Latest Release
The latest stable release of Umbraco
v7.2.2
Bug fixing on the 7.2.2 release. Note: This is a scheduled patch release, it will be released on the specified date, not when 100% of the issues are fixed (we might pull in more).
UmbracoCms.7.2.2.zip
This is the main Umbraco download, generally you won't need anything else.
Downloaded 510 times - uploaded Friday, February 20, 2015UmbracoCms.AllBinaries.7.2.2.zip
Downloaded 510 times - uploaded Friday, February 20, 2015UmbracoCms.AllBinaries.7.2.2.zip
Downloaded 510 times - uploaded Friday, February 20, 2015UmbracoCms.AllBinaries.7.2.2.zip
Downloaded 510 times - uploaded Friday, February 20, 2015Summary
Bug fixing on the 7.2.1 release. Note: This is a scheduled patch release, it will be released on the specified date, not when 100% of the issues are fixed (we might pull in more).
Release notes
Read the release blog post.
Upgrading from 7.2.x/7.1.x
Follow the general upgrade guide.
Upgrading from 7.0.3/7.0.4
Follow the general upgrade guide.
Note that the only significant config file change is that the UmbracoMembershipProvider, UsersMembershipProvider and UmbracoRoleProvider have been updated in the web.config.
Please also remove the /Install folder if you haven't done so, it is no longer used and might conflict with the new upgrader if it's still there.
Upgrading from version 7.0.0/7.0.1./7.0.2/7.0.3
Follow the general upgrade guide AND the version specific upgrade guide.
Note that the only significant config file change is that the UmbracoMembershipProvider, UsersMembershipProvider and UmbracoRoleProvider have been updated in the web.config.
Please also remove the /Install folder if you haven't done so, it is no longer used and might conflict with the new upgrader if it's still there.
Upgrading from Version 6
This is a major version, there are therefore several things you must be aware of before attempting to upgrade a site.
Related changes from the issue tracker
Features
- U4-1736 Support element + class styles in TinyMCE
- U4-4358 Progress of upload in the Media Section
- U4-5918 Update Underscore.js
- U4-5940 Feature Request - Disable partial/macro caching when in debug mode
- U4-5973 Feature request: Casing of XSLT when saving the file
- U4-6071 Feature request: Add a specific section to all users
Breaking Changes
No items to display
Issues & Tasks
- U4-2038 Violation of PRIMARY KEY constraint 'PK_cmsContentPreviewXml' YSOD occurs when publishing content
- U4-2675 Create new controller service to output the json for localization for use in angular service
- U4-4049 Some images fail when uploading multiple media files
- U4-4093 Isn't drop zone supposed to replace click-to-upload area?
- U4-4158 Truncate strips out text between a href
- U4-4198 Related links editor does not update title property
- U4-4400 Make upload new media item button bit clearer
- U4-4418 UX: Tab sorting on document types
- U4-4802 Better visual feedback for large media uploads
- U4-5101 Property editors in Umbraco 7 have hardcoded, non-localizable text
- U4-5236 Change color of has-unpublished-version icon, when node is selected
- U4-5340 Backoffice user of type translator can't log in if they don't have access to Content section
- U4-5364 Email is invalid validation error - even not is set to required
- U4-5568 Numeric data type doesn't allow a zero entry
- U4-5658 Cannot launch dictionary overview report - dictionary tree JS: 'openDictionary' defined in Languages tree
- U4-5687 Fix issues with mini content editor that is launched from new Edit button in MNTP
- U4-5717 Member email validation does not allow valid domains (Umbraco 7.1.4)
- U4-5834 Change password description text is confusing
- U4-5843 Listview - empty result row doesn't have same number of columns as header
- U4-5852 After Updating Password using Dashboard, you are warned of unsaved changes.
- U4-5875 Prevent saving content/media if Mandatory validation fails for JSON property editors
- U4-5929 Can not add new domain to Culture and Hostnames dialog
- U4-5932 Grid editor has JS errors
- U4-5942 Issue with placing cursor in right part of richtext editor in Grid
- U4-5943 Cannot render a macro on a virtual page when using UmbracoVirtualNodeRouteHandler and assigning virtual Ids
- U4-5944 When image is inserted in richtext editor, alternative text for image is set to undefined
- U4-5959 Getting YSOD when wanting to create partial view in a subfolder in the backoffie
- U4-5961 UI inconsistency in the umbTreeDirective
- U4-5966 Move UI localization code to Core project
- U4-5974 User which is not administrator getting logged out when clicking recycle bin
- U4-5975 When editing and saving administrator user, username disapears in tree node
- U4-5976 When no Examine indexes exist they are rebuilt at startup, however the rebuilding happens too soon before any Examine event handlers are actually wired up.
- U4-5977 The debounce timeout on the main search is too big which produces a lag in searching
- U4-5981 Umbraco 7.2 Text encoding when saving partial view template
- U4-5993 Update Examine implementation to be able to sync index storage to local temp file system
- U4-5994 Global search needs to only show results for the 'last' search request made and ignore previous ones
- U4-5995 Update examine implementation to store indexes in machine specific folders
- U4-5996 CDF reference issue
- U4-6004 7.2.0 MediaService.GetMediaByPath returning null for paths containing both 'x' and '_'
- U4-6034 Umbraco 7.2.1 Renaming a tab in doctype editor will delete existing properties from that tab?
- U4-6044 Prevent google from indexing login page
- U4-6052 Umbraco 7.2.1 web app installed with Nuget fails at start with error :Cannot create an MVC Area path without the umbracoPath specified
- U4-6054 Unit test Umbraco.Tests.IO.AbstractFileSystemTests.Can_Get_Size() fails
- U4-6061 7.2.1, NodeCount for tags is not populated
- U4-6066 Cannot change the 'Property has errors' text during client side validation
- U4-6072 File upload, tags and color picker property editors do not perform client side validation when they are required
- U4-6073 Cannot edit items on macro containe
- U4-6080 Server side email validation doesn't occur on existing members when they are saved
- U4-6087 Fix Partial View macro repository usage so that when a macro is created from the FileService, the MacroService events are raised
- U4-6088 Publicize all partial view methods on IFileService
- U4-6093 ContentService.SaveAndPublishDo changes Culture
- U4-6096 Links in Translator emails incorrect
- U4-6103 Grid has style instead of ng-style
- U4-6106 Threading/Performance issue with MappingResolver and LazyManyObjectsResolverbase/ManyObjectsResolverBase
- U4-6111 GetGridHtml throws an exception if no grid value is available
- U4-6115 Add which user installs/uninstalls packages to the audit trail
- U4-6129 The Multiple Media Picker does not preserve the image order.
- U4-6133 Update builtin razor snippets to follow Query Builder conventions
- U4-6151 Datatype PreValues not updated in Datatype Saved Event
- U4-6154 Cannot unpublish document with changes
- U4-6165 Make Enum<T> public
- U4-6168 Custom Section is not showing correctly in 7.2 if custom tree is empty
- U4-6183 Change Document Type doesn't check for permitted parent node type
- U4-6184 Incorrect page served when request includes port number
- U4-6208 Cannot cancel grid 'Are you sure' button when deleting a cell
- U4-6209 GetGridHtml needs to be an extension on HtmlHelper not IPublishedContent so that ViewData and ModelState are inherited
- U4-6210 ExtensionMethodFinder doesn't work with overloaded extensions - this means overloaded extension methods for dynamic content won't work.
- U4-6219 Visual Studio 2013 Crashes When Opening umbraco.sln
- U4-6255 Add configurable option in base membership providers to toggle AllowManuallyChangingPassword
- U4-6257 The drop zone for uploading images is the entire browser window
- U4-6268 TreeController cannot exist in App_Code
- U4-6284 UmbracoCms.7.2.2-build.26 - missing feedback when file is too large
- U4-6288 RTE Toolbar within Grid inaccessible when adding long content
Getting started
Make sure to give the installation instructions a read.
Check the free foundation videos on how to get started building Umbraco sites. They're available from:
World's best community
You'll find our über active community working hard to help each other at Our Umbraco: https://our.umbraco.com
Upgrading from older versions
Make sure to read the upgrade instructions before you attempt an upgrade.
Found a bug?
We're happy to help you with any problems you encounter, so please report them in the issue tracker and use the “Affected versions” field to set it to 7.2.2.
Do a search first though, if the issue already exists, give it an extra vote.
Source code
The source for this specific release is available for download from the repository (click the "ZIP" button).


- Source:
Extends
- Set
Methods
add(value) → {SetWrap}
Wrapper for Set.prototype.add()
method.
Parameters:
| Name | Type | Description |
|---|---|---|
value |
* | Value to be added to the Set.
|
- Source:
Throws:
-
-
"Not all required parameters are supplied."
- Type
- SyntaxError
-
-
-
"Cannot utilize NULL primitives."
- Type
- TypeError
-
-
-
"Cannot utilize boolean primitives."
- Type
- TypeError
-
-
-
"Cannot utilize empty arrays."
- Type
- TypeError
-
-
-
"Cannot utilize empty objects."
- Type
- TypeError
-
Returns:
Mimicks the super method by allowing call chaining.
- Type
- SetWrap
setSelector(content, constructorNameopt) → {*|null}
Searches for the first match of an element, specified by parameters.
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
content |
* |
Only supplied content will be used throughout the search,
which may lead to multiple matches. |
|
constructorName |
String |
<optional> |
A name
of a class / constructor function
of an object being searched. |
- Source:
Throws:
-
-
content
isundefined
.
- Type
- SyntaxError
-
-
-
content
is an empty object.content
is an empty array.content
is aNull
primitive.
- Type
- TypeError
-
Returns:
- Element in Set that matched,
Null
of no element matched.
- Type
- * | null
Examples
setSelector({name "testRoutine", timesPerDay: 5}, "Routine")
setSelector({prop1: true, prop2: []})


Welcome to Flower seller, The place Where you find the best Flowers and gifts ready for delivery directly to your door!.
DESIGN YOUR OWN Flowers & Gift 🎕
Select a box, flowers, and get ready to make someone's day extra special with your gift!
150+
Products
Products
50+
Events Done
Events Done
89+
Happy Clients
Happy Clients
2
Branches
Branches
Flower seller was started in 2021 in (). Then we decided to spread
the joy to ().
Shop at one of our flowers Stores Near You!
- ▼ jadah - KSA
- ☎<PHONE_NUMBER>
- ✉<EMAIL_ADDRESS>- 🕗 Open hours (8:00AM - 10:00 PM)
- ▼ jadah - KSA
- ☎<PHONE_NUMBER>
- ✉<EMAIL_ADDRESS>- 🕗 Open hours (8:00AM - 10:00 PM)


❌Oh no! It's an Error - 404❌
An error has occured 😔, to continue:
* Return to our homepage.
* Send us an e-mail about this error and try later.
An error has occured 😔, to continue:
* Return to our homepage.
* Send us an e-mail about this error and try later.


