01
Home remedies
Click here to know the home remedies for some common diseases.
read moreA home remedy (sometimes also referred to as a granny cure) is a treatment to cure a disease or ailment that employs certain spices, herbs, vegetables, or other common items
Herbal medicines are generally considered to be safe and effective agents. Therefore, people more and more turn to herbal medicine because they believe that plant remedies are free from undesirable side effects. Nowadays, more and more people are turning away from conventional medicines, because they believe that ‘natural’ substances like medicinal plants are safer than synthetic substances. It is estimated that 80% of the world population consume different forms of herbal products and the use of herbal medicines is growing. It is estimated that the usage of herbal products increases annually at a rate of 10-20%
Due to the current pandemic situation the whole world is scared to go out out to hospital for their regular checkups and for the common disease like cold,cough etc...So for a cure they use the online apps and webpages.But checking these without knowing what the actual disease is,can be harmful because every allopathic medicine comes with side effects. So we have found out the best way to tackle this situation."The home remedies".Home remedies was the root of treatment during the previous century .So why dont we use them now?
Click here to know the precautions and uses of some common home remedies.
click here


Id: {{professor.professorId}}
Date Of Join: {{professor.dateOfJoin | date}}
Salary: {{professor.salary}}
Is Phd: {{professor.isPhd}}


You have a marketplace but would like to scale your business? We integrate Artificial Intelligence / Machine Learning algorithms to allow a better experience to your users and increase the user engagement.
Get benefit of the state-of-the-at technology, have a better user retention and higher conversion.


Check for updates immediately.
Alternatively, you can select Help | Check for Updates (for Windows or *NIX) or IntelliJ IDEA | Check for Updates (for macOS) from the main menu.


Lecture 4
Qi Wang, Department of Statistics
Aug 27, 2018
Drew is a risk taker. On any given weekend, Drew takes risks with or without monetary compensation. He gets paid 20% of the time he takes risks. The risks involved are to either drink something weird (like garlic butter) or do something silly (like shave his head into a mohawk). Drew gets paid and drinks something weird 16% of the time. Drew does not get paid and drinks something weird 72% of the time.
You are previewing movies for your young nephew. You have 1284 movies available to view, 272 of which are G-rated. Your nephew enjoys movies with puppets, which make up 94 of your G-rated movies. There are only 30 movies with puppets that are not G-rated. if you happen to pick a movie that has puppets, what is the probability that it is G-rated?
Three cards are dealt successively at random and without replacement from a standard deck of 52 playing cards. What is the probability of receiving, in order, a king, a queen, and a jack?


Whitelabel functionality will be limited to allowing users to customize the logos & options defined within MegaMeeting for an account, we will NOT white-label any of the developed solution, or notifications within the platform. (Users scheduling white-label mettings will need to send their own email notifications to attendees, to ensure proper white-label functionality)


DON JUAN DEMARCO A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 7.5
U.S. Availability: general release 4/7/95 Running Length: MPAA Classification: PG-13 (Mature themes, nudity, profanity)
Starring: Marlon Brando, Johnny Depp, Faye Dunaway, Rachel Ticotin, Bob Dishy, Geraldine Pailhas Director: Jeremy Leven Producers: Francis Ford Coppola, Fred Fuchs, and Patrick Palmer Screenplay: Jeremy Leven Cinematography: Ralf Bode Music: Michael Kamen Released by New Line Cinema
Over the past fifteen years, Marlon Brando (ON THE WATERFRONT, LAST TANGO IN PARIS, THE GODFATHER) has been mostly absent from the screen. With the exception of a few small parts--some good (A DRY WHITE SEASON, THE FRESHMAN) and some terrible (CHRISTOPHER COLUMBUS: THE DISCOVERY)-- the man once hailed as the greatest American actor has eschewed leading roles. Until now, that is. With DON JUAN DEMARCO, Brando is attempting something of a minor comeback.
The title character, a modern day Don Juan (with the Italian last name of DeMarco), is played by Johnny Depp. At the age of 21, he claims to have made love to more than 1500 women. He has been rejected only once, by the enchanting and innocent Ana (Geraldine Pailhas), but, as is so often the case, she was "the only one who really mattered." So, with no possibility of a life with his true love, Don Juan decides to kill himself. He climbs high atop a billboard and demands that the police send him the greatest living swordfighter so he can die in a duel. What he gets instead is Dr. Jack Mickler (Marlon Brando), a massively- overweight, near-to-retirement psychiatrist who pretends to be Don Octavio de Flores and talks Don Juan down from his perch. Then, at Woodhaven State Hospital, where the young man is temporarily held, Jack petitions his boss (Bob Dishy) for the right to supervise the newcomer's treatment.
DON JUAN DEMARCO is a lighthearted, frothy fable about romance and the way a patient's fantasies affect his doctor's reality. Despite several astute lines which give the film an intellectual appeal, this is not an exceptionally deep movie. The playful, often humorous tone, coupled with Don Juan's accent, suggests THE PRINCESS BRIDE. Writer/director Jeremy Leven takes pleasure in clouding the division between what's real and what isn't, but he never stretches matters to such an extreme that DON JUAN DEMARCO is reduced to a mindless farce.
Johnny Depp is appealing as the Latin lover, but the truly meaty role belongs to Brando. His Jack Mickler (aka Don Octavio) represents the audience's anchor as the film shifts between romantic illusion and clinical reality. There's nothing overtly original in the character, but Brando shows he can still give a strong performance. This is far from his best work, but he's more than a match for the part, and brings to Jack a human quality that encourages compassion and empathy.
DON JUAN DEMARCO has a seductively sensual veneer, but the full compliment of the title character's sexual conquests lacks the romance of the low-key relationship between Jack and his wife (Faye Dunaway). These two act like they've been married for thirty-two years, and her wonder at his sudden re-awakening is charmingly unaffected. There is proof here-- as if any is really needed--that couples don't have to be under thirty to capture an audience's heart.
With so much blurring between reality and fantasy, there are bound to be a few missteps, but Leven keeps these to a minimum, and the film is mostly a success in its approach to its themes. Don Juan asks the four essential questions about existence: "What is sacred?", "Of what is the spirit made?", "What is worth living for?", and "What is worth dying for?" The proposed answer to all is "love", that singular emotion which pervades this disarming motion picture.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Storage and retrieval for the Berkeley DB access methods are based on key/data pairs. Both key and data items are represented by the DBT data structure. (The name DBT is a mnemonic for data base thang, and was used because noone could think of a reasonable name that wasn't already in use somewhere else.)
typedef struct {
void *data; u_int32_t size; u_int32_t ulen; u_int32_t dlen; u_int32_t doff; u_int32_t flags;
} DBT;
Key and data byte strings may reference strings of essentially unlimited length. See Database limits for more information.
In order to ensure compatibility with future releases of Berkeley DB, all fields of the DBT structure that are not explicitly set should be initialized to 0 before the first time the structure is used. Do this by declaring the structure external or static, or by calling the C library routine bzero(3) or memset(3).
By default, the flags structure element is expected to be 0. In this default case, when being provided a key or data item by the application, the Berkeley DB package expects the data structure element to point to a byte string of size bytes. When returning a key/data item to the application, the Berkeley DB package will store into the data structure element a pointer to a byte string of size bytes.
The elements of the DBT structure are defined as follows:
Note that applications can determine the length of a record by setting
the ulen field to 0 and checking the return value in the
size field. See the DB_DBT_USERMEM flag for more information.
It is an error to specify both DB_DBT_MALLOC and DB_DBT_USERMEM.
For example, if the data portion of a retrieved record was 100 bytes,
and a partial retrieval was done using a DBT having a dlen
field of 20 and a doff field of 85, the get call would succeed,
the data field would reference the last 15 bytes of the record,
and the size field would be set to 15.
If the calling application is doing a put, the dlen bytes
starting doff bytes from the beginning of the specified key's
data record are replaced by the data specified by the data
and size structure elements.
If dlen is smaller than size, the record will grow,
and if dlen is larger than size, the record will shrink.
If the specified bytes do not exist, the record will be extended using
nul bytes as necessary, and the put call will succeed.
It is an error to attempt a partial put using the DB->get function
in a database that supports duplicate records.
Partial puts in databases supporting duplicate records must be done
using a DBcursor->c_put function.
It is an error to attempt a partial put with differing dlen and
size values in a recno database with fixed-length records.
For example, if the data portion of a retrieved record was 100 bytes,
and a partial put was done using a DBT having a dlen field of 20,
a doff field of 85, and a size field of 30, the resulting
record would be 115 bytes in length, where the last 30 bytes would be
those specified by the put call.
Retrieved key/data permanence: When using the non-cursor Berkeley DB calls to retrieve key/data items (e.g., DB->get), the memory referenced by the pointer stored into the DBT is only valid until the next call to Berkeley DB using the DB handle returned by db_open. (This includes any use of the returned DB handle, including by another thread of control within the process. For this reason, when multiple threads are using the returned DB handle concurrently, either the DB_DBT_MALLOC or DB_DBT_USERMEM flag must be specified for any non-cursor DBT used for key or data retrieval.) When using the cursor Berkeley DB calls to retrieve key/data items (e.g., DBcursor->c_get), the memory referenced by the pointer into the DBT is only valid until the next call to Berkeley DB using the DBC handle returned by DB->cursor.
The Berkeley DB access methods provide no guarantees about key/data byte string alignment, and applications are responsible for arranging any necessary alignment. The DB_DBT_MALLOC and DB_DBT_USERMEM flags may be used to store returned items in memory of arbitrary alignment.
Logical record numbers are 1-based, not 0-based, i.e., the first record in the database is record number 1.


class Request : HttpMessage
method
val method: HttpMethod
uri
val uri: CharSequence
version
val version: CharSequence
headers
val headers: HttpHeadersMap
release
fun release(): Unit


Effect position of STL/SLC/SVG source file being rendered in plate.
Auto Center
Center pieces in the middle of plate.
Disable
Respects zero point from the 3D program which file exported with. The 3D program's origin point will be at bottom left of the plate.
Center Origin
Draw 3D program's origin (center) point to the center of the plate.


Application submitted
You are entitled to some help with health costs
Reference number: 1Q3 4T6 7K9
Next Steps
You can download or print a copy of your certificate at any time by signing into this service, using your application number and memorable word.


