| |
Methods defined here:
- __init__(self)
- Create a default menu
PARAM self{Menu}
- gameInit(self)
- initialise the different paramater needed to launch a game
PARAM self{Menu}
RETURN {None}
- getColor(self)
- Get the color of the player
PARAM self {Menu}
RETURN {Int}-> the color, 0 for white, 1 black
- getLichess(self)
- Get if you chose to use Lichess
PARAM self {Menu}
RETURN {Boolean}-> True if you wanted to use Lichess, False if you don't wanted to
- getOpponent(self)
- Get the opponent of the game
PARAM self {Menu}
RETURN {Int}-> the opponent, 0 for player vs program, 1 for program vs program
- setColor(self, col)
- Set the color of the player
PARAM self {Menu}
col {Int}-> the color 0 for white, 1 for black
- setLichess(self, lic)
- Set if you want to use Lichess or not
PARAM self {Menu}
lic {Boolean}-> True if you want to use Lichess, False if you don't want to
- setOpponent(self, op)
- Set the opponent of the game
PARAM self {Menu}
op {Int}-> the opponent 0 for player vs program, 1 for program vs program
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- color = -1
- lichess = False
- opponent = ''
|


Dealing with Conflict
Posted on 27 July 2014 by Kyle Daugherty
One of the last times I had conflict with someone else was while at work. One of my direct reports came to me one day and expressed that she was having trouble with getting all of her tasks done. After speaking with this individual, it was clear that the root of the problem was that she was taking on too many special projects at one time. This was especially true given that she was still fairly new to her role and was still trying to learn the responsibilities of her position.
We talked about ways she could manage her time better and that when other areas of the business asked her to work on special projects that she would really need to evaluate if she could actually commit the time it would take before volunteering to help. She realized that she had a problem of saying "no" to people because she really enjoys helping others.
We also talked about the special projects she was helping with and she identified one specifically that she felt like she didn't need to be involved in because it would be easy for someone else to transition over to the project and cover her part. This project was also one of the ones taking up large amount of her time.
After speaking with her, she said she felt better about the situation and she was planning to notify the manager over the project that she was going to be stepping away from. She seemed a little hesitant about notifying the other manager, but when I asked her if she was comfortable with doing that she said yes so I didn't think much of it.
After our initial talk, I later found out that she was still working on the special project and she was continuing to fall behind on her primary responsibilities. It was really starting to affect her performance. I found this information out from another party and I felt like she was intentionally trying to hide it from me (even thought I had no idea if this was her actual intent or not). My initial gut reaction was to confront her immediately. However, I realized I was too upset to approach her at that moment.
I decided to wait a few hours to calm down and ended up speaking with her later that afternoon. She admitted that she did not follow through on the plan we had discussed and she was falling behind. She expressed that she was afraid of disappointing the other manager that she had made a committment to.
We visited again about other ways she could manager her time more effectively and still work on this special project since she did make the committment. In the end, we were able to come up with the right solution and she helped with the project and caught up on her work.
If I could go back and do anything differently, it would be that I should have picked up on the fact that she seemed hesitant about speaking with the other manager. I should have went with her or just done that piece for her. I could have spoken up on her behalf, especially since I worked closely with the other manager, to where they would have been understanding of the siutation. If I would have done that, I think it would have taken some stress off of her and would have made things go better. I also wish she would have just been straightforward with me and told me that she didn't feel comfortable speaking with the other manager. I think that this miscommunication made the situation worse.
I was really proud of myself for taking a few hours to calm down before approaching her. I think it would have caused more stress and tension (for both of us) if I had gone to her right away while I was still upset. I also shouldn't have been so upset inthe first place because after speaking with her again I knew she wasn't intentionally trying to hide anything, she was just stressed about how to handle everything.


What's Jekyll?
Jekyll is a static site generator, an open-source tool for creating simple yet powerful websites of all shapes and sizes. From the project’s readme:
Jekyll is a simple, blog aware, static site generator. It takes a template directory […] and spits out a complete, static website suitable for serving with Apache or your favorite web server. This is also the engine behind GitHub Pages, which you can use to host your project’s page or blog right here from GitHub.
It’s an immensely useful tool. Find out more by visiting the project on GitHub.


This paragraph is about my favorite spot at King's My favorite spot is in the cafeteria because that is where they keep all the food and I cannot live without food. I figure it makes sense for my favorite spot to be the place where there is stuff that I need to survive.
This is another paragraph about another one of my favorite spots at King's. This spot is in the computer lab. I like the computers because they can generate the following text: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum magna arcu, elementum vel feugiat ac, egestas sit amet lectus. Nulla nunc sapien, condimentum id congue quis, molestie non velit. Pellentesque sit amet sem at sem rhoncus fermentum in vel risus. Curabitur lacinia id quam a eleifend. Etiam placerat augue nulla, a ornare ante tempor ac. Maecenas congue risus at ex egestas dictum ac ac turpis. Mauris at arcu congue, iaculis orci at, sollicitudin lorem.
Last edited September 17


