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typetracer is an R package to trace function parameter types. The R language includes a set of defined types, but the language itself is “absurdly dynamic”[1], and lacks any way to specify which types are expected by any expression. The typetracer package enables code to be traced to extract detailed information on the properties of parameters passed to R functions. typetracer can trace individual functions or entire packages, as demonstrated below.


The stable version of the package can be installed with one of the following commands:

# Stable version from CRAN:
install.packages ("typetrace")
# Current development version from r-universe:
install.packages (
    repos = c ("https://mpadge.r-universe.dev", "https://cloud.r-project.org")

Alternatively, for those who prefer to use other source code platforms, the package can also be installed by running any one of the following lines:

remotes::install_git ("https://git.sr.ht/~mpadge/dodgr")
remotes::install_git ("https://codeberg.org/UrbanAnalyst/dodgr")
remotes::install_bitbucket ("UrbanAnalyst/dodgr")
remotes::install_gitlab ("UrbanAnalyst/dodgr")

The package can then loaded for use by calling library:

library (typetracer)

Example #1 - A Single Function

typetracer works by “injecting” tracing code into the body of a function using the inject_tracer() function. Locally-defined functions can be traced by simply passing the functions directly to inject_tracer(). The following example includes four parameters, including ... to allow passing of additional and entirely arbitrary parameter types and values.

f <- function (x, y, z, ...) {
    x * x + y * y
inject_tracer (f)

After injecting the typetracer code, calls to the function, f, will “trace” each parameter of the function, by capturing both unevaluated and evaluated representations at the point at which the function is first called. These values can be accessed with the load_traces function, which returns a data.frame object (in tibble format) with one row for each parameter from each function call.

val <- f (
    x = 1:2,
    y = 3:4 + 0.,
    a = "blah",
    b = list (a = 1, b = "b"),
    f = a ~ b
x <- load_traces ()

## # A tibble: 7 × 12
##   trace_number fn_name fn_call_hash par_name class     typeof mode  storage_mode
##          <int> <chr>   <chr>        <chr>    <I<list>> <chr>  <chr> <chr>       
## 1            0 f       uDgEbied     x        <chr [1]> integ… nume… integer     
## 2            0 f       uDgEbied     y        <chr [1]> double nume… double      
## 3            0 f       uDgEbied     z        <chr [1]> NULL   NULL  NULL        
## 4            0 f       uDgEbied     ...      <chr [1]> NULL   NULL  NULL        
## 5            0 f       uDgEbied     a        <chr [1]> chara… char… character   
## 6            0 f       uDgEbied     b        <chr [1]> list   list  list        
## 7            0 f       uDgEbied     f        <chr [1]> langu… call  language    
## # ℹ 4 more variables: length <int>, formal <named list>, uneval <I<list>>,
## #   eval <I<list>>

Each row of the result returned by load_traces() represents one parameter passed to one function call. Each function call itself represents a single “trace” as enumerated by the trace_number column, and also uniquely identified by an arbitrary function call hash (fn_call_hash). The remaining columns of the trace data define the properties of each parameter, p, as:

  1. par_name: Name of parameter.
  2. class: List of classes of parameter.
  3. typeof: Result of typeof(p).
  4. mode: Result of mode(p).
  5. storage_mode: Result of storage.mode(p).
  6. length: Result of length(p).
  7. formal: Result of formals(f)[["p"]], as named list item with default value where specified.
  8. uneval: Parameters as passed to the function call prior to evaluation within function environment.
  9. eval: Evaluated version of parameter.

The results above show that all parameters of the function, f(), were successfully traced, including the additional parameters, a, b, and f, passed as part of the ... argument. Such additional parameters can be identified through having a "formal" entry of NULL, indicating that they are not part of the formal arguments to the function.

That result can also be used to demonstrate the difference between the unevaluated and evaluated forms of parameters:

x$uneval [x$par_name %in% c ("b", "f")]

## $b
## [1] "list(a = 1, b = \"b\")"
## $f
## [1] "a ~ b"

x$eval [x$par_name %in% c ("b", "f")]

## $b
## $b$a
## [1] 1
## $b$b
## [1] "b"
## $f
## a ~ b
## <environment: 0x560b15656ca8>

Unevaluated parameters are generally converted to equivalent character expressions.

The typeof, mode, and storage_mode columns are similar, yet may hold distinct information for certain types of parameters. The conditions under which these values differ are complex, and depend among other things on the version of R itself. typeof alone should generally provide sufficient information, although this list of differences may provide further insight into whether the other columns may provide useful additional information.

Traces themselves are saved in the temporary directory of the current R session, and the load_traces() function simply loads all traces created in that session. The function clear_traces() removes all traces, so that load_traces() will only load new traces produced after that time.

Uninjecting Traces

It is important after applying the inject_tracer() function to restore the functions back to their original form through calling the obverse uninject_tracer() function. For the function, r, above, this simply requires,

uninject_tracer (f)

## [1] TRUE

All traces can also be removed with this functions:

clear_traces ()

Because typetracer modifies the internal code of functions as defined within a current R session, we strongly recommend restarting your R session after using typetracer, to ensure expected function behaviour is restored.

Example #2 - Recursion into lists

R has extensive support for list structures, notably including all data.frame-like objects in which each column is actually a list item. typetracer also offers the ability to recurse into the list structures of individual parameters, to recursively trace the properties of each list item. To do this, the traces themselves have to be injected with the additional parameter, trace_lists = TRUE.