Britney Ever After
Britney Ever After is a TV movie starring Natasha Bassett, Nathan Keyes, and Nicole Oliver. A chronicle of the life and career of singer Britney Spears, from humble beginnings on the Mickey Mouse Club to mega stardom, a heavily...


Our country is made up of thousands of islands, skerries, and hundreds of lakes. There are many fjords in Åland, and various forests dot the islands.
Åland has very few natural resources, besides clay, wood and granite, which can be found throughout the islands.
Our population estimate is 28,500 people, of which about 11000 live in the capital, 90% live on the main island, and the rest live on all the other islands.
The main industries are Shipping, Tourism, and Trade, which most people work in.
The population of our country are unilingually Swedish and ethnically Swedish
There are ferries between islands, and roads between most islands, as well as roads across the main island.


--- layout: page title: WORK header: WORK group: navigation --- {% include JB/setup %}
{{ post.blurb }}


%inherit file="master.mak" />
PrintLure allows its users to create their perfect lure in a fun and interactive virtual workshop. No need to settle for anything less than your dream made into reality. Share your perfect lure online to allow others to benefit from your vision.
Our lures are made using ground breaking 3D Printing technologies. This leads to stronger, more durable lures with superior control over density, splash, drag and zara-pup.
We're not just building lures, we're building a community. Our code, designs, models, and art assets are publicly available under Free/Open Source licenses.
Your lures are made from completely biodegradable cornstarch based Polylactic Acid (PLA). Unlike traditional lures, PLA based lures do not release harmful chemicals into the environment.


We prepared a survey about COVID-19 current testing methods and people’s experiences with these tests. We got their thoughts about a new possible diagnostic test and if they would be willing to use it.
We gave a presentation to students around our age and explained what iGEM is. We explained the issue and the solution that we have been working on. We aimed to make known the knowledge of synthetic biology and recruit new students who might be interested. After our presentation, our team answered the audience’s questions to further improve their comprehension.
We conducted public interviews and got their thoughts on these methods. We questioned their knowledge about it and furthered their understanding. We explained our goals and what we were trying to accomplish by doing these interviews.


IMAGE ESSENTIALS VAGICAINE- benzocaine and resorcinol cream
Kmart Corporation
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
For external use only
adults and children 12 years and older |
apply a fingertip amount (approximately 1-inch strip) to the affected area not more than 3 to 4 times daily |
children under 12 years |
ask a doctor |
aloe barbadensis leaf extract, carbomer homopolymer, cetyl alcohol, cholecalciferol, corn oil, fragrance, glyceryl monostearate, isopropyl myristate, isopropyl palmitate, isopropyl stearate, lanolin alcohol, methylparaben, mineral oil, PEG-100 stearate, propylene glycol, purified water, retinyl palmitate, sodium sulfite, tocopheryl acetate, trisodium HEDTA, trolamine
Compare to the active ingredients of Vagisil® Maximum Strength
Helps Relieve External Feminine Itching, Burning and Irritation, Helps Soothe With Vitamins A, D and E Plus Aloe.
MAXIMUM STRENGTH
Pain Relieving Cream
Anti-Itch Cream
Benzocaine 20%, Resorcinol 3%
Vagicaine
NET WT 1 OZ (28 g)
|
IMAGE ESSENTIALS VAGICAINE
benzocaine, resorcinol cream | ||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||
|
| Labeler - Kmart Corporation (008965873) |


Home
Resume
Blog
Gallery
Contact
Shaikh Safiya Naaz
Hello guys !!! I love to create about new design of web
know more about me!
Contact me
See My Writtings
Gallery
Computer Engineering
studing in Anjuman Kalseker Technical Campus
Learing C,Java Python.


{% extends "mailinglist/email/base.html" %} {% block body %}
Someone (hopefully you) just subscribed to {{ mailinglist }}.
To confirm your subscription click here.
If you don't confirm, you won't hear from {{ mailinglist }} ever again.
Thanks,
Your friendly internet Mail Ape 🦍!
{% endblock %}


Second year student studying software engineering at the Amirkabir University of technology and currently an ERASMUS+ student at the University of Almeria.


|
Opcode/Instruction VEX.NDS1.LZ.0F38.W0 F5 /r BZHI r32a, r/m32, r32b VEX.NDS1.LZ.0F38.W1 F5 /r BZHI r64a, r/m64, r64b |
Op/En RMV RMV |
64/32 -bit Mode V/V V/N.E. |
CPUID Feature Flag BMI2 BMI2 |
Description Zero bits in r/m32 starting with the position in r32b, write result to r32a. Zero bits in r/m64 starting with the position in r64b, write result to r64a. |
NOTES:
1. ModRM:r/m is used to encode the first source operand (second operand) and VEX.vvvv encodes the second source operand (third
operand).
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| RMV | ModRM:reg (w) | ModRM:r/m (r) | VEX.vvvv (r) | NA |
BZHI copies the bits of the first source operand (the second operand) into the destination operand (the first operand) and clears the higher bits in the destination according to the INDEX value specified by the second source operand (the third operand). The INDEX is specified by bits 7:0 of the second source operand. The INDEX value is saturated at the value of OperandSize -1. CF is set, if the number contained in the 8 low bits of the third operand is greater than OperandSize -1.
This instruction is not supported in real mode and virtual-8086 mode. The operand size is always 32 bits if not in 64-bit mode. In 64-bit mode operand size 64 requires VEX.W1. VEX.W1 is ignored in non-64-bit modes. An attempt to execute this instruction with VEX.L not equal to 0 will cause #UD.
N ← SRC2[7:0] DEST ← SRC1 IF (N < OperandSize) DEST[OperandSize-1:N] ← 0 FI IF (N > OperandSize - 1) CF ← 1 ELSE CF ← 0 FI
ZF, CF and SF flags are updated based on the result. OF flag is cleared. AF and PF flags are undefined.
BZHI:
unsigned __int32 _bzhi_u32(unsigned __int32 src, unsigned __int32 index);
BZHI:
unsigned __int64 _bzhi_u64(unsigned __int64 src, unsigned __int32 index);
None
See Section 2.5.1, “Exception Conditions for VEX-Encoded GPR Instructions”, Table 2-29; additionally
| #UD | If VEX.W = 1. |


{{ package.description }}
Use the following snippets to share this package quality:
{{ package | json }}


IS
CA
E
Search
OUR LOGO
THiS logo is crated since 2010.
SEE MORE ABOUT US
US
YOU CAN TAKE YOUR PLACE WHITH US NOW !.
booking whith us
Card title
ENjoy WHith us !.
lLOGING


Johnatan's Portfolio
If you are reading this, that means i figured out how to work with HTML. My name is Johnatan Garcia and I am a sophmore at the university of Texas at El Paso.
Click here to see content created by server code.
Click here to get a random message:
Ever since I was I have always been involded in STEM activities. That's me in the middle :) 💻
After my studies I hope to work companies that are involved in the future of space exploration or artificial intelegance.


Weekly standup meetings have been scheduled and added to the calendar:
- Phabricator: Monday, 4:30 - 4:50
- MongoDB: Monday, 5:00 - 5:20
- Socket IO: Wednesday, 4:30 - 4:50
- Rails: Wednesday, 5:00 - 5:20
- Review Board: Wednesday, 5:30 - 5:50


| Liquid Web |
Website Hosting, Server Hosting: Cloud, Dedicated Server, HIPAA Server, and Word Press plans, within a fully managed environment |
VISIT NOW
|
| A2Hosting |
Website Hosting, Server Hosting: Cloud, Dedicated Server, HIPAA Server, and Word Press plans, within a fully managed environment |
VISIT NOW |
| Greengeeks |
Website Hosting, Server Hosting: Cloud, Dedicated Server, HIPAA Server, and Word Press plans, within a fully managed environment |
VISIT NOW |
| Namecheap |
Website Hosting, CDN Service, Server Hosting Domains, SSL certificates, hosting |
VISIT NOW |
| InMotion Hosting |
Website Hosting |
VISIT NOW |
| Hostgator |
Website Hosting – shared, reseller, VPS, & dedicated hosting solutions |
VISIT NOW |
| Hostens |
Website HostingServer HostingB2B |
VISIT NOW |


I’m a Lead Software Engineer who has been working in the industry since 2000. I worked as CTO for a small startup a few years ago and helped them to go overseas by providing them an affordable and stable cloud platform capable of handling hundreds of millions of users per day. Since then I’ve been putting myself as the bridge between C-Level stakeholders and technical teams in the companies I’ve been working for, helping them to achieve their KPIs. While my heart is passionate for well crafted and easily maintainable software, which is the reason I’ve been actively coding since then, my brain is motivated on making software development that delivers actual value to my customers.
I created this blog to express my ideas, observations and thoughts. This is a blog composed of my personal observations and thoughts. I write about mostly about technology, science and software engineering, but I might write occasionally about history and politics as well. I shall try my best to keep it as scientific as possible, using facts to support my point of view. Hopefully, you will give me a hand whenever I did a mistake.
You can find me in a few places over the internet. Feel free to contact me, I’ll try my best to answer you as soon as possible.


"But I must explain to you how all this mistaken idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the actual teachings of the great explorer of the truth, the master-builder of human happiness."
By Unknown
VANILLA WITH CHOCOLATE BUTTERCREAM
Buttery vanilla cupcake meets a fluffy chocolaty buttercream
THE SALTY CAPTAIN
Rich and fudgy chocolate cake topped with a smooth caramel buttercream, finished with a drizzle of caramel sauce and sprinkling of sea salt
BIG TOP
Buttery vanilla cake with chocolate chips baked right in, then topped with our classic vanilla cream cheese icing that is tinted each day to match the mood of the bakery
CARROT CAKE
Moist carrot cake with cream cheese icing. A true classic
CHOCOLATE WITH CREAM CHEESE ICING
Rich and fudgy chocolate cake topped with our classic vanilla cream cheese icing
DA' PDX
Of course rich with the flavors of Pork Belly and Bacon with the sweetness of maple


About
-
We have Free Templates for Everyone
Our website templates are created with inspiration, checked for quality and originality and meticulously sliced and coded. What's more, they're absolutely free! You can do a lot with them. You can modify them. You can use them to design websites for clients, so long as you agree with the Terms of Use. You can even remove all our links if you want to.
-
We Have More templates for you
Looking for more templates? Just browse through all our Free Website Templates and find what you're looking for. But if you don't find any website template you can use, you can try our Free Web Design service and tell us all about it. Maybe you?re looking for something diferent, something special. And we love the challenge of doing something diferent and something special.
-
Be part of Our Community
If you're experiencing issues and concerns about this website template, join the discussion on our forum and meet other people in the community who share the same interests with you.
-
Template Details
Design version 10
Code version 2
Website Template details, discussion and updates for this Retro Diner Website Template.
Website Template design by Free Website Templates.
Please feel free to remove some or all the text and links of this page and replace it with your own About content.


