Let us all Welcome 2014
With a Bright Smile and Happiness
Share Good deeds to everyone
Happy New Year to all of you!
mardi 31 décembre 2013
jeudi 26 décembre 2013
US Government Extends insurance Enrollment deadline by 24 hours
The deadline for registration for health insurance coverage on healthcare.gov has been extended by the Obama Government by 1 day. The deadline used be 11:59 p.m. last Monday.
Times said that on Monday there's a huge traffic on Healthcare.gov and the Whitehouse wants to be sure that people who wants coverage can have one.
Monday, December 23 had been the deadline for selecting a plan, it will take effect on the 1st day of the new year 2014.
Penalty: Under the Affordable Care Act or Obamacare, you will have to pay $95 penalty or 1% of income in 2014 if you don't have health insurance coverage. It is set to rise to $695 or 2% of income by 2016.
To avoid the penalty, you will need to enroll in a plan by February 15 or qualify for an exemption from the penalty.
Times said that on Monday there's a huge traffic on Healthcare.gov and the Whitehouse wants to be sure that people who wants coverage can have one.
Monday, December 23 had been the deadline for selecting a plan, it will take effect on the 1st day of the new year 2014.
Penalty: Under the Affordable Care Act or Obamacare, you will have to pay $95 penalty or 1% of income in 2014 if you don't have health insurance coverage. It is set to rise to $695 or 2% of income by 2016.
To avoid the penalty, you will need to enroll in a plan by February 15 or qualify for an exemption from the penalty.
mardi 24 décembre 2013
Merry Christmas To All!
Christmas season is the time for Peace and love
I wish you a peaceful Christmas
Filled with love and joy
Merry Christmas To you
I wish you a peaceful Christmas
Filled with love and joy
Merry Christmas To you
jeudi 19 décembre 2013
"Honda" Front Ranks the Insurance Industry 2014 Safety List
Honda Motor Company got the first place on the yearly insurance industry ranking of the safest new vehicles. The Insurance Institute for Highway Safety (IIHS) released the ranking on Thursday December 19, 2013, they award 39 vehicles top safety ratings for 2014. It went down by 70%, last year they have 130 vehicles on the list.
This year the vehicles needed to top the crash test scores and to have a high level front crash prevention system like automatic braking for it to be able to receive the highest classification. It's rank either "Top Safety Pick" or "op Safety Pick +".
Of the 39 vehicles, 22 go the top classification while the 12 receive the classification just below it.
Honda Motor Co got 8 vehicles on the list making them the automaker with the most vehicle on the list. 6 of their vehicles go the "Top Safety Pick +":
1. Honda Accord 2-door
2. Honda Accord 4-door
3. Honda Civic 4-door *hybrid version
4. Acura RL
5. Acura MDX
6. Honda Odyssey
7. Honda Civic 2-door
8. Acura TL
This is the list used by car shoppers who buy vehicles with high safety ratings. It is also use for advertising by the automakers.
The institute indicate that the "Top Safety Pick +" are those with optional front crash prevention systems, if they didn't get that rank they still met the Top Safety Pick criteria.
Here is the Full List:
2014 2014 IIHS TOP SAFETY PICK+
- Honda Civic 4-door *hybrid version
- Mazda 3 - built after October 2013
- Toyota Prius - built after November 2013
- Ford Fusion
- Honda Accord 2-door
- Honda Accord 4-door
- Mazda 6
- Subaru Legacy
- Subaru Outback
- Infiniti Q50
- Lincoln MKZ
- Volvo S60
- Acura RL
- Volvo S80
- Mazda CX-5 - built after October 2013
- Mitsubishi Outlander
- Subaru Forester
- Toyota Highlander
- Acura MDX
- Mercedes-Benz M-Class - built after August 2013
- Volvo XC60
- Honda Odyssey
- Chevrolet Spark
- Dodge Dart
- Ford Focus
- Honda Civic 2-door
- Hyundai Elantra
- Scion tC
- Subaru Impreza
- Subaru XV Crosstrek
- Chrysler 200 4-door
- Dodge Avenger
- Kia Optima
- Nissan Altima
- Toyota Camry - built after November 2013
- Volkswagen Passat
- Acura TL
- Mitsubushi Outlander Sport
- Volvo XC90
mardi 17 décembre 2013
Review: K�lner R Meeting 13 December 2013
Last week's Cologne R user group meeting was the best attended so far. Well, we had a great line up indeed. Matt Dowle came over from London to give an introduction to the
Matt's goal with the
I said early that
Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not
The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.
Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
data.table package. He was joined by his collaborator Arun Srinivasan, who is based in Cologne. Their talk was followed by Thomas Rahlf on Datendesign mit R (Data design with R).data.table
 |
| Download slides |
Matt's goal with the
data.table package is to reduce times; time to write code and to execute code. His talk illustrated how the syntax of data.table, not unlike SQL, can produce shorter and more readable code that at the same time provides an efficient and fast way to analyse big in memory data sets with R. Arun presented on new developments in data.table 1.8.11, which not only fixes bugs but adds many new features such as melt/cast and further speed gains. I said early that
data.table rocks. For more details see the data.table home page.Data design with R
 |
| Thomas Rahlf: Datendesign mit R |
Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not
grid or any add-ons such as lattice or ggplot2. The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.