Chapter 2 Getting Started with R
Learning Objectives
- Create R objects and and assign values to them.
- Use comments to inform script.
- Do simple arithmetic operations in R using values and objects.
- Call functions with arguments and change their default options.
- Inspect the content of vectors and manipulate their content.
- Subset and extract values from vectors.
- Correctly define and handle missing values in vectors.
- Use the built-in RStudio help interface
- Interpret the R help documentation
- Provide sufficient information for troubleshooting with the R user community.
- Download, install, and load R packages.
2.1 Creating objects in R
To do useful and interesting things in R, we need to assign values to
objects. To create an object, we need to give it a name followed by the
assignment operator <-
, and the value we want to give it:
<-
is the assignment operator. It assigns values on the right to objects on
the left. So, after executing weight_kg <- 55
, the value of weight_kg
is 55
. The arrow can
be read as 55
goes into weight_kg
. You can also use =
for assignments, but not in every context. Because of
the
slight differences in
syntax, it is good practice to always use <-
for assignments.
In RStudio, typing Alt + - (push Alt at the
same time as the - key) will write <-
in a single keystroke.
Here are a few rules as of how to name objects in R.
- Objects can be given any name such as
x
,current_temperature
, orsubject_id
. - You want your object names to be explicit and not too long.
- They cannot start with a number (
2x
is not valid, butx2
is). - R is case sensitive
(e.g.,
weight_kg
is different fromWeight_kg
). - There are some names that
cannot be used because they are the names of fundamental functions in R (e.g.,
if
,else
,for
, see here for a complete list). In general, even if it is allowed, it’s best to not use other function names (e.g.,c
,T
,mean
,data
,df
,weights
). If in doubt, check the help to see if the name is already in use. - It’s also best to
avoid dots (
.
) within a variable name as inmy.dataset
. There are many functions in R with dots in their names for historical reasons, but because dots have a special meaning in R (for methods) and other programming languages, it is best to avoid them. - It is also recommended to use nouns for variable names, and verbs for function names.
- It’s important to be consistent in the styling of your code (where you put spaces, how you name variables, etc.). Using a consistent
coding style makes your code clearer to read for your future self and your
collaborators.
In R, three popular style guides are Google’s, Jean Fan’s and the tidyverse’s. The tidyverse’s is very comprehensive and may seem overwhelming at first. You can install thelintr
to automatically check for issues in the styling of your code.
When assigning a value to an object, R does not print anything. You can force R to print the value by using parentheses or by typing the object name:
weight_kg <- 55 # doesn't print anything
(weight_kg <- 55) # but putting parenthesis around the call prints the value of `weight_kg`
weight_kg # and so does typing the name of the object
Now that R has weight_kg
in memory, we can do arithmetic with it. For
instance, we may want to convert this weight into pounds (weight in pounds is 2.2 times the weight in kg):
We can also change a variable’s value by assigning it a new one:
This means that assigning a value to one variable does not change the values of
other variables. For example, let’s store the weight in pounds in a new
variable, weight_lb
:
and then change weight_kg
to 50.
Challenge
What do you think is the current content of the object
weight_lb
? 110 or 220?
2.1.2 Functions and their arguments
Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc.
They all have in common that they are executed by typing their name followed by round brackets, in which we provide one or more parameters (or arguments) for the function to do something, separated by commas.
Many functions are predefined, or can be
made available by importing R packages (more on that later). A function
usually gets one or more inputs called arguments. Functions often (but not
always) return a value. A typical example would be the function sqrt()
. The
input (the argument) must be a number, and the return value (in fact, the
output) is the square root of that number. Executing a function (‘running it’)
is called calling the function. An example of a function call is:
Here, we assign the value 64
to he variable a
, which is then given to the sqrt()
function. The sqrt()
function
calculates the square root, and returns the value which is then assigned to
variable b
. This function is very simple, because it takes just one argument.
The return ‘value’ of a function need not be numerical (like that of sqrt()
),
and it also does not need to be a single item: it can be a set of things, or
even a dataset. We’ll see that when we read data files into R.
Arguments can be anything, not only numbers or filenames, but also other objects. Exactly what each argument means differs per function, and must be looked up in the documentation (see below). Some functions take arguments which may either be specified by the user, or, if left out, take on a default value: these are called options. Options are typically used to alter the way the function operates, such as whether it ignores ‘bad values’, or what symbol to use in a plot. However, if you want something specific, you can specify a value of your choice which will be used instead of the default.
Let’s try a function that can take multiple arguments: round()
.
#> [1] 3
Here, we’ve called round()
with just one argument, 3.14159
, and it has
returned the value 3
. That’s because the default is to round to the nearest
whole number. If we want more digits we can see how to do that by getting
information about the round
function. We can use args(round)
or look at the
help for this function using ?round
.
#> function (x, digits = 0)
#> NULL
We see that if we want a different number of digits, we can
type digits=2
or however many we want.
#> [1] 3.14
If you provide the arguments in the exact same order as they are defined you don’t have to name them:
#> [1] 3.14
And if you do name the arguments, you can switch their order:
#> [1] 3.14
Note:
R evaluates function arguments in three steps: first, by exact matching on argument name, then by partial matching on argument name, and finally by position.
you do not have to specify all of the arguments. If you don’t, R will use default values if they are specified by the function. If no default value is specified, you will receive an error.
It’s good practice to put the non-optional arguments (like the number you’re rounding) first in your function call, and to specify the names of all optional arguments. If you don’t, someone reading your code might have to look up the definition of a function with unfamiliar arguments to understand what you’re doing.
Functions usually return someting back to you as output. Whatever they return (a table, some informational text, a logical value, …) is by default written to the console, so you can see it right away.
Oftentimes, however, we want re-use the output of such a function. That is when you assign the output to an R object to be accessed later on.
2.1.3 Objects vs. variables
What are known as objects
in R
are known as variables
in many other
programming languages. Depending on the context, object
and variable
can
have drastically different meanings. However, in this lesson, the two words are
used synonymously. For more information see:
https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects
2.2 Vectors and data types
A vector is the most common and basic data type in R, and is pretty much
the workhorse of R. A vector is composed by a series of values, which can be
either numbers or characters. We can assign a series of values to a vector using
the c()
function. For example we can create a vector of weights and assign
it to a new object weight_g
:
#> [1] 21 34 39 54 55
There are many functions that allow you to inspect the content of a
vector. length()
tells you how many elements are in a particular vector:
#> [1] 5
An important feature of a vector, is that all of the elements are the same type of data.
The function class()
indicates the class (the type of element) of an object:
#> [1] "numeric"
The function str()
provides an overview of the structure of an object and its
elements. It is a useful function when working with large and complex
objects:
#> num [1:5] 21 34 39 54 55
You can use the c()
function to add other elements to your vector:
weight_g <- c(weight_g, 90) # add to the end of the vector
weight_g <- c(30, weight_g) # add to the beginning of the vector
weight_g
#> [1] 30 21 34 39 54 55 90
In the first line, we take the original vector weight_g
,
add the value 90
to the end of it, and save the result back into
weight_g
. Then we add the value 30
to the beginning, again saving the result
back into weight_g
.
We can do this over and over again to grow a vector, or assemble a dataset. As we program, this may be useful to add results that we are collecting or calculating.
A vector can also contain characters:
The quotes around “mouse”, “rat”, etc. are essential here. Without the quotes R
will assume there are objects called mouse
, rat
and dog
. As these objects
don’t exist in R’s memory, there will be an error message.
Lastly, we will introduce a vector with logical values (the boolean data type).
We just saw 3 of the 6 main atomic vector types (or data types) that R
uses: "character"
, "numeric"
and "logical"
.  The other 3 are:
"integer"
for integer numbers (e.g.,2L
, theL
indicates to R that it’s an integer)"complex"
to represent complex numbers with real and imaginary parts (e.g.,1 + 4i
) and that’s all we’re going to say about them"raw"
that we won’t discuss further
Challenge
We’ve seen that atomic vectors can be of type character, numeric, integer, and logical. But what happens if we try to mix these types in a single vector?
What will happen in each of these examples? (hint: use
class()
to check the data type of your objects):Why do you think it happens?
You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?
2.3 Subsetting vectors
If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:
#> [1] "rat"
#> [1] "dog" "rat"
:
is a special function that creates numeric vectors of integers in increasing
or decreasing order, test 1:10
and 10:1
for instance. We can use this to select a sequence, like this:
#> [1] "rat" "dog" "frog"
You can exclude elements of a vector using the “-
” sign:
#> [1] "mouse" "dog" "frog"
#> [1] "frog"
We can also repeat the indices to create an object with more elements than the original one:
#> [1] "mouse" "rat" "dog" "rat" "mouse" "frog"
R indices start at 1. Programming languages like Fortran, MATLAB, Julia, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.
2.3.1 Conditional subsetting
Another common way of subsetting is by using a logical vector. TRUE
will
select the element with the same index, while FALSE
will not.
#> [1] TRUE TRUE TRUE FALSE
#> [1] "mouse" "rat" "dog"
Typically, these logical vectors are not typed out by hand, but are the output of other functions or logical tests.
A typical example is to search for certain strings in a vector. One could use the
“or” operator |
to test for equality to multiple values, but this can quickly
become tedious. The function %in%
allows you to test if any of the elements of
a search vector are found:
#> [1] "rat" "frog"
#> [1] FALSE TRUE TRUE FALSE
#> [1] "rat" "dog"
Equivalently, if you wanted to select only the weights above 50:
#> [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#> [1] 54 55 90
You can combine multiple tests using &
(both conditions are true, AND) or |
(at least one of the conditions is true, OR):
#> [1] 21 54 55 90
#> [1] 30 34 39 54 55 90
Here, <
stands for “less than”, >
for “greater than”, >=
for “greater than
or equal to”, and ==
for “equal to”. The double equal sign ==
is a test for
numerical equality between the left and right hand sides, and should not be
confused with the single =
sign, which performs variable assignment (similar
to <-
).
Challenge
- Can you figure out why
"four" > "five"
returnsTRUE
?
2.4 Missing data
As R was designed to analyze datasets, it includes the concept of missing data
(which is uncommon in other programming languages). Missing data are represented
in vectors as NA
.
When doing operations on numbers, most functions will return NA
if the data
you are working with include missing values. This feature
makes it harder to overlook the cases where you are dealing with missing data.
You can add the argument na.rm=TRUE
to calculate the result while ignoring
the missing values.
#> [1] NA
#> [1] NA
#> [1] 6
#> [1] 16
If your data include missing values, you may want to become familiar with the
functions is.na()
, na.omit()
, and complete.cases()
. See below for
examples.
#> [1] 2 4 4 6
#> [1] 2 4 4 6
#> attr(,"na.action")
#> [1] 4
#> attr(,"class")
#> [1] "omit"
#> [1] 2 4 4 6
Challenge
Using this vector of length measurements, create a new vector with the NAs removed.
Use the function
median()
to calculate the median of thelengths
vector.
2.5 Common R Data Structures
Vectors are one of the many data structures that R uses. Other important
ones are matrices (matrix
), tables (data.frame
), lists (list
), and
factors (factor
).
2.5.1 Matrix
If we arrange data elements of a vector in a two-dimensional rectangular layout we have a matrix. To construct a matrix, we use a function conveniently called matrix()
.
Subset a matrix with [row ,
column]:
2.5.2 List
Lists can have elements of any type. Here is how we construct lists. You may have guessed that to construct a list, we use the list()
function:
2.5.3 Data frame
Data frames in R are a special case of lists, as they can have elements of any type, but they have to all be of the same length.
A data frame is the most common way of storing tabular data in R and something you will likely deal with a lot. As a first approximation, which holds true, probably in the most cases, you can really think of it as a table or a spreadsheet.
Here is how you could construct a data frame.
mydf <- data.frame(ID=c(1:4),
Color=c("red", "white", "red", NA),
Passed=c(TRUE,TRUE,TRUE,FALSE),
Weight=c(99, 54, 85, 70),
Height=c(1.78, 1.67, 1.82, 1.59))
mydf
We will go into more detail about data frames. For now, try the following:
Challenge
- Create a data frame that holds the following information for yourself, your right and your left neighbor:
- first name
- last name
- lucky number
There are a few mistakes in this hand-crafted
data.frame
, can you spot and fix them? Don’t hesitate to experiment!
2.6 Extending R base functionality
R comes with a base system and some contributed core packages. This is what you just downloaded. The functionality of R can be significantly extended by using additional contributed packages. Those packages typically contain commands (functions) for more specialized tasks. They can also contain example datasets. We will make use of external packages later.
2.6.1 Installing additional packages
To install additional packages there are two main options:
- You can use the RStudio interface like this:
- You can install from the R console like this:
2.6.2 Make use of the installed packages
In order to actually use commands from the installed packages you also will need to load the installed packages. This can be automated (whenever you launch R it will also load the libraries for you - see for example here) or otherwise you need to sumbit a command:
or
The difference between the two is that library
will result in an error, if the library does not exist, whereas require
will result in a warning.
Challenge
- Google for an R package that might be of interest for your research.
- Install and load it into R.
2.7 Seeking help
2.7.1 Use the built-in RStudio help interface
One of the most immediate ways to get help, is to use the RStudio help interface (Figure 2.2. In the default conficuration this panel by default can be found at the lower right hand panel of RStudio. As seen in the screenshot, by typing the word “Mean”, RStudio tries to also give a number of suggestions that you might be interested in. The description is then shown in the display window.
2.7.2 I know the name of the function, but I’m not sure how to use it
If you need help with a specific function, let’s say barplot()
, you can type:
If you just need to remind yourself of the names of the arguments, you can use:
2.7.3 There must be a function to do X but I don’t know which one…
If you are looking for a function to do a particular task, you can use the
help.search()
function, which is called by the double question mark ??
.
However, this only looks through the installed packages for help pages with a
match to your search request
If you can’t find what you are looking for, you can use the rdocumentation.org website that searches through the help files across all packages available.
Finally, a generic Google or internet search “R <task>” will often either send you to the appropriate package documentation or a helpful forum where someone else has already asked your question.
2.7.4 I am stuck… I get an error message that I don’t understand
Start by googling the error message. However, this doesn’t always work very well because often, package developers rely on the error catching provided by R. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”). If the message is very generic, you might also include the name of the function or package you’re using in your query.
However, you should check Stack Overflow. Search using the [r]
tag. Most
questions have already been answered, but the challenge is to use the right
words in the search to find the
answers:
http://stackoverflow.com/questions/tagged/r
The Introduction to R can also be dense for people with little programming experience but it is a good place to understand the underpinnings of the R language.
The R FAQ is dense and technical but it is full of useful information.
2.7.5 How to ask for help
The key to receiving help from someone is for them to rapidly grasp your problem. You should make it as easy as possible to pinpoint where the issue might be.
Try to use the correct words to describe your problem. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. Be as precise as possible when describing your problem.
If possible, try to reduce what doesn’t work to a simple reproducible example. If you can reproduce the problem using a very small data frame instead of your 50,000 rows and 10,000 columns one, provide the small one with the description of your problem. When appropriate, try to generalize what you are doing so even people who are not in your field can understand the question. For instance instead of using a subset of your real dataset, create a small (3 columns, 5 rows) generic one. For more information on how to write a reproducible example see this article by Hadley Wickham.
To share an object with someone else, if it’s relatively small, you can use the
function dput()
. It will output R code that can be used to recreate the exact
same object as the one in memory:
#> structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4),
#> Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4,
#> 1.4, 1.3, 1.5, 1.4, 1.7), Petal.Width = c(0.2, 0.2, 0.2,
#> 0.2, 0.2, 0.4), Species = structure(c(1L, 1L, 1L, 1L, 1L,
#> 1L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), row.names = c(NA,
#> 6L), class = "data.frame")
If the object is larger, provide either the raw file (i.e., your CSV file) with your script up to the point of the error (and after removing everything that is not relevant to your issue). Alternatively, in particular if your question is not related to a data frame, you can save any R object to a file:
The content of this file is however not human readable and cannot be posted
directly on Stack Overflow. Instead, it can be sent to someone by email who can
read it with the readRDS()
command (here it is assumed that the downloaded
file is in a Downloads
folder in the user’s home directory):
Last, but certainly not least, always include the output of sessionInfo()
as it provides critical information about your platform, the versions of R and
the packages that you are using, and other information that can be very helpful
to understand your problem.
#> R version 4.0.3 (2020-10-10)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Catalina 10.15.6
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] compiler_4.0.3 magrittr_2.0.1 bookdown_0.21 htmltools_0.5.0
#> [5] tools_4.0.3 rstudioapi_0.11 yaml_2.2.1 stringi_1.4.6
#> [9] rmarkdown_2.5 highr_0.8 knitr_1.29 stringr_1.4.0
#> [13] digest_0.6.27 xfun_0.16 rlang_0.4.9 evaluate_0.14
2.7.6 Where to ask for help?
- The person sitting next to you during the workshop. Don’t hesitate to talk to your neighbor during the workshop, compare your answers, and ask for help. You might also be interested in organizing regular meetings following the workshop to keep learning from each other.
- Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
- Stack Overflow: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 min. Remember to follow their guidelines on how to ask a good question.
- The R-help mailing list: it is read by a lot of people (including most of the R core team), a lot of people post to it, but the tone can be pretty dry, and it is not always very welcoming to new users. If your question is valid, you are likely to get an answer very fast but don’t expect that it will come with smiley faces. Also, here more than anywhere else, be sure to use correct vocabulary (otherwise you might get an answer pointing to the misuse of your words rather than answering your question). You will also have more success if your question is about a base function rather than a specific package.
- If your question is about a specific package, see if there is a mailing list
for it. Usually it’s included in the DESCRIPTION file of the package that can
be accessed using
packageDescription("name-of-package")
. You may also want to try to email the author of the package directly, or open an issue on the code repository (e.g., GitHub). - There are also some topic-specific mailing lists (GIS, phylogenetics, etc…), the complete list is here.
2.7.7 Resources on getting help
- The Posting Guide for the R mailing lists.
- How to ask for R help useful guidelines
- This blog post by Jon Skeet has quite comprehensive advice on how to ask programming questions.
- The reprex package is very helpful to create reproducible examples when asking for help. The [rOpenSci community call “How to ask questions so they get answered”], Github link and video recording includes a presentation of the reprex package and of its philosophy.
2.1.1 Comments
The comment character in R is
#
, anything to the right of a#
in a script will be ignored by R. It is useful to leave notes, and explanations in your scripts. RStudio makes it easy to comment or uncomment a paragraph: after selecting the lines you want to comment, press at the same time on your keyboard Ctrl + Shift + C. If you only want to comment out one line, you can put the cursor at any location of that line (i.e. no need to select the whole line), then press Ctrl + Shift + C.


