“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.
## 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 Speciesversicolor is positive and can be considered as very large and significant (beta = 1.12, SE = 0.12, 95% CI [0.88, 1.37], std. beta = 1.12, p < .001).
## - The effect of Speciesvirginica is positive and can be considered as very large and significant (beta = 1.91, SE = 0.12, 95% CI [1.66, 2.16], std. beta = 1.91, p < .001).
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!
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 to_text(), or as a table, using to_table(). Moreover, you can access a more detailed (but less digested) version of the report using to_fulltext() and to_fulltable(). Finally, to_values() makes it easy to access all the internals of a model.
Features
The report() function works on a variety of models, as well as dataframes:
## 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:
You can also create tables with the table_short() and table_long() functions:
# Table report for a linear model
lm(Sepal.Length ~ Petal.Length + Species, data=iris) %>%
report() %>%
table_short()
## Parameter | Coefficient | CI_low | CI_high | p | Std_Coefficient | Fit
## ----------------------------------------------------------------------------------
## (Intercept) | 1.50 | 1.12 | 1.87 | 0.00 | 1.50 |
## Petal.Length | 1.93 | 1.66 | 2.20 | 0.00 | 1.93 |
## Speciesversicolor | -1.93 | -2.40 | -1.47 | 0.00 | -1.93 |
## Speciesvirginica | -2.56 | -3.21 | -1.90 | 0.00 | -2.56 |
## | | | | | |
## R2 | | | | | | 0.84
## R2 (adj.) | | | | | | 0.83Examples
Supported Packages
Currently supported objects by report include cor.test, t.test, correlation, glm, lme4::merMod, rstanarm::stanreg, modelbased.
Miscellaneous
Report participants details
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 = 30.00, Mean = 30.00, SD = 16.02, Median = 22.50, MAD = 1.48, range: [21, 54], Skewness = 1.98, Kurtosis = -0.67; 50.00% females) were recruited in the study by means of torture and coercion."
