem
• How common things like data manipulation and visualization work
• How to use it for machine learning
• Webdevelopment and report generation in R
• Integrating R in your system
License:
Introduction To R by Samuel Bosch
To the extent possible under law, the person who associated CC0 with Introduction To R has waived all copyright and related or neighboring rights
to Introduction To R.
http://creativecommons.org/publicdomain/zero/1.0/
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
The goal of this workshop is to introduce fundamental capabilities of R as a tool for performing data analysis. Here, we learn about the most comprehensive statistical analysis language R, to get a basic idea how to analyze real-word data, extract patterns from data and find causality.
Vibrant Technologies is headquarted in Mumbai,India.We are the best r programming training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best r programming classes in Mumbai according to our students and corporates
Slides for a Machine Learning Course in R,
includes an introduction to R and several ML methods for classification, regression, clustering and dimensionality reduction.
Is it easier to add functional programming features to a query language, or to add query capabilities to a functional language? In Morel, we have done the latter.
Functional and query languages have much in common, and yet much to learn from each other. Functional languages have a rich type system that includes polymorphism and functions-as-values and Turing-complete expressiveness; query languages have optimization techniques that can make programs several orders of magnitude faster, and runtimes that can use thousands of nodes to execute queries over terabytes of data.
Morel is an implementation of Standard ML on the JVM, with language extensions to allow relational expressions. Its compiler can translate programs to relational algebra and, via Apache Calcite’s query optimizer, run those programs on relational backends.
In this talk, we describe the principles that drove Morel’s design, the problems that we had to solve in order to implement a hybrid functional/relational language, and how Morel can be applied to implement data-intensive systems.
(A talk given by Julian Hyde at Strange Loop 2021, St. Louis, MO, on October 1st, 2021.)
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
The goal of this workshop is to introduce fundamental capabilities of R as a tool for performing data analysis. Here, we learn about the most comprehensive statistical analysis language R, to get a basic idea how to analyze real-word data, extract patterns from data and find causality.
Vibrant Technologies is headquarted in Mumbai,India.We are the best r programming training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best r programming classes in Mumbai according to our students and corporates
Slides for a Machine Learning Course in R,
includes an introduction to R and several ML methods for classification, regression, clustering and dimensionality reduction.
Is it easier to add functional programming features to a query language, or to add query capabilities to a functional language? In Morel, we have done the latter.
Functional and query languages have much in common, and yet much to learn from each other. Functional languages have a rich type system that includes polymorphism and functions-as-values and Turing-complete expressiveness; query languages have optimization techniques that can make programs several orders of magnitude faster, and runtimes that can use thousands of nodes to execute queries over terabytes of data.
Morel is an implementation of Standard ML on the JVM, with language extensions to allow relational expressions. Its compiler can translate programs to relational algebra and, via Apache Calcite’s query optimizer, run those programs on relational backends.
In this talk, we describe the principles that drove Morel’s design, the problems that we had to solve in order to implement a hybrid functional/relational language, and how Morel can be applied to implement data-intensive systems.
(A talk given by Julian Hyde at Strange Loop 2021, St. Louis, MO, on October 1st, 2021.)
Introduction to the R Statistical Computing Environmentizahn
Get an introduction to R, the open-source system for statistical computation and graphics. With hands-on exercises, learn how to import and manage datasets, create R objects, and conduct basic statistical analyses. Full workshop materials can be downloaded from http://projects.iq.harvard.edu/rtc/event/introduction-r
(Presented by David Smith at useR!2016, June 2016. Recording: https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/R-at-Microsoft )
Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use the R language and R packages to analyze data in their data centers and in cloud environments.
In this talk I will give an overview (and a demo or two) of how R has been integrated into various Microsoft products. Microsoft data scientists are also big users of R, and I'll describe a couple of examples of R being used to analyze operational data at Microsoft. I'll also share some of my experiences in working with open source projects at Microsoft, and my thoughts on how Microsoft works with open source communities including the R Project.
Ejercicios de estilo en la programaciónSoftware Guru
El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
En esta plática realizaremos el mismo ejercicio con un programa de software. Abarcaremos distintos estilos y paradigmas: programación monolítica, orientada a objetos, relacional, orientada a aspectos, monadas, map-reduce, y muchos otros, a través de los cuales podremos apreciar la riqueza del pensamiento humano aplicado a la computación.
Esto va mucho más allá de un ejercicio académico; el diseño de sistemas de gran escala se alimenta de esta variedad de estilos. También platicaremos sobre los peligros de quedar atrapado bajo un conjunto reducido de estilos a lo largo de tu carrera, y la necesidad de verdaderamente entender distintos estilos al diseñar arquitecturas de sistemas de software.
Semblanza del conferencista:
Crista Lopez es profesora en la Facultad de Ciencias Computacionales de la Universidad de California en Irvine. Su investigación se enfoca en prácticas de ingeniería de software para sistemas de gran escala. Previamente, fue miembro fundador del equipo en Xerox PARC creador del paradigma de programación orientado a aspectos (AOP). Crista es una de las desarrolladoras principales de OpenSimulator, una plataforma open source para crear mundos virtuales 3D. También es fundadora de Encitra, empresa especializada en la utilización de la realidad virtual para proyectos de desarrollo urbano sustentable. @cristalopes
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
A high level introduction to R statistical programming language that was presented at the Chicago Data Visualization Group's Graphing in R and ggplot2 workshop on October 8, 2012.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
1. Introduction to R
Basic Teaching module
EMBL International PhD Program
13-10-2010
Sander Timmer & Myrto Kostadima
2. Overview
What is R
Quick overview datatypes, input/output and
plots
Some biological examples
I’m not a particular good teacher, so please
ask when you’re lost!
