chore: numeric_leading_zero

R/centrality.R

@@ -1913,19 +1913,19 @@ bonpow.sparse <- function(
 #'   dir = FALSE
 #' )
 #' # Compute power centrality scores
-#' for (e in seq(-0.5, .5, by = 0.1)) {
+#' for (e in seq(-0.5, 0.5, by = 0.1)) {
 #'   print(round(power_centrality(g.c, exp = e)[c(1, 2, 4)], 2))
 #' }
 #'
-#' for (e in seq(-0.4, .4, by = 0.1)) {
+#' for (e in seq(-0.4, 0.4, by = 0.1)) {
 #'   print(round(power_centrality(g.d, exp = e)[c(1, 2, 5)], 2))
 #' }
 #'
-#' for (e in seq(-0.4, .4, by = 0.1)) {
+#' for (e in seq(-0.4, 0.4, by = 0.1)) {
 #'   print(round(power_centrality(g.e, exp = e)[c(1, 2, 5)], 2))
 #' }
 #'
-#' for (e in seq(-0.4, .4, by = 0.1)) {
+#' for (e in seq(-0.4, 0.4, by = 0.1)) {
 #'   print(round(power_centrality(g.f, exp = e)[c(1, 2, 5)], 2))
 #' }
 #'

R/games.R

@@ -2443,7 +2443,7 @@ cit_cit_types <- function(
 #' sample_bipartite(10, 5, p = 1)
 #'
 #' ## random bipartite graph
-#' sample_bipartite(10, 5, p = .1)
+#' sample_bipartite(10, 5, p = 0.1)
 #'
 #' ## directed bipartite graph, G(n,m)
 #' sample_bipartite(10, 5, type = "Gnm", m = 20, directed = TRUE, mode = "all")
@@ -2581,7 +2581,7 @@ bipartite_gnp <- function(
 #' sample_bipartite_gnp(10, 5, p = 1)
 #'
 #' ## random bipartite graph
-#' sample_bipartite_gnp(10, 5, p = .1)
+#' sample_bipartite_gnp(10, 5, p = 0.1)
 #'
 #' ## directed bipartite graph, G(n,m)
 #' sample_bipartite_gnm(10, 5, m = 20, directed = TRUE, mode = "all")
@@ -2673,7 +2673,7 @@ sample_bipartite_gnp <- function(
 #' @examples
 #'
 #' ## Two groups with not only few connection between groups
-#' pm <- cbind(c(.1, .001), c(.001, .05))
+#' pm <- cbind(c(0.1, 0.001), c(0.001, 0.05))
 #' g <- sample_sbm(1000, pref.matrix = pm, block.sizes = c(300, 700))
 #' g
 #' @family games
@@ -3412,7 +3412,7 @@ sample_forestfire <- function(
 #' @family games
 #' @export
 #' @examples
-#' g <- sample_gnp(1000, .1)
+#' g <- sample_gnp(1000, 0.1)
 #' g2 <- sample_correlated_gnp(g, corr = 0.5)
 #' cor(as.vector(g[]), as.vector(g2[]))
 #' g
@@ -3461,7 +3461,7 @@ sample_correlated_gnp <- function(
 #' @export
 #' @examples
 #' gg <- sample_correlated_gnp_pair(
-#'   n = 10, corr = .8, p = .5,
+#'   n = 10, corr = 0.8, p = 0.5,
 #'   directed = FALSE
 #' )
 #' gg

R/hrg.R

@@ -785,7 +785,7 @@ hrgPlotHclust <- function(
   x,
   rect = 0,
   colbar = rainbow(rect),
-  hang = .01,
+  hang = 0.01,
   ann = FALSE,
   main = "",
   sub = "",
@@ -819,7 +819,7 @@ hrgPlotDendrogram <- function(x, ...) {
 #' @importFrom grDevices rainbow
 hrgPlotPhylo <- function(
   x,
-  colbar = rainbow(11, start = .7, end = .1),
+  colbar = rainbow(11, start = 0.7, end = 0.1),
   edge.color = NULL,
   use.edge.length = FALSE,
   ...

R/layout.R

@@ -2276,7 +2276,7 @@ with_mds <- function(...) layout_spec(layout_with_mds, ...)
 #' ), 1, which))
 #'
 #' ## Simple plot, not very nice
-#' par(mar = rep(.1, 4))
+#' par(mar = rep(0.1, 4))
 #' plot(DC, layout = lay1$layout, vertex.label.cex = 0.5)
 #'
 #' ## Sugiyama plot
@@ -2344,7 +2344,7 @@ with_mds <- function(...) layout_spec(layout_with_mds, ...)
 #' realedge <- as_edgelist(layex$extd_graph)[, 2] <= vcount(ex)
 #' plot(layex$extd_graph,
 #'   vertex.label.cex = 0.5,
-#'   edge.arrow.size = .5,
+#'   edge.arrow.size = 0.5,
 #'   vertex.size = ifelse(origvert, 5, 0),
 #'   vertex.shape = ifelse(origvert, "square", "none"),
 #'   vertex.label = ifelse(origvert, V(ex)$name, ""),
@@ -2923,14 +2923,14 @@ igraph.drl.default <- list(
   cooldown.iterations = 200,
   cooldown.temperature = 2000,
   cooldown.attraction = 1,
-  cooldown.damping.mult = .1,
+  cooldown.damping.mult = 0.1,
   crunch.iterations = 50,
   crunch.temperature = 250,
   crunch.attraction = 1,
   crunch.damping.mult = 0.25,
   simmer.iterations = 100,
   simmer.temperature = 250,
-  simmer.attraction = .5,
+  simmer.attraction = 0.5,
   simmer.damping.mult = 0
 )
 
