[ The judges |
IOCCC home page |
How to enter |
FAQ |
IOCCC news |
People who have won |
Winning entries ]
Q: How many entries do you receive each year?
A: By tradition, we do not say.Q: How many judging rounds do you have?
A: Are you trying to trick us? We will not say that either.Q: I cannot get entry xyzzy from year 19xx to compile!
A: Some winners, particularly winners from long ago, no longer compile on more modern systems because the C language has evolved (i.e. the modern C compilers are much more strict in what they accept as a valid program), or the entry depended on operation system and library features that where common back then but are different/missing today.
E.g. it is highly unlikely that you'll be able to execute 1984/mullender on a computer you're using to read this FAQ.Q: I found a bug in a previous winner, what should I do?
A: We do not ``maintain'' the contest winners. The code is made available on an ``AS IS'' basis. If you have a FIX for a entry, we suggest that you try and EMail it to the authors and to CC: us using the instructions on the contacting the IOCCC Judges page.
IF you have a FIX, and IF the author does not object, and IF your fix looks reasonable, we will add a web link to a copy of your EMail message for others to see.Q: Are there types of entries that are submitted so frequently that the judges get tired of them?
A: Yes, there are types of entries that show up over and over again.
The guidelines say:We tend to dislike programs that:We like variety. However too often we see (please look at the winning examples given to be aware of the level of the competition):are similar to previous winners
- maze generator
- tic-tac-toe game
- solitaire/Othello game
- generating small primes (below is the list of all prime related winners) As you can see, just a list of primes (let alone small primes) does not cut it anymore.
- self reproducing program
- 1990/scjones
- 1994/smr - do not claim your program is the smallest one without seeing it
- 2000/dhyang - unless you beat this one, your chances are slim
- entries that just print "Hello, world!" It's so 20th century...
- entries that use some complex state machine/table to print something Unbeaten for 12 years and counting...
- rot13
- pi or e computation
HINT: It is not fatal to send in those types of entries, it is just MUCH HARDER to win. Such an entry would have to do something really unique and interesting to even make it into the final judging rounds. Entries that re-use these 'worn out' themes frequently lose to other entries that do something different.
HINT: If you really MUST send in an entry based on an 'over-used theme', be sure that it is obfuscated in several new and novel ways. Be sure to clearly explain near the beginning of your remarks why you are submitting a entry based on an 'over-used theme' and why the judges should not simply toss it out as being boring.
Q: What should I write in the 'remark' section of my entry, if anything at all?
A: As much or as little as you wish.What helps:
- explaining what your entry does
- how to entice it to do what it is suppoosed to do
- what obfuscations are used
- what are the limitations of your entry in respect of portability and/or input data
- how it works (if you are really condescending)
What does not help:
- admitting that your entry is not very obfuscated (you see, the contest is called IOCCC, not INVOCCC); but even if you do not admit it, not very obfuscated entries have a minuscule chance to win (although 2000/tomx is a notable counterexample)
- mentioning your name or any identifying information in the remark section (or in the C code for that matter) - we like to be unbiased during the judging rounds; we look at the author name only if an entry wins
- leaving the remark section empty
Q: I am confused: what should I put to the 'build' section?
A: The 'build' section must contain UNIX shell-style commands to compile your C language entry in a file named prog.c and produce an executable file. More than one command is allowed, as far as the size rule is followed. Usually one (or more) of these commands will be an invocation of a C compiler (use cc or gcc), but there may be exceptions (or not anymore, see 2000/tomx).
If your entry depends on a particular source file name during compilation or execution, you will have to put a linecp prog.c desired_name.cbefore the compiler invocation line.
If in doubt, putcc -o prog prog.cin the 'build' section.Q: What happened to the winners for 1997, 1999 and 2002?
A: There are none. There was no IOCCC in those years.Q: Why don't you publish non-winners?
A: Because the publication on the IOCCC site IS the award! Anyone is free to put their IOCCC hopefuls, lookalikes and/or non-winners on their web page for everyone to see.Q: How much time does it take to judge the contest?
A: It takes a fair amount of time to setup, run, answer EMail, process (note that we do not contact the author if an entry does not compile or does not work as advertised, we might attempt to fix obvious compilation problems or incompatibilities, but no more than that - so be sure that your entry does work on at least a couple different platforms, at least one of them being UNIX or POSIX-conforming) entries, review entries, trim down the set entries to a set of winners, doing the write-up of the winners, announcing the winners, reviewing final edits of the winning entry set, posting the winners, being flamed :-), tell folks who send in late entries to wait until the next contest, etc... It takes a few weekends and a number nights of study and work ... which is hard given that we are busy with many other activities as well.Q: What platform should I assume for my entry?
A: Your entry must compile with GCC and run under at least one flavor of UNIX (e.g. Linux or Solaris). To improve chances to win, it should work under both BSD and SysV style UNIX (think stty cbreak), and should not depend on endianness. If it can compile and run on Windows and/or Mac (see 2000/thadgavin), even better.Q: I would like to mirror the IOCCC web site. May I do so?
A: Please see: http://www.ioccc.org/mirror-request.html for more information.Q: I want to publish some parts of the IOCCC in an article, or book, or newsletter, or use then in class/instructional notes, or quote from the IOCCC. May I do so?
A: Please ask the IOCCC judges first. Please send your request using the instructions on the contacting the IOCCC Judges page.Q: What are the grand prize / Best of Show winners?
A: In 1984-1987, the grand prize winners are:
Starting from 1988, the entry we liked the most in that year is called "Best of Show". Here are the "Best of Show" entries:
In 1993, 1994 and 1995 the judges were unable to select a clear overall winner. So to give a nod to the entry that had the highest approval ranking from the judges, they used the following awards:These could be considered the 'better entry' for those years with 1 or more other entries that came in close behind.
- 1993/rince - Most Well Rounded
- 1994/shapiro - Most Well Rounded
- 1995/leo - Best Use of Obfuscation
Q: I managed to get entry xyzzy from year 19xx to compile; now it fails to run!
A: switch(19xx/xyzzy) {}
- case 1984/mullender: try
main(){int i=512;do write(1," :-)\b\b\b\b",9),usleep(i);while(--i);}instead.
Enable reverse wraparound in your xterm window for best results.
break;- case 1991/davidguy: disable access control of your X server (xhost +). If it does not help (or if you see black screen), restart the X server without access control and with visual depth of 8 bits or less.
break;- default: Try enabling -fwritable-strings for GCC or its equivalent for other compilers.
Q: How did the IOCCC get started?
A: One day (23 March 1984 to be exact), back Larry Bassel and I (Landon Curt Noll) were working for National Semiconductor's Genix porting group, we were both in our offices trying to fix some very broken code. Larry had been trying to fix a bug in the classic Bourne shell (C code #defined to death to sort of look like Algol) and I had been working on the finger program from early BSD (a bug ridden finger implementation to be sure). We happened to both wander (at the same time) out to the hallway in Building 7C to clear our heads.We began to compare notes: ''You won't believe the code I am trying to fix''. And: ''Well you cannot imagine the brain damage level of the code I'm trying to fix''. As well as: ''It more than bad code, the author really had to try to make it this bad!''.
After a few minutes we wandered back into my office where I posted a flame to net.lang.c inviting people to try and out obfuscate the UN*X source code we had just been working on.
BTW: I (Landon Curt Noll) had to post this typo correction. Thus began the tradition of putting typos in the contest rules and guidelines ... to make them more obfuscated of course! :-)BTW: This posting was made back in the days when AT&T was the evil giant. Now, Microsoft makes AT&T look mild and kind in comparison. :-( (IMHO) ).
BTW: See the story about the '' Bill Gates'' award. :-)
OK, back to the story. We (Larry and I) received a number of entries by EMail. When we began to receive messages from outside of the US, Larry and I decided to include International in the name. The 1st IOCCC winners were posted on 17 April 1984.
There were 4 winners in 1984: BTW: The <dis>honorable mention wished to remain anonymous. While many have asked who it was, we have continued to follow the author's wish to remain anonymous. A few years ago, we asked the author if they still wanted to remain anonymous. They said: ''Yes, I want to keep my anonymity. But you can tell them that I am a well known for my connection to the C language"''. It was not until 2001 that another anonymous entry received an award.The name used in the posting of the 1st IOCCC winner posting was International Obfuscated C Code Contest or IOCCC for short.BTW: The 1984 winner remains one of my (Landon Curt Noll) all time favorites.
The posting said ''1st annual'', so in 1985 we held the 2nd IOCCC contest and the tradition continues as the longest running contest on the Internet.
P.S. Part of the inspiration for making the IOCCC a contest goes to the Bulwer-Lytton fiction contest.
P^2.S. See the overall README for more details.
© Copyright 1984-2020,
Leo Broukhis, Simon Cooper, Landon Curt Noll
- All rights reserved |


