mages' blog

Minimal examples help

The other day I got stuck working with a huge data set using data.table in R. It took me a little while to realise that I had to produce a minimal reproducible example to actually understand why I got stuck in the first place. I know, this is the mantra I should follow before I reach out to R-help, Stack Overflow or indeed the package authors. Of course, more often than not, by following this advise, the problem becomes clear and with that the solution obvious.

Ok, here is the problem. Well, easy to write down now, after I understood it.

Suppose, I have some data that describes my sales targets by product and quarter:

library(data.table)
Plan <- data.table(
  Product=c(rep("Apple",3),rep("Kiwi",3),rep("Coconut",3)),
  Quarter=rep(c(1,2,3), 3),
  Target=1:9)
Plan
##    Product Quarter Target
## 1:   Apple       1      1
## 2:   Apple       2      2
## 3:   Apple       3      3
## 4:    Kiwi       1      4
## 5:    Kiwi       2      5
## 6:    Kiwi       3      6
## 7: Coconut       1      7
## 8: Coconut       2      8
## 9: Coconut       3      9

Further, I have some actual data, which is also broken down by region, but has no data for coconut:

Actual <- data.table(
 Region=rep(c("North", "South"), each=4),
 Product=rep(c("Apple", "Kiwi"), times=4),
 Quarter=rep(c(1,1,2,2), 2), Sales=1:8)
Actual
##    Region Product Quarter Sales
## 1:  North   Apple       1     1
## 2:  North    Kiwi       1     2
## 3:  North   Apple       2     3
## 4:  North    Kiwi       2     4
## 5:  South   Apple       1     5
## 6:  South    Kiwi       1     6
## 7:  South   Apple       2     7
## 8:  South    Kiwi       2     8

What I would like to do is to join both data sets together, so that I can compare my sales figures with my targets. In particular, I would like to see also my targets for future quarters. However, I would like to filter out the target data for those products that are not available in a region, coconut in my example.

First I have to set keys for my data sets on which I would like to join them:

setkey(Actual, Product, Quarter)
setkey(Plan, Product, Quarter)

Because I want to see also future targets I am not using Plan[Actual]. Instead I join the Plan data for each region; but then I get also the target data for coconut:

Actual[, .SD[Plan], by=list(Region)]
##     Region Product Quarter Sales Target
##  1:  North   Apple       1     1      1
##  2:  North   Apple       2     3      2
##  3:  North   Apple       3    NA      3
##  4:  North Coconut       1    NA      7
##  5:  North Coconut       2    NA      8
##  6:  North Coconut       3    NA      9
##  7:  North    Kiwi       1     2      4
##  8:  North    Kiwi       2     4      5
##  9:  North    Kiwi       3    NA      6
## 10:  South   Apple       1     5      1
## 11:  South   Apple       2     7      2
## 12:  South   Apple       3    NA      3
## 13:  South Coconut       1    NA      7
## 14:  South Coconut       2    NA      8
## 15:  South Coconut       3    NA      9
## 16:  South    Kiwi       1     6      4
## 17:  South    Kiwi       2     8      5
## 18:  South    Kiwi       3    NA      6

Ok, that means I have to filter for the products in my actual data to match the relevant planning data:

Actual[, .SD[
  Plan[
    Product %in% unique(.SD[, Product])
    ]
  ], by=list(Region)]
##     Region Product Quarter Sales Target
##  1:  North   Apple       1     1      1
##  2:  North   Apple       2     3      2
##  3:  North   Apple       3    NA      3
##  4:  North    Kiwi       1     2      4
##  5:  North    Kiwi       2     4      5
##  6:  North    Kiwi       3    NA      6
##  7:  South   Apple       1     5      1
##  8:  South   Apple       2     7      2
##  9:  South   Apple       3    NA      3
## 10:  South    Kiwi       1     6      4
## 11:  South    Kiwi       2     8      5
## 12:  South    Kiwi       3    NA      6

That's it. Now I can get back to my original huge and complex data set and move on.

Please let me know if there is a better way of achieving the above.

