Before R became available, I was a heavy
user of the S-PLUS™ software, a commercial declination of
R’s ancestor S. It
had a very practical function called objects.summary(),
which would list objects from an environment in a tabular form
(basically as a data.frame) with some interesting
attributes including class, mode, dimensions, and size. I couldn’t find
its equivalent in R, so I wrote one 😊
You can install the current stable version of osum from
CRAN:
install.packages("osum")Windows and macOS binary packages are available from here.
You can install the development version of osum
including latest features from GitHub:
require(remotes)
install_github("zivankaraman/osum")First, we need to populate the session environment with a few objects.
a <- month.name
b <- sample(c("FALSE", "TRUE"), size = 5, replace = TRUE)
cars <- mtcars
.hidden <- -1L
.secret <- "Shhht!"
x1 <- rnorm(n = 10)
x2 <- runif(n = 20)
x3 <- rbinom(n = 30, size = 10, prob = 0.5)
lst <- list(first = x1, second = x2, third = x3)
fun <- function(x) {sqrt(x)}By default, the environment of the call to
objects.summary is used, here .GlobalEnv.
objects.summary()
#> data.class storage.mode mode typeof extent object.size
#> a character character character character 12 880
#> b character character character character 5 152
#> cars data.frame list list list 32 x 11 7208
#> fun function function function closure 1 4552
#> lst list list list list 3 1000
#> x1 numeric double numeric double 10 176
#> x2 numeric double numeric double 20 208
#> x3 numeric integer numeric integer 30 176The hidden objects are not shown by default. One has to provide
argument all.objects=TRUE to see them (not unlike the
all.names argument to the ls function)
objects.summary(all.objects = TRUE)
#> data.class storage.mode mode typeof extent object.size
#> .Random.seed numeric integer numeric integer 626 2552
#> .hidden numeric integer numeric integer 1 56
#> .secret character character character character 1 112
#> a character character character character 12 880
#> b character character character character 5 152
#> cars data.frame list list list 32 x 11 7208
#> fun function function function closure 1 4552
#> lst list list list list 3 1000
#> x1 numeric double numeric double 10 176
#> x2 numeric double numeric double 20 208
#> x3 numeric integer numeric integer 30 176If the objects.summary is called inside the function, it
is the calling function’s environment that is used by default.
# shows an empty list because inside myfunc no variables are defined
myfunc <- function() {objects.summary()}
myfunc()
#> [1] data.class storage.mode mode typeof extent
#> [6] object.size
#> <0 rows> (or 0-length row.names)
# define a local variable inside myfunc
myfunc <- function() {y <- 1; objects.summary()}
myfunc()
#> data.class storage.mode mode typeof extent object.size
#> y numeric double numeric double 1 56We can limit the output to objects with names matching the regular
expression provided as the pattern argument. Alternatively,
we can provide a character vector naming objects to summarize in the
names argument.
objects.summary(pattern = "^x")
#> data.class storage.mode mode typeof extent object.size
#> x1 numeric double numeric double 10 176
#> x2 numeric double numeric double 20 208
#> x3 numeric integer numeric integer 30 176
objects.summary(names = c("a", "b"))
#> data.class storage.mode mode typeof extent object.size
#> a character character character character 12 880
#> b character character character character 5 152We can list the objects from any environment, not just the current environment. The environment can be provided as an integer indicating the position in the search list or a character giving the name of an environment in the search list.
idx <- grep("package:graphics", search())
objects.summary(idx, pattern = "^plot")
#> data.class storage.mode mode typeof extent object.size
#> plot function function function closure 1 1408
#> plot.default function function function closure 1 127632
#> plot.design function function function closure 1 479960
#> plot.function function function function closure 1 64880
#> plot.new function function function closure 1 17880
#> plot.window function function function closure 1 11136
#> plot.xy function function function closure 1 75352
objects.summary("package:graphics", pattern = "^plot")
#> data.class storage.mode mode typeof extent object.size
#> plot function function function closure 1 1408
#> plot.default function function function closure 1 127632
#> plot.design function function function closure 1 479960
#> plot.function function function function closure 1 64880
#> plot.new function function function closure 1 17880
#> plot.window function function function closure 1 11136
#> plot.xy function function function closure 1 75352We can also explicitly provide an environment.
e <- new.env()
e$a <- 1:10
e$b <- rnorm(25)
e$df <- iris
e$arr <- iris3
objects.summary(e)
#> data.class storage.mode mode typeof extent object.size
#> a numeric integer numeric integer 10 96
#> arr array double numeric double 50 x 4 x 3 5816
#> b numeric double numeric double 25 248
#> df data.frame list list list 150 x 5 7256Unless an explicit environment is provided, where
argument should designate an element of the search list. However, if it
is a character of the form “package:pkg_name” and if the
package named “pkg_name” is installed, it is silently loaded,
its objects retrieved, and then it is unloaded when the function exits.
