Farmer John's fields are broken into fields, with paths between some of them. Unfortunately, some fields are not reachable from other fields via the paths.
Define a superfield as a collection of fields that are all reachable from each other. Calculate the number of superfields.
Given: an undirected graph
The component of a graph is a maximal-sized (though not necessarily maximum) subgraph which is connected.
Calculate the component of the graph.
This graph has three components: {1,4,8}, {2,5,6,7,9}, and {3}.
Flood fill can be performed three basic ways: depth-first, breadth-first, and breadth-first scanning. The basic idea is to find some node which has not been assigned to a component and to calculate the component which contains it. The question is how to calculate the component.
In the depth-first formulation, the algorithm looks at each step through all of the neighbors of the current node, and, for those that have not been assigned to a component yet, assigns them to this component and recurses on them.
In the breadth-first formulation, instead of recursing on the newly assigned nodes, they are added to a queue.
In the breadth-first scanning formulation, every node has two values: component and visited. When calculating the component, the algorithm goes through all of the nodes that have been assigned to that component but not visited yet, and assigns their neighbors to the current component.
The depth-first formulation is the easiest to code and debug, but can require a stack as big as the original graph. For explicit graphs, this is not so bad, but for implicit graphs, such as the problem presented has, the numbers of nodes can be very large.
The breadth-formulation does a little better, as the queue is much more efficient than the run-time stack is, but can still run into the same problem. Both the depth-first and breadth-first formulations run in N + M time, where N is the number of vertices and M is the number of edges.
The breadth-first scanning formulation, however, requires very little extra space. In fact, being a little tricky, it requires no extra space. However, it is slower, requiring up to N*N + M time, where N is the number of vertices in the graph.
This code uses a trick to not use extra space, marking nodes to be visited as in component -2 and actually assigning them to the current component when they are actually visited.
# component(i) denotes the
# component that node i is in
1 function flood_fill(new_component)
2 do
3 num_visited = 0
4 for all nodes i
5 if component(i) = -2
6 num_visited = num_visited + 1
7 component(i) = new_component
8 for all neighbors j of node i
9 if component(j) = nil
10 component(j) = -2
11 until num_visited = 0
12 function find_components
13 num_components = 0
14 for all nodes i
15 component(node i) = nil
16 for all nodes i
17 if component(node i) is nil
18 num_components =
num_components + 1
19 component(i) = -2
20 flood_fill(component
num_components)
Running time of this algorithm is O(N 2), where N is the numbers of nodes. Every edge is traversed twice (once for each end-point), and each node is only marked once.
Consider the graph from above.
The algorithm starts with all nodes assigned to no component.
Going through the nodes in order first node not assigned to any component yet is vertex 1. Start a new component (component 1) for that node, and set the component of node 1 to -2 (any nodes not shown are unassigned).
| Node | Component |
| 1 | -2 |
Now, in the flood_fill code, the first time through the do loop, it finds the node 1 is assigned to component -2. Thus, it reassigns it to component 1, signifying that it has been visited, and then assigns its neighbors (node 4) to component -2.
| Node | Component |
| 1 | 1 |
| 4 | -2 |
As the loop through all the nodes continues, it finds that node 4 is also assigned to component -2, and processes it appropriately as well.
| Node | Component |
| 1 | 1 |
| 4 | 1 |
| 8 | -2 |
Node 8 is the next to be processed.
| Node | Component |
| 1 | 1 |
| 4 | 1 |
| 8 | 1 |
Now, the for loop continues, and finds no more nodes that have not been assigned yet. Since the until clause is not satisfied ( num_visited = 3), it tries again. This time, no nodes are found, so the function exits and component 1 is complete.
The search for unassigned nodes continues, finding node 2. A new component (component 2) is allocated, node 2 is marked as in component -2, and flood_fill is called.
| Node | Component |
| 1 | 1 |
| 2 | -2 |
| 4 | 1 |
| 8 | 1 |
Node 2 is found as marked in component -2, and is processed.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 4 | 1 |
| 7 | -2 |
| 8 | 1 |
| 9 | -2 |
Next, node 7 is processed.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 4 | 1 |
| 5 | -2 |
| 7 | 2 |
| 8 | 1 |
| 9 | -2 |
Then node 9 is processed.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 4 | 1 |
| 5 | -2 |
| 7 | 2 |
| 8 | 1 |
| 9 | 2 |
The terminating condition does not hold ( num_visited = 3), so the search through for nodes assigned to component -2 starts again. Node 5 is the first one found.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 4 | 1 |
| 5 | 2 |
| 6 | -2 |
| 7 | 2 |
| 8 | 1 |
| 9 | 2 |
Node 6 is the next node found to be in component -2.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 4 | 1 |
| 5 | 2 |
| 6 | 2 |
| 7 | 2 |
| 8 | 1 |
| 9 | 2 |
No more nodes are found assigned to component -2, but the terminating condition does not hold, so one more pass through the nodes is performed, finding no nodes assigned to component -2. Thus, the search for unassigned nodes continue from node 2, finding node 3 unassigned.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 3 | -2 |
| 4 | 1 |
| 5 | 2 |
| 6 | 2 |
| 7 | 2 |
| 8 | 1 |
| 9 | 2 |
Node 3 is processed.
| Node | Component |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 1 |
| 5 | 2 |
| 6 | 2 |
| 7 | 2 |
| 8 | 1 |
| 9 | 2 |
From here, the algorithm eventually terminates, as there are no more nodes assigned to component -2 and no unassigned nodes. The three components of the graph have been determined, along with the component to which each node belongs.
Generally, these types of problem are fairly clear. If it asks for sets of "connected" things, it's probably asking for components, in which case flood fill works very well. Often, this is a step in solving the complete problem.
The notion of "components" becomes muddied when you go to directed graphs.
However, the same flooding idea can be used to determine the points which are reachable from any given point even in a directed graph. At each recursive step, if the point isn't marked already, mark the point as reachable and recurse on all of its neighbors.
Note that to determine which points can reach a given point in a directed graph can be solved the same, by looking at every arc backwards.
Given: A weighted directed graph, with weights between 0 and 100.
Some vertex A "owns" another vertex B if:
Find all (a,b) pairs such that a owns b.
Analysis: This can be solved via an adaptation of the calculating the vertices reachable from a vertex in a directed graph. To calculate which vertices vertex A owns, keep track of the "ownership percentage" for each node. Initialize them all to zero. Now, at each recursive step, mark the node as owned by vertex A and add the weight of all outgoing arcs to the "ownership percentages". For all percentages that go above 50, recurse into those vertices.
Given: a directed graph, and a start point and an end point.
Find all points p that any path from the start point to the end must travel through p.
Analysis: The easiest algorithm is to remove each point in turn, and check to see if the end point is reachable from the start point. This runs in O(N (M + N)) time. Since the original problem stated that M <= 100, and N <= 50, this will run in time easily.
The diameter of a connected graph is defined as the maximum distance between any two nodes of the graph, where the distance between two nodes is defined as the length of the shortest path.
Given a set of points in the plane, and the connections between those points, find the two points which are currently not in the same component, such that the diameter of the resulting component is minimized.
Analysis: Find the components of the original graph, using the method described above. Then, for each pair of points not in the same component, try placing a connection between them. Find the pair that minimizes the diameter.
Farmer John contracted out the building of a new barn. Unfortunately, the builder mixed up the plans of Farmer John's barn with another set of plans. Farmer John's plans called for a barn that only had one room, but the building he got might have many rooms. Given a grid of the layout of the barn, tell Farmer John how many rooms it has.
Analysis: The graph here is on the non-wall grid locations, with edge between adjacent non-wall locations, although the graph should be stored as the grid, and not transformed into some other form, as the grid is so compact and easy to work with.