–[[ example useless code to show lua syntax highlighting this is multiline comment ]] function blahblahblah(x) local table = { “asd” = 123, “x” = 0.34, } if x ~= 3 then print( x ) elseif x == “string” my_custom_function( 0x34 ) else unknown_function( “some string” ) end –single line comment end function blablabla3() for k,v in ipairs( table ) do –abcde.. y=[=[ x=[[ x is a multi line string ]] but its definition is iside a highest level string! ]=] print(" \“\” ") s = math.sin( x ) end end
Loosely based on Franciszek Wawrzak’s CodeMirror 1 mode. One configuration parameter is supported, specials
, to which you can provide an array of strings to have those identifiers highlighted with the lua-special
style.
MIME types defined: text/x-lua
.


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Permission Denied
You have tried to do something that you don't have permission for.
If you want to, you can request the permission by asking for a role
that allows you to perform that action. To do so, select a role
below, and a user to make the request to. You should also include
a message with information to help the recipient decide whether
or not to grant you the role.
{% endblock %}


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import partials.DependenciesPartial @import uk.gov.hmrc.cataloguefrontend.ViewMessages @import uk.gov.hmrc.cataloguefrontend.service.ConfigService @import uk.gov.hmrc.cataloguefrontend.service.ConfigService.{ConfigByEnvironment, ConfigEnvironment} @import scala.collection.immutable.ListMap @this(dependenciesPartial: DependenciesPartial, viewMessages: ViewMessages) @(serviceName: String, configByEnvironment: ConfigByEnvironment)(implicit messages: Messages, request: Request[_])


composer
trumpeter
music technologist
I use the vivid and immersive experiences I have when reading fiction as the basis for the emotional trajectory of a new work.
Light is like Water
| Years | Section | Minutes | Ranking |
|---|---|---|---|
| 1929-1931 | part 1 | 5.347524149 | 3 |
| 1932-1934 | part 2 | 2.042572466 | 5 |
| 1934-1935 | destroyed | 0.780193256 | 7 |
| 1934-1941 | part 3 | 14 | 1 |
| 1940-1943 | missing | 0.48218595 | 8 |
| 1941-1947 | part 4 | 8.652475836 | 2 |
| 1948-1950 | part 5 | 3.304951677 | 4 |
| 1950-1951 | loose pages | 1.262379208 | 6 |
| Total | 35.872 minutes |
Excerpt 1 (Part 1)
Excerpt 2 (Parts 4, 5 and "loose pages")
How do we find meaning and direction in our work as artists?
My personal answer is try to shape every part of my multi-faceted career to reflect my value system.
SPLICE is an organization dedicated to the performance, creation, and development of music for performers and electronics.
Applications for SPLICE Institute 2019 are due 2/15!
Join us for the weeklong, intensive program designed for performers and composers making music for live performance and electronics. This year's featured guests include Ensemble Dal Niente, Kyong Mee Choi, and Ben Melsky.
June 23-29, 2019, Kalamazoo, MI
SPLICE is an organization dedicated to the performance, creation, and development of music for performers and electronics.
Applications for SPLICE Institute 2019 are due 2/15!
Join us for the weeklong, intensive program designed for performers and composers making music for live performance and electronics. This year's featured guests include Ensemble Dal Niente, Kyong Mee Choi, and Ben Melsky.
June 23-29, 2019, Kalamazoo, MI
You can reach me here:
sam@samwellsmusic.com


Balanced attack helps Yale rout Oberlin College 94-37
by EndPlayNEW HAVEN, Conn. (AP) - Paul Atkinson tossed in 12 points on 5-of-7 shooting and Azar Swain scored 11 as Yale routed Division III member Oberlin College 94-37 on Friday night.
Jordan Bruner added eight rebounds and blocked three shots for the Bulldogs (2-0). Thirteen of 14 players scored for Yale. Starting guard Eric Monroe was the only Bulldog who didn't score, but he took only one shot and handed out five assists.
Yale plays the Ohio school because it is coached by former Bulldogs standout guard Isaiah Cavaco, a 2001 graduate who played and served as an assistant under Yale coach James Jones.
Yale plays San Francisco on the road on Monday.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
This was generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com