Web RTC
Home
About
Contact
Dropdown
Action
Another action
Something else here
Separated link
One more separated link
Logout
"; } else { $link=$_COOKIE[$cookie_name]; } echo "
Logout
"; ?>
Click Me
Setup New Conference
Close Conference


French Cookies
Efficient aggregate maximize clicks-and-motar infrastructures communities weblogs best-of-breed blogospheres frictionless interfaces B2C.
Overview
Difficulty
€€
Cost
45 mn
Preparation
Time
0 mn
Cooking
Time
10 mn
Resting
Time
Ingredients
Serving : - 4 +
| # | Name | Quantity | Metric |
|---|---|---|---|
| 1 | Tomato | 2 | Piece |
| 2 | Chocolate | 300 | Gram |
| 3 | Flour | 150 | Gram |
| 4 | Milk | 25 | Cl |
List of the recipes ingredients


My name is Farah Al-Khateeb, I studied communications and software engineering at Al-Balqa Applied University and graduated in 2020, I taught scientific subjects in an academy specialized in education for a whole year, but I aspire to excel in the field of programming
List of top ten meals that i fill in love
- shawrma
- pizza
- zinger
- burger
- hotdog
- mansaf
- mujadra
- sushi
- noodles
- chilli chicken
list for my education
- study communication and software engineer
- graduated in 2020 corona time
- after university i work as a teacher in mera acadmy
- i attend her to improve my skills in software


Discover how iterative testing
Gets you winning designs
I do UX Research, Design, and Product Management.
Digital optimization agency called Clearhead.
I do user research and A/B testing for companies like Adidas and Tesco.
Cool story Marisa...
what are we doing today?
Today we'll:
- Create a design prototype
- Validate that design with real data
- Learn to understand the feedback you get
- Learn when your prototype is ready
- Learn strategies for continually gathering user data
Why care about prototype testing?
Testing allows you to:
- Save money
- Save time
- Ensure awesome-ness
You've been hired.
Now you need to fix this site.
Your mission:
get people to sign up for the newsletter.
You're about to make a new friend
- You have 15 minutes
- Sketch the changes you would make
#2. Design your prototype
- I'm going to put you into groups
- Take 45 minutes as a group to create your design
Free Add-on Resource: Sketch Grid Wireframe
- Take 45 minutes as a group to put the design in InVision
#4. Create your questions
We're going to come up with 5 questions to ask users
Remember
When user testing, we want to falsify our idea. Not validate it.
If we lead with validation, we'll skew the test trying to get the answers we want.
You're the user
- Trade prototypes
- Go through the questions
- Write down notes and feedback
What was the feedback?
What changes should we make to these prototypes?
#7. Putting it all together
When is my design “good enough”?
Strategies for gathering user data
Free methods
- Surveying users at the location
- Reaching out to your extended network
- Watching family or friends on the site
- Critically reviewing the site yourself
- Usability Hub
Resources
- User Experience Strategy by Jamie Levy
- Nielsen Norman Group
- Small Data: The Tiny Clues that Uncover Huge Trends by Martin Lindstrom
Thanks!
Questions... comments... cookies?


This notebook is meant to address to a shared concern from two referees. The motivating example in the manuscript was designed to be a simple toy for illustrating the novel type of inference SuSiE offers. Here are some slightly more complicated examples, based on the motivating example, but with variables in high (rather than perfect) correlations with each other.
Following a reviewer's suggestion, we simulated two variables, $x_1$ and $x_2$, with high but not perfect correlation ($0.9$). Specifically, we simulated $n = 600$ samples stored as an $X_{600 \times 2}$ matrix, in which each row was drawn i.i.d. from a normal distribution with mean zero and $\mathrm{cor}(x_1, x_2) = 0.9$.
We then simulated $y_i = x_{i1} \beta_1 + x_{i2} \beta_2 + \varepsilon_i$, with $\beta_1 = 1, \beta_2 = 0$, and $\varepsilon_i$ i.i.d. normal with zero mean and standard deviation of 3. We performed 1,000 replicates of this simulation (generated with different random number seeds).
In this simulation, the correlation between $x_1$ and $x_2$ is still
sufficiently high (0.9) to make distinguishing between the two
variables somewhat possible, but not non entirely straightforward. For
example, when we run lasso (using cv.glmnet
from the glmnet
R package) on these data it wrongly selected $x_2$ as having
non-zero coefficient in about 10% of the simulations (95 out of
1,000), and correctly selected $x_1$ in about 96% of simulations (956
out of 1,000). Note that the lasso does not assess uncertainty in
variable selection, so these results are not directly comparable
with SuSiE CSs below; however, the lasso results demonstrate that
distinguishing the correct variable here is possible, but not so easy
that the example is uninteresting.
Ideally, then, SuSiE should identify variable $x_1$ as an effect variable and drop $x_2$ as often as possible. However, due to the high correlation between the variables, it is inevitable that some 95% SuSiE credible sets (CS) will also contain $x_2$. Most important is that we should avoid, as much as possible, reporting a CS that contains only $x_2$, since the goal is that 95% of CSs should contain at least one effect variable. The SuSiE results (SuSiE version 0.9.1 on R 3.5.2) are summarized below. The code used for the simulation can be found here.
| CSs | count |
|---|---|
| (1) | 829 |
| (1,2) | 169 |
| (2) | 2 |
Highlighted in bold are CSs that do not contain the true effect variable --- there are 2 of them out of 1,000 CSs detected. In summary, SuSiE precisely identifies the effect variable in a single CS in the majority (83%) of the simulations, and provides a "valid" CS (i.e., one containing an effect variable) in almost all simulations (998 out of 1,000). Further, even when SuSiE reports a CS including both variables, it consistently assigns higher posterior inclusion probability (PIP) to the correct variable, $x_1$: among the 169 CSs that contain both variables, the median PIPs for $x_1$ and $x_2$ were 0.86 and 0.14, respectively.
Another referee suggested the following:
Suppose we have another predictor $x_5$, which is both correlated with $(x_1, x_2)$ and $(x_3, x_4)$. Say $\mathrm{cor}(x_1, x_5) = 0.9$, $\mathrm{cor}(x_2, x_5) = 0.7$, and $\mathrm{cor}(x_5, x_3) = \mathrm{cor}(x_5, x_4) = 0.8$. Does the current method assign $x_5$ to the $(x_1, x_2)$ group or the $(x_3, x_4)$ group?
Following the suggestion, we simulated $x_1, \ldots, x_5$ from a
multivariate normal with zero mean and the covariance matrix
approximately as given in Table below. (Since this matrix is
not quite positive definite, in our R code we used nearPD
from
the Matrix
package to generate the nearest positive definite
matrix --- the entries of the resulting covariance matrix differ only
very slightly from those in Table below, with a maximum
absolute difference of 0.0025 between corresponding elements in the
two matrices).
| $x_1$ | $x_2$ | $x_3$ | $x_4$ | $x_5$ | |
|---|---|---|---|---|---|
| $x_1$ | 1.00 | 0.92 | 0.70 | 0.70 | 0.90 |
| $x_2$ | 0.92 | 1.00 | 0.70 | 0.70 | 0.70 |
| $x_3$ | 0.70 | 0.70 | 1.00 | 0.92 | 0.80 |
| $x_4$ | 0.70 | 0.70 | 0.92 | 1.00 | 0.80 |
| $x_5$ | 0.90 | 0.70 | 0.80 | 0.80 | 1.00 |
We simulated $n = 600$ samples from this multivariate normal distribution, then we simulated $n = 600$ responses $y_i$ from the regression model $y_i = x_{i1} \beta_1 + \cdots x_{i5} \beta_5 + \varepsilon_i$, with $\beta = (0, 1, 1, 0, 0)^T$, and $\varepsilon_i$ i.i.d. normal with zero mean and standard deviation of 3. We repeated this simulation procedure 1,000 times with different random seeds, and each time we fit a SuSiE model to the simulated data by running the IBSS algorithm. To simplify the example, we ran the IBSS algorithm with $L = 2$, and fixed the $\sigma_0^2 = 1$. Similar results were obtained when we used larger values of $L$, and when $\sigma_0^2$ was estimated. For more details on how the data were simulated and how the SuSiE models were fitted to the data sets, see this script.
Like the toy motivating example given in the paper, in this simulation the first two predictors are strongly correlated with each other, so it may be difficult to distinguish among them, and likewise for the third and fourth predictors. The fifth predictor, which has no effect on $y$, potentially complicates matters because it is also strongly correlated with the other predictors. Despite this complication, our basic goal remains the same: the Credible Sets inferred by SuSiE should capture the true effects most of the time, while also minimizing "false positive" CSs that do not contain any true effects. (Further, each CS should, ideally, be as small as possible.)
Table below summarizes the results of these simulations: the left-hand column gives a unique result (a combination of CSs), and the right-hand column gives the number of times this unique result occurred among the 1,000 simulations. The CS combinations are ordered by the frequency of their occurrence in the simulations. We highlight in bold CSs that do not contain a true effect.
| CSs | count |
|---|---|
| (2), (3) | 551 |
| (2), (3,4) | 212 |
| (1,2), (3) | 176 |
| (1,2), (3,4) | 38 |
| (2), (3,4,5) | 9 |
| (1), (3,4) | 3 |
| (2), (4) | 3 |
| (1,2), (3,4,5) | 2 |
| (1), (3) | 1 |
| (1,2), (4) | 1 |
| (2), (3,5) | 1 |
| (3), (1,2,5) | 1 |
| (3), (1,2,3) | 1 |
| (3,4), (1,2,4) | 1 |
In the majority (551) of the simulations, SuSiE precisely identiied the true effect variables, and no others. In most other cases, SuSiE identified two CSs, each containing a correct effect variable, and with one or more other variables included due to high correlation with the true-effect variable. The referee asks specifically about how the additional variable $x_5$ behaves in this example. In practice, $x_5$ was rarely included in a CS. In the few cases where $x_5$ was included in a CS, the results were consistent with the simulation setting; $x_5$ was included more frequently with $x_3$ and/or $x_4$ (12 times) rather than $x_2$ and/or $x_1$ (only once). In no simulations did SuSiE form a large group that contains all five predictors.
This example actually highlights the benefits of SuSiE compared to alternative approaches (e.g., hierinf) that first cluster the variables into groups based on the correlation structure, then test the groups. As we pointed out in the manuscript, this alternative approach (first cluster variables into groups, then test groups) would work well in the toy example in the paper, but in general it requires ad hoc decisions about how to cluster variables. In this more complex example raised by the referee, it is far from clear how to cluster the variables. SuSiE avoids this problem because there is no pre-clustering of variables; instead, the SuSiE CSs are computed directly from an (approximate) posterior distribution (which takes into account how the variables $x$ are correlated with each other, as well as their relationship with $y$).
Exported from manuscript_results/motivating_example_high_corr.ipynb
committed by Gao Wang on Fri Feb 7 13:49:00 2020 revision 3, 3708092