Flask App with MQTT
This is an example of a Flask Application. Check out their documentation to learn more about flask. Based on the flask sample by digitalocean.
This is an example of a Flask Application. Check out their documentation to learn more about flask. Based on the flask sample by digitalocean.


Details
A casual virtual weekly chat that open to everyone and anyone. A place to ask questions, hear different view points, and, most importantly, get to know your fellow participants.
Every week there is a new initial software development topic posted on Dev Edmonton Slack to get the discussion started:
TODO - Link to join the slack...
Sometimes we discus the initial topic the entire chat, other times the topic changes several times through the natural flow of the conversation. If you have a initial topic suggestion please post it to the #meetup-weekly-chat channel
Please contact me if you have any suggestions to improve the chat or have any trouble connecting. Hope to see you at the chat.
P.S. - Zoom link for chat is under the Online Event section of this Meetup event and is visible once you register. The Zoom link is also posted on the Dev Edmonton Slack.


Tik Tok is a Javascript tool to easily create beautiful, simple, mobile-friendly, vertical timelines.
HTML used in this example. Note that the inner loading <div> is not necessary but a nice note while things are loading.
<div>
CSS used in this example.
Javascript used in this example:


--- title: Saved by Dangling Blobs author: John Kane date: '2017-12-11' slug: saved-by-dangling-blobs categories: - git - version-control tags: - git - version-control ---
import numpy as np
np.random.randn(10)
perhaps a google search result for someone who discovers they did the same thing I did and hope that they may be able to recover them.
For most of 2017 I have worked on a project and stored the files locally, not doing version control with git. As the project matured I realized I would soon need to share the code and to make use of all git has to offer, I should connect it to a repository. The order in which I did it mattered.
Let’s illustrate with an example.
Let’s say you have some files in a directory
image of files via ls
cd /home/john
mkdir sample-dir
cd sample-dir
echo 'this is file1' > file1.txt
echo 'this is file2' > file2.txt
ls -l
echo ' '
echo 'now print the contents of the files'
cat file1.txt
cat file2.txt
## mkdir: cannot create directory ‘sample-dir’: File exists
## total 16
## -rw-rw-r-- 1 john john 14 Feb 11 19:38 file1.txt
## -rw-rw-r-- 1 john john 14 Feb 11 19:38 file2.txt
## -rw-rw-r-- 1 john john 14 Jan 14 18:29 file3.txt
## -rw-rw-r-- 1 john john 13 Jan 14 18:30 README.md
##
## now print the contents of the files
## this is file1
## this is file2
So in this scenario we start to think that it would be nice to connect these files to a github repository.
Commit, commit, commit!
As stated in a Stack Overflow post anything that is committed can be
Another realization is that when it is appropriate to commit.
Should you: - commit every time you edit/save a file? that seems excessive. - commit every time you make a major change? that seems to infrequent
When to push commits? - tempted to push every day to see and show off that you “worked” that day
These are questions that likely have “good” or at least moderately well accepted answers in software development circles. Likely data science circles as well. But this is all very new to me (which is hopefully apparent by the fact that I nearly lost six months of work by attempting to use git).


{% extends "base.html" %} {% block page_content %}
product image
Navn
Pris
Antal
{% for product in products %}
product image
{{ product.name }}
{{ product.salesPrice }} kr
{% if product.amount >= 0 %}
{% else %}
SOLD OUT!!!!!!!
{% endif %} {% endfor %}
Buy
You are buying the products right now.
Make sure that it is the right product before buying.
{% endblock %}


