Lint codes

Author: randef1ned

.lintr

@@ -1,17 +1,37 @@
-linters: list(linelen=line_length_linter(80),
-              abspathlintr=absolute_path_linter(),
-              assignlintr=assignment_linter(),
-              notab=no_tab_linter(),
-              namelinter=object_name_linter(styles = c("snake_case", "camelCase", "symbols")),
-              brace=brace_linter(),
-              trailingwhites=trailing_whitespace_linter())
+linters: list(
+              abspathlintr  = absolute_path_linter(),
+              assignlintr   = assignment_linter(),
+              brace         = brace_linter(),
+              comma         = commas_linter(),
+              comment_code  = commented_code_linter(),
+              cyclocomp     = cyclocomp_linter(),
+              equal_na      = equals_na_linter(),
+              left_par      = function_left_parentheses_linter(),
+              infix_spaces  = infix_spaces_linter(),
+              linelen       = line_length_linter(80),
+              notab         = whitespace_linter(),
+              object_length = object_length_linter(),
+              object_usage  = object_usage_linter(),
+              par_body      = paren_body_linter(),
+              quotes        = quotes_linter(),
+              semicolon     = semicolon_linter(),
+              seq           = seq_linter(),
+              spaces        = spaces_inside_linter(),
+              space_par     = spaces_left_parentheses_linter(),
+              t_f_symbol    = T_and_F_symbol_linter(),
+              blank_lines   = trailing_blank_lines_linter(),
+              trailingwhites= trailing_whitespace_linter(),
+              vector_logic  = vector_logic_linter())
 exclusions: list("tests/testthat.R",
                  "tests/testthat/test_knn.R",
                  "tests/testthat/test_mrw.R",
                  "tests/testthat/test_heat.R",
                  "tests/testthat/test_util.R",
                  "inst/doc/diffusr.R",
+                 "vignettes/diffusr.R",
+                 "vignettes/diffusr.Rmd",
                  "R/RcppExports.R")
 exclude: "# Exclude Linting"
 exclude_start: "# Begin Exclude Linting"
 exclude_end: "# End Exclude Linting"
+

NAMESPACE

@@ -8,7 +8,6 @@ export(nearest.neighbors)
 export(normalize.laplacian)
 export(normalize.stochastic)
 export(random.walk)
-import(Rcpp)
 importFrom(Rcpp,sourceCpp)
 importFrom(checkmate,assert)
 importFrom(checkmate,assert_int)

R/diffusr-package.R

@@ -29,20 +29,29 @@
 #' @keywords package
 #'
 #' @references
+#'
 #' Tong, H., Faloutsos, C., & Pan, J. Y. (2006),
-#' Fast random walk with restart and its applications.\cr \cr
+#' Fast random walk with restart and its applications.
+#'
 #' Koehler, S., Bauer, S., Horn, D., & Robinson, P. N. (2008),
 #' Walking the interactome for prioritization of candidate disease genes.
-#' \emph{The American Journal of Human Genetics}\cr \cr
+#' \emph{The American Journal of Human Genetics}
+#'
 #' Bonacich, P. (1987),
 #' Power and centrality: A family of measures.
-#' \emph{American Journal of Sociology}\cr \cr
-#' Leiserson, M. D., Vandin, F., Wu, H. T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., ... & Lawrence, M. S. (2015),
-#' Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes.
-#' \emph{Nature genetics}\cr \cr
-#' \url{https://en.wikipedia.org/wiki/Laplacian_matrix} \cr
+#' \emph{American Journal of Sociology}
+#'
+#' Leiserson, M. D., Vandin, F., Wu, H. T., Dobson, J. R., Eldridge, J. V.,
+#' Thomas, J. L., ... & Lawrence, M. S. (2015),
+#' Pan-cancer network analysis identifies combinations of rare somatic mutations
+#' across pathways and protein complexes.
+#' \emph{Nature genetics}
+#'
+#' \url{https://en.wikipedia.org/wiki/Laplacian_matrix}
+#'
 #' \url{https://en.wikipedia.org/wiki/Heat_equation}
 #'
 #' @useDynLib diffusr
-#' @import Rcpp
+#'
+#' @importFrom Rcpp sourceCpp
 NULL