The Schnitzel
Of course the evening ended with Schnitzel and K�lsch at the Lux. |
| The Luxus Schnitzel. Photo by G�nter Faes |
Next K�lner R meeting
The next meeting is scheduled for 26 February 2013 (Wednesday before Altweiber), with two talks by Diego de Castillo (Connecting R with databases) and Kim Kuen Tang (R and kdb+).Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
Review: K�lner R Meeting 13 December 2013
Last week's Cologne R user group meeting was the best attended so far. Well, we had a great line up indeed. Matt Dowle came over from London to give an introduction to the
Matt's goal with the
I said early that
Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not
The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.
Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
data.table package. He was joined by his collaborator Arun Srinivasan, who is based in Cologne. Their talk was followed by Thomas Rahlf on Datendesign mit R (Data design with R).data.table
|
| Download slides |
Matt's goal with the
data.table package is to reduce times; time to write code and to execute code. His talk illustrated how the syntax of data.table, not unlike SQL, can produce shorter and more readable code that at the same time provides an efficient and fast way to analyse big in memory data sets with R. Arun presented on new developments in data.table 1.8.11, which not only fixes bugs but adds many new features such as melt/cast and further speed gains. I said early that
data.table rocks. For more details see the data.table home page.Data design with R
|
| Thomas Rahlf: Datendesign mit R |
Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not
grid or any add-ons such as lattice or ggplot2. The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.
The Schnitzel
Of course the evening ended with Schnitzel and K�lsch at the Lux. |
| The Luxus Schnitzel. Photo by G�nter Faes |
Next K�lner R meeting
The next meeting is scheduled for 26 February 2013 (Wednesday before Altweiber), with two talks by Diego de Castillo (Connecting R with databases) and Kim Kuen Tang (R and kdb+).Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
jeudi 12 décembre 2013
Obamacare Enrollment Stats
Obamacare enrollment statistics released last Wednesday showed that 364,682 people in the US have already signed up for private coverage as of November 30, 2013. It is still less than 1/3 of the 1.2 million people officials had originally projected would enroll nationwide by the end of November.
The Obama government also projected that 7 million consumers would sign up for coverage during the first year. The government and insurers targeting desperately healthy, young adults to convince them to enroll to pay for the costs of paying for older, sicker consumers. Obamacare is designed to get money from people who value their health.
Critics says that "The president's health care law (Obamacare) has driven up costs, reduced choices and resulted in the cancellation of over 5 million health care plans that the president promised the American people they could keep," Rep. Congressman Jim Renacci said. "Though we know that problems with the president's health care law run far deeper than a broken website, it also is our responsibility to ensure that we reduce the negative effects of the law. It is time for the administration to update Congress and the American people on its efforts to correct serious back-end issues within its government-run health care system that could potentially be disastrous for insurers and Americans everywhere."
The Obama government also projected that 7 million consumers would sign up for coverage during the first year. The government and insurers targeting desperately healthy, young adults to convince them to enroll to pay for the costs of paying for older, sicker consumers. Obamacare is designed to get money from people who value their health.
Critics says that "The president's health care law (Obamacare) has driven up costs, reduced choices and resulted in the cancellation of over 5 million health care plans that the president promised the American people they could keep," Rep. Congressman Jim Renacci said. "Though we know that problems with the president's health care law run far deeper than a broken website, it also is our responsibility to ensure that we reduce the negative effects of the law. It is time for the administration to update Congress and the American people on its efforts to correct serious back-end issues within its government-run health care system that could potentially be disastrous for insurers and Americans everywhere."
lundi 9 décembre 2013
Next K�lner R User Meeting: 13 December 2013
Quick reminder: The next Cologne R user group meeting is scheduled for this Friday, 13 December 2013. We are delighted to welcome:
Please sign up if you would like to come along. Notes from past meetings are available here.
The organisers, Bernd Wei� and Markus Gesmann, gratefully acknowledge the sponsorship of Revolution Analytics, who support the Cologne R user group as part of their vector programme.
View Larger Map
- Matt Dowle and Arun Srinivasan: Introduction to
data.table - Thomas Rahlf: Book presentation - Datendesign mit R
Please sign up if you would like to come along. Notes from past meetings are available here.
The organisers, Bernd Wei� and Markus Gesmann, gratefully acknowledge the sponsorship of Revolution Analytics, who support the Cologne R user group as part of their vector programme.
View Larger Map
Next K�lner R User Meeting: 13 December 2013
Quick reminder: The next Cologne R user group meeting is scheduled for this Friday, 13 December 2013. We are delighted to welcome:
Please sign up if you would like to come along. Notes from past meetings are available here.
The organisers, Bernd Wei� and Markus Gesmann, gratefully acknowledge the sponsorship of Revolution Analytics, who support the Cologne R user group as part of their vector programme.
View Larger Map
- Matt Dowle and Arun Srinivasan: Introduction to
data.table - Thomas Rahlf: Book presentation - Datendesign mit R
Please sign up if you would like to come along. Notes from past meetings are available here.