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General support queries. User Guides: https://support.komodoplatform.com/ Support staff: Angelina#6231 gcharang#6833. If you want to ask JL777 a question, use the #ask-jl777 channel.
100 messages
Before 05-Dec-19 12:00 AM
mcgs08-Jun-19 05:38 PM
Question about the hack reclaim process: A small amount of KMD has been sent into compromised addresses and the rightful owners are to access the compromised address and send the small amount to their new destination address. My question is this: There is a tx fee, so are users to send the small amount minus the tx fee? Is that sufficient?
One more question, how do i activate chips 3? I only see a "CHIPS" option in Verus Agama
jspook1608-Jun-19 06:02 PM
disregard.... the steps in the link had the step number for "3." in the line for step 2. I just realized that
bushtea08-Jun-19 06:55 PM
After submitting the form, what is the next step in the process? Do I have to do anything?
gcharang08-Jun-19 06:55 PM
@bushtea
After submitting the form, please read this document carefully to understand the refund process. https://support.komodoplatform.com/support/solutions/articles/29000029973-steps-to-reclaim-funds-after-the-agama-incident
Summary: We have sent a small amount of KMD to your compromised address as confirmation that we have moved those funds. Send 0.0001 KMD from the compromised address to the New address you have submitted in the form to prove ownership.
1) login using compromised seed
2) send 0.0001 KMD to the new address you specified in the form
bushtea08-Jun-19 06:56 PM
thank you
mcgs08-Jun-19 07:13 PM
@gcharang I sent the whole amount in the transaction. It was like .00123456 that I received, then I just sent that same amount to the new address. Elsewhere in a different document it doesn't say to send exactly 0.0001 KMD, it said to just send 'the small amount'. Is this a problem? Or is it enough to have successfully sent a small tx?
gcharang08-Jun-19 07:14 PM
@mcgs that is fine
yanomi08-Jun-19 07:22 PM
I have 0.00020509 still in the old compromised address, this was before i sent the form in. Do i need to wait for the 0.0001 KMD from you guys?
gcharang08-Jun-19 07:26 PM
no @yanomi just send it to the new address you submitted
yanomi08-Jun-19 07:30 PM
@gcharang ok thanks
orange sunshine08-Jun-19 07:51 PM
hello, seems something happened on the 4th which also affected my account. Where is the best spot to start in the threads to get up to speed on what happened and how to recover
yanomi08-Jun-19 07:52 PM
the link above
orange sunshine08-Jun-19 07:52 PM
sorry, which link above ?
yanomi08-Jun-19 07:52 PM
check the pin messages
LB08-Jun-19 07:52 PM
And what if you dont need to reclaim but are having issues just getting into Agama?
How do I import a private key into Bitcoin-Qt? bitcoind is nowhere to be found, and pywallet says it requires "bsddb" when I run it, which is also nowhere to be found. I generated some vanity addre...
jspook1608-Jun-19 09:30 PM
are you using qt on windows?
farl08-Jun-19 09:30 PM
Mac
jspook1608-Jun-19 09:31 PM
i was unable to launch it in linux
ok copy
I was missing some libraries and i didnt want to start changing things
hopefully you were able to safegaurd all of your assets
farl08-Jun-19 09:34 PM
No, unfortunately not
all KMD is gone
jspook1608-Jun-19 09:35 PM
to safe address?
farl08-Jun-19 09:35 PM
to hacker
jspook1608-Jun-19 09:36 PM
sorry to hear that. I think the community is coming up with a method to try and replinish lost assets
I plan to contribute if there is a donation address. Im guessing every little helps
farl08-Jun-19 09:37 PM
That's nice to hear. I have faith in the team and the community. I will wait the next phase for claiming lost funds
jspook1608-Jun-19 09:37 PM
Copy that
farl08-Jun-19 09:38 PM
Good night
jspook1608-Jun-19 09:51 PM
were barterdex and hyperdex and atomicdex affected as well?
gcharang08-Jun-19 09:53 PM
@jspook16 no
the exploit was targetted specifically at agama
jspook1608-Jun-19 09:55 PM
copy
Quarterback08-Jun-19 10:16 PM
Hey there! I have writen an wrong address in the Form for missing KMD which i made on 5th of June. where can i correct this?
Deleted User08-Jun-19 10:19 PM
no chance LOL @Quarterback
@gcharang sleep buddy ?
gcharang08-Jun-19 10:21 PM
@Quarterback did you already send the 0.0001 transaction to the new address?
Deleted User08-Jun-19 10:21 PM
I completed the questionnaire 2 days ago, when to expect an answer ?
i didn't send 0.0001
Should i send 0.0001 KMD from address where i need to received KMD ?
gcharang08-Jun-19 10:22 PM
@Deleted User
After submitting the form, please read this document carefully to understand the refund process. https://support.komodoplatform.com/support/solutions/articles/29000029973-steps-to-reclaim-funds-after-the-agama-incident
Summary: We have sent a small amount of KMD to your compromised address as confirmation that we have moved those funds. Send 0.0001 KMD from the compromised address to the New address you have submitted in the form to prove ownership.
1) login using compromised seed
2) send 0.0001 KMD to the new address you specified in the form
Quarterback08-Jun-19 10:24 PM
ok, so first i have to sync the chain in the Verus Agama i imported the seed already. I will see then a small Amount on my old Address and will transfer 0.00001 KMD to my new Address, did i understand that right?
sync cause the Lite Mode dis not run
gcharang08-Jun-19 10:25 PM
@Quarterback you need to first submit the form with updated claim address
then send 0.0001 to it from the old address


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Mary Magdalene in The DaVinci Code
"The woman they are speaking of," Teabing explained, "is Mary Magdalene. Peter is jealous of her." "Because Jesus preferred Mary?" "Not only that. The stakes .
http://www.haverford.edu/relg/faculty/amcguire/relg221b/DAVinciCodeExc.htm
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Peter, Paul And Mary Magdalene (2006) - The Bible Exposed
past as Simon Peter, the apostle Paul, and Mary Magdalene is knowing what to leave out. . New Testament, Peter speaks in the name of God to Simon Magus, .
http://thebibleisnotholy.files.wordpress.com/2010/06/ehrman-peter-paul-and-mary-magdalene-2006.pdf
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Primary Sources - The Gospel Of Mary | From Jesus To Christ ... - PBS
In this gnostic gospel, Mary Magdalene appears as a disciple, singled out by Jesus for . Peter asks her to tell them the words of Jesus which she remembers. . And she began to speak to them these words: "I," she said, "I saw the Lord in a .
http://www.pbs.org/wgbh/pages/frontline/shows/religion/maps/primary/mary.html
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Mary Magdalene - Wikipedia, the free encyclopedia
Mary Magdalene (original Greek ????? ? ?????????), or Mary of Magdala and . Talmudic passages speak of a Miriam "hamegadela se'ar nasha", "Miriam, the . Peter said to Mary, "Sister we know that the Savior loved you more than the .
http://en.wikipedia.org/wiki/Mary_Magdalene
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Mary Magdalene - Sedona Institute for Comparative Theology ...
The intent of the author of Philip was to suggest that Mary Magdalene was more . Peter states, Did he really speak with a woman without our knowledge (and) .
http://sanctuarypublications.com/articles/Mary_Magdalene.shtml
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Gospel of Thomas Saying 114 - GospelThomas.com
(114) Simon Peter said to them, "Mary should leave us, for females are not worthy . (whether one speaks of Mary the mother of Jesus or of Mary Magdalene).
http://www.earlychristianwritings.com/thomas/gospelthomas114.html
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Mary Magdalene - Crystalinks
In our current timeline - one wonders if Mary Magdalene was the wife of Jesus . As the first witness to the Resurrection, Mary Magdalene hastened to tell Peter . "Did he then speak secretly with a woman, in preference to us, and not openly?
http://www.crystalinks.com/marymagdalene.html
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Gospel of Mary - Wikipedia, the free encyclopedia
Arguments in favor of Mary Magdalene are based on her status as a known . Levi, in his defense of Mary and her teaching, tells Peter "Surely the Savior . According to the story, Mary speaks up with words of comfort and encouragement .
http://en.wikipedia.org/wiki/Gospel_of_Mary
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Was Jesus Married? A Careful Look at the Real Evidence
What Language Did Jesus Speak? . This is all we know about Mary Magdalene from the biblical gospels. . Part 4b: Mary Magdalene in The Gospel of Peter .
http://www.patheos.com/blogs/markdroberts/series/was-jesus-married-a-careful-look-at-the-real-evidence/
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Who saw the risen Jesus first? Mary Magdalene? Peter? Cleopas ...
Response: Who saw Jesus first? I would say that Mary Magdalene was the first person to see the risen Jesus. I base this on Mark 16:9 because there it .
http://www.aboutbibleprophecy.com/q40.htm
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Peter, Paul and Mary Magdalene: The Followers of Jesus in History ...
Peter, Paul and Mary Magdalene: The Followers of Jesus in History and Legend . After Jesus, Saint Peter, Saint Paul and Mary Magdalene are the most .
http://books.google.com/books/about/Peter_Paul_and_Mary_Magdalene_The_Follow.html?id=Tlvgp7WggvEC
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Who is Mary Magdalene?
But I think the number of visits to my website speaks for itself - many people have . There are a large number of books written about Mary Magdalene, but I can . the apostles and Peter created a new image of Mary as the penitent prostitute, .
http://www.frcoulter.com/presentations/marymagdalene.html
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Mary Magdalen in Gnostic Tradition
In Gnostic myths Mary Magdalen was portrayed as a key figure among Jesus' . she agrees, and teaches them until Peter, furious, asks, 'Did he really speak .
http://www.womenpriests.org/magdala/gnostic.asp
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ST. PETER, ST. JAMES, AND MARY MAGDALENE - Facts and Details
Peter, James the Elder and John are regarded as the Apostle inner circle. . The Holy Spirit gave the disciples the ability to speak a number of different languages, . Some think Mary Magdalene may have been a close adviser of Jesus with .
http://factsanddetails.com/world.php?itemid=1420
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Mary Magdalene and Two Disciples Visit the Tomb - Bible Gateway
Disciple at the expense of Peter has become popular, but is not supported by the text, . If they do speak to the other disciples, John does not mention it. . Jesus Appears to Mary Magdalene (20:11-18) John does not describe when Mary .
http://www.biblegateway.com/resources/commentaries/IVP-NT/John/Mary-Magdalene-Two-Disciples
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Oxford University Press: Peter, Paul and Mary Magdalene: Bart D ...
Description. Bart Ehrman, author of the highly popular books Truth and Fiction in The Da Vinci Code, Lost Christianities, and the New York Times bestseller .
http://www.oup.com/us/catalog/general/subject/ReligionTheology/BiblicalStudies/NewTestament/~~/dmlldz11c2EmY2k9OTc4MDE5NTM0MzUwMg==
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HE IS RISEN! Mary Magdalene
Mary Magdalene and the angels at the tomb of Jesus; Jesus speaks to Mary; Magdalene, apostle to the apostles. . Mary Magdalene, weeping at the empty tomb, sees a rather unsympathetic angel. Then she mistakes . See Peter & John .
http://www.jesus-story.net/mary_magdalene_at_tomb.htm
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Ministry Matters | Blog | 4 Names: Mary Magdalene, Philip ...
Mar 1, 2013 . 4 Names: Mary Magdalene, Philip, Lazarus, and Peter. By Adam . He calls to each of us, speaking our names, and thus ourselves, into being.
http://www.ministrymatters.com/teach/blog/entry/2658/4-names-mary-magdalene-philip-lazarus-and-peter
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the pope speaks about mary magdalene on her feast day - News.va
Jul 22, 2012 . THE POPE SPEAKS ABOUT MARY MAGDALENE ON HER FEAST DAY . Out of the whole world one man, Peter, is chosen to preside at the .
http://www.news.va/en/news/the-pope-speaks-about-mary-magdalene-on-her-feast
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Witnesses to the Resurrection
It says that Mary Magdalene went to the tomb shortly before dawn and saw that it was open. Thinking that someone had moved the body, she ran and told Peter .
http://www.gospel-mysteries.net/witnesses-resurrection.html