Depending on the time it takes to load the package, the execution might
be slower than getting the information about an attached package.
# check if the package foreign is attached
length(grep("package:foreign", search())) > 0L
#> [1] FALSE
objects.summary("package:foreign", pattern = "^write")
#> data.class storage.mode mode typeof extent object.size
#> write.arff function function function closure 1 234168
#> write.dbf function function function closure 1 308496
#> write.dta function function function closure 1 224872
#> write.foreign function function function closure 1 12720
# check if the package foreign is attached
length(grep("package:foreign", search())) > 0L
#> [1] FALSEWe don’t need to display all the attributes, the what
argument controls which information is returned. Partial matching is
used, so only enough initial letters of each string element are needed
to guarantee unique recognition. For example,
“data[.class]”, “stor[age.mode]”,
“ext[ent]”, “obj[ect.size]”.
objects.summary(what = c("data.class", "storage.mode", "extent", "object.size"))
#> data.class storage.mode extent object.size
#> a character character 12 880
#> b character character 5 152
#> cars data.frame list 32 x 11 7208
#> fun function function 1 4552
#> idx numeric integer 1 56
#> lst list list 3 1000
#> x1 numeric double 10 176
#> x2 numeric double 20 208
#> x3 numeric integer 30 176
objects.summary(what = c("data", "stor", "ext", "obj"))
#> data.class storage.mode extent object.size
#> a character character 12 880
#> b character character 5 152
#> cars data.frame list 32 x 11 7208
#> fun function function 1 4552
#> idx numeric integer 1 56
#> lst list list 3 1000
#> x1 numeric double 10 176
#> x2 numeric double 20 208
#> x3 numeric integer 30 176In fact, just providing the first letter is sufficient, since all the
possible values start with a different letter. The order of columns in
the summary respects the order in which their names are listed in the
what argument.
objects.summary(what = c("m", "s", "t", "o", "d", "e"))
#> mode storage.mode typeof object.size data.class extent
#> a character character character 880 character 12
#> b character character character 152 character 5
#> cars list list list 7208 data.frame 32 x 11
#> fun function function closure 4552 function 1
#> idx numeric integer integer 56 numeric 1
#> lst list list list 1000 list 3
#> x1 numeric double double 176 numeric 10
#> x2 numeric double double 208 numeric 20
#> x3 numeric integer integer 176 numeric 30It should be noted that attributes storage.mode,
mode, and typeof are somewhat redundant, so
you can select only those that are relevant to you. You can set your
personal preferences using the osum.options function, as
explained in Options.
The subset of objects from the environment where which
should be selected for summary is specified with either an explicit
vector of names provided in argument names, or with some
combination of the subsetting criteria pattern (as seen in
Restricting the Objects
List), data.class, storage.mode,
mode, and typeof. If argument
names is given, the other criteria are ignored. If more
than one criterion is given, only objects which satisfy all of them are
selected. In the absence of both names and criteria, all
objects in where are selected.
objects.summary("package:datasets", pattern = "^[sU]", what = c("dat", "typ", "ext", "obj"),
data.class = c("data.frame", "matrix"))
#> data.class typeof extent object.size
#> USArrests data.frame list 50 x 4 5736
#> USJudgeRatings data.frame list 43 x 12 9328
#> USPersonalExpenditure matrix double 5 x 5 1448
#> sleep data.frame list 20 x 3 2984
#> stack.x matrix double 21 x 3 1168
#> stackloss data.frame list 21 x 4 1792
#> state.x77 matrix double 50 x 8 7672
#> swiss data.frame list 47 x 6 6464Objects can have more than one class, but only the first class
element is used by default. Specifying all.classes=TRUE
allows to consider the entire class vector of an object, both in
selection based on argument data.class and in the returned
summary.
