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Berry
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The color scale with white after red mimics geography maps, where this looks quite natural (white being associated with mountain peak snow). It's a common color scale in environmental sciences. In R, it's available in terrain.colors(). "Maybe on average the ground level is collapsing?" In the long term yes, and then it's called subsidence. I think that's what the second map displays. "shouldn't the sinusoidal curve [in the first map] drift downward over time?" Yes. And it shouldn't be that smooth in the first place, as you already pointed out.
jlbriggs: Nice idea with the vertical US average lines! However, we have a different understanding of the original numbers (not our fault!). Using the US-average as example: I understood delinquent to be 10.1% of 31.4% underwater, hence 3.2% of total mortgage owners. Same with the cities... You can see how the values in our charts differ. I have Las Vegas total underwater at 71%, in your graph it is displayed at slightly over 80%.
Here's a quick R script for bar plots comparing all the listed places and the resulting image: https://github.com/brry/misc/blob/master/housing.R https://github.com/brry/misc/blob/master/housing.png
Sooner yet, there's the Free and Open Source Software Developers Meeting (FOSDEM) in Brussels, Feb 4+5: https://fosdem.org/2017/ Not an R conference specifically, but I know there'll be at least one R talk (mine ^^)
Toggle Commented Jan 24, 2017 on Upcoming R Conferences at Revolutions
Wait. You show five excellent reasons not to use dual axis plots and then proceed using exactly those? That seems ironic. Am I missing something? My take away is: multipanel plots are usually the best solution. "will often be my preferred approach to this type of data". Also, adding a third series is easy...
I have a base R solution for the Joy Divison Album recreation, where the plotting part only has a few lines of code. It assumes a different data structure: a dataframe with the signal over time for each column. (This is how I tend to record or obtain data in environmental science). I'm not sure getting into the base/ggplot war* is good idea, but here's my contribution anyways. https://github.com/brry/misc/blob/master/unknown_pleasures.R https://github.com/brry/misc/blob/master/unknown_pleasures.png *: The Great War: http://simplystatistics.org/2016/02/11/why-i-dont-use-ggplot2/ http://varianceexplained.org/r/why-I-use-ggplot2/ http://flowingdata.com/2016/03/22/comparing-ggplot2-and-r-base-graphics/
Toggle Commented Apr 2, 2016 on Two fun plots with R at Revolutions
In case it's interesting: clay soil doesn't let water in easily (but retains nutrients well), sand doesn't hold water, silt is more prone to erosion. So the 'optimum' soil has a mixture of all (called loam) and resides in the middle of the texture triangle. (Yes, this is a strong symplification. Read a soil science book for the nuances and details^^)
It's quite useful in some geosciences, like soil science. Classifying a soil by grain size distribution (which has all sorts of implications on how the plants can take up water, how water is retained in the soil and thus e.g. how irrigation needs to be done) is often done on a ternery diagram called texture triangle like this one: http://i.stack.imgur.com/r7fYI.png clay are the very small particles (not visible to naked eye), sand is what you'd typically know as grains from the beach, silt is inbetween. There's an exact definition for grain size classes, btw. So if you have several soil samples, on the diagramm it's very easy to get a rough idea of soil texture and thus, usability for agriculture.
That's what I was thinking too, RossD. as.Date("2015010100", "%Y%m%d") perfectly yields: "2015-01-01" Joseph Rikkert: thanks for this awesome list. I'd love to see medium length blogposts looking at one package at a time. I would probably take notice of more of them that way ;-)
Toggle Commented Feb 4, 2016 on New Data Sources for R at Revolutions
Nice Post, clear code! I'll be pointing to it in optim tutorials! The last two lines of plotGamma could be simplified to: lines(gx, gy, col="blue", type="h") text(gx, 0, p, adj=c(1.1, -0.2), cex=cex)
Or you can use base R. Shorter code... Here's the result, also with my idea for the title. pen <- c(627, 625, 653, 617, 661, 730) pen_av <- mean(head(pen,-1)) plot(2010:2015, pen, ylim=c(600,750), las=1, bty="n", ylab="number of penalties", xlab="", pch=4, lwd=3, cex=1.3) abline(h=pen_av, lty=2) arrows(x0=2015, y0=pen_av, y1=tail(pen,1)-5, col="blue", lwd=2) title(main="NFL Penalties jumped 15%\n in the first 3 weeks of 2015\ncompared to previous seasons") text(2014, pen_av, "5 year average", adj=c(0,1.3)) text(2015.1, 700, "15%\nincr.", adj=0, col="blue", xpd=TRUE)
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Sep 30, 2015