Fix deprecation warning from igraph

R/euclidean_logical_joins.R

@@ -6,11 +6,11 @@
 #'   about the distance and not the similarity. Therefore, a lower value means a
 #'   higher similarity.
 #' @param r Hyperparameter used to govern the sensitivity of the locality
-#' sensitive hash. Corresponds to the width of the hash bucket in the LSH
-#' algorithm. Increasing values of `r` mean more hash collisions and higher
-#' sensitivity (fewer false-negatives) at the cost of lower specificity (more
-#' false-positives and longer run time). For more information, see the
-#' description in  <doi:10.1145/997817.997857>.
+#'   sensitive hash. Corresponds to the width of the hash bucket in the LSH
+#'   algorithm. Increasing values of `r` mean more hash collisions and higher
+#'   sensitivity (fewer false-negatives) at the cost of lower specificity (more
+#'   false-positives and longer run time). For more information, see the
+#'   description in \doi{10.1145/997817.997857}.
 #'
 #' @return A tibble fuzzily-joined on the basis of the variables in `by.` Tries
 #'   to adhere to the same standards as the dplyr-joins, and uses the same

R/string_group.R

@@ -1,7 +1,7 @@
 #' Fuzzy String Grouping Using Minhashing
 #'
 #' Performs fuzzy string grouping in which similar strings are assigned to the
-#' same group. Uses the `fastgreedy.community` community detection algorithm
+#' same group. Uses the `cluster_fast_greedy()` community detection algorithm
 #' from the `igraph` package to create the groups. Must have igraph installed
 #' in order to use this function.
 #'
@@ -67,7 +67,12 @@ jaccard_string_group <- function(string, n_gram_width = 2, n_bands = 45, band_wi
 
 
     graph <- igraph::graph_from_edgelist(pairs)
-    fc <- igraph::fastgreedy.community(igraph::as.undirected(graph))
+    if (packageVersion("igraph") < "2.0.0") {
+        fc <- igraph::fastgreedy.community(igraph::as.undirected(graph))
+    } else {
+        fc <- igraph::cluster_fast_greedy(igraph::as.undirected(graph))
+    }
+
 
     groups <- igraph::groups(fc)
     lookup_table <- vapply(groups, "[[", integer(1), 1)