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--- layout: post title: Seb does Ireland – 30th of September to 3rd of October date: '2009-09-03T15:31:00.001+01:00' tags: [user-groups] modified_time: '2009-09-03T15:31:27.876+01:00' blogger_id: tag:blogger.com,1999:blog-4015568221071268916.post-9175724124068385768 comments: true blogger_orig_url: http://serialseb.blogspot.com/2009/09/seb-does-ireland-30th-of-september-to.html ---
And because I’m equally excited about going back to Ireland as I am going back to Scotland, I’ll be doing a bunch of things there.
Dublin alt.net at 8pm in Seagrass http://www.seagrassdublin.com
An intro to OpenRasta
Dublin
Not confirmed yet but should be a talk on agility in Dublin
Galway
Speaking at a conference on agility, will post about it when I get more details.


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The most fascinating thing about emotional intelligence is that it can be learnt, by anyone willing to put the time and effort into it. I've seen how important having emotional intelligence can impact the workspace in a positive way. Its interesting to see that having emotional intelligence is regarded as some of the most important traits people look for in others when hiring.
I practice mindfulness meditation at work everyday for 10-15 minutes using the headspace app. it gives me time to pause and reflect on my thoughts and to bring focus and clarity to my daily workflow. Often times, after doing mindfulness, I find myself in quite a reflective state. I thoroughly enjoy it, as our minds can often become overly cluttered with thoughts, and its very easy to loose touch with whats important to you.


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{% url "heading-detail-northern-ireland" country_code=selected_origin_country|lower commodity_code=heading.commodity_code nomenclature_sid=heading.goods_nomenclature_sid as ni_details_url %}
{% include "hierarchy/_ni_warning.html" with ni_details_url=ni_details_url %}


#include <hwlib-xy.hpp>
Public Member Functions | |
| constexpr | xy (int_fast16_t x, int_fast16_t y) |
| construct from its x and y values | |
| constexpr | xy () |
| default constructor, zero's the x and y | |
| constexpr xy | operator+ (const xy rhs) const |
| add two | |
| constexpr xy | operator- (const xy rhs) const |
| subtract two | |
| constexpr xy | operator/ (const int_fast16_t rhs) const |
| divide by an integer | |
| constexpr xy | operator* (const int_fast16_t rhs) const |
| multiply by an integer | |
| constexpr bool | operator== (const xy &rhs) const |
| test whether two xy values are equal | |
| constexpr bool | operator!= (const xy &rhs) const |
| test whether two xy values are unequal | |
Public Attributes | |
| int_fast16_t | x |
| x value of the pair | |
| int_fast16_t | y |
| y value of the pair | |
Friends | |
| constexpr xy | operator* (int n, const xy &rhs) |
| reverse multiply by an integer | |
| constexpr xy | transpose (const xy &rhs) |
Related Functions | |
(Note that these are not member functions.) | |
| class xy_all_t | all (xy v) |
| template<typename T > | |
| T & | operator<< (T &lhs, xy rhs) |
an xy pair
This class abstracts an (at least 16 bit) integer pair. It is used for instance for the size of a graphics or character screen, and to identify a single pixel location, or the distance between two pixel locations.
|
related |
iterate over all values in an xy range
This function returns an object that can be used to iterate over all values in the range [ 0 ... x-1 ] * [ 0 ... y-1 ].
|
related |
print an xy pair
An xy pair is printed in [x,y] format


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/future.20unbundled.20class.20hierarchy.html ---
Sebastian Ullrich said (in the multiset thread)
Just idle speculation, I suppose in a future unbundled class hierarchy we would rather have an instance
is_zero ∅ (multiset a)
instead ofhas_zero (multiset a)
?
Does this say "in Lean 4, the whole type class system is going to change a lot"?
I realised over the last two weeks that mathematicians think "G is a group" is a proposition, but that Hyp : group G
is not. Maybe if a mathematician is pushed they might concede that actually it's "the pair (G,*)
is a group" which is the proposition, but Lean still wants more (identity and inverse). I guess what I'm saying is that questions from students have made me myself question why things are set up this way.
The last bit has to do with constructivity in type theory
Even if a function is uniquely defined in the relational sense, it's not quite the same as a function in type theory
I thought it might do. So if someone decided to make the fundamental typeclass is_group
we'd end up carrying around all three things (mul, inv, identity)
So while in ZFC it suffices to have (G,*) since 1 is unique, in lean you really want a concrete witness to this
What about making G
plus mul
+ inv
+ 1
into a structure, and making is_group
a propositional typeclass?
In classical lean, you can nevertheless define 1 from * if you wanted, but its definitional reductions won't be great
I'm just trying to question the way we do it, not because of groups, but because of diamonds in general. Presumably if every typeclass was a Prop the diamond problems would go away
That option has been discussed (group_struct
plus is_group
)
My response is, if group_struct
isn't useful on its own it's not worth the definition
I was wondering whether Sebastian's comments meant that some changes would be implemented
if every interesting group_struct
is also a group then it may as well be bundled in the definition
Remember there were problems with product of metric / top spaces (which I think were solved) and problems with modules over rings (which I think were not)
Can this sort of approach be used to solve them?
Sebastian's comments relate to Leo's plans with the new algebraic hierarchy
If every interesting group_struct
were a group, but this caused problems later down the line, then this would be an argument for not bundling.
i.e. the @[algebra]
classes
that's a rather vague worry
Where do I read about Leo's plans and the @[algebra]
classes?
there's a wiki page for it
https://github.com/leanprover/lean/wiki/Refactoring-structures
I remain skeptical. I will wait for Leo's implementation before considering anything along these lines
So here we are just waiting to see what happens in Lean 4 and then we go with whatever changes get made?
pretty much
it's not like there is any other option
I thought that another vague plan was to get a whole bunch of maths out of core Lean
I believe that will happen as well
and then it's up to mathlib to decide whether it's group
or is_group
It's possible that changes to simp
will necessitate a certain design
And it's not just "leo broke today's system", it could just as easily be "Leo's new system is way better and we would be stupid not to use it"
I predict that most of the large scale structural changes in mathlib after lean 4 will be done because we want to take advantage of some new feature, not to fix a breaking change
I'd seen that wiki page several times before, and my interpretation of it was "it's mostly CS stuff that I don't understand, but it documents an old change to the structure command which was already made"
I now think this interpretation might be incorrect -- it might say "even though there is use_old_structure_command
or whatever it's called, there are still some things here which are not in Lean and might in the future be in Lean"
Last updated: Apr 24 2020 at 19:02 UTC


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For service details visit the Pricing Page.
To purchase some services visit the Selection Page.