change land LUCC forest agricultural tropical Mexico climate use deforestation natural population socioeconomic covers dry expansion variables {border-style cover temperate areas evergreen px;} year #ffffff;} model spatial drivers forests, Environmental urban based related scenario scenarios transition Change area biodiversity grassland maps Global different dynamics high pessimistic processes (Table agriculture ecosystems environmental human models observed study (BAU) Land TDF TEF associated changes future global increase map production rural used Agricultural International Mexican Modeling S). Therefore, agreement biophysical conservation ecosystem forests impacts influence land-use national px; rates {font-family ./s--- Lambin Latin agriculture, change. disagreement economic example, extent font-size food forest, growth loss low medium patterns potential relationship services settlements showed studies vegetation, water (Fig. ./S-()- Applied Ecosystems GCMs GDP Mexico. Pontius Research Science Table Urban activities assessment causes double; especially forces hotspots information local mainly marginalization municipalities new percentage quantity roads scrubland simulated understanding (pessimistic ./j.gloenvcha... BAU Bonham-Carter Corona Ecology However, Institute Landscape Mas Mexico, Mexico: National Sciences Systems allocation analysis aridity border-color border-width consumption countries data ecosystems, evapotranspiration factors higher highest important land-cover linked main major management particularly period policies protected reduction region regional solid; suggests total validation value van variable {font-style {font-weight (Soares-Filho Agriculture, BioScience Bocco Climate Consequently, Conservation Deforestation Easterling Ellis Environment FAO Fig. Galicia Geography Millones Modelling Oaxaca, Policy Socioeconomic Southern TWofE Total WofE assumptions availability bold case central complex country courier; density distance driver driving effect effects em; expected grasslands, greatest highly hydrophilic international landscape location past pathways present products projected projections pt;} rate resources scales scrubland, scrublands, similarity specific supplementary terms trajectories transitions values vegetation yields


Built-in support for responsive Side Navbars, enables to rapidly prototype different layout styles.
Side Navbars DO NOT require additional JS code. They're compatible with any implementation of Bootstrap. Anyway, you may need to use custom or built-in handlers to toggle the collapsible bar.
Side navbar
This is the main component, it always has the following classes: navbar navbar-side collapse.
According to your desing you'll need to use navbar-side-left or navbar-side-right, defining a side for your navbar.
You should define a breaking point to display or hide your navbar: navbar-side-{BREAKING-POINT}, where BREAKING-POINT is sm, md, lg, xl.
It is recommended to wrap your navbar-brand inside a div with class navbar-brand-container, so you can add a background color to it.
You must include a navbar-toggler to hide/show the menu.
For SPAs (Single Page Applications) consider adding the attributes data-toggle="collapse" data-target=".navbar.show" to your links if you're using the jQuery default implementation, otherwise you'll need to add a code to handle navbar collapsing.
Sample Sidenavbar code
Sample React implementation
This example shows how to implement custom handlers for a React implementation with React Router.
For more a complete example and integrations with React-Bootstrap check this repo.
More info
Use the class m-navbar-side-{SIDE}-{BREAK-POINT}, where SIDE is left or right and BREAKING-POINT is sm, md, lg, xl, to define a margin between your main content, while the side navbar is visible.
Side navbars are fully compatible with Bootstrap color utilities and base navbars.


Bachelorarbeit 'Immersiv Augmented Reality'
Maurus Dähler
''Virtual Reality (VR) refers to the use of three-dimensional displays and interaction devices to explore real-time computer generated environments.''
''Virtual Reality refers to immersiv, interactive, multi-sensory, viewer-centered, three-dimensional computer generated environments and the combination of technologies requiered to build these environments.''
package main;
import java.awt.Color;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.net.MalformedURLException;
import java.net.URL;
import javax.imageio.ImageIO;
public class Main {
public final static String PAGE = "http://www.stadtplan.bs.ch/tmp/kacache/default/500/"
+ "LAYEROBJ_PARZPLAN_VEKTOR_FARB_500/def/t-@estart/l@nstart/t-<EMAIL_ADDRESS>//http://www.stadtplan.bs.ch/tmp/kacache/default/500/LAYEROBJ_PARZPLAN_VEKTOR_FARB_500/def/t-9592320/l19725312/t-9591808l19726336.png
public final static int HIGHESTE = 9598464;
public final static int HIGHESTN = 19725312;
public final static int LOWESTE = 9590784-1536;
public final static int LOWESTN = 19732992+1536;
public final static int TILESIZE = 256;
public final static int FOLDERSIZE = 1536;
public static int startEast;
public static int endEast;
public static int startNorth;
public static int endNorth;
public static void main(String[] args) {
// TODO Auto-generated method stub
startEast = HIGHESTE;
startNorth = HIGHESTN;
endEast = LOWESTE;
endNorth = LOWESTN;
int rows = (LOWESTN-HIGHESTN)/TILESIZE; //we assume the no. of rows and cols are known and each chunk has equal width and height
int cols = (HIGHESTE-LOWESTE)/TILESIZE;
int chunks = rows * cols;
System.out.println("rows: " + rows + " cols: " + cols + " chunks: " + chunks);
//Initializing the final image
BufferedImage finalImg = new BufferedImage(TILESIZE*cols, TILESIZE*rows, BufferedImage.TYPE_INT_RGB);
Graphics2D g = (Graphics2D) finalImg.getGraphics();
int num = 0;
for (int i = 0; i < rows; i++) {
for (int j = 0; j < cols; j++) {
try {
String img = (((PAGE.replace("@estart", ""+
findFolder(startEast-(i*TILESIZE),false))).
replace("@nstart",""+findFolder(startNorth+(j*TILESIZE),true))).
replace("@ecoord",""+(startEast-(i*TILESIZE)))).
replace("@ncoord",""+(startNorth+(j*TILESIZE)));
BufferedImage buffImages = ImageIO.read(new URL(img));
g.drawImage(buffImages, TILESIZE * j, TILESIZE * i, null);
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
num++;
System.out.println("Chunk " + num + " added... " + (chunks-num) + " to go");
}
}
g.dispose();
System.out.println("Image concatenated.....");
try {
File outimg = new File("C:/temp/finalImg.png");
ImageIO.write(finalImg, "png", outimg);
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
System.out.println("transformed coords: " + startEast + " / " + startNorth + " to " + endEast + " / " + endNorth);
}
private static int findFolder(int coord, boolean isNorth){
int start = HIGHESTE;
if(isNorth){
start = HIGHESTN;
}
return start+(((coord-start)/(1536))*(1536));
}
}
//we can't jump in the air
if (grounded)
velocity.y = Mathf.Min(0, velocity.y) - movement.gravity * Time.deltaTime;
else {
velocity.y = movement.velocity.y - movement.gravity * Time.deltaTime;
// When jumping up we don't apply gravity when the user is holding the jump button.
if (jumping.jumping && jumping.holdingJumpButton) {
// Fly upwards until we have the max height
if (Time.time < jumping.lastStartTime + jumping.extraHeight / CalculateJumpVerticalSpeed(jumping.baseHeight)) {
// Negate the gravity we just applied, except we push in jumpDir rather than jump upwards.
velocity += jumping.jumpDir * movement.gravity * Time.deltaTime;
}else{
//disable gravity as long as the jump button is hold
velocity.y = 0.0;
}
}
// Make sure we don't fall any faster than maxFallSpeed. This gives our character a terminal velocity.
velocity.y = Mathf.Max (velocity.y, -movement.maxFallSpeed);
}
private function GetDesiredHorizontalVelocity () {
// Find desired velocity
var desiredLocalDirection : Vector3 = tr.InverseTransformDirection(inputMoveDirection);
var maxSpeed : float = MaxSpeedInDirection(desiredLocalDirection);
if (grounded) {
// Modify max speed on slopes based on slope speed multiplier curve
var movementSlopeAngle = Mathf.Asin(movement.velocity.normalized.y) * Mathf.Rad2Deg;
maxSpeed *= movement.slopeSpeedMultiplier.Evaluate(movementSlopeAngle);
}else{
// Modify max speed in the air
maxSpeed *= 15.0;
}
return tr.TransformDirection(desiredLocalDirection * maxSpeed);
}


class AdvertisementController extends Controller
Methods
| preExecute() | ||
| Response |
indexAction(Request $request)
Management page of Advertisements. |
|
|
addBasicDetailAction(Request $request)
This is the first step when adding an advertisement |
||
|
addSpecificDetailAction(Request $request)
This is the second step in creating an advertisement |
||
| Response |
addInvoiceAction(Request $request)
This page will be the third step when creating a new advertisement. |
|
| Response |
previewAction(Request $request)
This will be the last page when creating a new advertisement |
|
| Response |
editAction(Request $request)
This is the edit advertisement page |
Details
at line 35
public
preExecute()
at line 54
public Response
indexAction(Request $request)
Management page of Advertisements.
at line 64
public
addBasicDetailAction(Request $request)
This is the first step when adding an advertisement
at line 101
public
addSpecificDetailAction(Request $request)
This is the second step in creating an advertisement
at line 159
public Response
addInvoiceAction(Request $request)
This page will be the third step when creating a new advertisement.
at line 172
public Response
previewAction(Request $request)
This will be the last page when creating a new advertisement
at line 186
public Response
editAction(Request $request)
This is the edit advertisement page