The organisers, Bernd Wei� and Markus Gesmann, gratefully acknowledge the sponsorship of Revolution Analytics, who support the Cologne R user group as part of their vector programme.
View Larger Map
lundi 2 décembre 2013
R in Insurance Conference, London, 14 July 2014
The second conference on R in Insurance will be held on Monday 14 July 2014 at Cass Business School in London, UK.
This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation. Topics covered may include actuarial statistics, capital modelling, pricing, reserving, reinsurance and extreme events, portfolio allocation, advanced risk tools, high-performance computing, econometrics and more. All topics will be discussed within the context of using R as a primary tool for insurance risk management, analysis and modelling.
The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance.
Invited talks will be given by:
- Arthur Charpentier, D�partement de math�matiques Universit� du Qu�bec � Montr�al
- Montserrat Guillen, Dept. Econometrics University of Barcelona together with Leo Guelman, Royal Bank of Canada (RBC Insurance division)
Details about the registration and abstract submission process will be published soon on www.RinInsurance.com.
You can contact us via rinsuranceconference at gmail dot com.
The organisers, Andreas Tsanakas and Markus Gesmann, gratefully acknowledge the sponsorship of Mango Solutions, RStudio, Cybaea and PwC.
R in Insurance Conference, London, 14 July 2014
The second conference on R in Insurance will be held on Monday 14 July 2014 at Cass Business School in London, UK.
This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation. Topics covered may include actuarial statistics, capital modelling, pricing, reserving, reinsurance and extreme events, portfolio allocation, advanced risk tools, high-performance computing, econometrics and more. All topics will be discussed within the context of using R as a primary tool for insurance risk management, analysis and modelling.
The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance.
Invited talks will be given by:
- Arthur Charpentier, D�partement de math�matiques Universit� du Qu�bec � Montr�al
- Montserrat Guillen, Dept. Econometrics University of Barcelona together with Leo Guelman, Royal Bank of Canada (RBC Insurance division)
Details about the registration and abstract submission process will be published soon on www.RinInsurance.com.
You can contact us via rinsuranceconference at gmail dot com.
The organisers, Andreas Tsanakas and Markus Gesmann, gratefully acknowledge the sponsorship of Mango Solutions, RStudio, Cybaea and PwC.
mercredi 27 novembre 2013
Small Businesses cancelling Health Insurance plans for Employees
The government estimated that millions of workers would be dropped from their work insurance under the Affordable Care Act, it's already happening now.
Nancy Clark owns a small business in New Hampshire, she was featured last year in a White House video blog, said that things are not right for her plan. She said that her insurer will increase her rates by 39: starting next year. Insurance that will cost her an additional $30,000.
Because of this she decided to terminate the insurance she's offered her 8 employees and turn to Obamacare, but there's been one problem after another.
�We�re experiencing technical difficulties. That's the nature of the beast,� said Clark.
Betsy Atkinson owns a business in Virginia Beach is also cancelling company insurance because her plan doesn't meet new Obamacare requirements and she can't afford to offer employees one that does.
�They�re going to have to go find their own insurance,� she said. �I�m sorry.�
lundi 25 novembre 2013
Not only verbs but also believes can be conjugated
Following on from last week, where I presented a simple example of a Bayesian network with discrete probabilities to predict the number of claims for a motor insurance customer, I will look at continuos probability distributions today. Here I follow example 16.17 in Loss Models: From Data to Decisions [1].
Suppose there is a class of risks that incurs random losses following an exponential distribution (density \(f(x) = \Theta {e}^{- \Theta x}\)) with mean \(1/\Theta\). Further, I believe that \(\Theta\) varies according to a gamma distribution (density \(f(x)= \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha \,-\, 1} e^{- \beta x } \)) with shape \(\alpha=4\) and rate \(\beta=1000\).
In the same way as I had good and bad driver in my previous post, here I have clients with different characteristics, reflected by the gamma distribution. I shall call the gamma distribution with the above parameters my prior parameter distribution and the exponential distribution the prior predictive distribution.
The textbook tells me that the unconditional mixed distribution of an exponential distribution with parameter \(\Theta\), whereby \(\Theta\) has a gamma distribution, is a Pareto II distribution (density \(f(x) = \frac{\alpha \beta^\alpha}{(x+\beta)^{\alpha+1}}\)) with parameters \(\alpha,\, \beta\). Its k-th moment is given in the general case by
\[
E[X^k] = \frac{\beta^k\Gamma(k+1)\Gamma(\alpha - k)}{\Gamma(\alpha)},\; -1 < k < \alpha. \] Thus, I can calculate the prior expected loss (\(k=1\)) as \(\frac{\beta}{\alpha-1}=\,\)333.33.
Now suppose I have three independent observations, namely losses of $100, $950 and $450 over the last 3 years. The mean loss is $500, which is higher than the $333.33 of my model.
Question: How should I update my belief about the client's risk profile to predict the expected loss cost for year 4 given those 3 observations?
Visually I can regard this scenario as a graph, with evidence set for years 1 to 3 that I want to propagate through to year 4.
