The Java EE 7 Tutorial
1.7 Java EE 7 APIs
Figure 1-6 shows the relationships among the Java EE containers.
Figure 1-7 shows the availability of the Java EE 7 APIs in the web container.
Figure 1-7 Java EE APIs in the Web Container
Description of "Figure 1-7 Java EE APIs in the Web Container"
Figure 1-8 shows the availability of the Java EE 7 APIs in the EJB container.
Figure 1-8 Java EE APIs in the EJB Container
Description of "Figure 1-8 Java EE APIs in the EJB Container"
Figure 1-9 shows the availability of the Java EE 7 APIs in the application client container.
Figure 1-9 Java EE APIs in the Application Client Container
Description of "Figure 1-9 Java EE APIs in the Application Client Container"
The following sections give a brief summary of the technologies required by the Java EE platform and the APIs used in Java EE applications.
1.7.1 Enterprise JavaBeans Technology
An Enterprise JavaBeans (EJB) component, or enterprise bean, is a body of code that has fields and methods to implement modules of business logic. You can think of an enterprise bean as a building block that can be used alone or with other enterprise beans to execute business logic on the Java EE server.
Enterprise beans are either session beans or message-driven beans.
-
A session bean represents a transient conversation with a client. When the client finishes executing, the session bean and its data are gone.
-
A message-driven bean combines features of a session bean and a message listener, allowing a business component to receive messages asynchronously. Commonly, these are Java Message Service (JMS) messages.
In the Java EE 7 platform, new enterprise bean features include the following:
-
Asynchronous local session beans in EJB Lite
-
Nonpersistent timers in EJB Lite
The Java EE 7 platform requires Enterprise JavaBeans 3.2 and Interceptors 1.2. The Interceptors specification is part of the EJB specification.
1.7.2 Java Servlet Technology
Java Servlet technology lets you define HTTP-specific servlet classes. A servlet class extends the capabilities of servers that host applications accessed by way of a request-response programming model. Although servlets can respond to any type of request, they are commonly used to extend the applications hosted by web servers.
In the Java EE 7 platform, new Java Servlet technology features include the following:
-
Nonblocking I/O
-
HTTP protocol upgrade
The Java EE 7 platform requires Servlet 3.1.
1.7.3 JavaServer Faces Technology
JavaServer Faces technology is a user interface framework for building web applications. The main components of JavaServer Faces technology are as follows:
-
A GUI component framework.
-
A flexible model for rendering components in different kinds of HTML or different markup languages and technologies. A
Renderer
object generates the markup to render the component and converts the data stored in a model object to types that can be represented in a view. -
A standard
RenderKit
for generating HTML 4.01 markup.
The following features support the GUI components:
-
Input validation
-
Event handling
-
Data conversion between model objects and components
-
Managed model object creation
-
Page navigation configuration
-
Expression Language (EL)
All this functionality is available using standard Java APIs and XML-based configuration files.
In the Java EE 7 platform, new features of JavaServer Faces technology include the following:
-
HTML5-friendly markup
-
Faces Flows
-
Resource library contracts
The Java EE 7 platform requires JavaServer Faces 2.2 and Expression Language 3.0.
1.7.4 JavaServer Pages Technology
JavaServer Pages (JSP) technology lets you put snippets of servlet code directly into a text-based document. A JSP page is a text-based document that contains two types of text:
-
Static data, which can be expressed in any text-based format, such as HTML or XML
-
JSP elements, which determine how the page constructs dynamic content
For information about JSP technology, see the The Java EE 5 Tutorial at http://docs.oracle.com/javaee/5/tutorial/doc/
.
The Java EE 7 platform requires JavaServer Pages 2.3 for compatibility with earlier releases but recommends the use of Facelets as the display technology in new applications.
1.7.5 JavaServer Pages Standard Tag Library
The JavaServer Pages Standard Tag Library (JSTL) encapsulates core functionality common to many JSP applications. Instead of mixing tags from numerous vendors in your JSP applications, you use a single, standard set of tags. This standardization allows you to deploy your applications on any JSP container that supports JSTL and makes it more likely that the implementation of the tags is optimized.
JSTL has iterator and conditional tags for handling flow control, tags for manipulating XML documents, internationalization tags, tags for accessing databases using SQL, and tags for commonly used functions.
The Java EE 7 platform requires JSTL 1.2.
1.7.6 Java Persistence API
The Java Persistence API (JPA) is a Java standards–based solution for persistence. Persistence uses an object/relational mapping approach to bridge the gap between an object-oriented model and a relational database. The Java Persistence API can also be used in Java SE applications outside of the Java EE environment. Java Persistence consists of the following areas:
-
The Java Persistence API
-
The query language
-
Object/relational mapping metadata
The Java EE 7 platform requires Java Persistence API 2.1.
1.7.7 Java Transaction API
The Java Transaction API (JTA) provides a standard interface for demarcating transactions. The Java EE architecture provides a default auto commit to handle transaction commits and rollbacks. An auto commit means that any other applications that are viewing data will see the updated data after each database read or write operation. However, if your application performs two separate database access operations that depend on each other, you will want to use the JTA API to demarcate where the entire transaction, including both operations, begins, rolls back, and commits.
The Java EE 7 platform requires Java Transaction API 1.2.
1.7.8 Java API for RESTful Web Services
The Java API for RESTful Web Services (JAX-RS) defines APIs for the development of web services built according to the Representational State Transfer (REST) architectural style. A JAX-RS application is a web application that consists of classes packaged as a servlet in a WAR file along with required libraries.
The Java EE 7 platform requires JAX-RS 2.0.
1.7.9 Managed Beans
Managed Beans, lightweight container-managed objects (POJOs) with minimal requirements, support a small set of basic services, such as resource injection, lifecycle callbacks, and interceptors. Managed Beans represent a generalization of the managed beans specified by JavaServer Faces technology and can be used anywhere in a Java EE application, not just in web modules.
The Managed Beans specification is part of the Java EE 7 platform specification (JSR 342). The Java EE 7 platform requires Managed Beans 1.0.
1.7.10 Contexts and Dependency Injection for Java EE
Contexts and Dependency Injection for Java EE (CDI) defines a set of contextual services, provided by Java EE containers, that make it easy for developers to use enterprise beans along with JavaServer Faces technology in web applications. Designed for use with stateful objects, CDI also has many broader uses, allowing developers a great deal of flexibility to integrate different kinds of components in a loosely coupled but typesafe way.
The Java EE 7 platform requires CDI 1.1.
1.7.11 Dependency Injection for Java
Dependency Injection for Java defines a standard set of annotations (and one interface) for use on injectable classes.
In the Java EE platform, CDI provides support for Dependency Injection. Specifically, you can use injection points only in a CDI-enabled application.
The Java EE 7 platform requires Dependency Injection for Java 1.0.
1.7.12 Bean Validation
The Bean Validation specification defines a metadata model and API for validating data in JavaBeans components. Instead of distributing validation of data over several layers, such as the browser and the server side, you can define the validation constraints in one place and share them across the different layers.
The Java EE 7 platform requires Bean Validation 1.1.
1.7.13 Java Message Service API
The Java Message Service (JMS) API is a messaging standard that allows Java EE application components to create, send, receive, and read messages. It enables distributed communication that is loosely coupled, reliable, and asynchronous.
In the platform, new features of JMS include the following.
-
A new, simplified API offers a simpler alternative to the previous API. This API includes a
JMSContext
object that combines the functions of aConnection
and aSession
. -
All objects with a
close
method implement thejava.lang.Autocloseable
interface so that they can be used in a Java SE 7try
-with-resources statement.
The Java EE 7 platform requires JMS 2.0.
1.7.14 Java EE Connector Architecture
The Java EE Connector Architecture is used by tools vendors and system integrators to create resource adapters that support access to enterprise information systems that can be plugged in to any Java EE product. A resource adapter is a software component that allows Java EE application components to access and interact with the underlying resource manager of the EIS. Because a resource adapter is specific to its resource manager, a different resource adapter typically exists for each type of database or enterprise information system.
The Java EE Connector Architecture also provides a performance-oriented, secure, scalable, and message-based transactional integration of Java EE platform–based web services with existing EISs that can be either synchronous or asynchronous. Existing applications and EISs integrated through the Java EE Connector Architecture into the Java EE platform can be exposed as XML-based web services by using JAX-WS and Java EE component models. Thus JAX-WS and the Java EE Connector Architecture are complementary technologies for enterprise application integration (EAI) and end-to-end business integration.
The Java EE 7 platform requires Java EE Connector Architecture 1.7.
1.7.15 JavaMail API
Java EE applications use the JavaMail API to send email notifications. The JavaMail API has two parts:
-
An application-level interface used by the application components to send mail
-
A service provider interface
The Java EE platform includes the JavaMail API with a service provider that allows application components to send Internet mail.
The Java EE 7 platform requires JavaMail 1.5.
1.7.16 Java Authorization Contract for Containers
The Java Authorization Contract for Containers (JACC) specification defines a contract between a Java EE application server and an authorization policy provider. All Java EE containers support this contract.
The JACC specification defines java.security.Permission
classes that satisfy the Java EE authorization model. The specification defines the binding of container-access decisions to operations on instances of these permission classes. It defines the semantics of policy providers that use the new permission classes to address the authorization requirements of the Java EE platform, including the definition and use of roles.
The Java EE 7 platform requires JACC 1.5.
1.7.17 Java Authentication Service Provider Interface for Containers
The Java Authentication Service Provider Interface for Containers (JASPIC) specification defines a service provider interface (SPI) by which authentication providers that implement message authentication mechanisms may be integrated in client or server message-processing containers or runtimes. Authentication providers integrated through this interface operate on network messages provided to them by their calling containers. The authentication providers transform outgoing messages so that the source of each message can be authenticated by the receiving container, and the recipient of the message can be authenticated by the message sender. Authentication providers authenticate each incoming message and return to their calling containers the identity established as a result of the message authentication.
The Java EE 7 platform requires JASPIC 1.1.
1.7.18 Java API for WebSocket
WebSocket is an application protocol that provides full-duplex communications between two peers over TCP. The Java API for WebSocket enables Java EE applications to create endpoints using annotations that specify the configuration parameters of the endpoint and designate its lifecycle callback methods.
The WebSocket API is new to the Java EE 7 platform. The Java EE 7 platform requires Java API for WebSocket 1.0.
1.7.19 Java API for JSON Processing
JSON is a text-based data exchange format derived from JavaScript that is used in web services and other connected applications. The Java API for JSON Processing (JSON-P) enables Java EE applications to parse, transform, and query JSON data using the object model or the streaming model.
JSON-P is new to the Java EE 7 platform. The Java EE 7 platform requires JSON-P 1.0.
1.7.20 Concurrency Utilities for Java EE
Concurrency Utilities for Java EE is a standard API for providing asynchronous capabilities to Java EE application components through the following types of objects: managed executor service, managed scheduled executor service, managed thread factory, and context service.
Concurrency Utilities for Java EE is new to the Java EE 7 platform. The Java EE 7 platform requires Concurrency Utilities for Java EE 1.0.
1.7.21 Batch Applications for the Java Platform
Batch jobs are tasks that can be executed without user interaction. The Batch Applications for the Java Platform specification is a batch framework that provides support for creating and running batch jobs in Java applications. The batch framework consists of a batch runtime, a job specification language based on XML, a Java API to interact with the batch runtime, and a Java API to implement batch artifacts.
Batch Applications for the Java Platform is new to the Java EE 7 platform. The Java EE 7 platform requires Batch Applications for the Java Platform 1.0.