Testimonials
( Customer: Harish Chandra from Hyderabad ) September 27, 2017, 2:26 pm
I've purchased about 6 times from this website so far, and barring one time (which possibly was a mixup on the courier service's part), EVERY SINGLE TIME, the order arrived within 2 days (shipping and delivery included), and EVERY SINGLE TIME the packaging and the product qualities were superb, dare i say flawless. This is my Go-to for electronic components. There is a request/suggestion though: please increase item listings, include sparkfun/adafruit/ other products in your listings as well. Good job - Cheers #2
( Customer: Baala from BLR ) August 23, 2017, 4:00 pm
Ref: Invoice No.: 0346 Order ID: #64954 Date Added: 22/08/2017 Good Quality Stuff; Especially 12W Soldering station. All 10 items I ordered reached good. Good packing too. The best is REAL QUICK delivery! Once ordered in 15 Mins I rcvd SMS with FedEx Tracking and I rcvd the Item in 24 Hrs.!!! Real Awesome! Area to improve: 1. Communication was pain, as ONLY 1 tel line and 90% it is engaged! 2. Post REAL pics that too in multiples to help make decision. 3. Post Max Tech Specs so that one need to interact (call) minimal. 4. Try yo make simple / Comprehensive / Ultimate Kits with discounts!!! Thnx.
( Customer: Mark Collins from Vishakhapatnam ) September 26, 2017, 3:28 pm
This site is perfect to buy items that can only be bought in bulk on other sites like Amazon another plus is that I receive the parcel the next day(This may differ based on your location).
( Customer: Gireesh kumar from vishakhapattanam ) May 29, 2017, 9:49 pm
I have availed products from electroncomponents.com from many of the states in india. earlier i was in punjab and they have delivered material within 2 day. still im in vishakhapattanam and they are sending material on the next day. i consider this as a lightning speed. when ever i order at morning, the product reaches to me on next day morning. price also same as the market price . little variation on some items . thanks you .
( Customer: venkat from villupuram ) July 15, 2017, 8:32 am
Lightning fast shipping.You are at the right place for your project with lots of components availability also ask for an item if not available.They Packed shipment and booked courier in 1 hour on placing order.Excellent service and keep it up.But local courier making delay would be better if multiple selection courier option added. Thankyou electroncomponents.com!!!
( Customer: Shiny from Vizianagaram ) March 8, 2017, 2:20 pm
May God bless your team. I ordered three times till now. Your team is so faithful. I love the components i receive, the way they are delivered, neat packing...thank you electron components.
( Customer: Aravind Chowdary from Nizamabad ) January 14, 2017, 12:15 pm
It gives it's services very fast and give best components for buyers
SOCIAL MEDIA


#+title: Building a SOTA Search Ranking System *** Ranking errors on the first page are worse than those on later pages


Microsoft Agent Character Viewer
This page lets you view the animations in any Microsoft Agent .ACF character file.


| Book of Mormon |
Annotations |
|
Chapter 1
|
|
|
1 And now there was no more contention in all the land of
Zarahemla, among all the people who belonged to king Benjamin, so that king
Benjamin had continual peace all the remainder of his days.
|
|
|
2 And it came to pass that he had three sons; and he
called their names Mosiah, and Helorum, and Helaman. And he caused that they
should be taught in all the language of his fathers, that thereby they might
become men of understanding; and that they might know concerning the
prophecies which had been spoken by the mouths of their fathers, which were
delivered them by the hand of the Lord.
|
|
|
3 And he also taught them concerning the records which
were engraven on the plates of brass, saying: My sons, I would that ye should
remember that were it not for these plates, which contain these records
and these commandments, we must have suffered in ignorance, even at this
present time, not knowing the mysteries of God.
|
This seems to deny the claimed ability of God to reveal
his will.
The context is that King Benjamin was clearly acting with ecclesiastic
authority. See Mosiah 6:3 for reference to him appointing priest to teach the
people. He was also keeper of the plates according to Omni 1:25 and Words of
Mormon 1:10-11. His son, Mosiah, was a seer according to Mosiah 8:13-18, and
apparently there is no greater gift or calling than this. But, this verse
tells us that without the plates of brass, they would have had suffered in
ignorance.
|
|
4 For it were not possible that our father, Lehi, could
have remembered all these things, to have taught them to his children, except
it were for the help of these plates; for he having been taught in
the language of the Egyptians therefore he could read these engravings,
and teach them to his children, that thereby they could teach them to their
children, and so fulfilling the commandments of God, even down to this
present time.
|
So, the plates of brass are written in Egyptian. The
plates of brass “contained ‘the five books of Moses, … and also a record of
the Jews from the beginning, … down to the commencement of the reign of
Zedekiah, king of Judah; and also the prophecies of the holy prophets’”
(Brief Explanation about the Book of Mormon, Book of Mormon, ellipses in the
original). As such, the plates of brass would have been what one might
consider the Jewish Bible (The Tanakh) up to that point in time (though the
Jewish Bible wasn’t canonized by this point in history, and it’s even
questionable as to whether the various writings indicated would have been
gathered into one book). As such, the original language of the writings
included in the brass plates would have been predominantly Hebrew. It would
have been an enormous process to translate these writings into Egyptian and
then engrave them unto metal plates into one book.
|
|
5 I say unto you, my sons, were it not for these things,
which have been kept and preserved by the hand of God, that we might read and
understand of his mysteries, and have his commandments always before our
eyes, that even our fathers would have dwindled in unbelief, and we should
have been like unto our brethren, the Lamanites, who know nothing concerning
these things, or even do not believe them when they are taught them, because
of the traditions of their fathers, which are not correct.
|
|
|
6 O my sons, I would that ye should remember that these
sayings are true, and also that these records are true. And behold, also the
plates of Nephi, which contain the records and the sayings of our fathers
from the time they left Jerusalem until now, and they are true; and we can
know of their surety because we have them before our eyes.
|
|
|
7 And now, my sons, I would that ye should remember to
search them diligently, that ye may profit thereby; and I would that ye
should keep the commandments of God, that ye may prosper in the land
according to the promises which the Lord made unto our fathers.
|
|
|
8 And many more things did king Benjamin teach his sons,
which are not written in this book.
|
|
|
9 And it came to pass that after king Benjamin had made an
end of teaching his sons, that he waxed old, and he saw that he must very
soon go the way of all the earth; therefore, he thought it expedient that he
should confer the kingdom upon one of his sons.
|
|
|
10 Therefore, he had Mosiah brought before him; and these
are the words which he spake unto him, saying: My son, I would that ye should
make a proclamation throughout all this land among all this people, or the
people of Zarahemla, and the people of Mosiah who dwell in the land, that
thereby they may be gathered together; for on the morrow I shall proclaim
unto this my people out of mine own mouth that thou art a king and a ruler
over this people, whom the Lord our God hath given us.
|
|
|
11 And moreover, I shall give this people a name, that
thereby they may be distinguished above all the people which the Lord God
hath brought out of the land of Jerusalem; and this I do because they have
been a diligent people in keeping the commandments of the Lord.
|
|
|
12 And I give unto them a name that never shall be blotted
out, except it be through transgression.
|
|
|
13 Yea, and moreover I say unto you, that if this highly
favored people of the Lord should fall into transgression, and become a
wicked and an adulterous people, that the Lord will deliver them up, that
thereby they become weak like unto their brethren; and he will no more
preserve them by his matchless and marvelous power, as he has hitherto
preserved our fathers.
|
|
|
14 For I say unto you, that if he had not extended his arm
in the preservation of our fathers they must have fallen into the hands of
the Lamanites, and become victims to their hatred.
|
|
|
15 And it came to pass that after king Benjamin had made
an end of these sayings to his son, that he gave him charge concerning all
the affairs of the kingdom.
|
|
|
16 And moreover, he also gave him charge concerning the
records which were engraven on the plates of brass; and also the plates of
Nephi; and also, the sword of Laban, and the ball or director, which led our
fathers through the wilderness, which was prepared by the hand of the Lord
that thereby they might be led, every one according to the heed and diligence
which they gave unto him.
|
|
|
17 Therefore, as they were unfaithful they did not prosper
nor progress in their journey, but were driven back, and incurred the
displeasure of God upon them; and therefore they were smitten with famine and
sore afflictions, to stir them up in remembrance of their duty.
|
|
|
18 And now, it came to pass that Mosiah went and did as
his father had commanded him, and proclaimed unto all the people who were in
the land of Zarahemla that thereby they might gather themselves together, to
go up to the temple to hear the words which his father should speak unto
them.
|
|


Clear uploads is used to reset uploads for a new conference year.
Clear uploads will remove all references to:
Note that this only clears references to the files in this app, the files will remain in your dropbox account until you delete them.
Are you sure you want to clear uploads?