Read more �
Suppose there is a class of risks that incurs random losses following an exponential distribution (density \(f(x) = \Theta {e}^{- \Theta x}\)) with mean \(1/\Theta\). Further, I believe that \(\Theta\) varies according to a gamma distribution (density \(f(x)= \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha \,-\, 1} e^{- \beta x } \)) with shape \(\alpha=4\) and rate \(\beta=1000\).
In the same way as I had good and bad driver in my previous post, here I have clients with different characteristics, reflected by the gamma distribution. I shall call the gamma distribution with the above parameters my prior parameter distribution and the exponential distribution the prior predictive distribution.
The textbook tells me that the unconditional mixed distribution of an exponential distribution with parameter \(\Theta\), whereby \(\Theta\) has a gamma distribution, is a Pareto II distribution (density \(f(x) = \frac{\alpha \beta^\alpha}{(x+\beta)^{\alpha+1}}\)) with parameters \(\alpha,\, \beta\). Its k-th moment is given in the general case by
\[
E[X^k] = \frac{\beta^k\Gamma(k+1)\Gamma(\alpha - k)}{\Gamma(\alpha)},\; -1 < k < \alpha. \] Thus, I can calculate the prior expected loss (\(k=1\)) as \(\frac{\beta}{\alpha-1}=\,\)333.33.
Now suppose I have three independent observations, namely losses of $100, $950 and $450 over the last 3 years. The mean loss is $500, which is higher than the $333.33 of my model.
Question: How should I update my belief about the client's risk profile to predict the expected loss cost for year 4 given those 3 observations?
Visually I can regard this scenario as a graph, with evidence set for years 1 to 3 that I want to propagate through to year 4.
Not only verbs but also believes can be conjugated
Following on from last week, where I presented a simple example of a Bayesian network with discrete probabilities to predict the number of claims for a motor insurance customer, I will look at continuos probability distributions today. Here I follow example 16.17 in Loss Models: From Data to Decisions [1].
Suppose there is a class of risks that incurs random losses following an exponential distribution (density \(f(x) = \Theta {e}^{- \Theta x}\)) with mean \(1/\Theta\). Further, I believe that \(\Theta\) varies according to a gamma distribution (density \(f(x)= \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha \,-\, 1} e^{- \beta x } \)) with shape \(\alpha=4\) and rate \(\beta=1000\).
In the same way as I had good and bad driver in my previous post, here I have clients with different characteristics, reflected by the gamma distribution. I shall call the gamma distribution with the above parameters my prior parameter distribution and the exponential distribution the prior predictive distribution.
The textbook tells me that the unconditional mixed distribution of an exponential distribution with parameter \(\Theta\), whereby \(\Theta\) has a gamma distribution, is a Pareto II distribution (density \(f(x) = \frac{\alpha \beta^\alpha}{(x+\beta)^{\alpha+1}}\)) with parameters \(\alpha,\, \beta\). Its k-th moment is given in the general case by
\[
E[X^k] = \frac{\beta^k\Gamma(k+1)\Gamma(\alpha - k)}{\Gamma(\alpha)},\; -1 < k < \alpha. \] Thus, I can calculate the prior expected loss (\(k=1\)) as \(\frac{\beta}{\alpha-1}=\,\)333.33.
Now suppose I have three independent observations, namely losses of $100, $950 and $450 over the last 3 years. The mean loss is $500, which is higher than the $333.33 of my model.
Question: How should I update my belief about the client's risk profile to predict the expected loss cost for year 4 given those 3 observations?
Visually I can regard this scenario as a graph, with evidence set for years 1 to 3 that I want to propagate through to year 4.
Read more �
Suppose there is a class of risks that incurs random losses following an exponential distribution (density \(f(x) = \Theta {e}^{- \Theta x}\)) with mean \(1/\Theta\). Further, I believe that \(\Theta\) varies according to a gamma distribution (density \(f(x)= \frac{\beta^\alpha}{\Gamma(\alpha)} x^{\alpha \,-\, 1} e^{- \beta x } \)) with shape \(\alpha=4\) and rate \(\beta=1000\).
In the same way as I had good and bad driver in my previous post, here I have clients with different characteristics, reflected by the gamma distribution. I shall call the gamma distribution with the above parameters my prior parameter distribution and the exponential distribution the prior predictive distribution.
The textbook tells me that the unconditional mixed distribution of an exponential distribution with parameter \(\Theta\), whereby \(\Theta\) has a gamma distribution, is a Pareto II distribution (density \(f(x) = \frac{\alpha \beta^\alpha}{(x+\beta)^{\alpha+1}}\)) with parameters \(\alpha,\, \beta\). Its k-th moment is given in the general case by
\[
E[X^k] = \frac{\beta^k\Gamma(k+1)\Gamma(\alpha - k)}{\Gamma(\alpha)},\; -1 < k < \alpha. \] Thus, I can calculate the prior expected loss (\(k=1\)) as \(\frac{\beta}{\alpha-1}=\,\)333.33.
Now suppose I have three independent observations, namely losses of $100, $950 and $450 over the last 3 years. The mean loss is $500, which is higher than the $333.33 of my model.
Question: How should I update my belief about the client's risk profile to predict the expected loss cost for year 4 given those 3 observations?