Find the same photos
Answer the questions
Score points if correct
...and remember: be quick and beat the clock!
The game
Front-Face is a simple HTML5 game,
a mix of memory-like style game and trivia about the speakers,
made to promote Front-Trends 2012 conference.
The conference
Front-Trends 2012 is a gathering for
Front-End loversto discover the current trends to build a professional career
out of innovative front-end development.
The conference will take place in SOHO Factory (Warsaw, Poland)
on 26-27 April 2012.
The authors


This page includes an overview of the key results from the paper. Although we provide them here we highly encourage you to read the paper for more in-depth discussion.
These tables show the overall scores for the best performing methods on each task as well as an overall summary score. Methods that failed to run for a particular task were assigned the unintegrated ranking for that task. An asterisk after a method name (scANVI and scGen) indicates that cell identity information was passed to this method.
For more details on how methods were scored and ranked please refer to the methods section of the paper.
For each method we selected the best performing combination of input features and scaling and then ranked those combinations. Methods were only ranked using scores from real RNA datasets, not simulations. We also show the summarised usability and scalability scores here. For ComBat and MNN these scores correspond to the Python implementation of the methods (Scanpy and mnnpy).
Batch removal and conservation of biological variance (bio-conservation) are often in competition with each each. These plots show the trade-off between these for each of the top method versions. Each point shows the mean across tasks and the error bars represent one standard deviation.
This table presents some of our main observations from the results of the study (click to enlarge). We hope this will be useful for deciding which methods to use for your analysis.
Ticks show which methods fulfil each criterion, grey dashes indicate partial fulfilment, and crosses are used to highlight methods that do not fulfil a criterion when more than half of the methods do. Python and R symbols indicate the primary language in which the method is programmed and used.
Considerations are divided into the five broad categories (input, scIB results, task details, speed, and output), which cover usability (input, output), scalability (speed), and expected performance (scIB results, task details). We also suggest using the scIB pipeline to test all methods on your own integration task.
Part (b) shows the relative strength of batch effect contributors in our study.


 For example, $69! \approx 1.7\times 10^{98}$ is the maximum factorial a calculator can handle. And Google maxes out around 170!
The issue is that the numbers get too big for the computer to store in memory in its default way to store numbers.
Logarithms cut numbers down to size, but we cannot use the logarithm of the answer without computing it. Or can we?
The logarithm of a product is the sum of the logarithms...
Using logarithms, I claim $1000! \approx 4.02\times 10^{2567}$.
To start with, computing $\ln(1000!)$ does not help since $1000!$ is computed first before the logarithm can act.
But remember that logarithms convert products to sums: $$\ln (1000* 999* 998* ... *3 *2 *1) = \ln(1000) + \ln(999) + \ln(998) + \cdots + \ln(3)+\ln(2) + \ln(1) $$
For example, if you run the following command in GeoGebra: sum(sequence(ln(n), n, 1, 1000))
you will get $5912.13$.
Now we cannot exponentiate that directly since it would generate too large a number. It is currently in base $e$, i.e., \(e^{5912} \approx 1000!\)
How do we get it to base 10? Divide by $\ln(10)$
Once in base 10, the integer part is the power of 10. The fractional part could then be exponentiated.
sum(sequence(ln(n), n, 1, 1000))/ln(10) = 2567.6046
Therefore, $$1000! \approx 10^{0.6046} \times 10^{2567} \approx 4.02 \times 10^{2567}$$
To see the growth of the factorial, let's compute a few of them.
| n | n! | \(\log_{10}(n!)\) |
|---|---|---|
| 5 | 120 | 2.08 |
| 10 | 3.62880E6 | 6.56 |
| 50 | 3.04141E64 | 64.5 |
| 100 | 9.33262E157 | 158 |
| 500 | 1.22014E1134 | 1.13e+3 |
| 1000 | 4.02387E2567 | 2.57e+3 |
| 5000 | 4.22858E16325 | 1.63e+4 |
| 10000 | 2.84626E35659 | 3.57e+4 |
| 50000 | 3.34732E213236 | 2.13e+5 |
| 100000 | 2.82423E456573 | 4.57e+5 |
| 500000 | 1.02280E2632341 | 2.63e+6 |
| 1000000 | 8.26393E5565708 | 5.57e+6 |
To use, put in a positive integer.
Would you like to play around with the code that generated this? Try editing the follow code block:
The result of the above computation is .
This is the implementation described above. We first compute the some of the logarithms, then we compute the logarithm relative to 10. The final line is just an attempt to present the result nicely.
Feel free to modify the code to see how it works.
Factorials are large. For example, suffling a deck of cards leads to about $52! \approx 8.13 \times 10^{67}$ different possible outcomes. That is a number with 67 digits. To put that into perspective, the number of estimated molecules in the observed universe is about $10^{80}$.
While Google maxes out at $170!$, one can use Wolfram Alpha to compute the factorial and check our work.
Always keep in mind the power of logarithms.


I'm terrible at analogies, so I'm just going to tell you the Udacity one we were told during the lessons with them.
Html are the framework of the building, the css is the interior decoration. And then the Dom is like a tree where you can pluck the leaves off and rearrange them in a different pattern.
Every website is made up of many boxes, some within others. Boxifying a design, means that one needs to design a website using boxes, some times even using boxes within boxes, to make up the website into a whole.
The CSS box model is essentially a box that wraps around every HTML element. It consists of: margins, borders, padding, and the actual content.


NC of Rock
______________________________
I am a member of NC of Rock @ NCSOFT since March 2023.
NC of rock is a band that plays rock music with passion and energy.
We are a group of friends who love music and want to share it with the world.
We are currently preparing for our show and we hope to see you there!


Note name must be unique
close
more_vert
border_vertical
rendered mode
attachment
attachments
({{this.attachedFiles?.length || '0'}})
wifi_protected_setup
backreferences
delete_outline
delete


pyQBTNs.QBTNs API
pyQBTNs.pyQBTNs module
|
Initilize the QBTNs class. |
|
Computes the factors and score for a given factorization method of the input Tensor with the specified rank. |
Returns the hamming distance of the fitted factors to the original tensor |
|
Returns factors computed by the factorization algorithm. |
|
Gets the boolean numpy array consstructed from the factors found by the tensor factorization algorithm. |
This software was developed as a tool to factor tensors using quantum annealers. Right now this software includes 5 different tensor factorization methods, making up three distinct types of tensor networks.
The software allows the user to specify local solvers that do not require a connection to a quantum annealer, but still solve the optimization problems the annealer would solve during the factorization algorithm.
In order to use a D-Wave quantum annealer as the solver for this software, the user must set up a D-Wave configuration file.
- class pyQBTNs.pyQBTNs.QBTNs(**parameters)[source]
Bases:
object
- fit(Tensor, Rank, **parameters)[source]
Computes the factors and score for a given factorization method of the input Tensor with the specified rank.
- Parameters
Tensor (numpy.array(dtype=bool)) – Boolean numpy array with at least two dimensions.
Rank (int) – Rank of the factors. Rank >= 2. The size of the quantum annealing hardware limits the size of the rank. For the LANL D-Wave 2000Q, the safe limit is rank 8, although in some cases much higher rank factorization can be achieved.
dimensions (list, optional) – Optional argument which supplies the tensor dimensions. Required for Hierarchical tucker because the input is a dict of an HT structure
ranks (list, optional) – Optional argument which supplies the tensor factorization ranks. Required for Hierarchical tucker
- Returns
- Return type
None.
- get_factors()[source]
Returns factors computed by the factorization algorithm.
- Returns
returns some sort of data structure containing the computed factors. Each factorization algorithm returns slightly different formats for the factors.
- Return type
list or dict or tuple
Module contents
© 2021. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.


I'm ZACCHEAUS NG'ANG'A ,An aspiring web developer who just began the journey to fulfil that at Moringa school and welcome to my portfolio page!
I'm currently undertaking my bachelor's degree in communication and computer networks at the Technical university of Kenya I am also an entry level Huawei Certified Network Engineer.
My hobbies include
My very first webpage at Moringa
Practised lists by listing my favorite things
Created a webpage for an animal shelter
Created a webpage to display my resume
Made a practice website for a travel agency


new
Until recently the design world was dominated by positions which were closed, symmetric, and static. With 2016 came a lot of websites that strayed from this style.
Open compositions of loosely suspended elements that are fleeing somewhere off-screen are gaining opularity - examples of such work can be seen at romainpsd durimel.io, or booneselections.com.
new
Until recently the design world was dominated by positions which were closed, symmetric, and static. With 2016 came a lot of websites that strayed from this style.
Open compositions of loosely suspended elements that are fleeing somewhere off-screen are gaining opularity.
new
2016 also broke the rule of symmetry, which dominated the industry for quite a long time.
Many designers created asymmetric layouts which are not perfectly balanced on the left and right sides. As examples I would like to show you a great website culture.