The filter module provides components that allow users to select filter criteria. These components create a button to expose filtering options, a summary to indicate which filters have been applied, and a area for filtering inline.
Indicates whether the filter button should be given active indication. This property should be set when indication of filtering is not visible to the user, such as when a filter summary is not shown.
An event that is emitted when the filter button is clicked.
Indicates whether the filter summary item has a close button.
An event that is emitted when the summary item close button is clicked.
An event that is emitted when the summary item is clicked.
Modal Filters
Inline Filters


Dependency injection container interface and supporting artifacts.
In the order of expected frequency of use, this package contains the followings:
- annotations to designate
- dependency injected and injectable {@linkplain org.fluidity.composition.Component components}
- dependency injected and injectable {@linkplain org.fluidity.composition.ComponentGroup groups of components}
- {@linkplain org.fluidity.composition.Component.Qualifiers context qualifiers} for dependency injected components
- dependency {@linkplain org.fluidity.composition.Inject injectable} constructors and fields
- {@linkplain org.fluidity.composition.Optional optional} dependencies
- {@linkplain org.fluidity.composition.Defer lazily instantiated} dependencies
- JAR file {@linkplain org.fluidity.composition.ServiceProvider service providers}
- interfaces to
- access a dependency injection container {@linkplain org.fluidity.composition.ComponentContainer internally}
- access a dependency injection container {@linkplain org.fluidity.composition.Containers externally}
- … and various exceptions and utilities


blog
resources
twtxt
e-library
serafina * b. may 1st, 2001
fond of
anime, manga, manhua, manhwas
law and order: svu, twitter
nostalgia tech, digital preservation
moss, foss, oss, open internet
privacy, security, retro-futurism
silence, being alone, the fetal position
computer science, cli, unix, www
learning, helping, sleeping, libraries
water, my cat, version control, archiving
true crime, podcasts, the wayback machine
pleasant reads
permanent record, edward snowden
nausea, jean-paul sartre
do androids dream of electric sheep? philip k. dick
generation ecstasy, simon reynolds
stay sexy & don't get murdered: the definitive how-to guide, karen kilgariff & georgia hardstark
feel free to hit me up, twitter dm is probably the best way to reach me.
2020-12-18 23:28:45


Documentation
What Exactly is this?
First of all, thank you for visiting this experimental project that I've been working on. In its essence, "Dungeon Chest" is a web 3D rendering engine that allowed me to visualize how 3D objects, lighting, JSON models, textures and animations would interact in a simple browser window. It turned out to be quite the task to program this environment and at the same time stylize it under an "Adventure" theme.
Controls
Use your mouse together with left and right click. It doesn't require any keyboard input!
Requirements for Correct Validation
Since this project utilizes SVG files please set your validator to CSS3 + SVG for correct validation.
How does it work?
I can write an entire research paper on the engine behind this project, but in short: it uses a 3D Web GL based JS framework "THREE.js" which
simplifies a lot of the calculations neccessary for a 3D scene with perspective rendering, but most importantly, it enables the possibility of adding lighting.
I encorporated multiple JSON files (Built a separate script that converted 3D models into JSON) that allow the engine to produce 3D models on screen.
For animations I prioritized simple tools like built in css keyframe transitions, but for in scene animations I utilized a library called "TWEEN.js",
which made my life rotating and translating object on triggered events so much easier. Lastly, for some of the controls I used pre-made libraries like "ORBITCONTROLS.js"
as they fit my project like a glove. Lastly, the entirety of the 3D code runs on a server so it requires a server configuration to run the scripts.
Known Issues
As with all 3D Engines, this one isn't perfect. Due to margin adjustments for example there is no proper way in THREE.js to adjust the raycaster
so that the collisions would be as precise as possible. As a result there are occasional incorrect collisions. This effect is especially visible
on the item selection scene where you'd have to go a little out of bounds in order to select something on the sides.
Another small, but present problem,
is the difficulty of optimizing the website for mobile. The project runs without problems, but due to the way the THREE.js library works it's very hard to adjust
UI elements to be mobile-friendly. It's a task for the future that I hope to be able to fix at one point!
How I went Above and Beyond
The actual fact of dealing with THREE.js deserves a medal, as the documentation pages for it are HEAVILY outdated and the majority of the scripts are deprecated. Otherwise, I've stepped up my design game and created a large amount of 2D/3D assets found in the project. (the ones not made by me are listed in Resources). The rest of my above and beyond lies within the programming of the 3D scenes where I made sure that I was able to fit the "adventure theme" and making the project as optimized as possible through WebGL. I played a lot with lighting settings and shadow calculations in order to get the best looking output. And, I've also utilized JQuery for some of the UI elements that are present within the simulation. I've managed to squeeze in a particle system, that both looks nice, but also showcases how performant correctly written particle systems can be. Lastly, I've used a Sound.js library that allowed me to play Hearthstone audio effects very efficiently, which is quite a good alternative to HTML audio.
Future Developments Planned
Currently there is a place holder right underneath the render view. There I planned to add a way for the adventurer to pick a specific location on the map for him to explore throught the render view. The 'x' mark selection will always be at one point for now. This effect was achieved by moving around svg elements, which in their turn could also be animated!
Conslusion
I would say this was one of the hardest projects I've ever attempted and the experience of programming 3D Graphics is invaluable to me. Even though I might have not been able to accomplish some of the stretch goals that I've set out for myself, this project holds a lot of room for incredible additions and improvements and partially that was my goal. In the future, I hope to return to this project with an abundance of new features and by continuing to work on it I will increase my level of 3D Graphics development even further beyond!


%- include('partials/builder-parts/head.html'); %>
A collaborative teambuilding environment complete with Smogon dex integration!


Comet parameter: database_name
- A full or relative path to the sequence database, in FASTA format, to search. Example databases
include RefSeq or UniProt.
- Database can contain amino acid sequences or nucleic acid sequences. If sequences are amino acid
sequences, set the parameter "nucleotide_reading_frame = 0". If the sequences are nucleic acid
sequences, you must instruct Comet to translate these to amino acid sequences. Do this by setting
"nucleotide_reading_frame" to a value between 1 and 9.
- There is no default value if this parameter is missing.
Example:
database_name = /usr/local/db/yeast.fasta
database_name = c:\local\db\yeast.fasta
database_name = yeast.fasta


BANGKOK (Reuters) - Pope Francis condemned on Thursday the exploitation of women and children for prostitution in Thailand, which is notorious for its sex tourism, saying the violence, abuse and enslavement they suffer are evils that must be uprooted.
Francis, on the first full day of his visit to mainly Buddhist Thailand, led a Mass in Bangkok’s National Stadium for tens of thousands of exuberant Roman Catholics in a country where they make up less than one percent of the population.
“For us, it is as if God himself is here,” said Nutaporn Kwanmuang, 27, who attended the Mass, the largest gathering of Catholics since Pope John Paul II visited in 1984. Tens of thousands of others watched on screens in an adjacent stadium.
In his homily, Francis mentioned the exploitation of women and children and the plight of refugees and migrants for the second time in one day.
“Here I think of children and women who are victims of prostitution and human trafficking, humiliated in their essential human dignity,” said the 82-year-old pope, who looked tired at the end of a long day.
Thailand, which attracts some 35 million tourists a year, has sought to shed its reputation for sex tourism. But repeated crackdowns have not rid Bangkok and other tourist hubs of go-go bars and massage parlors that often offer sex.
There are around 123,530 sex workers in Thailand, according to a 2014 UNAIDS report.
Earlier, in his first address on Thursday, Francis expressed appreciation for the Thai government’s efforts “to extirpate this scourge, and for all those private individuals and organizations working to uproot this evil and to provide ways to restore their (victims’) dignity”.
At both venues, he defended migrants and refugees and condemned human trafficking.
The number of human trafficking victims rescued in Thailand is set to hit a record high this year. Demand for cheap labor in neighboring Malaysia has caused a jump in the illegal trade, according to government data.
INTER-FAITH DIALOGUE
Thailand is home to some of Asia’s oldest refugee camps.
About 100,000 refugees from Myanmar have been living in nine camps along the border for decades, many since the early 1980s.
More recently, Thailand has been a major venue for human smugglers and traffickers of tens of thousands of Muslim Rohingya who have fled crackdowns in Myanmar.
In a gesture of inter-religious dialogue, Francis on Thursday morning visited the gilded Wat Ratchabophit Sathit Maha Simaram Temple to pay his respects to Thailand’s Supreme Buddhist Patriarch, the 91-year-old Somdet Phra Maha Muniwong.
Thailand’s Catholics number around 380,000 in a country of more than 65 million, but the small community has thrived and relations with the Buddhists are generally good.
Francis, who is accused by ultra-conservative Catholics of being too accommodating to other religions, praised the effects he said Buddhism has had on Thais.
Slideshow (37 Images)
“The majority of Thais have drunk deeply from the sources of Buddhism, which have imbued their way of venerating life and their ancestors, and leading a sober lifestyle based on contemplation, detachment, hard work and discipline,” Francis told the patriarch.


