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distr_characteristics uses violin and bar plots to visualise the
distribution of each characteristic in the dataset either per comparison
or cluster of comparisons.
distr_characteristics(
input,
drug_names,
rename_char = NULL,
cluster = NULL,
label_size = 4,
title_size = 14,
axis_title_size = 14,
axis_text_size = 14,
axis_x_text_angle = 0,
legend_text_size = 13
)distr_characteristics returns a list of plots using the proper plot
(violin or bar plot) for each characteristic. The size of the dots in the
violin plot (with amalgamated box plots and dots) are proportional to the
total sample size of the study: the large the sample size of the study, the
larger the size of the corresponding point.
A data-frame in the long arm-based format. Two-arm trials occupy one row in the data-frame. Multi-arm trials occupy as many rows as the number of possible comparisons among the interventions. The first three columns refer to the trial name, first and second arm of the comparison (their identifier number), respectively. The remaining columns refer to summary characteristics. See 'Details' for specifying the columns.
A vector of labels with the name of the interventions
in the order they appear in the argument input.
A list of two elements: (i) a numeric vector with the
position of the characteristics in input, and (ii) a character
vector with the names of the characteristics, as they are wished to
appear in the title of the plots. This argument is optional, in case the
user wants to control the appearance of the titles.
An object of S3 class comp_clustering that has
information on the cluster of each comparison. See 'Value' in
comp_clustering. If cluster is not provided, the
function presents the distribution of characteristics per comparison;
otherwise per cluster. In the latter, the function prints a table with the
comparisons and the corresponding cluster.
A positive integer for the font size of labels in the
plots. label_size determines the size argument found in the geom's
aesthetic properties in the R-package
ggplot2).
A positive integer for the font size of legend title in
the plots. title_size determines the title argument
found in the theme's properties in the R-package
ggplot2.
A positive integer for the font size of axis title in
the plots. axis_title_size determines the axis.title argument found
in the theme's properties in the
R-package ggplot2.
A positive integer for the font size of axis text in
the plots. axis_text_size determines the axis.text argument found in
the theme's properties in the R-package
ggplot2).
A positive integer for the angle of axis text in
the plots. axis_text_angle determines the axis.text.x argument found
in the theme's properties in the R-package
ggplot2).
A positive integer for the font size of legend text
in the plots. legend_text_size determines the legend.text argument
found in the theme's properties in the R-package
ggplot2.
Loukia M. Spineli
The correct type mode of columns in input must be ensured to use
the function distr_characteristics. The first three columns
referring to the trial name, first and second arm of the comparison,
respectively, must be character. The remaining columns referring
to the characteristics must be double or integer
depending on whether the corresponding characteristic refers to a
quantitative or qualitative variable. The type mode of each column is
assessed by distr_characteristics using the base function
typeof.
The interventions should be sorted in an ascending order of their
identifier number within the trials so that the first treatment column
(second column in input) is the control arm for every pairwise
comparison. This is important to ensure consistency in the order of
interventions within the comparisons obtained from the other related
functions.
comp_clustering
# \donttest{
# Fictional dataset
set.seed(13022024)
data_set <- data.frame(Trial_name = as.character(1:(5 + 7 +2)),
arm1 = rep(c("1", "2"), c(5 + 7, 2)),
arm2 = rep(c("2", "3"), c(5, 7 + 2)),
sample = as.numeric(sample(50:300, 5 + 7 + 2)),
age = as.numeric(sample(18:50, 5 + 7 + 2)),
blinding = factor(rep(c("yes", "no", "yes"), c(5, 7, 2))))
distr_characteristics(input = data_set,
drug_names = c("A", "B", "C"))
# }
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