Session Info

R version 3.1.2 Patched (2015-01-20 r67564)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.2 (Yosemite)

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] data.table_1.9.4

loaded via a namespace (and not attached):
[1] chron_2.3-45 plyr_1.8.1 Rcpp_0.11.4 reshape2_1.4.1 stringr_0.6.2 

Reading Arduino data directly into R

I have experimented with reading an Arduino signal into R in the past, using Rserve and Processing. Actually, it is much easier. I can read the output of my Arduino directly into R with the scan function.

Here is my temperature sensor example again:


And all it needs to read the signal into the R console with my computer is:

> f <- file("/dev/cu.usbmodem3a21", open="r")
> scan(f, n=1)
Read 1 item
[1] 20.8
> close(f)

Super simple: Open the file connection. Scan n lines of data. Close the file connection. Job done.

Note: This worked for me on my Mac and I am sure it will work in a very similar way on a Linux box as well, but I am not so sure about Windows. Crucially, I had to learn the difference between the tty* and cu* devices. I found the following statement in Mike's PBX Cookbook particular insightful:

You might notice that each serial device shows up twice in /dev, once as a tty.* and once as a cu.*. So, what's the difference? Well, TTY devices are for calling into UNIX systems, whereas CU (Call-Up) devices are for calling out from them (eg, modems). We want to call-out from our Mac, so /dev/cu.* is the correct device to use.

You find the file address of your Arduino by opening the Arduino software and looking it up under the menu Tools > Port.

With a little more R code I can create a 'live' data stream plot of my Arduino.

Reload this page to see the animated Gif again.

R code

Here is the original Arduino sketch as well:

Session Info

R version 3.1.2 (2014-10-31)
Platform: x86_64-apple-darwin13.4.0 (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     

loaded via a namespace (and not attached):
[1] tools_3.1.

What have a physicist, an entrepreneur and an actor in common?

They all try to do something new and take the risk to be seen as a fool.

Over the last few days I stumbled over three videos by a physicist, an entrepreneur and an actor, which at first have little in common, but they do. They all need to know when they are wrong in order to progress. If you are not wrong, then you are likely to be right, but that is often difficult to prove - often not at all.

• The physicist has an idea for a new law. How does he/she know if it is wrong?
• The entrepreneur has an idea for a new business. How does he/she know if it won't make money?
• The actor is rehearsing a new scene. How does he/she know if the acting is not believable?

Here I have Richard Feynman, Rob Fitzpatrick and Michael Caine.

The physicist

Start with a guess for a new law. Predict the consequences and compare the prediction with the results of experiments. If the experiments disagree with your prediction, then your idea is wrong.

The entrepreneur

Ask your mum questions about the assumptions of your new business idea, without telling her anything about it. Do this in the same way with friends, without them knowing that you talk about a new business idea. This will require a great care in the way you phrase your questions. Don't fish for compliments. If the answers are different from your exceptions, then your assumptions are wrong and perhaps your business idea as well.

The actor

Rehearse your dialogue and observe how other people react to it. If they say something like "I am sorry, I see you are rehearsing, but I need to talk to you", then you are not doing it well. If on the other hand they join the conversation, so that you have to say: "I am sorry, but we are rehearsing" then you are getting there.

Willing/wanting to know when you are wrong is one the hardest things to accept, and yet the best way to progress quickly.

R in Insurance 2015: Registration Opened

The registration for the third conference on R in Insurance on Monday 29 June 2015 at the University of Amsterdam has opened.


This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for statistical computation.

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:

• Prof. Richard Gill, Leiden University
• Dr James Guszcza, FCAS, Chief Data Scientist, Deloitte - US

Perhaps you have seen Richard's TED talk on Statistical Errors in Court or Jim's talk on Predictive Modelling and Behaviour Insight or Actuarial Analytics in R. We are thrilled that they accepted our invitation.

We invite you to submit a one-page abstract for consideration. Both academic and practitioner proposals related to R are encouraged. The submission deadline for abstracts is 28 March 2015.
Please email your abstract of no more than 300 words (in text or pdf format) to r-in-insurance@uva.nl.

Details about the registration and abstract submission are given on the dedicated R in Insurance page at the University of Amsterdam.

For more information about the past events visit www.rininsurance.com.