let's jump into it
first
Four Card Feature Master
secend
signUp Form Master
secend
Single Price Grid


The rapaciousradiologist greedily dug up countless radishes from the garden, quickly devouring them before anyone else could take a single one. [G|D]
The ram was readmitted to the farm after its reissue of the proper credentials, and the farmer rejoiced to see it return. [G|D]
Every day, the reactant worked hard to remain in readiness, refusing to refrain from doing its best despite the increasing difficulty of the tasks.  [G|D]


Here is some flowing text. This item has
margin-left: 10px;
added on hover. Notice how it moves the text along on hover?
Here is some flowing text. This item has
transform: translateX(10px)
added on hover. Notice how the
text stays in position on hover?


DAYANANDA SAGAR COLLEGE OF ENGINEERING
Department of Computer Science & Engineering
LAB PROGRAM 1B: Demonstration of Popup Menu
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termex.core.feature
Classes
AbstractFeature
AbstractFeatureBuilder
FeatureBuilderCorpusTermFrequency
FeatureBuilderDocumentTermFrequency
FeatureBuilderRefCorpusTermFrequency
FeatureCorpusTermFrequency
FeatureDocumentTermFrequency
FeatureRefCorpusTermFrequency


Nest is a nestable server templating library. It lets you create pages that look like standard html and easily add functionality with custom tag libraries. Nest templates are well-formed html that is then compiled into standard php. A Phing task has been included so that in a production environment all the templates can be pre-parsed and only the php files pushed to the production web server.
Nest is based on some of the concepts of JSP 2.0. Templates are well-formed html. Tag libraries are included by namespace. There are tags and tag files, and there is a simple expression language typing everything together.
While some of the better concepts of JSP have been carried over, Nest was designed first and foremost to be a PHP templating library.
Unlike JSP, Nest was designed to make it easy for web designers to add new tags. The level of programming skill needed with JSP to add a simple styled div tag was simply to high. It's easier now, but most web designers have moved on.
The PHP templating libraries I've looked at are often like writing in a different language. I tried using just PHP, but found it got too difficult to read even when using shrort tags. I moved to using xslt, but that didn't have a particularly good capacity for dealing with nested tags. So Nest was born.
Nest's tag libraries make it easy to encapsulate complex functionality and then simply work on the design concerns.


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update()
- Method in class models.directfire.view.
MenuPanelModelTargetSelection
updateClock(double)
- Method in interface view.
ClockListener
updateClock(double)
- Method in class view.
ClockView
updateClock(double)
- Method in class view.
SimpleView
updateClockListeners()
- Method in class sim.
Scenario
updateConicalView()
- Method in class sim.entity.
Entity
updateConicalView()
- Method in interface sim.entity.
MoverEntity
Force the entity to recalculate its conical view settings.
updateData()
- Method in class tests.
TestEvent
updateData()
- Method in class tests.
TestEventDetection
updateData()
- Method in class tests.
TestEventTargetGather
updateDetection(Entity, int)
- Method in class sim.entity.
Entity
updateDetection(EntityDetection)
- Method in class sim.forces.
Force
updateDetections(Entity)
- Method in class models.detection.
TestTargetGatherEvent
updateDetections()
- Method in class tests.
TestEventDetection
updateDetections()
- Method in class tests.
TestEventTargetGather
updateDirectionFace()
- Method in class sim.entity.
Entity
updateDirectionFace(double)
- Method in class sim.entity.
Entity
updateDirectionFace()
- Method in interface sim.entity.
MoverEntity
Update the entity's direction of facing relative to its current direction of movement.
updateDirectionView()
- Method in class sim.entity.
Entity
updateDirectionView(double)
- Method in class sim.entity.
Entity
updateDirectionView()
- Method in interface sim.entity.
MoverEntity
Update the entity's direction of view relative to its current direction of movement.
updateEntity(MoverEntity)
- Method in class sim.
Scenario
updateGUI()
- Method in class models.directfire.
DirectFireImpactEvent
updateLocation()
- Method in class models.movement.
MoveEvent
Update the entity's location and other related settings.
updateLocation(Entity, Coordinate)
- Method in interface view.
EntityListener
updateLocations(Scenario)
- Method in class models.detection.
TestTargetGatherEvent
updateLocations()
- Method in class tests.
TestEventDetection
updateLocations()
- Method in class tests.
TestEventTargetGather
updateMount()
- Method in class sim.entity.
Entity
updateMount()
- Method in interface sim.entity.
MoverEntity
Force the entity to recalculate its mounted status.
uploadTime
- Variable in class data.csd.
PlatformWeapon
uploadTime
- Variable in class models.directfire.view.
MenuPanelModelTargetSelection
uploadTime
- Variable in class sim.entity.
Entity
upperRight
- Variable in class data.map.
Map
userMaxRange
- Variable in class sim.entity.
Entity
userMinRange
- Variable in class sim.entity.
Entity
userPHThreshold
- Variable in class sim.entity.
Entity
userTargetPriority
- Variable in class sim.entity.
Entity
utils
- package utils
Utils
- Class in
utils
Utils()
- Constructor for class utils.
Utils
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- TabSelectedEvent - Class in com.travelbank.knit.viewevents
-
Tab selection events from TabLayout
.
- TabSelectedEvent() - Constructor for class com.travelbank.knit.viewevents.TabSelectedEvent
-
- TabSelectedEvent.State - Enum in com.travelbank.knit.viewevents
-
Type of callbacks fired by TabLayout.OnTabSelectedListener
.
- TabSelectionEventPool - Class in com.travelbank.knit.viewevents
-
- TabSelectionEventPool() - Constructor for class com.travelbank.knit.viewevents.TabSelectionEventPool
-
- tag - Variable in class com.travelbank.knit.components.graph.EntityNode
-
- target(Class<? extends Activity>) - Method in class com.travelbank.knit.KnitNavigator.ActivityNavigator
-
Stubber/Setter for this#target
- target(Class<? extends Fragment>) - Method in class com.travelbank.knit.KnitNavigator.FragmentNavigator
-
Stubber/Setter for this#target)
- target(Class<? extends Fragment>) - Method in class com.travelbank.knit.KnitNavigator.SupportFragmentNavigator
-
Stubber/Setter for this#target)
- targetView - Variable in class com.travelbank.knitprocessor.vp.KnitPresenterMirror
-
- task(Callable<T>) - Method in class com.travelbank.knit.schedulers.KnitTaskFlow.ScheduleFlower
-
Stub method to accept Callable
.
- TaskPackage - Class in com.travelbank.knit.schedulers.heavy
-
This class contains everything that a
HeavyThread
needs to perform a task.
- TaskPackage() - Constructor for class com.travelbank.knit.schedulers.heavy.TaskPackage
-
- TestKnit - Class in com.travelbank.knit
-
Created by Omer Ozer on 4/3/2018.
- TestKnit() - Constructor for class com.travelbank.knit.TestKnit
-
- TestModelManager - Class in com.travelbank.knit
-
Created by omerozer on 2/20/18.
- TestModelManager(KnitInterface) - Constructor for class com.travelbank.knit.TestModelManager
-
- TestObject - Class in com.travelbank.sample
-
Created by omerozer on 2/4/18.
- TestObject() - Constructor for class com.travelbank.sample.TestObject
-
- TestSchedulers - Class in com.travelbank.knit
-
Created by omerozer on 2/26/18.
- TestSchedulers() - Constructor for class com.travelbank.knit.TestSchedulers
-
- ThirdModel - Class in com.travelbank.sample.models
-
Created by omerozer on 3/1/18.
- ThirdModel() - Constructor for class com.travelbank.sample.models.ThirdModel
-
- toActivity() - Method in class com.travelbank.knit.KnitNavigator
-
Stubbing initialization for when navigation to an activity is needed.
- toFragment() - Method in class com.travelbank.knit.KnitNavigator
-
Stubbing initialization for when navigation to a Fragment is needed.
- toView(Object) - Method in class com.travelbank.knit.AttachmentMap.ViewAttachmentStubber
-
Stub method that sets the view object.
- Tuple2<A,B> - Class in com.travelbank.knit.tuples
-
N-Tuple data container to wrap multiple object into one quickly.
- Tuple2(A, B) - Constructor for class com.travelbank.knit.tuples.Tuple2
-
- Tuple2<A,B> - Class in com.travelbank.knitprocessor
-
- Tuple2(A, B) - Constructor for class com.travelbank.knitprocessor.Tuple2
-
- Tuple3<A,B,C> - Class in com.travelbank.knit.tuples
-
N-Tuple data container to wrap multiple object into one quickly.
- Tuple3(A, B, C) - Constructor for class com.travelbank.knit.tuples.Tuple3
-
- Tuple4<A,B,C,D> - Class in com.travelbank.knit.tuples
-
N-Tuple data container to wrap multiple object into one quickly.
- Tuple4(A, B, C, D) - Constructor for class com.travelbank.knit.tuples.Tuple4
-
- Tuple5<A,B,C,D,E> - Class in com.travelbank.knit.tuples
-
N-Tuple data container to wrap multiple object into one quickly.
- Tuple5(A, B, C, D, E) - Constructor for class com.travelbank.knit.tuples.Tuple5
-
- type - Variable in class com.travelbank.knit.components.graph.EntityNode
-
Type of the contained entity.
- TYPE_NAME_CALLABLE - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_CLASS - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_CONSUMER - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_ENTITY - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_KNIT - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_KNIT_MESSAGE - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_KNIT_PRESENTER - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_LIST - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_SCHEDULER_ENUM - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_SCHEDULER_PROVIDER - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-
- TYPE_NAME_STRING - Static variable in class com.travelbank.knitprocessor.KnitFileStrings
-


