README.Rmd

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# dupree

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.

## Installation

You can install `dupree` from github with:

```{r gh-installation, eval = FALSE}
if (!"dupree" %in% installed.packages()) {
  # Alternatively:
  # install.packages("dupree")
  remotes::install_github("russHyde/dupree")
}
```

## Example

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:

```{r example}
## basic example code
library(dupree)

files <- dir(pattern = "*.R(md)*$", recursive = TRUE)

dupree(files)
```

Any top-level code blocks that contain at least 
`r formals(dupree)$min_block_size` non-trivial tokens are
included in the above analysis (a token being a function or variable name, an
operator etc; but ignoring comments, white-space and some really common tokens:
`[](){}-+$@:,=`, `<-`, `&&` etc). To be more restrictive, you could consider
larger code-blocks (increase `min_block_size`) within just the `./R/` source
code directory:

```{r}
# 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)
```

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`](https://github.com/markvanderloo/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`.

```{r}
# Analyse all R files in the R/ directory:
dupree_dir(".", filter = "R/")
```

```{r}
# Analyse all R files except those in the tests / presentations directories:
# `dupree_dir` uses grep-like arguments
dupree_dir(
  ".",
  filter = "tests|presentations", invert = TRUE
)
```

```{r}
# Analyse all R source code in the package (only looking at the ./R/ directory)
dupree_package(".")
```