R/4_1_textPlotCentrality.R
textCentralityPlot.RdPlot words according to semantic similarity to the aggregated word embedding.
textCentralityPlot(
word_data,
min_freq_words_test = 1,
plot_n_word_extreme = 10,
plot_n_word_frequency = 10,
plot_n_words_middle = 10,
titles_color = "#61605e",
x_axes = "central_semantic_similarity",
title_top = "Semantic Centrality Plot",
x_axes_label = "Semantic Centrality",
scale_x_axes_lim = NULL,
scale_y_axes_lim = NULL,
word_font = NULL,
centrality_color_codes = c("#EAEAEA", "#85DB8E", "#398CF9", "#9e9d9d"),
word_size_range = c(3, 8),
position_jitter_hight = 0,
position_jitter_width = 0.03,
point_size = 0.5,
arrow_transparency = 0.1,
points_without_words_size = 0.5,
points_without_words_alpha = 0.5,
legend_title = "SC",
legend_x_axes_label = "x",
legend_x_position = 0.02,
legend_y_position = 0.02,
legend_h_size = 0.2,
legend_w_size = 0.2,
legend_title_size = 7,
legend_number_size = 2,
seed = 1007
)Tibble from textPlotData.
Select words to significance test that have occurred at least min_freq_words_test (default = 1).
Number of words per dimension to plot with extreme Supervised Dimension Projection value. (i.e., even if not significant; duplicates are removed).
Number of words to plot according to their frequency. (i.e., even if not significant).
Number of words to plot that are in the middle in Supervised Dimension Projection score (i.e., even if not significant; duplicates are removed).
Color for all the titles (default: "#61605e").
Variable to be plotted on the x-axes (default is "central_semantic_similarity", could also select "n", "n_percent").
Title (default " ").
Label on the x-axes.
Length of the x-axes (default: NULL, which uses c(min(word_data$central_semantic_similarity)-0.05, max(word_data$central_semantic_similarity)+0.05); change this by e.g., try c(-5, 5)).
Length of the y-axes (default: NULL, which uses c(-1, 1); change e.g., by trying c(-5, 5)).
Type of font (default: NULL).
Colors of the words selected as plot_n_word_extreme (minimum values), plot_n_words_middle, plot_n_word_extreme (maximum values) and plot_n_word_frequency; the default is c("#EAEAEA", "#85DB8E", "#398CF9", "#9e9d9d", respectively.
Vector with minimum and maximum font size (default: c(3, 8)).
Jitter height (default: .0).
Jitter width (default: .03).
Size of the points indicating the words' position (default: 0.5).
Transparency of the lines between each word and point (default: 0.1).
Size of the points not linked to a word (default is to not show the point; , i.e., 0).
Transparency of the points that are not linked to a word (default is to not show it; i.e., 0).
Title of the color legend (default: "(SCP)").
Label on the color legend (default: "(x)".
Position on the x coordinates of the color legend (default: 0.02).
Position on the y coordinates of the color legend (default: 0.05).
Height of the color legend (default 0.15).
Width of the color legend (default 0.15).
Font size of the title (default = 7).
Font size of the values in the legend (default = 2).
Set different seed.
A 1-dimensional word plot based on similarity to the aggregated word embedding, as well as tibble with processed data used to plot.
see textCentrality and textProjection
# The test-data included in the package is called: centrality_data_harmony
names(centrality_data_harmony)
#> [1] "words" "n"
#> [3] "central_semantic_similarity" "n_percent"
# Plot
# centrality_plot <- textCentralityPlot(
# word_data = centrality_data_harmony,
# min_freq_words_test = 10,
# plot_n_word_extreme = 10,
# plot_n_word_frequency = 10,
# plot_n_words_middle = 10,
# titles_color = "#61605e",
# x_axes = "central_semantic_similarity",
#
# title_top = "Semantic Centrality Plot",
# x_axes_label = "Semantic Centrality",
#
# word_font = NULL,
# centrality_color_codes = c("#EAEAEA", "#85DB8E", "#398CF9", "#9e9d9d"),
# word_size_range = c(3, 8),
# point_size = 0.5,
# arrow_transparency = 0.1,
# points_without_words_size = 0.5,
# points_without_words_alpha = 0.5,
# )
# centrality_plot