ldp01

> My point is, the statistics may be painting a different picture when viewed in the whole. Even if this specific case is true, we may just be trading one cause of death for another. There's no guarantee overall injury or "suffering" will be reduced.

The same is true when you prevent death by any means at all. Everyone dies eventually by some cause. This does not mean we shouldn't try to prolong life where we can by avoiding early, needless death! (Otherwise why would we even have airbags or seatbelts, etc?)

Another powerful tool I will keep in mind!

I think this thread illustrates a kind of tension between those coming from an IT/big-data/web oriented background and the more traditional statistics/science/engineering side.

The IT side bring a lot of very powerful and scalable tools to the table. However there are aspects of traditional work which I suspect are lost on some big-data people.

For example, in my line of work (physical asset mgmt) we deal with a lot of very small datasets, very poor quality datasets (e.g. some guy's favourite spreadsheet) and also cultural issues (some engineers are inherently averse to changing systems, and spending decisions are inherently political). In this situation, there is a limit to the benefit of more powerful/scalable tools, and it is advantageous to use tools which are considered high quality and vetted by the community.

R is in a good position here as it has the pedigree of being accepted by the academic stats community, as well as actually being a great tool.

A reply from the man himself! Thanks for the link. I'll have a go.

I do like the look of the dplyr library a lot. Combining functions like select and group_by with the pipe operators creates code that is reminiscent of SQL- very nice for readability.

There are many things wrong with R but basic plotting functions are one of its strengths.

Is this the way you did it? It seems pretty intuitive...

a=pi/180

x=1:360

plot(x,sin(a * x))

plot(x,a * cos(a * x))

plot(x,-a^2 * sin(a * x))

I'm glad I read this comment. After checking some of the docs I think I will have a go at Python for data wrangling. List comprehensions look... friendly.

R still rules for plotting and running canned statistical procedures but sometimes I feel like if I stop programming R for a week I forget how to use it effectively... E.g. Forgetting to add stringsAsFactor=FALSE to everything, forgetting rbind() can overwrite column names, forgetting I have to define my own string concatenation operator in every script.

If Python can save me some of the frustration involved in manipulating data frames that will be nice.

Note that's a winter peak you're looking at.

The highest summer peak appears to have been just above 13GW. http://www.wattclarity.com.au/2016/02/highest-electricity-de...

However your point still stands I believe.

Very neat! Would you consider adding a TeX() function to format the output strings into LaTeX? This functionality is very useful for someone who needs to present their results.

I'm doing some coursework for my MSc in math, and have taken to using CASs (Maxima, and Yacas via RYacas) to explore/validate my assignment questions/answers. The automatic TeX formatting in those systems has been an essential feature.

Sorry to hear about your sister, that's horrible. I absolutely agree that there is a role for these devices in monitoring medical conditions.

I have a sister who was born with a tachycardia condition which recurred in high school. I got her one of the Garmin wrist watches with a heart rate sensor so she could monitor the condition over time. Obviously it doesn't fix anything on its own, but it can help to understand the problem and feel more in control.