I like using for many reasons. Two of those are (1) easy integration with almost whichever software you can think of, and (2) for its graphical powers. Color-wise, I dare to assume you probably plotted, re-specified your colors, plotted again, and iterated until you found what works for your specific chart. Here you can find modern visualization so you are able to quickly find the colors you look for, and to quickly see how it looks on screen. See below for quick demo.
In this post you will learn how to properly paste an R plot\chart\image to a word file. There are few typical problems that occur when people try to do that. Below you can find a simple, clean and repeatable solution.
Principal component analysis (PCA) is one of the earliest multivariate techniques. Yet not only it survived but it is arguably the most common way of reducing the dimension of multivariate data, with countless applications in almost all sciences.
Mathematically, PCA is performed via linear algebra functions called eigen decomposition or singular value decomposition. By now almost nobody cares how it is computed. Implementing PCA is as easy as pie nowadays- like many other numerical procedures really, from a drag-and-drop interfaces to
prcomp in R or
from sklearn.decomposition import PCA in Python. So implementing PCA is not the trouble, but some vigilance is nonetheless required to understand the output.
This post is about understanding the concept of variance explained. With the risk of sounding condescending, I suspect many new-generation statisticians/data-scientists simply echo what is often cited online: “the first principal component explains the bulk of the movement in the overall data” without any deep understanding. What does “explains the bulk of the movement in the overall data” mean exactly, actually?
This post shares short code snippet to make your own screen saver in R, The Matrix-style:
This post has two goals. I hope to make you think about your graphics, and think about the future of data-visualization. An example is given using some simulated time series data. A very quick read.
Admit it, you always thought there is something off with how boxplot look like. You can tell there should be some way in which more information can be depicted, they simply look much too spacious. Evidently you are not the only one. Many have tried to suggest better ways to plot the same information. Here on 40 years of boxplots.
Tail risk conventionally refers to the risk of a large and sharp draw down of the portfolio. How large is subjective and depends on how you define what is a tail.
A lot of research is directed towards having a good estimate of the tail risk. Some fairly new research also now indicates that investors perceive tail risk to be a stand-alone risk to be compensated for, rather than bundled together with the usual variability of the portfolio. So this risk now gets even more attention.
Google “K-means clustering”, and you usually you find ugly explanations and math-heavy sensational formulas*. It is my opinion that you can only understand those explanations if you don’t need them; meaning you are already familiar with the topic. Therefore, this is a more gentle introduction to K-means clustering. Here you will find out what K-Means Clustering, an algorithm, actually does. You will get only the basics, but in this particular topic, the extensions are not wildely different.
How many times have you placed the legend in R plot to discover it is being overrun by some points or lines in the chart? Usually what comes next is a trial-and-error phase where you adjust the location, changing the arguments of the x and y coordinates, and re-drawing the plot again to check if the legend or text are now positioned such that they are fully readable.
Open CPU is a great project. Few months back, I wrote a function for plotting a moving window of the market average correlation. Jeroen C.L. Ooms was nice enough to upload it to their server. Something is now changed. Quotes now return as a character class, as oppose to numeric. This messes up the function and the plot does not renders. I don’t wish to disturb Jeroen C.L. Ooms again with the correction for the code (despite his kind replies in the past). This problem creates the opportunity to look at the glistening “Shiny” package. I used it to (quickly..) build an app for the plot. You can now view a live correlation plot with the moving window of your choice. Live, as the app requests current market data. The width of the window for correlation calculation is given as an input parameter.
Open source is amazing! I cannot even start to imagine the amount of work invested in R, in firefox browser (Mozilla), or Rstudio IDE, all of which are used extensively around the globe, free. Not free as in: free sample till you decide to upgrade, or: sure it’s free, just watch this one minute commercial every time you need to use it, but free, as in: we think it might make your life better, enjoy. Warms the heart, in direct opposite to the fabulous fabs out there, that instead of contributing to a better, safer society, set it back and get paid for it (see appendix). Character is also normally distributed I guess.