textPCAPlot: Plot words according to 2-D plot from 2 PCA components.

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

Plot words according to 2-D plot from 2 PCA components.

Description

Plot words according to 2-D plot from 2 PCA components.

Usage

textPCAPlot(
  word_data,
  min_freq_words_test = 1,
  plot_n_word_extreme = 5,
  plot_n_word_frequency = 5,
  plot_n_words_middle = 5,
  titles_color = "#61605e",
  title_top = "Principal Component (PC) Plot",
  x_axes_label = "PC1",
  y_axes_label = "PC2",
  scale_x_axes_lim = NULL,
  scale_y_axes_lim = NULL,
  word_font = NULL,
  bivariate_color_codes = c("#398CF9", "#60A1F7", "#5dc688", "#e07f6a", "#EAEAEA",
    "#40DD52", "#FF0000", "#EA7467", "#85DB8E"),
  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 = 0.2,
  points_without_words_alpha = 0.2,
  legend_title = "PC",
  legend_x_axes_label = "PC1",
  legend_y_axes_label = "PC2",
  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 = 1002
)

Arguments

word_data	Dataframe from textPCA
min_freq_words_test	Select words to significance test that have occurred at least min_freq_words_test (default = 1).
plot_n_word_extreme	Number of words that are extreme on Supervised Dimension Projection per dimension. (i.e., even if not significant; per dimensions, where duplicates are removed).
plot_n_word_frequency	Number of words based on being most frequent. (i.e., even if not significant).
plot_n_words_middle	Number of words plotted that are in the middle in Supervised Dimension Projection score (i.e., even if not significant; per dimensions, where duplicates are removed).
titles_color	Color for all the titles (default: "#61605e")
title_top	Title (default " ")
x_axes_label	Label on the x-axes.
y_axes_label	Label on the y-axes.
scale_x_axes_lim	Manually set the length of the x-axes (default = NULL, which uses ggplot2::scale_x_continuous(limits = scale_x_axes_lim); change e.g., by trying c(-5, 5)).
scale_y_axes_lim	Manually set the length of the y-axes (default = NULL; which uses ggplot2::scale_y_continuous(limits = scale_y_axes_lim); change e.g., by trying c(-5, 5)).
word_font	Font type (default: NULL).
bivariate_color_codes	The different colors of the words (default: c("#398CF9", "#60A1F7", "#5dc688", "#e07f6a", "#EAEAEA", "#40DD52", "#FF0000", "#EA7467", "#85DB8E")).
word_size_range	Vector with minimum and maximum font size (default: c(3, 8)).
position_jitter_hight	Jitter height (default: .0).
position_jitter_width	Jitter width (default: .03).
point_size	Size of the points indicating the words' position (default: 0.5).
arrow_transparency	Transparency of the lines between each word and point (default: 0.1).
points_without_words_size	Size of the points not linked with a words (default is to not show it, i.e., 0).
points_without_words_alpha	Transparency of the points not linked with a words (default is to not show it, i.e., 0).
legend_title	Title on the color legend (default: "(PCA)".
legend_x_axes_label	Label on the color legend (default: "(x)".
legend_y_axes_label	Label on the color legend (default: "(y)".
legend_x_position	Position on the x coordinates of the color legend (default: 0.02).
legend_y_position	Position on the y coordinates of the color legend (default: 0.05).
legend_h_size	Height of the color legend (default 0.15).
legend_w_size	Width of the color legend (default 0.15).
legend_title_size	Font size (default: 7).
legend_number_size	Font size of the values in the legend (default: 2).
seed	Set different seed.

Value

A 1- or 2-dimensional word plot, as well as tibble with processed data used to plot..

See Also

see textPCA

Examples

# The test-data included in the package is called: DP_projections_HILS_SWLS_100

# Supervised Dimension Projection Plot
principle_component_plot_projection <- textPCAPlot(PC_projections_satisfactionwords_40)
principle_component_plot_projection

names(DP_projections_HILS_SWLS_100)

text documentation built on Aug. 9, 2023, 5:08 p.m.