R/heat_diffusion.R

@@ -36,7 +36,8 @@
 #'
 #' @useDynLib diffusr
 #'
-#' @importFrom checkmate assert_int assert_integer assert test_matrix test_numeric test_atomic_vector
+#' @importFrom checkmate assert_int assert_integer assert test_matrix
+#'                       test_numeric test_atomic_vector
 #' @importFrom Rcpp sourceCpp
 #'
 #' @references
@@ -51,20 +52,22 @@
 #' # adjacency matrix (either normalized or not)
 #' graph <- matrix(abs(rnorm(n*n)), n, n)
 #' # computation of stationary distribution
-#' ht <- heat.diffusion(h0, graph)
+#' heat <- heat.diffusion(h0, graph)
 heat.diffusion <- function(h0, graph, t = 0.5, ...) {
   assert_number(t, lower = 0, na.ok = FALSE, finite = TRUE, null.ok = FALSE)
   n_elements <- nrow(graph)
 
   # convert h0 if h0 is vector
-  if (test_numeric(h0, lower = 0, len = n_elements, finite = TRUE, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE) &&
+  if (test_numeric(h0, lower = 0, len = n_elements, finite = TRUE,
+                   any.missing = FALSE, all.missing = FALSE, null.ok = FALSE) &&
       test_atomic_vector(h0, len = n_elements)) {
     h0 <- as.matrix(h0)
   } else {
     assert(
-      test_matrix(h0, mode = 'numeric', nrows = n_elements, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      test_matrix(h0, mode = "numeric", nrows = n_elements, any.missing = FALSE,
+                  all.missing = FALSE, null.ok = FALSE),
       any(h0 >= 0),
-      combine = 'and'
+      combine = "and"
     )
   }
 
@@ -75,12 +78,13 @@ heat.diffusion <- function(h0, graph, t = 0.5, ...) {
     # TODO: sparse matrix
   } else {
     assert(
-      test_matrix(graph, mode = 'numeric', nrows = n_elements, ncols = n_elements, min.rows = 3, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      test_matrix(graph, mode = "numeric", nrows = n_elements,
+                  ncols = n_elements, min.rows = 3, any.missing = FALSE,
+                  all.missing = FALSE, null.ok = FALSE),
       any(graph >= 0),
-      combine = 'and'
+      combine = "and"
     )
     heat <- heat_diffusion_(h0, laplacian_(graph), t)
   }
   return(heat)
 }
-

R/is.dgCMatrix.R

@@ -8,7 +8,7 @@
 #' @importFrom methods is
 #'
 is.dgCMatrix <- function(mat) {
-  return(is(mat, 'dgCMatrix'))
+  return(is(mat, "dgCMatrix"))
 }
 
 #' Check if the sparse matrix valid
@@ -22,10 +22,10 @@ assert_dgCMatrix <- function(adj_matrix, non_negative = TRUE) {
   if (adj_matrix@Dim[1] != adj_matrix@Dim[2]) {
     stop(paste("Error: Assertion on 'adj_matrix' failed: Must have exactly",
                adj_matrix@Dim[2], "rows, but has",
-               adj_matrix@Dim[1] ,"rows."))
+               adj_matrix@Dim[1], "rows."))
   } else if (adj_matrix@Dim[1] < 4) {
-    stop(paste("Error: Assertion on 'adj_matrix' failed: Must have at least 3 rows, but has",
-               adj_matrix@Dim[1] ,"rows."))
+    stop(paste("Error: Assertion on 'adj_matrix' failed: Must have at least",
+               "3 rows, but has", adj_matrix@Dim[1], "rows."))
   } else if (non_negative && any(adj_matrix@x < 0)) {
     stop(paste("Error: Assertion on 'adj_matrix' failed: Element",
                seq_along(adj_matrix@x)[adj_matrix@x < 0][1], "is not >= 0."))