Wesberry V Sanders - Yahoo! Voices - voices.yahoo.com
Mar 5, 2010 . Wesberry v. Sanders(1964), was a case involving congressional districts in the state of Georgia, presented to the Supreme Court of the United .
http://voices.yahoo.com/wesberry-v-sanders-5583819.html
Treatment for Herniated or Bulging Discs
New Georgia Encyclopedia: Wesberry v. Sanders (1964)
Oct 4, 2004 . Wesberry v. Sanders (1964). In its 1964 ruling in Wesberry v. Sanders a suit pursued by a group of Fulton County voters against Georgia .
http://www.georgiaencyclopedia.org/nge/Article.jsp?id=h-2984
Thigpen v. Meyers, June 22, 1964 - Washington Secretary of State
The 1960s: 1964. Court Cases. Wesberry v. Sanders, February 1964. The Georgia court case of Wesberry v. Sanders significantly expanded the concept of "one .
http://www.sos.wa.gov/legacyproject/ShiftingBoundaries/1960s/1964/1964_courtCases.aspx
wesberry vs sanders 1964 Herniated or Bulging Discs
Wesberry v. Sanders, 376 US 1
Wesberry v. Sanders, 376 U.S. 1 (1964) was a case involving congressional districts in the state of Georgia, brought before the Supreme Court of the United .
http://www.jimwes.com/id20.html
Key Court Cases
Wesberry v. Sanders, 376 U.S. 1 (1964). Case Summary. Wesberry, a voter of the 5th District of Georgia, filed suit on the basis that his Congressional district had .
http://www.redistrictinginamerica.org/learn/courtcases
29 Wesberry v Sanders
Sep 17, 2012 . Wesberry v. Sanders (1964) Chief Justice: Earl Warren (1953-1969) Issue/Topic Fundamental Rights Under Due Process and Equal Protection, .
http://www.docstoc.com/docs/130278761/29-Wesberry-v-Sanders
Official Impairment Listing for Spinal Disorders
Westlaw Insider | Blog | Tags | Wesberry-v-sanders
Jul 20, 2012 . On February 17, 1964, the Supreme Court decided Wesberry v. Sanders, which held that state Congressional districts must be proportional in .
http://westlawinsider.com/tag/wesberry-v-sanders/
- leanne mcjanet may 21 (narrowing of the spine)
Wesberry v. Sanders (1964)
Wesberry v. Sanders (1964). A group of voters in Georgia charged that population variations among the legislative districts of Georgia were so great as to violate .
http://library.thinkquest.org/11572/cc/cases/wesberry.htmlWesberry v. Sanders - Wikipedia, the free encyclopedia
Wesberry v. Sanders, 376 U.S. 1 (1964) was a U.S. Supreme Court case involving U.S. Congressional districts in the state of Georgia. The Court issued its ruling .
http://en.wikipedia.org/wiki/Wesberry_v._Sanders
Wesberry v. Sanders | The Oyez Project at IIT Chicago-Kent College ...
Appellee. Sanders. Appellant. Wesberry. Decided By. Warren Court (1962-1965). Opinion. 376 U.S. 1 (1964). Argued . Monday, February 17, 1964. Advocates .
http://www.oyez.org/cases/1960-1969/1963/1963_22
Wesberry v. Sanders - Wikisource, the free online library
Jun 26, 2011 . Wesberry v. Sanders, 376 U.S. 1 (1964) was a case involving congressional districts in the state of Georgia, brought before the Supreme Court .
http://en.wikisource.org/wiki/Wesberry_v._Sanders
Medical-Vocational Allowance for Herniated Disc
Wesberry v. Sanders - 376 U.S. 1 (1964) :: Justia US Supreme Court ...
Sanders, 376 U.S. 1 (1964). Wesberry v. Sanders. No. 22. Argued November 18- 19, 1963. Decided February 17, 1964. 376 U.S. 1. APPEAL FROM THE UNITED .
http://supreme.justia.com/cases/federal/us/376/1/case.html
Westlaw Insider | Blog | Today in 1964: Supreme Court rules that ...
Feb 17, 2012 . On February 17, 1964, the Supreme Court decided Wesberry v. Sanders, which held that state Congressional districts must be proportional in .
http://westlawinsider.com/today-in-legal-history/today-in-1964-supreme-court-rules-that-congressional-districts-must-be-proportional/
Appealing a Denial of Benefits
What was the Supreme Court case Wesberry v Sanders about
Wesberry v. Sanders, (1964) required that Districts of the US House of Representatives be composed of approximately equal populations in order to ensure fair .
http://wiki.answers.com/Q/What_was_the_Supreme_Court_case_Wesberry_v_Sanders_about
Redistricting Court Cases
Wesberry v. Sanders, 376 U. S. 1 (1964). The Court held that as nearly as is practicable one man's vote in a congressional election is to be worth as much as .
http://www.malegislature.gov/District/CourtCases


DeAndre Yedlin: Why Newcastle United dressing room have 'bought into' Steve Bruce and his tactics
The American full-back is enjoying life at the moment after scoring his first ever goal at St James' Park on the back of tip from Bruce
by Lee RyderDeAndre Yedlin has spoken about life under Steve Bruce at Newcastle United.
The USA international has had to fight his way back into the team following a summer operation on his groin and the arrival of Sweden international Emil Krafth.
Bruce has challenged Yedlin to offer more going forward from right wing-back and the former Spurs defender responded with a diving header to haul his team back into the game in the 2-1 win over AFC Bournemouth last weekend.
When asked about how he is enjoying life under a different boss, Yedlin told the Chronicle: "We all bought into his game plan and all bought into his tactics.
"I think it shows.
"When we go a goal down everybody is fighting on the pitch.
"That is the main thing with his teams, it's to fight.
"I think it has been a recurring thing over the years too, the guys on this team are fighters.
"It's great for the club."
Bruce has altered the United tactics in recent weeks and wants his wing-backs to be more of a threat as he looks to move away from the ultra defensive unit that kept Newcastle in the Premier League in recent years.
He said: "In recent weeks Steve told me and Jetro Willems to get in the box a bit more to add another guy in the box.
"Looking at it from a wing-back point of view when an extra body is coming forward it is tough to defend.
"We have been trying to do that.
"It has worked out a bit.
"We're coming into the front and back post and hoping for something.
NUFC latest
The latest Newcastle United news and views
- Newcastle are ready to move in January transfer window if right player is available
- Steve Bruce reveals factor which could be key to Newcastle United's success
- Newcastle United fans are all saying the same thing after Miguel Almiron scored for Paraguay
- Rafa Benitez hints at Premier League return as he discusses Newcastle record and Steve Bruce
"First things first, it's about defending.
"But as a defender if I can help produce goals and assists it brings even more to the team."
United checkout for the international break with 15 points in the bag from their first 12 games.
And while they are still 13th in the table, the Magpies are within swinging distance of the upper reaches of the Premier League table.
When asked if this season is an opportunity for one team to break away from the pack, Yedlin said: "Yeah, it's been on odd season.
"You have - quote unquote - the so-called smaller teams beating bigger teams.
Newcastle are ready to move in January transfer window if right player is available
"So it's been quite odd.
"It just means that we are going to need to get as many points as possible because I think the bar for relegation will be raised a bit higher.
"We just need to keep getting wins and points on the board."


/inc
/clear
/show
/sms : return SMS TwiML which uses the session key, counter.
Current counter value = {{ counter }}.Note, you may need to refresh the page to get the latest value.


This is the demo for Browser Radar. It's a javascript helper that allows detection of browser/os, outputting that data and handling urls to share the data with others (support teams).
Your browser details:
Copy the link below to share with your support team.
Here are some other user agents for testing this demo. Select an option from the drop down to reload the page - uses share links.


 %}
{% trans "When you update your profile, you will be redirected back to this page." %}


When installed, this plugin only allow bidirectional subscriptions. Basically, what it does is : 1 - When an user accepts a sub request, he automatically sends one back. 2 - When an user is subscribed (or requesting subscription) to a contact, he automatically accept subscription requests from that contact 3 - When an user recieve an unsubscribe request, he automatically sends one (if he's subscribed) or cancel any pending subscription requests Since it does not make sense when interaction with other domains (which may not have the same policy), it can be enabled for local requests only.
Copy the file, symmetricalsub.jar into the plugins directory of your Openfire installation. The plugin will then be automatically deployed. To upgrade to a new version: 1) go to the plugin screen of the Admin Console, 2) click on the delete icon on the same row as the currently installed symmetricalsub plugin, 3) copy the new symmetricalsub.jar into the plugins directory of your Openfire installation.
The symmetricalsub plugin is configured via Symmetrical Subscription Properties sidebar item which can found under the Sever tab in Openfire Admin Console. The symmetricalsub plugin can be configured in the following ways:
Once the plugin has been installed and configured there is no additional steps are required to use the plugin.


create game
Home
(current)
Home
Profile
Start Game
Design Game
Users
max score:
hold number:
number of dices:
1
2
4
number of dice roll:
enter how many times can a player roll a dice.
create game


By Noah Huppert
/acme-company/
web server directory.
/acme-company/
directory. Simply navigate to /acme-company/. Do you see any files that look interesting?
/acme-company/customer-dashboard.htpasswd
file is visible and available to download. This is a password hash file. Perhaps it contains the passwords to something important. See if you can crack it.
/acme-company/customer-dashboard.htpasswd
file can be cracked using John the Ripper. We can see that it contains an entry for a user named admin
.
cewl
tool can be used to do this. Scan the ACME Company home page and make it a word list:
cewl --write ctf-wordlist.txt http://<IP_ADDRESS>:1111/acme-company/
This will generate a word list based off of the words on the ACME company web page. Next use John the Ripper on this downloaded htpasswd file with our word list:
john --wordlist ctf-wordlist.txt customer-dashboard.htpasswd
Running John the Ripper will give you the login information.
admin
with password money
.