_Why_Do_Fools_Fall_in_Love_ (R) *** (out of ****)
As the summer popcorn season winds down, and the weightier fall/holiday season begins, here arrives a smoothly executed "transition" film, which tackles some fairly serious subject matter in a fun, entertaining, guilt-free package. _Why_Do_Fools..._ centers on the court battle between the late doo wop singer Frankie Lymon's (Larenz Tate) three wives--glamorous singer Zola Taylor (Halle Berry), petty thief Elizabeth Waters (Vivica A. Fox), and demure schoolmarm Emira Eagle (Lela Rochon)-- for his estimated $4 million estate . Testimony-stemmed flashbacks chronicle Lymon's quick rise to fame as the lead singer of the Teenagers (famous for the classic title song), his equally quick fall into obscurity and drugs, and the circumstances behind his untimely death.
Director Gregory Nava's recreation of Lymon's late '50s-early '60s performances are exuberant, buoyed by Tate's charismatic performance, which, in turn, provides a sympathetic anchor when things turn dark. The lighter court storyline is a bit more cliched and stereotyped, with all the verbal catfights between the women, but the appealing trio of Berry, Rochon, and especially Fox (in the showiest role of the three) make the proceedings enjoyable. These two contrasting sides, Lymon's tragically short existence and the battle between the three widows, are meshed together with ease into a pleasing cinematic confection by Nava, a proven hand musical biographies (_Selena_).
Michael Dequina
<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown
CompuServe Hollywood Hotline: http://www.HollywoodHotline.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Page under constreuction
Please be awear before the update U can't reach this from the main page
Hi! I'm csshall; dont ask about the name!
Dis = img
Trollololol!#lolol


Your session will time out soon
In order to keep your information secure, we will sign you out in [time].
Stay signed in
Sign out


Aman Zishan M.A
Hey!, I'm Aman Zishan M.A(u can call me Zishan).I'm currently doing my undergraduate studies in CUSAT .I chose b.tech I.T.Learning to code gave me problem-solving skills and a way to communicate with others on a technical level. And I'm a person who love to get to know more about coding.
"Declare variables,not war"
For now it would show an error cause i have to learn PHP to do form submission


SEEGATESITE
Dashboard
link1
link2
Shortcut
link1
link2
Overview
Events
About
Services
Contact
geojson.io


Barça defender Junior Firpo: When I came I knew how it would be
The defender says he's not worried about his situation in the squad
by Jordi DelgadoJunior Firpo started for Barcelona again against Celta after a while out of Ernesto Valverde's plans. The ex Real Betis defender played the 90 minutes, largely thanks to Jordi Alba's injury.
"I'm not worried about my situation, I've not been here long and I think everyone needs time to adapt," said Firpo.  It was hard not to play against Levante but in the end I have to respect the decision of the coach.
"We'd all like regular games but when I came I knew what I was in for. I don't like not being in the squad but I understand that the full-back who doesn't play sits in the stands. I'll keep working and I'm ready."
He also said the team is doing fine. "When you come from a good run then have two slip ups, it affects you mentally. There's a team and players to have a great season. All the teams have runs of form, we're leaders in La Liga and in the Champions League, we're happy with the win."


Connor's very much a work in progress EDV Blog
Kia ora Taiao!
Connor's very much a work in progress EDV Blog
Kia ora Taiao!
this is the beginning of my wbsite home page.
it will evenually contain a list of links to all my blogs.
sprint 1
sprint 2
sprint 3
sprint 4
sprint 5


Simple lightweight site for web apps with corolfull design.
#disigned by ‘GoogleSearch’
Simple lightweight site for web apps with corolfull design.
#disigned by ‘GoogleSearch’
Simple lightweight site for web apps with corolfull design.
#disigned by ‘GoogleSearch’
Simple lightweight site for web apps with corolfull design.
#disigned by ‘GoogleSearch’
*Those projects was made in educational purpose.


Coders
2020
Bruna Gomes
View profile
Bruna Gomes
View profile
Bruna Gomes
View profile
Bruna Gomes
View profile
Bruna Gomes
View profile
Bruna Gomes
View profile


Harry Fisher
R engineer at Seven Bridges
Prolific tinkerer // Runner // Penchant for coffee
Posts 📝
About 🙋
Software 🛠
Harry Fisher
R engineer at Seven Bridges
Prolific tinkerer // Runner // Penchant for coffee
Posts 📝
About 🙋
Software 🛠


Reports for week: {{ reports.beg | date:"EEEE d MMM yyyy" }} - {{ reports.end | date:"EEEE d MMM yyyy" }}
Average speed: {{ reports.average.speed }}
Average distance: {{ reports.average.distance }}
No entries for the selected week
No entries, nothing to display here.


{% extends "account/__l_base.html" %} {% block title %}{% trans "Reset your password" %} - {{ block.super }}{% endblock %} {% block content %}
{% trans "We've emailed you instructions for setting your password. Please check your inbox or spam folder. If you don't receive an email, please make sure you've entered the address you registered with." %}


lreverse - Reverse the order of a list
lreverse list
The lreverse command returns a list that has the same elements as its
input list, list, except with the elements in the reverse order.
lreverse {a a b c}
→ c b a a
lreverse {a b {c d} e f}
→ f e {c d} b a
list, lsearch, lsort
element, list, reverse
Copyright © 1995-1997 Roger E. Critchlow Jr.
Copyright © 2006 by Donal K. Fellows. All rights reserved.