should not fail to load
SpecificationException
Machine.Specifications.SpecificationException: Expected object to be <null>, but found System.IO.FileNotFoundException with message "Could not load file or assembly 'file:///D:\dev\GitHub\MSpec.Extensions\MSpec.Extensions.Vanilla.Spec\bin\Debug\MSpec.Extensions.Vanilla.Spec.dll' or one of its dependencies. The system cannot find the file specified."
at System.Reflection.RuntimeAssembly._nLoad(AssemblyName fileName, String codeBase, Evidence assemblySecurity, RuntimeAssembly locationHint, StackCrawlMark& stackMark, IntPtr pPrivHostBinder, Boolean throwOnFileNotFound, Boolean forIntrospection, Boolean suppressSecurityChecks)
at System.Reflection.RuntimeAssembly.InternalLoadAssemblyName(AssemblyName assemblyRef, Evidence assemblySecurity, RuntimeAssembly reqAssembly, StackCrawlMark& stackMark, IntPtr pPrivHostBinder, Boolean throwOnFileNotFound, Boolean forIntrospection, Boolean suppressSecurityChecks)
at System.Reflection.RuntimeAssembly.InternalLoadAssemblyName(AssemblyName assemblyRef, Evidence assemblySecurity, RuntimeAssembly reqAssembly, StackCrawlMark& stackMark, Boolean throwOnFileNotFound, Boolean forIntrospection, Boolean suppressSecurityChecks)
at System.Reflection.RuntimeAssembly.InternalLoadFrom(String assemblyFile, Evidence securityEvidence, Byte[] hashValue, AssemblyHashAlgorithm hashAlgorithm, Boolean forIntrospection, Boolean suppressSecurityChecks, StackCrawlMark& stackMark)
at System.Reflection.Assembly.LoadFrom(String assemblyFile)
at MSpec.Extensions.Model.xBehaveModeler.CreateBddModelFromAssembly(Assembly mspecAssembly) in d:\dev\git\MSpec.Extensions\MSpec.Extensions.Model\xBehaveModeler.cs:line 101
at MSpec.Extensions.Model.xBehaveModeler.ProcessModel(Stream stream) in d:\dev\git\MSpec.Extensions\MSpec.Extensions.Model\xBehaveModeler.cs:line 53
at MSpec.Extensions.Spec.Epics.Feature.Include_stories_in_code.Story.Access_existing_MSpec_Xml_report_output.Read_into_MSpec_model_instance.<.ctor>b__2() in d:\dev\git\MSpec.Extensions\MSpec.Extensions.Spec\Epics\Feature.Include_stories_in_code\Story.Access_existing_MSpec_Xml_report_output\Read_MSpec_Xml_ouput_into_model.cs:line 31
at Machine.Specifications.Catch.Only[TException](Action throwingAction)
.
at FluentAssertions.Execution.LateBoundTestFramework.Throw(String message) in d:\Workspaces\FluentAssertions\FluentAssertions.Net35\Execution\LateBoundTestFramework.cs:line 25
at FluentAssertions.Execution.AssertionScope.FailWith(String failureMessage, Object[] failureArgs) in d:\Workspaces\FluentAssertions\FluentAssertions.Net35\Execution\AssertionScope.cs:line 168
at FluentAssertions.Primitives.ReferenceTypeAssertions`2.BeNull(String reason, Object[] reasonArgs) in d:\Workspaces\FluentAssertions\FluentAssertions.Net35\Primitives\ReferenceTypeAssertions.cs:line 42
at MSpec.Extensions.Spec.Epics.Feature.Include_stories_in_code.Story.Access_existing_MSpec_Xml_report_output.Read_into_MSpec_model_instance.When_reading_the_xml_output_the_MSpec_model.<.ctor>b__6() in d:\dev\git\MSpec.Extensions\MSpec.Extensions.Spec\Epics\Feature.Include_stories_in_code\Story.Access_existing_MSpec_Xml_report_output\Read_MSpec_Xml_ouput_into_model.cs:line 37


| Class | Description |
|---|---|
| ActionReceiver |
Alternation of the BroadcastReceiver class that receives and handles
broadcast intents sent by sendBroadcast(Intent).
|
| BuildConfig | |
| Category |
This class is for an object representing an item Category.
|
| Cook |
The Cook interface
|
| Dialogues |
Alternation of the AlertDialog.Builder.
|
| EncryptionAlgorithm |
Convert string to encrypted message using the SHA1 algorithm
|
| ExampleInstrumentedTest |
Instrumentation test, which will execute on an Android device.
|
| ExampleUnitTest |
Example local unit test, which will execute on the development machine (host).
|
| Item |
This class is for an object representing an Item.
|
| ItemOrder |
This class is for an object representing an ItemOrder which represents each
item that belongs to an order.
|
| JDBC | |
| MainActivity |
Login screen of the application and main thread of reloading waiter,chef and printing receipts
|
| Manager |
The main screen of the manager
|
| ManagerEditUser |
The manager edit user screen
|
| ManagerMenu |
The manager screen to be able to edit , add and delete items and categories
|
| ManagerShowUsers |
Show all the users to the manager screen
|
| myCsvCreator |
A csv creator class
|
| Order |
This class is for an object representing an Order.
|
| OrderArrayAdapter |
Alternation of the ArrayAdapter
|
| OrderView |
The screen of the order view to edit, add or delete an order
|
| R | |
| R.anim | |
| R.animator | |
| R.array | |
| R.attr | |
| R.bool | |
| R.color | |
| R.dimen | |
| R.drawable | |
| R.id | |
| R.integer | |
| R.layout | |
| R.mipmap | |
| R.string | |
| R.style | |
| R.styleable | |
| ReportView |
The screen that is responsible to generate reports
|
| User |
This class is for an object representing a User.
|
| UserArrayAdapter |
Alternation of the ArrayAdapter
|
| Waiter |
This is the Waiter class.
|


Download NHANES data files.
nhanes_load_data(file_name, year, destination = tempdir(), demographics = FALSE, cache = TRUE, recode = FALSE, recode_data = FALSE, recode_demographics = FALSE, allow_duplicate_files = FALSE, method = "auto")
| file_name | NHANES file name (e.g. "EPH") or a vector of filenames (e.g c("EPH", "GHB")) |
|---|---|
| year | NHANES cycle year (e.g. "2007-2008") or a vector of cycle years |
| destination | directory to download the files to |
| demographics | include demographics data into the dataset |
| cache | whether to cache the file to disk |
| recode | whether to recode the data and demographics (overrides other parameters) |
| recode_data | whether to recode just the data |
| recode_demographics | whether to recode just the demographics |
| allow_duplicate_files | how to handle a request that has duplicate file names/cycle years. By default duplicates will be removed. |
| method | download method passed to download.file |
if file_name or year is a vector, returns a list containing a data frame for each file_name. If file_name and year are both singletons, then a data frame is returned.
If you supply vectors for both file_name and year, then the vectors are paired and each file_name/year pair is downloaded. For example, file_name = c("EPH, GHB"), year = c("2009-2010", "2011-2012") will download "EPH_F.XPT" and "EPH_G.XPT". In other words, the function does not download every possible combination of file_name and year.
You can specify file names in several formats. In order of specificity: You can supply the complete filename: "EPH_F.XPT" You can supply the filename without an extension: "EPH_F" You can supply the filename without a suffix: "EPH", year = "2009-2010"
If you are loading the same file across multiple years, you must supply the filename without a suffix so that the correct suffix for each year can be used.
This function returns either a list or a data frame. If you load multiple files, the return value will always be a list. This is because the columns may not match in between files. If you load one file, the result will be a data frame.
# NOT RUN { nhanes_load_data("UHG", "2011-2012") # Load data with demographics nhanes_load_data("UHG", "2011-2012", demographics = TRUE) # Download to /tmp directory and overwrite the file if it already exists nhanes_load_data("HDL_E", "2007-2008", destination = "/tmp", cache = FALSE) # }


| public class | BinaryExpressionNode |
| public abstract class | BinaryNode |
| public static class | Box |
| public class | Breakpoint |
| public class | Breakpoints |
| class | ByStart |
| public class | CommandWindow |
| public class | Debugger |
| public class | DecNumberNode |
| public class | Disassembly |
| public class | DissamblyFormatter |
| public class | EvaluationException |
| public class | Evaluator |
| public class | FormatExpressionNode |
| public class | HeaderLine |
| public class | HexNumberNode |
| public class | InputWindow |
| public class | Memory |
| public abstract class | Node |
| public class | Output |
| public class | ParenthesizedExpressionNode |
| public class | PrintStatementNode |
| public class | RegNumberNode |
| public class | ResultFormat |
| public class | Screen |
| public class | SegmentNode |
| public class | SymbolNode |
| public class | TraceStatementNode |
| public class | UnaryExpressionNode |
| public abstract class | UnaryNode |
| public class | Visitor |
| public abstract class | Window |
| public void | Evaluate(Debugger* debugger, const String<uchar>& line) |
| public String<uchar> | FormatStr(ResultFormat format) |
| public nothrow bool | InEpilog(uint currentInst) |
| public nothrow bool | InProlog(uint currentInst) |
| public String<uchar> | ModifierStr(Modifier modifier) |
| public String<uchar> | OperatorStr(Operator op) |
| public uint | ReadInst(Process* process, ulong address) |
| public String<uchar> | TypeStr(Type type) |
| public void | WriteInst(Process* process, ulong address, uint inst) |
| public enum | CommandResult |
| public enum | Modifier |
| public enum | Operator |
| public enum | State |
| public enum | Type |
| public const short | blackOnWhite = 1 |
| public const short | cyanOnBlue = 3 |
| public const uint | oriFpSpInst = (cmsx.machine.ORI << 24u) | (cmsx.machine.regFP << 16u) | (cmsx.machine.regSP << 8u) | (0u << 0u) |
| public const short | redOnWhite = 5 |
| public const uint | retInst = (cmsx.machine.RET << 24u) | (0u << 16u) | (0u << 8u) | (0u << 0u) |
| public const uint | stoiFpSpInst = (cmsx.machine.STOI << 24u) | (cmsx.machine.regFP << 16u) | (cmsx.machine.regSP << 8u) | (0u << 0u) |
| public const short | whiteOnBlue = 2 |
| public const short | whiteOnCyan = 6 |
| public const short | whiteOnRed = 4 |