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Id | {{ element.id }} | Employee Name | {{ element.employee_name }} | Employee age | {{ element.employee_age }} | Employee salary | {{ element.employee_salary }} | Total: {{ getTotalSal() | currency }} sum of subset : {{ getTotalsubset() | currency }} |
Employee profile image | {{ element.profile_image == "" ? "Not Available" : element.profile_image }} |
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Declare the e-mail headers. It accepts anything the mail method in ActionMailer accepts.
Source: show
# File app/models/contact.rb, line 8 def headers { :subject => "Extinguish Form", :to =><EMAIL_ADDRESS>:from => %Q("#{name}" <#{email}>) } end


Week 7 cultural blog post
Confidence
During my most happy time of my life I didn't really think about anything. I never had to worry about anything. I feel a lot of confidence in my life during that time and feel a lot of success.
In General I try to be the best person I can, so I would say 5 for agree. The last time someone ask me for advice was about life situation and coding which I felt happy I could help on!
I hate people who sterotype mainly because I dont believe anyone should be judging a book by its cover for no reason. Everyone has a good and bad side and it's the individual's job to show it differently.


We are a team of undergraduate students from Maturi Venkata Subba Rao Engineering College(MVSREC), who have come together to put their engineering skills to use and implement ideas to design and fabricate a Formula style race car. We have participated in SUPRA SAE-INDIA 2015 and were nominated for the 'BEST INNOVATION AWARD' for building India's first All-wheel drive car'RONIN'. And now, we are charged up and are ready to fire again with the new version, 'ZERO'
SAE India is an affiliate society of SAE International, registered as an Indian non profit engineering and scientific society dedicated to the advancement of mobility community in India. As an individual member driven society of mobility practitioners, SAEINDIA comprises members who are individuals from the mobility community, which includes engineers, executives from industry, government officials, academics and students.
SUPRA SAE-INDIA is an event that challenges young minds to compete as teams in designing a Formula category vehicle, there is no other event like this in the country. To develop a vehicle consisting of various sub systems, meeting engineering specifications, timelines and above all meeting safety standards is a challenge not for the faint hearted. And then making the rubber hit the road at a premium race track under gazing eyes of experienced technical experts and other enthusiasts is a thrill not just for the participants, but for the mobility community at large.
| Wheel Base | 65 in |
| Track Width | 45F/43R in |
| Length | 2833 mm |
| Width | 1301 mm |
| Height | 1150mm |
| Gross Weight | 280 Kg |
| Engine Displacement | 499 cc, Supercharged |
| Max. Power(bhp) | 31 @ 5000RPM |
| Max. Torque(Nm) | 44 @ 4000RPM |
| Top speed | 100kmph |
| Layout | RWD |
| Suspension | Double A-arm, Direct acting Dampers |
| Steering | Rack and Pinion |
| Braking | 4 outboard brake discs,Hydraulic |
| Wheels | 155/55 R14, Bridgestone tyres with premium X2 Alloys |


The Infromation about the gates can be accessed by selecting the Gate from home Screen.
The Simulations are run by clicking on the Input switches
The color Red denotes 0, False or LOW value of Input
The color Green denotes 1, True or High value of Input
The Symbol ' denotes the compliment or Inverted value of any input
In the equations of Logic Gates, x is assumed to be the output of the gate.
This app is Created by Kanwar Ujjaval Singh
This app is targeted for High School Electronics students who may wish to learn the basic concepts of Logic Gates.
You may contact me through App Home Page.


- Developer 🥑
- Currently: Gatsby
- Formerly: IBM
- Portland, OR
You should use GraphQL for everything and serve your entire app as static assets
Development today isdifferent
The monolithic CMS is dying
The future is thecontent mesh
This is amazing
- Manage data using purpose-built tools
- Load data from anywhere using APIs
- Build custom UIs that combine multiple data sources
This is terrible
- Atrocious performance pitfalls
- Complex business logic to combine data sources
- UIs can become tightly coupled to back-ends
- State management leads to nightmarish complexity
Also: async is hard
How to tame the Content Mesh
- Centralize data access with GraphQL
- Serve only optimized static assets
Centralize data with GraphQL
- Better explorability
- Single source of truth
- More scalable & team-friendly
- Consistent, shareable dev workflow
Go Static for everything
- Blazing fast performance
- Simpler deployment flow
- Reduced DevOps overhead
How this works in Gatsby
- Node APIs to load any data source into GraphQL
- “Early bind” data at build time
- Generate static assets
- Automate performance tuning
- Deploy to a CDN (e.g. Netlify)
- Rehydrate to a React app in the browser
Make the Right Thingthe Easy Thing
Design your tools so the laziest, shortcuttiest,
under-the-gunniest decisions a developer can make
will still result in an excellent user experience.
But...
“Isn’t Gatsby a static site generator?
My app has dynamic data!”
static assets !== static apps
React apps are dynamicGatsby sites are React apps
Something something transitive property...Gatsby sites are dynamic
You should use GraphQL for everything and serve your entire app as static assets
Thanks!
Jason Lengstorf
Follow me on Twitter: @jlengstorf


{% include "head.html" %}
{% include "navbar.html" %} {% include "header.html" %} {% include "services.html" %} {% include "tech.html" %} {% include "about.html" %} {% include "team.html" %} {% include "contact.html" %} {% include "footer.html" %} {% include "modals/development.html" %} {% include "modals/integration.html" %} {% include "modals/consulting.html" %} {% include "scripts.html" %}


If you feel you're not yet ready to propose and would like us to help you put together a good proposal, please send us a few lines about what you'd like to speak about and we will get back to you and work on this together. Hebrew or English.
This form will be not published. It will be sent to the moderators team to help you propose your talk.


--- layout: page title: "about me" header-img: "img/bg-post.jpg" ---
test automation engineer with the experience in Web, Mobile (Android/iOS), API test automation.
Passionate about absorbing new ideas, adopting new tools and technologies for better product quality.


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# File lib/rake/contrib/rubyforgepublisher.rb, line 10 10: def initialize(projname, user) 11: super( 12<EMAIL_ADDRESS>13: "/var/www/gforge-projects/#{projname}", 14: "html") 15: end


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This host page loads two gadgets (which should appear in the table below) and provides the left gadget a function "set", and the right gadget "get", by which they can communicate.
| Writes | Reads |
|---|---|
See comments in the source of this page for more information.