Pankaj KulshreshthaPankaj has 20 years of experience in analytics and risk management. Before starting Scienaptic, he led and grew Analytics business at Genpact three folds to 6000+ people. He was one of the first few people to join the Decision Science Center of GE in 1998. Here he built many predictive models himself and then led teams while designing scalable analytic processes. Since then he has been practising and advising in the art and science of Analytics across marketing and risk functions. Pankaj is a Mechanical Engineer and has a PhD in Decision Sciences. He loves to read about the confluence of mind, life, and machines. Muthukumar.NMuthu has16 years of experience in the field of Decision Sciences. Before co-founding Scienaptic, Muthu has worked with Amazon, Citi Bank and GE in the area of CRM, Operations and Risk Management. Throughout his career, Muthu has focused on designing impactful solutions combining analytics and technology. He has two patents to his credit, a methodology to drive Incremental Sales and an Offer Optimization Framework. Muthu is an engineer and has a Masters in Operations Research. He likes to learn and design new AI Algorithms in his spare time. |


| Brand | Origin |
| Ibanez | Japan |
| Ormsby | Australia |
| Skervesen | Sweden |
| Jackson | USA |
| Kiesel Custom Guitars | USA |
| Caparison | Japan |
|
||||||
|
| My Favorites Guitar Companies | |
| Brand | Origin |
| Ibanez | Japan |
| Ormsby | Australia |
| Skervesen | Sweden |
| My Second Favorites Guitar Companies | |
| Jackson | USA |
| Kiesel Custom Guitars | USA |
| Caparison | Japan |
| Brand | Origin |
| Ibanez | Japan |
| Ormsby | Australia |
| Brand | Origin |
| Ibanez | Japan |
| Ormsby | Australia |
| Brand | Origin |
| Ibanez | Japan |
| Ormsby | Australia |


1 package net.sourceforge.pmd.ast;
2
3 public interface AccessFlags {
4
5 /*** Access flags for classes, fields and methods.
6 */
7 public final static short ACC_PUBLIC = 0x0001;
8 public final static short ACC_PRIVATE = 0x0002;
9 public final static short ACC_PROTECTED = 0x0004;
10 public final static short ACC_STATIC = 0x0008;
11
12 public final static short ACC_FINAL = 0x0010;
13 public final static short ACC_SYNCHRONIZED = 0x0020;
14 public final static short ACC_VOLATILE = 0x0040;
15 public final static short ACC_TRANSIENT = 0x0080;
16
17 public final static short ACC_NATIVE = 0x0100;
18 public final static short ACC_INTERFACE = 0x0200;
19 public final static short ACC_ABSTRACT = 0x0400;
20 public final static short ACC_STRICT = 0x0800;
21
22
23 public final static short ACC_SUPER = 0x0020;
24
25 }


Ben is a current student at DeltaV code school in Cedar Rapids Iowa.
One night while in the shower the idea
for this simple game came to him and so he decided to just make it happen.
He was quite tired when he started
on this as his Fiance wouldn't let him go to bed because he still had to take the dogs out,
but he couldn't take them out too early or they'd wake up early in the morning asking to go out.


FormatDesigner is an application to experiment with the format strings used to format data in String.Format and various type's ToString functions.
Click here to launch the application directly (requires the .NET Framework 1.1).
Click here to download the source code.
Enjoy.


Github’s Octocat was designed by Simon Oxley, alongside the white bird Twitter used (before they received a proper logo) as part of a usual routine of cranking out images for iStock. GitHub saw it, and wanted it, presumably under the notion that it can represent how complex code combines to create peculiar things, much like the octopuss… except the CEO of GitHub called it an octocat, and it has been the octocat since via.
Octocat is Github’s logo and trademark and here is used to illustrate a fictional character (John Smith) that used the { Personal } Jekyll Theme to build his website.
Master Studies @ Universidad Nacional de Colombia Facultad de Ingenieria on Telecommunications + Platform for Analysis Biological Data + Group Research LISI
Architect @ Uniminuto, Architecture Development for IT Area
Coordinator @ Uniminuto, Free Software and Open Source Area
Bachelor studies @ Uniminuto Virtual y Distancia Facultad de Ingenieria on Computer Science
Send me an Email, ask whatever! (about of my projects)
Github’s Octocat was designed by Simon Oxley, alongside the white bird Twitter used (before they received a proper logo) as part of a usual routine of cranking out images for iStock. GitHub saw it, and wanted it, presumably under the notion that it can represent how complex code combines to create peculiar things, much like the octopuss… except the CEO of GitHub called it an octocat, and it has been the octocat since via.
Octocat is Github’s logo and trademark and here is used to illustrate a fictional character (John Smith) that used the { Personal } Jekyll Theme to build his website.
Github’s Octocat was designed by Simon Oxley, alongside the white bird Twitter used (before they received a proper logo) as part of a usual routine of cranking out images for iStock. GitHub saw it, and wanted it, presumably under the notion that it can represent how complex code combines to create peculiar things, much like the octopuss… except the CEO of GitHub called it an octocat, and it has been the octocat since via.
Octocat is Github’s logo and trademark and here is used to illustrate a fictional character (John Smith) that used the { Personal } Jekyll Theme to build his website.


Before Change
def setup_platform(hass, config, add_devices, discovery_info=None):
Setup Nest binary sensors.
logger = logging.getLogger(__name__)
try:
for structure in nest.NEST.structures:
for device in structure.devices:
for variable in config["monitored_conditions"]:
if variable in BINARY_TYPES:
add_devices([NestBinarySensor(structure,
device,
variable)])
else:
logger.error("Nest sensor type: "%s" does not exist",
variable)
except socket.error:
logger.error(
"Connection error logging into the nest web service."
)
class NestBinarySensor(NestSensor, BinarySensorDevice):
Represents a Nest binary sensor.
@property
After Change
def setup_platform(hass, config, add_devices, discovery_info=None):
Setup Nest binary sensors.
for structure, device in nest.devices():
add_devices([NestBinarySensor(structure, device, variable)
for variable in config[CONF_MONITORED_CONDITIONS]])


iOS7 icon font
A webfont of iOS 7 icons, based on Free iOS 7 icons in vector. Original Github Project by AKIRA-MIYAKE.
Forked to add keywords for easier searching, and roughly sorted by category.


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1 write to _members
System.Management.Automation (1)
engine\MshMemberInfo.cs (1)
3895
_members
= members;
6 references to _members
System.Management.Automation (6)
engine\MshMemberInfo.cs (6)
3913
return
_members
[name];
3930
return
_members
.Match(name);
3947
return
_members
.Match(name, memberTypes);
3965
return
_members
.GetEnumerator();
3971
public int Count =>
_members
.Count;
3978
public T this[int index] =>
_members
[index];


How to view details of users who are enrolled for an event on the Events module. The Enrolled Users list displays the Display Name, email address and number of attendees enrolled against that user's account, as well as whether the enrollment has been approved. Editors can click on the Display Name to view the enrollee's user profile, or click on their email address to send them a message. Note: This option may not be available to all users.
Viewing Enrolled Users as an Administrator


Smart and bring you income later on.
Yes, trusted and high quality cosence data bring climatecab fundamental computing.
Yes, trusted and high quality cosence data bring climatecab fundamental computing.
Yes, trusted and high quality cosence data bring climatecab fundamental computing.
So far you can place an order online through our email.
From cell phone wireless part , you can contact to phone, as like below :
Step 1 : After about 30 seconds of Powering On the device, if it cannot connect to any nearby Wi-Fi network, It will create a Wi-Fi Hotspot of it own by the name “ClimateOrb-xxxx”. We should now open Wi-Fi settings from the Settings Apps on our Smartphone. You can see the ClimateOrb-28b8 from the list of "CHOOSE A NETWORK...". Then tap the “ClimateOrb-28b8” network and enter password “climateorb”.
Step 2 : After This is the PRiDA statistics screen. This screen displays the current status of the established connection, WiFi mode, IP address, etc. Also, the top right icon is the control menu. Clicking on that icon, the control menu appears as below:
Step 3 : Now join to the new access point by clicking on "Configure new AP" option. This allows you to connect the device to a new access point.
Step 4 : Now you will see the SSID configuration screen. Enter the SSID and Passphrase and tap “Apply” to start connecting to the access point.
Step 5 : After connection is established, the display will turn on the Device and sensor data will be visible. Once the device is operational, you can close this page and disconnect from the Wi-Fi network. Also depending on Wi-Fi channel settings, the current status screen may not appear. In this case,there is no need to do anything as the device is already connected to the Wi-Fi.
Yes, you can set an alarm later.
Temperature, humidity, eCo2, Ph1.0. Pf2.5, ph10, etc.
6000 hours or two years
Yes , we are planning to do this exchange from next year.
Yes, you can only download app and subscribe data.
The accuracy is 0.1ppm.


Robin:
So I hope you’ve managed to calculate the median number of download for these journals and get the impact factor!
If you visualized the data, you might have seen something like the plot below. There is a really strong relationship between number of downloads of a preprint and the impact factor of the journal. Now, this may be a bit of a chicken-and-egg story. I’m not sure whether articles eventually published in a high-impact journal will get more downloads on their preprint, or whether preprints who have been very popular before being published are more likely to be published in a high-impact journal? Now, you may have a think about this with your group for a few minutes before you move on.
Below are a few answers you could have gotten, click the right one to continue to the next step!


| Shell Commands | Effect | Examples |
|---|---|---|
| $ 'command' --help | hows help for the shell command | $ mv --help |
| $ clear | Clears terminal screen | |
| $ mv 'source' 'destination' | Rename/move source to destination | $ mv wronNameFile correctNameFile |
| $ rm | Remove files and directories |
$ rmdir (Delete directories.) $ rm -i valueable.txt (removes file with "interactive"/warning prompt) |
| $ mkdir and $ touch | Commands to create new directory and files | |
| $ start . | Opens terminal directory folder in windows file explorer. | |
| $ cd "path" | Change working directory |
$ cd (change to top shell dierctory) $ cd .. (change to parent dir. ) $ cd Documents/Yellowstone/Bears/ (Change Shell working directory to bear folder) |
| $ ls | List the files in the directory. |
$ ls . = $ ls (list files in current dir.) $ ls ~ (list files in my home dir.) $ ls -l (option to list in a long /detailed form) $ ls *.pdf (list of all .pdf files) |
| $ echo | Print message back in terminal | $ echo $COLUMNS x $LINES |
| ; | Allows 2 Shell commands on same line | $ cd.. ; ls |
| pwd | Print Working Directory | $ pwd |
| $ curl | "see url" is used in command lines or scripts to transfer data. |
$ curl 'http://google.com' (Show source code of a web page) $ curl -L 'http:...' (Follow redirects) $ curl -o google.html -L 'http://google.com' (save code directly to g.html file) |
| $ cat | View/concatenate file content. | $ cat text.txt (text content) |
| $ less | View file content screen by screen. | $ less longbigtext.txt (text content with space and arrows, "q" to quit) |
| $ wc | "word count" reads either standard input or a list of files and generates one or more of the following statistics: newline count, word count, and byte count | $c ws -l (line count) |
| $ grep | "Global regular expression print,” processes text line by line and prints any lines which match a specified pattern. |
$ grep wordToFind textFile.txt (Output all lines containing wordToFind) $ grep wordToFind textFile.txt | less (Output to Less which results in page by page find) $ curl -L 'http...' | grep -c fish (-c counts how many fish in url) |
| variableName=Value | Variables in Shell. More here |
$ varName='variable' (assignment) $ echo $varName (Variable call) $ echo $LINES x $COLUMNS (Internall Shell variables: prints terminal window dimension) $ echo $PATH (Enviroment variable are shered within programs run from within the Shell: returns path to Program Shell files) |
| $ PS1 | Controlling the shell prompt ($PS1) | The tool is here by Julien Ricard. Try it out! |
| $ alias | Allows you to create short names for commands. |
$ alias ll='ls -la' $ alias (show all alias created) |
| Last updated: 5/2020 | Shell resources: The Bash Academy, Bash Beginners Guide, Bash Programming HOWTO, Regexr — Learn Regular Expressions. |


