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Generate a correlation table for all numeric variables in your dataset.

Source: R/datasummary_correlation.R
datasummary_correlation.Rd

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://vincentarelbundock.github.io/modelsummary/

  • https://vincentarelbundock.github.io/modelsummary/articles/datasummary.html

Usage

datasummary_correlation(
  data,
  output = "default",
  method = "pearson",
  fmt = 2,
  align = NULL,
  add_rows = NULL,
  add_columns = NULL,
  title = NULL,
  notes = NULL,
  escape = TRUE,
  ...
)

Arguments

data

A data.frame (or tibble)

output

filename or object type (character string)

  • Supported filename extensions: .docx, .html, .tex, .md, .txt, .png, .jpg.

  • Supported object types: "default", "html", "markdown", "latex", "latex_tabular", "data.frame", "gt", "kableExtra", "huxtable", "flextable", "DT", "jupyter". The "modelsummary_list" value produces a lightweight object which can be saved and fed back to the modelsummary function.

  • Warning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the 'Details' section.

  • LaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the 'Details' section.

  • The default output formats and table-making packages can be modified with global options. See the 'Details' section.

method

character or function

  • character: "pearson", "kendall", "spearman", or "pearspear" (Pearson correlations above and Spearman correlations below the diagonal)

  • function: takes a data.frame with numeric columns and returns a square matrix or data.frame with unique row.names and colnames corresponding to variable names. Note that the datasummary_correlation_format can often be useful for formatting the output of custom correlation functions.

fmt

how to format numeric values: integer, user-supplied function, or modelsummary function.

  • Integer: Number of decimal digits

  • User-supplied functions:

    • Any function which accepts a numeric vector and returns a character vector of the same length.

  • modelsummary functions:

    • fmt = fmt_significant(2): Two significant digits (at the term-level)

    • fmt = fmt_sprintf("%.3f"): See ?sprintf

    • fmt = fmt_identity(): unformatted raw values

align

A string with a number of characters equal to the number of columns in the table (e.g., align = "lcc"). Valid characters: l, c, r, d.

  • "l": left-aligned column

  • "c": centered column

  • "r": right-aligned column

  • "d": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. These commands must appear in the LaTeX preamble (they are added automatically when compiling Rmarkdown documents to PDF):

    • \usepackage{booktabs}

    • \usepackage{siunitx}

    • \newcolumntype{d}{S[ input-open-uncertainty=, input-close-uncertainty=, parse-numbers = false, table-align-text-pre=false, table-align-text-post=false ]}

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

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. This setting does not affect captions or notes.

...

other parameters are passed through to the table-making packages.

Global Options

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.

Model labels: default column names

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"

Table-making packages

modelsummary supports 4 table-making packages: kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. For example, 3 of those packages can export to LaTeX. 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_latex = 'gt')

options(modelsummary_factory_word = 'huxtable')

options(modelsummary_factory_png = 'gt')

Table themes

Change the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette: https://vincentarelbundock.github.io/modelsummary/articles/appearance.html

  • modelsummary_theme_gt

  • modelsummary_theme_kableExtra

  • modelsummary_theme_huxtable

  • modelsummary_theme_flextable

  • modelsummary_theme_dataframe

Model extraction functions

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 = "broom")

options(modelsummary_get = "easystats")

options(modelsummary_get = "all")

Formatting numeric entries

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")

References

Arel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. doi:10.18637/jss.v103.i01 .'

Examples

if (FALSE) {
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)

# 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)
}