The AstroStat Slog

The first step of data analysis or applications is reading the data sets into a tool of choice. Recent years, I’ve been using R (see also Learning R) for that regard but I’ve enjoyed freedoms for the same purpose from these languages and tools: BASIC, fortran77/90/95, C/C++, IDL, IRAF, AIPS, mongo/supermongo, MATLAB, Maple, Mathematica, SAS, SPSS, Gauss, ARC, Minitab, and recently Python and ciao which I just began to learn. Many of them I lost the fluency of how to use it. Quick learning tends to be flash memory. Some will need brain defragmentation and recovering time for extensive scientific work. A few I don’t like to use at all. No matter what, I’m not a computer geek. I’m not good at new gadgets, new softwares, nor welcome new and allegedly versatile computing systems. But one must be if he/she want to handle data. Until recently I believed R has such versatility in the aspect of reading in data. Yet, there is nothing without exceptions. Continue reading ‘read.table()’ »

Hyunsook and I have preliminary findings (work done with the help of the X-Atlas group) on the efficacy of using spectral proxies to classify low-mass coronal sources, put up as a poster at the XGratings workshop. The workshop has a “poster haiku” session, where one may summarize a poster in a single transparency and speak on it for a couple of minutes. I cannot count syllables, so I wrote a limerick instead: Continue reading ‘Summarizing Coronal Spectra’ »