The goal of dupree is to identify chunks / blocks of highly duplicated code within a set of R scripts.
A very lightweight approach is used:
The user provides a set of *.R and/or *.Rmd files;
All R-code in the user-provided files is read and code-blocks are identified;
The non-trivial symbols from each code-block are retained (for instance, really common symbols like <-, ,, +, ( are dropped);
Similarity between different blocks is calculated using stringdist::seq_sim by longest-common-subsequence (symbol-identity is at whole-word level - so “my_data”, “my_Data”, “my.data” and “myData” are not considered to be identical in the calculation - and all non-trivial symbols have equal weight in the similarity calculation);
Code-blocks pairs (both between and within the files) are returned in order of highest similarity
To prevent the results being dominated by high-identity blocks containing very few symbols (eg, library(dplyr)) the user can specify a min_block_size. Any code-block containing at least this many non-trivial symbols will be kept.
You can install dupree from github with:
if (!"dupree" %in% installed.packages()) {
# Alternatively:
# install.packages("dupree")
remotes::install_github("russHyde/dupree")
}
To run dupree over a set of R files, you can use the dupree(), dupree_dir() or dupree_package() functions. For example, to identify duplication within all of the .R and .Rmd files for the dupree package you could run the following:
## basic example code library(dupree) files <- dir(pattern = "*.R(md)*$", recursive = TRUE) dupree(files) #> # A tibble: 14 × 7 #> file_a file_b block_a block_b line_a line_b score #> <chr> <chr> <int> <int> <int> <int> <dbl> #> 1 R/dupree_classes.R tests… 33 8 57 13 0.296 #> 2 tests/testthat/test_dupree_clas… tests… 8 10 13 118 0.248 #> 3 R/dupree_classes.R R/dup… 33 61 57 117 0.218 #> 4 tests/testthat/test_dupree_clas… tests… 8 11 13 64 0.216 #> 5 R/dupree_classes.R R/dup… 33 88 57 180 0.215 #> 6 tests/testthat/test_dupree_clas… tests… 11 1 64 1 0.185 #> 7 tests/testthat/testdata/anRpack… tests… 2 1 132 1 0.172 #> 8 R/dupree.R R/dup… 111 33 124 57 0.146 #> 9 tests/testthat/test_dupree_clas… tests… 8 6 13 25 0.120 #> 10 R/dupree.R tests… 111 4 124 4 0.114 #> 11 R/dupree_classes.R R/dup… 88 48 180 90 0.111 #> 12 R/dupree_classes.R prese… 61 28 117 316 0.105 #> 13 tests/testthat/test-dupree_dir_… tests… 3 6 11 25 0.0972 #> 14 R/dupree_code_enumeration.R tests… 48 1 90 1 0.00298
Any top-level code blocks that contain at least 40 non-trivial tokens are included in the above analysis (a token being a function or variable name, an operator etc; but ignoring comments, white-space and some really common tokens: [](){}-+$@:,=, <-, && etc). To be more restrictive, you could consider larger code-blocks (increase min_block_size) within just the ./R/ source code directory:
# R-source code files in the ./R/ directory of the dupree package: source_files <- dir(path = "./R", pattern = "*.R(md)*$", full.names = TRUE) # analyse any code blocks that contain at least 50 non-trivial tokens dupree(source_files, min_block_size = 50) #> # A tibble: 1 × 7 #> file_a file_b block_a block_b line_a line_b score #> <chr> <chr> <int> <int> <int> <int> <dbl> #> 1 ./R/dupree_classes.R ./R/dupree_classes.R 61 88 117 180 0.104
For each (sufficiently big) code block in the provided files, dupree will return the code-block that is most-similar to it (although any given block may be present in the results multiple times if it is the closest match for several other code blocks).
Code block pairs with a higher score value are more similar. score lies in the range [0, 1]; and is calculated by the stringdist package: matching occurs at the token level: the token “my_data” is no more similar to the token “myData” than it is to “x”.
If you find code-block-pairs with a similarity score much greater than 0.5 there is probably some commonality that could be abstracted away.
Note that you can do something similar using the functions dupree_dir and (if you are analysing a package) dupree_package.
# Analyse all R files in the R/ directory:
dupree_dir(".", filter = "R/")
#> # A tibble: 6 × 7
#> file_a file_b block_a block_b line_a line_b score
#> <chr> <chr> <int> <int> <int> <int> <dbl>
#> 1 ./R/dupree_classes.R ./R/d… 33 61 57 117 0.218
#> 2 ./R/dupree_classes.R ./R/d… 33 88 57 180 0.215
#> 3 ./tests/testthat/testdata/anRpac… ./tes… 2 1 132 1 0.172
#> 4 ./R/dupree.R ./R/d… 111 33 124 57 0.146
#> 5 ./R/dupree_classes.R ./R/d… 88 48 180 90 0.111
#> 6 ./R/dupree_code_enumeration.R ./tes… 48 1 90 1 0.00298
# Analyse all R files except those in the tests / presentations directories: # `dupree_dir` uses grep-like arguments dupree_dir( ".", filter = "tests|presentations", invert = TRUE ) #> # A tibble: 4 × 7 #> file_a file_b block_a block_b line_a line_b score #> <chr> <chr> <int> <int> <int> <int> <dbl> #> 1 ./R/dupree_classes.R ./R/dupree_classes.R 33 61 57 117 0.218 #> 2 ./R/dupree_classes.R ./R/dupree_classes.R 33 88 57 180 0.215 #> 3 ./R/dupree.R ./R/dupree_classes.R 111 33 124 57 0.146 #> 4 ./R/dupree_classes.R ./R/dupree_code_enum… 88 48 180 90 0.111
# Analyse all R source code in the package (only looking at the ./R/ directory)
dupree_package(".")
#> # A tibble: 6 × 7
#> file_a file_b block_a block_b line_a line_b score
#> <chr> <chr> <int> <int> <int> <int> <dbl>
#> 1 ./R/dupree_classes.R ./R/d… 33 61 57 117 0.218
#> 2 ./R/dupree_classes.R ./R/d… 33 88 57 180 0.215
#> 3 ./tests/testthat/testdata/anRpac… ./tes… 2 1 132 1 0.172
#> 4 ./R/dupree.R ./R/d… 111 33 124 57 0.146
#> 5 ./R/dupree_classes.R ./R/d… 88 48 180 90 0.111
#> 6 ./R/dupree_code_enumeration.R ./tes… 48 1 90 1 0.00298
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