This course aims at giving you an overview of important numerical methods for solving quantum mechanical problems. In particular, in this course we will focus on
While developing the mathematical tools and a numerical project we aim at giving you an insight in how to:
We hope we can give you some useful hints and guidance on
With computing I will mean solving scientific problems using computers. It covers numerical, analytical as well as symbolic computing. Computing is also about developing an understanding of the scientific process by enhancing algorithmic thinking when solving problems.
Computing competence has always been a central part of the science and engineering education. Traditionally, such competence meant mastering mathematical methods to solve science problems - by pen and paper.
Today you are expected to use all available tools to solve scientific problems; computers primarily, but also pen and paper.
I will use the term/word algorithms in the broad meaning: methods (for example mathematical) to solve science problems, with and without computers.
Computing competence is about
Algorithms involving pen and paper are traditionally aimed at what we often refer to as continuous models.
Application of computers calls for approximate discrete models.
Much of the development of methods for continuous models are now being replaced by methods for discrete models in science and industry, simply because much larger problem classes can be addressed with discrete models, often also by simpler and more generic methodologies. However, verification of algorithms and understanding their limitations requires much of the classical knowledge about continuous models.
The power of the scientific method lies in identifying a given problem as a special case of an abstract class of problems, identifying general solution methods for this class of problems, and applying a general method to the specific problem (applying means, in the case of computing, calculations by pen and paper, symbolic computing, or numerical computing by ready-made and/or self-written software). This generic view on problems and methods is particularly important for understanding how to apply available, generic software to solve a particular problem.
Computing competence represents a central element in scientific problem solving, from basic education and research to essentially almost all advanced problems in modern societies. Computing competence is simply central to further progress. It enlarges the body of tools available to students and scientists beyond classical tools and allows for a more generic handling of problems. Focusing on algorithmic aspects results in deeper insights about scientific problems.
Today's project in science and industry tend to involve larger teams. Tools for reliable collaboration must therefore be mastered (e.g., version control systems, automated computer experiments for reproducibility, software and method documentation).
In this course we will try to focus on how to
The following simple example here illustrates many of the previous points. Python offers an extremely versatile programming environment, allowing for the inclusion of analytical studies in a numerical program. Here we show an example code with the trapezoidal rule using SymPy to evaluate an integral and compute the absolute error with respect to the numerically evaluated one of the integral \( 4\int_0^1 dx/(1+x^2) = \pi \):
The following extended version of the trapezoidal rule allows you to plot the relative error by comparing with the exact result. By increasing to \( 10^8 \) points one arrives at a region where numerical errors start to accumulate.
The last example shows the potential of combining numerical algorithms with symbolic calculations, allowing you thereby to
In this process we easily bake in
The conventions and techniques outlined here will save you a lot of time when you incrementally extend software over time from simpler to more complicated problems. In particular, you will benefit from many good habits:
If you wish to use C++ or Fortran, we recommend normally
python -v
Additional packages may have to be installed. For OSX users I highly recommend installing python and additional packages using brew. This makes it extremely simple to add packages and install additional software. With brew there is no point for OSX users to use virtual machines.
python nameofprog.py
If you use C++ or Fortran without an IDE, you would compile as
c++ -c mycode.cpp
c++ -o mycode.exe mycode.o
For Fortran replace with for example gfortran or ifort. This is what we call a flat compiler option and should be used when we develop the code. It produces normally a very large and slow code when translated into machine instructions. We use this option for debugging and for establishing the correct program output because every operation is done precisely as the user specified it.
It is instructive to look up the compiler manual for further instructions by writing for example
man c++
We have additional compiler options for optimization. These may include procedure inlining where performance may be improved, moving constants inside loops outside the loop, identify potential parallelism, include automatic vectorization or replace a division with a reciprocal and a multiplication if this speeds up the code.
c++ -O3 -c mycode.cpp
c++ -O3 -o mycode.exe mycode.o
This (other options are -O2 or -Ofast) is the recommended option.
It is also useful to profile your program under the development stage. You would then compile with
c++ -pg -O3 -c mycode.cpp
c++ -pg -O3 -o mycode.exe mycode.o
After you have run the code you can obtain the profiling information via
gprof mycode.exe > ProfileOutput
When you have profiled properly your code, you must take out this option as it slows down performance. For memory tests use valgrind. An excellent environment for all these aspects, and much more, is Qt creator.
Adding debugging options is a very useful alternative under the development stage of a program. You would then compile with
c++ -g -O0 -c mycode.cpp
c++ -g -O0 -o mycode.exe mycode.o
This option generates debugging information allowing you to trace for example if an array is properly allocated. Some compilers work best with the no optimization option -O0.
Depending on the compiler, one can add flags which generate code that catches integer overflow errors. The flag -ftrapv does this for the CLANG compiler on OS X operating systems.
To install MPI is rather easy on hardware running unix/linux as operating systems, follow simply the instructions from the OpenMPI website. See also subsequent slides. When you have made sure you have installed MPI on your PC/laptop,
# Compile and link
mpic++ -O3 -o nameofprog.x nameofprog.cpp
# run code with for example 8 processes using mpirun/mpiexec
mpiexec -n 8 ./nameofprog.x
If you wish to install MPI and OpenMP on your laptop/PC, we recommend the following:
brew install clang-omp
For linux/ubuntu users, you need to install two packages (alternatively use the synaptic package manager)
sudo apt-get install libopenmpi-dev
sudo apt-get install openmpi-bin
For OS X users, install brew (after having installed xcode and gcc, needed for the gfortran compiler of openmpi) and then install with brew
brew install openmpi
When running an executable (code.x), run as
mpirun -n 10 ./code.x
where we indicate that we want the number of processes to be 10.
With openmpi installed, when using Qt, add to your .pro file the instructions here
You may need to tell Qt where openmpi is stored.
We have an \( N\times N \) matrix A with \( N=100 \) In C/C++ this would be defined as
int N = 100;
double A[100][100];
// initialize all elements to zero
for(i=0 ; i < N ; i++) {
for(j=0 ; j < N ; j++) {
A[i][j] = 0.0;
Note the way the matrix is organized, row-major order.
We have \( N\times N \) matrices A, B and C and we wish to evaluate \( A=B+C \). $$ \mathbf{A}= \mathbf{B}\pm\mathbf{C} \Longrightarrow a_{ij} = b_{ij}\pm c_{ij}, $$ In C/C++ this would be coded like
for(i=0 ; i < N ; i++) {
for(j=0 ; j < N ; j++) {
a[i][j] = b[i][j]+c[i][j]
We have \( N\times N \) matrices A, B and C and we wish to evaluate \( A=BC \). $$ \mathbf{A}=\mathbf{BC} \Longrightarrow a_{ij} = \sum_{k=1}^{n} b_{ik}c_{kj}, $$ In C/C++ this would be coded like
for(i=0 ; i < N ; i++) {
for(j=0 ; j < N ; j++) {
for(k=0 ; k < N ; k++) {
a[i][j]+=b[i][k]*c[k][j];
ALLOCATE (a(N,N), b(N,N), c(N,N))
DO j=1, N
DO i=1, N
a(i,j)=b(i,j)+c(i,j)
ENDDO
ENDDO
...
DEALLOCATE(a,b,c)
Fortran 90 writes the above statements in a much simpler way
a=b+c
Multiplication
a=MATMUL(b,c)
Fortran contains also the intrinsic functions TRANSPOSE and CONJUGATE.
At least three possibilities in this course
int N;
double ** A;
A = new double*[N]
for ( i = 0; i < N; i++)
A[i] = new double[N];
Always free space when you don't need an array anymore.
for ( i = 0; i < N; i++)
delete[] A[i];
delete[] A;
#include <iostream>
#include <armadillo>
using namespace std;
using namespace arma;
int main(int argc, char** argv)
{
mat A = randu<mat>(5,5);
mat B = randu<mat>(5,5);
cout << A*B << endl;
return 0;
For people using Ubuntu, Debian, Linux Mint, simply go to the synaptic package manager and install armadillo from there. You may have to install Lapack as well. For Mac and Windows users, follow the instructions from the webpage http://arma.sourceforge.net. For Mac users we strongly recommend using brew.
To compile, use for example
c++ -O2 -o program.x program.cpp -larmadillo -llapack -lblas
where the -l
option indicates the library you wish to link to.
#include <iostream>
#include "armadillo"
using namespace arma;
using namespace std;
int main(int argc, char** argv)
{
// directly specify the matrix size (elements are uninitialised)
mat A(2,3);
// .n_rows = number of rows (read only)
// .n_cols = number of columns (read only)
cout << "A.n_rows = " << A.n_rows << endl;
cout << "A.n_cols = " << A.n_cols << endl;
// directly access an element (indexing starts at 0)
A(1,2) = 456.0;
A.print("A:");
// scalars are treated as a 1x1 matrix,
// hence the code below will set A to have a size of 1x1
A = 5.0;
A.print("A:");
// if you want a matrix with all elements set to a particular value
// the .fill() member function can be used
A.set_size(3,3);
A.fill(5.0); A.print("A:");
mat B;
// endr indicates "end of row"
B << 0.555950 << 0.274690 << 0.540605 << 0.798938 << endr
<< 0.108929 << 0.830123 << 0.891726 << 0.895283 << endr
<< 0.948014 << 0.973234 << 0.216504 << 0.883152 << endr
<< 0.023787 << 0.675382 << 0.231751 << 0.450332 << endr;
// print to the cout stream
// with an optional string before the contents of the matrix
B.print("B:");
// the << operator can also be used to print the matrix
// to an arbitrary stream (cout in this case)
cout << "B:" << endl << B << endl;
// save to disk
B.save("B.txt", raw_ascii);
// load from disk
mat C;
C.load("B.txt");
C += 2.0 * B;
C.print("C:");
// submatrix types:
//
// .submat(first_row, first_column, last_row, last_column)
// .row(row_number)
// .col(column_number)
// .cols(first_column, last_column)
// .rows(first_row, last_row)
cout << "C.submat(0,0,3,1) =" << endl;
cout << C.submat(0,0,3,1) << endl;
// generate the identity matrix
mat D = eye<mat>(4,4);
D.submat(0,0,3,1) = C.cols(1,2);
D.print("D:");
// transpose
cout << "trans(B) =" << endl;
cout << trans(B) << endl;
// maximum from each column (traverse along rows)
cout << "max(B) =" << endl;
cout << max(B) << endl;
// maximum from each row (traverse along columns)
cout << "max(B,1) =" << endl;
cout << max(B,1) << endl;
// maximum value in B
cout << "max(max(B)) = " << max(max(B)) << endl;
// sum of each column (traverse along rows)
cout << "sum(B) =" << endl;
cout << sum(B) << endl;
// sum of each row (traverse along columns)
cout << "sum(B,1) =" << endl;
cout << sum(B,1) << endl;
// sum of all elements
cout << "sum(sum(B)) = " << sum(sum(B)) << endl;
cout << "accu(B) = " << accu(B) << endl;
// trace = sum along diagonal
cout << "trace(B) = " << trace(B) << endl;
// random matrix -- values are uniformly distributed in the [0,1] interval
mat E = randu<mat>(4,4);
E.print("E:");
// row vectors are treated like a matrix with one row
rowvec r;
r << 0.59499 << 0.88807 << 0.88532 << 0.19968;
r.print("r:");
// column vectors are treated like a matrix with one column
colvec q;
q << 0.81114 << 0.06256 << 0.95989 << 0.73628;
q.print("q:");
// dot or inner product
cout << "as_scalar(r*q) = " << as_scalar(r*q) << endl;
// outer product
cout << "q*r =" << endl;
cout << q*r << endl;
// sum of three matrices (no temporary matrices are created)
mat F = B + C + D;
F.print("F:");
return 0;
#include <iostream>
#include "armadillo"
using namespace arma;
using namespace std;
int main(int argc, char** argv)
{
cout << "Armadillo version: " << arma_version::as_string() << endl;
mat A;
A << 0.165300 << 0.454037 << 0.995795 << 0.124098 << 0.047084 << endr
<< 0.688782 << 0.036549 << 0.552848 << 0.937664 << 0.866401 << endr
<< 0.348740 << 0.479388 << 0.506228 << 0.145673 << 0.491547 << endr
<< 0.148678 << 0.682258 << 0.571154 << 0.874724 << 0.444632 << endr
<< 0.245726 << 0.595218 << 0.409327 << 0.367827 << 0.385736 << endr;
A.print("A =");
// determinant
cout << "det(A) = " << det(A) << endl;
// inverse
cout << "inv(A) = " << endl << inv(A) << endl;
double k = 1.23;
mat B = randu<mat>(5,5);
mat C = randu<mat>(5,5);
rowvec r = randu<rowvec>(5);
colvec q = randu<colvec>(5);
// examples of some expressions
// for which optimised implementations exist
// optimised implementation of a trinary expression
// that results in a scalar
cout << "as_scalar( r*inv(diagmat(B))*q ) = ";
cout << as_scalar( r*inv(diagmat(B))*q ) << endl;
// example of an expression which is optimised
// as a call to the dgemm() function in BLAS:
cout << "k*trans(B)*C = " << endl << k*trans(B)*C;
return 0;


Truth Table Generator
This is a truth table generator for propositional logic formulas.
An example of a propositional formula would be
p ∧ q → ¬r which could also be written as
p ^ q -> ~r, as
p and q => not r, or as
p && q -> !r.
You can also enter an 'or' connective, ⊤, ⊥, and double implications.


to
All day event:
Description:
{evtdesc}
URL:
+
Location:
Contact Name:
Contact Email:
Contact Phone:
Organizer Name:
Organizer Email:
Organizer Phone:
Status:
Create Event
Update Event
Delete Event
Cancel
Cancel


| Reference Camera Path | |
| New Path Name: | |
| Length (sec): | |
| To use this utility, first create a reference camera path with a keyframe specifying the view at the beginning of the zoom and a keyframe at the end of the zoom. Enter a name for the new path and the total time in STK the zoom should take. |


{% extends 'animes/base.html' %} {% load staticfiles %} {% block title %} - Access Denied {% endblock %} {% block navbar %} {% include "navbar.html" with active='access_denied' %} {% endblock %} {% block content %}
Only authenticated users are allowed to access this content. Please sign in if you are authenticated or return to browsing the animes.


Create Stunning Email Campaigns
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Launch campaigns but also find new customers. Our unique platform handles campaigns from start to end.
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New Gilt Product Feature: “Fit Predictor”
Based on the feedback we receive from Gilt customers, we know that fit and sizing varies greatly between brands–which can make finding the right size very challenging. We always want our customers to feel great about their purchases and confident that an item will fit. With this in mind, we are excited to announce the launch of Fit Predictor: a new technology from our partners at Secret Sauce.
With Fit Predictor, simply tell us which sizes fit you best in some (or all) of your favorite brands. Based on the stats you provide, and the items from your fave brands that you select, we will calculate and recommend the right sizes for you. Here’s how it works:
-
While checking out select items on Gilt, you will notice the Fit Predictor above the size selections:
-
Click “calculate your size” to choose a brand that you know, an item type, and the size that fits you:
-
Based on your input, Fit Predictor will recommend the size that will best fit you for the item you are currently viewing:
-
The more sizing information about your favorite brands that you share with Fit Predictor, the better predictions we can make. Over time, build up your fit profile and receive the most accurate recommendations:
-
With a Fit Profile, every time you navigate to a product that is enabled by Fit Predictor, we will automatically suggest the best size for you:
We will be working hard to roll out Fit Predictor to as many items as possible, so you can always take advantage of its magic!
We launched Fit Predictor as part of our mission to provide the best shopping experience for our members. Let us know what you think of this and other new features by emailing us at productfeedback@gilt.com!