@@ -2953,14 +2953,14 @@ igraph.drl.coarsen <- list(
   cooldown.iterations = 200,
   cooldown.temperature = 2000,
   cooldown.attraction = 1,
-  cooldown.damping.mult = .1,
+  cooldown.damping.mult = 0.1,
   crunch.iterations = 50,
   crunch.temperature = 250,
   crunch.attraction = 1,
   crunch.damping.mult = 0.25,
   simmer.iterations = 100,
   simmer.temperature = 250,
-  simmer.attraction = .5,
+  simmer.attraction = 0.5,
   simmer.damping.mult = 0
 )
 
@@ -2983,14 +2983,14 @@ igraph.drl.coarsest <- list(
   cooldown.iterations = 200,
   cooldown.temperature = 2000,
   cooldown.attraction = 1,
-  cooldown.damping.mult = .1,
+  cooldown.damping.mult = 0.1,
   crunch.iterations = 200,
   crunch.temperature = 250,
   crunch.attraction = 1,
   crunch.damping.mult = 0.25,
   simmer.iterations = 100,
   simmer.temperature = 250,
-  simmer.attraction = .5,
+  simmer.attraction = 0.5,
   simmer.damping.mult = 0
 )
 
@@ -3000,27 +3000,27 @@ igraph.drl.refine <- list(
   edge.cut = 32 / 40,
   init.iterations = 0,
   init.temperature = 50,
-  init.attraction = .5,
+  init.attraction = 0.5,
   init.damping.mult = 1.0,
   liquid.iterations = 0,
   liquid.temperature = 2000,
   liquid.attraction = 2,
   liquid.damping.mult = 1.0,
   expansion.iterations = 50,
   expansion.temperature = 500,
-  expansion.attraction = .1,
-  expansion.damping.mult = .25,
+  expansion.attraction = 0.1,
+  expansion.damping.mult = 0.25,
   cooldown.iterations = 50,
   cooldown.temperature = 250,
   cooldown.attraction = 1,
-  cooldown.damping.mult = .1,
+  cooldown.damping.mult = 0.1,
   crunch.iterations = 50,
   crunch.temperature = 250,
   crunch.attraction = 1,
   crunch.damping.mult = 0.25,
   simmer.iterations = 0,
   simmer.temperature = 250,
-  simmer.attraction = .5,
+  simmer.attraction = 0.5,
   simmer.damping.mult = 0
 )
 
@@ -3030,7 +3030,7 @@ igraph.drl.final <- list(
   edge.cut = 32 / 40,
   init.iterations = 0,
   init.temperature = 50,
-  init.attraction = .5,
+  init.attraction = 0.5,
   init.damping.mult = 0,
   liquid.iterations = 0,
   liquid.temperature = 2000,
@@ -3043,14 +3043,14 @@ igraph.drl.final <- list(
   cooldown.iterations = 50,
   cooldown.temperature = 200,
   cooldown.attraction = 1,
-  cooldown.damping.mult = .1,
+  cooldown.damping.mult = 0.1,
   crunch.iterations = 50,
   crunch.temperature = 250,
   crunch.attraction = 1,
   crunch.damping.mult = 0.25,
   simmer.iterations = 25,
   simmer.temperature = 250,
-  simmer.attraction = .5,
+  simmer.attraction = 0.5,
   simmer.damping.mult = 0
 )
 

R/plot.R

@@ -458,10 +458,10 @@ plot.igraph <- function(
         c(
           x0,
           y0,
-          x0 + .4 * loopSize,
-          y0 + narrowing * .2 * loopSize,
-          x0 + .4 * loopSize,
-          y0 - narrowing * .2 * loopSize,
+          x0 + 0.4 * loopSize,
+          y0 + narrowing * 0.2 * loopSize,
+          x0 + 0.4 * loopSize,
+          y0 - narrowing * 0.2 * loopSize,
           x0,
           y0
         ),

