Miscellaneous Functions for ‘SciViews::R’

The {svMisc} package contains a large collection of functions that are useful in the context of (G)UI (Graphical) User Interface development, and also, of more general usage. Here is a series of functions you should look at.

Help

In R, you access man pages for the various R objects with help(topic), or ?topic. But, if topic is not found, these function do not return a very useful information. For instance, if you want to make a Kalman filtering in R, you may be inclined to search for the topic kalman

?kalman
#> No documentation for 'kalman' in specified packages and libraries:
#> you could try '??kalman'

OK, it is suggested to use ?? to search the documentation for kalman. However, using about() instead immediately produces a more useful result:

library(svMisc)
about("kalman")
#> 'kalman' not found, do you mean?
#> stats::KalmanForecast, stats::KalmanLike, stats::KalmanRun, stats::KalmanSmooth
#> Searching keyword in all R help pages for ''...

… and it also provides a list of potential man pages that could interest you. In case the topic is found, about() does the same as help() and shows the page (try with about("log"), for instance). If you still have not found what you are looking for, you could try to search on the Web by using search_web().

You may also be interested by is_help() that indicates if an object is associated with a man page, and if it has a running example.

Packages

In R, the use of library() to load a package is very confusing. Given the number of questions raised about it, one could consider another function to load R packages in memory. svMisc proposes package(). That function loads one or several R packages as silently as possible and it returns TRUE only if all the packages are loaded. Otherwise, the list of missing packages is recorded¹, and one could simply issue Install() to install them. This is indeed a semi-automatic installation mechanisms for R packages. The UseR still masters the process, but it is more straightforward.

Analyses in batch and show progression

If you need to perform an analysis in batch mode, you may be happy with batch() and progress(). The first function runs a function sequentially on all items allowing for an informative message in case of failure. Also, batch() provides a mechanism to recover from the error, so that following items in the list are also analyzed. Indeed, if you use a simple for() loop or applyXXX() functions, the execution is stopped at the first error encountered. Imagine 500 items to process, and an error that appears at the second one… it leaves you 498 items unanalyzed! allows to continue to the next item.

The example shows a fake batch process of files, which fails randomly. Here is the function to run sequentially:

fake_process <- function(file) {
  message("Processing ", file, "...")
  flush.console()
  Sys.sleep(0.5)
  if (runif(1) > 0.7) {# Fail
    warning("fake_process was unable to process ", file)
    invisible(FALSE)
  } else invisible(TRUE)
}

The key aspect here is that you function, instead of using stop() must use warning() and return FALSE. Otherwise, in case of success, it should return TRUE. Then, calling your function on a series of objects is straightforward:

# Run it in batch mode on ten items
batch(paste0("file", 1:10), fake_process)
#> Running the batch process with fake_process...
#> 
#> Progress:  1 on 10  Progress:  2 on 10  Progress:  3 on 10  Progress:  4 on 10
#> Warning in fun(item, ...): fake_process was unable to process file4
#> 
#> Progress:  5 on 10  Progress:  6 on 10  Progress:  7 on 10  Progress:  8 on 10  Progress:  9 on 10  Progress: 10 on 10                      
#> Processed successfully 9 items on 10 (see .last.batch)

In case an error occurred, the information is recorded i, .last.batch:

.last.batch
#> [1] FALSE
#> attr(,"items")
#>  [1] "file1"  "file2"  "file3"  "file4"  "file5"  "file6"  "file7"  "file8" 
#>  [9] "file9"  "file10"
#> attr(,"ok")
#>  [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

The items and ok attributes are also available from that object for further inspection and action.

If you run batch() in R, you noted also the progress()ion message that appeared. Indeed the progress() function allows to display such a message, either as a text at the R console, or in a dialog box. There are many different forms, see the man page ?progress. for instance, here is a progress bar in percent, stopped at 75% () you need to call progress() with a value higher than max.value = to dismiss it):

for (i in 0:75) {
  progress(i, progress.bar = TRUE)
  # Some process here...
}
#>           0%---------25%---------50%---------75%--------100%
#> Progress: ||||||||||||||||||||||||||||||||||||||

Subsettable functions

The $ operator is not applicable on functions. It is not meaningful in that context. Yet, it may be convenient to use it in certain conditions. From the example of ?subsettable:

foo <- structure(function(x, type = c("histogram", "boxplot"), ...) {
  type <- match.arg(type, c("histogram", "boxplot"))
  switch(type,
    histogram = hist(x, ...),
    boxplot = boxplot(x, ...),
    stop("unknow type")
  )
}, class = c("function", "subsettable_type"))
foo
#> function (x, type = c("histogram", "boxplot"), ...) 
#> {
#>     type <- match.arg(type, c("histogram", "boxplot"))
#>     switch(type, histogram = hist(x, ...), boxplot = boxplot(x, 
#>         ...), stop("unknow type"))
#> }
#> attr(,"class")
#> [1] "function"         "subsettable_type"

