Package 'rscc'

as.igraph

Description

Converts a data frame of similarity coefficients into a graph.

Usage

as_igraph(x, tol = 100 * .Machine$double.eps, tol1 = 8 * tol, ...)

Arguments

x	a similarity object
tol	numeric scalar >= 0. Smaller differences are not considered, see all.equal.numeric.
tol1	numeric scalar >= 0. isSymmetric.matrix() ‘pre-tests’ the first and last few rows for fast detection of ‘obviously’ asymmetric cases with this tolerance. Setting it to length zero will skip the pre-tests.
...	further parameters used by igraph::graph_from_adjacency_matrix

Value

an igraph object

Examples

files <- list.files(path=system.file("examples", package="rscc"), pattern="*.R$", full.names = TRUE)
prgs  <- sourcecode(files, title=basename(files))
docs  <- documents(prgs)
simm  <- similarities(docs)
# a similarity coefficients equal to zero does not create an edge!
g <- as_igraph(simm, diag=FALSE)
# thicker edges have higher similarity coefficients
plot(g, edge.width=1+3*igraph::E(g)$weight)

---

browse

Description

Creates a temporary HTML file with source codes and opens it into a browser using browseURL. Note that the source code is reformatted.

Usage

browse(prgs, simdf, n = (simdf[, 3] > 0), width.cutoff = 60, css = NULL)

Arguments

prgs	sourcecode object
simdf	similarity object
n	integer: comparisons to show (default: simf[,3]>0)
width.cutoff	integer: an integer in [20, 500]: if a line's character length is at or over this number, the function will try to break it into a new line (default: 60)
css	character: file name of CSS style for highlighting the R code

Value

invisibly the name of the temporary HTML file

Examples

# example files are taken from https://CRAN.R-project.org/package=SimilaR
files <- list.files(system.file("examples", package="rscc"), "*.R$", full.names=TRUE)
prgs  <- sourcecode(files)
simm  <- similarities(documents(prgs))
simdf <- matrix2dataframe(simm)
if (interactive()) browse(prgs, simdf)

---

documents

Description

Creates word vectors from parsed sourec code objects. If

• type=="vars" then the names of all.vars(.),

• type=="funs" then the namas of setdiff(all.names(.), all.vars(.), and

• type=="names" then the names of all.names(.)

are used.

Usage

documents(
  prgs,
  type = c("vars", "funs", "names"),
  ignore.case = TRUE,
  minlen = 2,
  ...
)

Arguments

prgs	prgs sourcecode object
type	character: either "vars", "funs", "names" (default: "vars")
ignore.case	logical: If TRUE, case is ignored for computing (default: TRUE)
minlen	integer: minimal name length to be considered (default: 2)
...	unused

Value

a

Examples

# example files are taken from https://CRAN.R-project.org/package=SimilaR
files <- list.files(system.file("examples", package="rscc"), "*.R$", full.names=TRUE)
prgs  <- sourcecode(files, basename=TRUE)
docs  <- documents(prgs)
docs

---

freq_table

Description

Computes a frequency table of words and documents.

Usage

freq_table(docs, ...)

Arguments

docs	documents object
...	unused

Value

a matrix with similarities

Examples

# example files are taken from https://CRAN.R-project.org/package=SimilaR
files <- list.files(system.file("examples", package="rscc"), "*.R$", full.names=TRUE)
prgs  <- sourcecode(files, basename=TRUE)
docs  <- documents(prgs)
freq_table (docs)

---

matrix2dataframe

Description

Converts a numeric matrix to a data frame with decreasing or increasing values: First column row index, second column col index and third column the value. If the matrix is symmetric, only the upper triangle is taken into account.

Usage

matrix2dataframe(
  m,
  decreasing = TRUE,
  tol = 100 * .Machine$double.eps,
  tol1 = 8 * tol,
  ...
)

Arguments

m	numeric: a matrix of values
decreasing	logical: should the sort order be increasing or decreasing (default: TRUE)
tol	numeric scalar >= 0. Smaller differences are not considered, see all.equal.numeric.
tol1	numeric scalar >= 0. isSymmetric.matrix() ‘pre-tests’ the first and last few rows for fast detection of ‘obviously’ asymmetric cases with this tolerance. Setting it to length zero will skip the pre-tests.
...	further arguments passed to methods; the matrix method passes these to all.equal. If the row and column names of object are allowed to differ for the symmetry check do use check.attributes = FALSE!