3. What is this R thing?
R is a powerful, general purpose language
and software environment for statistical
computing and graphics
Runs on Linux, OS X and for the unlucky few
also on Windows
R is open source and free!
6. Vectors
Many ways of generating a vector with a range of numbers:
x <- 1:10
assign(“x”, 1:10)
x <- c(1,2,3,4,5,6,7,8,9,10)
x <- seq(1,10, by=1)
x <- seq(length = 10, from=1,by=1)
x
[1] 1 2 3 4 5 6 7 8 9 10
7. Vectors
Common way to store multiple values
x <- c(1,2,4,5,10,12,15)
length(x)
mean(x)
summary(x)
9. Matrices
Common form of storing 2 dimensional data
Think about having an Excel sheet
m = matrix(1:10,2,5)
[,1] [,2] [,3] [,4] [,5]
[1,] 1 3 5 7 9
[2,] 2 4 6 8 10
summary(m)
10. Factors
Factors are vectors with a discrete number of
levels:
x <- factor(c(“Cancer”, “Cancer”, “Normal”,
“Normal”))
levels(x)
[1] “Cancer” “Normal”
table(x)
Cancer Normal
2 2
11. Lists
A list can contain “anything”
Useful for storing several vectors
list(gene=”gene 1”, expression=c(5,2,3))
$gene
[1] “gene 1”
$expression
[1] 5, 2, 4
12. If-else statements
Essential for any programming language
if state then do x else do y
if(p < 0.01){
print(“Significant gene”)
}else{
print(“Insignificant gene”)
}
13. Repetition
You want to apply 1 function to every
element of a list
for(element in list){ ....do something.... }
For loops are easy though tend to be slow
Apply is the fast way of getting things done
in R:
apply(List,1,mean)
14. Data input
R has countless ways of importing data:
CSV
Excel
Flat text file
15. Data input
Most simple, the CSV file:
read.csv(“mydata.csv”,
row.names=T,col.names=T)
Load a tab separated file
read.table(“mytable.txt”, sep=”t”)
Load Rdata file
load(“mydata.Rdata”)
16. Data input
Also for more specific data sources:
Excel
Database connections
Mysql -> Ensembl e.g.
Affy
Affymetrix chips data
HapMap
.........
17. Data output
Most simple, the CSV file:
write.csv(x, file=”myx.csv”)
Save Rdata file:
save(x, file=”myx.Rdata”)
Save whole R session:
save(file=”mysession.Rdata”)
18. Graphics
Quick way to study your data is plotting it
The function “plot” in R can plot almost
anything out of the box (even if this doesn’t
make sense!)
22. Basic graphics
With R you can plot almost any object
Multidimensional variables like matrixes
can be plotted with matplot()
Other often used plot functions are:
boxplot(), hist(), levelplot(), heatmap()
26. Before the example
Help page for functions in R can be called:
?plot, ?hist, ?vector
Examples for most functions can be runned:
example(plot)
Text search for functions can be done by
performing:
??plot
27. Example
Some example Affymetrix dataset to play
with
Checking distribution of data
Plotting data
Clustering data
Correlate data
30. Summary
Checking what we got
summary(dil)
mva.pairs(dil)
Or:
boxplot(log(dil.ex))
Or:
hist(dil.ex, xlim=c(0,500), breaks=1000)
31. We need to normalise
first
For almost all experiments you have to apply
some sort of normalisation
dil.norm = maffy.normalize(dil,
subset=1:nrow(dil))
colnames(dil.norm) = colnames(dil)
mva.pairs(dil.norm)
32. Most equal samples
Applying euclidian distance to detect most
equal samples
dil.norm.dist = dist(t(dil.norm))
dil.norm.dist.hc = hclust(dil.norm.dist)
plot(dil.norm.dist.hc)
Do the same for the non normalised dataset
33. Checking expression
Heatmap representation of expression levels
for different probes
heatmap(dil.ex.norm[1:50,])
You could apply a T-test for example to rank
to only plot the most significant probes
34. Checking expression
Heatmap representation of expression levels
for different probes
heatmap(dil.ex.norm[1:50,])
You could apply a T-test for example to rank
to only plot the most significant probes
35. Checking expression
You could apply a T-test for example to rank
to only plot the most significant probes
library(genefilter)
f = factor(c(1,1,2,2))
dil.exp.norm.t = rowttests(dil.exp.norm, fac=f)
heatmap(dil.exp.norm[order(dil.exp.norm.t
$dm)[1:10],])
36. Want to know more?
Using R will benefit all PhD’s in this room
Learning by doing
Loads of basic examples at:
http://addictedtor.free.fr/graphiques/
http://www.mayin.org/ajayshah/KB/R/
index.html
http://www.r-project.org/