“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)
# We fitted a linear model (estimated using OLS) to predict Sepal.Length with
# Species (formula: Sepal.Length ~ Species). The model explains a statistically
# 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
# Species = setosa, is at 5.01 (95% CI [4.86, 5.15], t(147) = 68.76, p < .001).
# Within this model:
#
# - The effect of Species [versicolor] is statistically significant and positive
# (beta = 0.93, 95% CI [0.73, 1.13], t(147) = 9.03, p < .001; Std. beta = 1.12,
# 95% CI [0.88, 1.37])
# - The effect of Species [virginica] is statistically significant and positive
# (beta = 1.58, 95% CI [1.38, 1.79], t(147) = 15.37, p < .001; Std. beta = 1.91,
# 95% CI [1.66, 2.16])
#
# Standardized parameters were obtained by fitting the model on a standardized
# version of the dataset. 95% Confidence Intervals (CIs) and p-values were
# computed using a Wald t-distribution approximation.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 onceLoad the package every time you start R
library("report")Tip
Instead of
library(report), uselibrary(easystats). This will make all features of the easystats-ecosystem available.To stay updated, use
easystats::install_latest().
The package documentation can be found here.
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)# The data contains 150 observations of the following 5 variables:
#
# - Sepal.Length: n = 150, Mean = 5.84, SD = 0.83, Median = 5.80, MAD = 1.04,
# range: [4.30, 7.90], Skewness = 0.31, Kurtosis = -0.55, 0% missing
# - Sepal.Width: n = 150, Mean = 3.06, SD = 0.44, Median = 3.00, MAD = 0.44,
# range: [2, 4.40], Skewness = 0.32, Kurtosis = 0.23, 0% missing
# - Petal.Length: n = 150, 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: n = 150, 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, namely setosa (n = 50, 33.33%), versicolor (n = 50,
# 33.33%) and virginica (n = 50, 33.33%)
These reports nicely work within the tidyverse workflow:
iris %>%
select(-starts_with("Sepal")) %>%
group_by(Species) %>%
report() %>%
summary()# The data contains 150 observations, grouped by Species, of the following 3
# variables:
#
# - setosa (n = 50):
# - Petal.Length: Mean = 1.46, SD = 0.17, range: [1, 1.90]
# - Petal.Width: Mean = 0.25, SD = 0.11, range: [0.10, 0.60]
#
# - versicolor (n = 50):
# - Petal.Length: Mean = 4.26, SD = 0.47, range: [3, 5.10]
# - Petal.Width: Mean = 1.33, SD = 0.20, range: [1, 1.80]
#
# - virginica (n = 50):
# - Petal.Length: Mean = 5.55, SD = 0.55, range: [4.50, 6.90]
# - Petal.Width: Mean = 2.03, SD = 0.27, range: [1.40, 2.50]
Reports can be used to automatically format tests like t-tests or correlations.
report(t.test(mtcars$mpg ~ mtcars$am))# Effect sizes were labelled following Cohen's (1988) recommendations.
#
# The Welch Two Sample t-test testing the difference of mtcars$mpg by mtcars$am
# (mean in group 0 = 17.15, mean in group 1 = 24.39) suggests that the effect is
# negative, statistically significant, and large (difference = -7.24, 95% CI
# [-11.28, -3.21], t(18.33) = -3.77, p = 0.001; Cohen's d = -1.41, 95% CI [-2.26,
# -0.53])
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()
# Pearson's product-moment correlation
#
# Parameter1 | Parameter2 | r | 95% CI | t(148) | p
# -----------------------------------------------------------------------------
# iris$Sepal.Length | iris$Sepal.Width | -0.12 | [-0.27, 0.04] | -1.44 | 0.152
#
# Alternative hypothesis: two.sidedThis works great with ANOVAs, as it includes effect sizes and their interpretation.
aov(Sepal.Length ~ Species, data = iris) %>%
report()# The ANOVA (formula: Sepal.Length ~ Species) suggests that:
#
# - The main effect of Species is statistically significant and large (F(2, 147)
# = 119.26, p < .001; Eta2 = 0.62, 95% CI [0.54, 1.00])
#
# Effect sizes were labelled following Field's (2013) recommendations.
Reports are also compatible with GLMs, such as this logistic regression:
model <- glm(vs ~ mpg * drat, data = mtcars, family = "binomial")
report(model)# We fitted a logistic model (estimated using ML) to predict vs with mpg and drat
# (formula: vs ~ mpg * drat). The model's explanatory power is substantial
# (Tjur's R2 = 0.51). The model's intercept, corresponding to mpg = 0 and drat =
# 0, is at -33.43 (95% CI [-77.90, 3.25], p = 0.083). Within this model:
#
# - The effect of mpg is statistically non-significant and positive (beta = 1.79,
# 95% CI [-0.10, 4.05], p = 0.066; Std. beta = 3.63, 95% CI [1.36, 7.50])
# - The effect of drat is statistically non-significant and positive (beta =
# 5.96, 95% CI [-3.75, 16.26], p = 0.205; Std. beta = -0.36, 95% CI [-1.96,
# 0.98])
# - The effect of mpg × drat is statistically non-significant and negative (beta
# = -0.33, 95% CI [-0.83, 0.15], p = 0.141; Std. beta = -1.07, 95% CI [-2.66,
# 0.48])
#
# Standardized parameters were obtained by fitting the model on a standardized
# version of the dataset. 95% Confidence Intervals (CIs) and p-values were
# computed using a Wald z-distribution approximation.
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)# We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)