Value

a data frame with an attribute matrix with m

Examples

# non-symmetric
x <- matrix(runif(9), ncol=3)
matrix2dataframe(x)

---

same_file

Description

same_file

Usage

same_file(m, replacement = 0)

Arguments

m	matrix object with row- and columnnames
replacement	value for replacement (default: 0)

Value

matrix

Examples

m <- matrix(runif(25), ncol=5)
colnames(m) <- rownames(m) <- c(sprintf("m[%.f]", 1:3), sprintf("m2[%.f]", 1:2))
m
same_file(m)

---

sim_coeff

Description

Internal function for faster computation. No checks on input will be performed.

Usage

sim_coeff(set1, set2, setfull, coeff)

Arguments

set1	character: unique vector of words
set2	character: unique vector of words
setfull	character: unique vector of texts to compare
coeff	character: name of similarity coefficient to use

Value

value of similarity coefficient

---

similarity_coeff

Description

Computes a similarity coefficient based on the unique elements set1 and set2 in relation to setfull. If setfull is NULL then setfull is set to unique(c(set1, set2)). For more details, see the vignette vignette("rscc").

Usage

similarity_coeff(
  set1,
  set2,
  setfull = NULL,
  coeff = c("jaccard", "braun", "dice", "hamann", "kappa", "kulczynski", "ochiai",
    "phi", "russelrao", "matching", "simpson", "sneath", "tanimoto", "yule")
)

Arguments

set1	vector: elements to compare
set2	vector: elements to compare
setfull	vector: elements to compare (default: NULL)
coeff	character: coefficient to compute (default: "jaccard"), abbreviations can be used

Value

a numeric similarity coefficient

Examples

s1 <- 1:3
s2 <- 1:5
similarity_coeff(s1, s2)
s1 <- letters[1:3]
s2 <- LETTERS[1:5]
similarity_coeff(s1, s2)

---

similarities

Description

sims and similarities both calculate for each pair of source code objects the similarity coefficients and return a data frame with the coefficients in descending order. A larger coefficient means a greater similarity.

Usage

sims(...)

similarities(
  docs,
  all = FALSE,
  coeff = c("jaccard", "braun", "dice", "hamann", "kappa", "kulczynski", "ochiai",
    "phi", "russelrao", "matching", "simpson", "sneath", "tanimoto", "yule")
)

Arguments

...	all parameters in sims are given to similarities
docs	document object
all	logical: should the similarity coefficients computed based on all sourcecode objects or just the two considered (default: FALSE)
coeff	character: coefficient to compute (default: "jaccard"), abbreviations can be used

Value

a data frame with the results

Examples

# example files are taken from https://CRAN.R-project.org/package=SimilaR
files <- list.files(system.file("examples", package="rscc"), "*.R$", full.names=TRUE)
prgs  <- sourcecode(files, basename=TRUE)
docs  <- documents(prgs)
similarities(docs)
# further steps
# m  <- similarities(docs)
# df <- matrix2dataframe(m)
# head(df, n=20)
# browse(prgs, df, n=5)

---

sourcecode

Description

Reads and parses files with R source code.

Usage

sourcecode(x, ...)

## Default S3 method:
sourcecode(x, title = x, silent = FALSE, minlines = -1, ...)

Arguments

x	character: filenames
...	unused
title	character: vector of program titles (default: x)
silent	logical: should the report of messages be suppressed (default: FALSE)
minlines	integer: only expressions with minlines lines are considered (default: -1), if minlines<0 then whole files will be considered

Value

a sourcecode object

Examples

# example files are taken from https://CRAN.R-project.org/package=SimilaR
files <- list.files(system.file("examples", package="rscc"), "*.R$", full.names=TRUE)
prgs  <- sourcecode(files)

---

tfidf

Description

Computes the term frequency–inverse document frequency uses tha cosine of the angles between the documents as similarity measure. Since R source code is provided no stemming or stop words are applied.

Usage

tfidf(docs)

Arguments

docs	document object

Value

similarity matrix

Examples

files <- list.files(system.file("examples", package="rscc"), "*.R$", full.names = TRUE)
prgs  <- sourcecode(files, basename=TRUE, silent=TRUE)
docs  <- documents(prgs)
tfidf(docs)
# further steps
# m  <- tfidf(docs)
# df <- matrix2dataframe(m)
# head(df, n=20)
# browse(prgs, df, n=5)

---