# This function can be used as usual:
foo(rnorm(50), type = "histogram")

# ... but also this way:
foo$histogram(rnorm(50))

foo$boxplot(rnorm(50))

Capture and parse R code

The capture.output() function from the ‘utils’ package can capture output usually send to the R console, but it does so in an imperfect way. If you want to capture output exactly as it would appear at the R console, you could use capture_all():

captured <- capture_all(parse_text('1:2 + 1:3'), split = FALSE)
#> Warning in 1:2 + 1:3: longer object length is not a multiple of shorter object
#> length
captured
#> [1] ":> 1:2 + 1:3" "[1] 2 4 4\n"

Only the prompt is changed to :>. You can use that content, or print it somewhere, for instance:

writeLines(captured)
#> :> 1:2 + 1:3
#> [1] 2 4 4

The parse_text() function parse one or more character strings exactly as if they were commands entered at the R prompt:

parse_text(c("1 + 1", "log(10)"))
#> expression(1 + 1, log(10))

… and for an incomplete expression:

parse_text("log(")
#> [1] NA

The source_clipboard() source code directly from the clipboard. All these functions form the basis to simulate an R console in a different context (a console widget in your own GUI). You can combine this with to_rjson()/eval_rjson to encode and decode R objects on both sides of a pipeline between the R process and your GUI.

Encode/decode R objects in Rjson

Rjson is a version of JSON that allows to encore and decode rapidly almost all R objects. From the example at ?to_rjson:

# A complex R object
# Note: we round doubles to 14 digits because precision is lost in the process
obj <- structure(list(
  a = as.double(c(1:5, 6)),
  LETTERS,
  c = c(c1 = 4.5, c2 = 7.8, c3 = Inf, c4 = -Inf, NA, c6 = NaN),
  c(TRUE, FALSE, NA),
  e = factor(c("a", "b", "a")),
  f = 'this is a "string" with quote',
  g = matrix(round(rnorm(4), 14), ncol = 2),
  `h&$@` = list(x = 1:3, y = round(rnorm(3), 14),
    fact = factor(c("b", "a", "b"))),
  i = Sys.Date(),
  j = list(1:5, y = "another item")),
  comment = "My comment",
  anAttrib = 1:10,
  anotherAttrib = list(TRUE, y = 1:4))

# Convert to RJSON
(rjson1 <- to_rjson(obj, attributes = TRUE))
#>  [1] list("Data_" := list("a" := c(1., 2., 3., 4., 5., 6.), "" := c("A",                                                                    
#>  [2] "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N",                                                                       
#>  [3] "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"),                                                                           
#>  [4]     "c" := c(c1 = 4.5, c2 = 7.8, c3 = Inf, c4 = -Inf,                                                                                  
#>  [5]     NA, c6 = NaN), "" := c(TRUE, FALSE, NA), "e" := list("Data_" := c(1,                                                               
#>  [6]     2, 1), "levels" := c("a", "b"), "class" := "factor"),                                                                              
#>  [7]     "f" := "this is a \\"string\\" with quote", "g" := list("Data_" := c(0.66154796025597,                                             
#>  [8]     -0.72882916871231, -0.15717688456231, -2.04773973331784), "dim" := c(2,                                                            
#>  [9]     2)), "h&$@" := list("x" := seq(1, 3), "y" := c(-0.416624068163,                                                                    
#> [10]     1.35212284484376, 0.09801472905977), "fact" := list("Data_" := c(2,                                                                
#> [11]     1, 2), "levels" := c("a", "b"), "class" := "factor")),                                                                             
#> [12]     "i" := list("Data_" := 20041., "class" := "Date"),                                                                                 
#> [13]     "j" := list("" := seq(1, 5), "y" := "another item")), "comment" := "My comment", "anAttrib" := seq(1, 10), "anotherAttrib" := list(
#> [14]     "" := TRUE, "y" := seq(1, 4)))
# Get back an R object from Rjson
(obj2 <- eval_rjson(rjson1))
#> $a
#> [1] 1 2 3 4 5 6
#> 
#> [[2]]
#>  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
#> [20] "T" "U" "V" "W" "X" "Y" "Z"
#> 
#> $c
#>   c1   c2   c3   c4        c6 
#>  4.5  7.8  Inf -Inf   NA  NaN 
#> 
#> [[4]]
#> [1]  TRUE FALSE    NA
#> 
#> $e
#> [1] a b a
#> Levels: a b
#> 
#> $f
#> [1] "this is a \"string\" with quote"
#> 
#> $g
#>            [,1]       [,2]
#> [1,]  0.6615480 -0.1571769
#> [2,] -0.7288292 -2.0477397
#> 
#> $`h&$@`
#> $`h&$@`$x
#> [1] 1 2 3
#> 
#> $`h&$@`$y
#> [1] -0.41662407  1.35212284  0.09801473
#> 
#> $`h&$@`$fact
#> [1] b a b
#> Levels: a b
#> 
#> 
#> $i
#> [1] "2024-11-14"
#> 
#> $j
#> $j[[1]]
#> [1] 1 2 3 4 5
#> 
#> $j$y
#> [1] "another item"
#> 
#> 
#> attr(,"anAttrib")
#>  [1]  1  2  3  4  5  6  7  8  9 10
#> attr(,"anotherAttrib")
#> attr(,"anotherAttrib")[[1]]
#> [1] TRUE
#> 
#> attr(,"anotherAttrib")$y
#> [1] 1 2 3 4
# Is it identical to obj?
identical(obj, obj2)
#> [1] TRUE

