tbl_likert | R Documentation |
experimental
tbl_likert()
creates a summary of Likert scales following the gtsummary structure.
add_n()
adds a column to the table with the total number of observations.
add_continuous_stat()
converts Likert scales into a numeric score and computes
continuous statistics based on this score.
tbl_likert( data, label = NULL, statistic = NULL, digits = NULL, include = everything(), sort = c("default", "ascending", "descending") ) ## S3 method for class 'tbl_likert' add_n( x, statistic = "{n}", col_label = "**N**", footnote = FALSE, last = FALSE, ... ) add_continuous_stat(x, ...) ## S3 method for class 'tbl_likert' add_continuous_stat( x, statistic = "{mean}", digits = NULL, col_label = NULL, footnote = FALSE, last = TRUE, score_values = NULL, stat_col_name = NULL, ... )
data |
A data frame |
label |
List of formulas specifying variables labels,
e.g. |
statistic |
String or formula indicating the statistic to be reported.
Default is the mean score. Other possible continuous statistics are described
in |
digits |
Formula or list of formulas indicating how to display the
computed statistics, see |
include |
variables to include in the summary table. Default is |
sort |
Sort table based on mean scores? Must be one of
|
x |
Object with class |
col_label |
String indicating the column label. Default is generated
from |
footnote |
Logical argument indicating whether to print a footnote
clarifying the statistics presented. Default is |
last |
Logical indicator to include the new column last in table.
Default is |
... |
not used |
score_values |
Vector indicating the numeric value of each factor level.
Default is |
stat_col_name |
Optional string indicating the name of the new column
added to |
Example 1
Other gtsummary-related functions:
add_inline_forest_plot()
,
add_sparkline()
,
as_ggplot()
,
bold_italicize_group_labels()
,
logistic_reg_adj_diff()
,
style_tbl_compact()
,
theme_gtsummary_msk()
library(dplyr) set.seed(1123) likert_lvls <- c("Never", "Sometimes", "Often", "Always") df <- tibble::tibble( Q1 = sample(likert_lvls, size = 100, replace = TRUE), Q2 = sample(likert_lvls, size = 100, replace = TRUE) ) %>% mutate(across(c(Q1, Q2), ~factor(., levels = likert_lvls))) tbl_likert_ex1 <- tbl_likert(df) %>% add_n() %>% add_continuous_stat(statistic = "{mean}") %>% add_continuous_stat(statistic = "{sd}")
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