This page contains the notes for the first part of R Workshop Module 3: Data Analysis with R, which is part of the R Workshop series prepared by ICJIA Research Analyst Bobae Kang to enable and encourage ICJIA researchers to take advantage of R, a statistical programming language that is one of the most powerful modern research tools.
Click here to go to the workshop home page.
Click here to go to the workshop Modules page.
Click here to view the accompanying slides for Module 3, Part 1.
Navigate to the other workshop materials:
tidyverse
Source: tidyverse.org
In Module 3, we will be exploring several tidyverse
packages that offer powerful functionalities for data analysis.
tidyverse
is a set of R packages as well as an opinionated philosophy underlying these packages on how the practice of working with data in R should be. Although it is not the only way of working with data in R, tidyverse
packages have become highly popular among R users.
Installing all tidyverse
packages can be easily done with the follwoing command:
# Install from CRAN
install.packages("tidyverse")
# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("hadley/tidyverse")
Installing tidyverse
package installs the following:
ggplot2
, dplyr
, tidyr
, readr
, purrr
, tibble
hms
, stringr
, lubridate
, forcats
feather
, haven
, httr
, jsonlite
, readxl
, rvest
, xml2
modelr
, broom
Alternatively, each pacakge can be installed separately:
# Install ggplot2
install.packages("ggplot2")
# Install both dplyr and tidyr with a single commend
# with a character vector of the package names
install.packages(c("dplyr", "tidyr"))
Once installed, we can now import the packages using library()
:
# This imports the core tidyverse packages
library(tidyverse)
# Or import packages separately
library(dplyr)
library(tidyr)
In the following, we will explore two powerful tidyverse
packages for wrangling tabluar data, dplyr
and tidyr
.
Source: tidyverse.org
In this section, we will learn the following dplyr
functions:
arange()
select()
rename()
filter()
mutate()
transmute()
left_join()
summarise()
group_by()
ungroup()
%>%
, the pipe operatorPlease note that this section is not intended to be an exhaustive documentation and description of the listed functions–or dplyr
package for that matter. For more information, please check out the reference materials listed below.
arrange(tbl, ...)
arrange()
is used to sort the rows by a selection of column variables. The first argument of arrange()
is a tabular data object. In fact, as we will see soon, almost all other functions of tidyverse
packages take a data object as the first input. This tabular data can be of the data.frame
class or any of its extensions, such as tidyverse
’s very own tibble
class.
arrange()
takes columns by which to sort the data as additional arguments. If multiple columns are provided, the sorting taks place in a hierarchical manner: the data is first sorted by the first column, then by the second column, and so on. By default, sorting is in an ascending order (smaller to larger). Sorting in a descending order can be done by wrapping the column with desc()
.
The following example sorts, or arranges, the ispcrime
data by the county
column. To make it manageable, we also use the head()
to print only the first few rows:
# without using pipe
head(arrange(ispcrime, county))
# with using pipe
ispcrime %>%
arrange(county) %>%
head()
## year county violentCrime murder rape robbery aggAssault propertyCrime
## 1 2011 Adams 218 0 37 15 166 1555
## 2 2012 Adams 205 0 28 16 161 1587
## 3 2013 Adams 222 1 33 21 167 1564
## 4 2014 Adams 222 2 34 13 173 1550
## 5 2015 Adams 227 5 39 9 174 1268
## 6 2011 Alexander 119 0 14 4 101 290
## burglary larcenyTft MVTft arson
## 1 272 1241 36 6
## 2 287 1261 30 9
## 3 297 1204 55 8
## 4 305 1201 39 5
## 5 274 944 43 7
## 6 92 183 11 4
As shown above, there are two ways to work with dplyr
functions:
%>%
We will take a closer look at %>%
below. In the meantime, it is sufficient to note that the object before %>%
is fed, or “piped,” into the following function as the input for its first argument.
The folloiwng code illustrates using desc()
to sort in a descending order:
# sort by county, in a DESCENDING order
ispcrime %>%
arrange(desc(county), desc(year)) %>%
head()
## year county violentCrime murder rape robbery aggAssault propertyCrime
## 1 2015 Woodford 29 0 5 0 24 227
## 2 2014 Woodford 25 0 4 0 21 283
## 3 2013 Woodford 17 0 10 2 5 349
## 4 2012 Woodford 23 0 3 1 19 405
## 5 2011 Woodford 15 1 4 0 10 350
## 6 2015 Winnebago 2692 22<PHONE_NUMBER> 8279
## burglary larcenyTft MVTft arson
## 1 35 186 6 0
## 2 50 218 13 2
## 3 81 257 11 0
## 4 65 329 10 1
## 5 90 247 10 3
## 6 1958 5635 618 68
filter(tbl, ...)
filter()
is used to filter data to keep rows that match given conditions. As in arrange()
, the first argument is a tabular data object. Conditional expressions to filter data, which should evaluate as TRUE
or FALSE
for each row, are given as additional arguments.
The following code filters ispcrime
to return a subset of data where year
value matches 2015 AND murder
value is greater than 0. As before, we use head()
to show only the first few rows.
ispcrime %>%
filter(
year == 2015,
murder > 0
) %>%
head()
## year county violentCrime murder rape robbery aggAssault propertyCrime
## 1 2015 Adams 227 5 39 9 174 1268
## 2 2015 Bond 5 1 0 0 4 130
## 3 2015 Bureau 53 1 17 4 31 360
## 4 2015 Champaign 918 7 127<PHONE_NUMBER>
## 5 2015 Coles 173 1 33 14 125 632
## 6 2015 Cook 28791 534 1911 11217 15129 124784
## burglary larcenyTft MVTft arson
## 1 274 944 43 7
## 2 39 84 7 0
## 3 110 244 6 0
## 4 1100 4235 196 37
## 5 146 463 13 10
## 6 20550 90675 12547 1012
select(tbl, ...)
rename(tbl, ...)
select()
and rename()
both take a tabular data object as the first argument input. The following arguments are columns to select or rename. These two functions are quite similar to each other, except that select()
returns the selected columns only while rename()
keeps all columns.
It is actually possible to renaming each selected column with select()
as well. select()
can be also used to exclude a section of columns using a minus (-
) sign while keeping the rest.
Let’s take a look at an example on how to use select()
to select columns, with changing their names in certain cases. The following code select four columns of ispcrime
while changing the names of violentCrime
and propertyCrime
columns.
# select with renaming columns
ispcrime %>%
select(year, county, v_crime = violentCrime, p_crime = propertyCrime) %>%
head()
## year county v_crime p_crime
## 1 2011 Adams 218 1555
## 2 2011 Alexander 119 290
## 3 2011 Bond 6 211
## 4 2011 Boone 59 733
## 5 2011 Brown 7 38
## 6 2011 Bureau 42 505
As noted earlier, select()
can be used to exclude certain columns. The following code selects all columns of ispcrime
except violentCrime
and propertyCrime
:
# excluding columns
ispcrime %>%
select(-violentCrime, -propertyCrime) %>%
head()
## year county murder rape robbery aggAssault burglary larcenyTft MVTft
## 1 2011 Adams 0 37 15<PHONE_NUMBER> 36
## 2 2011 Alexander 0 14 4 101 92 183 11
## 3 2011 Bond 1 0 0 5 58 147 5
## 4 2011 Boone 0 24 8 27 152 563 14
## 5 2011 Brown 0 1 0 6 14 22 1
## 6 2011 Bureau 0 4 3 35 90 405 8
## arson
## 1 6
## 2 4
## 3 1
## 4 4
## 5 1
## 6 2
transmute(tbl, ...)
mutate(tbl, ...)
transmute()
and mutate()
both take a tabular data object as the first argument input. The following arguments are expressions to transform existing columns or add new ones. An exisiting column is modified with an expression with the same column name. On the other hand, a new column is created with an expression having a new column name.
As with select()
and rename()
, the two functions here are quite similar to each other, except that transmute()
returns the trasnformed/aded columns only while mutate()
keeps all columns.
The following code adds a new column named totalCrime
, whose value equals the sum of violentCrime
and propertyCrime
values:
ispcrime %>%
mutate(totalCrime = violentCrime + propertyCrime) %>%
head()
## year county violentCrime murder rape robbery aggAssault propertyCrime
## 1 2011 Adams 218 0 37 15 166 1555
## 2 2011 Alexander 119 0 14 4 101 290
## 3 2011 Bond 6 1 0 0 5 211
## 4 2011 Boone 59 0 24 8 27 733
## 5 2011 Brown 7 0 1 0 6 38
## 6 2011 Bureau 42 0 4 3 35 505
## burglary larcenyTft MVTft arson totalCrime
## 1 272 1241 36 6 1773
## 2 92 183 11 4 409
## 3 58 147 5 1 217
## 4 152 563 14 4 792
## 5 14 22 1 1 45
## 6 90 405 8 2 547
If it were with transmute()
, the result would include only the totalCrime
column.
left_join(tbl1, tbl2, by = NULL, ...)
left_join()
takes two tabular data objects to join as the first two arguments. by
takes a chracter vector containing a selection of variables to join tables by. By default, all columns with common names are used. The ouput is a merged table where the columns in the second table is added to the first table as new columns where the values of by
columns match.
left_join()
is named so because it keeps all observations in the first (left) table. There, in fact, are other types of joins including: inner_join()
, right_join()
, full_join()
, semi_join()
, and anti_join()
.
Source: Wickham, H. (2017). R for Data Science
The following code left-joins regions
table to ispcrime
table on the county
values. The regions
table has two columns: region
and county
. Accordingly, the final output is basically the ispcrime
table with one extra column, region
, that corresponds to the county
value:
ispcrime %>%
left_join(regions, by = "county") %>%
head()
## year county violentCrime murder rape robbery aggAssault propertyCrime
## 1 2011 Adams 218 0 37 15 166 1555
## 2 2011 Alexander 119 0 14 4 101 290
## 3 2011 Bond 6 1 0 0 5 211
## 4 2011 Boone 59 0 24 8 27 733
## 5 2011 Brown 7 0 1 0 6 38
## 6 2011 Bureau 42 0 4 3 35 505
## burglary larcenyTft MVTft arson region
## 1 272 1241 36 6 Central
## 2 92 183 11 4 Southern
## 3 58 147 5 1 Southern
## 4 152 563 14 4 Northern
## 5 14 22 1 1 Central
## 6 90 405 8 2 Central
summarise(tbl, ...)
summarize(tbl, ...)
summarise()
and summairze()
are identical except who they are spelled. This is because the original package author, Hadly Wickham, is an Australian and wrote the names accordingly. Later, due to the demand from American programmers, summarize()
was added.
summarise()
takes a tabular data object as its first argument, and the following arguments are expressions to “summarize” data. Each of these expressions creates a summary column, which can be given a name just as in the case of transmute()
or mutate()
.
Here, we use summarise()
to get the average count of violentCrime
and propertyCrime
, which are named violentCrimeAverage
and propertyCrimeAverage
respectively:
ispcrime %>%
summarise(
violentCrimeAverage = mean(violentCrime, na.rm = TRUE),
propertyCrimeAverage = mean(propertyCrime, na.rm = TRUE)
)
## violentCrimeAverage propertyCrimeAverage
## 1 500.9702 2912.905
group_by(tbl, ...)
ungroup(tbl, ...)
group_by
is a powerful function that allows us to manipulate a data object in “groups,” which is defined by the discrete values in a column, or a selection of columns. As in other dplyr
functions, the first argument is a tabular object. The following arguments are columns by which to group the data.
group_by()
and ungroup()
are the opposite operations. group_by()
creates a data object with groups. ungroup()
removes the groups.
The following example shows how a groupd data table looks like:
ispcrime %>%
group_by(year)
## # A tibble: 510 x 12
## # Groups: year [5]
## year county violentCrime murder rape robbery aggAssault propertyCrime
## <int> <fct> <int> <int> <int> <int> <int> <int>
## 1 2011 Adams 218 0 37 15 166 1555
## 2 2011 Alexa~ 119 0 14 4 101 290
## 3 2011 Bond 6 1 0 0 5 211
## 4 2011 Boone 59 0 24 8 27 733
## 5 2011 Brown 7 0 1 0 6 38
## 6 2011 Bureau 42 0 4 3 35 505
## 7 2011 Calho~ 13 0 0 0 13 56
## 8 2011 Carro~ 8 0 1 0 7 206
## 9 2011 Cass 12 0 1 0 11 119
## 10 2011 Champ~ 1210 5 127<PHONE_NUMBER>
## # ... with 500 more rows, and 4 more variables: burglary <int>,
## # larcenyTft <int>, MVTft <int>, arson <int>
Now, let’s see how group_by()
can be used in combination with another dplyr
function. In the following, we group our data by year
values and get summaries (average crime counts) for each year:
ispcrime %>%
group_by(year) %>%
summarise(
violentCrimeAverage = mean(violentCrime, na.rm = TRUE),
propertyCrimeAverage = mean(propertyCrime, na.rm = TRUE)
)
## # A tibble: 5 x 3
## year violentCrimeAverage propertyCrimeAverage
## <int> <dbl> <dbl>
## 1 2011 538. 3329.
## 2 2012 520. 3192.
## 3 2013 503. 2940.
## 4 2014 464. 2609.
## 5 2015 478. 2481.
We have been using the pipe operator, %>%
, in our code examples. Now is a good time to take a closer look at it.
data %>% function1(arg2, ...) %>% ...
%>%
is a binary operator that requires two objects for it to work on. Think of +
as another example of a binary operator. In the case of %>%
the object preciding the operator has to be the first argument for the function that follows the oprator. In other words, the preceding object is injected into the following function as its first argument. The result of the %>%
operator is the output of the function.
In fact, as we have already seen before, multiple functions can be chained, or “piped,” using %>%
as long as the output of the first “piping” generates a suitable object for the first argument of the next function to be chained.
with piping, we can have a coding style that is cleaner and more intuitive–that reflects the way we think about how we apply the operations to the object, rather than the tranditional syntax where the function that acts on the last comes at the beginning of the statement:
# piping style
object %>%
function1(arguments1) %>%
function2(arguments2) %>%
function3(arguments3)
# traditional style
function3(
function2(
function1(
object,
arguments1
),
arguments2
),
arguments3
)
# or
function3(function2(function1(object, arguments1), arguments2), arguments3)
dplyr
in actionLet’s take a look at more concrete examples of using dplyr
functions to work on data manipulation and analysis. For those of you who were there at my presentation for the February R&A meeting, these are the some of the examples used in that presentation–now we can understand them better!
ispcrime %>%
filter(substr(county, 1, 1) == "D", year %in% c(2014, 2015)) %>%
mutate(totalCrime = violentCrime + propertyCrime) %>%
select(year, county, totalCrime)
## year county totalCrime
## 1 2014 De Kalb 2218
## 2 2014 De Witt 182
## 3 2014 Douglas 116
## 4 2014 Du Page 12576
## 5 2015 De Kalb 2173
## 6 2015 De Witt 140
## 7 2015 Douglas 173
## 8 2015 Du Page 12538
In this example, we we want to answer the following question: What are the total crime count in 2014 and 2015 for counties whose name starts with letter D?
ispcrime
dataispcrime
data into filter()
county
column value matches “D” (substr(county, 1, 1) == "D"
)year
column value has to be either 2014 or 2015 (year %in% c(2014, 2015)
)mutate()
totalCrime
, which is the sume of violentCrime
and propertyCrime
valuesselect()
year
, county
, and totalCrime
columns only.ispcrime %>%
left_join(regions) %>%
group_by(region, county) %>%
summarise(annualAvgCrime = sum(violentCrime, propertyCrime, na.rm = TRUE) / n()) %>%
arrange(desc(annualAvgCrime))
## # A tibble: 102 x 3
## # Groups: region [4]
## region county annualAvgCrime
## <fct> <fct> <dbl>
## 1 Cook Cook 182818.
## 2 Northern Du Page 14316.
## 3 Northern Lake 12779.
## 4 Northern Winnebago 12275.
## 5 Northern Will 11078.
## 6 Southern St. Clair 9262.
## 7 Central Sangamon 8876.
## 8 Northern Kane 8332.
## 9 Central Peoria 7229.
## 10 Central Champaign 6567.
## # ... with 92 more rows
In this example, we we want to answer the following question: what is the annual average of the crime count per county, shown with the region it belongs to and sorted by the average count?
ispcrime
dataispcrime
data into left_join()
region
column from regions
table to our datagroup_by()
region
and county
column.summarise()
violentCrime
and propertyCrime
, divided by total counts. Since the input is grouped, the summarising work is also done by group.arrange()
annualAvgCrime
, in a descending orderdplyr
What we have seen is only a tip of an iceberg that is dplyr
. Although the aforementioned functions are among the most commonly used in usual data manipulation and analysis tasks, the description and examples here are by no means comprehensive. Also, dplyr
offers many other functions to facilitate data manipulation work. I recommend you to check out the following resources to learn more about dplyr
:
dplyr
on tidyverse.orgdplyr
CRAN documentationdplyr
Github repositorySource: tidyverse.org
Source: R for Data Science
We have learned the notion of “tidy” data in the second part of the previous module. In short, a dataset is “tidy” when:
tidyr
functionsIn this section, we will learn the following tidyr
functions:
gather()
spread()
unite()
separate()
separate_rows()
Please note that, as before, this section is not intended to be an exhaustive documentation and description. For more information, please check out the reference materials listed below.
gather(tbl, key = "key", value = "value", ..., na.rm = FALSE, ...)
gather()
is a function to make “wide” data longer, by “gathering” multiple columns into two columns of key-value pairs. The function takes a tabular data object, such as data.frame
or tibble
, as its first argument. The second argument, key
, defines the name of a new column for key and the third argument, value
, defines the name of a new column for value. These two arguments have default values that are, not surprisingly, "key"
and "value"
, respectively.
The following arguments (...
) are a selection of columns to be used whose name will be made into values for the key
column and whose value will be made into the values of the value
column.
spread(tbl, key, value, fill = NA, ...)
spread()
is the opposite of gather()
. As in gather()
, the first argument is a tabular data object. Then, key
is an existing column containing the names of new columns, and value
is another column containing the values for the new columns.
Let’s take a look at an example illustrating how gather()
and spread()
work. First, we prepare a data to be used, which is the familiar ispcrime
dataset, except we exclude violentCrime
and propertyCrime
columns using select()
. We assign this to ispcrime_2
.
ispcrime_2 <- ispcrime %>%
select(-violentCrime, -propertyCrime) %>%
as_tibble()
ispcrime_2
## # A tibble: 510 x 10
## year county murder rape robbery aggAssault burglary larcenyTft MVTft
## <int> <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 2011 Adams 0 37 15<PHONE_NUMBER> 36
## 2 2011 Alexan~ 0 14 4 101 92 183 11
## 3 2011 Bond 1 0 0 5 58 147 5
## 4 2011 Boone 0 24 8 27 152 563 14
## 5 2011 Brown 0 1 0 6 14 22 1
## 6 2011 Bureau 0 4 3 35 90 405 8
## 7 2011 Calhoun 0 0 0 13 14 41 1
## 8 2011 Carroll 0 1 0 7 38 165 2
## 9 2011 Cass 0 1 0 11 41 71 3
## 10 2011 Champa~ 5 127<PHONE_NUMBER> 3756 164
## # ... with 500 more rows, and 1 more variable: arson <int>
Now, let’s try gather()
. We will name the key column type
for the type of crime, and the value column count
. Then we will “gather” all 8 crime type columns, from murder
to arson
. The output is a reshaepd dataset that is 8-times “longer” than the input (4,080 = 510 * 8). The name of each input column is now turned into a value for the type
column, and the corresponding crime count is a value for the count
column.
ispcrime_2 %>%
gather(key = "type", value = "count", murder:arson)
## # A tibble: 4,080 x 4
## year county type count
## <int> <fct> <chr> <int>
## 1 2011 Adams murder 0
## 2 2011 Alexander murder 0
## 3 2011 Bond murder 1
## 4 2011 Boone murder 0
## 5 2011 Brown murder 0
## 6 2011 Bureau murder 0
## 7 2011 Calhoun murder 0
## 8 2011 Carroll murder 0
## 9 2011 Cass murder 0
## 10 2011 Champaign murder 5
## # ... with 4,070 more rows
Now, to see that spread()
is the opposite process, here we show the result of gather()
-ing and than spread()
-ing the ispcrime_2
object. The final output is in the same shape as the input.
ispcrime_2 %>%
gather(key = "type", value = "count", murder:arson) %>%
spread(key = type, value = count)
## # A tibble: 510 x 10
## year county aggAssault arson burglary larcenyTft murder MVTft rape
## <int> <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 2011 Adams 166 6 272 1241 0 36 37
## 2 2011 Alexander 101 4 92 183 0 11 14
## 3 2011 Bond 5 1 58 147 1 5 0
## 4 2011 Boone 27 4 152 563 0 14 24
## 5 2011 Brown 6 1 14 22 0 1 1
## 6 2011 Bureau 35 2 90 405 0 8 4
## 7 2011 Calhoun 13 0 14 41 0 1 0
## 8 2011 Carroll 7 1 38 165 0 2 1
## 9 2011 Cass 11 4 41 71 0 3 1
## 10 2011 Champaign 870 28 1384 3756 5 164 127
## # ... with 500 more rows, and 1 more variable: robbery <int>
unite(tbl, col, ..., sep = "_", remove = TRUE)
unite()
works to combine multiple columns into a single column by concatenating their values. Its first argument is a tabular data object. Then, col
is a new column created by “uniting” the following column inputs (...
). sep
input is a string to be inserted as a seperator between column values when concatenation takes place. The default value for sep
is the underscore symbol (_
). Finally, remove
takes a boolean value for whether removing the original columns or not.
separate(tbl, col, into, sep = "[^[:alnum:]]+", remove = TRUE, ...)
separate()
is the opposite of unite()
. As in unite()
, the first argument is a tabular data object. Here, col
is a column to be split into multiple columns. into
is a character vector for separated column names, which may have two or more elements. sep
is a separator between values, which could be _
, or any other. Finally, remove
is a boolean for whether removing the original columns or not.
unite()
and sperate()
are great for cleaning up date or name variables. One common application is to convert two columns for “last name” and “first name” into a single column for “full name”, or vice versa.
The following example illustrates the use of unite()
and separate()
. We first prepare a data object for this exercise: we left_join()
ispcrime
and regions
and select()
only six columns in the following order: year
, region
, county
, volentCrime
, and propertyCrime
. This transformed data is assigned to ispcrime_3
.
ispcrime_3 <- ispcrime %>%
left_join(regions) %>%
select(year, region, county, violentCrime, propertyCrime) %>%
as_tibble()
## Joining, by = "county"
ispcrime_3
## # A tibble: 510 x 5
## year region county violentCrime propertyCrime
## <int> <fct> <fct> <int> <int>
## 1 2011 Central Adams 218 1555
## 2 2011 Southern Alexander 119 290
## 3 2011 Southern Bond 6 211
## 4 2011 Northern Boone 59 733
## 5 2011 Central Brown 7 38
## 6 2011 Central Bureau 42 505
## 7 2011 Southern Calhoun 13 56
## 8 2011 Northern Carroll 8 206
## 9 2011 Central Cass 12 119
## 10 2011 Central Champaign 1210 5332
## # ... with 500 more rows
Now, we will try unite()
on region
and county
columns to create a region_county
column. Notice that we do not provide any input for sep
argument and, accordingly, the default separator, "_"
, is used to concatenate the two columns.
ispcrime_3 %>%
unite(col = region_county, region, county)
## # A tibble: 510 x 4
## year region_county violentCrime propertyCrime
## <int> <chr> <int> <int>
## 1 2011 Central_Adams 218 1555
## 2 2011 Southern_Alexander 119 290
## 3 2011 Southern_Bond 6 211
## 4 2011 Northern_Boone 59 733
## 5 2011 Central_Brown 7 38
## 6 2011 Central_Bureau 42 505
## 7 2011 Southern_Calhoun 13 56
## 8 2011 Northern_Carroll 8 206
## 9 2011 Central_Cass 12 119
## 10 2011 Central_Champaign 1210 5332
## # ... with 500 more rows
To see that unite()
and separate()
are the opposite procedures, we show the result of unite()
-ing and separate()
-ing the ispcrime_3
data. Note that we have to provide "_"
as the sep
input to gain back the original input.
ispcrime_3 %>%
unite(col = region_county, region, county) %>%
separate(col = region_county, into = c("region", "county"), sep = "_")
## # A tibble: 510 x 5
## year region county violentCrime propertyCrime
## <int> <chr> <chr> <int> <int>
## 1 2011 Central Adams 218 1555
## 2 2011 Southern Alexander 119 290
## 3 2011 Southern Bond 6 211
## 4 2011 Northern Boone 59 733
## 5 2011 Central Brown 7 38
## 6 2011 Central Bureau 42 505
## 7 2011 Southern Calhoun 13 56
## 8 2011 Northern Carroll 8 206
## 9 2011 Central Cass 12 119
## 10 2011 Central Champaign 1210 5332
## # ... with 500 more rows
separate_rows(tbl, ..., sep = "[^[:alnum:]]+", ...)
We also have separate_rows()
, which is similar to separate()
, except the former results in a longer table while the latter results in a wider table.
tidyr
As in the earlier dplyr
case, what we have seen is only part of what tidyr
offers. I recommend you to check out the following resources to learn more about tidyr
tidyr
on tidyverse.orgtidyr
CRAN documentationtidyr
Github repository