SmartThings By Samsung
Smart Things App allows the user to connect his phone to all the Samsung products like refrigerators,
frames, washing machines, TV, watches, sensors, among others.
The advantage of this application is that is not neccesary to have a Samsung device to have them and
its
free.
This app allows devices like TVs, Air conditioner, Washer/Dryer, Robot Vacuum, Refrigerattor,
WiFi Hub, Motion sensors, cameras, doorbell, light bulb, lock, outlet, swit/dimmer and thermostat.
SmartThings conects all the Samsung devices to intereact with eachother, for example you can watch
TV from your Family Hub Refrigerator.
HOME APP by Apple
This is a free app designed by Apple that allows the user to connect this mobile to different home
devices and personalize them. The application is only available for apple devices, which could
represent a problem for someone without an apple devices. It only requires WiFi.
It allows the user to connect to devices of differents brands like Philips, D-Link, Eve, Samsung,
Vizio, Sony, LG and Nanoleaf. The user can personalize by room, hour, actions, ubication, and
others.
The devices allowed are cameras, lights, spotlights, plugs, sensors, fans, airconditioners, TV,
thermometer, looks, house alarms and switches.


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Status
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departure Time:
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Status:
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模态框（Modal）标题


Gonville and Caius College
Cambridge, UK
In 1995, John Simpson & Partners won a limited competition for Gonville and Caius College, University of Cambridge. The College required new spaces to accommodate the rapidly growing number of fellows and students, and had recently purchased new neighbouring buildings which released space in the centre to create new function rooms, student and staff accommodation and new service spaces. The project had two main centrepieces, the new Lord Colyton Hall, and the Fellows' Dining Room. The project incorporated new kitchen and service facilities and a new building was inserted in the kitchen court with a dramatically top-lit servery to serve the main College Hall. The scheme was completed in 1998 and received the Royal Institution of Chartered Surveyors Award in 1999. Project opened by The Prince of Wales in March 1998.
Gonville and Caius College
Cambridge, UK
The design for the Lord Colyton Hall was inspired by an original scheme for the College carried out by Sir John Soane in 1793. Through careful archaeological and historical research, John Simpson was able to trace the original design and create a new interpretation of it, adapting the original concept to suit its new requirements.
Gonville and Caius College
Cambridge, UK
The work at Gonville and Caius College in the heart of medieval Cambridge involved ten buildings ranging in age from 1430 to 1860. The work revolved around the careful reconfiguration of the spaces within the buildings to ensure their historic integrity remained intact while bringing the buildings up-to-date as regards health and safety legislation, fire regulations, and accessibility for the disabled. In addition to this, it was necessary to introduce up-to-date services and technology in order for the buildings to serve the University in a manner that befits the twenty first century. The scheme involved a new building within the old kitchen court. The project involved the major College public spaces, the fellows' combination rooms, the undergraduate and graduate members' combination rooms, the dining hall, the fellows' dining room, the College bar as well as the kitchens, student and fellows' accommodation and the Master's lodge.
Gonville and Caius College
Cambridge, UK
The new Fellow's Dining Room was created out of space that used to be the College Library, following its relocation to the University Library which was recently acquired by the College. The new design for the room makes reference to this by picking up on detail from the Temple of Apollo at Bassae used by C.R. Cockrell at the University Library. The design follows Cockerell's important discovery that Greek temples and buildings were often highly decorated with polychromatic designs, and includes a large and complex painted ceiling along with painted and gilded column capitals.
Gonville and Caius College
Cambridge, UK
The College accepted the recommendation that the opportunity should be taken to improve the entire circulation pattern and service areas of this most important part of the College. The entrance undercroft and Salvin's stairs up to the Hall have all been remodelled to create a procession of spaces worthy of such a significant public part of the College. This view shows the rooflight above the Fellows' stair.The design incorporates a new lift which provides access for the disabled to the combination rooms and hall.
Gonville and Caius College
Cambridge, UK
A new service building was inserted into the kitchen court providing additional space for the kitchens at ground level, new toilets and pantries at first floor level to serve the combination rooms and a new top lit servery at the uppermost level to serve Salvin's Hall. All the servery furniture was also designed by the practice.
Gonville and Caius College
Cambridge, UK
The area below the Fellows' new dining room was enlarged and refurbished to create a new bar area to be used by members of the College. It was designed as part of a suite of rooms linking the Junior and Middle Combination Rooms and a general student recreation area within the basement of the College.
Gonville and Caius College
Cambridge, UK
The new Fellow's Dining Room was created out of space that used to be the College Library, following its relocation to the University Library which was recently acquired by the College. The new design for the room makes reference to this by picking up on detail from the Temple of Apollo at Bassae used by C.R. Cockrell at the University Library. The design follows Cockerell's important discovery that Greek temples and buildings were often highly decorated with polychromatic designs, and includes a large and complex painted ceiling along with painted and gilded column capitals.
Gonville and Caius College
Cambridge, UK
Quote from HRH The Prince of Wales speech in March 1998 when he opened the new building: "These (works) show how adaptable and flexible traditional approaches to architecture can be and how you can go on using the Classical Tradition in a fresh and really contemporary manner."


Galvanize Delivers
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This menu is disabled if no game is active.
1. Choosing an Info menu option while a human player command is executing or during an interactive game replay will automatically complete the execution or stop the replay, respectively. This does not apply to the first three menu options which display only static scenario data.
2. Placement Sites is disabled if a computer player is active, if the game is being replayed, or if the game has been won. However, you can always use the Classes page of the Faction Status dialog to show the Placement Sites dialog for any desired faction.


--- title: Home description: layout : page ---
Ruhoh is all about the command-line. Check out the goods:
$ ruhoh help


"ITURAEA "a district in the north-east of Palestine, forming, along with the adjacent territory of Trachonitis, the tetrarchy of Philip (Luke 3:1). The present Jedur comprehends the chief part of Ituraea. It is bounded on the east by Trachonitis, on the south by Gaulanitis, on the west by Hermon, and on the north by the plain of Damascus.
"ITURAEA "a district in the north-east of Palestine, forming, along with the adjacent territory of Trachonitis, the tetrarchy of Philip (Luke 3:1). The present Jedur comprehends the chief part of Ituraea. It is bounded on the east by Trachonitis, on the south by Gaulanitis, on the west by Hermon, and on the north by the plain of Damascus.


| In this grisly remake of the 2002 horror hit, five college chums rent an isolated woodland cabin for a party. But their fun quickly ends when the group is exposed to a hideous flesh-eating virus, and survival becomes the name of the game. | |
| Genres: | Horror |
| Director: | Travis Zariwny |
| Writer: | Randy Pearlstein, Eli Roth |
Cast: | Karen - Gage Golightly Jeff - Matthew Daddario Paul - Samuel Davis Marcy - Nadine Crocker Bert - Dustin Ingram Henry - Randy Schulman Cadwell - George Griffith Connor / Grim - Tim Zajaros Tommy - Aaron Trainor Deputy Winston - Louise Linton Hog Lady - Laura Kenny Dennis - Derrick R. Means Fenster - Jason Rouse Baily - Benton Morris Guitar Man - Dawson Doupé Emily - Teresa Decher Woman - Lorraine Bahr Sheriff Lincoln - Travis Zariwny Harmonica Girl - Michelle Damis Man - Bradford Farwell Hazmat - Richard G. Boron Sheriff Silver - Ted Pfeifer |


Considerations¶
When using the IATI activity standard to produce an activity-file, the following should be considered:
- An IATI publisher should publish at least one activity file.
- An activity file should contain at least one
iati-activity
- It should be updated at least annually.
- It must be well-formed XML.


Home
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About
Contact
About Us
This application is built for your aid to help you save money
and a keep an eye out for unwanted
Our Team
AGreetings of appreciation to those who are,
designers and programmers on this project
Nemh Ablan
computer science
Ruba Kanaan
computer science
Shahed Al-Jalam
Civil Engineering
Mahmoud Yassin
Software engineering
Ashraf Sao
Culinary arts
Nedal Alashqar
Management Information Systems


This is about our industries, our goverments around the world, taking devisive large scale actions. Evidences section. All the charts for the causes will come here.
We need to put a price tag on Carbon emissions, and eliminate government subsidies for all oil, coal and gas. What are the main causes for Global Warming.
 If antatrica starts melting with the same rate as Greenland, we are in for trouble. Inface whole ice sheet of Antartica is actually melting away.
Good news is Reneuable eneergy is not only achievable but good economic policy. Clean air a livable climate are human rights. And solving this crisis is not a question of politics, but its the question of our own survival.
"Solving this crisis is not a question of politics, but its the question of our own survival."
"Antartica is melting. And if this happens, humanity is in big trouble."
Our rising Oceans
Our oceans are acifying with methane plumes rising out from the ocean floor
Deforestration
Run virtually any language at any scale.Run virtually any language at any scale.
CO Emission
Run virtually any language at any scale.Run virtually any language at any scale.
Extreme Temperature change
With West Antartica and Greenland ice-sheets melting at un-precedented rates
California Drought
Droughts are intensifying


wall wart
`Prev `__
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--------------
**wall wart**: n.
A small power-supply brick with integral male plug, designed to plug
directly into a wall outlet; called a ‘wart’ because when installed on a
power strip it tends to block up at least one more socket than it uses.
These are frequently associated with modems and other small electronic
devices which would become unacceptably bulky or hot if they had power
supplies on board (there are other reasons as well having to do with the
cost of UL certification).
--------------
+------------------------------+----------------------------+-----------------------------+
| `Prev `__ | `Up <../W.html>`__ | `Next `__ |
+------------------------------+----------------------------+-----------------------------+
| wall time | `Home <../index.html>`__ | wallhack |
+------------------------------+----------------------------+-----------------------------+


Chardin.js
Simple overlay instructions for your apps.
See it in action
Or open it again
Baked with
<3
by
@heelhook
.


