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textPCAPlot

Source: R/4_2_textPlotPCA.R
textPCAPlot.Rd

textPCAPlot() plots 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
#> $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 × 13
#>    words     n Dim_PC1 Dim_PC2 check_extreme_max_PC1 check_extreme_max_PC2
#>    <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
#> # ℹ 282 more rows
#> # ℹ 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] "word_data"

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