Turkey's pro-Kurdish party calls for early elections, won't leave parliament
by ReutersISTANBUL: Turkey’s pro-Kurdish opposition party called on Wednesday for an early election but ruled out withdrawing from parliament to protest the government’s dismissal of dozens of its mayors who were elected earlier this year.
Authorities have removed 24 mayors of the Peoples’ Democratic Party (HDP) since August, mainly due to alleged links to terrorism, and appointed replacements to run their municipalities.
Several members of the HDP had called for the party to with draw from the national parliament or from local assemblies which the party still holds.
The HDP said in a statement that President Tayyip Erdogan’s AK Party and its ally, the Nationalist Movement Party (MHP), were “stealing the will of the peoples” by appointing trustees.
“We say ‘early election’ for the peoples of Turkey to be rid of the AKP-MHP authority. This is a call for confrontation.
We’re saying bring it on,” the party said. “We call on all of the opposition to unite around this request for an early election and to take action,” it said, also appealing for a campaign of civil disobedience.
The HDP has previously complained about a lack of opposition solidarity. The party ruled out withdrawing from national parliament or local assemblies, saying it had paid “heavy prices” to achieve gains and “will not withdraw from any area of struggle”.
Erdogan and his AK Party accuse the HDP of links to the outlawed Kurdistan Workers Party (PKK), which has led a separatist insurgency since 1984 in which some 40,000 people have been killed. The HDP denies any links to terrorism.
Hundreds of the HDP members, including its lawmakers and former co-leaders, have been put on trial on terrorism charges and many of those have been sentenced to jail.
The HDP has been the only party in the Turkish parliament that opposed Turkey’s offensive in northeast Syria against the Kurdish YPG militia that began on October 9.


{% extends "eatshop/base_business_list.html" %} {% block "ogtitle" %}On-Campus Eateries{% endblock %} {% block "title" %}On-Campus Eateries{% endblock %} {% block "page_title" %}On-Campus Eateries{% endblock %} {% block "ogdesc" %}Information on all of the on-campus eateries available for Claremont Colleges students.{% endblock %} {% block "metadesc" %}Information on all of the on-campus eateries available for Claremont Colleges students.{% endblock %} {% block "eatshop_breadcrumbs" %}


Easy Photo Gallery developed by Keegan Donley - all images © 2015 Keegan Donley. Used by permission. GitHub Number of files:


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BACHELOR OF SCIENCE IN PSYCHOLOGY
Is a (4) four-year degree design to provide initial training on any of the three different fields of career. (Industrial, Educational and Clinical Setting). The curriculum provides an overview of the concepts in Psychology and involves basic and applied research and interventions for the development of well-functioning individual. Furthermore, they provide preparation for graduate studies in Psychology as well as further studies in other professions such as medicine, law and business management. It offers an in-house review for those students who are willing to take the Licensure Examination for Psychometrician.
Graduates of the BA/BS Psychology program could be licensed psychometricians or work in the academe, in HR, research, graduates may also pursue careers in medicine, law, management, etc. With further specialized training, graduates of this program may pursue specializations in Psychology such as clinical, counseling, developmental, educational, social, industrial/organizational, etc.


I WOKE UP EARLY THE DAY I DIED
Reviewed by Harvey Karten Cinequanon Pictures International Director: Aris Iliopulos Writer: Edward D. Wood, Jr. Cast: Billy Zane, Sandra Bernhard, Karen Black, Conrad Brooks, Tippi Hedren, Eartha Kitt, Ann Magnuson, Andrew McCarthy, Will Patton, Max Perlich, Ron Perlman, Summer Phoenix, Rain Phoenix, Christina Ricci, John Ritter, Rick Schroder, Nicollette Sheridan, Carel Struycken, Johnathan Taylor Thomas, Vampira, Steven Weber
Aris Iliopulos, who directed this film written by Ed Wood, must have woken up early one day and decided to make a film out of a script that had apparently died and rested in peace for over twenty years. Having won from the writer's widow, Kathleen Wood, the right to translate the text to celluloid after convincing her that he would not put a word of dialogue into the movie, Iliopulos--a portrait painter and photographer as well as a filmmaker-- somehow scraped up some of Hollywood's celebrated stars to appear in this no-budget silent. With Billy Zane ("Titanic," "Orlando," "Dead Calm," "Back to the Future") in virtually every scene of the 90-minute, unabashedly "B" movie, the entire cast seem to be having a ball hamming it up, struggling to keep a straight face in a movie with Ed Wood's characteristic 1950s ambiance. Wood, considered by cognoscenti to be the worst American director of all-time, gained celebrity status when Johnny Depp and Martin Landau starred in Tim Burton's 1994 film "Ed Wood," a film with a decidedly loving look at the no-talent helmer whose "Glen or Glenda" (1953) docu-fantasied about transvestism (Wood himself starred as the title character who cannot decide how to tell his fiancee he wants to wear her clothes) and "Plan 9 from Outer Space" (1959), about aliens seeking to conquer our planet by resurrecting corpses from a San Fernando Valley cemetery.
Iliopolus could have made a film to satirize Wood, but why beat a dead horse? Besides he obviously has a soft spot in his heart. How can you help loving a guy with no aptitude who carried on making film after film while riding on pure enthusiasm? Though "I Woke Up Early" has the feel of early Wood, Iliopulos takes advantage of a few modern special effects techniques to the extent that his budget would allow, juxtaposing black-and-white dream sequences from the principal character's youth with boldly colorful scenes depicting his current odyssey. This is the sort of film that kids in the seventies might have turned out for at midnight, toking their weed and establishing their hip quotient by turning out for the show on Saturday nights for a few weeks.
We can guess that widow Kathy Wood insisted no dialogue mar the film because any verbal exchanges would put "I Woke Up Early" in the category of just another movie, one to be judged critically by the usual standards. By barring conversations, she perhaps hoped to cement the movie's cult status, signalling reviewers and Joe Sixpack alike that this work should be judged on its own unique merits. This is not a silent film in the way we perceive the genre, though at times Billy Zane may remind us of Chaplin and Keaton. Instead the soundtrack is filled with shrieks and eerie music from the haunting, even maddening, strains of a Scottish bagpipe (actually played by Ron Perlman who spent quite a while learning to conquer the basics of the unusual instrument) to the mellifluous tones of Nat King Cole crooning "Nature Boy."
While there is no maxim to be learned from the film--no "love conquers all," or "life is short," or "persistence pays"-- the movie enjoys incorporating elements of Hitchcock, Brecht and Fellini, challenging serious viewers to show their erudition by analyzing the work semiotically and casual moviegoers to open their minds to an experience unlike any other they're likely to see this year.
The plot is anything but confusing. The Thief (Billy Zane) escapes from a California mental institution by knocking out or perhaps killing a nurse (he plunges the long syringe all the way into her arm), donning her uniform, and making his getaway. Quickly stealing clothes more appropriate to his gender from the local clothes lines, he robs a lending institution of $15,000 and becomes a wanted man after shooting the owner. Attending what he thinks is the victim's funeral, he hides the money in a coffin which locks the cash in, making it inaccessible to the thief. He then shadows each of the mourners in turn hoping to find the loot.
Staying a step or two ahead of the police (Andrew McCarthy, Steven Weber and Rick Schroder), he confronts the caretaker (Ron Perlman) and a succession of mourners whose names he finds on a list. During his murderous adventures, he attracts women at a dance hall and, in one more or less compelling scene is invited to have fun in his flophouse room by a teenaged hooker (Christina Ricci).
If you judge the picture as though it were just another entry in the annual march of cinema, you will likely conclude that "I Woke Up Early" is a hollow experience: unemotional, bereft of traditional character development, pointless, just one- damn-thing-after-another. The movie is all that, but what redeems the whole enterprise is the affection that paradoxically comes through from its director, who obviously has tender memories of Mr. Wood. From time to time, we regular moviegoers need to watch both serious and comic stars simply cut loose and have a good time with a script that is so silly that we wonder whether its writer was as nuts as The Thief who's in the limelight. If nothing else, the film may encourage us to go back to the silents to see how all those movies made from 1895 to 1927 succeeded in riveting audiences purely on their visual imagery. While "I Woke Up Early" can hardly be called riveting--it's no "Birth of a Nation"- -there is somehow enough going on visually and sufficient charm exuded by the admittedly undeveloped characters to keep us in our seats, despite its absence of either flat-out laughs or authentic Hitchcockian suspense, genuinely Brechtian devices or seriously amusing Felliniesque characterizations.
Not Rated. Running Time: 90 minutes. (C) 1999 Harvey Karten
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.
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Related links: index of all rec.arts.movies.reviews reviews