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.Buy a Home .Buy a Property .Sell your Home/Property
Since 2000, Nila Property Management has been the dominant force in Kerala real estate, building a record of success and longevity unparalleled in the industry. Our devoted team is doing the job right, building a presence in the area, and making a difference for people in real estate. Browse our listings of Thir area properties.
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{% extends "base.html" %} {% block title %}Philip Kiely: Press{% endblock %} {% block head %} {% endblock %} {% block content %}
I'm honored to have been invited to speak on a number of podcasts and channels. Thank you to every host who has generously shared their audience with me. If you want to create an engaging episode together for your show, please contact me at<EMAIL_ADDRESS>Saron sits down with writer, programmer, and creator, Philip Kiely. Philip is the author of Writing for Software Developers and Life-Changing Email. They discuss how he first got into technical writing while studying abroad in Budapest, what technical writing is, what technical content platforms are out there, why new developers should publish content early in their careers, and more.
Philip Kiely discusses his recently published book, Writing for Software Developers. While software development primarily involves writing code, strong written communication skills are critical for multiple tasks.
After a computer programmer with a knack for technical writing is approached for a freelance gig, he ends up writing the book on the subject.
“The business of life” is one of Philip Kiely’s ’20 trademark phrases. It won’t take you long to figure out why! On this episode, Philip shares his tips, tricks, and wisdom for maximizing your professional trajectory.
Philip Kiely, author of Writing for Software Developers talks about how technical professionals can write content users love. We discuss the writing process, how to assemble articles effectively, how to get users to care about your topics, and why the Heart Bleed Bug was so well named.
In such a complex and fast-moving industry as tech writing, it can be interesting to see how burgeoning tech writers get started—and become successful.
n this episode I talk to Philip Kiely, Head of Marketing at Gumroad, about how he made 20k in his first week selling an info product, why the creator economy is booming, and how you can launch your own product.
Ever feel impostor syndrome because you don't have a CS degree? Philip Kiely joins Swyx and Randall to talk about 3 big misconceptions that people have about CS degrees!
Philip Kiely graduated from college in May 2020 with a degree in Computer Science. Six days before graduation, he published his first book called Writing for Software Developers, which made over $15,000 in the first 24 hours after launch.
Since he published Writing for Software Developers, Philip has detailed every step of the process, from writing, marketing, and selling the product, to building the landing page and transcribing audio interviews. His radical transparency offers a blueprint for other creators, and his habit of sharing metrics mirrors the philosophy of Gumroad itself.
In this episode we talking about authoring books for developers with Philip Kiely.
Phillip Kiely is a technical writer whose new book "Writing for Software Developers" did over $20,000 of gross sales in the first week.
Philip talks about writing effective emails, competing with stack overflow, launching his book, and how developers skim tutorials.
Philip takes us through his journey writing and launching his new book, Writing for Software Developers, and how he nailed the launch with a very small audience.
Our conversation covers a wide range of topics on software, writing, the future of digital information distribution and more. Philip goes deep on the craft of technical writing and interviewing as well as marketing, distribution and connecting with audiences. We discuss the success of creator-focused content platform Gumroad and the economics of Gumroad versus Amazon. Philip analyzes the impact of COVID on digital content, universities and more.


--- layout: default group: "navigation" title: Executives permalink: /executives/ order: 2 ---
Our talented executive board consists of the eight dedicated community members listed below.
I’m a third year computer science student who is deeply inspired by this community and the endless possibilities of technology. Apart from ACM@UC, I’m a RevolutionUC organizer and a member of Cyber@UC. My other passions are music and fitness.
I'm a 3rd year computer science and business analytics student excited to be part of ACM@UC's exec! I am also secretary/webmaster for ACM-W and got involved with RevUC the past 2 years as a participant and an organizer.
I am a third year student majoring in computer science. I am passionate about software and algorithm development Currently working as a research intern in the lab of iCDCU.
I am a second year computer science student with an interest in mobile development. I was a member of the logistics team that help organize Revolution UC 2020. I am a Domino's pizza lover and MARVEL movie fan.
I am a third year IT student in the Software development and Game design tracks. I'm a very passionate software developer and metalhead. I'm a big advocate for bots and automation, so catch me on Slack if you like to talk about the future of AI.
I'm an international student from Vietnam, and currently a sophomore pursuing Computer Science. I love the innovative power of computers, softwares, and technology, and in the future, I hope to create more of technology wonders for people all around the world to enjoy. I also love playing chess and the guitar.
I'm a third year majoring in Computer Engineering and working on my MBA (through ACCEND). I enjoy working with new and innovative tech by attending hackathons and working on random hobby projects. Feel free to reach out on Slack!
I'm a 3rd year CS Major (Mathematics Minor), have a huge Legend of Zelda collection, and enjoy rollerblading and running in my downtime.


My life is dull and grey just like this website (I really need to change the color scheme soon...it's so depressing...)
What is Rails??
Posted on May 31, 2014
Introduction
Ruby on rails is an open source web application that uses Ruby language as its framework. It was essentially, developed for easier programming and allow for less code. It is also known as a opinionated software in that it usually encourage programmer to code in a certian way.


--- layout: post status: publish published: true title: Podcast 2.09 - It's more about loyalty than satisfaction. author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 2645 wordpress_url: http://ivyenvy.com/?p=2645 date: '2010-05-17 21:36:51 +0000' date_gmt: '2010-05-18 03:36:51 +0000' categories: - Podcast tags: [] comments: [] ---
Andy, Corey and Jeremy discuss a terrible first half of May for the Cubs, the lack of production from the middle of the line-up and Starlin Casto being showered with boo's in his first game at Wrigley Field. We also consider what Lou Piniella's role in this is and what the future holds for him. We question the validity of the no-hitter thrown by Nick Struck of the Peoria Chiefs. And we read your answers from the Question of the Week.


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{% load widget_tweaks %} {% include 'common/snippets/modal_header.html' with modal_title='Reservation details' %}
| Name | {{ reservation.name }} |
| Consumer: | {{ reservation.consumer }} |
| Title: | {{ reservation.title }} |
| Start Date: | {{ reservation.start_date }} |
| Start Time: | {{ reservation.start_time }} |
| End Date: | {{ reservation.end_date }} |
| End Time: | {{ reservation.end_time }} |
| Info: | {{ reservation.info }} |
| Created by | {{ reservation.created_by }} |
| Created at | {{ reservation.created_at }} |
| Last modified by | {{ reservation.last_modified_by }} |
| Last modified at | {{ reservation.last_modified_at }} |


The Atlantic County Economic Alliance, a private sector directed, nonprofit economic development corporation that was established for business attraction, retention, and marketing efforts in Atlantic County.
The mission of the Atlantic County Economic Alliance is to drive economic growth and cultivate economic diversity in Atlantic County through the execution of the Atlantic County Economic Development Strategy and Action Plan developed in 2015 by AngelouEconomics, a nationally recognized independent consultant from Austin, Texas.
To raise the profile of Atlantic County as the place to do business, we are empowering regional economic development, driving industry diversification, improving delivery of economic development services, and rethinking destination attraction.


Well google sounds like a cool place to work. I'v found it very enlightening and makes heaps of sence.
I found it peacful, calming.


For our first project, we're going to take a look at SAT and ACT scores around the United States. Suppose that the College Board - the organization that administers the SAT - seeks to improve the participation rate of its exams. Your presentation should be geared toward **non-technical** executives with the College Board and you will use, at minimum, the provided data to make recommendations about how the College Board might work to increase the participation rates of these exams.


{{< layout}} {{$propositionHeader}}{{/propositionHeader}} {{$pageTitle}}Information about urgent passport{{/pageTitle}} {{$header}} {{/header}} {{$main-content}}
You'll need to attend an appointment at a passport office in the UK. You can choose an appointment online when you apply.
There are 3 ways to get a passport urgently:
Who can apply? |
Adults 16 or older |
How do I apply? |
Paper form |
When do I get my new passport? |
4 hours after your appointment |
What photos do I need? |
2 printed photos |
How much is an adult passport? |
£128 |
Who can apply? |
Adults 26 or older |
How do I apply? |
Online |
When do I get my new passport? |
At your appointment |
What photos do I need? |
|
How much is an adult passport? |
£128 |
Who can apply? |
Adults and children |
How do I apply? |
Paper form |
When do I get my new passport? |
1 week after your appointment (by post) |
What photos do I need? |
2 printed photos |
How much is an adult passport? |
£103 |


Members must have a public username and public
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{% if form.non_field_errors %}


{{ now }}
Please uploading gesture digit image files and then selection want to prediction targets to prediction.


Upload your file to automatically generate a .objjs file
If anything breaks feel free to complain to<EMAIL_ADDRESS>. PLEASE INCLUDE THE FILES THAT DID IT


Instructional Technology Facilitator and Computer Science Teacher at Manteo High School
I work with teachers to integrate technology into their lessons. I help design learning experiences that are enhanced by the use of instructional technology
I teach AP Computer Science Principles. I am also the sponsor of the Girls Who Code club and the Computer Science Club. I use Scratch and Python mostly to teach my classes.
I love video games. I have been playing them my whole life. Like many other nerds, that was the reason I got into computers. I also enjoy Disc Golf, Kayaking, and Scuba Diving.
I love picking up projects that involve learning new things. I might not finish them, but they are always educational.


