Limitations to the model formula method are discused.
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Limitations to the model formula method are discused.
In a recent post, I highlighted several new packages that arrived on CRAN in January that provided R users with access to data. In this post, I present additional selections for interesting January packages, organized into the categories Miscellaneous, Machine Learning, Statistics and Utilities.
Miscellaneous rcss v1.2: Provides functions for Solving Control Problems with Linear State Dynamics.
stormwindmodel v0.1.0: Provides functions to calculate wind speeds for hurricanes and tropical storms in the North American Atlantic basin.
Would you like to teach people to use R? If so, I would like to jump-start your efforts.
I’m one half of RStudio’s education team, and I’ve taught thousands of people to use R, usually in face-to-face workshops. Over time, I’ve come to appreciate that teaching R in a short workshop is an unusual challenge that requires an unusual approach: you cannot teach a short workshop in the same way that you would teach a college course, and you should not teach R in the same way you would teach Python, UNIX or C.
As forecast, the number of R packages hosted on CRAN exceed 10,000 in January. Dirk Eddelbuettel, who has been keeping track of what’s happeining on CRAN with his CRANberies site for a number of years, called [hurricaneexposure]() as the 10K package in a tweet on January 27th. hurricaneexposure was of two hundred and six new packages that arrived on CRAN in January. I thought many of them were of high quality so picking out recommendations was unusually difficult this month.
by Joseph Rickert
I have always been attracted to the capricious. So, it was no surprise that I fell for the Cauchy distribution at first sight. I had never seen such unpredictability! You might say that every distribution has its moments of unpredictability, but the great charm of Cauchy is that it has no moments. (No finite moments, anyway.)
Before discussing why momentlessness (not being in the moment :) ) leads to unpredictability, let’s derive the Cauchy distribution.
“I’m not a coder” or “I was never good at math” is a frequent refrain I hear when I ask professionals about their data analysis skills. Through popular culture and stereotypes, most people who don’t have a background in programming automatically underestimate their ability to create amazing things with code. However, Data Society has proven that this is a false narrative through our training program – with students in over 20 countries and many government and enterprise clients, we’ve seen so-called “non-coders” proficiently put together automated data cleaning code scripts and analyses within a few weeks.
Today, we are going to tackle a project that has long been on my wishlist - a Shiny app to take a fund or portfolio, analyze its exposure to different countries and display those exposures on a world map. Now you know how exciting my wishlists are.
Before describing our data importing/wrangling work here in the Notebook, it mgight be helpful to look at where we’re headed.
interactive rolling correlations sectors and SP500
Introduction The formula interface to symbolically specify blocks of data is ubiquitous in R. It is commonly used to generate design matrices for modeling function (e.g. lm). In traditional linear model statistics, the design matrix is the two-dimensional representation of the predictor set where instances of data are in rows and variable attributes are in columns (a.k.a. the X matrix).
A simple motivating example uses the inescapable iris data in a linear regression model:
This month’s collection of Tips and Tricks comes from an excellent talk given at the 2017 RStudio::Conf in Orlando by RStudio Software Engineer Kevin Ushey. The slides from his talk are embedded below and cover features from autocompletion to R Markdown shortcuts. Use the left and right arrow keys to change slides.
Enjoy!