[Recieve Contribution Bonus]
[Ancient Adena Recieved]
Here is your well-deserved reward for your contribution during the quest event period!


| DOSE: T+ 0:00 |
5.0 g |
oral |
Mushrooms |
(plant material) |
| T+ 2:47 |
2.0 g |
oral |
Mushrooms |
(plant material) |
Friday 26th January 2007
5g Koh Samui & Dutch King mix @ 11am
1g Ecuador & 1g Dutch King @ 1.47pm
This journey was heavily influences by the myriad things recently digested and rolling about in my brain, primarily Daniel Pinchbeck's book '2012 The Year of the Mayan Prophesy', the recent downfall of snow and the films 'Grizzly Man' and 'A Scanner Darkly'.
I felt pleasantly and surprisingly energized with a positive vibe in the morning, having a confidence in the trip, and myself, which I have never quite felt before. With no real nerves or nausea at all, the trip had a calm natural evolution, the transition from ground state through visionary states and back again was smooth, feeling absolutely integrated with my life, as natural as the tide rolling in and out through the passage of a day.
'Medulla' playing, just into the early stages I reflected on the utter clarity of mind mushrooms bring, I feel totally in the moment with no thoughts at all other than the immediacy of existence through all senses, focus is completely on just the reception of information.  I added to the 'L' shaped pattern again, which is still not finished!
Surprisingly early on, the first closed eye visuals I had whooshed me up into a cloudlike murk of greens. It was as if I had poked my head up into an intergalactic/interdimensional meeting place. The impression was of various species sat round in a circle about to discuss things of great import, perhaps involving me, or maybe I was an emissary as well. Even though I couldn't see desks, I sensed them and I particularly recall a being with a squid-like head.
I think of the peak as 'The Download'. I was hurtling through worlds and landscapes of information. Two three dimensional shadows hung in the air in the air in front of then beside me. Initially it was overwhelmingly rapid, and I felt concern about freaking out. I relinquished my fear and relaxed into it passively letting them pour the information into my head. By doing so the CEVs melted through and into me, becoming a 360-degree experience in which I was enmeshed with what I perceived. The clear light circled and arched around the edges of everything. I can recall with clarity a scroll rolled down by two 'Greys', an absurd mix of ancient and futuristic connotations. Along with the ideas in the Pinchbeck book, the new laptop, with its CD burner, Amy had just bought, definitely influenced the theme of downloaded information.
There was the feeling of vast amounts of ancient and alien knowledge being stored in hitherto unused parts of my brain, which I moved my thoughts through as if I were rediscovering and exercising them. Wondering what 'powers' I may have gained I later, half jokingly, tried to make Socrates and myself levitate. During the onslaught of information I telepathically asked 'Why me? Why this time?' and received the answer 'It's always like this. This is what always happens!' The essence of what they were imparting/teaching me was a message of promoting harmony and interconnection, the notion of 'god' in everyone with a Jesus style acceptance of responsibility. I felt deemed a potentially harmonious man, with a sense of congratulations towards me for trying to be as good a person as I can.
To counterbalance this positiveness, thoughts turned to the issues I'd been having with a person at work I had got along with until recently, who in the trip I saw as both a reflection (He has a slightly similar temperament to me, or rather how I used to be) and utter diametrically opposed contrast. I must admit to feeling some jealousy, as he is a talented guitarist, sometimes playing gigs, which reflects my wish to be musical again. Recently he seemed to have taken umbrage at something and appeared to mock aspects of my outlook on life.
There was an undercurrent that all the signals being thrown out were so vague that the whole thing could have been in my head. I needed the quiet time of not talking to people at work to gain perspective on life anyway though. I saw him running away from me, looking nervous and scared and hiding behind a pillar. I must note that since this trip things have smoothed out and we are on good terms again.
I was overcome with feeling positively freezing. I had a numb little finger on my left hand and was shivering down to me feet, which brought a brief worry about pneumonia. I would also be concerned about having a heart attack later due to a rapid pulse and chest pain. I wrapped myself up in the sleeping blanket. Bestowing sentience on him I said, 'Come here you' to Socrates, a stuffed grey squirrel monkey soft toy and the best tripping companion ever, as I pulled him over to join me in my snuggled up state. That it had snowed and settled earlier in the week had a great deal of influence on what transpired next.
Whilst the opening of the Younger Brother album 'A Flock of Beeps' played I went into a daydream-like reverie in which I imagined myself as part of an expedition in a snowstorm, which was attacked by bear, and someone was bleeding to death. Blood lapped up over my field of vision, and the sense of this was so strong I had to check to see if I really was bleeding and hadn't noticed, or even if I had pissed myself and become highly confused. This whole passage of the trip was tinged with deja vu, so much so that I thought I had experienced the exact same thing whilst listening to that album before, but I've since realised I hadn't.
I quickly realised that the album was wrong for the mood, perhaps contributing to conjuring up this imagery, and I turned it off. Unlike Shpongle, the music felt cheap, disingenuous, non-organic, especially the jarring samples from 'Alice in Wonderland'. I even had to put it back on the CD shelf, to get it out of the way, beyond my sight. I came face-to-face encounter with a female entity. Part of this is wiped from memory, and only echo remains. I give her a look as if to say, 'I don't deserve this' which she reflected back at me and relented in agreement.
What haunted me briefly post trip was a realisation that the couple of times I felt I might die I had a willing acceptance of it. I continue to wonder if I really thought I might die and was truly accepting of the possibility or if underneath I knew I was, hence my acceptance feeling so easy, or if the whole emotional response related to ego death, or was a metaphor for something else entirely!
Transitioning to a less weird and uneasy frame of mind I sensed of an alien family, with the inference that we were all doing an activity like skiing. There was an annoying father figure who was taking charge and felt an utter cliché. I opened my eyes to, successfully, break this vignette.
 My eyes all misty everything blanked out to the view of a snowstorm, with white sweeping through everything my body turned into a mountainscape and I was hit by the thought 'Why am I buried in snow?'
 There was an acceptance of errors in a very matter of fact manner. I understood my development through re-experiencing vital junctures of my existence and was imbued with an joy at a complete understanding of myself and how I am who I am. I felt like I was viewing a map of my psyche with a beautiful feeling of fully understanding myself and seeing how you are assembled throughout your life experiences.
The process of re-familiarising myself with the keynotes of my life so far segued into a preparation of what advice I would want/hope to pass on to Jack. The greatest advice was undoubtedly, and undoubtedly based on the follies of my oversexed teenage years: 'Be a gentleman', especially with regards to women. The clouds broke many times and where I was positioned on the sofa the sun (echoing 'son' in my mind) shone down on me many times, surrounding me in a cleansing heavenly glow.
Somewhere along the line I got up and went to the window and looked outside, all I could see had the rotorscope painted look of the films 'Waking Life' and 'A Scanner Darkly'. Surveying the view of the back gardens, filled with kids toys and other paraphernalia I thought 'Am I Bob Arctor?' For a split second I believed I was, illustrating a fusion of various realms of thought and reality in my mind and how deep the trip went into my subconscious.
The music line up was again a mixed bag. After the iconic Floyd the Super Furry Animals album 'Phantom Power' was an okay album to trip to, it filled the gap fairly pleasantly, if a little disappointingly. There was a doubling and tripling of images and ancient/alien text/symbols I have seen many times before rose up through all surfaces emitting meaning beyond ordinary language.
Perhaps echoing Jack's womb bound maneuver, working my way rotating round clockwise I ended up lying upside down on the sofa, my head hanging down. I considered that everything's much better that way round, for one thing there's NO DUST! This may have been somewhat inspired by a photo I had seen at the Tate Modern earlier in the week of the artist upside-down, tied to a chair, reading a book (held upside down). The artist was hanging upside down, but the picture had been inverted. With pleasure at feeling utterly abstracted from ordinary reality I kept completely losing track of time, I felt outside of it, minutes passing ridiculously slowly. The carpet and ceiling swirled with patterns rising and falling, which I seemed to be able to control with my mind somewhat.
As the experience had been quite profound and intense I had delayed taking the second dosage. I was still tripping very hard when I took it at around a quarter to two. I had a bad case of the fidgets, so I forced myself to sit and relax in silence. Chirruping and other sounds familiar from psychedelic music emerged. A golden Buddha/Alien statue confronted me about my identity, in a scene very reminiscent of the stare of the 'confuse a cat' sketch cat. Opening eyes from this, all was roughly, warmly painted (a la Scanner Darkly) and fluid looking/feeling. I felt like I was floating through the sensation of the happiest sunshine day of the year, a comforting, cozy, connected, contented feeling. The light from outside became bright flaring sunlight, the opposite of the snow scene section of the journey, it felt like peak of summer.
I was in hysterics about having to call Amy at 2.30pm due to the incredibly intangible state of reality I found myself in, and the potential impossibility of being able to communicate in a lucid manner.
I only made it through a few track of 'Ziggy Stardust', imagining 70s Bowie as fake, fag, hag scarecrow. Lester Bangs's assessment of him as a poser and phony came to mind and after a few sublime tracks the album became annoying. I turned it off and put on the mix disc I had made the night before. With the mix disc whispering through the flat, after swift rejection of venturing outside into the cold, I went to the bedroom for change of scene. On top of the covers, curled in ball, I found myself on the 'Lost' island, sensing the character Hurly around. Staring into a rock I drifted out of my body and this reality and down through and into holes in the finest detail of the stone, which became hive-like segments, this came with an a feeling of utter comfort and relaxation. I felt in place and utterly peaceful.
I considered the spark of energy and attraction I sometimes feel between myself and one of the women at work. I had had a vivid dream recently in which we had expressed love towards each other, with the true feeling of falling in love again, exchanging a passionate emotionally explosive kiss, only to be upset to wake and find it all to be a dream.
I considered a fantasy of having a relationship with this woman, in an alternate reality where I wasn't with Amy, or if I could split myself in two and live both lives. A lot of this had to do with feelings of loneliness, sexual frustration (I spent a fair amount of time rolling about on the bed in semi sexual positions, or even in anticipation of Amy's birthing positions), a longing for intimacy and a feeling of aging and longing for some aspects of youth, surrounding the pregnancy. I don't really have any feelings of anything other than friendship towards this woman at all, but she seemed to be the closest person in my life I admire beyond my wife. I found myself saying, 'I don't know where I am in my life, or who I am'. This is not really true, I was testing the waters of my subconsciousness, worried that I'm not more worried about the future. Strangely, as if an afterthought on the experience I had already had/was having, I wrote, 'I need a break from myself' in the notebook.
The alarm on my pocket clock went off to indicate that I should call Amy. I rushed into the blindingly bright living room, light flaring in through the window. I nearly tripped over my trouser leg, but my hands quickly restored balance. Amy says I was slurry and odd, in a funny way, on the phone, very vague and talking much nonsense.
Thinking about what I had, perhaps, learnt during the experience I found it hard to define what had actually happened, asking of myself questions that often occur. Was I operated on/modified by an alien intelligence? Do I know have a mass of new information in my mind? Can I access it? How will this modification manifest itself? Thinking about the TV series 'Lost', the ultimate cliffhanger show, I came with what I think is the best, and most intriguing mystery story title ever - 'I wanted to know'.
With the feeling that I hadn't eaten in years, like a madman. I leapt and squealed with excitement about the idea of food. I sat down perched on the precipice of exploding with delight and joy and had the most exciting bowl of cereal ever. I was so amped up that I took some neurofen to take the edge off and avoid the inevitably imminent ache to my mind and eyes. With extreme keenness I organised a mix disc of the only decent songs on various soundtrack and freebie albums we have.
I suddenly, amusedly, realised 'I Am The Walrus' taking that definition to mean that socially I am an outcast. Briefly, due to the extremely draining tone of the experience I worried I'd driven myself insane, or damaged my brain, I also had concerns about not coming down, my brain felt very strange, more so than usual. The odd brain feeling faded and gradually relative normality was back into the driving seat. I barely noticed the transference back to ordinary perception, comfortably drifting into the evening routine.
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.


