dplyr issue 2916.
The following appears to work.
COL <- "homeworld"
## # A tibble: 1 x 14
## # Groups: COL 
## name height mass hair_color skin_color eye_color birth_year
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl>
## 1 Luke Skywalker 172 77 blond fair blue 19
## # ... with 7 more variables: gender <chr>, homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>, COL <chr>
Though notice it reports the grouping is by "
COL", not by "
homeworld". Also the data set now has
14 columns, not the original
13 from the
starwars data set.
Continue reading Please Consider Using wrapr::let() for Replacement Tasks
In this article we will discuss composing standard-evaluation interfaces (SE) and composing non-standard-evaluation interfaces (NSE) in
R the package
rlang is a tool for building domain specific languages intended to allow easier composition of NSE interfaces.
To use it you must know some of its structure and notation. Here are some details paraphrased from the major
rlang client, the package dplyr:
vignette('programming', package = 'dplyr')).
:=" is needed to make left-hand-side re-mapping possible (adding yet another "more than one assignment type operator running around" notation issue).
!!" substitution requires parenthesis to safely bind (so the notation is actually "
(!! )", not "
- Left-hand-sides of expressions are names or strings, while right-hand-sides are
Continue reading Non-Standard Evaluation and Function Composition in R
Here is an absolutely horrible way to confuse yourself and get an inflated reported
R-squared on a simple linear regression model in
We have written about this before, but we found a new twist on the problem (interactions with categorical variable encoding) which we would like to call out here. Continue reading An easy way to accidentally inflate reported R-squared in linear regression models
Let’s worry a bit about substitution in
R. Substitution is very powerful, which means it can be both used and mis-used. However, that does not mean every use is unsafe or a mistake.
Continue reading More on safe substitution in R
Python has a fairly famous design principle (from “PEP 20 — The Zen of Python”):
There should be one– and preferably only one –obvious way to do it.
R (especially once you add many packages) there is usually more than one way. As an example we will talk about the common
head(), and the
glimpse(). Continue reading There is usually more than one way in R
Here is a really nice feature found in the current 3.4.0 version of R: summary() has become a lot more reasonable.
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 15555 15555 15555 15555 15555 15555
Please read on for some background. Continue reading R summary() got better!
One thing I teach is: when evaluating the performance of regression models you should not use correlation as your score.
This is because correlation tells you if a re-scaling of your result is useful, but you want to know if the result in your hand is in fact useful. For example: the Mars Climate Orbiter software issued thrust commands in pound-seconds units to an engine expecting the commands to be in newton-seconds units. The two quantities are related by a constant ratio of 1.4881639, and therefore anything measured in pound-seconds units will have a correlation of 1.0 with the same measurement in newton-seconds units. However, one is not the other and the difference is why the Mars Climate Orbiter “encountered Mars at a lower than anticipated altitude and disintegrated due to atmospheric stresses.”
The need for a convenient direct F-test without accidentally triggering the implicit re-scaling that is associated with calculating a correlation is one of the reasons we supply the sigr R library. However, even then things can become confusing.
Please read on for a nasty little example. Continue reading Be careful evaluating model predictions
My criticism of R‘s numeric
summary() method is: it is unfaithful to numeric arguments (due to bad default behavior) and frankly it should be considered unreliable. It is likely the way it is for historic and compatibility reasons, but in my opinion it does not currently represent a desirable set of tradeoffs.
summary() likely represents good work by high-ability researchers, and the sharp edges are due to historically necessary trade-offs.
The Big Lebowski, 1998.
Please read on for some context and my criticism.
Edit 8/25/2016: Martin Maechler generously committed a fix! Assuming this works out in testing it looks like we could see an improvement on this core function in April 2017. I really want to say “thank you” to Martin Maechler and the rest of the team for not only this, for all the things they do, and for putting up with me.
Continue reading My criticism of R numeric summary
geom_step is an interesting geom supplied by the R package ggplot2. It is an appropriate rendering option for financial market data and we will show how and why to use it in this article.
Continue reading Using geom_step
It is often said that “R is its packages.”
One package of interest is ranger a fast parallel C++ implementation of random forest machine learning. Ranger is great package and at first glance appears to remove the “only 63 levels allowed for string/categorical variables” limit found in the Fortran randomForest package. Actually this appearance is due to the strange choice of default value
ranger::ranger() which we strongly advise overriding to
respect.unordered.factors=TRUE in applications. Continue reading On ranger respect.unordered.factors