Package index • Networds

Sample Data

Sample data for networds examples and for function params

ex_prince: The Prince by Niccolò Machiavelli

g: A sample graph dataframe (tbl-graph)

layouts: layouts available to plot networks

text_sample: A Text Sample

txt_wiki: A Text Sample from wikipedia

Preparing the data

filter_by_query(): Tokenize and filter text by a query

filter_graph(): Filter both nodes at the same time

tblgraph_to_tbl(): Transform a tbl_graph into a labeled tibble

tokenize_by_words(): Tokenize words

txt_subst(): Make compound words into a single word

Extract graph

extract cooccurrence of (almost) all words

cooccur_words(): Get co-occurrence of all words from pure text

Extracting graphs using regex

connectors(): A lowercase connectors between two proper names

connectors_list: list of proper names connectors

extract_entities2(): extract_entity for each sentence

extract_entity_rb(): A rule based entity extractor

extract_graph_df(): Extract a non directional graph based on co-occurrence in the token and returns a tibble It extracts only if two entities are mentioned in the same token (sentence or paragraph)

extract_graph_rb(): rule based entity graph extractor

extract_graph_rgx(): extract a graph from text, using custom regex pattern as nodes.

extract_relation(): tokenize and selects only sentences/paragraphs with more than one entity per sentence or paragraph

get_graph_from_txt(): Graph from text and query

subs_ppn(): Substitute proper names/entities spaces with underscore in the text.

Extracting graphs with Part of Speech Tagging (POS)

collapse_adp(): Collapse ADP

entity_list_2_graph2(): from a list of entities (generated by extract_entities2) returns a tibble/dataframe with co-occurence pairs

extract_entities(): Extract entities from POS

extract_entities_l(): Extract entities in list format, by sentence

extract_entities_v(): extract entities, returns a vector

extract_graph_pos(): extract graph of co-ocurrence from a POS dataframe

filter_ppn(): extract proper name and nouns from POS DF

get_cooc(): get graph (co-occurrence of entities and/or grammar class)

get_cooc_entities(): Get graph (co-occurrence of entities)

get_entities(): get entities from POS DF

graph_from_cooccurrence(): Graph from word co-occurrence

group_entities(): Group a sequence of entities in a POS dataframe

group_ppn(): Group a POS of proper names

group_seq_pos(): collapse sequence of repeated POS into a single one

parsePOS(): parse tokenized text into POS

Analize graphs

count_graphs(): Count the graph frequency of co-occurences/Co-mentioning of triplets

count_vec(): count a vector of elements

extract_nodes(): Extract nodes from a dataframe of two columns of nodes

extract_triplets(): from semgram output, join passive and active voices

filter_ego(): Filter a graph / create an ego graph

filter_graph_g(): Filter both nodes at same time using grep

filter_nodes(): filter nodes in both columns, at the same time

freq_nodes(): extract the frequency of nodes in a text

get_neighbors(): Get the neighbors of a node

get_neighbors_df(): Get the neighbors of a word in a dataframe

get_node_id(): Search for the id of a node in a tbl_graph

graph_subs(): graph substitution

reduce_freq(): Set a maximum value to the frequency of pairs

rename_cols(): rename cols from dplyr::count

scale_to_range(): Change the vector min and max values proportionally

split_graph(): split a tidy graph into a list of dataframe

visualize graphs

visualize static and dynamic graphs

net_wordcloud(): plot net wordcloud

plot_graph(): Plot simple graph

plot_graph2(): weighted word graph viz

plot_graph_i(): viz graph interactively

plot_pos_graph(): plot static graph from POS list

q_plot(): quick word graph

viz_triplets_i(): viz graph interactively