[Rd] Five functions proposed for base

[Arni Magnusson]

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)