really good restaurants for people who eat food
Search Restaurants
Restaurant List
No matches!
Name:
{{item.name}}
{{types}}
Price point:
{{item.price}}
$
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Name
Type of food
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title=Getting Started with Cloud Foundry for Java and Spring Developers date=2012-11-16 type=post tags=blog status=published ~~~~~~
I received the following (very awesome) e-mail the other day:
Josh-
I have been watching a couple of your videos on YouTube, and I have some questions that are decidedly remedial, which I recognize, but i am also having trouble getting answer to them.
 I understand the basics of HTML web development, and have even built a .NET application using Visual Studio. But I am not a developer - you could call me a hobbyist and have some nominal experience around building websites using HTML and Dreamweaver.
However, I am running into more and more situations where knowing how to write real web applications will be beneficial... I have installed the latest rev of STS, signed up for CloudFoundry, even configured the micro cloud on my macbook.... but now I don't know how to get started! what do I do? how do I build a database (vPostgres, MySQL, I don't care), and connect to it with a web application? I can't seem to find the tutorials that are remedial enough for me.
Any suggestions?
Thanks, Josh -...
A few points: he is referring to the SpringSourceDev YouTube channel, where I have a few videos (and where you can find a slew of content from other great presenters on all things SpringSource). Additionally, I omitted his name from this email.
So, what we have here is an essentially tech-minded, but not particularly advanced, developer looking to take the plunge into the wide world of web development, and he wants to get started with Cloud Foundry. This is an awesome (and increasingly common) occurence. Cloud Foundry gives an outlet to all the people and departments that want to get things on the web and in production quickly. The cloud is an important next step, not only for the hard core developers out there, but also for the infinite many that need technology in place, quickly, and can't or won't wait for IT to deliver it. This happens a lot with what Patrick Chanezon and others refer to as situational applications.
I've updated and included my initial response to him below in the hopes that it will help others.
You say remedial like it's a bad thing! We all get started at some point.
Cloud Foundry is a good way to get started (and continue to mastery!) with web development.
An important tool is the
vmc
command line tool.vmc
is the simplest way to get started with this stuff. Setup vmc and interact with the command line and understand the tool's capabilities. Here are some docs on getting started with vmc and the vmc tunnel. If you have questions don't hesitate to reach out to me. Once you've gotvmc
and the tunnel installed, there's a lot you can do!If you're doing Java development, specifically, then the SpringSource Tool Suite (STS) is a huge start. STS is a free, Eclipse-based distribution. It is a super set of the Java EE distribution of Eclipse, and includes a slew of handy plugins pre-installed, including Maven support. Maven is a tool that lets you easily compile your code. While you can install STS's plugins on top of an existing Eclipse distribution, you won't get the Maven support, and that's required for this tutorial.
Cloud Foundry lets you run and manage web applications. You don't need to worry about the hard disk, servers, databases, RAM, etc. However, the compromise is that you can only run well known application stacks. Cloud Foundry.com supports Java (including Spring applications, and anything else you might want to run on a command line or inside of Tomcat 6), Ruby, Node.js, and Scala. Other ecosystem partners have taken the code and run their own version of Cloud Foundry, and often include extra features. For example, AppFog has support for Python and PHP, above and beyond what Cloud Foundry offers. Tier3 and Uhuru both support .NET on their derivatives. So, what type of language will you use? I've written the following assuming you're going to use Spring and Java, a very common combination.
To make sure that everything is easy to find in STS for this tutorial, use the
Window
->Reset Perspective
command.If you're using STS (which is an Eclipse -based distribution) you'll need to install the Cloud Foundry WTP (web tools project) connector. Go to the
Help
->Eclipse Marketplace
. Search for "CloudFoundry" and you'll find the "Cloud Foundry integration for Eclipse."
Then choose
VMWare
->Cloud Foundry
.Click
Next
on the first screen (there's no need to change anything there.) and then enter your credentials to log in to the cloud.You can verify your credentials by clicking
Validate Account
. Then, clickFinish
.Once installed, you can double click on the Cloud Foundry server to see what's going on in your account, just as you would from the
vmc
command line tool. The tab that presents itself can take up some serious screen real estate, so you might close theSpring Explorer
,Outline
, andTask List
panels, as I have. The Cloud Foundry tab has two sub tabs at the bottom: one calledOverview
, and another calledApplications
. You can click on an application to see the resources provisioned for the application, including which services (databases, message queues, etc) have been apportioned for it and how much RAM and CPU the application is taking.
To create an application, go to the
File
menu, chooseNew
->Spring Template Project
.Then choose
Spring MVC project
and clickNext
.It might ask you to download a few kilobytes to get the tutorial if you haven't already downloaded this before. If so, click
Yes
.Then fill in an application name and a top-level package. The wizard requires 3 tokens in the top-level package name, so
com.joshlong.cloudfoundrydemo
would work, butcom.joshlong
won't.Once you have an application, it's easy enough to deploy it to Cloud Foundry. Simply drag the project from the
Project
panel onto the Cloud Foundry server in theServers
panel. It'll ask you to specify information about the application: what runtime, what language, etc. The defaults are good enough in this case, so just leave them as-is and clickNext
.Next, you'll be asked to specify the URL for the application as well as how much memory to allocate. Again, the defaults are fine in this case.
The application will deploy, and - if you open the URL given in a browser window - you'll be greeted with a new application on Cloud Foundry!
Getting started with programming Spring is another story for another blog post at least! In the meantime, you might checkout the Green Beans blogs on SpringSource.org
I hope this post has been helpful. If not, always feel free to reach me at my email.


Featured Matches
Manchester United vs Chelsea
Currently Live
{% for live in broadcastList %}
Manchester United vs Chelsea
{% endfor %}
Start Your Own Room
Sports should be enjoyed together!


--- layout: post title: Get Slimmer Pets categories: - flickr ---
I was shopping at Lidl last weekend and saw this for sale. It might be a bit of a drastic measure, but I think it would work.


"x" "1" "LEE,BH, LEE,S, KIM,YS, BERTRAM,B AND WIESSLER,M, MIXED DISULFIDES FROMDISULFIRAM INHIBIT THE BENZO[A]PYRENE INDUCED MUTAGENESIS, MUTAT. RES.371(3-4):215-220, 1996"


I had a few issues installing Rails for the first time with PostgreSQL locally, and since the problems I was having as a newbie in both were not well-documented on the internet, I thought I’d post a rundown of what happened and how I fixed it.
Installing pgsql with home-brew
This topic has been covered exhaustively on the web, but the way I did it was to run:
1 2 |
|
Then, I followed the rest of the directions in the terminal to finish my install, and to install the pg gem.
Big thing of note: If you are not sure you followed all the directions, and you’re getting crazy errors, and you’ve already closed your terminal, all is not lost!
You can run brew info postgresql
to see the instructions again and double check.
Fixing your broken PG Connection in Rails
I decided to start my first rails project with pgsql, because I wanted to give it a try and after using sqlite3 a few times already, I was ready to graduate to something I could actually deploy on Heroku.
So I created my brand-new rails app by running this command in my terminal:
rails new blogger -d postgresql
Everything looked gravy, until I loaded up http://localhost:3000 and got the big red Rails error with something I did not understand:
PG::ConnectionBad (FATAL: role "blogger" does not exist
):
So I found that after doing that, I did some research and found that I needed to run the rake task to actually set up the databases. That’s fine by me! So I ran this in my terminal:
rake db:create:all
Then, my terminal blew up and the error I would get would be:
FATAL: role "blogger" does not exist
Not knowing much about this, I spent some time searching and found out that I needed to create a new user named the same thing as the user in my database.yml
file in the Rails app. Mine happens to be blogger
, so I then went into my terminal and ran the following command:
createuser -s -r blogger
Then, I ran this command again, this time successfully: rake db:create:all
After doing this, and restarting my Rails server, I went back to http://localhost:3000 and I was happily riding the Rails!


Panda is a wonderful animal! Let's learn more about them.
Get started! »
Panda is a giant herbivore. Let's learn more about them.
Learn More! »
Although rather plain, user can create a blog post using HTML markup. We'll try to let them use Markdown later.
See it in action! »


Trader Alexandria:
Welcome! Thank you so much for visiting our boutique. We sell precious and valuable things that you would never find anywhere else. We've also added some luxury accessories this time. Look around see if you can find anything you like.
Ah! Now, you can use Adena to pay for your goods.
Purchase Specialty Items.
Purchase Agathion Bracelet.
Quest


Dr M officiates soft launch of KL Summit 2019
PUTRAJAYA, Nov 21 ― Prime Minister Tun Dr Mahathir Mohamad today officiated the soft launch of Kuala Lumpur Summit 2019, scheduled to be held from December 18 to December 21 at the Kuala Lumpur Convention Centre.
It aims to revive Islamic civilisation, deliberate and find new and workable solutions for problems afflicting the Muslim world, to contribute in the improvement of the state of affairs among Muslims and Muslim nations and to form a global network between Islamic leaders, intellectuals, scholars and thinkers.
With the theme, “The Role of Development In Achieving National Sovereignty”, the summit would be attended by 450 Muslim leaders, thinkers, intellectuals and scholars from all over the world.
Dr Mahathir, who is also Kuala Lumpur Summit chairman, announced that the head of states from Qatar, Turkey and Pakistan have agreed to attend the summit. ― Bernama


About four million (60% of) veterans diagnosed with PTSD are not reaching out for help with their diagnoses. PTSD is a serious mental health issue, and puts veterans at severe risk. This is clear when we look at the numbers. In the United States in 2017, 40,000 veterans were homeless. Furthermore, in 2016, an average of 20 veterans committed suicide per day. While PTSD does not necessarily account for all of these numbers, it puts veterans in a vulnerable state that increases their risk to become one of these statistics. There are a few factors contributing to this staggering problem, such as poor access to resources, stigma surrounding mental health, and a lack of community or support between veterans once retired from service. Veterist seeks to find solutions to these problems.
Our solution is a mobile application designed to provide direct support to veterans with PTSD on an as-needed basis. This addresses some of the aspects of the problem described above in the following ways. First, we hope to increase veterans’ access to both long term and immediate resources by connecting them with organizations based on their personal needs. Second, we want to challenge the stigma surrounding mental health by connecting veterans with others experiencing the same hardship. Through these connections, we hope to foster community and support between veterans with each other and mental health professionals. Below, you will see the features of Veterist designed to execute this solution.
Veterist matches users to the resource that best supports their needs based on their personal symptoms.
Users are presented with organizations that can best treat their symptoms, as well as others near them.
Selecting an organization presents users with more information and options to call or get directions.
Users can also reach out to Emergency Contacts to get support for more severe symptoms.
Selecting a contact gives users additional information, along with the ability to call or message the contact.
How we assist users in finding the help they need
Select the symptoms most relevant to you to find the best resources for your needs!
Find specific information about the organizations we think are right for you - including mission statements and treatment specialties!
Call an organization to schedule an appointment, or find directions on how to get there - all from our app!
Chat with other veterans and mental health professionals on your schedule - whenever, wherever you are!
Computer Science
HCDE
Computer Science
Computer Science


A hybrid of C and C++, it is a Microsoft programming language developed to compete with Java. C# is an object oriented programming language used on the .NET platform.
Ruby on Rails is a server-side web application framework written in Ruby under the MIT License. Rails is a model-view-controller framework, providing default strutures for a database, a web service, and web pages.
An object-oriented computer programming language commonly used to create interactive effects within web browsers. JavaScript is a client-side scripting language, which means the source code is processed by the client's web browser rather than on the web server.