Visually I can regard this scenario as a graph, with evidence set for years 1 to 3 that I want to propagate through to year 4.
mercredi 20 novembre 2013
Chao: Health Insurance Marketplace is Still Incomplete
Henry Chao the deputy chief information officer at the Centers for Medicare and Medicaid Services said that the federal health insurance marketplace is not yet complete. He said that they are still building the �back office systems."
�we still have to build the financial management aspects of the system, which includes our accounting system and payment system and reconciliation system,� he said. "This part is still being developed and will be tested."
He admitted Tuesday that up to 40 percent of IT systems supporting the exchange still need to be built.
The Obama government completed the online system which allowed consumers to apply for insurance, compare health plans and enroll however, many parts of the system were still being repaired and were not performing as well as they had hoped.
�It�s not that it�s not working,� Chao told lawmakers at an Energy and Commerce Oversight and Investigations subcommittee hearing. �It�s still being developed and tested.�
Financial management tools are not yet done, he said, particularly the process that will deliver payments to insurers.
lundi 18 novembre 2013
Predicting claims with a Bayesian network
Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.15 in [1].
Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively.
Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers. Therefore the average number of claims per policyholder across the whole customer base would be:
To answer the above question I present the data here as a Bayesian Network using the
Next, I set the client's evidence (0 claims in year one and 1 claim in year two) and propagate these back through my network to estimate the probabilities that the customer is either a good (73.68%) or a bad (26.32%) driver. Knowing that a good driver has on overage 0.4 claims a year and a bad driver 0.7 claims I predict the number of claims for my customer with the given claims history as 0.4789.
Alternatively I could have added a third node for year 3 and queried the network for the probabilities of 0, 1 or 2 claims given that the customer had zero claims in year 1 and one claim in year 2. The sum product of the number of claims and probabilities gives me again an expected claims number of 0.4789.
[2] S�ren H�jsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. URL http://www.jstatsoft.org/v46/i10/
Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively.
Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers. Therefore the average number of claims per policyholder across the whole customer base would be:
0.75*(0*0.7 + 1*0.2 + 2*0.1) + 0.25*(0*0.5 + 1*0.3 + 2*0.2) = 0.475
To answer the above question I present the data here as a Bayesian Network using the
gRain package [2]. I start with the contingency probability tables for the driver type and the conditional probabilities for 0, 1 and 2 claims in year 1 and 2. As I assume independence between the years I set the same probabilities. I can now review my model as a mosaic plot (above) and as a graph (below) as well.
Next, I set the client's evidence (0 claims in year one and 1 claim in year two) and propagate these back through my network to estimate the probabilities that the customer is either a good (73.68%) or a bad (26.32%) driver. Knowing that a good driver has on overage 0.4 claims a year and a bad driver 0.7 claims I predict the number of claims for my customer with the given claims history as 0.4789.
Alternatively I could have added a third node for year 3 and queried the network for the probabilities of 0, 1 or 2 claims given that the customer had zero claims in year 1 and one claim in year 2. The sum product of the number of claims and probabilities gives me again an expected claims number of 0.4789.
References
[1] Klugman, S. A., Panjer, H. H. & Willmot, G. E. (2004), Loss Models: From Data to Decisions, Wiley Series in Proability and Statistics.[2] S�ren H�jsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. URL http://www.jstatsoft.org/v46/i10/
Session Info
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Rgraphviz_2.6.0 gRain_1.2-2 gRbase_1.6-12 graph_1.40.0
loaded via a namespace (and not attached):
[1] BiocGenerics_0.8.0 igraph_0.6.6 lattice_0.20-24 Matrix_1.1-0
[5] parallel_3.0.2 RBGL_1.38.0 stats4_3.0.2 tools_3.0.2Predicting claims with a Bayesian network
Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.15 in [1].
Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively.
Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers. Therefore the average number of claims per policyholder across the whole customer base would be:
To answer the above question I present the data here as a Bayesian Network using the
Next, I set the client's evidence (0 claims in year one and 1 claim in year two) and propagate these back through my network to estimate the probabilities that the customer is either a good (73.68%) or a bad (26.32%) driver. Knowing that a good driver has on overage 0.4 claims a year and a bad driver 0.7 claims I predict the number of claims for my customer with the given claims history as 0.4789.
Alternatively I could have added a third node for year 3 and queried the network for the probabilities of 0, 1 or 2 claims given that the customer had zero claims in year 1 and one claim in year 2. The sum product of the number of claims and probabilities gives me again an expected claims number of 0.4789.
[2] S�ren H�jsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. URL http://www.jstatsoft.org/v46/i10/
Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively.
Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers. Therefore the average number of claims per policyholder across the whole customer base would be:
0.75*(0*0.7 + 1*0.2 + 2*0.1) + 0.25*(0*0.5 + 1*0.3 + 2*0.2) = 0.475To answer the above question I present the data here as a Bayesian Network using the
gRain package [2]. I start with the contingency probability tables for the driver type and the conditional probabilities for 0, 1 and 2 claims in year 1 and 2. As I assume independence between the years I set the same probabilities. I can now review my model as a mosaic plot (above) and as a graph (below) as well.Next, I set the client's evidence (0 claims in year one and 1 claim in year two) and propagate these back through my network to estimate the probabilities that the customer is either a good (73.68%) or a bad (26.32%) driver. Knowing that a good driver has on overage 0.4 claims a year and a bad driver 0.7 claims I predict the number of claims for my customer with the given claims history as 0.4789.