{% load icon %}
{% block console_css %}
{% endblock console_css %}
{% block console_body %}
{% include "console/components/links/breadcrumbs.html" with
base_page="Analytes"
base_page_url="/analytes"
current_page=id
%}
{% if id and id != 'new' %}
{% include "console/components/forms/form_tabs.html" with
id=id
model="analytes"
%}
{% endif %}
{% if unit == 'files' %}
{% include "console/pages/settings/files/files.html" with
model='analytes'
model_singular='analyte'
organizations=organizations
section=section
user=user
%}
{% elif unit == 'logs' %}
{% include "console/pages/settings/logs/logs.html" with
model='analytes'
model_singular='analyte'
organizations=organizations
section=section
user=user
%}
{% else %}
{% include "console/components/forms/form_title.html" with
abbreviation=data_model.abbreviation
id=id
model='analytes'
model_singular='analyte'
description="Manage analyte details to prepare the analyte for analysis."
options=True
export=id
%}
{% if id and id != 'new' %}
{% include "console/components/forms/delete_option.html" with
id=id
model="analytes"
model_singular="analyte"
%}
{% endif %}
{% endif %}
{% endblock console_body %}
{% block console_js %}
{% endblock console_js %}


| Concept Framework 2.2 documentation | Contents | Index |
| Name | Version | Deprecated |
| fseek | version 1.0 | no |
| number fseek(number fd, number offset, number origin) |
|
The fseek function moves the file pointer (if any) associated with fd to a new location that is offset bytes from origin. The next operation on the file takes place at the new location.
The argument origin must be one of the following constants: SEEK_CUR Current position of file pointer. SEEK_END End of file. SEEK_SET Beginning of file. For files opened in text mode, fseek has limited use, because carriage return-linefeed translations can cause fseek to produce unexpected results. The only fseek operations guaranteed to work on streams opened in text mode are: Seeking with an offset of 0 relative to any of the origin values. Seeking from the beginning of the file with an offset value returned from a call to ftell. Also in text mode, CTRL+Z is interpreted as an end-of-file character on input. In files opened for reading/writing, fopen and all related routines check for a CTRL+Z at the end of the file and remove it if possible. This is done because using fseek and ftell to move within a file that ends with a CTRL+Z may cause fseek to behave improperly near the end of the file. |
import standard.C.io import standard.C.string define FILENAME "test.txt" class Main { function Main() { var fd; var result; var buffer = ""; // Open file for read and write if (fd = fopen(FILENAME, "w+")) { buffer = "The fseek begins here: This is the file 'fseek.out'.\n"; fwrite(buffer, 1, strlen(buffer), fd); result = fseek(fd, 23, SEEK_SET); if (result) echo "Fseek failed\n"; else { buffer = ""; echo "File pointer is set to the middle of the file\n"; fgets(buffer, 30, fd); echo buffer; } fclose(fd); } else echo "Problem opening the file\n"; } } Results File pointer is set to the middle of the file This is the file 'fseek.out'. |
| Documented by Simona Plesuvu, generation time: Sun Jan 27 18:15:15 2013 GMT | (c)2013 Devronium Applications |