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We use AI and Machine Learning to improve as you gain more data. The ladies always say Khaled you smell good, I use no cologne. Cocoa butter is the key. Another one. I’m giving you cloth talk, cloth. Special cloth alert, cut from a special cloth. Hammock talk come soon. They key is to have every key, the key to open every door.
It takes no effort to go from your dashboard to your team presentation. Lorem Khaled Ipsum is a major key to success. Always remember in the jungle there’s a lot of they in there, after you overcome they, you will make it to paradise. Wraith talk.  How’s business? Boomin.
Reach out and let us know what's on your mind. We can't wait to help you optimize your business.


Julia
This game is very fun! It is a walking cupcake that can jump over gates and collect coins.  It automatically restarts and than you can play again and again. YAY!!!


Missing Publicity Materials We're missing publicity materials from the show. {% include boxes/phrase_if_you_can_help.html %}


{% from 'macros.html' import form_field_group_macro %} {% macro login_form_macro(form, url) %}
Date & Time should be in UTC Time Zone. If you don't need to specify running time, make the end date's year far, far away in the future.


The Hot Spot
We only have 5 tables, be quick!
View Tables
Make Reservation
View Table API
View Waitlist API


October 2013 - June 2014
This car was originally built during the 2012-2013 school year by 15 students in Foothill High School's advanced engineering course. Everything, including the welded steel-tubing frame and all the electronics, were built and assembled by students. In UCI's 2013 Energy Invitational race this car placed 2nd overall.
As president of the school's Engineering Club, I led a team to improve the car for the 2014 race. We worked for several months to address 5 main issues. Improvements included inserting a gear reduction box to change the steering ratio, fabricating boxes to make batteries interchangeable, remounting shocks to lower the front end, adjusting wheel toe and camber, and adding sheet metal to the cockpit to protect the driver. The car finished much improved, passed all pre-race safety inspections, and we won the 2014 competition!
A high steering ratio on the original car meant the driver had to awkwardly shuffle the steering wheel during tight corners, costing precious seconds. To remedy this issue, we replaced a portion of the steering column with a gear reduction box. In the proper orientation, this amplifies steering input allowing the driver to reach both extreme steering positions with ~270° of steering input and without changing hand position.
The biggest hinderance to the car's success in 2013 was power. The car is powered with 6 deep-cycle batteries, which can discharge a large capacity--good for racing--but take long to recharge. The competition allows one hour of recharge time between each of four 15 minute heats--not enough to fully recharge the batteries. By the fourth heat in 2013, the car struggled to accelerate past walking speed. The club's budget allowed one major expenditure, and we elected to purchase a second set of batteries. This would allow over closer to two hours of charge time for each set of batteries.
To make this strategy viable, removable battery trays were constructed to allow quick battery pack changes. Shown below, they replaced the semi-permanent mounting system previously used. The trays slide out horizontally toward the back of the car, are held in place by 4 bolts and both can be switched in about five minutes. Four trays were fabricated in total. I designed the pack and welded all the components together.


Regex
(Evidently there aren't any funny regex cartoons out there... ergo cute overload.)
Defined
Regex - short for Regular Expression - is a special sequence of characters that allows you to match/find other strings or sets of strings. They also allow you to extract the portions that match.
One of the beautiful things about Regex is that they're ubiquitous - they work across all languages and any capable text editor.
Within the scope of Dev Bootcamp it's also a wonderful thing that there is a Regexp class.
Syntax
The basic Ruby syntax is /pattern/ or %r{pattern} You can also search using the Regexp.new constructor.
The =~ operator is Ruby's basic pattern-matching operator. If a match is found the operation returns the index of the first match in the string otherwise it returns nil.
Metacharacters and Escapes
Metacharacters have a specific meaning when appearing in a pattern. To match them literally many of them must be backslash escaped. Ruby Metacharacters requiring an escape are:
- (, )
- [, ]
- {, }
- .
- ?
- +
- *
Anchors are a type of metacharacter that match the zero-width positions between characters, anchoring the match to a specific position.
- ^ matches beginning of the line
- $ matches end of line
- \A matches beginning of string
- \Z matches end of string (if string ends with a new line it matches just before the new line)
- \z matches end of string
- \G matches point where last match finished
- \b matches word boundaries when outside brackets; backspace when inside them
- \B matches non-word boundaries
I got a bit lost with the following ones and so am listing but not trying to define them.
- (?=pat) is positive look ahead assertion
- (?!pat) is negative look ahead assertion
- (? =pat) is positive look back (minus the space after the )
- (? !pat)" is negative look back (minus the space after the )
Repetition
- * stands for zero or more times
- + stands for one or more times
- ? zero or one times
- {n} stands for exactly n times
- {,m} stands for m or less times
- {n,m} stands for at least n and at most m times
Examples
Delete empty lines: /^\s*\n/
The caret (^) stands for the start of the line - indicating we'd like to start the search from the beginning of the line. The \s combination represents whitespace The * says that we are looking for 0 or more of the whitespaces And the \n is, of course, our special character for a newline.
As it stands the above example will read through whatever text you have it search on from the beginning of each line through to the end of the line. Depending on what you tell it to (vis-a-vis replacement) it will either be modified as instructed or it will remove all empty lines (if you replace it with nothing)
Remove spaces from beginning of the line: /^ +/ (or /^\s+/)
A variation on the above would be to remove any/all leading white spaces from lines. You can either use a literal space or the \s to indicate the white space.
So we know the ^ is start of line (and it's $ if you're looking to match end of line) The space or \s is our white space And the + looks for one or more of the previous tokens (in this case a white space)
For future consideration
This is but the tip of the proverbial iceberg. There is SO much more to this regex thing than I can begin to capture in a single blog post. What is apparent to me is the HUGE value it adds to a programmer's toolbox and I'm excited to work with it more in the weeks ahead!