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"), data.class = "array")
#> data.class typeof extent object.size
#> iris3 array double 50 x 4 x 3 5816
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"),
all.classes = TRUE, data.class = "array")
#> data.class typeof extent object.size
#> EuStockMarkets mts, ts,.... double 1860 x 4 60824
#> Seatbelts mts, ts,.... double 192 x 8 13872
#> USPersonalExpenditure matrix, .... double 5 x 5 1448
#> VADeaths matrix, .... double 5 x 4 1264
#> WorldPhones matrix, .... double 7 x 7 1800
#> euro.cross matrix, .... double 11 x 11 2944
#> freeny.x matrix, .... double 39 x 4 2008
#> iris3 array double 50 x 4 x 3 5816
#> stack.x matrix, .... double 21 x 3 1168
#> state.x77 matrix, .... double 50 x 8 7672
#> volcano matrix, .... double 87 x 61 42672Besides simple filtering criteria by values of attributes, we can
also filter on logical expression indicating elements (rows) to keep.
The expression is evaluated in the data frame with object attributes, so
columns should be referred to (by unquoted attribute name) as variables
in the expression (not unlike the select argument of the
base subset function). This can be particularly helpful
when we want to exclude some values, avoiding explicit listing of all
other (possible) values, as shown in the example below.
objects.summary("package:grDevices", filter = mode != "function")
#> data.class storage.mode mode typeof extent object.size
#> Hershey list list list list 3 2024
#> blues9 character character character character 9 680
#> colorspaces list list list list 6 478024The filter expression can involve more than one attribute.
objects.summary("package:datasets", filter = mode != storage.mode)[1:10, ]
#> data.class storage.mode mode typeof extent object.size
#> AirPassengers ts double numeric double 144 1616
#> BJsales ts double numeric double 150 1664
#> BJsales.lead ts double numeric double 150 1664
#> EuStockMarkets mts double numeric double 1860 x 4 60824
#> HairEyeColor table double numeric double 4 x 4 x 2 2040
#> JohnsonJohnson ts double numeric double 84 1136
#> LakeHuron ts double numeric double 98 1248
#> Nile ts double numeric double 100 1264
#> Seatbelts mts double numeric double 192 x 8 13872
#> Titanic table double numeric double 4 x 2 x 2 x 2 2152It can also be quite complex, as long as it yields a logical value for every object (row).
objects.summary("package:datasets", all.classes = TRUE,
filter = sapply(data.class, length) > 2L)
#> data.class storage.mode mode typeof extent object.size
#> CO2 nfnGroup.... list list list 84 x 5 9976
#> ChickWeight nfnGroup.... list list list 578 x 4 22696
#> DNase nfnGroup.... list list list 176 x 3 8256
#> EuStockMarkets mts, ts,.... double numeric double 1860 x 4 60824
#> Indometh nfnGroup.... list list list 66 x 3 5904
#> Loblolly nfnGroup.... list list list 84 x 3 12120
#> Orange nfnGroup.... list list list 35 x 3 5232
#> Seatbelts mts, ts,.... double numeric double 192 x 8 13872
#> Theoph nfnGroup.... list list list 132 x 5 10000By default, the object entries (printed as rows) in the summary are
sorted alphabetically by object name. By providing the
order argument, they can be sorted on any other column(s).
The order argument should be (unquoted) column names. For
numeric columns, one can precede the name by “-” to sort in descending
order, with the expression enclosed in parentheses (see examples). To
sort on more than one column, the expression must be provided as a
vector c(., .) (again see examples). Feature inspired by
the standard R order
function.