--- layout: post status: publish published: true title: The Cubs are losing? author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 917 wordpress_url: http://ivyenvy.com/?p=917 date: '2009-05-26 18:50:39 +0000' date_gmt: '2009-05-27 00:50:39 +0000' categories: - The Cubs tags: - Brewers - Wisconsin - The Brewers give tickets away - Snickers comments: - id: 239 author: Jimmy author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-05-27 15:12:33 +0000' date_gmt: '2009-05-27 21:12:33 +0000' content: I'd rather have a free pizza. - id: 241 author: Kurt author_email<EMAIL_ADDRESS>author_url: http:// date: '2009-05-28 07:35:23 +0000' date_gmt: '2009-05-28 13:35:23 +0000' content: Why exclude the "Panormous"? Very un-Sconsin. ---
Well, I think I picked a good weekend to get out of town and miss Cubs games. I was up in Wisconsin eating cheese and drinking Spotted Cow. Maybe I'll just become a Brewers fan. Hell, I can get free tickets to Brewers games with the purchase of a Snickers bar at the Piggly Wiggly or a pizza from Pizza Hut.
Make your case, Cubs.


Notifies application of button press events for buttons to which the application is subscribed. SDL supports two button press events defined as follows:
See
SDLSubscribeButtonSee
SDLUnsubscribeButton@since SDL 1.0
@abstract the button’s name
See
SDLButtonNameRequired
@property (readwrite, strong, nonatomic) SDLButtonName _Nonnull buttonName;
var buttonName: SDLButtonName { get set }
@abstract Indicates whether this is a LONG or SHORT button press event
See
SDLButtonPressModeRequired
@property (readwrite, strong, nonatomic)
SDLButtonPressMode _Nonnull buttonPressMode;
var buttonPressMode: SDLButtonPressMode { get set }
@abstract If ButtonName is CUSTOM_BUTTON
, this references the integer ID passed by a custom button. (e.g. softButton ID)
@since SDL 2.0
Optional, Integer 0 - 65536
@property (readwrite, strong, nonatomic, nullable)
NSNumber<SDLInt> *customButtonID;
var customButtonID: (NSNumber & SDLInt)? { get set }


5 Ways to Blog From Your Mobile
Developing apps for mobile devices requires considering the constraints and features of these devices. Mobile devices run on battery and have less powerful processors than personal computers and also have more features such as location detection and cameras. Developers also have to consider a wide array of screen sizes, hardware specifications and nfigurations because of intense competition in mobile software and changes within each of the platforms (although these issues can be overcome with mobile device detection).
Mobile UIs, or front-ends, rely on mobile back-ends to support access to enterprise systems. The mobile back-end facilitates data routing, security, authentication, authorization, working off-line, service.
Mobile UIs, or front-ends, rely on mobile back-ends to support access to enterprise systems. The mobile back-end facilitates data routing, security, authentication, authorization, working off-line, and service orchestration. This functionality is supported by a mix of middleware components including mobile app servers, Mobile Backend as a service.
5 Comments
Vanessa Price
January 07, 2018 ReplyMobile devices run on battery and have less powerful processors than personal computers and also have more features such as location detection and cameras. Developers also have to consider a wide array of screen sizes, hardware specifications.
Charles Reynolds
January 08, 2018 ReplyMobile devices run on battery and have less powerful processors than personal computers and also have more features such as location detection and cameras.
Brooke Marshall
January 09, 2018 ReplyMobile devices run on battery and have less powerful processors than personal computers and also have more features such as location detection and cameras. Developers also have to consider a wide array of screen sizes, hardware specifications.


This website homes the premium content related to nitty gritty of web development and various responsive web templates.
All things related to html markup and its related domain
All things related to CSS
All things related to Graphic design and related contents


MAY-DAY
THE first of May is inaugurated with much uproar. As soon as the clock has told of midnight, a loud blast on tin trumpets proclaims the advent of May. This is long continued. At daybreak, with their "tintarrems," they proceed to the country, and strip the sycamore-trees (called May-trees) of all their young branches, to make whistles. With these shrill musical instruments they return home. Young men and women devote May-day to junketing and picnics.
It was a custom at Penzance, and probably at many other Cornish towns, when the author was a boy, for a number of young people to sit up until twelve o'clock, and then to march round the town with violins and fifes, and summon their friends to the Maying.
When all were gathered, they went into the country, and were welcomed at the farmhouses at which they called, with some refreshment in the shape of rum and milk, junket, or something of that sort.
They then gathered the "May," which included the young branches of any tree in blossom or fresh leaf. The branches of the sycamore were especially cut for the purpose of making the "May-music." This was done by cutting a circle through the bark to the wood a few inches from the end of the branch. The, bark was wetted and carefully beaten until it was loosened and could be slid off from the wood. The wood was cut angularly at the end, so as to form a mouth-piece, and a slit was made in both the bark and the wood, so that when the bark was replaced a whistle was formed. Prepared with a sufficient number of May whistles, all the party returned to the town, the band playing, whistles blowing, and the young people singing some appropriate song.


CLOSET LAND A film review by Mark R. Leeper Copyright 1991 Mark R. Leeper
Capsule review: A great idea for a film disappointingly squandered. This is a two-person play about a woman accused of treason and a government interrogator trying to force her to sign a confession. This could have been a powerful statement for Amnesty International, but its special power is lost on far-fetched plot contrivances and misjudgements in atmosphere. It's still worth seeing, though. Rating: 0 (-4 to +4).
Charlie Brown asked it after a particularly bad performance by his baseball team: "How can we lose if we're so sincere?" Sad to say, sincerity is not enough. Radha Bharadwaj wrote the play CLOSET LAND and directed the film with what I am sure was nothing but great sincerity and the best of intentions. And if this film had been done correctly, it would have been a film that is desperately needed with what is a very important message. That message is blunted with what turns out to be a contrived plot and a set of unbelievable circumstances.
In an unnamed country a woman (played by Madelaine Stowe) has been kidnapped and is brought before an interrogator (played by Alan Rickman). At first it appears to have all been a mistake. She is non-political--a simple writer of children's stories. One of the stories, "Closet Land"-- still unpublished--has fallen into the hands of the government. They interpret it as a bitter anti-government allegory and now the government wants the woman to sign a confession of sedition. She is put through a mind-numbing succession of mental and physical tortures to debase and humiliate her in an attempt to get her to sign.
So far, so good. If that was all there was to this film it would be a painful film to watch but it would make a powerful statement for Amnesty International, for whose benefit this film seems to have been made. The incident portrayed here could be seen to be in many ways typical of crimes committed by far too many governments today. This view of government political sadism is and should be a bitter pill to swallow. But there is more of a plot to CLOSET LAND than that and there is where the film goes frustratingly wrong, making this incident anything but typical.
When we see the real reasons behind the woman writing her story "Closet land" and the real motives of the interrogator, both seem extremely contrived and built around an incredible coincidence. The interrogator pulls off a number of odd deceptions; some require talents beyond even the capabilities of Rickman to bring off. One doubts that a man as talented as the interrogator would become a government interrogator/torturer. Surely we are not meant to believe most people in this profession are this intelligent. Further undercutting the credibility is the set design of the interrogation center. One suspects in real life such places are at best utilitarian. This chamber, with its fancy furniture, its sound and light equipment, its decorative columns, its functional yet decorative file drawers, creates just the wrong feel.
What is needed is a story that one can tell oneself is being repeated on a daily basis in many countries around the world. Instead, we have a story we doubt could have ever happened. That makes this film a curiosity rather than a powerful statement. I give it a 0 on the -4 to +4 scale.
Mark R. Leeper att!mtgzy!leeper<EMAIL_ADDRESS>.
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{% extends "base.html" %} {% load static %} {% block content %}
We are not just a copycat of Stack Overflow, Quora and Chegg. It is actually a downgrade version of thoes websites! :)
The website is the final project, Kwounsu Lee has created to pass the web programming class, CINS465.
Nothing to tell you more..
Oh! And this is how the logo of Stack Quoregg created.
There are two main contents in this website to help people share their knowledge.
You can post questions and answer to them by comments in "Q & A". Upvote for the good answers to give them credit!
You can also ask questions to people with chatting in "Tutor". People can enter the chat room through tutor registration post in Tutor.
But this is not a individual separated chat room. Becuase the spirit of Stack Quoregg is 'to share knowledge'!
Therefore, everyone will be joined to the chat and the conversations will be gone.


Mappify
A tool to easily create interactive maps backed by Semantic Web technologies
To be filled with content...


Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.
| Version: | 0.2 |
| Depends: | R (≥ 3.1.1) |
| Imports: | stats, graphics |
| Published: | 2018-05-16 |
| Author: | Tim Lamont-Smith [aut, cre] |
| Maintainer: | Tim Lamont-Smith <t.lamontsmith at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | Distributions |
| CRAN checks: | kdist results |
| Reference manual: | kdist.pdf |
| Package source: | kdist_0.2.tar.gz |
| Windows binaries: | r-devel: kdist_0.2.zip, r-release: kdist_0.2.zip, r-oldrel: kdist_0.2.zip |
| OS X binaries: | r-release: kdist_0.2.tgz, r-oldrel: kdist_0.2.tgz |
Please use the canonical form https://CRAN.R-project.org/package=kdist to link to this page.