Get system file or directory

There are several different functions in R to access system files, or files inside R packages: R.home(), system.file(), Sys.which(), tempdir(). The system_dir() and system_file() functions centralize their functionalities. For instance:

Get the temporary directory used by this R process

system_dir("temp")
#> [1] "/tmp/Rtmpi3Ie35"

Get the system temporary directory

system_dir("sysTemp")
#> [1] "/tmp"

Get the home directory of the current user

system_dir("user")
#> [1] "/github/home"

Get the R home directory

system_dir("home")
#> [1] "/usr/lib/R"

Get the path to an executable

system_dir("zip", exec = TRUE)
#> [1] "/usr/bin"

Get the file of that executable

system_file("zip", exec = TRUE)
#> [1] "/usr/bin/zip"

Get the root directory of a package

system_dir(package = "stats")
#> [1] "/usr/lib/R/library/stats"

Get a file from a package

system_file("help", "AnIndex", package = "splines")
#> [1] "/usr/lib/R/library/splines/help/AnIndex"

There are other possibilities. See ?system_dir. You may also be interested by file_edit() that allows to create and edit a text file from a template.

Various information functions

compare_r_version() conveniently compares the current R version with a specified one. It returns 1 if it is newer, 0, if it is equal and -1 if it is older.

compare_r_version("5.6.0") # Probably older
#> [1] -1

compare_r_version("0.6.0") # Probably newer
#> [1] 1

Check the environment:

is_win() # Windows?
#> [1] FALSE
is_mac() # MacOS?
#> [1] FALSE
is_rgui() # Is it RGui under Windows?
#> [1] FALSE
is_sdi() # Is RGui run in SDI mode (separate windows)?
#> [1] FALSE
is_rstudio() # Is it RStudio?
#> [1] FALSE
is_rstudio_desktop() # RStudio desktop?
#> [1] FALSE
is_rstudio_server() # RStudio server?
#> [1] FALSE
is_jgr() # Is R running under JGR?
#> [1] FALSE

Miscellaneous

Make sure a vector is of a defined mode and length (possibly by applying recycling rule) using def():

def(0:2, mode = "logical", length.out = 5) # logical, size 5
#> [1] FALSE  TRUE  TRUE FALSE  TRUE

Get a nicely formatted args() (see ?arg_tips for other functions to get short textual information about functions):

args_tip("ls")
#> [1] "ls(name, pos = -1L, envir = as.environment(pos), all.names = FALSE, pattern,\n    sorted = TRUE)"

Get the name of an (unused) temporary variable:

temp_var("my_var")
#> [1] "my_var84542"

Manage a temporary environment attached to the search path using TempEnv() and the temp_XXX() functions. The temporary_environment vignette gives more details on this series of functions.

search()
#>  [1] ".GlobalEnv"        "package:svMisc"    "package:rmarkdown"
#>  [4] "package:stats"     "package:graphics"  "package:grDevices"
#>  [7] "package:utils"     "package:datasets"  "package:methods"  
#> [10] "SciViews:TempEnv"  "Autoloads"         "package:base"
# Assign a variable in a temporary environment
assign_temp("my_var", 1:5)
# The environment is named SciViews:TempEnv
search()
#>  [1] ".GlobalEnv"        "package:svMisc"    "package:rmarkdown"
#>  [4] "package:stats"     "package:graphics"  "package:grDevices"
#>  [7] "package:utils"     "package:datasets"  "package:methods"  
#> [10] "SciViews:TempEnv"  "Autoloads"         "package:base"
# Get the variable
get_temp("my_var")
#> [1] 1 2 3 4 5
# List variables in the temporary environment
ls(envir = TempEnv())
#> [1] "my_var"
# Delete the variable
rm_temp("my_var")

The list of missing packages is written in a variable named .packages_to_install located in the SciViews:TempEnv environment↩︎