| Type: | Package |
| Title: | Cluster Strings by Edit-Distance |
| Version: | 1.0 |
| Author: | Dan S. Reznik |
| Maintainer: | Dan S. Reznik <dreznik@gmail.com> |
| Description: | Returns an edit-distance based clusterization of an input vector of strings. Each cluster will contain a set of strings w/ small mutual edit-distance (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by stringdist::stringdist(). The set of all mutual edit-distances is then used by graph algorithms (from package 'igraph') to single out subsets of high connectivity. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | magrittr, dplyr, stringi, stringr, stringdist, igraph, assertthat, forcats, rlang, tidygraph, ggraph, ggplot2 |
| Depends: | R (≥ 3.1) |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2019-03-26 18:10:58 UTC; dreznik |
| Repository: | CRAN |
| Date/Publication: | 2019-03-30 16:10:03 UTC |
Plot string clusters as graph.
Description
Plot string clusters as graph.
Usage
cluster_plot(cluster, min_cluster_size = 2, label_size = 2.5,
repel = T)
Arguments
cluster |
string clusters returned from 'cluster_strings()' |
min_cluster_size |
minimum size for clusters to be plotted. |
label_size |
how big should the cluster name fonts be. |
repel |
whether to "repel" (so cluster names won't overlap) |
Value
a graph plot (using 'ggraph') of the string clusters.
Examples
s_vec <- c("alcool","alcohol","alcoholic","brandy","brandie","cachaça")
s_clust <- cluster_strings(s_vec,method="lv",max_dist=3,algo="cc")
cluster_plot(s_clust,min_cluster_size=1)
Cluster Strings by Edit-Distance
Description
Cluster Strings by Edit-Distance
Usage
cluster_strings(s_vec, clean = T, method = "osa", max_dist = 3,
algo = "cc")
Arguments
s_vec |
a vector of character strings |
clean |
whether to space-squish and de-duplicate s_vec |
method |
one of "osa","lv","dl" (as in 'stringdist') |
max_dist |
max distance (typically damerau-levenshtein) between related strings. |
algo |
one of "cc" (connected components) or "eb" (edge betweeness) |
Value
a data frame containing cluster membership for each input string
Examples
s_vec <- c("alcool","alcohol","alcoholic","brandy","brandie","cachaça")
s_clust <- cluster_strings(s_vec,method="lv",max_dist=3,algo="cc")
s_clust$df_clusters
Distinct words in Cervantes' "Don Quijote".
Description
Dataframe listing all distinct words (length>3), their length, and frequency of appearance in text.
Usage
quijote_words
Format
A data frame w/ ~22k rows and 3 cols:
- word
the unique word, in Spanish
- len
the word's length
- freq
number of appearances in text