layout: true class: center, middle --- # Navigating a Data Warehouse
via CLI
Chris Fournier (@cfournie)
Pycon Canada 2017, Montréal, QC, Canada
??? Hi, I'm Chris Fournier, I work at Shopify, and I'm going to talk to you about some simple scripts we wrote to navigate our data warehouse and the UNIX tools that we combine them with to get the most out of them. In case you haven't heard of Shopify... --- background-color: #FFFFFF background-image: url(./frontpage.png) background-position: center background-repeat: no-repeat ??? ... we provide a platform for people to sell products online or in person. And the specific team that I work on develops software for and maintains a data warehouse ... --- ## Data Warehouse *Platform* to collect, store, analyze, and report on *data* ??? ... or a platform used to collect, store, analyze, and report on data from our systems. Our analysts use it to answer questions about the performance of Shopify as a business, our products, and our features. ---
Gross Merchandise Volume
??? One example of a question that we try to answer for Shopify as a whole is: "What is the total value of everything sold through our platform?" Or Gross Merchandise Volume (GMV). That's one of the metrics that we use as a company to gauge our success. To make this chart we first create a dataset with one row per order transaction. This is not a trivial dataset to create given the volume of orders and transactions that we process for our merchants and what we join this dataset against to be able to break it down by payment gateway, industry, monthly recurring revenue, etc. In 2016 alone we had over $15 billion dollars worth of GMV and that's a lot of rows. We're also continuously processing data to keep this dataset up to date. But from a broad technical perspective, how is this data processed? Sources: https://www.slideshare.net/SolmazShahalizadeh/building-a-financial-data-warehouse-a-lesson-in-empathy https://s2.q4cdn.com/024715446/files/doc_presentations/2017/11/Investor-Deck-Q3-2017.pdf --- class: center, middle ## Batch jobs ### (E)xtract (T)ransform (L)oad
graph LR A(Extract
Orders)-->C(Transform to
GMV per Shop) B(Extract
Transactions)-->C C-->D(Load to Database)
??? We predominantly run ETL jobs every few hours that: - Extract batches of data from operational systems (like MySQL) - Transform that extracted data into a dataset (for example "GMV per shop"), and then - Load that into a database for analysts to issue queries against and create reports upon The batch jobs that we run in our warehouse are written in Python on top of PySpark by our analysts. The number of these batch jobs, their implementations, and whether a job reads a specific dataset or not changes frequently, making it difficult to keep an up to date picture of how everything works in ones head. How frequently do our jobs change? --- ## Development speed .center[
3000+
ETL jobs
(as of Nov 1017)
15.2
Deploys per day
on avg (±8.6 std dev)
]
??? We have over 100 developers making changes to over 3000+ jobs, and on average we deploy about 15 different versions of these jobs per day. The engineering team that I work on not only maintains the platform and frameworks that these jobs run on, but also helps our analysts maintain their jobs and the integrity of the data that they produce. To help make sure that these jobs are working, and to see which jobs/datasets are affected by bugs that pop up, we need to be able to navigate this ever-changing set of jobs, their implementations, and the relationships between them to debug issues. How do we do that? --- background-color: #FFFFFF background-image: url(./hue_workflow.png) background-position: center background-repeat: no-repeat ??? One way that we navigate our jobs is through a Django app called Hue. It's a very handy way to visualize groups of jobs (which we refer to as flows) and the jobs themselves. --- background-color: #FFFFFF background-image: url(./hue_workflows.png) background-position: center background-repeat: no-repeat ??? Hue can also show lists of jobs that are completed or being run by our scheduler, Oozie. One downside to using Hue is that we can't search by all of our job metadata, like where a job reads/writes, which team maintains it, how often it runs, what resources it uses, etc. This is an obstacle when we're trying to explore our jobs, understand how they're related, and break them down by their properties to see if a bug applies to them or not. So how can we gain access to more metadata to search through? --- ### Search YAML schedule definitions
.left[ ```yaml gross-merchandise-volume-flow: frequency: 2h owner<EMAIL_ADDRESS>compute-gmv-job: resource_class: xxlarge executable: jobs/compute_gmv.py inputs: - /data/raw/orders/ - /data/raw/transactions/ output: /data/frontroom/gmv-facts/ extract-orders-job: ... ``` ]
??? Well, all the metadata used to schedule our jobs is contained within YAML files in a git repository alongside the job implementations. These files are at most 20 minutes newer than what's actually running in production, so they're a reliable approximation for the metadata in our scheduler, and if we need precision, we can check out the exact git sha of what we're running. We can search these YAML files to learn more about our jobs. Here's an example of what that YAML looks like. This is the "gross-merchandise-volume-flow" group of jobs that I showed you a graph of earlier. This group of jobs: - Runs every 2 hours - Has a team that owns them - There's a "compute-gmv-job" job that: - Uses a lot of resources (`xxlarge`) - Is implemented by a specific Python script - Takes "orders" and "transactions" as input, and - Outputs a dataset on GMV There are other jobs that are part of this flow as well, for example the "extract-orders-job", and others not shown for brevity. Using an editor we can browse this YAML to view metadata, but except for finding details for specific jobs and some basic aggregate statistics (like using your editor's "find" functionality and seeing the number of occurrences of something), there's not a lot of analytical power here, and remember that we have over 3000 jobs that consume each others outputs. When we find a bug, they usually apply to a set of jobs that all share some similar properties. We need to be able to query our schedule to identify these similar jobs and then find all data that is produced directly or indirectly by them and fix both the jobs and sometimes their data. YAML has the metadata that we want, but it's not lain out in an easy to consume way when we're concerned with many jobs at once. --- ### Tables of metadata
.left[ Workflow (group) | Freq. (h) | Job | Resources | Etc. --------------------- | ------------- | ------- | --------- | --- abiding-poetry |6 |comp |small | ... abiding-poetry |6 |defeated |medium | abiding-poetry |6 |kind-big |xxlarge | abiding-poetry |6 |load-com |small | abiding-poetry |6 |load-kind-big |xlarge | abiding-poetry |6 |swift-energy |medium | confused-indy |7 |eight |small | confused-indy |7 |load-eight |small | confused-indy |7 |load-two-orig |small | confused-indy |7 |luxury-med |xlarge | ]
??? What we really want is to view our job metadata as a table. That way each row consolidates all the information about a job, and if we were able to filter this table by its columns, then we could find groups of jobs that share similar properties. --- ### Parse and print metadata
.left[ ```python for flow in flows: for job in flow.jobs: print('\t'.join(( flow.name, str(flow.frequency), job.name, job.resource_class.value, job.output ))) ``` ]
??? Thankfully, we already had some Python YAML parsing code laying around to schedule our jobs, so we reused those parsers and made a script to generate this table! All that script does is parse our schedule and print to the console one row of tab-separated values at a time. I can't show you our actual schedule, but I can show you an automatically generated one, and you can visit the git repo at the bottom of these slides and follow along if you like. Here's a snippet from the `jobs.py` script. First it iterates over each flow, or group of jobs, and then each job within. Then it prints the flow name, schedule frequency (in hours), the job name, resources it uses, and its output. There's a lot of other metadata that we could print, like the owning team, job implementation file, inputs, etc. but I'm skipping that in this presentation for brevity. This will print the information that I just showed you in the previous slide. Note that I'm printing the flow name and frequency repeatedly, once for each of the flow's jobs. If this were a database table, it would be bad to design a denormalized table like this (we'd have a "flows" table and a "jobs" table), but in the terminal, pre joining like this makes filtering jobs by properties that they inherit from flows easier. Denormalized tables are your friend on the CLI. So, what does this table look like when we script it? --- ??? ```shell python jobs.py ``` Not bad, but it's a little hard to distinguish the columns. Is there anything that can help us pretty-print this table? We could change our Python script to format our columns with variable whitespace so that the table is nice and easy to read. There are some great libraries for formatting output like this, `tabulate` for example, but let's keep our script simple and just output tab-separated values. It's currently making an on-demand TSV file for us and writing it to stdout, there must be some UNIX tools that can take advantage of this. Yes, yes there are, for example there's a handy little tool called `column` that we can use to format our whitespace for us. All we need to do is generate our output and then pipe it to the column program like so: ```shell python jobs.py | column -t ``` And look at that, we have a nicely formatted table that we can look at! But what was that piping wizardry that I just mentioned? Pipes are the glue that UNIX-like operating systems (like OSX) provide you to combine programs together by passing output through them. --- ## What is piping?
.left[ ```shell python jobs2.py | column -t ``` ]
graph LR A[
python jobs2.py
stdin=stdin
stdout=pipefile write]-->C(pipe file) C-->D[
column -t
stdin=pipefile read
stdout=stdout]
https://brandonwamboldt.ca/how-linux-pipes-work-under-the-hood-1518/
??? Here's the command that I just ran. It's generating tab-separated values and passing them directly to the column program which then prints them to `stdout` which we see in our terminal. This chaining is called a pipeline. Roughly how this works is: 1. Bash creates a special in-memory pipe file in the pipe filesystem 2. Bash then forks twice to create two child processes, one for the python program and one for column program 3. Bash then sets python's stdout (where it writes output to) to the write end of the pipe file 4. Bash then sets column's stdin (where it reads input from) to the read end of that same pipe file 5. Both processes then start and run roughly in parallel writing and reading from that pipefile, blocking on write when the pipefile is full or blocking on read when it's empty, 6. Stopping usually when they're done writing or reading output. This lets us create a very simple Python script that just creates raw tab-separated output that we can then pipe to other UNIX programs to massage into whatever form we desire. By keeping the output simple, we don't narrow the possibilities of what we can use this output for. We make our script do one thing and do that one thing well: create tab-separated metadata. And the best part about relying on pipelines to adjust our output is that we get to spread work over multiple concurrent processes without writing any threading code ;) Now all I've shown you so far is how to show a table. That's still not very useful for navigating our warehouse of 3000 jobs. What other UNIX tools are available to us and what can we use them for? --- ??? Well, for starters, if we want to throw some columns away, there's a tool called cut. We can use that to take our large table and narrow it down to just the flow name and job name columns: ```shell python jobs.py | cut -f1 -f3 ``` That's still just so-far a display-related tool, what about some real analytics, what about filtering? How do we answer questions like: "Which jobs use the large resource class?" For that we can use a tool called "awk" to filter rows and only print them if they meet certain conditions, such as whether the 4th column contains the string "large". ```shell python jobs.py | awk '$4 == "large"' | column -t ``` "grep" can also be used for filtering, but when you have variable-sized columns, "awk" is easier to work with. Now that we have filtering, what about some aggregate statistics? Can we count? Yes we can, using the word count program to ask how many lines of filtered output that it sees. "How many jobs use the large resource class?" ```shell python jobs.py | awk '$4 == "large"' | wc -l ``` Some of those calls produced a lot of output and sometimes you're going to do something very expensive with that output later in your pipeline, so it would be helpful if we could just pass a short bit of output through a pipeline to prototype it. For that, we can use the "head" tool to just get the first 10 lines of output. ```shell python broken_jobs.py | head ``` Uh oh! There's a garbled stack trace in there. What happened to our table! --- ## Piping to `head`