Note
Click here to download the full example code
A Complete Guide to Matrix Notation and Linear Regression¶
Let’s really understand matrix notation in context of linear regression, from the ground up.
Linear Regression finds the best line, or hyperplane \(\hat{y}\) in higher dimension, or generally a function \(f\):
that fits the whole data. This is just a dot product between vector \(w\) and a data point \(x\) in \(d\) dimension:
Notice that we use \(w_0\) as an intercept term, and thus we need to add a dummy dimension with value of “1” (\(x_0\)) for all data points \(x\). Thus, \(x\) here is on \(d+1\) dimension. Think of it as the y-intercept term \(c\) in 2-dimension (\(y = mx + c\)).
Another way to look at this is that \(f(x)\) transforms a data point \(x\) on \(d+1\) dimension into a predicted scalar value \(\hat{y}\) that is close to target \(y\):
The Sum of Squared Error Loss¶
The best way to solve this is to find \(w\) that minimizes the sum of squared errors (SSE), or the “error” between all of predicted value \(\hat{y}^i\) and the target \(y^i\) of \(i^{th}\) data point for \(i = 1\) to \(n\), writing this as a loss function \(L(w)\):
From now on we refer to a data point (d+1 vector) as \(x^i\) and its corresponding target (scalar) as \(y^i\). I know it’s confusing for the first time, but you’ll get used to using superscript for indexing data points.
Surprisingly, the SSE loss is not from someone’s intuition, but it’s from the assumption that there is Gaussian noise in our observation of the underlying linear relationship. We will show how this leads to SSE loss later, but first let’s visualize what we’re trying to do.
There is a true line, the true linear relationship that we want to discover (blue line).
The data points are then observed through noise deviating from that line, with Gaussian distribution.
Suppose we predict a random line (red), not the best one yet.
We calculate the distance or difference between the predicted points (along the line) and the actual data points. This is then squared and sum up to get sum of squared error.
Linear regression is the method to get the line that fits the given data with the minimum sum of squared error.
How to Find the Optimal Solution¶
An optimal solution (\(w\)) for this equation can be found either using closed-form solution or via iterative methods like gradient descent.
A closed-form solution means we figure out the formula for \(w = ...\). Implementing that formula in a program directly solves the problem. The thing is you have to come up with the correct one yourself, by hand.
Do you remember how to find a minimum (or maximum) value for a function? We take the derivative of the function above with respect to \(w\), set it to zero, and solve for the \(w\) in terms of other parameters. This is like taking a single jump to the optimal value. We do all the hard work for computers.
Luckily we can do this for linear regression, but not all loss functions be solved this way, actually, only a few. In those cases, we use iterative methods like gradient descent to search for the solution. In contrast to closed-form solution, we do not jump directly to the optimal answer, instead, we take many steps that lead us near to where the optimal answer lives.
Next let’s derive the closed-form solution for linear regression. In order to do that efficiently, we need some matrix notations.
Going into Matrix Notation¶
Writing things down in matrix notation makes things much faster in NumPy. But it’s not easy to read matrix notation, especially if you study machine learning on your own. There’re things like dot product, matrix multiplication, transpose and stuff that you need to keep track of in your head. If you’re starting out, then please write them on papers, drawing figures as needed to make you understand. It really pays off.
On top of that, these few key standards will make our lives with linear algebra easier:
1. Always a column vector¶
When you see standalone vectors in a matrix notation formula, assumes it’s a column vector. E.g.,
and so its transpose is a row vector,
Likewise, you should try to make the final result of matrix operation to be a column vector.
Note that the NumPy vector created by np.zeros
, np.arange
, etc.,
is not really a column vector. It has only one dimension (N,)
. So,
you cannot transpose it directly (x.T
still gives you x
.) To
convert it to a column vector, we use x.reshape(N,1)
or
x[:, None]
.
2. Feature matrix \(X\) is rows of data points¶
Our data points \(x^i\) are on \(d+1\) dimension, and there is \(n\) of them, we store them all in a 2-d matrix \(X\):
Each row in \(X\) is a row vector for each data point. Also note that we use uppercase letter for matrix.
3. Again, \(w\) is a column vector¶
Like the first point, our \(w\) will be \(d+1\) dimension column vector with w_0 as an intercept term:
4. Dot products of rows in matrix \(X\) with vector \(w\) is \(Xw\)¶
Sometimes we want the dot product of each row in matrix with a vector:
given that \(X\) contains rows of vectors we want to dot product with.
Interestingly, this gives us a column vector of our predictions \(\hat{y}\):
It’s also good to remind yourself that it sums along dimension of \(x^i\) and \(w\):
5. Sum of Squared is \(x^Tx\)¶
This is a useful pattern to memorize. Sometimes we want the sum of squared of each element in arbitrary d-dimension vector \(x\):
which is simply \(x^Tx\):
Notice that the result of \(x^Tx\) is scalar, e.g., a number.
In fancy term, \({\left\lVert x \right\rVert} = \sqrt{\sum_{j=1}^{d} x_i^2}\) is L2-norm (or Euclidean norm) of \(x\). So we can write sum of squared as \({\left\lVert x \right\rVert}^2 = \sum_{j=1}^{d} x_i^2\). For now, let’s not care what norm actually means.
Writing SSE Loss in Matrix Notation¶
Now you’re ready, let’s write the above SSE loss function in matrix notation. If you look at \(L(w)\) closely, it’s a sum of squared of vector \(y - \hat{y}\). This means we can kick-off by applying our fifth trick:
Next we just have to find \(y - \hat{y}\). First we encode target
\(y\) in a long column vector of shape [n, 1]
:
(Remember that we use superscript for indexing the \(i^{th}\) data point.)
Next we encode each of our predicted values (\(\hat{y}^i\)) in a column vector \(\hat{y}\). Since \(\hat{y}^i\) is a dot product between \(w\) and each of \(x^i\), we can apply 4:
Putting all together we get our loss function for linear regression:
In NumPy code, we can compute \(L(w) = (y - Xw)^T(y - Xw)\).
There’s no intuitive way to come up with this nice formula the first time you saw it. You have to work it out and put things together yourself. Then you’ll start to memorize the pattern and it’ll become easier.
Deriving a Closed-form Solution¶
To do that, we’ll take derivative of \(L(w)\) with respect to \(w\), set to zero and solve for \(w\).
Writing matrix notation is already hard, taking derivative of it is even harder. I recommend writing out partial derivatives to see what happens. For \(L(w) = L_w\), we have to take derivative with respect to each dimension of \(w\):
Looks like we might be able to apply our fourth point (\(Xw\), but in this case \(w\) is \((y - Xw)\). But unlike our fourth point, we now sum along data points (\(n\)) instead of dimensions (\(d\)). For this, we want each row of \(X\) to be one given dimension along all data points instead of one data point with all dimensions, and thus we use \(X^T\) instead of \(X\). Finally, here’s the full derivative in matrix notation:
Setting to zero and solve:
Move \(X^TX\) to other side and we get a closed-form solution:
In NumPy, this is:
w = np.linalg.inv(X.T @ X) @ X @ y
A NumPy Example¶
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
We will create a fake dataset from the underlying equation \(y = 2x + 7\):
def true_target(x):
return 2*x + 7
In practical settings, there is no way we know this exact equation. We only get observed targets, and there’s some noise on it. The reason is that it’s impossible to measure any data out there in the world perfectly:
def observed_target(x):
"""Underlying data with Gaussian noise added"""
normal_noise = np.random.normal() * 3
return true_target(x) + normal_noise
Creating data points¶
Next, make 50 data points, observations and targets:
N = 50
# Features, X is [1,50]
X = np.random.rand(N).reshape(N, 1) * 10
# Observed targets
y = np.array([observed_target(x) for x in X]).reshape(N, 1)
Adding dummy dimension term to each \(x^i\):
# Append 1 for intercept term later
X = np.hstack([np.ones((N, 1)), X])
Note that it doesn’t matter here whether we add it to the front or back, it will simply reflect correspondingly in our solution \(w\).
Visualize our data points with respect to the true line¶
# For plotting
features = X[:,1:] # exclude the intercept for plotting
target = y
true_targets = true_target(X[:,1:])
plt.scatter(features, target, s=10, label='Observed data points')
plt.plot(features, true_targets, c='blue', label='True target line y = 2x + 7', alpha=0.3)
plt.xlabel('Feature')
plt.ylabel('Target')
plt.legend(loc='best')
plt.title('True and observed data points')
plt.show()
Compute a closed-form solution¶
Our goal is to get the line that is closest to that true target (blue) line as possible, without the knowledge of its existence. For this we use linear regression to fit observed data points by following the formula from the previous section:
w = np.linalg.inv(X.T @ X) @ X.T @ y
To predict, we compute \(\hat{y} = xw\) for each data point
\(x^i\). Here we predict the training set (X
) itself:
predicted = X @ w # y_hat
To predict a set of new points, you just make it the same format as
X
, e.g., rows of data points.
Visualize best fit line vs. true target line¶
plt.scatter(features, target, s=10, label='Data points')
plt.plot(features, true_targets, c='blue', label='True target line', alpha=0.3)
plt.plot(features, predicted, c='red', label='Best fit line')
plt.xlabel('Feature')
plt.ylabel('Target')
plt.legend(loc='best')
plt.show()
That’s pretty close.
Understanding the result¶
And our \(w\) is:
print(w)
Out:
[[7.00426874]
[2.08162643]]
Since we append ones in front of each data point \(x\), w[0]
will be the intercept term and w[1]
will be the slope. So our
predicted line will be in the format of y = w[1]*x + w[0]
. Recall
the true equation \(y = 2x + 7\), you can see that we almost got
the true slope (2):
print("Our slope is", w[1][0])
Out:
Our slope is 2.081626431082087
The intercept seems a little off, but that’s okay because our data is in a big range (\(x \in [0, 50], y \in [7, 107]\)). If we normalize the data into \([0, 1]\) range, expect it to be much closer.
Below is our sum of squared error for the best fit line. Note that the number doesn’t mean anything much, apart from that this is the least possible loss we would get from any lines that try to fit the data:
diff = (y - X @ w)
loss = diff.T @ diff
print(loss)
Out:
[[368.25248566]]
If you don’t want intermediate variable, you can use np.linalg.norm
,
but to get the sum of squared loss, you have to square that after:
loss = np.linalg.norm(y - X @ w, ord=2) ** 2
print(loss)
Out:
368.252485661978
Visualize the loss surface¶
Let’s confirm that our solution is really the one with lowest loss by seeing the loss surface.
Our loss function \(L(w)\) depends on two dimensions of \(w\),
e.g., w[0]
and w[1]
. If we plot \(L(w)\) over possible
values of w[0]
and w[1]
, the minimum of \(L(w)\) should be
near w = [5.17,2.066]
, which is our solution.
To plot that out, first we have to create all possible values of w[0] and w[1] in a grid.
from mpl_toolkits.mplot3d import Axes3D
# Ranges of w0 and w1 to see, centering at the true line
spanning_radius = 10
w0range = np.arange(7-spanning_radius, 7+spanning_radius, 0.05)
w1range = np.arange(2-spanning_radius, 2+spanning_radius, 0.05)
w0grid, w1grid = np.meshgrid(w0range, w1range)
range_len = len(w0range)
print("Number of values in each axis:", range_len)
Out:
Number of values in each axis: 400
This means we’ll look into a total of 400*400 = 160,000 values of w
.
We have to calculate loss for each pair of w0, w1
:
# Make [w0, w1] in (2, 14400) shape
all_w0w1_values = np.hstack([w0grid.flatten()[:,None], w1grid.flatten()[:,None]]).T
# Compute all losses, reshape back to grid format
all_losses = (np.linalg.norm(y - (X @ all_w0w1_values), axis=0, ord=2) ** 2).reshape((range_len, range_len))
Then, we can plot the loss surface (with minimum at the red point):
fig = plt.figure(figsize=(10,6))
ax = fig.gca(projection='3d')
ax.plot_surface(w0grid, w1grid, all_losses, alpha=0.5, cmap='RdBu')
ax.contour(w0grid, w1grid, all_losses, offset=0, alpha=1, cmap='RdBu')
ax.scatter(w[0], w[1], loss, lw=3, c='red', s=100, label="Minimum point (5.9,2.2)")
ax.legend(loc='best')
ax.set_xlabel('w[0]')
ax.set_ylabel('w[1]')
ax.set_zlabel('L(w)')
ax.set_xticks(np.arange(7-spanning_radius, 7+spanning_radius, 2))
ax.set_yticks(np.arange(2-spanning_radius, 2+spanning_radius, 2))
ax.set_zticks([loss])
plt.show()
You can notice the bowl centers at the solution.
Using sklearn¶
Using sklearn
for linear regression is very simple (if you already
understand all the concepts above).
from sklearn.linear_model import LinearRegression
First we create the classifier clf
. If fit_intercept
is True
(default), then it adds the dummy ‘1’ to the X
. But we already did
that manually, so set it to False
here.
clf = LinearRegression(fit_intercept=False)
Then fit the data:
clf.fit(X,y)
Check the \(w\) learned, it’s the same as ours:
print(clf.coef_)
Out:
[[7.00426874 2.08162643]]
Previously we use X @ w
to predict data. For sklearn
we can use
clf.predict
:
predicted = clf.predict(X)
And the result is the same:
plt.figure()
plt.scatter(X[:,1:], y, s=10, label='Data points')
plt.plot(X[:,1:], true_targets, c='blue', label='True line', alpha=0.3)
plt.plot(X[:,1:], predicted, c='red', label='Best fit line')
plt.legend(loc='best')
plt.show()
Total running time of the script: ( 0 minutes 1.096 seconds)


Episode nine in our not-quite-daily serialisation of The Immense Duckpond Pamphlet, this time without any potato pictures
Like the novelist Samuel Richardson, Doctor Cack was an enthusiastic indexer of his own work. Even before he arrived at the House, he had been issuing his Bulletin Of Potato Science & Related Matters every quarter. The bulk of the contents he wrote himself, allowing the occasional interjection from Ruhugu, Moop, Trellis and the others. Only Jubble had been barred from its pages, because he was unhinged.
Every five years, Doctor Cack published, as a separate volume, a cumulative index to the Bulletin. His skills lay in thematic rather than purely alphabetic indexing. Indeed, Scridge has remarked that, like Prynne's Histrio-Mastix (1633), Doctor Cack's indices are often more readable than the texts from which they are eked.
Of the most recent edition of the Quinquennial Collection Of Instructive Sentiments, Maxims, Descriptions, Footnotes, Evasions, Queries & Accusations Contained In The Bulletin Of Potato Science & Related Matters, Digested Under Proper Heads, Scridge reported that he was “driven to hilarity” by the entry for Potato Cyst Eelworm, an infection which stunts and withers the crop, with haulm dying down prematurely and tubers the size of marbles resulting.
Of course, Scridge cannot always be trusted, for he is a deceitful toad. There are those who assert that he has never read a single word of Doctor Cack's majestic works, indeed that he has never read a single potato-related text in his entire sorry life.