Chapter 3 Importing Data into R with readr
3.1 Instructions
This tutorial will teach you the basics of importing data from your hard drive into R. We will cover how to import a Comma-Separated Values (csv) file into R using the read_csv()
function in the readr package. We will also be covering the different data types that R can recognize from a csv file, how to manipulate how data show up on R, as well as how to export the manipulated file back into a csv file.
Accompanying this tutorial is a short Google quiz for your own self-assessment. The instructions of this tutorial will clearly indicate when you should answer which question.
3.2 Learning Objectives
- Know how to import a Comma-Separated Values (csv) file from a hard drive into R.
- Understand the basics of
read_csv()
including how to use it to import data and how to manipulate the presentation of the data on R. - Be familiar with the different data types that R can recognize.
- Know how to import other data files such as txt, xlsx, xpt, and sas into R.
- Know how to export a csv file from R into a hard drive.
3.3 Set Up
In this tutorial, we will be using the readr package, so we will need to install and attach this package onto our R and R session. The readr package is part of a larger tidyverse core. This tidyverse core contains many R packages that give us access to functions that mainly work to organize data. In this tutorial series, we will be covering three packages from tidyverse: readr (tutorial 2), dplyr (tutorial 4), and ggplot (tutorial 5).
#install.packages("readr")
library(readr)
For this tutorial, we will be using the demo_csv.csv
file. This data is a subset of the National Health and Nutrition Examination Survey (NHANES) conducted by the National Center for Health Statistics (NCHS). Our demo_csv.csv
, in particular, contains a portion of the information about the demographic of the survey’s participants in the years 2013-2014. We will cover NHANES in more detail in tutorial 3. For now, you can explore NHANES in general by visiting this website.
After attaching the readr package, one other thing that we need to complete this tutorial is a csv file. Note that the csv file should be in your working directory - this just makes out lives much easier when we want to import data from a hard drive onto R. Recall that we can use the function dir()
to check if all of the files we need are in our working directory.
dir()
## [1] "_book"
## [2] "_bookdown.yml"
## [3] "_bookdown_files"
## [4] "_build.sh"
## [5] "_deploy.sh"
## [6] "_output.yml"
## [7] "0-r-and-rstudio-set-up.Rmd"
## [8] "1-introduction-to-r.Rmd"
## [9] "2-importing-data-into-r-with-readr.Rmd"
## [10] "3-introduction-to-nhanes.Rmd"
## [11] "4-data-analysis-with-dplyr.Rmd"
## [12] "5-data-visualization-with-ggplot.Rmd"
## [13] "6-date-time-data-with-lubridate.Rmd"
## [14] "7-data-summary-with-tableone.Rmd"
## [15] "8-Exercise-Solutions.Rmd"
## [16] "9-references.Rmd"
## [17] "book.bib"
## [18] "data"
## [19] "DESCRIPTION"
## [20] "Dockerfile"
## [21] "docs"
## [22] "header.html"
## [23] "images"
## [24] "index.Rmd"
## [25] "intro2R.log"
## [26] "intro2R.Rmd"
## [27] "intro2R.tex"
## [28] "intro2R_cache"
## [29] "intro2R_files"
## [30] "LICENSE"
## [31] "now.json"
## [32] "packages.bib"
## [33] "preamble.tex"
## [34] "R.Rproj"
## [35] "README.md"
## [36] "style.css"
## [37] "toc.css"
You may be a bit confused about the output of the previous code. This is because we are working on Kaggle and the csv file that we will be using is not located in our working directory. More specifically, the csv file is in the input folder, whereas our working directory is the output folder.
getwd()
## [1] "C:/Users/ehsan/Documents/GitHub/intro2R"
There are two ways that we can approach this problem. We will go over how to do both in the next section of this tutorial.
DO QUESTION 1 OF THE QUIZ NOW
- REVIEW: Which of the following functions lets us set a new working directory?
3.4 Basics Of Importing a CSV File Into R
3.4.1 Method 1: Setting a Different Working Directory
As we have sort of alluded to in tutorial 1, we can set our working directory as the location of the csv file to import it into R. After successfully importing the file, we can then set the working directory back to our original folder.
setwd("./data")
Perfect! Now that we know the csv file is in our working directory, we can now import it using read_csv()
. This function is relatively easy to use. All we need to do is add the name of our csv file along with the .csv
extension in ""
within the brackets, and we are good to go!
read_csv("data/demo_csv.csv")
## Rows: 15 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): gender, race, edu
## dbl (2): id, age
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American <NA>
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ <NA>
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American <NA>
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American <NA>
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic <NA>
## 15 83731 Male 11 Non-Hispanic Asian <NA>
You should see a list of “Column specification” and the demo_csv.csv
file imported into a data frame in R after running the codes above. We will go over what “Column specification” means later in this tutorial.
Now that we have successfully imported our csv file into R, it is time for us to set our working directory back to our original directory.
setwd("..")
getwd()
## [1] "C:/Users/ehsan/Documents/GitHub/intro2R"
3.4.2 Method 2: Copying the Exact Pathway of the File
Another way for us to import the csv file into R is to copy and paste the exact pathway of the file into read_csv()
. You should see the exact same “Column specification” and data frame as before!
read_csv("data/demo_csv.csv")
## Rows: 15 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): gender, race, edu
## dbl (2): id, age
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American <NA>
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ <NA>
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American <NA>
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American <NA>
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic <NA>
## 15 83731 Male 11 Non-Hispanic Asian <NA>
Note that you can also store this imported data into an object using <-
.
<- read_csv("data/demo_csv.csv") DEMO
## Rows: 15 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): gender, race, edu
## dbl (2): id, age
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Now, we can just type DEMO to see the data frame.
DEMO
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American <NA>
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ <NA>
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American <NA>
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American <NA>
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic <NA>
## 15 83731 Male 11 Non-Hispanic Asian <NA>
DO QUESTIONS 2-4 OF THE QUIZ NOW
REVIEW: Which of the following codes will print the entire DEMO data frame?
read_csv can also be used to import Excel and txt files. (True or False)
Which R package does the function
read_csv()
belong to?
Try it yourself 3.1
Can you try importing the bpx.csv
file into R using the function read_csv()
?
3.4.3 Key Notes About Importing Data into R
There are a few key things that we should note when using read_csv()
:
1. The file name or pathway to the file needs to be in ""
,
2. The file extension, .csv
, needs to be present, and
3. The name of the file needs to be exact.
The third point is related to one of the most common mistakes. When importing any data from your hard drive onto R, you need to make sure that the file name that you write in R is exactly what it displays on your hard drive. For instance, take note of spaces, capital letters, spelling of words, as well as the correct extensions. In other words, demo_csv-1.csv
or Demo_csv.csv
is much different than demo_csv.csv
.
Another point to note is that read_csv()
automatically assumes that the first row of your csv file is the header. We will learn how to tell R this assumption is not correct in section 3 of this tutorial.
DO QUESTION 5 OF THE QUIZ NOW
- REVIEW: R is case sensitive. (True or False)
Try it yourself 3.2
Can you identify the mistakes of the following codes?
# a.
# read_csv(../input/import/demo_csv.csv)
# b.
# read_csv("data/DEMO_csv.csv")
# c.
# Read_csv("data/demo_csv.csv")
# d.
# read_csv(data/"demo_csv.csv")
DO QUESTION 6 OF THE QUIZ NOW
- Which of the following statements about
read_csv()
are correct? (select all that apply)
3.4.4 Column Specification
You may notice that when you import a data into R by running read_csv()
, a “Column specification” list appears. This list tells us two things:
1. The names of our columns and
2. The type of data that each column contains.
read_csv("data/demo_csv.csv")
## Rows: 15 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): gender, race, edu
## dbl (2): id, age
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American <NA>
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ <NA>
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American <NA>
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American <NA>
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic <NA>
## 15 83731 Male 11 Non-Hispanic Asian <NA>
As we can see after running the code above, there are five columns in our data frame: id (the participant’s unique ID number), gender, age, race, and edu (highest level of education).
We can also see that there are two types of data in this data frame col_double()
and col_character()
.
DO QUESTION 7 OF THE QUIZ NOW
- Which of the following is the best an example of a data that would be classified as
col_double()
?
Try it yourself 3.3
Just by looking at the actual data frame, can you guess what type of data col_double()
and col_character()
are?
(HINT: doubles? integers? logical? character?)
3.5 More Arguments Of read_csv
3.5.1 Skip
There are a range of other arguments that we can use with read_csv()
. Firstly, we can nest skip
inside the ()
of read_csv()
to tell R to skip (AKA not import) a certain number of rows when importing our data.
<- read_csv("data/demo_csv.csv", skip = 2) Skip_2
## Rows: 13 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): Female, Non-Hispanic Black, High school graduate/GED or equi
## dbl (2): 83718, 60
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Skip_2
## # A tibble: 13 x 5
## `83718` Female `60` `Non-Hispanic Black` `High school graduate/GED o~
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83719 Male 3 Mexican American <NA>
## 2 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 3 83721 Male 52 Non-Hispanic White College graduate or above
## 4 83722 Male 0 Other Race - Including Mul~ <NA>
## 5 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 6 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 7 83725 Male 7 Mexican American <NA>
## 8 83726 Male 40 Mexican American Less than 9th grade
## 9 83727 Male 26 Other Hispanic College graduate or above
## 10 83728 Female 2 Mexican American <NA>
## 11 83729 Female 42 Non-Hispanic Black College graduate or above
## 12 83730 Male 7 Other Hispanic <NA>
## 13 83731 Male 11 Non-Hispanic Asian <NA>
DEMO
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American <NA>
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ <NA>
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American <NA>