.left[ ```shell python jobs3.py | head ``` ] .left[ ``` Traceback (most recent call last): File "jobs3.py", line 13, in
job.output BrokenPipeError: [Errno 32] Broken pipe ``` ]
??? Getting rid of some of the output we can see that what we got was a `BrokenPipeError`. That happened because `head` does something special. Head is reading from the pipe file all the output from the python script up until it sees 10 lines, and then it closes the read end of the pipe file. But our Python script doesn't know that `head` has had enough and it still wants to write more output to the write end of the pipe file. If the Python script tries to write output to that pipefile and the OS has closed it, then we get back from the OS an `EPIPE` error which Python wraps for us as a `BrokenPipeError`. Thankfully though, the OS provides us a way to tell that we should stop writing. --- ## Handling SIGPIPE
.left[ ```python import signal def handle_sigpipe(signum, frame): sys.stdout.close() sys.exit(0) signal.signal(signal.SIGPIPE, handle_sigpipe) ``` ]
??? When all file descriptors pointing to the read end of the pipe have been closed, our Python script is sent a signal called SIGPIPE, indicating that the pipe that we were writing to is closed and that we should stop writing to it. All we need to do in our script is write and register a signal handler that closes the write end of our pipe file (stdout) and exits the program when we receive the SIGPIPE signal. Now that we've fixed that, what other UNIX tools are there that can help us use these tables to find types of jobs? --- ??? There's a `sort` utility that we can use to sort text by entire rows or by columns which can even re-interpret text as numbers which we can use to answer: "What are the most frequently-run jobs?" ```shell python jobs.py | sort -k2n | head ``` Now we could have done that in Python very easily just using the `sorted` function, but that's costly because it needs to see all of the output and sometimes we don't need sorting (for example when we call `head`). Let's keep our program simple and focues on one job and leave sorting to another program. Now that we're sorted, how about removing duplicates? There's also `uniq` utility that can take sorted output and filter out duplicates which we can use to answer: "Which resource classes are used in the schedule?" To do that we can use cut to narrow down the output to the column that we're interested in, sort, and then use uniq. ```shell python jobs.py | cut -f4 | sort | uniq ``` Neat, they're all used, that wasn't incredibly useful, but how frequently are they used? Uniq has that covered as well, with one flag, we can also ask for counts of duplicates in a column. ```shell python jobs.py | cut -f4 | sort | uniq -c ``` Both uniq and sort are useful on their own, but where they really shine is in an application that I'll show you next. ---
Dataset dependencies & paths
graph LR A(/data/orders)-->C(/data/gmv_per_shop) B(/data/transactions)-->C C-->D("gmv_per_shop@db")
.left[ Start | End -------------------- | -------------- `/data/orders` | `/data/gmv_per_shop` `/data/orders` | `gmv_per_shop@db` `/data/transactions` | `/data/gmv_per_shop` `/data/transactions` | `gmv_per_shop@db` `/data/gmv_facts` | `gmv_per_shop@db` ]
??? Before I delve into that application though, I want to talk about another type of table that we can generate. So far we've been filtering a table of jobs, but these jobs have output datasets and those outputs are often consumed by other "downstream jobs" that produce their own output datasets. For example, as shown in this graph, "Orders" and "Transactions" datasets feed into the "GMV per Shop" dataset, which itself is loaded into a database, creating another instance of this dataset. Graphs like this are useful because we can use them to assess the impact that a mistake in producing one of our datasets would have on our other datasets. For example if we accidentally miss some "Orders", there are two downstream datasets either directly or indirectly that would also be affected. If only "GMV per Shop" incorrectly produced data, then we only have a single database to worry about. We can represent these relationships in a tabular form with one row per path through the graph using path starts and ends as columns. That generates a lot of rows, but it allows us to capture not only direct, but also indirect dependencies between datasets. --- #### What's *downstream* of a *bad dataset*?
.left[ Start | End ------------------------ | -------------- `/data/raw/orders` | `/data/gmv_per_shop` `/data/raw/orders` | `gmv_per_shop@database` `/data/raw/transactions` | `/data/gmv_per_shop` `/data/raw/transactions` | `gmv_per_shop@database` `/data/gmv_facts` | `gmv_per_shop@database` ]
.left[ ```shell python downstream.py | awk '$1 == "bad"' ``` ]
??? From a table of path starts and ends, we could then use `awk` to ask: "Which datasets are downstream of this bad dataset?" This would filter all rows (or paths) that have a "start" equal to the bad dataset, giving us all affected downstream datasets in the "end" column. Great! We can find all dataset impacted when a single job outputs bad data. Now let's imagine a more alarming scenario. What if we had many bad datasets all at once? What if we had a bug and we first had to figure out which jobs the bug applies to of our 3000 jobs and datasets that they produce? And then which datasets are downstream of those? --- #### What is downstream of this *list* of bad datasets?
.left[ ```shell python jobs.py | awk '$2 < 7 && $4 == "small"' > bad python downstream.py > downstream ``` ]
??? As an example, let's say that we identified a bug in our software where jobs that don't use a lot of memory, and that we run frequently, lose data. Let's also say that we've narrowed down the bug to the "small" resource class jobs that run more frequently than 7 hours. Using "awk" we can filter our jobs table to only show those that match those two conditions. That gives us a list of bad jobs and their datasets, but it doesn't tell us what is downstream of all of those bad datasets. We need to join this bad dataset list against the list of downstream paths. Is there a UNIX tool that can do this for us? ---
*`join`* two files
.left[ ``` a b c ``` ]
.left[ ``` a 1 b 2 b 3 d 4 ``` ]
.left[ ```shell join -1 1 -2 1 file_a file_b ``` ]
.left[ ``` a 1 b 2 b 3 ``` ]
??? Yes, yes there is! We can use the `join` command to perform an equality join between two files. It's a very simple tool and it requires that your data is pre-sorted by the column that you want to join on. It produces one row for each pair of lines that have a matching column in the two files. For example, we can take these two files at the top here and join them on the first column of each file to produce the result below: - `a` is in both files, so join emits a row `a 1`, then - `b` is in both files, so join emits a row `b 2`, then - `b` is in the second file again, so join also emits a row `b 3`, - `c` is only in one file so we don't emit a row, and - `d` is only in one file so we also don't emit a row for it. If instead of letters and numbers we had bad datasets in the left file and paths in right, we could join these together and get a table with two columns: bad datasets and each dataset downstream of them. So let's do that! --- ??? First let's get a sorted list of bad datasets: ```shell python jobs.py | column -t python jobs.py | awk '$2 < 7 && $4 == "small"' | head python jobs.py | awk '$2 < 7 && $4 == "small"' > a cut -f5 a | sort > b head b wc -l b ``` Next, let's get a sorted list of all path starts and ends: ```shell python downstream.py | head | column -t python downstream.py | sort -k1 > c head c ``` Now let's join these two tables together on the first column of both: ```shell join -1 1 -2 1 -t $'\t' b c > joined head joined ``` Now we have a list of all of the datasets downstream of the bad ones! There are duplicates though, because two bad datasets could affect the same dataset, so let's filter those out and combine them with the bad datasets to get a total list of everything that we need to worry about. ```shell cut -f2 joined > d sort b d | uniq > affected wc -l affected wc -l b python jobs.py | wc -l python -c 'print(504 / 590)' ``` And there you have it, a way to generate metadata tables about jobs/datasets using simple Python scripts and how to use UNIX tools to filter and join those tables to answer real questions about a data warehouse. --- ## *SQL-like* CLI using Python & UNIX tools
.left[ 1. *Filter* table of jobs and outputs 2. *Join* against table of downstream outputs ]
??? So that's how you get a SQL-like interface on the command-line using Python and some UNIX tools, and how you'd navigate a data warehouse with it! What I showed you here was an artificial scenario, but all of the principles are the same in the version of these scripts that we use on my team day to day. Why do we use this interface to do impact assessment? This can't be the easiest way to do this. And you need to learn a lot of commands. Well, that's my fault. A while ago I took an interest in developing a minimalist programming experience, moving from using Eclipse as an editor to vi/emacs, from relying on my editor to tell me about a software project to instead using UNIX tools and the CLI instead, and trying as much as possible to only interact with the CLI when developing. I originally built these scripts and learned to use these tools to supplement my own dev environment and because it's fun. But, then late one night on Slack we needed an answer to the question "how many datasets were affected by this incident"? And I grabbed the tool that I had on hand. And we liked it. Now instead of writing bespoke Python scripts to find jobs affected by an issue, we have prepared scripted tables of metadata. No more reading someones untested code at 1AM to figure out the list of affected jobs. With a git SHA, these tables (along with some UNIX tools) provide a reproducible way to analyse our schedule that doesn't rely on one of the systems that we're debugging. --- ### UNIX tools for tables
.left[ - *`head`* to truncate - *`column -t`* to pretty-print - *`grep`* and *`awk`* to filter - *`wc -l`* to count - *`sort`* to sort - *`uniq`* to dedupe / count freqs. (*`-c`*) - *`join`* to join sorted files ]
??? So to summarize, I showed you the benefit of keeping your Python script really simple and instead of adding features to it, leveraging the power of a few UNIX tools to make the most out of tabular output. Those tools being: ... And there are so many more that I didn't discuss. That's all the time that I have unfortunately, Check out the repo link below if you want these slides or to play around with these scripts. --- ## Testing
.left[ - Move script code into modules - Scripts produce parseable output ]
--- ## Performance
.left[ - [`cProfile`](https://docs.python.org/3.6/library/profile.html) - Use [generators](https://docs.python.org/3/howto/functional.html#generators) (speeds up `head`) - Use [`pandas`](http://pandas.pydata.org/) (and [`numpy`](http://www.numpy.org/)) - Make expensive columns flags - `--expensive-col` - Cache output (e.g. [`joblib.memory`](https://pythonhosted.org/joblib/memory.html)) ]
---
## Thanks for listening!
??? Thanks for listening, I'll take questions in the hall afterwards, and if you thought this presentation was cool, get in touch with me, my team is hiring! Thanks!