md-grid-list
is a two-dimensional list view that arranges cells into grid-based layout.
See Material Design spec here.
An md-grid-list
must specify a cols
attribute which sets the number of columns in the gird. The
number of rows will be automatically determined based on the number of columns and the number of
items.
The height of the rows in a grid list can be set via the rowHeight
attribute. Row height for the
list can be calculated in three ways:
Fixed height: The height can be in px
, em
, or rem
. If no units are specified, px
units are assumed (e.g. 100px
, 5em
, 250
).
Ratio: This ratio is column-width:row-height, and must be passed in with a colon, not a
decimal (e.g. 4:3
).
Fit: Setting rowHeight
to fit
This mode automatically divides the available height by
the number of rows. Please note the height of the grid-list or its container must be set.
If rowHeight
is not specified, it defaults to a 1:1
ratio of width:height.
The gutter size can be set to any px
, em
, or rem
value with the gutterSize
property. If no
units are specified, px
units are assumed. By default the gutter size is 1px
.
It is possible to set the rowspan and colspan of each md-grid-tile
individually, using the
rowspan
and colspan
properties. If not set, they both default to 1
. The colspan
must not
exceed the number of cols
in the md-grid-list
. There is no such restriction on the rowspan
however, more rows will simply be added for it the tile to fill.
A header and footer can be added to an md-grid-tile
using the md-grid-tile-header
and
md-grid-tile-footer
elements respectively.


