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

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

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
#> $final_plot
#> #> $description #> [1] "INFORMATION ABOUT THE PROJECTION INFORMATION ABOUT THE PLOT word_data = PC_projections_satisfactionwords_40 min_freq_words_test = 1 plot_n_word_extreme = 5 plot_n_word_frequency = 5 plot_n_words_middle = 5 bivariate_color_codes = #398CF9 #60A1F7 #5dc688 #e07f6a #EAEAEA #40DD52 #FF0000 #EA7467 #85DB8E word_size_range = 3 - 8 position_jitter_hight = 0 position_jitter_width = 0.03 point_size = 0.5 arrow_transparency = 0.5 points_without_words_size = 0.2 points_without_words_alpha = 0.2 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" #> #> $processed_word_data #> # A tibble: 292 x 13 #> words n Dim_PC1 Dim_PC2 check_extreme_m… check_extreme_m… #> <chr> <int> <dbl> <dbl> <dbl> <dbl> #> 1 acce… 1 -11.4 4.79 0 0 #> 2 acco… 2 -10.3 7.52 0 0 #> 3 achi… 1 3.08 17.1 0 0 #> 4 acti… 1 -3.79 8.28 0 0 #> 5 adeq… 1 -7.50 8.40 0 0 #> 6 alive 1 2.25 -1.43 0 0 #> 7 alone 1 8.31 -3.75 0 0 #> 8 ambi… 1 -8.43 -4.33 0 0 #> 9 amus… 1 11.8 -4.24 0 0 #> 10 anal… 1 2.28 6.42 0 0 #> # … with 282 more rows, and 7 more variables: check_extreme_min_PC1 <dbl>, #> # check_extreme_min_PC2 <dbl>, check_extreme_frequency <dbl>, #> # check_middle_PC1 <dbl>, check_middle_PC2 <dbl>, extremes_all <dbl>, #> # colour_categories <chr> #>
names(DP_projections_HILS_SWLS_100)
#> [1] "words" "dot.x" #> [3] "p_values_dot.x" "n_g1.x" #> [5] "n_g2.x" "dot.y" #> [7] "p_values_dot.y" "n_g1.y" #> [9] "n_g2.y" "n" #> [11] "n.percent" "N_participant_responses"