OGNL stands for Object-Graph Navigation Language; it is an expression language for getting and setting properties of Java objects. You use the same expression for both getting and setting the value of a property.
OGNL started out as a way to set up associations between UI components and controllers using property names. As the desire for more complicated associations grew, Drew Davidson created what he called KVCL, for Key-Value Coding Language, egged on by Luke Blanshard. Luke then reimplemented the language using ANTLR, came up with the new name, and, egged on by Drew, filled it out to its current state.
We pronounce OGNL as a word, like the last syllables of a drunken pronunciation of "orthogonal" or like "ogg-null".


flask-chatterbot
A web implementation of
ChatterBot
using Flask.
Usage example:
/get/how are you?
Browser handles spaces (
/get/how%20are%20you?
)
Returns raw response (ex: I am good).
Contributing:
Project is hosted on
GitHub
.


REC INFO[p!]=p
This line is malformed and does not end with a BR tag. THIS FIELD[would]=work but it doesn't end with a BR tag either. This line is malformed and contains no equal sign.
MESSAGE[pm]=This message
includes
HTML
.
P BARCODE[pb]=abcd


Responsibilities include designing, producing, maintaining, and refining tire models and tire analysis tools for the purposes of real-time vehicle performance simulation and real-world tire performance optimization.
Qualifications
Requirements include: Degree in Mechanical Engineering or Mathematics or equivalent years of experience. 3-5 years industry experience in motorsport applications/industry including programming in MATLAB or Python; supervised machine learning techniques – for non-linear regression; time-series data processing; signal processing and limit-handling vehicle dynamics.
Must be able to communicate technical information effectively both verbally and in written form to a wide variety of audiences. Ability to work in team environment. Ability to work under minimal supervision. Detailed oriented individual who can effectively work well under pressure in a culturally diverse environment. Traveling will be required.
HPD offers a competitive compensation & benefits program & an opportunity to further develop a career in motorsports racing.
Interested candidates should submit resume to:
http://corporate.honda.com/careers/find-a-job.aspx
An Equal Opportunity-Affirmative Action Employer. Women & minorities encouraged to apply


Like "White Balloon", it focuses on the lives of ordinary children. Unlike "White Balloon" which stretches over one incident in realtime, "Children...." involves a string of events and problems encountered by them. It was very interesting to see how they go about solving it. These solutions, funny or ineffective they might be, exude innocence, which is the very soul of this masterpiece.That untainted innocence is unattainable by conventional city dwellers, both young and old. Like almost all Iranian productions, everything was done in the most authentic way, more real than dramatic.It would have easily taken the dramatic turn by exploiting the hard lives the kids are having to gain sympathy. Fortunately, that fact was downplayed so as not to get in the way of storytelling or perhaps, what I perceived as hardships might be a common sight in Iran ( I hope not).
Iranian productions like to end on a pondering note but that does not always mean open ended endings. I guess with all the loose ends tied up there's no need to go any further by including a most probably corny conclusion. So they left the obvious to imagination and fired off a last burst of lingering creativity, then subtly the curtain fell.
If you have not seen any Iranian films and wonder what's the big fuss about? This will be a very good one to start with because of its accessibility, in other words, it's of relatively high entertainment value for an arthouse fare. But if you adore Iranian films, then this one could be a little less intriguing. Nonetheless, it's still as endearingly heartfelt.
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READERS’ LETTERS: Deafening silence at Dundee University over principal departure
Sir, – The fact that Dundee University has to find a new principal, following the appointment and resignation of the previous one within 11 months, deserves some comment.
by Courier ReadersWe are informed that Professor Atherton’s appointment followed a “rigorous” examination of his suitability for the post and that his resignation factored in that, apparently, he wasn’t the right person for the job, or for the “aspirations” of the university.
So what exactly went wrong? For a university that prides itself on openness, there has been a deafening silence on detail.
Presumably the salary and any add-ons offered and accepted were formally agreed and signed up to, by both the University Court and Professor Atherton.
The fact that salaries paid to university principals, in general, are ridiculously high is a separate issue that needs attention, but it is not unique to Dundee University.
Over 50-odd years, the university, often through trying times, has become a hugely successful, globally recognised centre of international excellence, notably in the life sciences but also in many other areas. The city of Dundee is rightly proud of this.
But, within a few months, its hard-won reputation has been tarnished.
Surely, suspending the principal for alleged non-payment of rent, for example, is at variance with a common sense approach to the handling of this matter, irrespective of other issues. The whole saga is deeply worrying.
Let’s hope it does not deter outstanding potential applicants from applying for the principal post. At the highest levels of the University Court, some introspection and outcomes would not go amiss.
William Stewart.
1 Clarendon Drive,
Dundee.
More dirty tricks to come over oil
Sir, – Oil expert Nick Dekker in his letter (Scotland’s £350m untapped opportunity, Courier, November 18) tells us the reason Norway, which discovered oil at the same time as Scotland in our shared North Sea, is one of the world’s richest countries and Scotland is not is because it is an independent country.
I’m sure Mr Dekker will be aware of the McCrone Report, commissioned by the UK Government in 1974 from economist Gavin McCrone, to establish the likely effects of North Sea oil on the economic viability of an independent Scotland.
The report was marked “secret”, and quietly shelved, when McCrone predicted Scotland would have a “chronic surplus to a quite embarrassing degree, and its currency would become the hardest in Europe”.
Mr Dekker was probably thinking of such subterfuge as he warned that “Westminster will use every dirty trick in the book to maintain control of our oil”.
Dirty tricks of which they are masters, having honed their skills around the world for centuries and, as another independence referendum nears, we can expect much more.
Les Mackay.
5 Carmichael Gardens,
Dundee.
UK commitment is wavering
Sir, – I read with some incredulity that the Liberal Democrats have made an electoral pact not to stand against Plaid Cymru in Wales.
This is surely a risky and hypocritical development.
If the Welsh nationalists make progress towards independence thanks to assistance from the Liberal Democrats, what will be the constitutional consequences for Scotland?
Arguably, in contrast to their stated unionist credentials, Jo Swinson has potentially divided the United Kingdom by her opportunistic action.
This leads me to believe that the Liberal Democrats are only in favour of the United Kingdom when it suits them and it is not an enduring commitment, rather like the strident assertions of Jo Swinson that she will not put Jeremy Corbyn in No 10.
How long will that promise endure in the cold morning light of Friday December 13?
Dr Brenda Gillies.
12 Victoria Street,
Newport-on-Tay.
SNP lying back and waiting
Sir, – Ian Blackford, the SNP’s leader at Westminster, tells us that “Scotland’s desire for independence is unstoppable”.
This is another example of an SNP representative conflating “Scotland” with the SNP.
We know the SNP’s desire for secession from the UK is “unstoppable”, but that does not mean that “Scotland”, or a majority of Scots, feel the same way.
Having tried every trick in the book to achieve another referendum in the few years since we had a “once in a generation/lifetime” referendum, the SNP now resorts to a “lie back and enjoy it” ploy.
Since it is inevitable, they say, why not abandon all resistance to it and just let it happen?
I get the impression that some are falling for this scam. I doubt that Mr Blackford’s much-mentioned “people of Scotland” will.
Jill Stephenson.
Glenlockhart Valley,
Edinburgh.
Taking voters for granted
Sir, – Soon we will have them at our doors peddling their lies and asking for your vote.
I will be asking questions such as, have you any plans for the safe disposal of the nuclear subs at Rosyth?
Would you have any member of your party or family take part in cutting them up?
Then there is the question how safe are the old Forth Road Bridge cables, bearing in mind a suspension bridge collapsed in Europe.
We need to run new main cables above the existing ones with new droppers and anchors before the same happens here.
It is time our politicians wrote a truthful CV, but I know this would be out of character for them.
What would be a surprise in my town is to find a candidate that speaks in a Kirkcaldy dialect, was born in the town and would stay in the town no matter what.
What we have in Scotland is a political clique who would join any party that would get them in to a safe seat.
They are taking us for granted.
John G Phimister.
63 St Clair Street,
Kirkcaldy.
Homelessness must be priority
Sir, – Watching the news tonight, I had to listen to talks from all the parties due to the election.
Above all, one heard about the nightmare of Indyref2.
But why do I not hear the SNP or the first minister talk about the rising number of homeless people in Scotland?
People are dying because they have no roof over their head, for whatever reason.
Homelessness just should not be in this day and age.
It is an absolute scandal and the SNP should be deeply ashamed that they can only think of their own dreams, and do nothing at all to help the homeless in their own back garden.
How many homeless people need to die before the first minister and her SNP wake up and realise that the people of Scotland need help now?
Homelesness is an acute emergency in Scotland, which is largely ignored by the SNP and the first minister.
Ignored simply because it is not a “popular” item or not as “fashionable” as the environmental emergency?
Maaike Cook.
Cash Feus,
Strathmiglo.


