---

output: github_document

report

knitr::opts_chunk$set(
  collapse = TRUE,
  dpi = 300,
  fig.path = "man/figures/",
  comment = "#",
  message = FALSE,
  warning = FALSE
)

options(
  knitr.kable.NA = "",
  digits = 4,
  width = 80
)

library(dplyr)
library(report)

“From R to your manuscript”

report’s primary goal is to bridge the gap between R’s output and the formatted results contained in your manuscript. It automatically produces reports of models and data frames according to best practices guidelines (e.g., APA’s style), ensuring standardization and quality in results reporting.

library(report)

model <- lm(Sepal.Length ~ Species, data = iris)
report(model)

Installation

The package is available on CRAN and can be downloaded by running:

install.packages("report")

If you would instead like to experiment with the development version, you can
download it from GitHub:

install.packages("remotes")
remotes::install_github("easystats/report") # You only need to do that once

Load the package every time you start R

library("report")

Tip

Instead of library(report), use library(easystats).
This will make all features of the easystats-ecosystem available.

To stay updated, use easystats::install_latest().

Documentation

The package documentation can be found here.

Report all the things

General Workflow

The report package works in a two step fashion. First, you create a report object with the report() function. Then, this report object can be displayed either textually (the default output) or as a table, using as.data.frame(). Moreover, you can also access a more digest and compact version of the report using summary() on the report object.

The report() function works on a variety of models, as well as other objects such as dataframes:

report(iris)

print(report(iris), width = 80)

These reports nicely work within the tidyverse workflow:

iris %>%
  select(-starts_with("Sepal")) %>%
  group_by(Species) %>%
  report() %>%
  summary()

iris %>%
  select(-starts_with("Sepal")) %>%
  group_by(Species) %>%
  report() %>%
  summary() %>%
  print(width = 80)

t-tests and correlations

Reports can be used to automatically format tests like t-tests or correlations.

report(t.test(mtcars$mpg ~ mtcars$am))

t.test(mtcars$mpg ~ mtcars$am) %>%
  report() %>%
  print(width = 80)

As mentioned, you can also create tables with the as.data.frame() functions, like for example with this correlation test:

cor.test(iris$Sepal.Length, iris$Sepal.Width) %>%
  report() %>%
  as.data.frame()

ANOVAs

This works great with ANOVAs, as it includes effect sizes and their interpretation.

aov(Sepal.Length ~ Species, data = iris) %>%
  report()

aov(Sepal.Length ~ Species, data = iris) %>%
  report() %>%
  print(width = 80)

Generalized Linear Models (GLMs)

Reports are also compatible with GLMs, such as this logistic regression:

model <- glm(vs ~ mpg * drat, data = mtcars, family = "binomial")

report(model)

glm(vs ~ mpg * drat, data = mtcars, family = "binomial") %>%
  report() %>%
  print(width = 80)

Mixed Models

Mixed models, whose popularity and usage is exploding, can also be reported:

library(lme4)

model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)

report(model)

library(lme4)

lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris) %>%
  report() %>%
  print(width = 80)

Bayesian Models

Bayesian models can also be reported using the new SEXIT framework, which combines clarity, precision and usefulness.

library(rstanarm)

model <- stan_glm(mpg ~ qsec + wt, data = mtcars)

report(model)

options(mc.cores = parallel::detectCores())
library(rstanarm)

model <- stan_glm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 1000) %>%
  report() %>%
  print(width = 80)

Other types of reports

Specific parts

One can, for complex reports, directly access the pieces of the reports:

model <- lm(Sepal.Length ~ Species, data = iris)

report_model(model)

report_performance(model)

report_statistics(model)

Report participants’ details

This can be useful to complete the Participants paragraph of your manuscript.

data <- data.frame(
  "Age" = c(22, 23, 54, 21),
  "Sex" = c("F", "F", "M", "M")
)

paste(
  report_participants(data, spell_n = TRUE),
  "were recruited in the study by means of torture and coercion."
)

data <- data.frame(
  "Age" = c(22, 23, 54, 21),
  "Sex" = c("F", "F", "M", "M")
)

paste(
  report_participants(data, spell_n = TRUE),
  "were recruited in the study by means of torture and coercion."
) |>
  insight::format_message() %>%
  cat()

Report sample

Report can also help you create a sample description table (also referred to as Table 1).

report_sample(iris, group_by = "Species")

knitr::kable(report_sample(iris, group_by = "Species"))

Report system and packages

Finally, report includes some functions to help you write the data analysis paragraph about the tools used.

report(sessionInfo())

report(sessionInfo()) %>%
  print(width = 80)

Credits

If you like it, you can put a star on this repo, and cite the package as follows:

citation("report")

Contribute

report is a young package in need of affection. You can easily be a part of the developing community of this open-source software and improve science! Don’t be shy, try to code and submit a pull request (See the contributing guide). Even if it’s not perfect, we will help you make it great!

Code of Conduct

Please note that the report project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.