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.

From time to time, I talked about among many factors, FITS format data make it difficult statisticians and astronomers work together. Statisticians cannot read in FITS format unless astronomers convert it into ascii or jpeg format for them whereas astronomers do not want to wasted their busy time for doing a chore like file format conversion wasting computer resources as well. Only a peaceful reunion happens when the data analysis become intractable via traditional methodology described in Numerical Recipes or Bevington and Robinson. They realize statistical (new) theory need to be found and collaboration happens with involvement of graduate students from both fields who patiently do many tedious jobs while learning (I missed this part while I was graduate student, which sometimes I thank my advisor for).

Now, let’s get back to the title. read.table()[1] is a commonly used command line in R when you read in data in ascii format. It’ reads in data intelligently. As I said, it has been versatile enough. Numerals are in numeric format, letters are character format, missings are stored as NA, etc. read.table() make it easy to jump into data analysis right away. Well, now you know why I write this. I confronted a case read.table() does not read things correctly with astronomical data “even in ascii format.,” which I never had since I began to use S-Plus/R.

Although I know how to fix this simple problem that I’ll describe later, I want to point out the lack of compatibility in data formats between two communities and the common tools used for accessing data sets, which, I believe, is one of the biggest factors that prohibit astronomically uneducated statisticians from participating collaborations. I’ve mixed up tools for consulting courses to assist clients of various disciplines (grad students from agriculture, horticulture, physiology, social science, psychology were my clients) and for executing projects in electrical engineering and computational physics (these heavily rely on MATLAB) but reading data was the most simplest and fundamental step that I don’t have to worry about across various data sets with R (probably, those graduate students and professors of engineering and physics provided well trimmed and proven data sets).

When you have a long way to complete your mission and when you stumbled with your first step, I think it’s easy to loose eagerness for the future unless there’s support from your colleagues. Instead, I mostly likely receive discouraging comments such as “Why using R?” “You won’t have such problems if you use other tools” (Although it takes a bit of extra time to manuever, I eventually get to there). Such frustrating comments also degrade eagerness furthermore. So, from 100% I normally begin with, only 25% eagerness is left after two discouraging moments occurred at the initial step of data analysis whose end is invisibly far away. I only hang on to this 25%, still big by the normal standard and I wish for this last long until the final step without exponential decays that happened at the beginning.