The API Superstore provides a catalog of thirdparty APIs that you can use to enhance your business. From ecommerce to maps, API Superstore lists all of the services that customers expect and saves you the headache of sorting it out yourself! The longer you wait, the longer you’re not making more money and happier users!
Product Features
Insert javascript function tabs here


19. QBank - Developer’s Manual¶
Table of Contents
19.1. Overview¶
19.1.1. Introduction¶
QBank is a web application that assists Problem Authoring and Publishing. The user interface is intuitive and easy to understand. This document will give you a feel of the overall functionality of QBank tool and how to contribute to extending the code base.
19.1.2. QBank Features¶
Main Features Of QBank
Easy interfaces for Problem authoring based on the main Problem types:
- Static question – Multiple Choice Question
- Dynamic question – Parameterised Question
- Summative question – Multi-Part Question
- Tool Specific question – Khan Academy Exercise Question
Problem publishing - Parsing options to convert authored questions to different formats
- Comma Separated Format - csv
- Khan Academy Exercise Format
QBank is extremely flexible and extensible
- All display items are driven by templates using the powerful Django templating language
- All urls can be custom configured to your desired naming convention
- The formats that the questions can be parsed into can be extended to support specific needs of various publishing tool.
- Front ends specific to a Tool can also be easily extended from the existing(Khan Academy) interface.
Standardised Authoring Interfaces based on the Formal Problem Definition for different Problem Types.
19.2. Requirements¶
QBank is developed on the Django framework, therefore you do need a fully functioning Django instance The Django installation guide will step you through the process.
Please use Django 1.5.x at this time.
QBank requires Python 2.6.5 or later and a database supported by Django.
19.2.1. Directory Structure¶
The basic QBank directory structure looks as follows
. `-- QBank
|-- manage.py
|-- settings.py
|-- urls.py
`-- templates
`-- qtool
|-- admin.py
|-- models.py
|-- forms.py
|-- urls.py
|-- views.py
|-- tests.py
`media
`Exercises
- The templates directory contains all the HTML files for the tool.
- The media directory contains the JavaScript, CSS, and other important files that are served by Django which are used by the QBank tool.
- The Exercises directory in the media folder holds the generated exercises in the Khan Academy Exercise format that can be used as-is fully compatible to Khan Academy.
Run qtool app
Start it with ”./manage.py runserver” and browse to http://localhost:8000/qtool/
19.3. Installation¶
19.3.1. Setup¶
Check out the latest version of QBank into /home/user/src/:
git clone https://github.com/annp89/QBank.git
Note
This copies all the files in the QBank directory into src folder.
Install QBank onto your system:
cd /home/user/src/qtool/ sudo python setup.py install
Note
An alternative to running the install is ensuring that /path/to/QBank/qtool is on your python path. This can be done by modifying the PATH variable to point to the correct file location.
Once the above step in completed, a local database can be created for your copy:
cd /home/user/src/qtool/ sudo python manage.py syncdb
19.3.2. Configuration¶
After having cloned the QBank repository, you have to make some changes in the settings.py file to work on your local machine.
- Ensure that DJANGO_SETTINGS_MODULE environment variable is set to <project_name>.settings; e.g. QBank.settings
<IP_ADDRESS>. settings.py¶
TEMPLATE_DIRS should point to a relative location where the templates folder is located.
TEMPLATE_DIRS = ("/<project_name>/templates",)
<IP_ADDRESS>. urls.py¶
The media folder contains all the javascript, css and other image files that are accessed by the tool.
url(r'^(?P<path>.*)$', 'django.views.static.serve', {'document_root': '/quiz/qtool/media/'})
For viewing in Khan Academy Exercise Format, there are many javascript and css dependent files that have to be located in the media folder.
Django serves the static files in the above location since the url is specified with that particular regular expression.
19.3.3. View the Authoring tool¶
After you have completed the installation and the configuration, you can view the QBank tool using the commands below.
Start up the sample webserver to see your store:
python manage.py runserver
In order to see your sample store, point your browser to:
http://<IP_ADDRESS>:8000/qtool
If you want to see the admin interface, point your browser to:
http://<IP_ADDRESS>:8000/admin
Additional detailed documentation can be found here:
http://<IP_ADDRESS>:8000/admin/doc
Note
The above urls will be dependent on your Django setup. If you’re running the webserver on the same machine you’re developing on, the above urls should work. If not, use the appropriate url.
19.4. Developing a Tool-Specific Authoring Interface¶
Files that need to be manipulated in order to develop a tool specific to the interface
- forms.py:
A new form specific to the interface be written by extending the basic `Problem Form by either adding/ excluding fields to it.
``SimpleProblemForm`` was created to handle simple Multi Choice Questions.
- views.py:
For each type of form, a view (class definition) needs to exist to render the template specific to that particular form.
e.g. ``simple`` for is a view for ``SimpleProblemForm``
Another view needs to exist for parsing the form into a desired publishing format.
e.g.
1. ``write_csv`` to parse the authored Problem to a ``csv`` file.
2. ``ka_gen`` to parse the authored Problem to a ``Khan Academy Exercise format``
19.5. Defining another Publishing format¶
In the views.py, the parsing of the content entered in the Frontend Interface into another format is done in the ka_gen function. This function essentially takes all the content fed into the Front end and parses it with the additional information that converts it into a particular format.
- For instance, to write exercises in the Khan Academy exercise format, the content has to be converted into an html page. All the appropriate header files, scripts, css` and images have to be correctly linked to the question based on the options selected and the type of question being authored.
The ka_gen class parses the content and writes a file which is stored in the media folder. This allows the author to simply take the parsed file and plug it into the desired tool.
19.6. About the project¶
QBank - Problem Authoring Tool, was developed by Ann Paul, Masters student, Department of Computer Science, Virginia Polytechnic and State University.
This project was done under the supervision and able guidance of Dr. Cliff Shaffer, Professor, Department of Computer Science, Virginia Tech.


Dragon BASIC, online at http://github.com/uli/dragonbasic
Waits for a button to be pressed and released.ParametersFUNCTION WAITKEY( mask )
mask - a bit-mask of all buttons to test for, OR'ed together with NORReturn
Returns the value of the button in mask that was pressed and then released.Remarks
WAITKEY is perfect for menu selections. It allows you to wait for multiple buttons, but only return one. It also will wait for the button to be released before returning.See Also
INPUT


The Revenant
While exploring uncharted wilderness in 1823, legendary frontiersman Hugh Glass sustains injuries from a brutal bear attack. When his hunting team leaves him for dead, Glass must utilize his survival skills to find a way back home while avoiding natives on their own hunt. Grief-stricken and fueled by vengeance, Glass treks through the wintry terrain to track down John Fitzgerald, the former confidant who betrayed and abandoned him.


simple enough...
This post most likely contains either an error or something somebody disagrees with. This is just my way to do this,If you have other (or better) ways to do this, please share as well.
I have decided to put up my way (one of) of how to make a game map/world for a text rpg in the console.
After reading posts on various forums I have seen one common question is "How do I make the game map". One way to do this in the console is to use a double nested for loop to draw a x*x grid (see the dungeon crawler thread) and then place objects represented by text onto said grid and then have it do when you make a move it redraws the grid/map with the updated values. Blarg.
This is a much simpler and to the point way.
My example will use a coordinate systems to represent a game world and place events/objects/battles/etc at various X/Y coordinate values.
First with classes:
Lets say you have your Character class. In your main() you have made a new Character called Hero. Your Character class may look like:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
|
class Character{
public:
int getXCoord();
void setXCoord(int xCoord);
int getYCoord();
void setYCoord(int yCoord);
private:
int itsXCoord;
int itsYCoord;
};
| |
then in your main() you would put
1 2 3 4 5 6 7 8 9 10 11 12
|
#include <iostream>
#include "Character.h"
using namespace std;
int main()
{
Character Hero;
Hero.setXCoord(0);
Hero.setYCoord(0);
return 0;
}
| |
Ok, so now we have a Hero, and its X/Y values are 0,0. The point of this is so you can now create a move function or loop that will then change either the x or y Coord value +/-. With this system it would be something like:
You find yourself in a forest. You can move North/South/West/East. The player then picks one of the choices given and the program changes the values of X/Y. Say the player decides to move north. Your program will add 1 to the X value. Now you are at 1.0. How is this useful? It is useful because it allows you to make a virtually infinite sized world and set conditional events at certain X/Y locations. For example: If a player moves to 3/-2 on your world, then perhaps a battle happens:
1 2 3
|
if (Hero.getXCoord() == 3 && Hero.getYCoord() == -2){
orcAmbush(); // don;t bother trying to compile this, orcAmbush has not been declared, used as an example
}
| |
Now lets say we want to move around. You have told your player they are in *insert background story* and you cout the current location (0,0). You then want the player to be able to move around your world. Two ways to do this are by using either a playerMove(); function or a loop/nested if series. I will used the latter since using a playerMover function outside of the main scope requires pointers and ...no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
|
#include <iostream>
#include "Character.h"
using namespace std;
int main()
{
int move;
Character Hero;
Hero.setXCoord(0);
Hero.setYCoord(0);
cout << "You are at :" << Hero.getXCoord() << ", " << Hero.getYCoord() << endl;
cout << "You find yourself in a clearing. Make a move\n";
cout << "1)North 2)South 3)East 4)West: ";
cin >> move;
if (move == 1){
Hero.setXCoord(Hero.getXCoord()+1);
}
if (move == 2){
Hero.setXCoord(Hero.getXCoord()-1);
}
if (move == 3){
Hero.setYCoord(Hero.getYCoord()-1);
}
if (move == 4){
Hero.setYCoord(Hero.getYCoord()+1);
}
cout << "Currently you are at: " << Hero.getXCoord() << ", " << Hero.getYCoord();
return 0;
}
| |
Of course this only allows one move, but you can use this type of setup in a loop or a function as well.
Now, to do this all without classes, which is much simpler but much more restrictive on what your player can do (since you lose the class):
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
|
#include <iostream>
using namespace std;
int main()
{
int move;
int xCoord = 0;
int yCoord = 0;
cout << "You are at :" xCoord << yCoord <, endl;
cout << "You find yourself in a clearing. Make a move\n";
cout << "1)North 2)South 3)East 4)West: ";
cin >> move;
if (move == 1){
xCoord++;
}
/*repeat for rest of choices*/
return 0;
}
| |
This method is much shorter but is restrictive since you can't really do much if you want your x/y to be assigned to a player class. I wanted to show both methods though.
To be honest in my unprofessional opinion the best way to do this type of map/grid/world is to use the class and have a movePlayer() function. I would try to write it but its 4:40am and I just can't focus...
ps: the .cpp is:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
|
#include "Character.h"
int Character::getXCoord(){
return itsXCoord;
}
int Character::setXCoord(int xCoord){
itsXCoord = xCoord;
}
int Character::getYCoord(){
return itsYCoord;
}
int Character::setYCoord(int yCoord){
itsYCoord = yCoord;
}
| |