R/mat_util.R

@@ -32,23 +32,23 @@
 #' @examples
 #'  W <- matrix(abs(rnorm(10000)), 100, 100)
 #'  stoch.W <- normalize.stochastic(W)
-normalize.stochastic <- function(obj, ...)
-{
+normalize.stochastic <- function(obj, ...) {
   is_matrix <- FALSE
-  if (test_numeric(obj, lower = 0, finite = TRUE, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE) &&
+  if (test_numeric(obj, lower = 0, finite = TRUE, any.missing = FALSE,
+                   all.missing = FALSE, null.ok = FALSE) &&
       test_atomic_vector(obj)) {
-    if (!.equals.double(sum(obj), 1, .001))
-    {
+    if (!.equals.double(sum(obj), 1, .001)) {
       message("normalizing vector!")
     }
   } else if (is.dgCMatrix(obj)) {
     assert_dgCMatrix(obj)
     is_matrix <- TRUE
   } else {
     assert(
-      check_matrix(obj, mode = 'numeric', any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      check_matrix(obj, mode = "numeric", any.missing = FALSE,
+                   all.missing = FALSE, null.ok = FALSE),
       any(obj >= 0),
-      combine = 'and'
+      combine = "and"
     )
     is_matrix <- TRUE
   }
@@ -81,17 +81,17 @@ normalize.stochastic <- function(obj, ...)
 #' @examples
 #'  W <- matrix(abs(rnorm(10000)), 100, 100)
 #'  lapl.W <- normalize.laplacian(W)
-normalize.laplacian <- function(obj, ...)
-{
-  # UseMethod("normalize.laplacian")
+normalize.laplacian <- function(obj, ...) {
   if (is.dgCMatrix(obj)) {
     assert_dgCMatrix(obj)
     # TODO: sparse matrix
   } else {
     assert(
-      check_matrix(obj, mode = 'numeric', nrows = ncol(obj), ncols = nrow(obj), min.rows = 3, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      check_matrix(obj, mode = "numeric", nrows = ncol(obj), ncols = nrow(obj),
+                   min.rows = 3, any.missing = FALSE, all.missing = FALSE,
+                   null.ok = FALSE),
       any(obj >= 0),
-      combine = 'and'
+      combine = "and"
     )
     return(laplacian_(obj))
   }
@@ -113,28 +113,27 @@ normalize.laplacian <- function(obj, ...)
 #' @examples
 #' W <- matrix(abs(rnorm(10000)), 100, 100)
 #' cor.hub <- hub.correction(W)
-hub.correction <- function(obj)
-{
+hub.correction <- function(obj) {
   n_elements <- nrow(obj)
   if (is.dgCMatrix(obj)) {
     assert_dgCMatrix(obj)
     # TODO: sparse matrix
   } else {
     assert(
-      check_matrix(obj, mode = 'numeric', nrows = n_elements, ncols = n_elements, min.rows = 3, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      check_matrix(obj, mode = "numeric", min.rows = 3, nrows = n_elements,
+                   ncols = n_elements, any.missing = FALSE, all.missing = FALSE,
+                   null.ok = FALSE),
       any(obj >= 0),
-      combine = 'and'
+      combine = "and"
     )
     return(hub_normalize_(obj))
   }
 }
 
 #' @noRd
 #' @importFrom igraph graph_from_adjacency_matrix components
-.is.ergodic <- function(obj)
-{
-  adj   <- graph_from_adjacency_matrix(
-    obj, mode="directed", weighted=TRUE)
+.is.ergodic <- function(obj) {
+  adj   <- graph_from_adjacency_matrix(obj, mode = "directed", weighted = TRUE)
   comps <- components(adj)
   ifelse(length(comps$csize) == 1, TRUE, FALSE)
 }