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American <NA>
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic <NA>
## 15 83731 Male 11 Non-Hispanic Asian <NA>
When comparing the Skip_2 table with our original DEMO table, we can see that Skip_2 has two less rows. This is because the argument skip = 2
has told R to not import the first two rows of our demo.csv
.
DO QUESTION 8 OF THE QUIZ NOW
- Which of the following statements is true about the argument
skip
?
Try it yourself 3.4
You may also notice that the header of Skip_2 is incorrect. This is because R recognizes the header of our data as the first row, thus omiting it when importing demo.csv
into R.
Let’s say this is not what we really want. What we actually want to do is to remove the first two rows of actual data while keeping the header. What do you think we have to do to achieve this?
(HINT: Recall what we learn about extracting rows in tutorial 1)
3.5.2 Remove Header & Header Names
Recall how read_csv()
assumes that the first row of our data is the header. If this is not true, we can use col_names = FALSE
to tell R that the first row of our data do not contain headers and that R should add headers for our data.
<- read_csv("data/demo_csv.csv",
No_header col_names = FALSE)
## Rows: 16 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (5): X1, X2, X3, X4, X5
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
No_header
## # A tibble: 16 x 5
## X1 X2 X3 X4 X5
## <chr> <chr> <chr> <chr> <chr>
## 1 id gender age race edu
## 2 83717 Female 80 Mexican American Less than 9th grade
## 3 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 4 83719 Male 3 Mexican American <NA>
## 5 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 6 83721 Male 52 Non-Hispanic White College graduate or above
## 7 83722 Male 0 Other Race - Including Multi~ <NA>
## 8 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 9 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 10 83725 Male 7 Mexican American <NA>
## 11 83726 Male 40 Mexican American Less than 9th grade
## 12 83727 Male 26 Other Hispanic College graduate or above
## 13 83728 Female 2 Mexican American <NA>
## 14 83729 Female 42 Non-Hispanic Black College graduate or above
## 15 83730 Male 7 Other Hispanic <NA>
## 16 83731 Male 11 Non-Hispanic Asian <NA>
We can also change the names of our headers by using col_names =
following by a vector of names. For example:
<- read_csv("data/demo_csv.csv",
Header_names col_names = c("ID", "Gender", "Age", "Race", "Education"))
## Rows: 16 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (5): ID, Gender, Age, Race, Education
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Header_names
## # A tibble: 16 x 5
## ID Gender Age Race Education
## <chr> <chr> <chr> <chr> <chr>
## 1 id gender age race edu
## 2 83717 Female 80 Mexican American Less than 9th grade
## 3 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 4 83719 Male 3 Mexican American <NA>
## 5 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 6 83721 Male 52 Non-Hispanic White College graduate or above
## 7 83722 Male 0 Other Race - Including Multi~ <NA>
## 8 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 9 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 10 83725 Male 7 Mexican American <NA>
## 11 83726 Male 40 Mexican American Less than 9th grade
## 12 83727 Male 26 Other Hispanic College graduate or above
## 13 83728 Female 2 Mexican American <NA>
## 14 83729 Female 42 Non-Hispanic Black College graduate or above
## 15 83730 Male 7 Other Hispanic <NA>
## 16 83731 Male 11 Non-Hispanic Asian <NA>
DO QUESTION 9 OF THE QUIZ NOW
- In which of the following scenarios do you think we would NEED to use
col_names = FALSE
? (select all that apply)
With the addedcol_names
, you may notice that the column specification for our data is not incorrect (everything is recognized as col_character()
!
This is because R now reads “id,” “gender,” “age,” “race,” and “edu” as a content row, and since all of these are texts, R recognizes the entire column as col_character()
. This is something worthy to note when you are importing data into R.
We can solve this problem with this solution:
<- read_csv("data/demo_csv.csv",
(Skip_and_Header_Names skip = 1,
col_names = c("ID", "Gender", "Age", "Race", "Education")))
## Rows: 15 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): Gender, Race, Education
## dbl (2): ID, Age
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 15 x 5
## ID Gender Age Race Education
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American <NA>
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ <NA>
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American <NA>
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American <NA>
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic <NA>
## 15 83731 Male 11 Non-Hispanic Asian <NA>
3.5.3 Missing Values
We can also define missing values by using na =
. For example, if we want to assign “Some college or AA degree” under the edu column as NA, we can use the following code:
<- read_csv("data/demo_csv.csv",
Missing_values na = "Some college or AA degree")
## Rows: 15 Columns: 5
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): gender, race, edu
## dbl (2): id, age
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Missing_values
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American NA
## 4 83720 Male 36 Non-Hispanic Black <NA>
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ NA
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American NA
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American NA
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic NA
## 15 83731 Male 11 Non-Hispanic Asian NA
DO QUESTION 10 OF THE QUIZ NOW
- Only characters can be assigned a value of NA, there is a different missing-value designation for numeric values. (True or False)
3.6 Importing Other File Types into R
While csv is the most common file type to import into R, we can also import other types of data file into R using different functions. In this section, you will be introduced to the very basics of how to import txt, xlsx, xpt, and sas files into R.
3.6.1 Text file (txt)
The simplest function that we can use to import a txt file is read.table()
. This function belongs to the default Base R package, so we do not need to install or attach any packages before using it!
The first argument of this function is the file path. What do you think header = TRUE
mean?
read.table("data/demo_txt.txt", header = TRUE)
## id gender age
## 1 83717 Female 80
## 2 83718 Female 60
## 3 83719 Male 3
## 4 83720 Male 36
## 5 83721 Male 52
## 6 83722 Male 0
## 7 83723 Male 61
## 8 83724 Male 80
## 9 83725 Male 7
## 10 83726 Male 40
## 11 83727 Male 26
## 12 83728 Female 2
## 13 83729 Female 42
## 14 83730 Male 7
## 15 83731 Male 11
3.6.2 Excel file (xlsx)
To import an xlsx file into R, we use read_excel()
. But before we can use this function, we need to install and attach the readxl package. Similarly to read.table()
, this function requires a file path.
# install.packages("readxl")
library(readxl)
read_excel("data/demo_xlsx.xlsx")
## # A tibble: 15 x 5
## id gender age race edu
## <dbl> <chr> <dbl> <chr> <chr>
## 1 83717 Female 80 Mexican American Less than 9th grade
## 2 83718 Female 60 Non-Hispanic Black High school graduate/GED or~
## 3 83719 Male 3 Mexican American NA
## 4 83720 Male 36 Non-Hispanic Black Some college or AA degree
## 5 83721 Male 52 Non-Hispanic White College graduate or above
## 6 83722 Male 0 Other Race - Including Multi~ NA
## 7 83723 Male 61 Mexican American 9-11th grade (Includes 12th~
## 8 83724 Male 80 Non-Hispanic White High school graduate/GED or~
## 9 83725 Male 7 Mexican American NA
## 10 83726 Male 40 Mexican American Less than 9th grade
## 11 83727 Male 26 Other Hispanic College graduate or above
## 12 83728 Female 2 Mexican American NA
## 13 83729 Female 42 Non-Hispanic Black College graduate or above
## 14 83730 Male 7 Other Hispanic NA
## 15 83731 Male 11 Non-Hispanic Asian NA
Try it yourself 3.5
Import the bpx.xlsx
into R using the read_excel()
function.
3.6.3 XPT File Extension
Another file type that you may need to import into R is xpt. To do this, we need the function read.xport()
that belongs to the SASxport package.
# install.packages("SASxport")
library(SASxport)
read.xport("data/demo_xpt.xpt")
## ID GENDER RACE
## 1 83717 Female Mexican American
## 2 83718 Female Non-Hispanic Black
## 3 83719 Male Mexican American
## 4 83720 Male Non-Hispanic Black
## 5 83721 Male Non-Hispanic White
## 6 83722 Male Other Race - Including Multi-Rac
## 7 83723 Male Mexican American
## 8 83724 Male Non-Hispanic White
## 9 83725 Male Mexican American
## 10 83726 Male Mexican American
## 11 83727 Male Other Hispanic
## 12 83728 Female Mexican American
## 13 83729 Female Non-Hispanic Black
## 14 83730 Male Other Hispanic
## 15 83731 Male Other Race - Including Multi-Rac
3.6.4 Statistical Analysis Software (SAS)
We can use the function read_sas()
to import sas files into R. But before we do this, we need to install and attach the haven package.
# install.packages("haven")
library("haven")
read_sas("data/demo_sas.sas")
## # A tibble: 15 x 3
## id gender race
## <dbl> <dbl> <dbl>
## 1 83717 2 1
## 2 83718 2 4
## 3 83719 1 1
## 4 83720 1 4
## 5 83721 1 3
## 6 83722 1 5
## 7 83723 1 1
## 8 83724 1 3
## 9 83725 1 1
## 10 83726 1 1
## 11 83727 1 2
## 12 83728 2 1
## 13 83729 2 4
## 14 83730 1 2
## 15 83731 1 5
3.7 Exporting the Data Frame From R
After changing and manipulating our data in R, we can also export it back into a csv file to share it. To do this, we can use the function write_csv()
. For example, let’s say we want to export our “Missing_values” data frame.
write_csv(Missing_values, "data/Missing Values.csv")
We can also export it to an Excel file by using write_excel_csv()
.
write_excel_csv(Missing_values, "data/Missing Values.xlsx")
Now if we check our working directory, there should be 2 new files, “Missing Values.csv” and “Missing Values.xlsx!”
dir()
## [1] "_book"
## [2] "_bookdown.yml"
## [3] "_bookdown_files"
## [4] "_build.sh"
## [5] "_deploy.sh"
## [6] "_output.yml"
## [7] "0-r-and-rstudio-set-up.Rmd"
## [8] "1-introduction-to-r.Rmd"
## [9] "2-importing-data-into-r-with-readr.Rmd"
## [10] "3-introduction-to-nhanes.Rmd"
## [11] "4-data-analysis-with-dplyr.Rmd"
## [12] "5-data-visualization-with-ggplot.Rmd"
## [13] "6-date-time-data-with-lubridate.Rmd"
## [14] "7-data-summary-with-tableone.Rmd"
## [15] "8-Exercise-Solutions.Rmd"
## [16] "9-references.Rmd"
## [17] "book.bib"
## [18] "data"
## [19] "DESCRIPTION"
## [20] "Dockerfile"
## [21] "docs"
## [22] "header.html"
## [23] "images"
## [24] "index.Rmd"
## [25] "intro2R.log"
## [26] "intro2R.Rmd"
## [27] "intro2R.tex"
## [28] "intro2R_cache"
## [29] "intro2R_files"
## [30] "LICENSE"
## [31] "now.json"
## [32] "packages.bib"
## [33] "preamble.tex"
## [34] "R.Rproj"
## [35] "README.md"
## [36] "style.css"
## [37] "toc.css"
Congratulations! We have now succeeded in exporting a dataset from R to an external file! This will make our work much easier to share and access!
3.8 Summary and Takeaways
In this tutorial, we have learned how to import csv files from our hard drive into R using read_csv()
. This is an important first step in data analysis or manipulation since we need to be able to have the data in R in order to process it!