Alternatively I could have added a third node for year 3 and queried the network for the probabilities of 0, 1 or 2 claims given that the customer had zero claims in year 1 and one claim in year 2. The sum product of the number of claims and probabilities gives me again an expected claims number of 0.4789.
References
[1] Klugman, S. A., Panjer, H. H. & Willmot, G. E. (2004), Loss Models: From Data to Decisions, Wiley Series in Proability and Statistics.[2] S�ren H�jsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. URL http://www.jstatsoft.org/v46/i10/
Session Info
R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Rgraphviz_2.6.0 gRain_1.2-2 gRbase_1.6-12 graph_1.40.0
loaded via a namespace (and not attached):
[1] BiocGenerics_0.8.0 igraph_0.6.6 lattice_0.20-24 Matrix_1.1-0
[5] parallel_3.0.2 RBGL_1.38.0 stats4_3.0.2 tools_3.0.2lundi 11 novembre 2013
googleVis 0.4.7 with RStudio integration on CRAN
In my previous post, I presented a preview version of googleVis that provided an integration with RStudio's Viewer pane (introduced with version 0.98.441).
Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.
However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.
Hence, googleVis gained a new option
The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.
Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.
However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.
Hence, googleVis gained a new option
'googleVis.viewer' that controls the default output of the googleVis plot method. On package load it is set to getOption("viewer") and if you use RStudio, then its viewer pane will be used for displaying non-Flash and un-merged charts. You can set options("googleVis.viewer" = NULL) and the googleVis plot function will open all output in the default browser again. Thanks to J.J. from RStudio for the tip. The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.
Session Info
RStudio v0.98.456 and R version 3.0.2 (2013-09-25)Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] googleVis_0.4.7 XML_3.95-0.2
loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.2
googleVis 0.4.7 with RStudio integration on CRAN
In my previous post, I presented a preview version of googleVis that provided an integration with RStudio's Viewer pane (introduced with version 0.98.441).
Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.
However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.
Hence, googleVis gained a new option
The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.
Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.
However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.
Hence, googleVis gained a new option
'googleVis.viewer' that controls the default output of the googleVis plot method. On package load it is set to getOption("viewer") and if you use RStudio, then its viewer pane will be used for displaying non-Flash and un-merged charts. You can set options("googleVis.viewer" = NULL) and the googleVis plot function will open all output in the default browser again. Thanks to J.J. from RStudio for the tip. The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.
Session Info
RStudio v0.98.456 and R version 3.0.2 (2013-09-25)Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] googleVis_0.4.7 XML_3.95-0.2
loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.2
Goodbye Trans Fat?
The U.S. Food and Drug Administration said last Thursday it will phase out trans fats that
would eliminate artery-clogging from our diet. This will give manufacturers and restaurant some problems and may cause prices to shoot up but will be beneficial to our health.
Dr. Margaret A. Hamburg, commissioner of FDA said that this move will prevent 20,000 heart attacks and 7,000 deaths each year. But critics say that this is just a political move since the real threat to our health is not trans fat but the increased use of pesticides on our food and genetically engineered foods. Trans fats are identified and labeled on our food but in the case for genetically engineered foods and pesticide laden food no information are given to consumers.
Chris Shanahan of Frost & Sullivan market research firm said that if FDA bans trans fat "in the long term, prices of certain foods will increase and different foods will be discontinued."
-----------------------
Check out my friends blog http://maverikmaven.blogspot.com/ the blog covers a variety of topics including consumer goods.
would eliminate artery-clogging from our diet. This will give manufacturers and restaurant some problems and may cause prices to shoot up but will be beneficial to our health.
Dr. Margaret A. Hamburg, commissioner of FDA said that this move will prevent 20,000 heart attacks and 7,000 deaths each year. But critics say that this is just a political move since the real threat to our health is not trans fat but the increased use of pesticides on our food and genetically engineered foods. Trans fats are identified and labeled on our food but in the case for genetically engineered foods and pesticide laden food no information are given to consumers.
Chris Shanahan of Frost & Sullivan market research firm said that if FDA bans trans fat "in the long term, prices of certain foods will increase and different foods will be discontinued."
-----------------------
Check out my friends blog http://maverikmaven.blogspot.com/ the blog covers a variety of topics including consumer goods.
jeudi 7 novembre 2013
Will You Buy Twitter IPO shares?
New York Stock Exchange welcomes Twitter on Thursday November 7, 2013. They will have the symbol TWTR and their share is priced at $26 each to raise around $2.1 billion.
Twitter is part of our everyday life for most of us and it has a lot of media attention which is why there is a great demand for its shares. However, be very cautious remember Facebook? a lot of people were burned by it. The best thing to do is wait. There's no guarantee the stock will trade higher, and if you would look at several recent social media IPOs, the stocks actually dropped like facebook.
