39 karma · joined September 10, 2013
> as.chron("1970-01-01")+unclass(as.chron("2001-04-01")) [1] 04/01/01
> as.POSIXct("1970-01-01","EST")+unclass(as.POSIXct("2014-06-01","EST")) [1] "2014-06-01 05:00:00 EST"
If there is any conversion necessary it is difficult to get back the original intended time.
`emacs -nw -Q` or `emacsclient -nw`
when you want to open an instance in a terminal?
cat("options(stringsAsFactors=FALSE)\n",file="~/.Rprofile",append=TRUE)
at the R console.
I have gotten used to its behavior but only because I use factors sparingly, having set stringsAsFactors=FALSE as my default. But reshape2::melt() requires a separate argument (factorsAsStrings) if you don't want automatic conversion, and with plyr::ldply() you can't prevent the index column conversion to a factor at all. So factors creep in periodically into my data frames and burns me every now and then.
https://stat.ethz.ch/pipermail/r-help/2007-February/125438.h...
I have adopted this approach as well, and for all the scripts I distribute I set options(stringsAsFactors=FALSE) at the top.
His list is concise but man did he take a while to get to it!
Seriously though. The introduction was super relevant as I have wondered the exact same question about Lisp myself. What features make it so praise-worthy? Maybe X-expressions isn't a core feature for everyone to appreciate, but the fact that everything is an S-expression is an understated value. People complain about its syntax, but alternate versions (so many reincarnations of parentheses-less Lisps) have never caught on.
The thing is, Lisp is no longer unique in its feature set, and languages with more standard forms of syntax have incorporated some of its features. But it is uncommon to find all of these listed features in one language. In the domain of data analysis where I do most of my work, it still makes me sad that XLISP-STAT has been supplanted by other languages which leave the user wanting.
However, I have to issue a statement of warning that when an idea doesn't pan out like written out, people will go to great lengths to fulfill their agenda (e.g., put undue pressure on their students to come up with the "correct" result). For computer science and mathematics, it may be more difficult to fudge results. But in experimental work or data analysis, I've seen students suddenly come up with a solution that fits their advisor's expectation.
There has to be some mechanism to guard against this; the most common answer is the moral character and integrity of the scientist, but psychologists and economists will quickly point out that there are gray areas where people are willing to cheat just enough to get their reward while preserving their conscience.