Plot a network of co-occurrence of terms. Because word frequencies can vary significantly, differences in text size can be substantial. Therefore, instead of adjusting text size, we vary the dot/node size, ensuring the text remains consistently sized and maintains readability. It is also possible to normalize the result with log. plot a graph of co-occurrence of terms, as returned by extract_graph, cooccur
Usage
plot_graph2(
DF,
text,
head_n = 30,
lower = TRUE,
text_color = "black",
text_size = 3,
text_contour_color = NA,
node_alpha = 0.5,
node_color = NULL,
node_size = NULL,
edge_color = "lightblue",
edge_alpha = 0.5,
edge_cut = 0,
edge_type = "arc",
edge_bend = 0.5,
edge_fan = FALSE,
scale_graph = "scale_values",
layout = "kk"
)Arguments
- DF
a dataframe of co-occurrence, extracted with `extract_graph()` and `count(n1, n2)`
- text
an input text
- head_n
number of nodes to show - the more frequent
- lower
Convert words to lowercase. If the text is passed in all lowercase, it can return false sentence and paragraph tokenization.
- text_color
color of the text in nodes
- text_size
font size of the nodes. If empty (default), it will use the frequency of word.
- node_alpha
transparency of the nodes
- node_color
color of the nodes. If empty (default), it will use the same color of the edges.
- edge_color
color of the edges
- edge_alpha
transparency of the edges. Values between 0 and 1.
- edge_cut
in mm, how much you want that the edge stop before reach the node. Can improve readability.
- edge_fan
if TRUE, the edges are drawn in a fan shape. Default FALSE.
- scale_graph
name of a function to normalize the result. Sometimes, the range of numbers are so wide that the graph becomes unreadable. Applying a function to normalize the result can improve the readability, for example using `scale_graph = "log2"`, `"sqrt"`,`"log10"` or `none`.
- layout
the layout of the plot. Options are bipartite, star, circle, nicely, dh, gem, graphopt, grid, mds, sphere, randomly, fr, kk, drl, lgl. More info at ggraph documentation
Examples
text <- ex_prince[3, 2]
graph <- cooccur_words(text, sw = stopwords::stopwords())
#> tokenizing sentences...
#> tokenizing words...
plot_graph2(DF = graph, text = text, head_n = 50, scale_graph = "log2")
#>
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#> Using node_size proportional to word frequency as no node_size was provided in parameters