Billionaire boom slows down as uncertainty hits the super-wealthy
by Sebastian McCarthyThe billionaire boom has grounded to a halt, as a year of political upheaval took its toll on the world’s super-wealthy.
A new report has found that billionaires’ wealth dipped by $388bn in 2018, following five years of growth.
Read more: Bovis Homes agrees £1.1bn deal for Galliford Try housing business
While the Americas saw a slight wealth increase this year, led by prominent US tech billionaires, in Asia the number of billionaires edged down after five years of sharp growth.
Women are also joining the ultra-rich ranks in greater numbers, according to the findings from UBS and PwC, with the number of female billionaires growing by 46 per cent in the last five years.
“The stronger dollar, combined with greater uncertainty in equity markets amidst a tough geopolitical environment, has created the conditions for this dip,” said Josef Stadler, head of ultra high net worth at UBS Global Wealth Management.
He added: “Nevertheless, it’s clear that billionaire businesses continue to thrive. Billionaires are creating and steering businesses that consistently outperform equity markets.
“This business acumen has also translated into their philanthropy, as billionaires seek new ways to engineer far-reaching environmental and social change.”
Read more: Credit Suisse profit doubles amid rise in wealth management business
Despite slowing growth last year, billionaire wealth is over a third higher (34.5 per cent) than five years earlier, amounting to an increase of $2.2 trillion.
Billionaire companies listed on the equity markets also outperformed the global benchmark by 8.7 percent in the 15 years to the end of 2018.


Daily
Weekly
Monthly
Compression:
Points on chart:
History offset (sec):
Knoydart Hydro Data
by
Knoydart Foundation
is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Based on a work at
http://knoydart.modulo.ee/


{% extends 'base_nonav.html' %} {% block head %}
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Ireland remind Eriksen of adultery issue
by BeSoccerIn the match against Denmark on Monday, some Ireland fans sang about a controversial incident between Eriksen, his wife, and Vertonghen.
A few months ago there was a rumour that Vertonghen had had a relationship or affair with Eriksen's wife.
This provoked controversy in Tottenham's dressing room and the Ireland fans reminded him of it in the match against Denmark.
Featured links
- Pochettino sacked as Tottenham manager
- The latest football transfers news and rumours
- Who has the most free-kicks goals: Messi or Cristiano?
- Luis Enrique returns to Spain national team
At one point in the match, a section of the stand chanted at Eriksen to distract him and put him off: "He's sha***ng your wife, Jan Vertonghen he's sh***ng your wife".
In the end, the match ended in a 1-1 draw with goals from Braithwaite and Doherty, the latter in the last 5 minutes of the match.


https://avwx.rest/api/<report type>/<station | lat,lon>
Note: The url parameters shown in the METAR and TAF examples can also be placed in the request header instead.
format: JSON (default), XML, or YAML
options: info, translate, summary (details below)
METAR reports are surface observations for a particular airfield or other station location. There are two standard formats: International and North American. The parser works with both, but it is up to the developer to do the correct interpretation of the data. Use the 'Units' key for labling and conversions.
The METAR API can be called using the station's ICAO or coordinates (lat,lon), in which case the API will return the station nearest to those coordinates. Additionally, the call can include the desired format and will return JSON if none is given.
Returns a JSON, XML, or YAML document with the following keys: Station, Time, Flight-Rules, Wind-Direction, Wind-Speed, Wind-Gust, Wind-Variable-Dir, Visibility, Runway-Visibility, Altimeter, Temperature, Dewpoint, Cloud-List, Other-List, Remarks, Raw-Report, Units
Translations with the following keys: Wind, Visibility, Clouds, Temperature, Dewpoint, Altimeter, Other
Ficticious METAR XML with all fields present
Look-up by ICAO with JSON format: http://avwx.rest/api/metar/KJFK
Look-up by coordinates with info and translate options: http://avwx.rest/api/metar/51.47,-0.4?options=info,translate
TAF reports are atmospheric conditions for an area within five nautical miles of a particular airfield or other station location. Unlike METAR which only has two standard formats, TAF reports vary considderably more based on their geography. For example, Oceania TAF reports include two unique keys: Temp-List and Alt-List.
Like METAR, you can look-up TAF data by station ICAO or coordinates. Format option also exists.
Returns a JSON, XML, or YAML document with the following keys: Station, Time, Forecast, Remarks, Min-Temp, Max-Temp, Units [, Temp-List, Alt-List (Oceania only)]
Forecast is a list of forecasts in the TAF report with the following keys: Type , Start-Time, End-Time, Wind-Direction, Wind-Speed, Wind-Gust, Wind-Shear, Visibility, Altimeter, Cloud-List, Icing-List, Turb-List, Other-List, Probability, Raw-Line
Translations with the following keys: Forecast, Min-Temp, Max-Temp. Forecast keys: Flight-Rules, Wind, Visibility, Clouds, Altimeter, Wind-Shear, Turbulance, Icing, Other
Ficticious TAF XML with all fields present
Look-up by ICAO: http://avwx.rest/api/taf/KJFK?options=summary
Look-up by coordinates with JSON format: http://avwx.rest/api/taf/51.47,-0.4?format=XML
info - Provides basic info for the requested station along with the report. Info is sourced and cross-referenced from several databases. Contains the following keys: ICAO, IATA, Country, State, City, Name, Elevation (meters), Latitude, Longitude, Priority (mapping zoom)
translate - Provides basic translations for most fields in the report. See below for the keys in each report.
Reports include a key for 'Units' which contains the units associated with each measurement. Contains the following keys: Wind-Speed, Visibility, Altitude, Temperature, Altimeter
summary - Includes a translation summary string for METAR and TAF forecasts. Doesn't require calling translation
https://avwx.rest/api/parse/<report type>
Attempts to parse a given METAR or TAF report using the AVWX engine. A "report" parameter must be supplied in either the url (with spaces replaced with "%20") or in the header as is.
All response keys, shared options, and response formats for the station/coord API are supported.
Example with URL parameter: https://avwx.rest/api/parse/metar?report=KJFK%20012251Z%2004012KT%2010SM%20OVC011%2016/13%20A3014&options=info,translate


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include "pieces/layout/breadcrumbs.html"
include "pieces/layout/left-column.html"
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About
Whitman College, a small, residential liberal arts college in Eastern Washington's Walla Walla, provides an education that goes beyond the classroom.
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Whitman College Office of Admission
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Student Life
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Main and most useful functions
The A Function
- Example
The B Function
- Example
The C Function
*Example
Etc.
*Example
forcats
🤓By now, we already know that data cleaning is important. We have realized that treating certain variables is not always so straightforward and we struggle because we know that this can impact our analysis. This is always the case when we find Categorical Data.
In this workshop we are going to show you how to treat Categorical Data using the forcats
tool. It will help you to work with factors.
What do we need?
forcats
in R.Note:
You can also use the forcats
tool if you have already installed the tidyverse package.
Or the development version from GutHub: devtools::install_github("tidyverse/forcats")
.
What will we cover in this workshop?
Categorical Data
What are factors?
Application: why are factors useful?
Incorporating factors into the concept of tidy data: Hello forcats
!
Main and most useful functions from forcats
The A Function
The B Function
The C Function
Etc
Factors with forcats
: Cheat sheet
Bibliography
What is a Categorical Variable?
“Careless data cleaning operations can lead to errors or inconsistencies in analysis” (McNamara et all, 2007)
Categorical variables have a fixed and known set of possible values.
They represent types of data that may be divided into groups.
Examples of categorical variables: race, age, group, educational level, income brackets.
What is the problem with categorical variables?
Wrangling categorical data can be difficult because many variables are often coded with numerical values.
Categorical variables are often stored with different values in a dataset. For instance, we need to differentiate category numbers and category labels. We need consistency to work with them properly. - How annoying! 😒
We need to know their true value not to miss important information.
A factor is an integer vector with levels attribute that stores a set of mappings between integers and categorical values.
Factors are R’s data structure for categorical data. In simpler words, R represents categorical data with factors.
R displays not the integers from a variable, but the levels associated with them.
Factors in the R language allow to treat character variables in a different way.
Factors could be easier to work with than characters.
Factors are helpful for reordering character vectors in non alphabetical order and to improve display.
They allow to inspect levels in a more detailed way.
Working with factors is a nicer alternative that working only with dummies.
forcats
! 🐱Nice Anagram, Hadley!
Hello forcats
!
The forcats
package provides useful tools to solve probelms with factors.
It is better to work with categorical data using tidyverse than with base R. We will see why!
*Example
*Example
forcats
:: Cheat Sheet


We believe in a society where the power of data belongs to the people.
We believe in a society where the power of data belongs to the people.
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