report

by easystats

easystats / report

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

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report

CRAN downloads

“From R to 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 dataframes according to best practice guidelines (e.g., APA’s style guide), ensuring standardization and quality in results reporting.
library(report)

model

## We fitted a linear model (estimated using OLS) to predict Sepal.Length with Species (formula =

Sepal.Length ~ Species). Standardized parameters were obtained by fitting the model on a

standardized version of the dataset. Effect sizes were labelled following Funder's (2019)

recommendations.

The model explains a significant and substantial proportion of variance (R2 = 0.62, F(2, 147) =

119.26, p < .001, adj. R2 = 0.61). The model's intercept, corresponding to Sepal.Length = 0 and

Species = setosa, is at 5.01 (SE = 0.07, 95% CI [4.86, 5.15], p < .001). Within this model:

- The effect of (Intercept) is positive and can be considered as very large and significant (b =

5.01, SE = 0.07, 95% CI [4.86, 5.15], std. beta = -1.01, p < .001).

- The effect of Species [versicolor] is positive and can be considered as very large and

significant (b = 0.93, SE = 0.10, 95% CI [0.73, 1.13], std. beta = 1.12, p < .001).

- The effect of Species [virginica] is positive and can be considered as very large and significant

(b = 1.58, SE = 0.10, 95% CI [1.38, 1.79], std. beta = 1.91, p < .001).

Documentation

Documentation Blog Features

The package documentation can be found here. Check-out these tutorials:

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 by doing the following:

  • Create or check existing issues to report, replicate, understand or solve some bugs.
  • Create or check existing issues to suggest or discuss a new feature.
  • Check existing issues to see things that we’d like to implement, but where help is needed to do it.
  • Check existing issues to give your opinion and participate in package’s design discussions.

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!

Installation

Run the following:

install.packages("devtools")
devtools::install_github("easystats/report")
library("report")

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 (which takes different arguments depending on the type of object you are reporting). Then, this report object can be displayed either textually, using
text_short()
, or as a table, using
table_short()
. Moreover, you can access a more detailed (but less digested) version of the report using
text_long()
and
table_short()
.

workflow

The

report()
function works on a variety of models, as well as other objects such as dataframes:
report(iris)
## The data contains 150 observations of the following variables:
## 
##   - Sepal.Length: Mean = 5.84, SD = 0.83, Median = 5.80, MAD = 1.04, range: [4.30, 7.90], Skewness = 0.31, Kurtosis = -0.57, 0 missing
##   - Sepal.Width: Mean = 3.06, SD = 0.44, Median = 3.00, MAD = 0.44, range: [2, 4.40], Skewness = 0.32, Kurtosis = 0.18, 0 missing
##   - Petal.Length: Mean = 3.76, SD = 1.77, Median = 4.35, MAD = 1.85, range: [1, 6.90], Skewness = -0.27, Kurtosis = -1.40, 0 missing
##   - Petal.Width: Mean = 1.20, SD = 0.76, Median = 1.30, MAD = 1.04, range: [0.10, 2.50], Skewness = -0.10, Kurtosis = -1.34, 0 missing
##   - Species: 3 levels: setosa (n = 50, 33.33%); versicolor (n = 50, 33.33%) and virginica (n = 50, 33.33%)

These reports nicely work within the tidyverse workflow:

cor.test(iris$Sepal.Length, iris$Petal.Length) %>% report()
## The Pearson's product-moment correlation between iris$Sepal.Length and iris$Petal.Length is positive, significant and very large (r = 0.87, 95% CI [0.83, 0.91], t(148) = 21.65, p < .001).

You can also create tables with the

table_short()
and
table_long()
functions:
lm(Sepal.Length ~ Petal.Length + Species, data = iris) %>% report() %>% 
    table_short()
## Parameter            | Coefficient |             CI |      p | Coefficient (std.) |  Fit
## ----------------------------------------------------------------------------------------
## (Intercept)          |        3.68 | [ 3.47,  3.89] | < .001 |               1.50 |     
## Petal.Length         |        0.90 | [ 0.78,  1.03] | < .001 |               1.93 |     
## Species [versicolor] |       -1.60 | [-1.98, -1.22] | < .001 |              -1.93 |     
## Species [virginica]  |       -2.12 | [-2.66, -1.58] | < .001 |              -2.56 |     
##                      |             |                |        |                    |     
## R2                   |             |                |        |                    | 0.84
## R2 (adj.)            |             |                |        |                    | 0.83

t-tests and correlations

t.test(mtcars$mpg ~ mtcars$am) %>% report()
## The Welch Two Sample t-test suggests that the difference of mtcars$mpg by mtcars$am (mean in group
## 0 = 17.15, mean in group 1 = 24.39) is significant (difference = -7.24, 95% CI [-11.28, -3.21],
## t(18.33) = -3.77, p < .01) and can be considered as very large (Cohen's d = -1.76).

Report participants details

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

data 

Credits

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

  • Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2020). The report package for R: Ensuring the use of best practices for results reporting. CRAN.

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