View source: R/datasummary_correlation.R
datasummary_correlation | R Documentation |
The names of the variables displayed in the correlation table are the names
of the columns in the data
. You can rename those columns (with or without
spaces) to produce a table of human-readable variables. See the Details and
Examples sections below, and the vignettes on the modelsummary
website:
https://modelsummary.com/
https://modelsummary.com/articles/datasummary.html
datasummary_correlation(
data,
output = "default",
method = "pearson",
fmt = 2,
align = NULL,
add_rows = NULL,
add_columns = NULL,
title = NULL,
notes = NULL,
escape = TRUE,
stars = FALSE,
...
)
data |
A data.frame (or tibble) |
output |
filename or object type (character string)
|
method |
character or function
|
fmt |
how to format numeric values: integer, user-supplied function, or
|
align |
A string with a number of characters equal to the number of columns in
the table (e.g.,
|
add_rows |
a data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a "position" attribute of integers to set the row positions. See Examples section below. |
add_columns |
a data.frame (or tibble) with the same number of rows as your main table. |
title |
string. Cross-reference labels should be added with Quarto or Rmarkdown chunk options when applicable. When saving standalone LaTeX files, users can add a label such as |
notes |
list or vector of notes to append to the bottom of the table. |
escape |
boolean TRUE escapes or substitutes LaTeX/HTML characters which could
prevent the file from compiling/displaying. |
stars |
to indicate statistical significance
|
... |
other parameters are passed through to the table-making packages. |
The behavior of modelsummary
can be modified by setting global options. For example:
options(modelsummary_model_labels = "roman")
The rest of this section describes each of the options above.
These global option changes the style of the default column headers:
options(modelsummary_model_labels = "roman")
options(modelsummary_panel_labels = "roman")
The supported styles are: "model", "panel", "arabic", "letters", "roman", "(arabic)", "(letters)", "(roman)"
The panel-specific option is only used when shape="rbind"
modelsummary
supports 6 table-making packages: tinytable
, kableExtra
, gt
,
flextable
, huxtable
, and DT
. Some of these packages have overlapping
functionalities. To change the default backend used for a specific file
format, you can use ' the options
function:
options(modelsummary_factory_html = 'kableExtra')
options(modelsummary_factory_word = 'huxtable')
options(modelsummary_factory_png = 'gt')
options(modelsummary_factory_latex = 'gt')
options(modelsummary_factory_latex_tabular = 'kableExtra')
Change the look of tables in an automated and replicable way, using the modelsummary
theming functionality. See the vignette: https://modelsummary.com/articles/appearance.html
modelsummary_theme_gt
modelsummary_theme_kableExtra
modelsummary_theme_huxtable
modelsummary_theme_flextable
modelsummary_theme_dataframe
modelsummary
can use two sets of packages to extract information from
statistical models: the easystats
family (performance
and parameters
)
and broom
. By default, it uses easystats
first and then falls back on
broom
in case of failure. You can change the order of priorities or include
goodness-of-fit extracted by both packages by setting:
options(modelsummary_get = "easystats")
options(modelsummary_get = "broom")
options(modelsummary_get = "all")
By default, LaTeX tables enclose all numeric entries in the \num{}
command
from the siunitx package. To prevent this behavior, or to enclose numbers
in dollar signs (for LaTeX math mode), users can call:
options(modelsummary_format_numeric_latex = "plain")
options(modelsummary_format_numeric_latex = "mathmode")
A similar option can be used to display numerical entries using MathJax in HTML tables:
options(modelsummary_format_numeric_html = "mathjax")
When creating LaTeX via the tinytable
backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents (except when the modelsummary()
calls are cached).
\usepackage{tabularray} \usepackage{float} \usepackage{graphicx} \usepackage[normalem]{ulem} \UseTblrLibrary{booktabs} \UseTblrLibrary{siunitx} \newcommand{\tinytableTabularrayUnderline}[1]{\underline{#1}} \newcommand{\tinytableTabularrayStrikeout}[1]{\sout{#1}} \NewTableCommand{\tinytableDefineColor}[3]{\definecolor{#1}{#2}{#3}}
library(modelsummary) # clean variable names (base R) dat <- mtcars[, c("mpg", "hp")] colnames(dat) <- c("Miles / Gallon", "Horse Power") datasummary_correlation(dat) # clean variable names (tidyverse) library(tidyverse) dat <- mtcars %>% select(`Miles / Gallon` = mpg, `Horse Power` = hp) datasummary_correlation(dat) # `correlation` package objects if (requireNamespace("correlation", quietly = TRUE)) { co <- correlation::correlation(mtcars[, 1:4]) datasummary_correlation(co) # add stars to easycorrelation objects datasummary_correlation(co, stars = TRUE) } # alternative methods datasummary_correlation(dat, method = "pearspear") # custom function cor_fun <- function(x) cor(x, method = "kendall") datasummary_correlation(dat, method = cor_fun) # rename columns alphabetically and include a footnote for reference note <- sprintf("(%s) %s", letters[1:ncol(dat)], colnames(dat)) note <- paste(note, collapse = "; ") colnames(dat) <- sprintf("(%s)", letters[1:ncol(dat)]) datasummary_correlation(dat, notes = note) # `datasummary_correlation_format`: custom function with formatting dat <- mtcars[, c("mpg", "hp", "disp")] cor_fun <- function(x) { out <- cor(x, method = "kendall") datasummary_correlation_format( out, fmt = 2, upper_triangle = "x", diagonal = ".") } datasummary_correlation(dat, method = cor_fun) # use kableExtra and psych to color significant cells library(psych) library(kableExtra) dat <- mtcars[, c("vs", "hp", "gear")] cor_fun <- function(dat) { # compute correlations and format them correlations <- data.frame(cor(dat)) correlations <- datasummary_correlation_format(correlations, fmt = 2) # calculate pvalues using the `psych` package pvalues <- psych::corr.test(dat)$p # use `kableExtra::cell_spec` to color significant cells for (i in 1:nrow(correlations)) { for (j in 1:ncol(correlations)) { if (pvalues[i, j] < 0.05 && i != j) { correlations[i, j] <- cell_spec(correlations[i, j], background = "pink") } } } return(correlations) } # The `escape=FALSE` is important here! datasummary_correlation(dat, method = cor_fun, escape = FALSE)
Arel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v103.i01")}.'
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