The above code is assuming you have the
wraprpackage attached via already having run
Notice we picked R-related operator names. We stayed away from overloading the
+operator, as the arithmetic operators are somewhat special in how they dispatch in R. The goal wasn’t to make R more like Python, but to adapt a good idea from Python to improve R.
Also, it’s a little late to pick up the discount (though Manning has discounts pretty much every day so be patient and you’ll find 40+ percent off) but check out the second edition of Practical Data Science with R by Mount and Nina Zumel. I’ve held off on reading it so far because I want to wait until it’s closer to completion, but it is on my to-read list.
Now we’re going to look at movie reviews and predict whether a movie review is a positive or a negative review based on its words. If you want to play along at home, grab the data set, which is under 3MB zipped in 2000 reviews in total.
Unike last time, I’m going to break this out into sections with commentary in between. If you want the full script with notebook, check out the GitHub repo I put together for this talk.
Assuming I ever get a chance to do this talk again, I’m probably going to change the data sets in the example given how overplayed iris is.
Convolutional Neural Nets are usually abbreviated either CNNs or ConvNets. They are a specific type of neural network that has very particular differences compared to MLPs. Basically, you can think of CNNs as working similarly to the receptive fields of photoreceptors in the human eye. Receptive fields in our eyes are small connected areas on the retina where groups of many photo-receptors stimulate much fewer ganglion cells. Thus, each ganglion cell can be stimulated by a large number of receptors, so that a complex input is condensed into a compressed output before it is further processed in the brain.
If you’re interested in understanding why a CNN will classify the way it does, chapter 5 of Deep Learning with R is a great reference.
I was reminded of this recently as I was working with R, trying to read a nearly 2 GB data file. I wanted to read in 5% of the data and output it to a smaller file that would make the test code run faster. The particular function I was working with needed a row count as one of its parameters. For me, that meant I had to determine the number of rows in the source file and multiply by 0.05. I tied the code for all of those tasks into one script block.
Now, none to my surprise, it was slow. In my short experience, I’ve found R isn’t particularly snappy–even when the data can fit comfortably in memory. I was pretty sure I could beat R’s record count performance handily with C#. And I did. I found some related questions on StackOverflow. A small handful of answers discussed the efficiency of various approaches. I only tried two C# variations: my original attempt, and a second version that was supposed to be faster (the improvement was nominal).
To fact-check Dave (because this blog is about nothing other than making sure Dave is right), I checked the source code to the wc command. That command also streams through the entire file, so Dave’s premise looks good.
The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce a unified single plot.
In the below code, We will build three different histograms using the R’s in-built dataset iris and then assign one by one to an R object. Finally, we will use cowplot function
plot_grid()to combine the two plots of our interest.
The only thing that disappointed me with
cowplot is that its name has nothing to do with cattle.
The suite of AzureR packages for interfacing with Azure services from R is now available on CRAN. If you missed the earlier announcements, this means you can now use the
install.packagesfunction in R to install these packages, rather than having to install from the Github repositories. Updated versions of these packages will also be posted to CRAN, so you can get the latest versions simply by running
Read on for a summary of those packages.
Even more common than grouping columns is probably grouping data by rows. The htmlTable allows you to do this by
tspanner. The most common approach is by using
rgroupas the first row-grouping element but with larger tables you frequently want to separate concepts into separate sections. Here’s a more complex example. This has previously been a little cumbersome to to counting the rows of each tspanner but now you’re able to (1) leave out the last row, (2) specify the number of rgroups instead of the number of rows. The latter is convenient as the
n.tspannermust align with the underlying rgroup.
I haven’t used this package before, but it does look interesting. H/T R-bloggers
Docker is designed to enclose environments inside an image / a container. What this allows, for example, is to have a Linux machine on a Macbook, or a machine with R 3.3 when your main computer has R 3.5. Also, this means that you can use older versions of a package for a specific task, while still keeping the package on your machine up-to-date.
This way, you can “solve” dependencies issues: if ever you are afraid dependencies will break your analysis when packages are updated, build a container that will always have the software versions you desire: be it Linux, R, or any package.
Click through for the details. H/T R-bloggers
This sort of difference, scalar oriented
C++being so much faster than scalar oriented
R, is often distorted into “
This is just not the case. If we adapt the algorithm to be vectorized we get an
Ralgorithm with performance comparable to the
Not all algorithms can be vectorized, but this one can, and in an incredibly simple way. The original algorithm itself (
xlin_fits_R()) is a bit complicated, but the vectorized version (
xlin_fits_V()) is literally derived from the earlier one by crossing out the indices. That is: in this case we can move from working over very many scalars (slow in
R) to working over a small number of vectors (fast in
This is akin to writing set-based SQL instead of cursor-based SQL: you’re thinking in terms which make it easier for the interpreter (or optimizer, in the case of a database engine) to operate quickly over your inputs. It’s also one of a few reasons why I think learning R makes a lot of sense when you have a SQL background.
While this commit was done in the autumn 2017, nothing further happened until I decided to make gganimate the center of my useR 2018 keynote, at which point I was forced (by myself) to have some sort of package ready by the summer of 2018.
A fair amount of users have shown displeasure in the breaking changes this history has resulted in. Many blog posts have already been written focusing on the old API, as well as code on numerous computers that will no longer work. I understand this frustration, of course, but both me and David agreed that doing it this way was for the best in the end. I’m positive that the new API has already greatly exceeded the mind-share of the old API and given a year the old API will be all but a distant memory…
Read on for information on these breaking changes, and how the changes will make life easier in the long run. And stay for the fireworks. H/T R-Bloggers