library(iotables)
library(dplyr, quietly = T)
library(tidyr, quietly = T)
This introduction shows the reproducible workflow of iotables with the examples of the Eurostat Manual of Supply, Use and Input-Output Tables. by Joerg Beutel (Eurostat Manual).
This vignette uses library(tidyverse)
, more particularly
dplyr
and tidyr
, just like all analytical
functions of iotables
. Even if you do not use
tidyverse
, this packages will be installed together with
iotables
. These functions are only used in the vignette to
print the output, and they are not essential for the examples.
The germany_1995
dataset is a simplified 6x6 sized SIOT
taken from the Eurostat
Manual of Supply, Use and Input-Output Tables (page 481). It is
brought to a long form similar to the Eurostat bulk files. The testthat
infrastructure of the iotables package is checking data processing
functions and analytical calculations against these published
results.
The following data processing functions select and order the data
from the Eurostat-type bulk file. Since the first version of this
package, Eurostat moved all SIOT data products to the ESA2010
vocabulary, but the manual still follows the ESA95 vocabulary. The
labels of the dataset were slightly changed to match the current
metadata names. The changes are minor and self-evident, the comparison
of the germany_1995
dataset and the Manual should cause no
misunderstandings.
<- iotable_get( labelling = "iotables" )
germany_io <- input_flow_get (
input_flow data_table = germany_io,
households = FALSE)
<- primary_input_get ( germany_io, "output" )
de_output print (de_output[c(1:4)])
#> iotables_row agriculture_group industry_group construction
#> 15 output 43910 1079446 245606
The input_flow()
function selects the first quadrant,
often called as input flow matrix, or inter-industry matrix, from the
German input-output table. The primary_input_get()
selects
on of the primary inputs, in this case, the output from the table.
The input coefficient matrix shows what happens in the whole domestic economy when an industry is facing additional demand, and it increases production. In the Germany example, all results are rounded to 4 digits for easier comparison with the Eurostat manual.
The input coefficients for domestic intermediates are defined in the Eurostat Manual of Supply, Use and Input-Output Tables on page 486. You can check the following results against Table 15.8 of the Eurostat manual. (Only the top-right corner of the resulting input coefficient matrix is printed for readability.)
The input_coefficient_matrix_create()
function relies on
the following equation. The numbering of the equations is the numbering
of the Eurostat Manual.
It checks the correct ordering of columns, and furthermore it fills up 0 values with 0.000001 to avoid division with zero.
<- input_coefficient_matrix_create(
de_input_coeff data_table = germany_io,
digits = 4)
## which is equivalent to:
<- coefficient_matrix_create(
de_input_coeff data_table = germany_io,
total = "output",
return_part = "products",
households = FALSE,
digits = 4)
print (de_input_coeff[1:3, 1:3])
#> iotables_row agriculture_group industry_group
#> 1 agriculture_group 0.0258 0.0236
#> 2 industry_group 0.1806 0.2822
#> 3 construction 0.0097 0.0068
These results are identical after similar rounding to the Table 15.6 of the Manual (on page 485.)
Similarly, the output coefficient matrix is defined in the following way:
= output coefficient for domestic goods and services (i = 1, …, 6; j = 1, …, 6) \eqn{x_{ij}= flow of commodity i to sector j \eqn{x_{i} = output of sector i
#> iotables_row agriculture_group industry_group
#> 1 agriculture_group 0.0258 0.5803
#> 2 industry_group 0.0073 0.2822
#> 3 construction 0.0017 0.0299
These results are identical after similar rounding to the Table 15.7 of the Eurostat Manual of Supply, Use and Input-Output Tables on page 485. The diagonal values are the same in the input coefficient matrix and the output coefficient matrix.
The Leontief matrix is derived from Leontief equation system.
The Leontief matrix is defined as and it is created with the
leontief_matrix_create()
function.
The Leontief inverse is and it is created with the
leontief_inverse_create()
function from the
Leontief-matrix.
<- leontief_matrix_create (
L_de technology_coefficients_matrix = de_input_coeff
)<- leontief_inverse_create(de_input_coeff)
I_de <- leontief_inverse_create(technology_coefficients_matrix=de_input_coeff,
I_de_4 digits = 4)
print (I_de_4[,1:3])
#> iotables_row agriculture_group industry_group
#> 1 industry_group 1.4155 0.5605
#> 2 construction 0.0441 1.0462
#> 3 trade_group 0.2180 0.2292
#> 4 business_services_group 0.2892 0.3237
#> 5 other_services_group 0.1089 0.1014
#> 6 total 1.1152 1.3025
You can check the Leontief matrix against Table 15.9 on page 487 of the Eurostat Manual, and the Leontief inverse against Table 15.10 on page 488. The ordering of the industries is different in the manual.
Technical indicators assume constant returns to scale and fixed relationship of all inputs to each industry. With these conditions the technical input coefficients show how much input products, labour or capital is required to produce a unit of industry output.
The helper function primary_input_get()
selects a row
from the SIOT and brings it to a conforming form. The
input_indicator_create()
creates the vector of technical
input coefficients.
<- primary_input_get(germany_io,
de_emp primary_input = "employment_domestic_total")
<- input_indicator_create(
de_emp_indicator data_table = germany_io,
input_row = "employment_domestic_total")
vector_transpose_longer(de_emp_indicator)
#> # A tibble: 6 × 2
#> nace_r2 value
#> <chr> <dbl>
#> 1 agriculture_group 0.0250
#> 2 industry_group 0.00776
#> 3 construction 0.0132
#> 4 trade_group 0.0171
#> 5 business_services_group 0.00615
#> 6 other_services_group 0.0201
Often we want to analyse the effect of growing domestic demand on some natural units, such as employment or \(CO_2\) emissions. The only difficulty is that we need data that is aggregated / disaggregated precisely with the same industry breakup as our SIOT table.
European employment statistics have greater detail than our tables, so employment statistics must be aggregated to conform the 60 (61, 62) columns of the SIOT. There is a difference in the columns based on how national statistics offices treat imputed real estate income and household production, and trade margins. Czech SIOTs are smaller than most SIOTs because they do not have these columns and rows.
In another vignette we will show examples on how to work with these real-life data. For the sake of following the calculations, we are continuing with the simplified 1990 German data.
The input coefficients for value added are created with
input_indicator_create()
.
<- primary_input_get ( germany_io,
de_gva primary_input = "gva")
<- input_indicator_create(
de_gva_indicator data_table = germany_io,
input_row = "gva")
vector_transpose_longer(de_gva_indicator)
#> # A tibble: 6 × 2
#> nace_r2 value
#> <chr> <dbl>
#> 1 agriculture_group 0.493
#> 2 industry_group 0.366
#> 3 construction 0.471
#> 4 trade_group 0.577
#> 5 business_services_group 0.600
#> 6 other_services_group 0.717
This is equal to the equation on page 495 of the Eurostat Manual of Supply, Use and Input-Output Tables. The results above can be checked on the bottom of page 498.
You can create a matrix of input indicators, or direct effects on
(final) demand with direct_supply_effects_create()
. The
function by default creates input requirements for final demand. With
the code below it re-creates the Table 15.14 of the Eurostat
Manual.
<- coefficient_matrix_create(
direct_effects_de data_table = germany_io,
total = 'output',
return_part = 'primary_inputs')
1:6,1:4]
direct_effects_de[#> iotables_row agriculture_group industry_group construction
#> 7 total 0.41528126 0.482855094 0.468258104
#> 8 imports 0.06665908 0.145169837 0.054668860
#> 9 intermediate_consumption 0.50662719 0.634051171 0.529229742
#> 10 compensation_employees 0.21366431 0.274644586 0.320916427
#> 11 net_tax_production -0.04582100 0.001349766 0.003920914
#> 12 consumption_fixed_capital 0.17925302 0.059075674 0.023859352
The ‘total’ row above is labelled as Domestic goods and services in the Eurostat Manual. The table can be found on page 498.
The SIOTs contain (with various breakups) three types of income:
Employee wages, which is usually a proxy for all household income.
Gross operating surplus, which is a form of corporate sector income.
Taxes that are the income of government.
These together make gross value added (GVA). If you are working with SIOTs that use basic prices, then GVA = GDP at producers’ prices, or basic prices.
The GVA multiplier shows the additional gross value created in the economy if demand for the industry products is growing with unity. The wage multiplier (not shown here) shows the increase in household income.
The following equation is used to work with different income effects and multipliers:
B = vector of input coefficients for wages or GVA or taxes.
Z = direct and indirect requirements for wages (or other income)
The indicator shows that manufacturing has the lowest, and other services has the highest gross value added component. This is hardly surprising, because manufacturing needs a lot of materials and imported components. When the demand for manufacturing in the domestic economy is growing by 1 unit, the gross value added is 0.3659488.
You can check these values against the Table 15.16 of the Eurostat Manual of Supply, Use and Input-Output Tables on page 501 (row 10).
You can recreate the whole matrix, when the data data permits, with
input_multipliers_create()
as shown here. Alternatively,
you can create your own custom multipliers with
multiplier_create()
as shown in the following example.
<- coefficient_matrix_create(
input_reqr data_table = iotable_get(),
total = 'output',
return_part = 'primary_inputs')
<- input_multipliers_create(
multipliers input_requirements = input_reqr,
Im = I_de)
multipliers#> iotables_row
#> 7 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 8 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 9 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 10 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 11 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 12 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 13 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 14 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 15 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 16 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 17 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 18 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> 19 c("total", "imports", "intermediate_consumption", "compensation_employees", "net_tax_production", "consumption_fixed_capital", "os_mixed_income_net", "gva", "output", "net_tax_products", "employment_wage_salary", "employment_self_employed", "employment_domestic_total")
#> agriculture_group industry_group construction trade_group
#> 7 2.685405 2.697473 2.746360 2.773719
#> 8 2.350820 1.736823 2.960627 3.030228
