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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)*$", recursive = TRUE)
dupree(files)
#> # A tibble: 14 x 7
#> file_a file_b block_a block_b line_a line_b score
#> <chr> <chr> <int> <int> <int> <int> <dbl>
#> 1 R/dupree_classes.R tests/testthat/tes… 33 8 57 13 0.296
#> 2 tests/testthat/tes… tests/testthat/tes… 8 10 13 118 0.248
#> 3 R/dupree_classes.R R/dupree_classes.R 33 61 57 117 0.218
#> 4 tests/testthat/tes… tests/testthat/tes… 8 11 13 64 0.216
#> 5 R/dupree_classes.R R/dupree_classes.R 33 88 57 180 0.215
#> 6 tests/testthat/tes… tests/testthat/tes… 11 1 64 1 0.185
#> 7 tests/testthat/tes… tests/testthat/tes… 1 2 1 132 0.172
#> 8 R/dupree_classes.R R/dupree.R 33 111 57 124 0.146
#> 9 tests/testthat/tes… tests/testthat/tes… 8 6 13 25 0.120
#> 10 R/dupree_classes.R tests/testthat/hel… 33 4 57 4 0.114
#> 11 R/dupree_classes.R R/dupree_code_enum… 88 48 180 90 0.111
#> 12 presentations/clea… R/dupree_classes.R 28 61 316 117 0.105
#> 13 tests/testthat/tes… tests/testthat/tes… 6 3 25 11 0.0972
#> 14 R/dupree_code_enum… tests/testthat/tes… 48 1 90 1 0.00298Any top-level code blocks that contain at least 40 non-trivial tokens
are included in the above analysis (a token being a function or variable
name, an operator etc; but ignoring comments, white-space and some
really common tokens: [](){}-+$@:,=, <-,
&& 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 x 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 0.104For 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 x 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… 33 61 57 117 0.218
#> 2 ./R/dupree_classes.R ./R/dupree_classes… 33 88 57 180 0.215
#> 3 ./tests/testthat/te… ./tests/testthat/t… 1 2 1 132 0.172
#> 4 ./R/dupree_classes.R ./R/dupree.R 33 111 57 124 0.146
#> 5 ./R/dupree_classes.R ./R/dupree_code_en… 88 48 180 90 0.111
#> 6 ./R/dupree_code_enu… ./tests/testthat/t… 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 x 7
#> file_a file_b block_a block_b line_a line_b score
#> <chr> <chr> <int> <int> <int> <int> <dbl>
#> 1 ./R/dupree_class… ./R/dupree_classes.R 33 61 57 117 0.218
#> 2 ./R/dupree_class… ./R/dupree_classes.R 33 88 57 180 0.215
#> 3 ./R/dupree_class… ./R/dupree.R 33 111 57 124 0.146
#> 4 ./R/dupree_class… ./R/dupree_code_enumera… 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 x 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… 33 61 57 117 0.218
#> 2 ./R/dupree_classes.R ./R/dupree_classes… 33 88 57 180 0.215
#> 3 ./tests/testthat/te… ./tests/testthat/t… 1 2 1 132 0.172
#> 4 ./R/dupree_classes.R ./R/dupree.R 33 111 57 124 0.146
#> 5 ./R/dupree_classes.R ./R/dupree_code_en… 88 48 180 90 0.111
#> 6 ./R/dupree_code_enu… ./tests/testthat/t… 48 1 90 1 0.00298These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.