This work is copyright © Abhijit Dasgupta, 2013. All rights reserved.
Please run the file initialize.R first to ensure you have all the appropriate packages installed locally. For the reporting modules and examples, please install Pandoc directly from here.
The document Magic.Rmd contains a complete workflow: enter data into R from Excel, do some manipulations, draw a plot, and generate a report. The results of this can be seen in Magic.html and the actual R code is in Magic.R. You can create the report yourself using
library(knitr)
knit2html("Magic.Rmd")
This HTML file can be easily imported into Word, for example. Alternatively, you can do
library(pander)
library(knitr)
knit("Magic.Rmd")
Pandoc.convert("Magic.md", format = "docx")
to directly get a Word document.
R coding is free-form, in that you can split code over lines. If you want to put two commands on the same line of text, separate them with a ;
. Lines of code can be commented out using #
.
The most common type of data are text files, and often comma-separated files or csv files. R natively handles the import of data in csv format, using read.csv
.
pheno <- read.csv("data/pheno.csv")
pheno # This prints out the data
head(pheno) # This prints out first 6 lines
tail(pheno) # This prints out last 6 lines
Tip: you can just type an R object to see what it is or what it contains. For example, if you want to see how the function
read.csv
is coded, just typeread.csv
. You can also find out just what the arguments for a function and their default values are using the functionargs
, as below
read.csv
## function (file, header = TRUE, sep = ",", quote = "\"", dec = ".",
## fill = TRUE, comment.char = "", ...)
## read.table(file = file, header = header, sep = sep, quote = quote,
## dec = dec, fill = fill, comment.char = comment.char, ...)
## <bytecode: 0x7fb33621ed88>
## <environment: namespace:utils>
args(read.csv)
## function (file, header = TRUE, sep = ",", quote = "\"", dec = ".",
## fill = TRUE, comment.char = "", ...)
## NULL
We see that
read.csv
is build on the functionread.table
. To see more aboutread.table
, you can access its documentation by typing?read.table
orhelp(read.table)
help(read.table)
R has two powerful functions which give you a quick feel about a data set. The two major pieces of information you need are
str
)summary
)str(pheno)
tells us that we have a data.frame
object with 1000 observations of 6 variables. Each variable has a type -- in this case all are integer variables (int
) and sex
is a factor
variable. I'll describe factor
variables in a bit.
summary(pheno)
gives numerical summaries for int
variables (as it would for numeric
variables) and frequency tabulations for factor
variables. If you have a character
variable (denoted chr
in the output of str
), it would merely say that you have a character variable of 1000 observations
str(pheno)
## 'data.frame': 1000 obs. of 6 variables:
## $ X : int 486 79 511 304 933 47 584 585 589 659 ...
## $ sex: Factor w/ 2 levels "FEMALE","MALE": 2 1 1 2 2 1 1 1 1 1 ...
## $ sbp: int 132 122 173 151 148 159 149 145 158 153 ...
## $ dbp: int 76 60 63 81 89 79 90 100 80 68 ...
## $ bmi: int 16 17 17 18 18 19 19 19 19 19 ...
## $ id : int 45516 21869 24512 32125 21503 30728 49088 43523 41446 28797 ...
summary(pheno)
## X sex sbp dbp bmi
## Min. : 1 FEMALE:468 Min. : 87 Min. : 47.0 Min. :16.0
## 1st Qu.: 251 MALE :532 1st Qu.:129 1st Qu.: 76.0 1st Qu.:26.0
## Median : 500 Median :140 Median : 82.0 Median :29.0
## Mean : 500 Mean :141 Mean : 82.6 Mean :30.3
## 3rd Qu.: 750 3rd Qu.:152 3rd Qu.: 90.0 3rd Qu.:33.0
## Max. :1000 Max. :202 Max. :117.0 Max. :51.0
## NA's :1
## id
## Min. :10100
## 1st Qu.:20168
## Median :28914
## Mean :29713
## 3rd Qu.:39570
## Max. :49911
##
data.frame
The data.frame
looks like a matrix, but it really isn't. This is one of the pitfalls and quirks of R. We can see this by
is.data.frame(pheno)
## [1] TRUE
is.matrix(pheno)
## [1] FALSE
The data.frame
object is really another kind of R object, a list
. We will see later how this is useful, since R has very powerful list manipulation functions. Still, some matrix operations are allowed for data.frame
objects. For example, you can extract rows and columns and elements just like a matrix, using pheno[1,]
(1st row), pheno[,3]
(3rd column) or pheno[1,2]
(the (1,2) element).
You can extract variables (stored apparently in columns) either by the above matrix notation, or by variable name. For example, to just extract the sex
variable, you can use pheno$sex
or pheno[,'sex']
. The $
notation is useful to extract single variables, but the matrix-like notation is useful to extract multiple variables by name: pheno[,c('sex','sbp','dbp','bmi')]. As a side note,
c()` is a function called "concatenate", which creates vectors.
The study I took the data from has two files, geno and pheno, which collected gene data and clinical data respectively from 1000 subjects. We now read in the gene data, but we need to match up the two data sets so that the rows correspond. The simplest way to do this is to sort the rows of the data frame by the id
variable, so both have the same order. The data set pheno
is already sorted. We need to do this for pheno
. We will use the order
function on the id
variable to figure out its sort order. Basically, the rule is that, to sort a vector x
, you can do sort(x)
or x[order(x)]
. Here what we need is the correct re-ordering, not just the sorted values. So order(x)
is what's needed to re-order the rows.
A general practice: Never change data (be it a vector or a data.frame) in place. Always copy it to differently-named object and change that. That way, if you goof up, you're not having to re-generate everything. So, I saved the re-ordered
pheno
topheno2
. This keepspheno
unchanged.
geno <- read.table("data/geno.csv", sep = ",", header = T)
head(geno)
## snp1 snp2 snp3 snp4 snp5 snp6 snp7 snp8 snp9 snp10 snp11 id
## 1 CC TT TT TT CC AG TT CC TT CT TT 10100
## 2 TT TT CC CC CC AA AT CT CT CC TT 10110
## 3 CT AT TC TT <NA> AG AT CC TT CT TT 10177
## 4 CT TT CC CC CC AA AT CC TT CT CT 10180
## 5 TT TT CC CC CC AA TT CC TT CT TT 10244
## 6 CT AT <NA> CT CT AG AT CT CT CC CT 10245
head(pheno)
## X sex sbp dbp bmi id
## 1 486 MALE 132 76 16 45516
## 2 79 FEMALE 122 60 17 21869
## 3 511 FEMALE 173 63 17 24512
## 4 304 MALE 151 81 18 32125
## 5 933 MALE 148 89 18 21503
## 6 47 FEMALE 159 79 19 30728
o = order(pheno$id)
# pheno$id[o]
pheno2 = pheno[o, ]
# pheno2 <- pheno[order(pheno$id),]
head(pheno2)
## X sex sbp dbp bmi id
## 193 479 MALE 152 88 25 10100
## 621 711 FEMALE 187 88 31 10110
## 578 987 MALE 135 93 30 10177
## 80 110 FEMALE 148 92 23 10180
## 271 712 FEMALE 133 69 26 10244
## 33 472 FEMALE 112 47 21 10245
A better way to match the ordering of two data sets based on a common variable is to use the function match
args(match)
## function (x, table, nomatch = NA_integer_, incomparables = NULL)
## NULL
ind <- match(geno$id, pheno$id)
pheno3 <- pheno[ind, ]
You can check for yourself whether pheno2
and pheno3
are the same.
You can also merge the two datasets into a single data set based on a common variable, using the function merge
. This does not require the common variable (in this case, id
) to be sorted, unlike in SAS.
args(merge)
## function (x, y, ...)
## NULL
combined.data <- merge(pheno, geno, by = "id")
head(combined.data)
## id X sex sbp dbp bmi snp1 snp2 snp3 snp4 snp5 snp6 snp7 snp8
## 1 10100 479 MALE 152 88 25 CC TT TT TT CC AG TT CC
## 2 10110 711 FEMALE 187 88 31 TT TT CC CC CC AA AT CT
## 3 10177 987 MALE 135 93 30 CT AT TC TT <NA> AG AT CC
## 4 10180 110 FEMALE 148 92 23 CT TT CC CC CC AA AT CC
## 5 10244 712 FEMALE 133 69 26 TT TT CC CC CC AA TT CC
## 6 10245 472 FEMALE 112 47 21 CT AT <NA> CT CT AG AT CT
## snp9 snp10 snp11
## 1 TT CT TT
## 2 CT CC TT
## 3 TT CT TT
## 4 TT CT CT
## 5 TT CT TT
## 6 CT CC CT
Factor variables are very useful, but can be quirky. Factors look like character variables, but are really stored as numeric variables. They are meant to store categorical variables rather than strings.
s2 = as.character(pheno$sex)
str(s2)
## chr [1:1000] "MALE" "FEMALE" "FEMALE" "MALE" "MALE" ...
summary(s2)
## Length Class Mode
## 1000 character character
head(as.numeric(pheno$sex))
## [1] 2 1 1 2 2 1
head(as.numeric(s2))
## [1] NA NA NA NA NA NA
When we read in data sets, R, by default, converts any string variable to a factor. This is often not what we want. For example, an identifier variable needs to remain a character, not a factor. Most of us suggest making sure that strings are imported as characters, not factors. There is an option stringsAsFactors
which can be set globally using options(stringsAsFactors=FALSE)
. You can also set it on the fly when you import data
pheno <- read.csv("data/pheno.csv", stringsAsFactors = F)
You can also pre-specify what the types of variables are in each column you are importing, using the option colClasses
(note the camel-case). This is much faster if you are importing large data sets
pheno <- read.csv("data/pheno.csv", colClasses = c("integer", "factor", "integer",
"integer", "integer", "integer"))
R has packages to import data from most standard relational databases. The generic package is RODBC
which connects R to ODBC-compliant databases. Popular databases also have dedicated packages, including
RMySQL
),RPgSQL
),RSQLite
),MonetDB.R
)More recently, there are packages to import from MongoDB and CouchDB, two popular NoSQL databases.
I'm demonstrating using RSQLite, since it installs the sqlite database automatically. The other package you need is sqldb
, which allows you to manipulate R data frames and database objects using SQL commands.  You can see all the data sets that are loaded with R and other R packages you might have installed by typing data()
. We will use the mtcars
data set, which is a data set of car road tests published by Motor Trend magazine in 1974.
First, we will look at subsetting data by values of some variable. Note, if you wanted to subset particular rows, say the first 10 rows, you could just use matrix notation and do mtcars[1:10,]
.
The notation
1:10
denotes the sequence 1,2,3,4,5,6,7,8,9,10, stored in a numeric vector. This is similar to the functionrange
in Python.
Note that R starts counting at 1, so the first element of a vector
x
isx[1]
. This is unlike Python or C, which start counting at 0. This is because of R's close history with Fortran, which was a column-dominant language counting from 1.
We start by extracting the subset of data where number of cylinders is 6. Note that we are using ==
and not =
. You can also put multiple conditions in your subset conditions, either using &
(and) or |
(or)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
subset(mtcars, cyl == 6)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
subset(mtcars, cyl == 6 & mpg < 20)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Valiant 18.1 6 225.0 105 2.76 3.46 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.44 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.44 18.90 1 0 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.77 15.50 0 1 5 6
The non-destructive way of creating new variables in a data set is the function transform
. First we want to say that if a car gets less than 20 mpg, it is a gas guzzler. R has a convenient ifelse
function to do this (much like th ?
function in Python/C). Ideally you want to make this new variable a factor.
You can create multiple variables in one command using transform
. For example, I also convert mpg to kmpg.
ifelse(mtcars$mpg < 20, "gas.guzzler", "Econ")
## [1] "Econ" "Econ" "Econ" "Econ" "gas.guzzler"
## [6] "gas.guzzler" "gas.guzzler" "Econ" "Econ" "gas.guzzler"
## [11] "gas.guzzler" "gas.guzzler" "gas.guzzler" "gas.guzzler" "gas.guzzler"
## [16] "gas.guzzler" "gas.guzzler" "Econ" "Econ" "Econ"
## [21] "Econ" "gas.guzzler" "gas.guzzler" "gas.guzzler" "gas.guzzler"
## [26] "Econ" "Econ" "Econ" "gas.guzzler" "gas.guzzler"
## [31] "gas.guzzler" "Econ"
blah <- transform(mtcars, gas = ifelse(mpg < 20, "gas.guzzler", "econ"), kmpg = 1.6 *
mpg)
str(blah)
## 'data.frame': 32 obs. of 13 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
## $ gas : Factor w/ 2 levels "econ","gas.guzzler": 1 1 1 1 2 2 2 1 1 2 ...
## $ kmpg: num 33.6 33.6 36.5 34.2 29.9 ...
blah <- transform(mtcars, gas = ifelse(mpg < 20, "gas.guzzler", "econ"), kmpg = 1.6 *
mpg, score = 3 * cyl + 0.1 * wt - 0.01 * mpg)
## Warning: * not meaningful for factors
str(blah)
## 'data.frame': 32 obs. of 14 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ disp : num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat : num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec : num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear : Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
## $ carb : num 4 4 1 1 2 1 4 2 2 4 ...
## $ gas : Factor w/ 2 levels "econ","gas.guzzler": 1 1 1 1 2 2 2 1 1 2 ...
## $ kmpg : num 33.6 33.6 36.5 34.2 29.9 ...
## $ score: num NA NA NA NA NA NA NA NA NA NA ...
Note that the original mtcars
remains unchanged
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
R codes missing data as NA
, and provides the function is.na
to find missing values. Many of R's functions give a missing value or NA
if one of the components in the computation is missing. This behavior can be suppressed within the functions which accept it by the option na.rm=TRUE
. The summary
function we saw before already does this, and tells you how many values are missing for each variable in the data set
blah$mpg[3] <- NA
head(blah)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 NA 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## gas kmpg score
## Mazda RX4 econ 33.60 NA
## Mazda RX4 Wag econ 33.60 NA
## Datsun 710 econ 36.48 NA
## Hornet 4 Drive econ 34.24 NA
## Hornet Sportabout gas.guzzler 29.92 NA
## Valiant gas.guzzler 28.96 NA
mean(blah$mpg)
## [1] NA
mean(blah$mpg, na.rm = T)
## [1] 20
R provides a family of functions, all ending in apply
, which are meant to evaluate a function over different aspects of a data set. The function apply
works on matrices.
args(apply)
## function (X, MARGIN, FUN, ...)
## NULL
X = matrix(rnorm(100), ncol = 10) # rnorm generates normal random numbers
apply(X, 2, mean)
## [1] 0.30206 -0.22703 0.26977 0.02580 -0.18515 -0.23471 0.10943
## [8] -0.10705 0.02807 0.04476
lapply
does the same for components of a list
. Recall I said that a data.frame
object is really a list
. So the following works:
args(lapply)
## function (X, FUN, ...)
## NULL
lapply(pheno, mean, na.rm = T)
## Warning: argument is not numeric or logical: returning NA
## $X
## [1] 500.5
##
## $sex
## [1] NA
##
## $sbp
## [1] 141.4
##
## $dbp
## [1] 82.61
##
## $bmi
## [1] 30.26
##
## $id
## [1] 29713
R provides many ways to aggregate data, including the functions by
and aggregate
. The most powerful group of functions are from the package plyr
. These functions are all of the form (x)(y)ply
, where (x)
and (y)
can be any of d (data.frame),a (array), and l (list). (x)
denotes the type of data that is input, and (y)
denotes the type of data being output. These functions implement what Wickham calls the "split-apply-combine" paradigm, i.e., you split the data by some variable (usually of type factor
), apply a function to the split pieces, and put the results of the functions back together. The documentation is rather unfortunate for these functions, so I'll demonstrate by example.
We will first look at the average mpg of the cars in mtcars
by number of cylinders. This can be done by splitting the data set by cyl
, passing the split data sets through the function summarise
, and then putting the results back together into a data frame. For more information on the summarise
function, look at its documentation. There are two ways of denoting the splitting variable. I prefer ~cyl
, since it is similar to the formula interface for modeling in R that I am familiar with, and that you will learn later. You can also split on two or more factors quite easily. With summarise
, you can also compute more than one measure
library(plyr)
mtcars <- transform(mtcars, cyl = as.factor(cyl), gear = as.factor(gear)) #this creates a local copy
avg.by.cyl <- ddply(mtcars, ~cyl, summarise, mpg = mean(mpg, na.rm = T))
# avg.by.cyl <- ddply(mtcars, .(cyl), summarise, mpg = mean(mpg, na.rm=T))
avg.by.cyl
## cyl mpg
## 1 4 26.66
## 2 6 19.74
## 3 8 15.10
avg.by.cyl.gear <- ddply(mtcars, ~cyl + gear, summarise, mean.mpg = mean(mpg,
na.rm = T), median.mpg = median(mpg, na.rm = T))
avg.by.cyl.gear
## cyl gear mean.mpg median.mpg
## 1 4 3 21.50 21.50
## 2 4 4 26.93 25.85
## 3 4 5 28.20 28.20
## 4 6 3 19.75 19.75
## 5 6 4 19.75 20.10
## 6 6 5 19.70 19.70
## 7 8 3 15.05 15.20
## 8 8 5 15.40 15.40
If you want to just split the data set up by a variable, that is very easy as well
dlply(mtcars, ~cyl)
R provides several plotting frameworks. The basic one is known as base graphics, and one of the more popular frameworks is in the package ggplot2
. To make a few quick and dirty plots....
plot(mpg ~ disp, data = mtcars)
plot(mpg ~ disp, data = mtcars, xlab = "Displacement", ylab = "Miles per gallon",
main = "Fuel effiency by engine size") # adding labels
boxplot(mpg ~ cyl, data = mtcars)
You can make prettier plots with better defaults using ggplot2
which is based on the Grammar of Graphics. The idea is, like a painter, to layer different components of the plot on top of each other. The syntax is quirky again, but once you learn it, it is really very powerful.
library(ggplot2)
print(ggplot(mtcars, aes(x = disp, y = mpg)) + geom_point())
print(ggplot(mtcars, aes(x = disp, y = mpg)) + geom_point() + geom_smooth() +
labs(x = "Displacement", y = "Miles per gallon") + ggtitle("Fuel efficiency by engine size"))
print(ggplot(mtcars, aes(x = disp, y = mpg)) + geom_point() + geom_smooth() +
facet_wrap(~cyl, ncol = 1) + labs(x = "Displacement", y = "Miles per gallon"))
If you want to look at several variables and their inter-relationships, the functions pairs
(base graphics) and plotmatrix
(ggplot2) work
pairs(pheno[, c("sbp", "dbp", "bmi")])
print(plotmatrix(pheno[, c("sbp", "dbp", "bmi")]))
## This function is deprecated. For a replacement, see the ggpairs function
## in the GGally package. (Deprecated; last used in version 0.9.2)
I believe the future is in web-based content-rich interactive charts. The package rCharts
provides an interface from R to several Javascript graphing libraries. Running the following code will open up your web browser to display the charts. These charts can be integrated into HTML documents, but that's another day. See the rCharts webpage for more details.
library(rCharts)
r1 <- rPlot(mpg ~ wt | cyl, data = mtcars, type = "point")
r1
Fitting a linear regression model to two variables is a pretty basic task. R provies an intuitive formula interface for all its modeling functions, which reads just like an equation. The linear regression model is fit using the function lm
(linear model). Other modeling functions include glm
(generalized linear models), lrm
(logistic regression, in package rms), coxph
(Cox regression, in package survival), and several machine learning methods in the package caret
. There are many more.
Huge tip: Go to the Task Views page on CRAN to find packages addressing different topics in analytics
model1 <- lm(mpg ~ disp, data = mtcars)
model1
##
## Call:
## lm(formula = mpg ~ disp, data = mtcars)
##
## Coefficients:
## (Intercept) disp
## 29.5999 -0.0412
summary(model1)
##
## Call:
## lm(formula = mpg ~ disp, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.892 -2.202 -0.963 1.627 7.231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.59985 1.22972 24.07 < 2e-16 ***
## disp -0.04122 0.00471 -8.75 9.4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.25 on 30 degrees of freedom
## Multiple R-squared: 0.718, Adjusted R-squared: 0.709
## F-statistic: 76.5 on 1 and 30 DF, p-value: 9.38e-10
You can now export components of the results for use in your report. The most useful tools (in Windows) is the package R2wd
which will take the result of the model, format it and put it into your open Word document.
My preferred way now is using markdown, pandoc and the R package knitr, as demonstrated here. knitr
is a very powerful tool which is relatively easy to use (I'm using it right now), so is worth a look. This file uses these tools, and the process of converting it to Word and HTML are described at the end of this document. The results of the model above is formatted below:
require(pander)
pander(model1)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 29.6 | 1.23 | 24.07 | 3.577e-21 |
| disp | -0.04122 | 0.004712 | -8.747 | 9.38e-10 |
R treats graphs as printable objects, and so provides several "printers" to convert graphs to PDF, PNG, JPG, etc.
dev.off()
pdf(file = "graph.pdf")
# png(file='graph.png') jpeg(file='graph.jpg')
plot(mpg ~ disp, data = mtcars, xlab = "Displacement", ylab = "Miles per gallon")
dev.off()
You should be writing R in a script file and passing it to R, rather than writing directly in the R console. However, R automatically saves all your commands in a history file, so you can save that directly
savehistory("~/Downloads/workshop-master/WorkshopHistory.R")
No endorsement is implied in this list. There are many many R resources out there. This is the tip of the iceberg.
I would like to thank Sonya Snedecor and Varun Ektare for their help in organizing this workshop.
This document is written in the file WorkshopHistory.Rmd, which is converted to Markdown using the command knitr::knit('WorkshopHistory.Rmd')
to create the file WorkshopHistory.md. This was then converted to HTML (WorkshopHistory.md.html) and Word (WorkshopHistory.md.docx) using pandoc and the function Pandoc.convert
from the R package pander. The code has been extracted into a separate file code.R. This document contains both the text and the R code and can be reproduced locally by the commands
library(knitr)
library(pander)
Pandoc.convert("WorkshopHistory.md", format = "docx")
Pandoc.convert("WorkshopHistory.md", format = "html")
The R code can be extracted following the previous commands, using the commands
library(knitr)
purl("WorkshopHistory.Rmd", output = "WorkshopHistory.R")
I hope you have fun exploring R and the many tools it provides for data analytics.
Design: rapport Development Team © 2012 | Backend: pander | License: AGPL3 | Styled with skeleton