Twitter is part of our everyday life for most of us and it has a lot of media attention which is why there is a great demand for its shares. However, be very cautious remember Facebook? a lot of people were burned by it. The best thing to do is wait. There's no guarantee the stock will trade higher, and if you would look at several recent social media IPOs, the stocks actually dropped like facebook.
lundi 4 novembre 2013
Display googleVis charts within RStudio
The preview version 0.98.441 of RStudio introduced a new viewer pane to render local web content and with that it allows me to display googleVis charts within RStudio rather than in a separate browser window.
I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.
The behaviour can also be controlled via the option
Of course shiny apps can also run in the viewer pane. Here is the example of the
For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.
Is this a step in the right direction? Please use the voting buttons below.
I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.
The behaviour can also be controlled via the option
viewer. Set options("viewer"=NULL) and googleVis will plot all output in the browser again.Of course shiny apps can also run in the viewer pane. Here is the example of the
renderGvis help page of googleVis. For more information about the new viewer pane see the online RStudio documentation. 
For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.
Is this a step in the right direction? Please use the voting buttons below.
Session Info
R Under development (unstable) (2013-10-25 r64109)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] googleVis_0.4.6
loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.1.0 Display googleVis charts within RStudio
The preview version 0.98.441 of RStudio introduced a new viewer pane to render local web content and with that it allows me to display googleVis charts within RStudio rather than in a separate browser window.
I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.
The behaviour can also be controlled via the option
Of course shiny apps can also run in the viewer pane. Here is the example of the
For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.
Is this a step in the right direction? Please use the voting buttons below.
I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.
The behaviour can also be controlled via the option
viewer. Set options("viewer"=NULL) and googleVis will plot all output in the browser again.Of course shiny apps can also run in the viewer pane. Here is the example of the
renderGvis help page of googleVis. For more information about the new viewer pane see the online RStudio documentation. For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.
Is this a step in the right direction? Please use the voting buttons below.
Session Info
R Under development (unstable) (2013-10-25 r64109)
Platform: x86_64-apple-darwin10.8.0 (64-bit)
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] googleVis_0.4.6
loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.1.0 jeudi 31 octobre 2013
mercredi 30 octobre 2013
Infographics on Why Others Can't Keep Their Old Insurance Plans
Check out the Infographics done by NYTimes, which explains why some people can't keep their insurance plans. It is very detailed and easy to comprehend. President Barrack Obama promised that people can keep their old insurance plan under the Obamacare, however the truth is most need to buy a new plan.
mardi 29 octobre 2013
High resolution graphics with R
For most purposes PDF or other vector graphic formats such as windows metafile and SVG work just fine. However, if I plot lots of points, say 100k, then those files can get quite large and bitmap formats like PNG can be the better option. I just have to be mindful of the resolution.
As an example I create the following plot:
Hence, I increase the resolution to 150 dots per pixel.
This looks a bit odd. The file size is only 29 KB but the annotations look too big. Well, the file has only 400 x 400 pixels and the size of a pixel is fixed. Thus, I have to provide more pixels, or in other words increase the plot size. Doubling the width and height as I double the resolution makes sense.
Next I increase the resolution further to 300 dpi and the graphic size to 1600 x 1600 pixels. The file is still very crisp. Of course the file size increased. Now it is 654 KB in size, yet sill only about 1/8 of the PDF and I can embed it in LaTeX as well.
Note, you can click on the charts to access the original files of this post.
As an example I create the following plot:
x <- rnorm(100000)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
png("100kPoints72dpi.png", units = "px", width=400, height=400)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()
Hence, I increase the resolution to 150 dots per pixel.
png("100kHighRes150dpi.png", units="px", width=400, height=400, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()
This looks a bit odd. The file size is only 29 KB but the annotations look too big. Well, the file has only 400 x 400 pixels and the size of a pixel is fixed. Thus, I have to provide more pixels, or in other words increase the plot size. Doubling the width and height as I double the resolution makes sense.
png("100kHighRes150dpi2.png", units="px", width=800, height=800, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()
Next I increase the resolution further to 300 dpi and the graphic size to 1600 x 1600 pixels. The file is still very crisp. Of course the file size increased. Now it is 654 KB in size, yet sill only about 1/8 of the PDF and I can embed it in LaTeX as well.
png("100kHighRes300dpi.png", units="px", width=1600, height=1600, res=300)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()
Note, you can click on the charts to access the original files of this post.
High resolution graphics with R
For most purposes PDF or other vector graphic formats such as windows metafile and SVG work just fine. However, if I plot lots of points, say 100k, then those files can get quite large and bitmap formats like PNG can be the better option. I just have to be mindful of the resolution.
As an example I create the following plot:
Hence, I increase the resolution to 150 dots per pixel.
This looks a bit odd. The file size is only 29 KB but the annotations look too big. Well, the file has only 400 x 400 pixels and the size of a pixel is fixed. Thus, I have to provide more pixels, or in other words increase the plot size. Doubling the width and height as I double the resolution makes sense.
Next I increase the resolution further to 300 dpi and the graphic size to 1600 x 1600 pixels. The file is still very crisp. Of course the file size increased. Now it is 654 KB in size, yet sill only about 1/8 of the PDF and I can embed it in LaTeX as well.
Note, you can click on the charts to access the original files of this post.