Chapter 3 Drawing graphs
3.1 Orange juice
We will read in the orange juice data again, in link, and make a suitable graph.
The data values are separated by a space. Use the appropriate Tidyverse function to read the data directly from the course website into a “tibble”.
The juice manufacturer was interested in whether there was a relationship between sweetness and pectin. To assess this, draw a scatterplot. Does it look as if there is any kind of a relationship? (I think
sweetness
is the outcome variable andpectin
is explanatory, so draw your scatterplot appropriately.)
3.2 Making soap
Previously, we learned about a company that operates two production lines in a factory for making soap bars. The production lines were labelled A and B. A production line that moves faster may produce more soap, but may possibly also produce more “scrap” (that is, bits of soap that can no longer be made into soap bars and will have to be thrown away).
The data are in link.
Read the data into R again.
Obtain a histogram of the
scrap
values, using 10 bins for your histogram.Comment briefly on the shape of the histogram. Is it approximately symmetric, skewed to the left, skewed to the right or something else? (By “comment briefly” I mean “say in a few words why you gave the answer you did.”)
Make side-by-side boxplots of scrap values for each production line.
Do you think your boxplot says that there are differences in the amount of scrap produced by the two production lines, or not? Explain briefly.
We started out with the suspicion that if the line was run faster, there would be more scrap. We haven’t assessed this yet. Draw a scatter plot with
scrap
on the \(y\) axis andspeed
on the \(x\) axis.What do you think is the most important conclusion from your plot of the previous part? Describe your conclusion in the context of the data.
3.3 Handling shipments
We learned earlier about a company called Global Electronics that
imports shipments of a certain large part used as a
component in several of its products. The size of the shipment varies
each time. Each shipment is sent to one of two warehouses (labelled A
and B) for handling. The data in
link show the
size
of each shipment (in thousands of parts) and the direct
cost
of handling it, in thousands of dollars. Also shown is
the warehouse
(A or B) that handled each shipment.
Read the data into R and display your data frame.
Make a scatterplot of the cost of handling each shipment as it depends on the shipment’s size.
What kind of relationship do you see on the scatterplot? Do you think a straight line would describe it appropriately? Explain briefly.
When a shipment comes in, the cost of handling it is not known. A decision is made about which warehouse to send it to, and then, after it is handled, the cost is recorded. What do you think determines which warehouse an incoming shipment goes to? Provide a graph to support your answer.
3.4 Rainfall in Davis, California
One way to assess climate change is to look at weather records over a number of years and to identify any changes.
Annual rainfall data for the Davis, California is in here. (Right-click on the URL and select Copy Link Address, then paste into your R Notebook.) The rainfall is measured in inches.
Read in and display (some of) the data. Briefly justify why you coded it as you did.
Make a suitable plot of the rainfall values and years. Explain briefly why you drew the plot you did.
Add a regression line to your plot. Is there any convincing indication of a trend, upward or downward, in annual rainfall for Davis, California? Discuss briefly.
3.5 Students and exercise
Some students in a Statistics class were asked how many minutes they typically exercised in a week. The data are shown in http://ritsokiguess.site/datafiles/exercise.txt.
Some of the students identified as male and some as female. Our concern is how the males and females compare in terms of the amount of exercise they do.
Take a look at the data file. (Click on the link, or paste the copied link into your web browser.) How is it laid out?
Read in and display (some of) the data. (This means to display enough of what you read in to convince others that you read in the right kind of thing.)
Make a suitable plot of this data frame.
Does there appear to be any substantial difference in the average amount of time that males and females spend exercising? Explain briefly. (“average” could be mean or median. Which is it here?)
How do you know that both distributions, for males as well as females, are skewed to the right? Explain (very) briefly.
For data like this, why does it make practical sense that the distributions are skewed to the right?
My solutions follow:
3.6 Orange juice
We will read in the orange juice data again, in link, and make a suitable graph.
- The data values are separated by a space. Use the appropriate Tidyverse function to read the data directly from the course website into a “tibble”.
Solution
As before,
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## run = col_double(),
## sweetness = col_double(),
## pectin = col_double()
## )
## # A tibble: 24 x 3
## run sweetness pectin
## <dbl> <dbl> <dbl>
## 1 1 5.2 220
## 2 2 5.5 227
## 3 3 6 259
## 4 4 5.9 210
## 5 5 5.8 224
## 6 6 6 215
## 7 7 5.8 231
## 8 8 5.6 268
## 9 9 5.6 239
## 10 10 5.9 212
## # … with 14 more rows
24 runs, as we had before.
\(\blacksquare\)
- The juice manufacturer was interested in whether there was a
relationship between sweetness and pectin. To assess this, draw a
scatterplot. Does it look as if there is any kind of a
relationship? (I think
sweetness
is the outcome variable andpectin
is explanatory, so draw your scatterplot appropriately.)
Solution
This requires a ggplot
plot. You can go back and look
at the lecture notes to figure out how to make a scatterplot:
the “what to plot” is the \(x\)-axis and \(y\)-axis variables,
with the response on the \(y\)-axis (starting with a data frame
to get the variables from), and the “how to plot” is
geom_point
to plot the points:
It looks to me as if there is a negative relationship: as pectin goes up, sweetness tends to go down. The trend appears to go top left to bottom right.
Extra: having said that, I’m wondering how much of the apparent trend is caused by those two observations bottom right with pectin over 350. If you take those away, the trend seems to me to be a lot less convincing. You could add a smooth trend to the plot, thus:
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
The smooth trend is kind of downhill, but not very convincing.
\(\blacksquare\)
3.7 Making soap
Previously, we learned about a company that operates two production lines in a factory for making soap bars. The production lines were labelled A and B. A production line that moves faster may produce more soap, but may possibly also produce more “scrap” (that is, bits of soap that can no longer be made into soap bars and will have to be thrown away).
The data are in link.
- Read the data into R again.
Solution
Read directly from the URL, most easily:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## case = col_double(),
## scrap = col_double(),
## speed = col_double(),
## line = col_character()
## )
## # A tibble: 27 x 4
## case scrap speed line
## <dbl> <dbl> <dbl> <chr>
## 1 1 218 100 a
## 2 2 248 125 a
## 3 3 360 220 a
## 4 4 351 205 a
## 5 5 470 300 a
## 6 6 394 255 a
## 7 7 332 225 a
## 8 8 321 175 a
## 9 9 410 270 a
## 10 10 260 170 a
## # … with 17 more rows
27 rows. line
, which is either a
or b
, was
correctly deduced to be text.
\(\blacksquare\)
- Obtain a histogram of the
scrap
values, using 10 bins for your histogram.
Solution
\(\blacksquare\)
- Comment briefly on the shape of the histogram. Is it approximately symmetric, skewed to the left, skewed to the right or something else? (By “comment briefly” I mean “say in a few words why you gave the answer you did.”)
Solution
I would call this “bimodal”. There are two peaks to the histogram, one around 250 and one around 370, with a very small frequency in between (the bar around 300). Apart from the bimodality, there is no particular evidence for a long tail on either end, so I don’t think you could otherwise call it anything other than symmetric. Having said that (this is going beyond the question), the way a histogram looks can depend on the bins you choose to draw it with. This is 8 bins rather than 10:
The middle low-frequency bin has gone, and this one just looks symmetric, with a kind of “flat top”.
\(\blacksquare\)
- Make side-by-side boxplots of scrap values for each production line.
Solution
One categorical, one quantitative variable, so boxplots make sense.
\(\blacksquare\)
- Do you think your boxplot says that there are differences in the amount of scrap produced by the two production lines, or not? Explain briefly.
Solution
I would say that there is a difference between the two production lines, with line A producing an average (median) of about 330 and line B producing a median of about 275. But you could also make the case that, although the medians are rather different, there is a lot of variability and hence a lot of overlap between the two boxplots, and therefore that there is not a “substantial” difference. I would say that either of those answers are good if you back them up with proper reasons. This is going to be a common theme in this course: I am going to ask you to make a decision and support it, where the reasons you provide are often more important than the decision you make.
Extra: you might be wondering whether the medians, or means, since there is no serious skewness here and definitely no outliers, are “significantly different”. This is inference, which we will come to later, but a preview looks like this:
##
## Welch Two Sample t-test
##
## data: scrap by line
## t = 1.2493, df = 21.087, p-value = 0.2253
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -26.97888 108.21222
## sample estimates:
## mean in group a mean in group b
## 333.5333 292.9167
They are not: the P-value of 0.22 is not anywhere near as small as 0.05, so we can’t reject the null hypothesis that the two lines have equal mean amount of scrap.
Rusty on this stuff? Don’t worry; we’re going to come back to it later in the course.
I was also wondering about something else: that bimodal histogram. Could that be explained by the scrap values being two different production lines being mixed together? One way to understand that is to have two separate histograms, one for each line, side by side, which is what facetting does. There is an extra wrinkle here that I explain afterwards:
I could have used facet_wrap
, but that would have put the
histograms side by side, and I wanted them one above the other (for
ease of comparison, since they’ll be on the same
scale). facet_grid
is like facet_wrap
, but offers
you more control over where the facets go: you can arrange them above
and below by a variable, or left and right by a variable. Whatever is
facetting the plots up and down (on the \(y\) axis) goes before the
squiggle, and whatever facets them left and right goes after. If there
is nothing separating the facets in one direction, here horizontally,
the variable is replaced by a dot.
In some ways, facet_grid
is also less flexible,
because the facets have to be arranged up/down or left/right by a
variable. That worked here, but if you think back to the Australian
athletes, where there were ten different sports, it was
facet_wrap
that did the right thing, arranging the sports
along rows and columns to produce a pleasing display.
All right, that bimodality. I was expecting that the scrap values from one line would be centred about one value and the scrap values from the other line would be centred about a different value, with a gap in between. But that’s not what happened at all: the line B values are all over the place, while it’s the line A values that are actually bimodal all by themselves. I’m not sure whether that really means anything, since the data sets are pretty small, but it’s kind of interesting.
