report
“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 <- lm(Sepal.Length ~ Species, data = iris)
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
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().
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.) | | | | | 0.83t-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 <- 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.")
## [1] "Four participants (Mean age = 30.0, SD = 16.0, range = [21, 54]; 50.0% females) were recruited in the study by means of torture and coercion."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.