# filter on 'mode' and sort on 'data.class'
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"), mode = "numeric",
order = data.class)[1:10, ]
#> data.class typeof extent object.size
#> iris3 array double 50 x 4 x 3 5816
#> UScitiesD dist integer 45 2392
#> eurodist dist double 210 3688
#> state.region factor integer 50 904
#> state.division factor integer 50 1392
#> USPersonalExpenditure matrix double 5 x 5 1448
#> WorldPhones matrix double 7 x 7 1800
#> volcano matrix double 87 x 61 42672
#> VADeaths matrix double 5 x 4 1264
#> euro.cross matrix double 11 x 11 2944
# filter on 'mode' and sort (descending) on 'object.size'
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"), mode = "numeric",
order = (-object.size))[1:10, ]
#> data.class typeof extent object.size
#> treering ts double 7980 64304
#> EuStockMarkets mts double 1860 x 4 60824
#> volcano matrix double 87 x 61 42672
#> sunspot.month ts double 3177 25880
#> sunspots ts double 2820 23024
#> Seatbelts mts double 192 x 8 13872
#> crimtab table integer 42 x 22 8504
#> state.x77 matrix double 50 x 8 7672
#> iris3 array double 50 x 4 x 3 5816
#> precip numeric double 70 5560
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"),
order = c(data.class, -object.size))[1:10, ]
#> data.class typeof extent object.size
#> iris3 array double 50 x 4 x 3 5816
#> state.name character character 50 3496
#> state.abb character character 50 3248
#> quakes data.frame list 1000 x 5 33232
#> infert data.frame list 248 x 8 16064
#> attenu data.frame list 182 x 5 15648
#> randu data.frame list 400 x 3 10584
#> USJudgeRatings data.frame list 43 x 12 9328
#> morley data.frame list 100 x 3 8576
#> iris data.frame list 150 x 5 7256It should be noted that although the extent is by
default printed (by the specific print method for objects of class
objects.summary) as a product of dimensions (d1 x d2), it
is internally stored as a list, which allows sorting on a number of rows
or columns, for example.
# get all two-dimensional objects of from the datasets package, with more than 7 columns,
# sorted by number on columns (ascending) and then on number of rows (descending)
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"),
filter = sapply(extent, length) == 2L & sapply(extent, "[", 2L) > 7L,
order = c(sapply(extent, "[", 2L), -sapply(extent, "[", 1L)))
#> data.class typeof extent object.size
#> infert data.frame list 248 x 8 16064
#> Seatbelts mts double 192 x 8 13872
#> state.x77 matrix double 50 x 8 7672
#> anscombe data.frame list 11 x 8 2592
#> occupationalStatus table integer 8 x 8 2288
#> mtcars data.frame list 32 x 11 7208
#> euro.cross matrix double 11 x 11 2944
#> USJudgeRatings data.frame list 43 x 12 9328
#> crimtab table integer 42 x 22 8504
#> volcano matrix double 87 x 61 42672The entries are sorted in ascending order by default. They can be
sorted in descending order by specifying reverse=TRUE.
# get five biggest objects from package datasets
objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj"),
reverse=TRUE)[1:10, ]
#> data.class typeof extent object.size
#> women data.frame list 15 x 2 1104
#> warpbreaks data.frame list 54 x 3 2944
#> volcano matrix double 87 x 61 42672
#> uspop ts double 19 616
#> trees data.frame list 31 x 3 1728
#> treering ts double 7980 64304
#> swiss data.frame list 47 x 6 6464
#> sunspots ts double 2820 23024
#> sunspot.year ts double 289 2776
#> sunspot.month ts double 3177 25880It should be noted that the objects in the summary can be
filtered and/or sorted by the columns that will not be
part of the summary (i.e. are not listed in the what
argument).
objects.summary("package:datasets", what = c("dat", "typ", "ext"), pattern = "st",
filter = mode %in% c("list", "numeric"), order = object.size)
#> data.class typeof extent
#> stack.loss numeric double 21
#> state.area numeric double 50
#> state.region factor integer 50
#> stack.x matrix double 21 x 3
#> austres ts double 89
#> state.center list list 2
#> state.division factor integer 50
#> stackloss data.frame list 21 x 4
#> eurodist dist double 210
#> USArrests data.frame list 50 x 4
#> state.x77 matrix double 50 x 8The objects.summary function creates an object of class
objects.summary, which is an extension of the
data.frame class. The purpose of this class is being able
to propose custom print and summary
methods.
The number of rows printed can be limited by the
max.rows argument, which allows more straightforward
control than the max argument of the
print.data.frame.