\(\blacksquare\)
- We started out with the suspicion that if the line was run
faster, there would be more scrap. We haven’t assessed this
yet. Draw a scatter plot with
scrap
on the \(y\) axis andspeed
on the \(x\) axis.
Solution
Same mechanism as before:
\(\blacksquare\)
- What do you think is the most important conclusion from your plot of the previous part? Describe your conclusion in the context of the data.
Solution
There seems to be a pretty evident upward trend, apparently
linear, which means that if the speed of the production line is
higher, the amount of scrap produced is also higher.
My last sentence was meant to remind you that
“there is an upward trend” is not a complete answer:
we are concerned
with what that upward trend tells us about the data.
This, in other words, confirms the suspicion expressed in the
question, which was therefore a rather large clue: more speed
tends to go with more scrap.
That was as far as I wanted you to go: there seems to be an
association with speed, and there might be an association with
line
that turned out not to be statistically significant.
What we haven’t done is to assess the relationship between speed
and scrap for each production line. To do that, we want
to plot the scrap-speed points distinguished for each production
line. ggplot
makes that easy: you add a
colour
1 to say what you want to
distinguish by colour. This is two quantitative variables and one
categorical variable, if you want to think of it that way:
Notice that we get a legend, automatically.
What is interesting about this one is the red dots are mostly at the top (for any given speed), and the blue dots are mostly at the bottom. That seems to mean that when we account for speed, there is a difference between lines.
I want to show you one more embellishment, which is to put the
regression lines on the plot for each group separately. This is where
ggplot
is so nice, since I just have to add one thing:
ggplot(soap, aes(x = speed, y = scrap, colour = line)) +
geom_point() + geom_smooth(method = "lm", se = F)
## `geom_smooth()` using formula 'y ~ x'
The points and lines have come out in different colours, without our having to think too hard.
Both lines show an upward trend, with about the same slope, which means that regardless of line, increasing the speed goes with increasing the scrap by the same amount. The fact that the red line is above the blue one, however, suggests that production line A produces more scrap at the same speed than production line B.
From a management point of view, there is an interesting dynamic at work: if you run the production line faster, you’ll produce more bars of soap, but you’ll produce more scrap as well. The crucial thing for the people in the supervisor’s office is how much raw material is used per bar of soap, and if you make the soap bars faster, you might use more raw material, which will eat into your profits (from one angle), but you will also have more bars of soap to sell.
Here’s another way to see the same thing. I’m definitely not expecting you to follow the code, but you can admire the result!
soap2 <- soap %>% select(-line)
ggplot(soap, aes(x = speed, y = scrap)) +
geom_point(data = soap2, colour = "grey") +
geom_point(aes(colour = line)) + facet_wrap(~line)
$
The idea is that we plot all the points in grey (to “put them in the background”) and then in each plot we plot the points again, coloured, for the group we are looking at: line A in the left, line B on the right. This is another way of seeing that line A has more scrap than line B, given the speed at which the line was being run. (I discovered this technique only yesterday. I think the code is remarkably concise for what it does.)
The logic of the code is:
create a new data frame that contains everything in
soap
except forline
make a scatter plot of all the points in this new data frame, coloured grey
plot the points again (from the original data frame), coloured by which production line they’re from
produce a separate scatterplot for each production line.
The trick about creating the new data frame was to enable plotting of
all points regardless of group on each subplot (“facet” in
ggplot
terminology), as well as the ones that come from that
production line.
I don’t expect you to be able to follow all the details of the code
below, either,
but I would like you to try and get the logic. What we do is a
regression predicting scrap
from two things:
speed
and production line
. The results we get are these:
##
## Call:
## lm(formula = scrap ~ speed + line, data = soap)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.557 -14.161 -0.121 17.518 33.953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.41099 14.54379 5.529 1.10e-05 ***
## speed 1.23074 0.06555 18.775 7.48e-16 ***
## lineb -53.12920 8.21003 -6.471 1.08e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.13 on 24 degrees of freedom
## Multiple R-squared: 0.9402, Adjusted R-squared: 0.9352
## F-statistic: 188.6 on 2 and 24 DF, p-value: 2.104e-15
The P-values for speed
and line
are the second and
third things in the last column, \(7 \times 10^{-16}\) and \(1 \times 10^{-6}\)
respectively. These are both very strongly significant, in contrast to
the two-sample \(t\)-test where line
was not significant.
So does production line make a difference or not?
The plot says that it does, and the meaning of model scrap.1
just above is that
speed
affects scrap when you account for line
, and
line
affects scrap when you account for speed
.
(In the two-sample \(t\)-test above we didn’t
account for speed at all, since the various speeds were all mixed up.)
There is a moral to this story, which I would like you to get even if
you don’t get any of the statistics: if a variable makes a
difference, it should be in your model and on your
graph,2
because it enables you to get better (more precise) conclusions about your
other variables. Here, there really is a difference between the
production lines, but the \(t\)-test was too much of a blunt instrument
to unearth it (because speed
made a difference as well).
\(\blacksquare\)
3.8 Handling shipments
We learned earlier about a company called Global Electronics that
imports shipments of a certain large part used as a
component in several of its products. The size of the shipment varies
each time. Each shipment is sent to one of two warehouses (labelled A
and B) for handling. The data in
link show the
size
of each shipment (in thousands of parts) and the direct
cost
of handling it, in thousands of dollars. Also shown is
the warehouse
(A or B) that handled each shipment.
- Read the data into R and display your data frame.
Solution
A .csv
:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## warehouse = col_character(),
## size = col_double(),
## cost = col_double()
## )
## # A tibble: 10 x 3
## warehouse size cost
## <chr> <dbl> <dbl>
## 1 A 225 12.0
## 2 B 350 14.1
## 3 A 150 8.93
## 4 A 200 11.0
## 5 A 175 10.0
## 6 A 180 10.1
## 7 B 325 13.8
## 8 B 290 13.3
## 9 B 400 15
## 10 A 125 7.97
\(\blacksquare\)
- Make a scatterplot of the cost of handling each shipment as it depends on the shipment’s size.
Solution
The wording of the question says that cost is the response and so
belongs on the \(y\)-axis. To make the plot, ggplot
with an
x=
and a y=
in the aes
(the “what to plot”
part), and a geom_point()
after (the “how to plot it”):
As a matter of coding, there are usually two brackets to close
after the aes
, the one that begins the ggplot
and
the one that begins the aes
.
\(\blacksquare\)
- What kind of relationship do you see on the scatterplot? Do you think a straight line would describe it appropriately? Explain briefly.
Solution
I see an upward trend: a shipment with larger size
costs
more to handle.
If you look carefully at the scatterplot, you see that the cost of
handling a small shipment goes up fairly steeply with its size,
but the cost of handling a large shipment, while it still
increases with size
, does not increase so fast. Thus
having one straight line to describe the whole relationship would
not work so well.
The relationship is actually two different
straight lines joined end-to-end, which we will explore later, but
if you think the relationship is curved, I’ll accept that. The
point is to get at the idea that the rate of increase is not
constant.
\(\blacksquare\)
- When a shipment comes in, the cost of handling it is not known. A decision is made about which warehouse to send it to, and then, after it is handled, the cost is recorded. What do you think determines which warehouse an incoming shipment goes to? Provide a graph to support your answer.
Solution
The veiled hint in the question is that the decision must depend
on size
, since it cannot depend on cost
. So we
have one quantitative variable size
and one categorical
variable warehouse
, which suggests drawing boxplots:
Well, there’s the answer right there. When the shipment has small
size
, it goes to warehouse A, and when it’s large, it goes to
Warehouse B. We know this because all the shipments smaller
than about 250 (thousand parts) went to A and all the shipments
larger than that went to B. (If you want to provide a number to
delineate “small” and “large”, anything between the largest A,
about 225, and the smallest B, about 290, will do.)
Another way to think about this is to add something to the scatterplot you drew before. The obvious thing is to make the two warehouses different colours:
As a point of technique, you can split lines of code to make them fit
on your screen. You can do this as long as
the code that ends the line must be incomplete, so that R knows more is to
come. Ending a line with a pipe symbol, or, as here, with one of the
pluses in the middle of a ggplot
, will work. If you put the
plus on the start of the next line, you’ll get a blank plot, because R
thinks you’re done plotting. Try it and see.
Anyway, this plot tells exactly the same story: the small shipments
(in size or cost) go to Warehouse A and the large ones to Warehouse
B. But we don’t know cost when the decision is made about which
warehouse to send a shipment to, so the decision must be based on
size
.
In the place where I got these data, it said “larger shipments are sent to Warehouse B, since this warehouse has specialized equipment that provides greater economies of scale for larger shipments”. That is to say, very large shipments are more expensive to handle, but not as expensive as you might think.3 That makes sense with our scatterplot, because the slope for larger shipments is less than for smaller shipments.
When we get to regression later, we’ll see what happens if we fit a straight line to data like these, and how to tell whether we really ought to be able to do better with a different form of relationship. There is also a trick to fit what is called a “piecewise linear regression”, which has one slope for small shipment sizes, a different (smaller) slope for large ones, and joins up in the middle. But that’s well beyond our scope now.
\(\blacksquare\)
3.9 Rainfall in Davis, California
One way to assess climate change is to look at weather records over a number of years and to identify any changes.
Annual rainfall data for the Davis, California is in here. (Right-click on the URL and select Copy Link Address, then paste into your R Notebook.) The rainfall is measured in inches.
- Read in and display (some of) the data. Briefly justify why you coded it as you did.
Solution
Look at the data file, and see that the values are separated by a
single space, so will do it. (This is the brief justification.) You need to have some words in this part explaining your choice of read_delim
(or read_table
: see below).
Read straight from the URL rather than copying and pasting the data or doing anything like that:4
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## Year = col_double(),
