chore: seq_along(), seq_len()

R/centrality.R

@@ -1007,7 +1007,7 @@ arpack <- function(
       }
       res$values <- res$values[, 1]
     }
-    res$vectors <- res$vectors[, 1:length(res$values)]
+    res$vectors <- res$vectors[, seq_along(res$values)]
   }
 
   res

R/community.R

@@ -1090,7 +1090,7 @@ as.dendrogram.communities <- function(
   }
   z <- list()
   if (!use.modularity || is.null(object$modularity)) {
-    object$height <- 1:nrow(merges)
+    object$height <- seq_len(nrow(merges))
   } else {
     object$height <- object$modularity[-1]
     object$height <- cumsum(object$height - min(object$height))
@@ -1178,7 +1178,7 @@ as.phylo.communities <- function(x, use.modularity = FALSE, ...) {
   merges <- complete.dend(x, use.modularity)
 
   if (!use.modularity || is.null(x$modularity)) {
-    height <- 1:nrow(merges)
+    height <- seq_len(nrow(merges))
   } else {
     height <- x$modularity[-1]
     height <- cumsum(height - min(height))

R/glet.R

@@ -190,7 +190,7 @@ function() {
     co <- layout_with_kk(g)
     par(mar = c(1, 1, 1, 1))
     plot(g, layout = co)
-    for (i in 1:length(gl$Bc)) {
+    for (i in seq_along(gl$Bc)) {
       sel <- gl$Bc[[i]]
       V(g)$color <- "white"
       V(g)[sel]$color <- "#E495A5"

R/hrg.R

@@ -562,7 +562,7 @@ as.dendrogram.igraphHRG <- function(object, hang = 0.01, ...) {
     r
   }
 
-  oHgt <- 1:nrow(merges)
+  oHgt <- seq_len(nrow(merges))
   hMax <- oHgt[length(oHgt)]
   mynames <- if (is.null(object$names)) 1:(nMerge + 1) else object$names
   z <- list()
@@ -634,7 +634,7 @@ as.hclust.igraphHRG <- function(x, ...) {
   ## the left node.
   map2 <- numeric(nrow(merge))
   mergeInto <- merge
-  for (i in 1:nrow(merge)) {
+  for (i in seq_len(nrow(merge))) {
     mr <- mergeInto[i, ]
     mr[mr > 0] <- -map2[mr[mr > 0]]
     mergeInto[i, ] <- -mr
@@ -650,7 +650,7 @@ as.hclust.igraphHRG <- function(x, ...) {
   mynames <- if (is.null(x$names)) 1:n else x$names
   res <- list(
     merge = merge,
-    height = 1:nrow(merge),
+    height = seq_len(nrow(merge)),
     order = order,
     labels = mynames,
     method = NA_character_,

R/tkplot.R

@@ -1096,7 +1096,7 @@ tk_canvas <- function(tkp.id) {
     function(from, to, id) .tkplot.create.edge(tkp.id, from, to, id),
     edgematrix[, 1],
     edgematrix[, 2],
-    1:nrow(edgematrix)
+    seq_len(nrow(edgematrix))
   )
 }
 
@@ -1765,7 +1765,7 @@ tk_canvas <- function(tkp.id) {
         pady = 5
       )
       values[[i]] <- tcltk::tclVar(layout$params[[i]]$default)
-      for (j in 1:length(layout$params[[i]]$values)) {
+      for (j in seq_along(layout$params[[i]]$values)) {
         tmp <- tcltk::tkradiobutton(
           tmp.frame,
           variable = values[[i]],

tests/testthat/test-assortativity.R

@@ -40,7 +40,7 @@ test_that("nominal assortativity works", {
 
   el <- as_edgelist(o, names = FALSE)
   etm <- matrix(0, nrow = max(V(o)$value) + 1, ncol = max(V(o)$value) + 1)
-  for (e in 1:nrow(el)) {
+  for (e in seq_len(nrow(el))) {
     t1 <- V(o)$value[el[e, 1]] + 1
     t2 <- V(o)$value[el[e, 2]] + 1
     etm[t1, t2] <- etm[t1, t2] + 1

tests/testthat/test-community.R

@@ -217,7 +217,7 @@ test_that("cluster_leading_eigen works", {
     multiplier,
     extra
   ) {
-    M <- sapply(1:length(vector), function(x) {
+    M <- sapply(seq_along(vector), function(x) {
       v <- rep(0, length(vector))
       v[x] <- 1
       multiplier(v)
@@ -268,7 +268,7 @@ test_that("cluster_leading_eigen works", {
     multiplier,
     extra
   ) {
-    M <- sapply(1:length(vector), function(x) {
+    M <- sapply(seq_along(vector), function(x) {
       v <- rep(0, length(vector))
       v[x] <- 1
       multiplier(v)

tests/testthat/test-conversion.R

@@ -461,7 +461,7 @@ test_that("as_biadjacency_matrix() works -- dense", {
 test_that("as_biadjacency_matrix() works -- dense named", {
   biadj_mat <- matrix(sample(0:1, 35, replace = TRUE, prob = c(3, 1)), ncol = 5)
   g <- graph_from_biadjacency_matrix(biadj_mat)
-  V(g)$name <- letters[1:length(V(g))]
+  V(g)$name <- letters[seq_along(V(g))]
 
   expect_true(is_named(g))
 

tests/testthat/test-glet.R

@@ -120,7 +120,7 @@ graphlets.project.old <- function(graph, cliques, iter, Mu = NULL) {
 
   ## Create vertex-clique list first
   vcl <- vector(length = vcount(graph), mode = "list")
-  for (i in 1:length(clf)) {
+  for (i in seq_along(clf)) {
     for (j in clf[[i]]) {
       vcl[[j]] <- c(vcl[[j]], i)
     }
@@ -138,7 +138,7 @@ graphlets.project.old <- function(graph, cliques, iter, Mu = NULL) {
   ## We will also need a clique-edge list, the edges in the cliques
   system.time({
     cel <- vector(length = length(clf), mode = "list")
-    for (i in 1:length(ecl)) {
+    for (i in seq_along(ecl)) {
       for (j in ecl[[i]]) {
         cel[[j]] <- c(cel[[j]], i)
       }
@@ -153,10 +153,10 @@ graphlets.project.old <- function(graph, cliques, iter, Mu = NULL) {
   w <- numeric(length(ecl))
   a <- sapply(clf, function(x) length(x) * (length(x) + 1) / 2)
   for (i in 1:iter) {
-    for (j in 1:length(ecl)) {
+    for (j in seq_along(ecl)) {
       w[j] <- sum(Mu[ecl[[j]]])
     }
-    for (j in 1:length(clf)) {
+    for (j in seq_along(clf)) {
       Mu[j] <- Mu[j] * sum(origw[cel[[j]]] / (w[cel[[j]]] + .0001)) / a[j]
     }
   }

tests/testthat/test-make.R

@@ -393,7 +393,7 @@ test_that("make_bipartite_graph works", {
 
 test_that("make_bipartite_graph works with vertex names", {
   types <- c(0, 1, 0, 1, 0, 1)
-  names(types) <- LETTERS[1:length(types)]
+  names(types) <- LETTERS[seq_along(types)]
   edges <- c("A", "B", "C", "D", "E", "F", "A", "D", "D", "E", "B", "C", "C", "F")
   bip_grap <- make_bipartite_graph(types, edges)