The final call above included an additional parameter passed as a list. The following code re-injects a tracer with the ability to traverse into list structures:

inject_tracer (f, trace_lists = TRUE)
val <- f (
    x = 1:2,
    y = 3:4 + 0.,
    a = "blah",
    b = list (a = 1, b = "b"),
    f = a ~ b
x_lists <- load_traces ()
print (x_lists)

## # A tibble: 9 × 12
##   trace_number fn_name fn_call_hash par_name class     typeof mode  storage_mode
##          <int> <chr>   <chr>        <chr>    <I<list>> <chr>  <chr> <chr>       
## 1            0 f       LzZIbYvx     x        <chr [1]> integ… nume… integer     
## 2            0 f       LzZIbYvx     y        <chr [1]> double nume… double      
## 3            0 f       LzZIbYvx     z        <chr [1]> NULL   NULL  NULL        
## 4            0 f       LzZIbYvx     ...      <chr [1]> NULL   NULL  NULL        
## 5            0 f       LzZIbYvx     a        <chr [1]> chara… char… character   
## 6            0 f       LzZIbYvx     b        <chr [1]> list   list  list        
## 7            0 f       LzZIbYvx     f        <chr [1]> langu… call  language    
## 8            0 f       LzZIbYvx     b$a      <chr [1]> double nume… double      
## 9            0 f       LzZIbYvx     b$b      <chr [1]> chara… char… character   
## # ℹ 4 more variables: length <int>, formal <named list>, uneval <I<list>>,
## #   eval <I<list>>

And that result now has 9 rows, or 2 more than the previous example, reflecting the two items passed as a list to the parameter, b. List-parameter items are identifiable in typetracer output through the “dollar-notation” in the par_name field. The final two values in the above table are b$a and b$b, representing the two elements of the list passed as the parameter, b.

Example #3 - Tracing a Package

This section presents a more complex example tracing all function calls from the rematch package, chosen because it has less code than almost any other package on CRAN. The following single line traces function calls in all examples for the nominated package. The trace_package() function automatically injects tracing code into every function within the package, so there is no need to explicitly call the inject_tracer() function.

(This function also includes a trace_lists parameter, as demonstrated above, with a default of FALSE to not recurse into tracing list structures.)

res <- trace_package ("rematch")

## # A tibble: 8 × 14
##   trace_number source_file_name fn_name  fn_call_hash call_env par_name class   
##          <int> <chr>            <chr>    <chr>        <chr>    <chr>    <I<list>
## 1            0 man/re_match.Rd  re_match UJfHAIRp     <NA>     pattern  <chr>   
## 2            0 man/re_match.Rd  re_match UJfHAIRp     <NA>     text     <chr>   
## 3            0 man/re_match.Rd  re_match UJfHAIRp     <NA>     perl     <chr>   
## 4            0 man/re_match.Rd  re_match UJfHAIRp     <NA>     ...      <chr>   
## 5            1 man/re_match.Rd  re_match Anyflkea     <NA>     pattern  <chr>   
## 6            1 man/re_match.Rd  re_match Anyflkea     <NA>     text     <chr>   
## 7            1 man/re_match.Rd  re_match Anyflkea     <NA>     perl     <chr>   
## 8            1 man/re_match.Rd  re_match Anyflkea     <NA>     ...      <chr>   
## # ℹ 7 more variables: typeof <chr>, mode <chr>, storage_mode <chr>,
## #   length <int>, formal <named list>, uneval <I<list>>, eval <I<list>>

The data.frame returned by the trace_package() function includes three more columns than the result directly returned by load_traces(). These columns identify the sources and calling environments of each function call being traces. The “call_env” column identifies the calling environment which generated each trace, while “source_file_name” identifies the file.

unique (res$call_env)

## [1] NA

unique (res$source_file_name)

## [1] "man/re_match.Rd"

Although the “call_env” columns contains no useful information for that package, it includes information on the full environment in which each function was called. These “environments” include such things as tryCatch calls expected to generate errors, or the various expect_ functions of the “testthat” package. The above case of racing an installed package generally only extracts traces from example code, as documented in help, or .Rd, files. These are identified by the “rd_” prefix on the “source_file_name”, with the rematch package including only one .Rd file.

The trace_package() function also includes an additional parameter, types, which defaults to c ("examples", "tests"), so that traces are also by default generated for all tests included with local source packages (or for packages installed to include test files). The “source” column for test files identifies the names of each test, prefixed with “test_”.

The other two additional columns of “trace_file” and “call_env” respectively specify the source file and calling environment of each trace. These will generally only retain information from test files, in which case the source file will generally be the file name identified in the “source” column, and “call_env” will specify the environment from which that function call originated. Environments may, for example, include various types of expectation from the “testthat” package. These calling environments are useful to discern whether, for example, a call was made with an expectation that it should error.

Example #3(a) - Specifying Functions to Trace

The trace_package() function also accepts an argument, functions, specifying which functions from a package should be traced. For example,

x <- trace_package ("stats", functions = "sd")

## # A tibble: 2 × 16
##   trace_number trace_source fn_name fn_call_hash trace_file call_env par_name
##          <int> <chr>        <chr>   <chr>        <chr>      <chr>    <chr>   
## 1            0 examples     sd      EzasZOKV     <NA>       <NA>     x       
## 2            0 examples     sd      EzasZOKV     <NA>       <NA>     na.rm   
## # ℹ 9 more variables: class <I<list>>, typeof <chr>, mode <chr>,
## #   storage_mode <chr>, length <int>, formal <I<list>>, uneval <I<list>>,
## #   eval <I<list>>, source <chr>

Prior Art

This package extends on concepts previously developed in other R packages, notably including:

Plus work explained in detail in this footnote:

[1] Alexi Turcotte & Jan Vitek (2019), Towards a Type System for R, ICOOOLPS ’19: Proceedings of the 14th Workshop on Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and Systems. Article No. 4, Pages 1–5, https://doi.org/10.1145/3340670.3342426