When all.classes argument is set to TRUE,
the entire class vector is returned, and the data.class
column is a list of character vectors. When such data is printed, the
output is limited to a fixed number of characters (12 by default),
longer strings being shown as e.g. “matrix, …” or “nfnGroup….”. The
data.class.width argument to the print method
allows users to change this value (probably to increase it), in order to
see (almost) all the classes.
os <- objects.summary("package:datasets", what = c("dat", "ext", "obj"),
all.classes = TRUE, order = object.size, reverse = TRUE)
print(os, data.class.width = 25, max.rows = 12)
#> data.class extent object.size
#> treering ts 7980 64304
#> EuStockMarkets mts, ts, matrix, array 1860 x 4 60824
#> volcano matrix, array 87 x 61 42672
#> quakes data.frame 1000 x 5 33232
#> sunspot.month ts 3177 25880
#> sunspots ts 2820 23024
#> ChickWeight nfnGroupedData, nfGro.... 578 x 4 22696
#> infert data.frame 248 x 8 16064
#> attenu data.frame 182 x 5 15648
#> Seatbelts mts, ts, matrix, array 192 x 8 13872
#> Loblolly nfnGroupedData, nfGro.... 84 x 3 12120
#> randu data.frame 400 x 3 10584
#> [ reached 'max' / getOption("max.print") -- omitted 92 rows ]
multi_class_objects <- row.names(objects.summary("package:datasets", all.classes = TRUE,
filter = sapply(data.class, length) > 1L))
os <- objects.summary("package:datasets", names = multi_class_objects, all.classes = TRUE,
what = c("dat", "ext", "obj"))
print(os, data.class.width = 32, max.rows = 12)
#> data.class extent object.size
#> CO2 nfnGroupedData, nfGroupedDat.... 84 x 5 9976
#> ChickWeight nfnGroupedData, nfGroupedDat.... 578 x 4 22696
#> DNase nfnGroupedData, nfGroupedDat.... 176 x 3 8256
#> EuStockMarkets mts, ts, matrix, array 1860 x 4 60824
#> Indometh nfnGroupedData, nfGroupedDat.... 66 x 3 5904
#> Loblolly nfnGroupedData, nfGroupedDat.... 84 x 3 12120
#> Orange nfnGroupedData, nfGroupedDat.... 35 x 3 5232
#> Seatbelts mts, ts, matrix, array 192 x 8 13872
#> Theoph nfnGroupedData, nfGroupedDat.... 132 x 5 10000
#> USPersonalExpenditure matrix, array 5 x 5 1448
#> VADeaths matrix, array 5 x 4 1264
#> WorldPhones matrix, array 7 x 7 1800
#> [ reached 'max' / getOption("max.print") -- omitted 5 rows ]As already mentioned in Sorting
Objects, the extent column is internally stored as a
list, and we can explicitly control how it is printed by the
format.extent argument.
multi_dim_objects <- row.names(objects.summary("package:datasets", all.classes = TRUE,
data.class = c("array", "table")))
os <- objects.summary("package:datasets", names = multi_dim_objects,
what = c("dat", "ext", "obj"))
print(os[rev(order(sapply(os$extent, length))), ],
format.extent = TRUE, max.rows = 12) # default
#> data.class extent object.size
#> Titanic table 4 x 2 x 2 x 2 2152
#> iris3 array 50 x 4 x 3 5816
#> UCBAdmissions table 2 x 2 x 6 1992
#> HairEyeColor table 4 x 4 x 2 2040
#> volcano matrix 87 x 61 42672
#> state.x77 matrix 50 x 8 7672
#> stack.x matrix 21 x 3 1168
#> occupationalStatus table 8 x 8 2288
#> freeny.x matrix 39 x 4 2008
#> euro.cross matrix 11 x 11 2944
#> crimtab table 42 x 22 8504
#> WorldPhones matrix 7 x 7 1800
#> [ reached 'max' / getOption("max.print") -- omitted 4 rows ]
print(os[rev(order(sapply(os$extent, length))), ],
format.extent = FALSE, max.rows = 12)
#> data.class extent object.size
#> Titanic table 4, 2, 2, 2 2152
#> iris3 array 50, 4, 3 5816
#> UCBAdmissions table 2, 2, 6 1992
#> HairEyeColor table 4, 4, 2 2040
#> volcano matrix 87, 61 42672
#> state.x77 matrix 50, 8 7672
#> stack.x matrix 21, 3 1168
#> occupationalStatus table 8, 8 2288
#> freeny.x matrix 39, 4 2008
#> euro.cross matrix 11, 11 2944
#> crimtab table 42, 22 8504
#> WorldPhones matrix 7, 7 1800
#> [ reached 'max' / getOption("max.print") -- omitted 4 rows ]Other options can be passed down to the print.data.frame
function (not necessarily very useful).