Graduated from École de Technologie Supérieure in Montreal, I have a bachelor in IT engineering. Also I have a background in Networking from CEGEP Bois-de-Boulogne. I worked at Best Buy and Bell Center as a technician, Desjardins as a sysadmin and Fibrenoire as a python developer. I was a member of the student club Lan ETS for two years as a network administrator.
Programming is like talking, you must practice. If you don't, you lose the knowledge. At the beginning, I learned Java in the first place. After some security competition like NSEC, I started to learn Python, which is a powerful language. Easier to learn and to understand, Python was very useful to script and automate some tasks. Right now, I'm learning Ruby to improve my knowledge. Also I have some experience with Web framework like Flask and J2EE.
Networking is my passion. At the beginning of my three years of CEGEP, I knew that I want to know how computers are connected together, how Internet works and more. With that formation, I learned how to configure a Cisco equipements (routers and switches), separate a network (VLAN, IP subnetting), configure a DNS, Web server, DHCP server, firewall and more.
When I worked at Desjardins, I implemented OpenStack, an open source cloud solution. The versions that I used to work with was Folsom and Grizzly. I also had the possibility to work with ESXi, a hypervisor from VMware.


Toggle navigation
Your logo here
Home
Blog
Home
Blog
Maybe something here?
Lorem ipsum dolor sit amet


Free Sign Up
TOS At A Glance
All of your user information is completely private and confidential. You control what you want to
share about yourself, except in the following circumstances:
- In the event we go public, haven't figured out a business model, and third-party advertisers
are willing to pay us money for your information.
- The site is hacked and your information is sold to the highest bidder. Not unlike #1, except we
will issue an apology on our blog and have a meeting about how we should security and stuff.
- We decide to change our terms of service in a way that allows us to use all of your information
in advertisements and to make your profile publicly searchable unless you physically mail us
your desired privacy settings on 8x11.1 sheet of papyrus parchment, written in calligraphy
with invisible ink and delivered by a singing llama wearing bermuda shorts.
- It's a Monday. Just because Mondays are like that for everyone.


YANGON
Yangon Technological University(YTU, COE)
Myanmar Marintime University(MMU)
University of Economic, COE;Hlaing
University of Medical Technology
National Management Degree College(NMDC)
University of Foreign Language(YUFL)
University of Information Technology(UIT, COE)
University of Computer Studies, Yangon(UCSY)
Defence Services Institute of Nursing and Paramedical Academy
Defence Services Medical Academy
Yangon Institute of Marine Technology
Yangon University of Economics, Ywar Thar Gyi
West Yangon Technological University(WYTU)
Thanlyin Technological University(TTU)
MANDALAY
University of Medicine Mandalay
University of Medical Technology
University of Traditional Medicine
University of Nursing Mandalay
Mandalay University of Foreign Language
Yadanarbon Cyber City, Pyin Oo Lwin
Mandalay Technological University
Myanmar Institute of Information Technology
University of Computer Studies Mandalay
National University of Arts and Culture
Defence Service Technological Academy
Government Institute of Technology, Kyaukpadaung
Government Institute of Technology, Pyin Oo Lwin
Government Institute of Technology, Yamethin
Technological University, Kyaukse
Technological University, Meiktila
University of Economics Meiktila


Radioactive
Cavemen
highlight_off
Dig
whatshot
Build
search
Info
directions_run
Move
open_with
Pan
exit_to_app
LD
Air
Terrain
accessibility


Economy
BUDGET
- Search Database of 500 Stores
- 10 Searches Per Day
- Access to Single Item Search
FREE
(Current Plan)
Bronze
POPULAR
- Search Entire Walmart Database
- 25 Searches per Day
- Access to Walmart Store Database
- Access to Single Item Search
$7.99 / month
Select plan
Silver
POPULAR
- Search Entire Walmart Database
- Unlimited Daily Searches
- Access to Walmart Store Database
- Retail Arbitrage Toolkit
- Access to API
$24.99 / month
Select plan