DOM Scripting: Web Design with JavaScript and the Document Object Model 读书笔记
DOM: Document Object Model
W3C对DOM的定义是：“一个与系统平台和编程语言无关的接口，程序和脚本可以通过这个接口动态的对文档的内容、结构和样式进行访问和修改。”
var A="happy", B="3"
, 区分大小写。var beatles = Array();
或者var beatles = ["John", "Paul", "George", "Ringo"];
。function name(argument) {statements;}
document.getElementById()
方法，返回一个对象。element.getElementsByTagName(tag)
方法，返回一个对象数组。object.getAttribute(attribute)
方法，返回对象的一个属性。object.setAttribute(attribute, value)
方法，修改属性节点的值。if (!method) {use another method; }
。document.write()
and innerHTML
.
document.write()
method: write element into HTML. Cons: have to call function in HTML, JS isn’t separated from HTML.innerHTML
: read and change the HTML content of a certain element. Cons: not DOM standard. Which means, the inserted string is only read into HTML webpage as a string, not a DOM tree structure. Pros: can insert a large amount of code immediately.createElement()
, createTextNode()
, appendChild()
and insertBefore()
. Can create elements, better method. Working principle: HTML builds the structure of website, and JavaScript functions modify little details of HTML.var variable = setTimeout("function", interval)
to execute function with a certain time interval.To quote from Tim Berners Lee, inventor of World Wide Web:
Web users ultimately want to get at data quickly and easily. They don’t care as much about attractive sites and pretty design.
Intellectual property is an important legal and cultural issue. Society as a whole has complex issues to face here: private ownership vs. open source, and so on.