print(objects.summary("package:datasets", what = c("dat", "typ", "ext", "obj")),
format.extent = TRUE, max.rows = 12, right = FALSE, quote = TRUE)
#> data.class typeof extent object.size
#> AirPassengers "ts" "double" "144" " 1616"
#> BJsales "ts" "double" "150" " 1664"
#> BJsales.lead "ts" "double" "150" " 1664"
#> BOD "data.frame" "list" "6 x 2" " 1176"
#> CO2 "nfnGroupedData" "list" "84 x 5" " 9976"
#> ChickWeight "nfnGroupedData" "list" "578 x 4" "22696"
#> DNase "nfnGroupedData" "list" "176 x 3" " 8256"
#> EuStockMarkets "mts" "double" "1860 x 4" "60824"
#> Formaldehyde "data.frame" "list" "6 x 2" " 1320"
#> HairEyeColor "table" "double" "4 x 4 x 2" " 2040"
#> Harman23.cor "list" "list" "3" " 2712"
#> Harman74.cor "list" "list" "3" " 9624"
#> [ reached 'max' / getOption("max.print") -- omitted 92 rows ]The summary method shares the same specific arguments as
the print except for max.rows.
os <- objects.summary("package:datasets", all.classes = TRUE, what = c("dat", "ext", "obj"),
filter = sapply(data.class, length) > 1L)
summary(os, data.class.width = 32, format.extent = FALSE)
#> data.class extent object.size
#> matrix, array:8 5, 4: 1 Min. : 1168
#> mts, ts, matrix, array:2 5, 5: 1 1st Qu.: 2008
#> nfnGroupedData, nfGroupedDat....:7 7, 7: 1 Median : 7672
#> 21, 3: 1 Mean :12344
#> 35, 3: 1 3rd Qu.:12120
#> 39, 4: 1 Max. :60824
#> (Other):11Again, other options can be passed down to the
summary.data.frame function.
summary(os, data.class.width = 32, maxsum = 10, quantile.type = 5)
#> data.class extent object.size
#> matrix, array:8 11 x 11 :1 Min. : 1168
#> mts, ts, matrix, array:2 132 x 5 :1 1st Qu.: 1956
#> nfnGroupedData, nfGroupedDat....:7 176 x 3 :1 Median : 7672
#> 1860 x 4:1 Mean :12344
#> 192 x 8 :1 3rd Qu.:12558
#> 21 x 3 :1 Max. :60824
#> 35 x 3 :1
#> 39 x 4 :1
#> 5 x 4 :1
#> (Other) :8There are a few custom options dedicated to the package. The function
osum.options, crafted after the base package
options, allows the user to set and examine them. The
custom options mainly allow for providing the default values for the
specific arguments to the print and summary
methods (data.class.width, format.extent, and
max.rows), as seen in Printing and Summarizing.
# see all current options
osum.options()
#> $osum.format.extent
#> [1] TRUE
#>
#> $osum.information
#> [1] "data.class" "storage.mode" "mode" "typeof" "extent"
#> [6] "object.size"# set some values
old_opt <- osum.options(osum.data.class.width = 12, osum.max.rows = 25)
# previous values of the changed 'osum' options
old_opt
#> $osum.data.class.width
#> NULL
#>
#> $osum.max.rows
#> NULLIt is also possible to select what information will be returned by
default by the function objects.summary. It must be a
subset of
c("data.class", "storage.mode", "mode", "typeof", "extent", "object.size"),
partial matching is allowed.
# set which attributes are retrieved by default
osum.options(osum.information = c("dat", "mod", "ext", "obj"))
# get the current value of the option
osum.options("osum.information")
#> $osum.information
#> [1] "data.class" "mode" "extent" "object.size"
# if the argument 'what' is not specified, the new default values are used
objects.summary("package:base", filter = data.class != "function")
#> data.class mode extent object.size
#> F logical logical 1 56
#> LETTERS character character 26 1712
#> R.version simple.list list 14 3168
#> R.version.string character character 1 136
#> T logical logical 1 56
#> letters character character 26 1712
#> month.abb character character 12 848
#> month.name character character 12 880
#> pi numeric numeric 1 56
#> version simple.list list 14 3168
Created on 2024-08-21.