{{ partial "header.html" . }}
Much of my leisure time over the years has been spent merrily making and improving the sorts of websites and software utilities you see on this page. If you’re interested in learning more about any of these projects, I’d be delighted to hear from you!
The following tools were originally created to solve simple problems I stumbled across. I am releasing their code in the hope that they will be helpful to you too.


|
SphinxBase
5prealpha
|
Files | |
| file | ad.h [code] |
| generic live audio interface for recording and playback | |
| file | agc.h [code] |
| routine that implements automatic gain control | |
| file | bio.h [code] |
| Cross platform binary IO to process files in sphinx3 format. | |
| file | bitarr.h [code] |
| An implementation bit array - memory efficient storage for digit int and float data. | |
| file | bitvec.h [code] |
| An implementation of bit vectors. | |
| file | case.h [code] |
| Locale-independent implementation of case swapping operation. | |
| file | ckd_alloc.h [code] |
| Sphinx's memory allocation/deallocation routines. | |
| file | cmd_ln.h [code] |
| Command-line and other configurationparsing and handling. | |
| file | cmn.h [code] |
| Apply Cepstral Mean Normalization (CMN) to the set of input mfc frames. | |
| file | err.h [code] |
| Implementation of logging routines. | |
| file | feat.h [code] |
| compute the dynamic coefficients from the cepstral vector. | |
| file | filename.h [code] |
| File names related operation. | |
| file | genrand.h [code] |
| High performance prortable random generator created by Takuji Nishimura and Makoto Matsumoto. | |
| file | glist.h [code] |
| Generic linked-lists maintenance. | |
| file | hash_table.h [code] |
| Hash table implementation. | |
| file | heap.h [code] |
| Heap Implementation. | |
| file | jsgf.h [code] |
| JSGF grammar compiler. | |
| file | listelem_alloc.h [code] |
| Fast memory allocator for uniformly sized objects. | |
| file | logmath.h [code] |
| Fast integer logarithmic addition operations. | |
| file | matrix.h [code] |
| Matrix and linear algebra functions. | |
| file | mmio.h [code] |
| Memory-mapped I/O wrappers for files. | |
| file | ngram_model.h [code] |
| N-Gram language models. | |
| file | pio.h [code] |
| file IO related operations. | |
| file | prim_type.h [code] |
| Basic type definitions used in Sphinx. | |
| file | profile.h [code] |
| Implementation of profiling, include counting , timing, cpu clock checking. | |
| file | sbthread.h [code] |
| Simple portable thread functions. | |
| file | strfuncs.h [code] |
| Miscellaneous useful string functions. | |
| file | yin.h [code] |
| Implementation of pitch estimation. | |


Throughout my entire life, I have always enjoyed creating things on the computer. Whether that be art, games, programs, songs, I enjoyed spending my time working on projects involving the usage of a computer. Although when I found programming, I loved the aspect of finding the best ways to create the programs I want to make. While this was never fully practiced mind you when I was younger, I still enjoyed the challenge. I made sure I strived to create games during my high school life that no one else in the course would dare try. The best example of this fact was the Fully Three-Dimentional Spaceship shooter based on the popular francise known as Star Wars. This game was made using Unreal Engine 4.13 and had multiple levels, with different themed areas, adjustable camera, enemy AI tracking, etc. I have also made many other projects, just not as exciting as a Star Wars game. You can find examples of the projects i've worked on in the "Game Development" page referred on the main page of this website.
During my education, I've made games ranging from Sci-Fi Fighters to Platformers. I wanted to test myself, to prove to the teacher and other students that this was the career path for me.
I've learned about so many different programs to make, with a million combinations to make those work! As of 2018/03/30, I am only within my second semester. I have already learned so much and I have a far ways to go!


Player Name |
Team Name |
Player Id |
Player Position |
Points in average |
Rebounds in average |
Assists in average |
Steals in average |
|---|---|---|---|---|---|---|---|
| {{player[0]}} | {{player[1]}} | {{player[2]}} | {{player[3]}} | {{player[4]}} | {{player[5]}} | {{player[6]}} | {{player[7]}} |


Event Details
RedSnakePhilly ‘XIV
Date: April 5, 2014
Time: 4:00PM - 9:00PM
Location: Impact Hub Philadelphia
Get a ticket
Agenda
Agenda comming soon!
Agenda
Agenda comming soon!


http://mathiasbynens.be/demo/placeholder
html
<input type="text" name="name" placeholder="e.g. John Doe">
<input type="email" name="email" placeholder="e.g<EMAIL_ADDRESS><input type="url" name="url" placeholder="e.g. http://mathiasbynens.be/">
<input type="tel" name="tel" placeholder="e.g. +32 472 77 69 88">
<input type="password" name="password" placeholder="e.g. h4x0rpr00fz">
<input type="search" name="search" placeholder="Search this site…">
<textarea name="message" placeholder="Your message goes here"></textarea>
Use the plugin as follows:
js
$('input, textarea').placeholder();
You’ll still be able to use jQuery#val()
to get and set the input values. If the element is currently showing a placeholder, .val()
will return an empty string instead of the placeholder text, just like it does in browsers with a native @placeholder
implementation. Calling .val('')
to set an element’s value to the empty string will result in the placeholder text (re)appearing.
The plugin automatically adds class="placeholder"
to the elements who are currently showing their placeholder text. You can use this to style placeholder text differently:
css
input, textarea { color: #000; }
.placeholder { color: #aaa; }
I’d suggest sticking to the #aaa
color for placeholder text, as it’s the default in most browsers that support @placeholder
. If you really want to, though, you can style the placeholder text in some of the browsers that natively support it.
You can install jquery-placeholder by using Bower.
bash
bower install jquery-placeholder
placeholder
attribute for input
and textarea
elements. If this is the case, the plugin won’t do anything. If @placeholder
is only supported for input
elements, the plugin will leave those alone and apply to textarea
s exclusively. (This is the case for Safari 4, Opera 11.00, and possibly other browsers.)jQuery.fn.placeholder.input
and jQuery.fn.placeholder.textarea
. For example, if @placeholder
is natively supported for input
elements, jQuery.fn.placeholder.input
will be true
. After loading the plugin, you can re-use these properties in your own code.Makes sure it never causes duplicate IDs in your DOM, even in browsers that need an extra input
element to fake @placeholder
for password inputs. This means you can safely do stuff like:
html
<label for="bar">Example label</label>
<input type="password" placeholder="foo" id="bar">
And the <label>
will always point to the <input>
element you’d expect. Also, all CSS styles based on the ID will just work™.
This plugin is available under the MIT license.
– Mathias