Main page
Carousel
If you want to add new slide to carousel, you have to do four things:
- Add new item to slider box:
... ...
- Add new indicator to list:
-
...
...
- Add new background image do
resources/img/carousel/
directory. - Set slide background in javascript. Go to
main.js
and add new line slider section. Do not forget to use proper id.//setting up sliders images ... ... $('#slide4').css("background-image", "url(resources/img/carousel/photo4.jpg)"); //EOF setting up sliders images
Services
In services section it is important to add, that it uses "Stacked Icons" from Font Awesome. This means that if you are bored with circles :), you can use other shapes from Font Awesome, like squares or even bells. All you need to do is changing icon classes:
Web Development
...
Web Design
...
Training
...
Consulting
...
Quotation
Quotation background by default is located in resources/img/quotation-bg.jpg
.
Skills charts
Drawing skills charts is done by great
jquery-plugin-circliful.
Big advantage of using this plugin is integration with Font Awesome icons.
Most important data-*
attributes for you, are:
- data-text - big text displayed inside of the circle
- data-percent - chart's filling percentage
- data-icon - Font Awesome icon which you want to use inside of the circle
- data-info - smaller text displayed inside of the circle
Notice: you can change color themes on your website, so we decided to set color of the chart dynamically.
That means that we are setting this color by JavaScript (main.js
) and by default it uses styles
from footer
element. Because of that it is important to do not delete this element.
If you want to add new chart you should:
- Create new chart box with new ID add all needed data:
- Add ne line in
main.js
file for new created chart://drawing skills charts var el=document.getElementById('footer'); var cs=document.defaultView.getComputedStyle(el,null); var bg=cs.getPropertyValue('background-color'); $('#skillNo1').html("").data("fgcolor", bg).data("iconcolor", bg).circliful(); ... ... $('#skillNo5').html("").data("fgcolor", bg).data("iconcolor", bg).circliful(); //EOF skills charts
For more detailed documentation you should go to https://github.com/pguso/jquery-plugin-circliful.
Portfolio
Portfolio uses Isotope to display, filter and sort projects.
You can add new categories to portfolio section. Then you can add new projects to existing categories.
Adding new category should by done by adding input
field and it's label in
filter div
element. The most important is to define new category class in
data-filter
element:
... ...
After that you should add new project to isotope container:
... ...... ...Project name
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum semper gravida porta.
Notice: remember to add category name (used in data-filter
) as a class to project css classes.
Projects images by default are kept in resources/img/projects/
directory
Contact form
Contact form validation uses bootstrap validator by Cina Saffary. For more information please go to https://github.com/1000hz/bootstrap-validator
Google Maps
Using Google Maps is very simple. All you need to do is go to main.js
and edit
coordinates in LatLng()
function.
function initGoogleMap() { var var_location = new google.maps.LatLng(45.430817,12.331516); //change coordinates here var var_mapoptions = { scrollwheel: false, center: var_location, zoom: 14 }; var var_marker = new google.maps.Marker({ position: var_location, map: var_map, title:"Venice"}); //change marker's title var var_map = new google.maps.Map(document.getElementById("map-container"), var_mapoptions); var_marker.setMap(var_map); }


All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Published: | 2016-08-12 |
| Author: | Philipp Ottolinger |
| Maintainer: | Philipp Ottolinger <philipp at ottolinger.de> |
| BugReports: | https://github.com/ottlngr/bundesligR/issues |
| License: | GPL-3 |
| URL: | https://github.com/ottlngr/bundesligR |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | bundesligR results |
| Reference manual: | bundesligR.pdf |
| Package source: | bundesligR_0.1.0.tar.gz |
| Windows binaries: | r-devel: bundesligR_0.1.0.zip, r-release: bundesligR_0.1.0.zip, r-oldrel: bundesligR_0.1.0.zip |
| OS X binaries: | r-release: bundesligR_0.1.0.tgz, r-oldrel: bundesligR_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=bundesligR to link to this page.


| Foods | Games | Movies |
|---|---|---|
| Pizza | World of Warcraft | Avengers: Infinity War |
| Wings | Magic the Gathering | Avengers: Endgame |
| Lobster | Settlers of Catan | The Matrix |
| Strawberries | Overwatch | Aliens |
I really enjoyed Avatar: The Last Airbender when I was young and the show holds up great even today. I was able to share it with my son, Cooper, who also enjoyed it immensely. Some of my other favorite shows include Game of Thrones and Neon Genesis Evangelion.
I have had the incredible opportunity to travel to some pretty exotic places in my life including Australia, China, Greece, Italy, Costa Rica, just to name a few. These were great life experiences that I think helped me round out the person who I am today. Some day I'd really love to go visit Japan.
How I feel when me and the boys get together..


Classes and interfaces
PGraph¶
The main PGraph class that is intended for
user usage.
« More »
PGraphEdgeExistsException¶
Exception thrown if an edge between two vertices
exists.
« More »
PGraphException¶
The main Exception class that is intended for
being the benchmark for all Exceptions.
« More »
PGraphInvalidTypeException¶
Exception thrown if the parameter passed has an invalid
type.
« More »
PGraphLinkedList¶
Extension of the SPLDoublyLinkedList class.
« More »
PGraphNegativeCycleException¶
Exception thrown if a graph contains a negative cycle.
« More »
PGraphNoEdgeException¶
Exception thrown if an edge between
certain vertices doesn't exist.
« More »
PGraphNoPathException¶
Exception thrown if there is no path
betwee two vertices.
« More »
PGraphNoVertexException¶
Exception thrown if a vertex doesn't exist.
« More »
PGraphVertex¶
The main PGraph class that is intended for
user usage.
« More »
PGraphVertexExistsException¶
Exception thrown if a vertex exists.
« More »


link: In the Help screen, blue or green text that can be
{_tap tapped}. Blue text represents a link to another help page, while green
represents a link to a definition popup like this one. A blue link can also be
{_long-press long-pressed} to open the page in a new {_tab}.


| 014:022 Khan | : |
And Shaitan (Satan) will say when the matter has been decided: "Verily, Allah promised you a promise of truth. And I too promised you, but I betrayed you. I had no authority over you except that I called you, so you responded to me. So blame me not, but blame yourselves. I cannot help you, nor can you help me. I deny your former act in associating me (Satan) as a partner with Allah (by obeying me in the life of the world). Verily, there is a painful torment for the Zalimun (polytheists and wrong-doers, etc.)." |
| 014:022 Maulana | : |
And the devil will say, when the matter is decided: Surely Allah promised you a promise of truth, and I promised you, then failed you.  I cannot come to your help, nor can you come to my help. I deny your associating me with Allah before. Surely for the unjust is a painful chastisement. |
| 014:022 Pickthal | : |
And Satan saith, when the matter hath been decided: Lo!   So blame not, but blame yourselves. I cannot help you, nor can ye help me, Lo! I disbelieved in that which ye before ascribed to me. Lo! for wrong-doers is a painful doom. |
| 014:022 Rashad | : |
And the devil will say, after the judgment had been issued, "GOD has promised you the truthful promise, and I promised you, but I broke my promise. I had no power over you; I simply invited you, and you accepted my invitation. Therefore, do not blame me, and blame only yourselves. My complaining cannot help you, nor can your complaining help me. I have disbelieved in your idolizing me. The transgressors have incurred a painful retribution." |
| 014:022 Sarwar | : |
When the decree of God is issued, satan will say, "God's promise to you was true, but I, too, made a promise to you and disregarded it. I had no authority over you. I just called you and you answered. Do not blame me but blame yourselves. I cannot help you and you cannot help me. I did not agree with your belief that I was equal to God." The unjust will face a painful punishment. |
| 014:022 Shakir | : |
And the Shaitan shall say after the affair is decided: Surely Allah promised you the promise of truth, and I gave you promises, then failed to keep them to you, and I had no authority over you, except that I called you and you obeyed me, therefore do not blame me but blame yourselves: I cannot be your aider (now) nor can you be my aiders; surely I disbelieved in your associating me with Allah before; surely it is the unjust that shall have the painful punishment. |
| 014:022 Sherali | : |
And when the matter is decided, Satan will say, 'ALLAH promised you a promise of truth, But I promised you and failed you.  So blame me not, but blame your ownselves. I cannot succour you nor can you succour me. I have already disclaimed your associating me with ALLAH. For the wrongdoers there shall, surely, be a grievous punishment.' |
| 014:022 Yusufali | : |
And Satan will say when the matter is decided: "It was Allah Who gave you a promise of Truth: I too promised, but I failed in my promise to you.  I cannot listen to your cries, nor can ye listen to mine. I reject your former act in associating me with Allah. For wrong-doers there must be a grievous penalty." |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الكافرون > تخلي المتبوعين عن الأتباع Pillars of Islam> Islamic> The Disbelievers> The Masters' Abandonment of the Followers الإيمان > الغيب > الشيطان > أتباعه Faith> The Unseen World> Satan, the Devil> His Followers الإيمان > الغيب > الشيطان > عداوته لآدم وبنيه Faith> The Unseen World> Satan, the Devil> His Hostility to Adam and his Sons
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