| DOSE: |
1 hit |
inhaled |
Xenon |
(gas) |
I used to work as a chemist for a specialty compressed gas corporation. Before we could afford a compressor we had to just vent transfers of highly expensive rare gases such as krypton and xenon from filling manifolds to the outside air. (1 cylinder containing 50cu foot of xenon ~1400 liters currently sells for ~$20,000).
Having been curious about the vocal distortive effects of helium, I decided to vent a xenon manifold into my lungs. What a head-rush, the large size of a xenon atom apparently displaces oxygen quite readily. (I was standing by an emergency oxygen supply in case I felt I was going to pass out). The buzzed feeling only lasted for ~5min following a single deep breath of Xe. The buzz was only 1/4 the fun however. If one sings tenor, prepare to sing contra-bass; I found myself singing 'were so sorry Uncle Albert, but we havent done a bloody thing all day' about 2 octaves down from my normal register to a coworker.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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---
sort_order: 211
tei_id: rdx_b5j2m.p.idm204422432
annotation_count: 0
images:
small-thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p7bkj/mini-thumbnail/
json: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p7bkj/info/
full: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p7bkj/fs/
page: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p7bkj/single-page/
thumbnail: http://readux.library.emory.edu/books/emory:b5j2m/pages/emory:p7bkj/thumbnail/
title: Page 211
number: 211
---
{% raw %}MY THIRD AND LAST VTSIT TO LONDON. 207{% endraw %}
{% raw %}fort when a slave, and it has been a comfort to him{% endraw %}
{% raw %}through all the vicissitudes of life. Benevolence is{% endraw %}
{% raw %}also very large; he is full of the missionary{% endraw %}
{% raw %}element, delights to do good, and many years{% endraw %}
{% raw %}of his life have been spent in labours of love. He{% endraw %}
{% raw %}is liable to forget his own interests when he can{% endraw %}
{% raw %}make himself useful to others. In his mind, ' faith{% endraw %}
{% raw %}without works is dead.' He does not expect an{% endraw %}
{% raw %}answer to his prayers without he makes an effort in{% endraw %}
{% raw %}the right direction. He is active, industrious, and{% endraw %}
{% raw %}delights to be occupied ; is always busy in one way{% endraw %}
{% raw %}or another ; and is not afraid of hard work if neces¬{% endraw %}
{% raw %}sary. His mind works slowly but quite safely.{% endraw %}
{% raw %}When he has an object he holds on to it till his end{% endraw %}
{% raw %}is accomplished. He is one of the real plodding{% endraw %}
{% raw %}kind. He has not the qualities to render him showy{% endraw %}
{% raw %}and imaginative, but he has good powers of imitation,{% endraw %}
{% raw %}and can easily adapt himself to a change of situation{% endraw %}
{% raw %}and circumstances. He has a vast amount of dry{% endraw %}
{% raw %}humour, and is very direct, practical, natural, and{% endraw %}
{% raw %}truthful in his style of talking. His intellectual{% endraw %}
{% raw %}faculties are of the most practical and common-sense{% endraw %}
{% raw %}kind.{% endraw %}
{% raw %}"He has superior powers to draw correct conclusions{% endraw %}
{% raw %}and inferences, as he understands them. He deals{% endraw %}
{% raw %}mostly in facts, conditions, qualities, and bearings{% endraw %}
{% raw %}of things, and turns all his knowledge into useful{% endraw %}
{% raw %}channels. He has a remarkable gift for observing{% endraw %}
{% raw %}everything that is transpiring around him; has a{% endraw %}
{% raw %}superior memory of persons he sees, facts he hears,{% endraw %}
{% raw %}of places, events, and anecdotes; and his mind is{% endraw %}
{% raw %}like a great storehouse, in which he has collected a{% endraw %}
{% raw %}vast amount of interesting incidents. He has a good{% endraw %}


Type
the data you upload is created by Matlab or scipy.io.savemat() in Python,normally as xxx.mat
Format
including testing_result
,true_result
,location
,such as
scipy.io.savemat("whole_data.mat",{"testing_result": testing_result, "true_result": true_result,"location":location})
Detail