#> 9 2.648481 2.546448 2.848667 2.828821
#> 10 5.228504 4.828548 4.274868 3.134768
#> 11 1.754409 -31.761313 -9.914110 -4.386767
#> 12 2.270536 5.635210 12.323323 3.589129
#> 13 2.770958 10.779995 3.440553 3.383980
#> 14 3.747840 5.325994 4.348257 3.170864
#> 15 3.190875 3.563620 3.554629 3.026047
#> 16 2.831087 9.949035 9.478301 3.608658
#> 17 4.023060 6.239150 4.299240 3.096275
#> 18 1.596012 34.019675 8.518983 3.822717
#> 19 2.665597 7.395981 4.742600 3.196316
#> business_services_group other_services_group
#> 7 2.687728 2.733341
#> 8 5.097511 2.892089
#> 9 2.850498 2.686567
#> 10 5.684347 1.967902
#> 11 -1.624052 2.014459
#> 12 2.325327 2.310132
#> 13 1.820218 2.474001
#> 14 3.051751 2.084234
#> 15 2.971231 2.254549
#> 16 4.207374 2.022875
#> 17 6.792323 2.006822
#> 18 7.413313 4.250793
#> 19 6.880557 2.149956
You can check these results against the Table 15.16 on page 501 of the Eurostat Manual. The label ‘total’ refers to domestic intermediaries. The ordering of the rows is different from the Manual.
These multipliers are Type-I multipliers. The type-I GVA multiplier shows the total effect in the domestic economy. The initial extra demand creates new orders in the backward linking industries, offers new product to build on in the forward-linking industry and creates new corporate and employee income that can be spent. Type-II multipliers will be introduced in a forthcoming vignette [not yet available.]
The E matrix contains the input coefficients for labor (created by
input_indicator_create()
). The following matrix equation
defines the employment multipliers.
The multiplier_create()
function performs the matrix
multiplication, after handling many exceptions and problems with
real-life data, such as different missing columns and rows in the
national variations of the standard European table.
Please send a bug report on Github if you run into further real-life problems.
<- input_indicator_create (
de_emp_indicator data_table = germany_io,
input = 'employment_domestic_total')
<- multiplier_create (
employment_multipliers input_vector = de_emp_indicator,
Im = I_de,
multiplier_name = "employment_multiplier",
digits = 4 )
vector_transpose_longer(employment_multipliers,
values_to="employment_multipliers")
#> # A tibble: 6 × 2
#> nace_r2 employment_multipliers
#> <chr> <dbl>
#> 1 agriculture_group 0.0665
#> 2 industry_group 0.0574
#> 3 construction 0.0625
#> 4 trade_group 0.0548
#> 5 business_services_group 0.0423
#> 6 other_services_group 0.0431
You can check against the Eurostat Manual of Supply, Use and Input-Output Tables page 501 that these values are correct and on page 501 that the highest employment multiplier is indeed \(z_i\) = 0.0665, the employment multiplier of agriculture.
For working with real-life, current employment data, there is a
helper function to retrieve and process Eurostat employment statistics
to a SIOT-conforming vector employment_get()
. This function
will be explained in a separate vignette.
Output multipliers
and forward linkages
are
calculated with the help of output coefficients for product as defined
on p486 and p495 of the the Eurostat Manual. The Eurostat Manual uses
the definition of output at basic prices to define output
coefficients which is no longer part of SNA as of SNA2010.
\(x_i\): output of sector i
<- input_coefficient_matrix_create(
de_input_coeff data_table = iotable_get(),
digits = 4)
<- output_multiplier_create (input_coefficient_matrix = de_input_coeff)
output_multipliers
vector_transpose_longer(output_multipliers,
values_to = "output_multipliers")
#> # A tibble: 6 × 2
#> nace_r2 output_multipliers
#> <chr> <dbl>
#> 1 agriculture_group 1.70
#> 2 industry_group 1.84
#> 3 construction 1.81
#> 4 trade_group 1.60
#> 5 business_services_group 1.59
#> 6 other_services_group 1.38
These multipliers can be checked against the Table 15.15 (The 8th, ‘Total’ row) on the page 500 of the Eurostat Manual.
The backward linkages
, i.e. demand side linkages, show
how much incremental demand is created in the supplier sector when an
industry is facing increased demand, produces more, and requires more
inputs from suppliers.
Forward linkages
on the other hand show the effect of
increased production, which gives either more or cheaper supplies for
other industries that rely on the output of the given industry.
For example, when a new concert is put on stage, orders are filled for real estate, security services, catering, etc, which show in the backward linkages. The concert attracts visitors that creates new opportunities for the hotel industry in forward linkages.
<- input_coefficient_matrix_create(iotable_get(), digits = 4)
de_coeff <- leontief_inverse_create (de_coeff)
I_de
vector_transpose_longer(backward_linkages(I_de),
values_to = "backward_linkage_strength")
#> # A tibble: 6 × 2
#> nace_r2 backward_linkage_strength
#> <chr> <dbl>
#> 1 agriculture_group 1.70
#> 2 industry_group 1.84
#> 3 construction 1.81
#> 4 trade_group 1.60
#> 5 business_services_group 1.59
#> 6 other_services_group 1.38
You can check the results against Table 15.19 on page 506 of the Eurostat Manual.
Manufacturing has the highest backward linkages, and other services the least. An increased demand for manufacturing usually effects supplier industries. Service industry usually have a high labor input, and their main effect is increased spending of the wages earned in the services.
Forward linkages show the strength of the new business opportunities when industry i starts to increase its production. Whereas backward linkages show the increased demand of the suppliers in industry i, forward linkages show the increased availability of inputs for other industries that rely on industry i as a supplier.
The forward linkages are defined as the sums of the rows in the Ghosh-inverse. The Ghosh-inverse is not explicitly named in the Eurostat Manual, but it is described in more detail in the United Nations’ similar manual Handbook on Supply and Use Tables and Input-Output Tables with Extensions and Applications (see pp 636–638).
<- output_coefficient_matrix_create(
de_out data_table = germany_io,
total = "final_demand",
digits = 4
)
ghosh_inverse_create(de_out, digits = 4)[,1:4]
#> iotables_row agriculture_group industry_group construction
#> 1 agriculture_group 1.0339 0.8612 0.0560
#> 2 industry_group 0.0117 1.4292 0.0901
#> 3 construction 0.0037 0.0839 1.0290
#> 4 trade_group 0.0103 0.2426 0.0484
#> 5 business_services_group 0.0117 0.3229 0.0888
#> 6 other_services_group 0.0042 0.0625 0.0105
The Ghosh-inverse is
where B = the output coefficient matrix.
The forward linkages are the rowwise sums of the Ghosh-inverse
forward_linkages(output_coefficient_matrix = de_out)
#> iotables_row forward_linkages
#> 1 agriculture_group 2.112754
#> 2 industry_group 1.690891
#> 3 construction 1.355896
#> 4 trade_group 1.584989
#> 5 business_services_group 2.104036
#> 6 other_services_group 1.210504
You can check the values of the forward linkages against the Table 15.20 on page 507 of the Eurostat Manual.
At last, let’s extend the input-output system with emissions data.
For getting Eurostat’s air pollution account data, use
airpol_get()
. We have included in the package the German
emissions data from the Eurostat Manual.
data("germany_airpol")
<- germany_airpol[, -3] %>%
emissions_de vector_transpose_wider(names_from = "iotables_col",
values_from = "value")
emissions_de#> # A tibble: 9 × 9
#> airpol agriculture_g…¹ indus…² const…³ trade…⁴ busin…⁵ other…⁶ final…⁷ outpu…⁸
#> <chr> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 CO2 10448<PHONE_NUMBER>4 71269 8792 26990<PHONE_NUMBER>58
#> 2 CH4 1534 1160 1 4 1 1058 136 3894
#> 3 N2O 77 100 0 3 0 11 17 209
#> 4 SO2 12 1705 18 50 4 24 180 1994
#> 5 NOx 62 722 64 452 23 58 585 1967
#> 6 CO 43 1616 86 434<PHONE_NUMBER> 6667
#> 7 NMVOC 20 1209 17 101 15<PHONE_NUMBER>
#> 8 Dust 57 165 7 34 1 7 58 329
#> 9 Total 12252<PHONE_NUMBER>8 72347 8939 28479<PHONE_NUMBER>41
#> # … with abbreviated variable names ¹agriculture_group, ²industry_group,
#> # ³construction, ⁴trade_group, ⁵business_services_group,
#> # ⁶other_services_group, ⁷final_consumption_households, ⁸output_bp
<- output_get(germany_io) output_bp
The output coefficients are created from the emission matrix with =
<- germany_io %>%
emission_coeffs supplementary_add(emissions_de) %>%
input_indicator_create(input_row = as.character(emissions_de$airpol), digits = 4)
The emissions coefficients are expressed as 1000 tons per millions of euro output (at basic prices).
-1, 1:3]
emission_coeffs[#> iotables_row agriculture_group industry_group
#> 20 CO2_indicator 0.2379 0.5172
#> 21 CH4_indicator 0.0349 0.0011
#> 22 N2O_indicator 0.0018 0.0001
#> 23 SO2_indicator 0.0003 0.0016
#> 24 NOx_indicator 0.0014 0.0007
#> 25 CO_indicator 0.0010 0.0015
#> 26 NMVOC_indicator 0.0005 0.0011
#> 27 Dust_indicator 0.0013 0.0002
#> 28 Total_indicator 0.2790 0.5234
And the multipliers (which include both the direct and indirect emissions of the industries) are created with
You can create a single multiplier with
?multiplier_create
.
multiplier_create(
input_vector = emission_coeffs[2,],
Im = I_de,
multiplier_name = "CO2_multiplier",
digits = 4 )
#> iotables_row agriculture_group industry_group construction trade_group
#> 1 CO2_multiplier 0.4185 0.7686 0.2726 0.2358
#> business_services_group other_services_group
#> 1 0.0583 0.1234
To create a tidy table of indicators of using a loop:
names(emission_coeffs)[1] <- names(I_de)[1]
<- cbind (
emission_multipliers key_column_create(names(emission_coeffs)[1],
gsub("_indicator", "_multiplier", unlist(emission_coeffs[-1,1]))),
do.call( rbind,
lapply ( 2:nrow(emission_coeffs),
function(x) equation_solve(emission_coeffs[x, ], I_de) )
%>% as.data.frame()
)
)
-1] <- round(emission_multipliers[, -1], 4)
emission_multipliers[,
1:3,1:4]
emission_multipliers[#> iotables_row agriculture_group industry_group construction
#> 1 CO2_multiplier 0.4185 0.7686 0.2726
#> 2 CH4_multiplier 0.0365 0.0029 0.0008
#> 3 N2O_multiplier 0.0019 0.0002 0.0001
You can check the results against the Table 15.13 of the Eurostat Manual of Supply, Use and Input-Output Tables on page 494.
Because the equation of the final demand is:
final_demand = final_consumption_households + final_consumption_government + inventory_change + gross_capital_formation + exports
we can calculate the final demand for the products of industries with creating the rowwise sums of the appropriate columns
<- rowSums(germany_io[1:6, 9:13]) final_demand
And at last the emission content of the final demand is given by
where the term
is the definition of the emission multipliers.
<- as.data.frame(
emission_content round(as.matrix(emission_multipliers[1:3, -1]) %*% diag(final_demand), 0)
)names(emission_content) <- names(emission_multipliers[,-1])
<- data.frame (
emission_content iotables_row = gsub("_multiplier", "_content", emission_multipliers[1:3,1])
%>%
) cbind(emission_content )
1:4]
emission_content[,#> iotables_row agriculture_group industry_group construction
#> 1 CO2_content 6369<PHONE_NUMBER>7
#> 2 CH4_content 555 1796 157
#> 3 N2O_content 29 124 20
This final result can be found on the bottom of the page 494 of the Eurostat Manual of Supply, Use and Input-Output Tables.