## Rainfall = col_double()
## )
## # A tibble: 47 x 2
## Year Rainfall
## <dbl> <dbl>
## 1 1951 20.7
## 2 1952 16.7
## 3 1953 13.5
## 4 1954 14.1
## 5 1955 25.4
## 6 1956 12.0
## 7 1957 28.7
## 8 1958 11.0
## 9 1959 12.6
## 10 1960 12.8
## # … with 37 more rows
Note for later that the and the have Capital Letters. You can call the data frame whatever you like, but I think something descriptive is better than eg. .
Extra 1: read_delim
works because there is exactly one space between the year and the
rainfall amount. But the year is always four digits, so the columns
line up, and there is a space all the way down between the year and
the rainfall. That is to say, another possibility for your “brief justification” is that the columns are lined up all the way down.
That means that this will also work:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## Year = col_double(),
## Rainfall = col_double()
## )
## # A tibble: 47 x 2
## Year Rainfall
## <dbl> <dbl>
## 1 1951 20.7
## 2 1952 16.7
## 3 1953 13.5
## 4 1954 14.1
## 5 1955 25.4
## 6 1956 12.0
## 7 1957 28.7
## 8 1958 11.0
## 9 1959 12.6
## 10 1960 12.8
## # … with 37 more rows
This is therefore also good.
It also looks as if it could be tab-separated values, since the rainfall column always starts in the same place, but if you try it, you’ll find that it doesn’t work:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## `Year Rainfall` = col_character()
## )
## # A tibble: 47 x 1
## `Year Rainfall`
## <chr>
## 1 1951 20.66
## 2 1952 16.72
## 3 1953 13.51
## 4 1954 14.1
## 5 1955 25.37
## 6 1956 12.05
## 7 1957 28.74
## 8 1958 10.98
## 9 1959 12.55
## 10 1960 12.75
## # … with 37 more rows
This looks as if it worked, but it didn’t, because there is only one column, of years and rainfalls smooshed together as text, and if you try to do anything else with them later it won’t work.5
Hence those values that might have been tabs actually were not. There’s no way to be sure about this; you have to try something and see what works.6
Extra 2: read.table
with a dot will work, but it is in this course wrong. As I stated in the course outline, I expect you to do things as they are done in this course. Strictly speaking, if you use read.table
, which you did not learn from me, and you say nothing about where you got it from, you are guilty of plagiarism, which is an academic offence. I will probably not be that strict, but you should certainly expect to lose credit for doing things differently from how they are done in this course. The idea is that you need to demonstrate that you have learned something here.
\(\blacksquare\)
- Make a suitable plot of the rainfall values and years. Explain briefly why you drew the plot you did.
Solution
This is two quantitative variables, so a scatterplot makes sense. (That’s the brief explanation.) To decide which variable goes on which axis, think of rainfall as a response to the explanatory variable year, or note that time goes by tradition on the horizontal axis:
This is a time trend, so it would also be reasonable to join the points with lines:
and because any trend looks irregular, you could also justify putting a smooth trend through the points:
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
This one is rather interesting (as an aside): there is a more noticeable upward trend at the end, after about 1985. If you study environmental science, you may have seen that a lot of time plots of climate data show a bigger change after about 1990, and this is one of those.
If you treat the year as categorical, and try to draw a boxplot, it won’t work out so well:
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
This is actually a boxplot of rainfall in all the years together; it ignored year because it couldn’t figure out what you wanted to do with it. The warning message is indicating that you might have forgotten something that defines groups; the x
is quantitative here. So you have to clarify what you meant. For example, you might have meant to treat the years as categorical, which you could do like this:
Aside from the fact that you have too many years to see them all, you only have one observation per year (that year’s annual rainfall), so you don’t get an actual box. To get boxes, you need to organize groups where you have multiple observations, for example decades:
rain %>%
mutate(decade_number = Year %/% 10) %>%
mutate(decade = str_c(decade_number, "0s")) %>%
ggplot(aes(x = decade, y = Rainfall)) + geom_boxplot()
You see that there seems to have been an upward jump in rainfall in the 1990s (the last decade in the data).
About the code: `%/%’ does “integer division” (throws away the fractional part if there is one), so the calculation turns eg. 1954 into 195. I wanted to have the graph display something you could understand, so I constructed some text out of the decade number by gluing a 0 and a letter s onto the end.
Another kind of situation where this approach does work is if you have something like monthly rainfall over a number of years, and then you have multiple values for January over all the years that you have data for.
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- Add a regression line to your plot. Is there any convincing indication of a trend, upward or downward, in annual rainfall for Davis, California? Discuss briefly.
Solution
Thus:
## `geom_smooth()` using formula 'y ~ x'
The regression line goes uphill, but this is not convincing evidence of an overall upward trend for several reasons (pick one):
- the increase, if there is one, is very small
- the data points are mostly far away from the regression line
- there are points within the grey envelope all the way across (meaning, for example, that the mean annual rainfall could be 20 inches for all the years).
Further evidence comes from a regression (which we haven’t looked at yet):
##
## Call:
## lm(formula = Rainfall ~ Year, data = rain)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.670 -5.252 -1.905 7.088 17.804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -208.57325 158.42281 -1.317 0.195
## Year 0.11513 0.08025 1.435 0.158
##
## Residual standard error: 7.463 on 45 degrees of freedom
## Multiple R-squared: 0.04373, Adjusted R-squared: 0.02248
## F-statistic: 2.058 on 1 and 45 DF, p-value: 0.1583
The slope is indeed positive, but it is not significantly different from zero. Also, the R-squared is very small, 4.4%. Thus the apparent upward trend is no more than chance.
Extra: looking at the plot from earlier with the smooth trend suggests that there may not have been any real increase since 1950, but if you look only since about 1985 it may be different. There is a statistical question mark over that, though, because we started looking at 1985 only because there seemed to be an upward trend starting from about there.
\(\blacksquare\)
3.10 Students and exercise
Some students in a Statistics class were asked how many minutes they typically exercised in a week. The data are shown in http://ritsokiguess.site/datafiles/exercise.txt.
Some of the students identified as male and some as female. Our concern is how the males and females compare in terms of the amount of exercise they do.
- Take a look at the data file. (Click on the link, or paste the copied link into your web browser.) How is it laid out?
Solution
Aligned in columns. Or, separated by spaces, but a variable number of them. (The latter is a hint that will not work, and the former is a hint about what will work.)
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- Read in and display (some of) the data. (This means to display enough of what you read in to convince others that you read in the right kind of thing.)
Solution
“Aligned in columns” is the best way of saying it so that you know to use . Hence:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## gender = col_character(),
## minutes = col_double()
## )
## # A tibble: 29 x 2
## gender minutes
## <chr> <dbl>
## 1 female 60
## 2 female 240
## 3 female 0
## 4 female 360
## 5 female 450
## 6 female 200
## 7 female 100
## 8 female 70
## 9 female 240
## 10 female 0
## # … with 19 more rows
Just entering the data frame name displays the first ten rows, which is usually enough to convince anyone that you have the right thing.
Call the data frame what you like, as long as it describes what’s in the data frame in some way.
\(\blacksquare\)
- Make a suitable plot of this data frame.
Solution
Two variables, one quantitative and one categorical, means that a boxplot is the thing:
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- Does there appear to be any substantial difference in the average amount of time that males and females spend exercising? Explain briefly. (“average” could be mean or median. Which is it here?)
Solution
A boxplot shows the median. So we learn here that the median time spent exercising per week is very slightly higher for females. However, there is a lot of variability (the height of the boxes), and so the difference between the medians is very small. Hence, there is definitely no substantial difference between males and females.
Less insightfully, the difference in median between males and females is very small (but that doesn’t rule out the possibility that the spread is very small also).
\(\blacksquare\)
- How do you know that both distributions, for males as well as females, are skewed to the right? Explain (very) briefly.
Solution
The upper whiskers, at the top of the box, are longer than the lower ones (at the bottom).
\(\blacksquare\)
- For data like this, why does it make practical sense that the distributions are skewed to the right?
Solution
Nobody can exercise less than 0 minutes per week, but there is no upper limit: a student in the class can exercise as much as they want. This means that there could be unusually high values, but not unusually low values.
Extra: Distributions that have a limit on one side are often skewed to the other side. This one has a limit on the left, so it is skewed to the right. This is most likely to be true if there are observations close to the limit, such as the people in this data set that don’t exercise at all (and there are some of those).
\(\blacksquare\)
If you are concerned about the spelling: the guy who wrote ggplot is from New Zealand, where they spell colour the same way we do. However, if you want to use color, that works too.↩
Meaning that the model should contain all three variables,
speed
,scrap
andline
.↩This is the same idea that it costs more to ride the GO bus from UTSC to York U than it does to ride from UTSC to Scarborough Town, but if you work out how much it costs per kilometre, the longer journey costs less per km. As of when I’m writing this, $5.30 for the 7.2 km to Scarborough Town and $6.75 for the 38 km to York. That’s quite an economy of scale, isn’t it?↩
Reading from the URL is reproducible in that somebody else doing what you did will get exactly what results you did. Copying and pasting data is not in general reproducible because somebody else might do it differently from you, such as missing a line of data. Copying and pasting a URL, especially by right-clicking and Copy Link Address, has much less to go wrong. Physically selecting the URL text by selecting all the letters in the URL can go wrong, especially if the printed URL goes over two lines on the page.↩
It is actually possible to disentangle data like this and then work with it (we will see how later in the course), but the best way to do it is to make it easiest for yourself and anyone reading your code, and read the data file in a way that is appropriate for the layout you have in the file.↩
An indication, though: if you have more than one space, and the things in the later columns are left-justified, that could be tab-separated; if the things in the later columns are right-justified, so that they finish in the same place but don’t start in the same place, that is probably aligned columns.↩