Dear r-devel, Among the R functions I have written and later shared with colleagues, there are five that I hope will become a part of the R base package. The tasks are neither specific nor marginal, so rather than creating one more 'misc' package, I would be happy if the R Development Core Team would adopt these functions and hammer them into shape.
The functions are available from http://students.washington.edu/arnima/s, and a short demo session below explains their behaviour. Sorry about the length of this message, but it should be quick read. > env() Environment Objects Kb 1 .GlobalEnv 19 982 2 package:lme4 180 992 3 file:f:/gnu/home/r/toolbox.rdata 49 457 4 Autoloads 44 0 5 package:base 1657 8196 These environments are currently loaded. Verbose version of search(). > ll() Class Kb chinook data.frame 66 chinook.0 glm 244 coho data.frame 127 coho.0 glm 466 fig2 function 21 fig3 function 13 table3 data.frame 1 table4 data.frame 2 x numeric 1 y integer 1 z list 5 My workspace contains these objects. Verbose version of ls(). I think package:R.oo provides something similar. > elem(coho) Class Kb <row.names> character 21 Species factor 7 Estuary ordered 8 EstSize factor 7 EstSizeLog numeric 14 EstNatural numeric 14 Oyster factor 7 RelYear factor 7 SSTsummer numeric 14 Survival numeric 14 TxF numeric 14 The coho data frame contains these elements. Compactly describes the data frame, not overlapping with summary() or describe() in package:Hmisc. I use this function when choosing appropriate storage mode for elements in large data frames. It has also helped me locating errors in imported data (numbers containing both . and , decimal seperator are flagged by factor). > elem(coho.0, dim=T) Class Kb Dim coefficients numeric 0 1 residuals numeric 35 1768 fitted.values numeric 35 1768 effects numeric 35 1768 R matrix 0 1 x 1 rank integer 0 1 qr list 35 5 family family 7 11 linear.predictors numeric 35 1768 deviance numeric 0 1 aic numeric 0 1 null.deviance numeric 0 1 iter integer 0 1 weights numeric 35 1768 prior.weights numeric 35 1768 df.residual numeric 0 1 df.null numeric 0 1 y numeric 35 1768 converged logical 0 1 boundary logical 0 1 model data.frame 50 1768 x 2 call call 1 5 formula formula 0 3 terms terms 1 3 data data.frame 127 1768 x 10 offset NULL 0 control list 0 3 method character 0 1 contrasts NULL 0 xlevels NULL 0 Gives me a good idea about the elements of this GLM. > is.what(y) [1] "is.atomic" "is.finite" "is.integer" "is.numeric" "is.vector" Now I know which is.* tests are positive on an integer object. Inspired by demo(is.things). > keep(fig2, fig3) [1] "chinook" "chinook.0" "coho" "coho.0" "table3" "table4" [7] "x" "y" "z" Shows which objects will be removed, if I'm sure. > keep(fig2, fig3, sure=T) Default workspace has been cleared, only fig2 and fig3 were kept. --- I understand that functions for the base package have to be selected very carefully, but I believe the functions above can save a significant amount of time and effort for many R users. My implementations are reasonably generic and robust, but I'm hoping the R Development Core Team will adopt and improve improve them. Regards, Arni Magnusson (fish biologist) ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-devel
