plot method for class "data_descr".
# S3 method for data_descr
plot(
x,
cex_text_mean = 1,
cex_symb_mean = 12,
marg_by = "item",
cex_text_pc = 3,
cex_range_pc = c(8, 20),
...
)A list of 5 labelled plots displaying descriptive summaries of the partial ranking dataset, namely: i) n_ranked_distr: a barplot of the frequency distribution (%) of the number of items actually ranked in each partial sequence, ii) picto_mean_rank: a basic pictogram of the mean rank vector, iii) marginals: a heatmap of the marginal distributions (either by item or by rank), iv) ecdf: the ecdf of the marginal rank distributions and v) pc: a bubble plot of the pairwise comparison matrix.
An object of class "data_descr" returned by data_description.
Positive scalar: the magnification to be used for all the labels in the plot for the mean rank vector. Defaults to 1.
Positive scalar: the magnification to be used for the symbols in the pictogram of the mean rank vector. Defaults to 12.
Character indicating whether the marginal distributions must be reported by "item" or by "rank" in the heatmap. Defaults to "item".
Positive scalar: the magnification to be used for all the labels in the bubble plot of the paired comparison frequencies. Defaults to 3.
Numeric vector indicating the range of values to be used on each axis in the bubble plot of the paired comparison frequencies. Defaults to c(8,20).
Further arguments passed to or from other methods (not used).
The plots of the marginals distributions and pairwise comparisons are constructed if the object x was obtained from the data_description routine with arguments marg = TRUE and pc = TRUE; otherwise, a NULL element in the output list is returned.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
data_description
## Example 1. Plot the mean rank vector and marginal distributions for the Antifragility dataset.
r_antifrag <- ranks_antifragility[, 1:7]
desc <- data_description(r_antifrag)
p_desc <- plot(desc)
p_desc$picto_mean_rank()
p_desc$marginals()
## Example 2. Plot the distribution of the number of ranked items and the
# pairwise comparison matrix for the Sports dataset.
r_sports <- ranks_sports[, 1:8]
desc <- data_description(rankings = r_sports, borda_ord = TRUE)
p_desc <- plot(desc, cex_text_mean = 1.2)
p_desc$n_ranked_distr()
p_desc$pc()
## Example 3. Plot the ecdf's for the marginal rank distributions for the Sports dataset by gender.
r_sports <- ranks_sports[, 1:8]
desc_f <- data_description(rankings = r_sports, subset = (ranks_sports$Gender == "Female"))
p_desc_f <- plot(desc_f, cex_text_mean = 1.2)
p_desc_f$ecdf()
desc_m <- data_description(rankings = r_sports, subset = (ranks_sports$Gender == "Male"))
p_desc_m <- plot(desc_m, cex_text_mean = 1.2)
p_desc_m$ecdf()
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