As an example I create the following plot:
x <- rnorm(100000)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
png("100kPoints72dpi.png", units = "px", width=400, height=400)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()Hence, I increase the resolution to 150 dots per pixel.
png("100kHighRes150dpi.png", units="px", width=400, height=400, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()This looks a bit odd. The file size is only 29 KB but the annotations look too big. Well, the file has only 400 x 400 pixels and the size of a pixel is fixed. Thus, I have to provide more pixels, or in other words increase the plot size. Doubling the width and height as I double the resolution makes sense.
png("100kHighRes150dpi2.png", units="px", width=800, height=800, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()Next I increase the resolution further to 300 dpi and the graphic size to 1600 x 1600 pixels. The file is still very crisp. Of course the file size increased. Now it is 654 KB in size, yet sill only about 1/8 of the PDF and I can embed it in LaTeX as well.
png("100kHighRes300dpi.png", units="px", width=1600, height=1600, res=300)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()Note, you can click on the charts to access the original files of this post.
mercredi 23 octobre 2013
Obamacare website Utter Failure
The number of Obamacare website problems since the website opened has been deeply embarrassing for the White House. The drawbacks have called into question whether the Obama administration is capable of implementing the complex policy they seems to be unaware of the scope of the problems when the exchange sites opened.
Even Obama acknowledges problems:
�Nobody is madder than me about the fact that the website is not working as well as it should, which means it�s gonna get fixed,� Obama said.
He even turn to an ex-adviser Jeffrey Zients, he is the former acting director of the White House budget office.
A person close to the project said that "No way it was properly tested before it went live" since the website is full of bugs and junk computer codes.
lundi 21 octobre 2013
Review: K�lner R Meeting 18 October 2013
The Cologne R user group met last Friday for two talks on split apply combine in R and XLConnect by Bernd Wei� and G�nter Faes respectively, before the usual Schnitzel and K�lsch at the Lux.
The
Alternatively to the base R function Bernd touched also on the
G�nter presented the
Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
Split apply combine in R
The
apply family of functions in R is incredible powerful, yet for newcomers often somewhat mysterious. Thus, Bernd gave an overview of the different apply functions and their cousins. The various functions differ in their object inputs, e.g. vectors, arrays, data frames or lists, and their outputs. Other related functions are by, aggregate and ave. While functions like aggregate reduce the output size, others like ave will return as many rows as the input object and repeat the results where necessary. Alternatively to the base R function Bernd touched also on the
**ply functions of the plyr package. The function names are certainly easier to remember, but their syntax can be a little awkward (.()). Bernd's slides, in German, are already available from our Meetup site. XLConnect
When dealing with data stored in spreadsheets most member of the group rely onread.csv and write.csv in R. However, if you have a spreadsheet with multiple tabs and formatted numbers, read.csv becomes clumsy, as you would have to save each tab without any formatting in separate files. G�nter presented the
XLConnect as an alternative to read.csv or indeed RODBC for reading spreadsheet data. It uses the Apache POI API as the underlying interface. XLConnect requires a Java runtime environment on your computer, but no installation of Excel. That makes it a true platform independent solution to exchange data with spreadsheets and R. Not only can you read defined rows and columns from Excel into R, or indeed named ranges, but in the same way data can be stored in Excel files again and to top it all - also graphic output from R.Next K�lner R meeting
The next meeting is scheduled for 13 December 2013. A discussion of the data.table package is already on the agenda.Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
Review: K�lner R Meeting 18 October 2013
The Cologne R user group met last Friday for two talks on split apply combine in R and XLConnect by Bernd Wei� and G�nter Faes respectively, before the usual Schnitzel and K�lsch at the Lux.
The
Alternatively to the base R function Bernd touched also on the
G�nter presented the
Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
Split apply combine in R
The
apply family of functions in R is incredible powerful, yet for newcomers often somewhat mysterious. Thus, Bernd gave an overview of the different apply functions and their cousins. The various functions differ in their object inputs, e.g. vectors, arrays, data frames or lists, and their outputs. Other related functions are by, aggregate and ave. While functions like aggregate reduce the output size, others like ave will return as many rows as the input object and repeat the results where necessary. Alternatively to the base R function Bernd touched also on the
**ply functions of the plyr package. The function names are certainly easier to remember, but their syntax can be a little awkward (.()). Bernd's slides, in German, are already available from our Meetup site. XLConnect
When dealing with data stored in spreadsheets most member of the group rely onread.csv and write.csv in R. However, if you have a spreadsheet with multiple tabs and formatted numbers, read.csv becomes clumsy, as you would have to save each tab without any formatting in separate files. G�nter presented the
XLConnect as an alternative to read.csv or indeed RODBC for reading spreadsheet data. It uses the Apache POI API as the underlying interface. XLConnect requires a Java runtime environment on your computer, but no installation of Excel. That makes it a true platform independent solution to exchange data with spreadsheets and R. Not only can you read defined rows and columns from Excel into R, or indeed named ranges, but in the same way data can be stored in Excel files again and to top it all - also graphic output from R.Next K�lner R meeting
The next meeting is scheduled for 13 December 2013. A discussion of the data.table package is already on the agenda.Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.
Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.
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