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https://github.com/easystats/report

:scroll: :tada: Automated reporting of objects in R
https://github.com/easystats/report

anovas apa automated-report-generation automatic bayesian describe easystats hacktoberfest manuscript models r r-package report reporting reports rstats scientific statsmodels

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:scroll: :tada: Automated reporting of objects in R

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README 

        
---

output: github_document

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# report 



```{r, message=FALSE, warning=FALSE, echo=FALSE}

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)

```



[![R-CMD-check](https://github.com/easystats/report/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/report/actions)

[![CRAN](http://www.r-pkg.org/badges/version/report)](https://cran.r-project.org/package=report)

[![r-universe](https://easystats.r-universe.dev/badges/report)](https://easystats.r-universe.dev/report)

[![downloads](http://cranlogs.r-pkg.org/badges/report)](https://cran.r-project.org/package=report)

[![total](https://cranlogs.r-pkg.org/badges/grand-total/report)](https://cranlogs.r-pkg.org/)

[![stars](https://img.shields.io/github/stars/easystats/report?style=social)](https://github.com/easystats/report/stargazers)



***"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](https://apastyle.apa.org/)'s style), ensuring **standardization** and **quality** in results reporting.



```{r}

library(report)



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

report(model)

```



## Installation



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



```{r eval=FALSE}

install.packages("report")

```



If you would instead like to experiment with the development version, you can

download it from `GitHub`:



```{r eval=FALSE}

install.packages("remotes")

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

```



Load the package every time you start R



```{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**](https://easystats.github.io/report/). 



## Report all the things 



All the things meme by Allie Brosh



### 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.



[![workflow](man/figures/workflow.png)](https://easystats.github.io/report/)



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



```{r, eval=FALSE}

report(iris)

```

```{r echo=FALSE}

print(report(iris), width = 80)

```



These reports nicely work within the [*tidyverse*](https://github.com/tidyverse) workflow:



```{r, eval=FALSE}

iris %>%

  select(-starts_with("Sepal")) %>%

  group_by(Species) %>%

  report() %>%

  summary()

```



```{r echo=FALSE}

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.



```{r, eval=FALSE}

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

```



```{r echo=FALSE}

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:



```{r, error=TRUE}

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.



```{r, eval=FALSE}

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

  report()

```



```{r echo=FALSE}

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**:



```{r, eval=FALSE}

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



report(model)

```



```{r echo=FALSE}

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:



```{r, eval=FALSE}

library(lme4)



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



report(model)

```



```{r echo=FALSE}

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**](https://easystats.github.io/bayestestR/reference/sexit.html) framework, which combines clarity, precision and usefulness.



```{r, eval=FALSE}

library(rstanarm)



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



report(model)

```



```{r echo=FALSE}

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:



```{r}

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.



```{r, eval=FALSE}

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."

)

```



```{r echo=F}

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**).



```{r, eval=FALSE}

report_sample(iris, by = "Species")

```



```{r, echo=FALSE}

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

```



### Report system and packages



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



```{r, eval=FALSE}

report(sessionInfo())

```



```{r echo=FALSE}

report(sessionInfo()) %>%

  print(width = 80)

```



## Credits



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



```{r, citation, comment=""}

citation("report")

```



## Contribute



***report* is a young package in need of affection**. You can easily be a part of the [developing](https://github.com/easystats/report/blob/master/.github/CONTRIBUTING.md) 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](https://github.com/easystats/report/blob/master/.github/CONTRIBUTING.md)). 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](https://easystats.github.io/report/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.