R/rewire.R

@@ -35,7 +35,7 @@
 #' @examples
 #' g <- make_ring(10)
 #' g %>%
-#'   rewire(each_edge(p = .1, loops = FALSE)) %>%
+#'   rewire(each_edge(p = 0.1, loops = FALSE)) %>%
 #'   plot(layout = layout_in_circle)
 #' print_all(rewire(g, with = keeping_degseq(niter = vcount(g) * 10)))
 rewire <- function(graph, with) {

inst/benchmarks/correlated.R

@@ -7,6 +7,6 @@ time_that(
     library(igraph)
   },
   {
-    sample_correlated_gnp_pair(100, corr = .8, p = 5 / 100)
+    sample_correlated_gnp_pair(100, corr = 0.8, p = 5 / 100)
   }
 )

inst/benchmarks/local.scan.R

@@ -5,9 +5,9 @@ init <- expression({
   set.seed(42)
 })
 reinit <- expression({
-  g <- random.graph.game(1000, p = .1)
+  g <- random.graph.game(1000, p = 0.1)
   E(g)$weight <- sample(ecount(g))
-  gp <- random.graph.game(1000, p = .1)
+  gp <- random.graph.game(1000, p = 0.1)
   E(gp)$weight <- sample(ecount(gp))
 })
 

inst/benchmarks/time_sgm.R

@@ -10,9 +10,9 @@ time_that(
     nos = 10
   },
   reinit = {
-    g1 <- erdos.renyi.game(vc, .01)
+    g1 <- erdos.renyi.game(vc, 0.01)
     perm <- c(1:nos, sample(vc-nos)+nos)
-    g2 <- sample_correlated_gnp(g1, corr = .7, p = g1$p, permutation = perm)
+    g2 <- sample_correlated_gnp(g1, corr = 0.7, p = g1$p, permutation = perm)
   },
   {
     match_vertices(

tests/testthat/test-bipartite.R

@@ -44,7 +44,7 @@ test_that("bipartite_projection works", {
 test_that("bipartite_projection can calculate only one projection", {
   withr::local_seed(42)
 
-  g <- sample_bipartite_gnp(5, 10, p = .3)
+  g <- sample_bipartite_gnp(5, 10, p = 0.3)
   proj <- bipartite_projection(g)
   proj_false <- bipartite_projection(g, which = "false")
   proj_true <- bipartite_projection(g, which = "true")

tests/testthat/test-centrality.R

@@ -468,7 +468,7 @@ test_that("power_centrality() works", {
   )
 
   g.c <- make_graph(c(1, 2, 1, 3, 2, 4, 3, 5), dir = FALSE)
-  bp.c <- lapply(seq(-.5, .5, by = 0.1), function(x) {
+  bp.c <- lapply(seq(-0.5, 0.5, by = 0.1), function(x) {
     round(power_centrality(g.c, exponent = x), 2)[c(1, 2, 4)]
   })
 
@@ -490,7 +490,7 @@ test_that("power_centrality() works", {
   )
 
   g.d <- make_graph(c(1, 2, 1, 3, 1, 4, 2, 5, 3, 6, 4, 7), dir = FALSE)
-  bp.d <- lapply(seq(-.4, .4, by = 0.1), function(x) {
+  bp.d <- lapply(seq(-0.4, 0.4, by = 0.1), function(x) {
     round(power_centrality(g.d, exponent = x), 2)[c(1, 2, 5)]
   })
   expect_equal(
@@ -509,7 +509,7 @@ test_that("power_centrality() works", {
   )
 
   g.e <- make_graph(c(1, 2, 1, 3, 1, 4, 2, 5, 2, 6, 3, 7, 3, 8, 4, 9, 4, 10), dir = FALSE)
-  bp.e <- lapply(seq(-.4, .4, by = 0.1), function(x) {
+  bp.e <- lapply(seq(-0.4, 0.4, by = 0.1), function(x) {
     round(power_centrality(g.e, exponent = x), 2)[c(1, 2, 5)]
   })
   expect_equal(
@@ -530,7 +530,7 @@ test_that("power_centrality() works", {
   g.f <- make_graph(c(1, 2, 1, 3, 1, 4, 2, 5, 2, 6, 2, 7, 3, 8, 3, 9, 3, 10, 4, 11, 4, 12, 4, 13),
     dir = FALSE
   )
-  bp.f <- lapply(seq(-.4, .4, by = 0.1), function(x) {
+  bp.f <- lapply(seq(-0.4, 0.4, by = 0.1), function(x) {
     round(power_centrality(g.f, exponent = x), 2)[c(1, 2, 5)]
   })
   expect_equal(

tests/testthat/test-cliques.R

@@ -199,7 +199,7 @@ test_that("max_cliques() work", {
 
 test_that("max_cliques() work for subsets", {
   withr::local_seed(42)
-  gnp <- sample_gnp(100, .5)
+  gnp <- sample_gnp(100, 0.5)
 
   mysort <- function(x) {
     xl <- sapply(x, length)
@@ -219,7 +219,7 @@ test_that("max_cliques() work for subsets", {
 
 test_that("count_max_cliques works", {
   withr::local_seed(42)
-  gnp <- sample_gnp(100, .5)
+  gnp <- sample_gnp(100, 0.5)
 
   cl1 <- count_max_cliques(gnp, min = 8)
 

tests/testthat/test-embedding.R

@@ -386,7 +386,7 @@ test_that("embed_adjacency_matrix -- Directed, weighted case works", {
 test_that("embed_adjacency_matrix -- Issue #50 is resolved", {
   withr::local_seed(12345)
 
-  g <- sample_gnp(15, .4)
+  g <- sample_gnp(15, 0.4)
   w <- -log(runif(ecount(g)))
   X1 <- embed_adjacency_matrix(g, 2, weights = w)