vignettes/lgr.Rmd
lgr.Rmd
lgr is a logging framework for R inspired by Apache Log4j and Python logging. It follows a traditional object oriented design implemented through R6 classes. This enables lgr to have a larger set of features than other logging packages for R, and makes it easy to extend for people that have experience with R6 classes. This also makes Logger configuration a bit more verbose than for other packages, but I hope also more flexible and transparent.
If you are not sure if lgr is the right package for you, take a look examples to see what lgr can do for you.
lgr comes with a so-called root logger that is ready to go after you install it. If you are a package developer you can (and should) set up a new Logger for your package, but more on that later. For many use cases the root Logger will suffice.
# the root logger is called "lgr"
lgr$info("Vampire stories are generally located in Styria.")
#> INFO [12:43:17.496] Vampire stories are generally located in Styria.
You can use formatting strings that are passed on to sprintf()
in lgr.
lgr$error("Vampires generally arrive in carriages drawn by %i black horses.", 2)
#> ERROR [12:43:17.698] Vampires generally arrive in carriages drawn by 2 black horses.
Usually you don’t (only) want to log to the console, you want to log to files. Output of Loggers is managed by Appenders. The root Logger is preconfigured with a console Appender (that is why we see output in the console). Let’s add a file Appender:
tf <- tempfile(fileext = ".info")
lgr$add_appender(AppenderFile$new(tf), name = "file")
lgr$info("You must think I am joking")
#> INFO [12:43:17.744] You must think I am joking
readLines(tf)
#> [1] "INFO [2019-03-25 12:43:17.744] You must think I am joking"
The various Appenders available in lgr are R6 classes. To instantiate an object of that class (i.e. create a new appender) you have to use the $new()
function as in the example above. Whenever you see something in lgr that IsNamedLikeThis
, you can be sure that it is such an R6 class.
If you look at the output, you see that the timestamp format of the file appender is slightly different to the timestamp format of the console Appender. Formatting is handled by Layouts, and each Appender has exactly one:
lgr$appenders$file$set_layout(LayoutFormat$new(timestamp_fmt = "%B %d %T"))
lgr$info("No, I am quite serious")
#> INFO [12:43:17.759] No, I am quite serious
readLines(tf)
#> [1] "INFO [2019-03-25 12:43:17.744] You must think I am joking"
#> [2] "INFO [March 25 12:43:17] No, I am quite serious"
If you log to files, you should not log normal text. If you want to analyse your logs later, it’s much better to log to a format like JSON:
# cleanup behind the old Appender
unlink(tf)
lgr$remove_appender("file")
# setup a JSON appender
lgr$add_appender(AppenderJson$new(tf), name = "json")
lgr$info("We lived in Styria")
#> INFO [12:43:17.778] We lived in Styria
JSON is still somewhat human readable
cat(readLines(tf))
#> {"level":400,"timestamp":"2019-03-25 12:43:17","logger":"root","caller":"eval","msg":"We lived in Styria"}
and easy for machines to parse
read_json_lines(tf)
#> level timestamp logger caller msg
#> 1 400 2019-03-25 12:43:17 root eval We lived in Styria
$config()
The $config()
method provides a flexible alternative to configuring loggers with their $set_*()
methods.
# explicit setup with logger_config
lgr$config(logger_config(
threshold = "info",
appenders = AppenderFile$new(
file = "path/to/logfile",
layout = LayoutFormat(fmt = "[%L] %m")
)
))
You can also specify Logger configurations in yaml files
#### contents of path/to/config.yaml #####
# Logger:
# name: test/blubb
# threshold: info
# appenders:
# AppenderFile:
# file: /path/to/logfile
# LayoutFormat:
# fmt: '[%L] %m'
lgr$config("path/to/config.yaml")
Unlike many others Loggers for R, lgr treats a log event as a unit of data, not just a message with a timestamp. A log event can contain arbitrary data, though not all Appenders can handle that well. The JSON appender we added above is particularly good at handling most R objects.
# The default console appender displays custom fields as pseudo-json after the message
lgr$info("Styria has", poultry = c("capons", "turkeys"))
#> INFO [12:43:17.827] Styria has {poultry: [capons, turkeys]}
# JSON can store most R objects quite naturally
read_json_lines(tf)
#> level timestamp logger caller msg
#> 1 400 2019-03-25 12:43:17 root eval We lived in Styria
#> 2 400 2019-03-25 12:43:17 root eval Styria has
#> poultry
#> 1 NULL
#> 2 capons, turkeys
read_json_lines(tf)$poultry[[2]] # works because poultry is a list column
#> [1] "capons" "turkeys"
If the examples above have piqued your interest, the rest of this vignette will provide more details on the workings of lgr. Discussing all Appenders and configuration options is beyond the scope of this vignette, please refer to the function reference for that.
If you want custom logging configurations, you have to understand the structure of the logging process.
AppenderFile
uses LayoutFormat
by default to write human readable log events to a text file, but can also use LayoutJson
produce machine readable JSON lines logfiles.AppenderFormat
with LayoutFormat
you can only use the standard fields in your log message, while LayoutJson
supports custom fields in a quite natural manner. See examples 1 & 2
The elements described above are R6 classes. R6 is an object orientation system for R that is used by many popular packages such as shiny, dplyr, plumber, roxygen2, and testthat but often behind the scenes and not as exposed as in lgr.
You recognize R6 classes in this package because they are named following the UpperCamelCase
naming convention. While there is only one kind of Logger and one kind of LogEvent, there are several subclasses of Appenders and Layouts.
An introduction to R6 classes is beyond the scope of this document, but you can find the official documentation here and there is also this talk on Youtube. In short R6 classes store data (fields) together with functions (methods) and have to be instantiated with <classname>$new()
. So if you want to create a new AppenderFile
, you do this by calling AppenderFile$new(file = tempfile())
.
Please not that Loggers should never be instantiated directly with $new()
but always with get_logger()
.
lgr supports the standard log4j Log Levels outlined bellow. The Log Level of an event represents its severity. The named log levels are really just nicknames for integer values, and you can use the character
or integer
representations interchangeably. You can also use arbitrary integer values (greater than 0
), but you are encouraged to stick to the ones bellow.
| Level | Name | Description |
|---|---|---|
| 0 | off | A log level of 0/off tells a Logger or Appender to suspend all logging |
| 100 | fatal | Critical error that leads to program abort. Should always indicate a stop() or similar |
| 200 | error | A severe error that does not trigger program abort |
| 300 | warn | A potentially harmful situation, like warning()
|
| 400 | info | An informational message on the progress of the application |
| 500 | debug | Finer grained informational messages that are mostly useful for debugging |
| 600 | trace | An even finer grained message than debug |
| NA | all | A log level of NA/all tells a Logger or Appender to process all log events |
The list of named log levels is stored as a global option (getOption("lgr.log_levels")
) and you can use add_log_levels()
and remove_log_levels()
to define your own named levels if you want to. There are only predefined logging methods (lgr$fatal()
, etc..) for the standard log levels and you have to use lgr$log(level, message)
to create a LogEvent with a custom log level.
lgr comes with a pre-configured root Logger. It is called root because you can set up a tree of Loggers that descent from it, but for basic use you will not have to worry about that.
lgr Loggers are R6 objects with methods (functions) for logging. You can refer to the root logger with lgr
.
lgr$fatal("This is an important message about %s going wrong", "->something<-")
#> FATAL [12:43:17.892] This is an important message about ->something<- going wrong
lgr$trace("Trace messages are still hidden")
lgr$appenders$console$set_threshold("trace")
lgr$trace("Unless we lower the threshold")
#> TRACE [12:43:17.897] Unless we lower the threshold
lgr$appenders$memory$show(3)
You can use sprintf()
style formatting strings directly in log messages.
lgr$info("The sky was the color of %s, tuned to a dead chanel", "television")
#> INFO [12:43:17.909] The sky was the color of television, tuned to a dead chanel
LogEvents are objects that store all information collected by the Logger. They are passed on to Appenders that output them, but Appenders usually don’t utilize all the information present in a log event. The last event produced by a Logger is stored in its last_event
field.
lgr$info("Vampire stories are generally located in Styria")
#> INFO [12:43:17.917] Vampire stories are generally located in Styria
lgr$last_event # a summary output of the event
#> INFO [2019-03-25 12:43:17] Vampire stories are generally located in Styria
lgr$last_event$values # all values stored in the event as a list
#> $level
#> [1] 400
#>
#> $timestamp
#> [1] "2019-03-25 12:43:17 CET"
#>
#> $logger
#> [1] "root"
#>
#> $caller
#> [1] "eval"
#>
#> $msg
#> [1] "Vampire stories are generally located in Styria"
LogEvents can contain not only these standard values, but an arbitrary number of extra values. These extra values are passed as named arguments to the logging function (as opposed as to parameters to sprintf()
, which are passed as unnamed arguments). It is up to the Appender whether to process them further or not.
You should consider making use of custom fields liberally and using output formats that support them (such as JSON), rather than producing elaborately formatted but hard to parse log messages. If you ever want to analyse your log data, you will thank me for this tip.
# bad
lgr$info("Processing track '%s' with %s waypoints", "track.gpx", 32)
#> INFO [12:43:17.932] Processing track 'track.gpx' with 32 waypoints
# Good
tf <- tempfile()
lgr$add_appender(AppenderJson$new(tf), "json")
lgr$info("Processing track", file = "track.gpx", waypoints = 32)
#> INFO [12:43:17.940] Processing track {file: track.gpx, waypoints: 32}
lgr$appenders$json$data
#> level timestamp logger caller msg file
#> 1 400 2019-03-25 12:43:17 root eval Processing track track.gpx
#> waypoints
#> 1 32
To control the level of detail of the log output, you can set thresholds for Loggers and Appenders. A Logger with a threshold of warn
will only create LogEvents of the priorities warn
, error
and fatal
and dispatch them to its Appenders.
A threshold of a Logger or Appender is the minimum log level a LogEvent must have so that that Logger/Appender processes it.
If you require more complex logic to decide whether a LogEvent should be created/processed you can also assign filters to Loggers/Appenders. Filters are just functions that have exactly one argument, event
(the LogEvent to be filtered), and return TRUE
or FALSE
. They will be applied after the threshold is checked. Alternatively there is also a formal R6 class for Filters (?EventFilter) that you can use, but this is usually not necessary.
examples:
The root logger only logs to the console by default. If you want to redirect the output to a file you can just add a file appender to lgr.
tf <- tempfile()
# Add a new appender to a logger. We don't have to supply a name, but that
# makes it easier to remove later.
lgr$add_appender(AppenderFile$new(file = tf), name = "file")
# configure lgr so that it logs everything to the file, but only info and above
# to the console
lgr$set_threshold(NA)
lgr$appenders$console$set_threshold("info")
lgr$appenders$file$set_threshold(NA)
lgr$info("Another informational message")
#> INFO [12:43:18.001] Another informational message
lgr$debug("A debug message not shown by the console appender")
readLines(tf)
#> [1] "INFO [2019-03-25 12:43:18.001] Another informational message"
#> [2] "DEBUG [2019-03-25 12:43:18.004] A debug message not shown by the console appender"
# Remove the appender again
lgr$remove_appender("file")
Logger hierarchies are a powerful concept to organize logging for different parts of a larger system. This is mainly relevant for package developers. It is good practice to have a separate Logger for each package. Since it is not common in R to build complex systems of hierarchically organised packages, hierarchies will usually be pretty flat (i.e. most Loggers will only inherit from the root logger).
Each newly created Logger is child to a parent Logger, derived from its name. So lg <- get_logger("foo/bar")
creates the logger with the qualified name foo/bar
whose parent is the logger foo
whose parent is (implicitly) the root
logger. If the logger foo
