Shows the results of stacked frequencies (such as likert scales) as HTML table. This function is useful when several items with identical scale/categories should be printed as table to compare their distributions (e.g. when plotting scales like SF, Barthel-Index, Quality-of-Life-scales etc.).
tab_stackfrq(
items,
weight.by = NULL,
title = NULL,
var.labels = NULL,
value.labels = NULL,
wrap.labels = 20,
sort.frq = NULL,
alternate.rows = FALSE,
digits = 2,
string.total = "N",
string.na = "NA",
show.n = FALSE,
show.total = FALSE,
show.na = FALSE,
show.skew = FALSE,
show.kurtosis = FALSE,
digits.stats = 2,
file = NULL,
encoding = NULL,
CSS = NULL,
use.viewer = TRUE,
remove.spaces = TRUE
)Data frame, or a grouped data frame, with each column representing one item.
Vector of weights that will be applied to weight all cases.
Must be a vector of same length as the input vector. Default is
NULL, so no weights are used.
Character vector with table
caption(s) resp. footnote(s). For tab_df(), must be a character
of length 1; for tab_dfs(), a character vector of same length as
x (i.e. one title or footnote per data frame).
Character vector with variable names, which will be used to label variables in the output.
Character vector (or list of character vectors)
with value labels of the supplied variables, which will be used
to label variable values in the output.
Numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.
logical, indicates whether the items should be ordered by
by highest count of first or last category of items.
Use "first.asc" to order ascending by lowest count of first category,
"first.desc" to order descending by lowest count of first category,
"last.asc" to order ascending by lowest count of last category,
"last.desc" to order descending by lowest count of last category,
or NULL (default) for no sorting.
Logical, if TRUE, rows are printed in
alternatig colors (white and light grey by default).
Numeric, amount of digits after decimal point when rounding values.
label for the total N column.
label for the missing column/row.
logical, if TRUE, adds total number of cases for each
group or category to the labels.
logical, if TRUE, an additional column with each
item's total N is printed.
logical, if TRUE, NA's (missing values)
are added to the output.
logical, if TRUE, an additional column with each item's skewness is printed.
The skewness is retrieved from the describe-function
of the psych-package.
amount of digits for rounding the skewness and kurtosis valuess. Default is 2, i.e. skewness and kurtosis values have 2 digits after decimal point.
Destination file, if the output should be saved as file.
If NULL (default), the output will be saved as temporary file and
openend either in the IDE's viewer pane or the default web browser.
Character vector, indicating the charset encoding used
for variable and value labels. Default is "UTF-8". For Windows
Systems, encoding = "Windows-1252" might be necessary for proper
display of special characters.
A list with user-defined style-sheet-definitions,
according to the official CSS syntax.
See 'Details' or this package-vignette.
Logical, if TRUE, the HTML table is shown in the IDE's
viewer pane. If FALSE or no viewer available, the HTML table is
opened in a web browser.
Logical, if TRUE, leading spaces are removed from all lines in the final string
that contains the html-data. Use this, if you want to remove parantheses for html-tags. The html-source
may look less pretty, but it may help when exporting html-tables to office tools.
Invisibly returns
the web page style sheet (page.style),
the web page content (page.content),
the complete html-output (page.complete) and
the html-table with inline-css for use with knitr (knitr)
for further use.
# NOT RUN {
# -------------------------------
# random sample
# -------------------------------
# prepare data for 4-category likert scale, 5 items
likert_4 <- data.frame(
as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.2, 0.3, 0.1, 0.4))),
as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.5, 0.25, 0.15, 0.1))),
as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.25, 0.1, 0.4, 0.25))),
as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.1, 0.4, 0.4, 0.1))),
as.factor(sample(1:4, 500, replace = TRUE, prob = c(0.35, 0.25, 0.15, 0.25)))
)
# create labels
levels_4 <- c("Independent", "Slightly dependent",
"Dependent", "Severely dependent")
# create item labels
items <- c("Q1", "Q2", "Q3", "Q4", "Q5")
# plot stacked frequencies of 5 (ordered) item-scales
# }
# NOT RUN {
if (interactive()) {
tab_stackfrq(likert_4, value.labels = levels_4, var.labels = items)
# -------------------------------
# Data from the EUROFAMCARE sample dataset
# Auto-detection of labels
# -------------------------------
data(efc)
# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive first item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")
tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE)
tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,
show.n = TRUE, show.na = TRUE)
# --------------------------------
# User defined style sheet
# --------------------------------
tab_stackfrq(efc[, c(start:end)], alternate.rows = TRUE,
show.total = TRUE, show.skew = TRUE, show.kurtosis = TRUE,
CSS = list(css.ncol = "border-left:1px dotted black;",
css.summary = "font-style:italic;"))
}
# }
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