vignettes/customize_ExPanD.Rmd
customize_ExPanD.Rmd
By default, ExPanD offers a set of sample modification and data exploration/visualization components that support a typical EDA workflow. In many situations however, your data does not need all of these components or you might want to center the user experience on a sub-set of these components. In addition, you might want to change the ordering of components, add additional information or even allow the user to define additional variables interactively. You can customize ExPanD along the following dimensions:
This vignette will guide you through each of these opportunities by step-by-step developing a customized ExPanD app. The code in the vignette is additive, meaning that you can source each code block independently from the others to see how the ‘ExPanD’ app is changed by the customization step-by-step.
When you start ExPanD in default mode, e.g., by
ExPanD will include all available components in their default order:
| Name | Description |
|---|---|
sample_selection |
A drop down menu to select the sample to be used and whether it should be balanced |
subset_factor |
A drop down menu to select a factor on which to limit the sample and an additional menu to select the value of that factor on which you want to focus the analysis |
grouping |
A drop down menu that gives you the option to name a grouping factor. The grouping factor allows the user to focus certain components on a sub-sample defined by a value of that factor. Also, the grouping component contains the dialog four outlier treatments |
bar_chart |
A component displaying a bar chart reporting the observations by time period |
missing_values |
A visual displaying the frequency of missing values for each variable and time period |
udvars |
A component that allows users to define additional variables (see below) |
descriptive_table |
A descriptive table for all numerical and logical variables contained in the sample |
histogram |
A histogram to display the distribution of a chosen numerical or logical variable |
ext_obs |
A list displaying the 5 most extreme observations for a chosen variable, overall or by time period |
by_group_bar_graph |
A bar graph that visualizes a chosen descriptive statistic by a chosen grouping factor |
by_group_violin_graph |
A by group violin plot, where users can choose the variable and the grouping factor |
trend_graph |
A graph that visualizes the development of up to three variables over time |
quantile_trend_graph |
A graph that visualizes the distribution of one chosen variable over time |
by_group_trend_graph |
A grouped time trend graph that visualizes the development of one variable across groups over time |
corrplot |
A visual representation of Spearman and Pearson correlations of the numerical and logical variables in the sample. Exact correlations are displayed when you hover above the respective cell with your mouse |
scatter_plot |
A scatter plot, where you can present up to 4 dimensions of your data (x, y, size, color). You can choose whether you want a LOESS line to be displayed and whether you want your sample to be limited to 1,000 observations |
regression |
A regression component where you can estimate a linear or logit regression, with up to two fixed effect levels and standard error clustering |
To select the components that you want to display, you have two options: First, you can simply indicate the components that you want to omit. The remaining components stay in the original order. As an example, the ‘gapminder’ data set does not have any missing values and is a balanced sample. This mean that we can safely omit the sample selection component as well as the display of missing values.
library(ExPanDaR)
library(gapminder)
data(gapminder)
ExPanD(df = gapminder, cs_id = "country", ts_id = "year",
components = c(sample_selection = FALSE, missing_values = FALSE))
Alternatively, you can choose the components that you want to include. This also gives you the option to sort the components in your preferred order. Let us assume that we want our app to focus on the Preston curve association of GDP per capita and life expectancy in the ‘gapminder’ data. So maybe we want to include the following components:
library(ExPanDaR)
library(gapminder)
data(gapminder)
ExPanD(df = gapminder, cs_id = "country", ts_id = "year",
components = c(descriptive_table = TRUE,
by_group_violin_graph = TRUE,
scatter_plot = TRUE,
regression = TRUE))
OK. While we are at it, we can also include an informative title and a short intro sentence.
library(ExPanDaR)
library(gapminder)
data(gapminder)
ExPanD(df = gapminder, cs_id = "country", ts_id = "year",
title = "Explore the Preston Curve",
abstract = paste("This interactive display uses 'gapminder' data to",
"let you explore the Preston Curve. Scroll down and enjoy!"),
components = c(descriptive_table = TRUE,
by_group_violin_graph = TRUE,
scatter_plot = TRUE,
regression = TRUE))
When you look at the descriptive table you see that it does not offer self-explanatory variable names. Also, no tool-tips are displayed when you hover over the variable names with your mouse.
To add the tool-tip functionality, we need to specify a df_def
data frame that describes the variables. The upside of this is that we no longer need to specify the cross-sectional and time series identifiers as these are also defined in the data frame
library(ExPanDaR)
library(gapminder)
data(gapminder)
df_def <- data.frame(
var_name = names(gapminder),
var_def = c("Name of the country",
"Continent where country is located",
"Year of data",
"Life expectancy in years at birth",
"Population in million",
"Gross Domestic Product (GDP) per capita"),
type = c("cs_id", "factor", "ts_id", rep("numeric", 3))
)
gapminder$pop <- gapminder$pop / 1e6
ExPanD(df = gapminder,
title = "Explore the Preston Curve",
abstract = paste("This interactive display uses 'gapminder' data to",
"let you explore the Preston Curve. Scroll down and enjoy!"),
components = c(descriptive_table = TRUE,
by_group_violin_graph = TRUE,
quantile_trend_graph = TRUE,
scatter_plot = TRUE,
regression = TRUE),
df_def = df_def)
Now tool-tips for the descriptive table inform about our variables. Neat.
When you scroll down the components you will see that the scatter plot is not showing the typical Preston curve by default. We want to change that so that the scatter plot is based on the full sample and includes a LOESS line. It should show GDP per capita as X and life expectancy as Y, with population defining the size of the data points and the continent their color. Also, we want to estimate a panel model of life expectancy with country and year firm fixed effects, two-way clustered standard errors and GDP per capita as explanatory variable (Population is time constant in the ‘gapminder’ data set and as such subsumed by country fixed effects).
There are two ways to achieve that, a simple one and a hard one.
First, the simple one: Make the desired changes in the app, save the configuration of the app to a file that you can remember (button towards the bottom of the page), read that file with readRDS()
into your environment and provide the resulting list as the parameter config_list
to the ExPanD()
function call. Below, I assume that you saved the configuration of the app as my_config.RDS
to the working directory.
library(ExPanDaR)
library(gapminder)
data(gapminder)
df_def <- data.frame(
var_name = names(gapminder),
var_def = c("Name of the country",
"Continent where country is located",
"Year of data",
"Life expectancy in years at birth",
"Population in million",
"Gross Domestic Product (GDP) per capita"),
type = c("cs_id", "factor", "ts_id", rep("numeric", 3))
)
gapminder$pop <- gapminder$pop / 1e6
clist <- readRDS("my_config.RDS")
ExPanD(df = gapminder,
title = "Explore the Preston Curve",
abstract = paste("This interactive display uses 'gapminder' data to",
"let you explore the Preston Curve. Scroll down and enjoy!"),
components = c(descriptive_table = TRUE,
by_group_violin_graph = TRUE,
scatter_plot = TRUE,
regression = TRUE),
df_def = df_def,
config_list = clist)
Now the hard one. You can specify the changes that you want to apply to the default configuration directly in the code. Take a look at clist
to understand how the data is structured. I hope that most list member names are somewhat self explanatory.
library(ExPanDaR)
library(gapminder)
data(gapminder)
df_def <- data.frame(
var_name = names(gapminder),
var_def = c("Name of the country",
"Continent where country is located",
"Year of data",
"Life expectancy at birth, in years",
"Population in million",
"Gross Domestic Product (GDP) per capita in US-$, inflation-adjusted"),
type = c("cs_id", "factor", "ts_id", rep("numeric", 3)),
stringsAsFactors = FALSE
)
gapminder$pop <- gapminder$pop / 1e6
clist <- list(
scatter_x = "gdpPercap",
scatter_y = "lifeExp",
scatter_size = "pop",
scatter_color = "continent",
scatter_loess = TRUE,
scatter_sample = FALSE,
reg_y = "lifeExp",
reg_x = "gdpPercap",
reg_fe1 = "country",
reg_fe2 = "year",
cluster = "4" # Now this is hard to guess
# 1: none, 2: first FE, 3: second FE, 4: both FE
)
ExPanD(df = gapminder,
title = "Explore the Preston Curve",
abstract = paste("This interactive display uses 'gapminder' data to",
"let you explore the Preston Curve. Scroll down and enjoy!"),
components = c(descriptive_table = TRUE,
by_group_violin_graph = TRUE,
scatter_plot = TRUE,
regression = TRUE),
df_def = df_def,
config_list = clist)
Now we have our customized components well documented in code. The scatter plot exhibits the Preston curve. The association is non-linear, potentially explaining the negative coefficient in the panel model. Maybe we want to allow the user to address this issue on-the-fly by defining additional variables?
This can be achieved by adding a component udvars
.
library(ExPanDaR)
library(gapminder)
data(gapminder)
df_def <- data.frame(
var_name = names(gapminder),
var_def = c("Name of the country",
"Continent where country is located",
"Year of data",
"Life expectancy in years at birth",
"Population in million",
"Gross Domestic Product (GDP) per capita"),
type = c("cs_id", "factor", "ts_id", rep("numeric", 3)),
stringsAsFactors = FALSE
)
gapminder$pop <- gapminder$pop / 1e6
clist <- list(
scatter_x = "gdpPercap",
scatter_y = "lifeExp",
scatter_size = "pop",
scatter_color = "continent",
scatter_loess = TRUE,
scatter_sample = FALSE,
reg_y = "lifeExp",
reg_x = "gdpPercap",
reg_fe1 = "country",
reg_fe2 = "year",
cluster = "4" # No this is hard to guess 1: none, 2: first FE, 3: second FE, 4: both FE
)
ExPanD(df = gapminder,
title = "Explore the Preston Curve",
abstract = paste("This interactive display uses 'gapminder' data to",
"let you explore the Preston Curve. Scroll down and enjoy!"),
components = c(descriptive_table = TRUE,
by_group_violin_graph = TRUE,
scatter_plot = TRUE,
udvars = TRUE,
regression = TRUE),
df_def = df_def,
config_list = clist)
This generates another component in your app allowing the user to define additional variables. It is positioned just below the scatter plot. For fun and jiggles, you can define logGpdPercap <- log(gdpPercap)
and verify using the scatter plot that using a log-transformed explanatory variable makes the association more linear, reflecting also in a now positive coefficient in the regression model. If you worry about security, the code that is passed to the server via the user variable definition component is sandboxed in a sense that is only includes the variables of the sample and the functions listed in the help text of the component.
Time to move to the finishing touches.
From time to time it might be useful to include some additional text or other info in between components. For that, you have the option to include an arbitrary number of HTML blocks as additional components. Each html block will be wrapped in a fluid row, so you can use columns similar to what you would do in shiny.
library(ExPanDaR)
library(gapminder)
data(gapminder)
df_def <- data.frame(
var_name = names(gapminder),
var_def = c("Name of the country",
"Continent where country is located",
"Year of data",
"Life expectancy in years at birth",
"Population in million",
"Gross Domestic Product (GDP) per capita"),
type = c("cs_id", "factor", "ts_id", rep("numeric", 3)),
stringsAsFactors = FALSE
)
gapminder$pop <- gapminder$pop / 1e6
clist <- list(
scatter_x = "gdpPercap",
scatter_y = "lifeExp",
scatter_size = "pop",
scatter_color = "continent",
scatter_loess = TRUE,
scatter_sample = FALSE,
reg_y = "lifeExp",
reg_x = "gdpPercap",
reg_fe1 = "country",
reg_fe2 = "year",
cluster = "4" # No this is hard to guess 1: none, 2: first FE, 3: second FE, 4: both FE
)
html_blocks <- c(
paste('

Variation of life expectancy',
"across regions and income levels

",
'
',
"

As you see below, life expectancy varies widely",
"across countries and continents. One potential reason for this",
"variation is the difference in income levels across countries.",
"This association is visualized by the",
"",
"Preston Curve that you also find below.",
"
"),
paste('

Transform variables

',
'
',
"The Preston Curve is far from",
"linear. Maybe you can come up with a transformation",
"of GDP per capita that makes the association",
"a little bit more well behaved?",
"Use the dialog below to define a transformed",
"measure of GDP per capita and assess its association",
"with life expectancy in the scatter plot above.",
"
"),
paste('

Assess Robustness

',
'
',
"You see below that the linear regression coefficient",
"for GDP per capita is negative",
"and signficant in a panel model with country and year",
"fixed effects.",
"Does this also hold when you use a log-transformed version",
"of GDP per capita?",
"
")
)
ExPanD(df = gapminder,
title = "Explore the Preston Curve",
abstract = paste("This interactive display uses 'gapminder' data to",
"let you explore the Preston Curve. Scroll down and enjoy!"),
components = c(descriptive_table = TRUE,
html_block = TRUE,
by_group_violin_graph = TRUE,
scatter_plot = TRUE,
html_block = TRUE,
udvars = TRUE,
html_block = TRUE,
regression = TRUE),
df_def = df_def,
config_list = clist,
html_blocks = html_blocks)
This is it. Now we have a customized version of ExPanD focusing on communicating the robustness of the Preston Curve. If you do not want to run the code yourself, you can take a quick look at the customized version here.
If you want to take customization to the next level, you can always fork the code of the ‘ExPanDaR’ package on GitHub. The code for the shiny app is in the inst/app
folder of the package.
Enjoy.


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tigerforecast.methods.AutoRegressor () |
Description: Implements the equivalent of an AR(p) method - predicts a linear combination of the previous p observed values in a time-series |
tigerforecast.methods.LastValue () |
Description: Predicts the last value in the time series, i.e. |
tigerforecast.methods.PredictZero () |
Description: Predicts the next value in the time series to be 0, i.e. |
tigerforecast.methods.RNN () |
Description: Produces outputs from a randomly initialized recurrent neural network. |
tigerforecast.methods.LSTM () |
Description: Produces outputs from a randomly initialized LSTM neural network. |
tigerforecast.methods.LeastSquares () |
Description: Implements online least squares. |
tigerforecast.methods.WaveFiltering () |
Description: Predicts the last value in the time series, i.e. |


Application Code
SNES (Nonlinear Equation Solver)
TS (Timestepping)
KSP (Krylov Subspace Methods)
PC (Preconditioners)
Matrices
Vectors
Index Sets
BLAS
MPI


Welcome to the music shuffler web helper
Enter the absolute path to the folder you would like to use (i.e. the folder holding your music) :
Override the file in the chosen music folder
Maximum number of tracks to keep
/!\ if you chose to override your music folder, the tracks that do not make it will be deleted from your filesystem
Shuffle !
Progress :
0
% done.