R/mrw.R

@@ -38,9 +38,9 @@
 #'
 #' @param thresh  threshold for breaking the iterative computation of the
 #'  stationary distribution. If the absolute difference of the distribution at
-#'  time point $t-1$ and $t$ is less than \code{thresh}, then the algorithm stops.
-#'  If \code{thresh} is not reached before \code{niter}, then the algorithm stops
-#'  as well.
+#'  time point \eqn{t-1} and \eqn{t} is less than \code{thresh}, then the
+#'  algorithm stops. If \code{thresh} is not reached before \code{niter}, then
+#'  the algorithm stops as well.
 #'
 #' @param niter  maximal number of iterations for computation of the
 #'  Markov chain. If \code{thresh} is not reached, then \code{niter} is used as
@@ -62,21 +62,24 @@
 #' @return  returns a list with the following elements
 #'  \itemize{
 #'   \item p.inf  the stationary distribution as numeric vector
-#'   \item transition.matrix the column normalized transition matrix used for the random walk
+#'   \item transition.matrix the column normalized transition matrix used for
+#'         the random walk
 #'  }
 #'
 #' @references
 #' Tong, H., Faloutsos, C., & Pan, J. Y. (2006),
-#' Fast random walk with restart and its applications.\cr \cr
+#' Fast random walk with restart and its applications.
+#'
 #' Koehler, S., Bauer, S., Horn, D., & Robinson, P. N. (2008),
 #' Walking the interactome for prioritization of candidate disease genes.
-#' \emph{The American Journal of Human Genetics}\cr \cr
+#' \emph{The American Journal of Human Genetics}
 #'
 #' @export
 #'
 #' @useDynLib diffusr
 #'
-#' @importFrom checkmate assert_number assert_int assert_logical test_numeric assert test_matrix check_numeric
+#' @importFrom checkmate assert_number assert_int assert_logical test_numeric
+#'                       assert test_matrix check_numeric
 #' @importFrom Rcpp sourceCpp
 #'
 #' @examples
@@ -90,37 +93,46 @@
 #' pt    <- random.walk(p0, graph)
 #'
 random.walk <- function(p0, graph, r = 0.5, niter = 1e4, thresh = 1e-4,
-                               do.analytical = FALSE, correct.for.hubs = FALSE,
-                               ergodic.tolerance = FALSE, return.pt.only = FALSE) {
+                        do.analytical = FALSE, correct.for.hubs = FALSE,
+                        ergodic.tolerance = FALSE, return.pt.only = FALSE) {
   ## Check the fucking inputs
-  assert_number(r, lower = 0, upper = 1, na.ok = FALSE, finite = TRUE, null.ok = FALSE)
+  assert_number(r, lower = 0, upper = 1, na.ok = FALSE, finite = TRUE,
+                null.ok = FALSE)
   assert_int(niter, lower = 2, na.ok = FALSE, coerce = TRUE, null.ok = FALSE)
-  assert_number(thresh, lower = 0, na.ok = FALSE, finite = TRUE, null.ok = FALSE)
-  assert_logical(do.analytical, len = 1, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE)
-  assert_logical(correct.for.hubs, len = 1, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE)
+  assert_number(thresh, lower = 0, na.ok = FALSE, finite = TRUE,
+                null.ok = FALSE)
+  assert_logical(do.analytical, len = 1, any.missing = FALSE,
+                 all.missing = FALSE, null.ok = FALSE)
+  assert_logical(correct.for.hubs, len = 1, any.missing = FALSE,
+                 all.missing = FALSE, null.ok = FALSE)
 
   # graph must be either matrix or dgCMatrix
   n_elements <- nrow(graph)
   if (is.dgCMatrix(graph)) {
     assert_dgCMatrix(graph)
     sparse <- TRUE
+    # TODO: sparse matrix
   } else {
     assert(
-      test_matrix(graph, mode = 'numeric', nrows = n_elements, ncols = n_elements, min.rows = 3, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      test_matrix(graph, mode = "numeric", min.rows = 3, nrows = n_elements,
+                  ncols = n_elements, any.missing = FALSE, all.missing = FALSE,
+                  null.ok = FALSE),
       any(graph >= 0),
-      combine = 'and'
+      combine = "and"
     )
     sparse <- FALSE
   }
 
   # convert p0 if p0 is vector
-  if (test_numeric(p0, lower = 0, len = n_elements, finite = TRUE, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE)) {
+  if (test_numeric(p0, lower = 0, len = n_elements, finite = TRUE,
+                   any.missing = FALSE, all.missing = FALSE, null.ok = FALSE)) {
     p0 <- as.matrix(p0)
   } else {
     assert(
-      test_matrix(p0, mode = 'numeric', nrows = n_elements, any.missing = FALSE, all.missing = FALSE, null.ok = FALSE),
+      test_matrix(p0, mode = "numeric", nrows = n_elements, any.missing = FALSE,
+                  all.missing = FALSE, null.ok = FALSE),
       any(p0 >= 0),
-      combine = 'and'
+      combine = "and"
     )
   }
 
@@ -136,11 +148,9 @@ random.walk <- function(p0, graph, r = 0.5, niter = 1e4, thresh = 1e-4,
   }
 
   l <- mrwr_(normalize.stochastic(p0),
-              stoch.graph, r, thresh, niter, do.analytical)
+             stoch.graph, r, thresh, niter, do.analytical)
   if (!return.pt.only) {
-    l <- list(
-      p.inf=l,
-      transition.matrix=stoch.graph)
+    l <- list(p.inf = l, transition.matrix = stoch.graph)
   }
 
   return(l)