textPlot {text}

Plot words from textProjection() or textWordPrediction().

Description

Plot words from textProjection() or textWordPrediction().

Usage

textPlot(
  word_data,
  k_n_words_to_test = FALSE,
  min_freq_words_test = 1,
  min_freq_words_plot = 1,
  plot_n_words_square = 3,
  plot_n_words_p = 5,
  plot_n_word_extreme = 5,
  plot_n_word_frequency = 5,
  plot_n_words_middle = 5,
  titles_color = "#61605e",
  y_axes = FALSE,
  p_alpha = 0.05,
  overlapping = TRUE,
  p_adjust_method = "none",
  title_top = "Supervised Dimension Projection",
  x_axes_label = "Supervised Dimension Projection (SDP)",
  y_axes_label = "Supervised Dimension Projection (SDP)",
  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 = "SDP",
  legend_x_axes_label = "x",
  legend_y_axes_label = "y",
  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,
  group_embeddings1 = FALSE,
  group_embeddings2 = FALSE,
  projection_embedding = FALSE,
  aggregated_point_size = 0.8,
  aggregated_shape = 8,
  aggregated_color_G1 = "black",
  aggregated_color_G2 = "black",
  projection_color = "blue",
  seed = 1005,
  explore_words = NULL,
  explore_words_color = "#ad42f5",
  explore_words_point = "ALL_1",
  explore_words_aggregation = "mean",
  remove_words = NULL,
  n_contrast_group_color = NULL,
  n_contrast_group_remove = FALSE,
  space = NULL,
  scaling = FALSE
)

Arguments

word_data

Dataframe from textProjection.

k_n_words_to_test

Select the k most frequent words to significance test (k = sqrt(100*N); N = number of participant responses) (default = TRUE).

min_freq_words_test

Select words to significance test that have occurred at least min_freq_words_test (default = 1).

min_freq_words_plot

Select words to plot that has occurred at least min_freq_words_plot times (default = 1).

plot_n_words_square

Select number of significant words in each square of the figure to plot. The significant words, in each square is selected according to most frequent words (default = 3).

plot_n_words_p

Number of significant words to plot on each (positive and negative) side of the x-axes and y-axes, (where duplicates are removed); selects first according to lowest p-value and then according to frequency (default = 5). Hence, on a two dimensional plot it is possible that plot_n_words_p = 1 yield 4 words.

plot_n_word_extreme

Number of words that are extreme on Supervised Dimension Projection per dimension (default = 5). (i.e., even if not significant; per dimensions, where duplicates are removed).

plot_n_word_frequency

Number of words based on being most frequent (default = 5). (i.e., even if not significant).

plot_n_words_middle

Number of words plotted that are in the middle in Supervised Dimension Projection score (default = 5). (i.e., even if not significant; per dimensions, where duplicates are removed).

titles_color

Color for all the titles (default: "#61605e").

y_axes

(boolean) If TRUE, also plotting on the y-axes (default = FALSE, i.e, a 1-dimensional plot is generated). Also plotting on y-axes produces a two dimension 2-dimensional plot, but the textProjection function has to have had a variable on the y-axes.

p_alpha

Alpha (default = .05).

overlapping

(boolean) Allow overlapping (TRUE) or disallow (FALSE) (default = TRUE).

p_adjust_method

(character) Method to adjust/correct p-values for multiple comparisons (default = "none"; see also "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr").

title_top

Title (default " ").

x_axes_label

(character) Label on the x-axes (default = "Supervised Dimension Projection (SDP)").

y_axes_label

(character) Label on the y-axes (default = "Supervised Dimension Projection (SDP)").

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

(HTML color codes. Type = character) The different colors of the words. Note that, at the moment, two squares should not have the exact same colour-code because the numbers within the squares of the legend will then be aggregated (and show the same, incorrect value). (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: "SDP").

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).

group_embeddings1

(boolean) Shows a point representing the aggregated word embedding for group 1 (default = FALSE).

group_embeddings2

(boolean) Shows a point representing the aggregated word embedding for group 2 (default = FALSE).

projection_embedding

(boolean) Shows a point representing the aggregated direction embedding (default = FALSE).

aggregated_point_size

Size of the points representing the group_embeddings1, group_embeddings2 and projection_embedding (default = 0.8).

aggregated_shape

Shape type of the points representing the group_embeddings1, group_embeddings2 and projection_embedding (default = 8).

aggregated_color_G1

Color (default = "black").

aggregated_color_G2

Color (default = "black").

projection_color

Color (default = "blue").

seed

(numeric) Set different seed (default = 1005)..

explore_words

Explore where specific words are positioned in the embedding space. For example, c("happy content", "sad down") (default = NULL).

explore_words_color

Specify the color(s) of the words being explored. For example c("#ad42f5", "green") (default = "#ad42f5").

explore_words_point

Specify the names of the point for the aggregated word embeddings of all the explored words (default = "ALL_1").

explore_words_aggregation

Specify how to aggregate the word embeddings of the explored words (default = "mean").

remove_words

Manually remove words from the plot (which is done just before the words are plotted so that the remove_words are part of previous counts/analyses) (default = NULL).

n_contrast_group_color

Set color to words that have higher frequency (N) on the other opposite side of its dot product projection (default = NULL).

n_contrast_group_remove

Remove words that have higher frequency (N) on the other opposite side of its dot product projection (default = FALSE).

space

Provide a semantic space if using static embeddings and wanting to explore words (default = NULL).

scaling

Scaling word embeddings before aggregation (default = FALSE).

Value

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

See Also

See textProjection.

Examples

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

# Supervised Dimension Projection Plot
plot_projection <- textPlot(
  word_data = DP_projections_HILS_SWLS_100,
  k_n_words_to_test = FALSE,
  min_freq_words_test = 1,
  plot_n_words_square = 3,
  plot_n_words_p = 3,
  plot_n_word_extreme = 1,
  plot_n_word_frequency = 1,
  plot_n_words_middle = 1,
  y_axes = FALSE,
  p_alpha = 0.05,
  title_top = "Supervised Dimension Projection (SDP)",
  x_axes_label = "Low vs. High HILS score",
  y_axes_label = "Low vs. High SWLS score",
  p_adjust_method = "bonferroni",
  scale_y_axes_lim = NULL
)
plot_projection

names(DP_projections_HILS_SWLS_100)