R Tip: be wary of “
The following code example contains an easy error in using the R function
vec1 <- c("a", "b", "c")
vec2 <- c("c", "d")
#  "a" "b" "c"
Notice none of the novel values from
vec2 are present in the result. Our mistake was: we (improperly) tried to use
unique() with multiple value arguments, as one would use
union(). Also notice no error or warning was signaled. We used
unique() incorrectly and nothing pointed this out to us. What compounded our error was
...” function signature feature.
In this note I will talk a bit about how to defend against this kind of mistake. I am going to apply the principle that a design that makes committing mistakes more difficult (or even impossible) is a good thing, and not a sign of carelessness, laziness, or weakness. I am well aware that every time I admit to making a mistake (I have indeed made the above mistake) those who claim to never make mistakes have a laugh at my expense. Honestly I feel the reason I see more mistakes is I check a lot more.
Continue reading R Tip: Be Wary of “…”
R package wrapr 1.5.0 is now available on CRAN.
wrapr includes a lot of tools for writing better
I’ll be writing articles on a number of the new capabilities. For now I just leave you with the nifty operator coalesce notation.
Continue reading wrapr 1.5.0 available on CRAN
R tip: use slices.
R has a very powerful array slicing ability that allows for some very slick data processing.
Continue reading R Tip: Use Slices
R tip: first organize your tasks in terms of data, values, and desired transformation of values, not initially in terms of concrete functions or code.
I know I write a lot about coding in
R. But it is in the service of supporting statistics, analysis, predictive analytics, and data science.
R without data is like going to the theater to watch the curtain go up and down.
(Adapted from Ben Katchor’s Julius Knipl, Real Estate Photographer: Stories, Little, Brown, and Company, 1996, page 72, “Excursionist Drama 2”.)
Usually you come to
R to work with data. If you think and plan in terms of data and values (including introducing more data to control processing) you will usually work in much faster, explainable, and maintainable fashion.
Continue reading R Tip: Think in Terms of Values
Here is an R tip. Want to re-map a column of values? Use a named vector as the mapping.
Continue reading R Tip: Use Named Vectors to Re-Map Values
Another R tip. Need to replace a name in some R code or make R code re-usable? Use
Continue reading R Tip: Use let() to Re-Map Names
There are a number of easy ways to avoid illegible code nesting problems in
In this R tip we will expand upon the above statement with a simple example.
Continue reading R Tip: Break up Function Nesting for Legibility
R tip: use
stringsAsFactors = FALSE.
R often uses a concept of
factors to re-encode strings. This can be too early and too aggressive. Sometimes a string is just a string.
It is often claimed Sigmund Freud said “Sometimes a cigar is just a cigar.”
Continue reading R Tip: Use
stringsAsFactors = FALSE