does not exist in that scenario, it is created automatically. This behaviour might sound strange at first, but it mimics tried and tested behaviour of python logging. This way logging is decoupled from the business logic and your program will not abort if you forgot to initialize some logger up the hierarchy for whatever reason.
A logger dispatches the LogEvents it creates not only to its own Appenders, but also to the Appenders of all its ancestral Loggers (ignoring the threshold and Filters of the ancestral Loggers, but not of the Appenders). When you define Loggers for your package, you should not configure them (with custom Appenders or thresholds); that should be left to the user of the package.
If all this sounds confusing to you, take a look at the examples. The common use cases are pretty easy to understand and illustrate the how and why pretty well.
glue is very nicely designed package for string interpolation. It makes composing log messages more flexible and comfortable at the price of an additional dependency and slightly less performance than sprintf()
(which is used by normal Loggers). To take advantage of glue, simply create a new LoggerGlue like this:
# install.packages("glue")
lg <- get_logger_glue("glue/logger")
lg$info(
"glue automatically ",
"pastes together unnamed arguments ",
"and evaluates arbitray expressions inside braces {Sys.Date()}"
)
#> INFO [12:43:18.062] glue automatically pastes together unnamed arguments and evaluates arbitray expressions inside braces 2019-03-25
Glue lets you define temporary variables inside the glue()
call. As with the normal Logger, named arguments get turned into custom fields.
lg$info("For more info on glue see {website}", website = "https://glue.tidyverse.org/")
#> INFO [12:43:18.097] For more info on glue see https://glue.tidyverse.org/ {website: https://glue.tidyverse.org/}
You can suppress this behaviour by making named argument start with a "."
.
lg$info("Glue is available from {.cran}", .cran = "https://CRAN.R-project.org/package=glue")
#> INFO [12:43:18.107] Glue is available from https://CRAN.R-project.org/package=glue
lgr comes with simple but powerful formatting syntax for LogEvents. Please refer to ?format.LogEvent
for the full list of available placeholders.
lg <- get_logger("test")
lg$set_appenders(list(cons = AppenderConsole$new()))
lg$set_propagate(FALSE)
lg$info("the default format")
#> INFO [12:43:18.131] the default format
lg$appenders$cons$layout$set_fmt("%L (%n) [%t] %c(): !! %m !!")
lg$info("A more involved custom format")
#> INFO (400) [12:43:18.135] eval(): !! A more involved custom format !!
If this is not enough for you, you can use LayoutGlue
based on the awesome glue package. The syntax is a bit more verbose, and AppenderGlue
is a bit less performant than AppenderFormat
, but the possibilities are endless.
# install.packages("glue")
library(glue)
lg$appenders$cons$set_layout(LayoutGlue$new(
fmt = "{.logger$name} {level_name} {caller}: {toupper(msg)}"
))
lg$info("with glue")
#> test info eval: WITH GLUE
All fields of the [LogEvent] object are exposed through LayoutGlue, so please refer to ?LogEvent
for a list of all available Fields.
JavaScript Object Notation (JSON) is an open-standard file format that uses human-readable text to transmit data objects consisting of attribute–value pairs and array data types (Wikipedia). JSON is the recommended text-based logging format when logging to files 1, as it is human- as well as machine readable. You should only log to a different format if you have very good reasons for it. The easiest way to log to JSON files is with AppenderJson2
# install.packages("jsonlite")
tf <- tempfile()
lg <- get_logger("test")
lg$set_appenders(list(json = AppenderJson$new(file = tf)))
lg$set_propagate(FALSE)
lg$info("JSON naturally ", field = "custom")
lg$info("supports custom", numbers = 1:3)
lg$info("log fields", use = "JSON")
JSON is easy to parse and analyse with R. lgr provides the function read_json_lines()
that can be used to read JSON log files, but you can also use AppenderJson’s $data
binding for an even more convenient method to read the logfile.
lg$appenders$json$data
# same as
read_json_lines(tf)
#> level timestamp logger caller msg field numbers
#> 1 400 2019-03-25 12:43:18 test eval JSON naturally custom NULL
#> 2 400 2019-03-25 12:43:18 test eval supports custom <NA> 1, 2, 3
#> 3 400 2019-03-25 12:43:18 test eval log fields <NA> NULL
#> use
#> 1 <NA>
#> 2 <NA>
#> 3 JSON
JSON is also human readable, though this vignette does not transport that fact very well because of the lack of horizontal space.
#> {"level":400,"timestamp":"2019-03-25 12:43:18","logger":"test","caller":"eval","msg":"JSON naturally ","field":"custom"}
#> {"level":400,"timestamp":"2019-03-25 12:43:18","logger":"test","caller":"eval","msg":"supports custom","numbers":[1,2,3]}
#> {"level":400,"timestamp":"2019-03-25 12:43:18","logger":"test","caller":"eval","msg":"log fields","use":"JSON"}
The most common use cases for creating a new Logger rather than just using the root Logger is if you create a Package that should contain logging. This way you can have separate Appenders (e.g logfiles) and thresholds for each package.
# The logger name should be the same as the package name
tf <- tempfile()
lg <- get_logger("mypackage")
lg$add_appender(AppenderFile$new(tf))
The print()
method for Loggers gives a nice overview of the newly created Logger:
print(lg)
#> <Logger> [all] mypackage
#>
#> appenders:
#> [[1]]: <AppenderFile> [all] -> /tmp/RtmpMuA1OY/file1a7c45dac043
#>
#> inherited appenders:
#> console: <AppenderConsole> [info] -> console
#> memory : <AppenderDt> [ all] -> in memory data.table
This tells us that lg
logs all events of at least level info
. It does have a single (unnamed) Appender that logs to a temporary file, and dispatches all LogEvents it creates to the Appenders of the root Logger (ignoring the threshold and filters of the root Logger, but not of its Appenders).
We can use lg$fatal()
, lg$info()
, etc.. to log messages with this Logger:
lg$info("A test message for lg")
#> INFO [12:43:18.233] A test message for lg
If we do not want lg
to dispatch to the root Logger, we can set propagate
to FALSE
.
lg$set_propagate(FALSE)
When we take a look at the Logger again, we now see that it does not inherit any Appenders anymore
print(lg)
#> <Logger> [all] mypackage
#>
#> appenders:
#> [[1]]: <AppenderFile> [all] -> /tmp/RtmpMuA1OY/file1a7c45dac043
Consequently, lg
no longer outputs log messages to he console
lg$info("Nothing to see here")
The main purpose of AppenderBuffer is to retain LogEvents in memory and write them to destinations at a later point in time, e.g. when the Buffer is full and needs to be flushed. For example, if you log to a remote database you can postpone this costly operation until after your analysis is finished.
By setting a filter as a custom $should_flush()
method for an AppenderBuffer, you can define more complex conditions to trigger flushing. For example, the will output the last 5 LogEvents that happened before an error
occurred.
lg <- get_logger("buffer")
lg$config(logger_config(
threshold = NA,
propagate = FALSE, # to suppress console output
appenders = AppenderBuffer$new(
threshold = NA,
buffer_size = 5, # can hold 5 events, the 6th will trigger flushing
flush_on_exit = FALSE,
flush_on_rotate = FALSE,
flush_threshold = "error",
appenders = AppenderConsole$new(threshold = NA)
)
))
#> <Logger> [all] buffer
#>
#> appenders:
#> [[1]]: <AppenderBuffer> [all] -> 1 child Appenders
# The for loop below stores 8 log events in the Buffer
for (nm in month.name[1:8]) lg$debug("%s", nm)
# An event of level 'error' or 'fatal' triggers flushing of the buffer
lg$error("But the days grow short when you reach September")
#> DEBUG [12:43:18.299] April
#> DEBUG [12:43:18.299] May
#> DEBUG [12:43:18.299] June
#> DEBUG [12:43:18.299] July
#> DEBUG [12:43:18.300] August
#> ERROR [12:43:18.301] But the days grow short when you reach September
Logging to databases is simple, though a few aspects can be tricky to configure based on the backend used. For performance reasons database inserts are buffered by default. This works exactly identical as described above for AppenderBuffer. If you want to write each LogEvent directly to the database, just set the buffer size to 0
.
# install.packages("RSQLite")
lg <- get_logger("db_logger")
lg$set_propagate(FALSE)
lg$add_appender(
name = "db",
AppenderDbi$new(
conn = DBI::dbConnect(RSQLite::SQLite()),
table = "log",
buffer_size = 2L
)
)
#> Creating 'log' with manually specified column types
lg$info("Logging to databases uses a buffer")
lg$info("As the buffer size is 2, no insert took place till now")
lg$appenders$db$show()
#> [empty log]
lg$info("Now as the buffer is rotated, all events are output to the db")
lg$appenders$db$show()
#> INFO [2019-03-25 12:43:18] Logging to databases uses a buffer
#> INFO [2019-03-25 12:43:18] As the buffer size is 2, no insert took place till now
#> INFO [2019-03-25 12:43:18] Now as the buffer is rotated, all events are output to the db
By abusing Filters, lgr can modify LogEvents as they are processed. One example for when this is useful is assigning a grouping identifier to a series of log calls.
# setup an example function
clean <- function() lgr$info("cleaning data")
process <- function() lgr$info("processing data")
output <- function() lgr$info("outputing data")
analyze <- function(){
clean()
process()
output()
}
with_log_value()
provides a convenient wrapper to inject values into log calls.
with_log_value(
list(dataset_id = "dataset1"),
analyze()
)
An alternative way to achieve the same is to use one of the preconfigured Filters that come with lgr. This approach is more more comfortable for use within functions.
analyze <- function(id = "dataset1"){
lgr$add_filter(FilterInject$new(dataset_id = id), name = "inject")
on.exit(lgr$remove_filter("inject"))
clean()
process()
output()
}
analyze()
The result is the same in both cases:
#> INFO [12:43:18.713] cleaning data {dataset_id: dataset1}
#> INFO [12:43:18.715] processing data {dataset_id: dataset1}
#> INFO [12:43:18.716] outputing data {dataset_id: dataset1}
You can use with_log_level()
and FilterForceLevel
in a similar fashion to modify the log level of events conveniently.
Temporary disabling logging for portions of code is straight forward and easy with lgr:
without_logging({
lgr$warn("Oh Yeah?")
lgr$fatal("Oh No")
})
If you are a package author, it is good practice to define a separate logger for your package. This gives users the ability to easily enable/disable logging on a per-package basis. Loggers must be initialized in the packages .onLoad hook. You can do this by adding the following code to any .R
file inside the R/
directory of your package:
# mypackage/R/mypackage-package.R
.onLoad <- function(...){
assign(
"lg", # the recommended name for a logger object
lgr::get_logger(name = "mypackage"), # should be the same as the package name
envir = parent.env(environment())
)
}
You can also just use lgr::use_logger()
to generate the appropriate code for your package automatically.
 You do not have to define any appenders, since all log events will get redirected to the root Logger (see Inheritance).
Technically, the logger does not produce standard JSON files but JSON lines↩
AppenderJson is just an AppenderFile with LayotJson as default Layout and a few extra features↩


{HEADER}
Month:
{MESEC}
Company:
{FIRMA}
No
Employee
Salary
Last change on
Last change by
{ITEM_1}
{TOTAL}
{FOOTER}


My core personal values are:
A friend of mine had some legal issues in regards to his residency in a foreign country. He did not have much time or support as he was being told to leave the country. Because he was alone in a foreign country he needed help to relocate but also to help with the legal side. He could not find anyone to go there and help him. He called me asking for help so I booked a return ticket to go over there and help him move back. I did this at my own expense and while taking time off from work. The reason I went to this extent to help him was because a few years back I was going through tough times and he really helped me out when I needed it. Because loyalty is a very high up on my list of values I had left everything and went to help to show that I am very much loyal to the people who stood by me In times of difficulty. It had costed me time and money but this is representation of my value system.
My culture did not really influence me in my set of moral values and ethics but my religion definitely has. My religion has a legislation which one needs to adhere to so that he may be guided in this world so I try my best to act on the practises outlined in our holy book.
I believe a strength of mine is a willingness to learn new things even though they may be difficult.
Currently I am trying to overcome my limitations but have not been able to find good resources to help with my time management and my nature of procrastinating.