# to predict Sepal.Length with Petal.Length (formula: Sepal.Length ~
# Petal.Length). The model included Species as random effect (formula: ~1 |
# Species). The model's total explanatory power is substantial (conditional R2 =
# 0.97) and the part related to the fixed effects alone (marginal R2) is of 0.66.
# The model's intercept, corresponding to Petal.Length = 0, is at 2.50 (95% CI
# [1.19, 3.82], t(146) = 3.75, p < .001). Within this model:
#
# - The effect of Petal Length is statistically significant and positive (beta =
# 0.89, 95% CI [0.76, 1.01], t(146) = 13.93, p < .001; Std. beta = 1.89, 95% CI
# [1.63, 2.16])
#
# Standardized parameters were obtained by fitting the model on a standardized
# version of the dataset. 95% Confidence Intervals (CIs) and p-values were
# computed using a Wald t-distribution approximation.
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)# We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
# of 1000 iterations and a warmup of 500) to predict mpg with qsec and wt
# (formula: mpg ~ qsec + wt). Priors over parameters were all set as normal (mean
# = 0.00, SD = 8.43; mean = 0.00, SD = 15.40) distributions. The model's
# explanatory power is substantial (R2 = 0.81, 95% CI [0.71, 0.90], adj. R2 =
# 0.79). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.67
# (95% CI [8.34, 30.67]). Within this model:
#
# - The effect of qsec (Median = 0.93, 95% CI [0.39, 1.51]) has a 99.90%
# probability of being positive (> 0), 98.65% of being significant (> 0.30), and
# 0.30% of being large (> 1.81). The estimation successfully converged (Rhat =
# 1.000) and the indices are reliable (ESS = 1762)
# - The effect of wt (Median = -5.05, 95% CI [-6.01, -4.05]) has a 100.00%
# probability of being negative (< 0), 100.00% of being significant (< -0.30),
# and 100.00% of being large (< -1.81). The estimation successfully converged
# (Rhat = 1.000) and the indices are reliable (ESS = 2213)
#
# Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
# framework, we report the median of the posterior distribution and its 95% CI
# (Highest Density Interval), along the probability of direction (pd), the
# probability of significance and the probability of being large. The thresholds
# beyond which the effect is considered as significant (i.e., non-negligible) and
# large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
# outcome's SD). Convergence and stability of the Bayesian sampling has been
# assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
# Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
One can, for complex reports, directly access the pieces of the reports:
model <- lm(Sepal.Length ~ Species, data = iris)
report_model(model)
# linear model (estimated using OLS) to predict Sepal.Length with Species (formula: Sepal.Length ~ Species)
report_performance(model)
# The model explains a statistically significant and substantial proportion of
# variance (R2 = 0.62, F(2, 147) = 119.26, p < .001, adj. R2 = 0.61)
report_statistics(model)
# beta = 5.01, 95% CI [4.86, 5.15], t(147) = 68.76, p < .001; Std. beta = -1.01, 95% CI [-1.18, -0.84]
# beta = 0.93, 95% CI [0.73, 1.13], t(147) = 9.03, p < .001; Std. beta = 1.12, 95% CI [0.88, 1.37]
# beta = 1.58, 95% CI [1.38, 1.79], t(147) = 15.37, p < .001; Std. beta = 1.91, 95% CI [1.66, 2.16]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."
)# Four participants (Mean age = 30.0, SD = 16.0, range: [21, 54]; Sex:
# 50.0% females, 50.0% males, 0.0% other) were recruited in the study by
# means of torture and coercion.
Report can also help you create a sample description table (also referred to as Table 1).
report_sample(iris, by = "Species")| Variable | setosa (n=50) | versicolor (n=50) | virginica (n=50) | Total (n=150) |
|---|---|---|---|---|
| Mean Sepal.Length (SD) | 5.01 (0.35) | 5.94 (0.52) | 6.59 (0.64) | 5.84 (0.83) |
| Mean Sepal.Width (SD) | 3.43 (0.38) | 2.77 (0.31) | 2.97 (0.32) | 3.06 (0.44) |
| Mean Petal.Length (SD) | 1.46 (0.17) | 4.26 (0.47) | 5.55 (0.55) | 3.76 (1.77) |
| Mean Petal.Width (SD) | 0.25 (0.11) | 1.33 (0.20) | 2.03 (0.27) | 1.20 (0.76) |
Finally, report includes some functions to help you write the data analysis paragraph about the tools used.
report(sessionInfo())# Analyses were conducted using the R Statistical language (version 4.4.1; R Core
# Team, 2024) on Windows 11 x64 (build 22631), using the packages lme4 (version
# 1.1.35.5; Bates D et al., 2015), Matrix (version 1.7.0; Bates D et al., 2024),
# Rcpp (version 1.0.13; Eddelbuettel D et al., 2024), rstanarm (version 2.32.1;
# Goodrich B et al., 2024), report (version 0.5.9; Makowski D et al., 2023) and
# dplyr (version 1.1.4; Wickham H et al., 2023).
#
# References
# ----------
# - Bates D, Mächler M, Bolker B, Walker S (2015). "Fitting Linear Mixed-Effects
# Models Using lme4." _Journal of Statistical Software_, *67*(1), 1-48.
# doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
# - Bates D, Maechler M, Jagan M (2024). _Matrix: Sparse and Dense Matrix Classes
# and Methods_. R package version 1.7-0,
# <https://CRAN.R-project.org/package=Matrix>.
# - Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I,
# Bates D, Chambers J (2024). _Rcpp: Seamless R and C++ Integration_. R package
# version 1.0.13, <https://CRAN.R-project.org/package=Rcpp>. Eddelbuettel D,
# François R (2011). "Rcpp: Seamless R and C++ Integration." _Journal of
# Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08
# <https://doi.org/10.18637/jss.v040.i08>. Eddelbuettel D (2013). _Seamless R and
# C++ Integration with Rcpp_. Springer, New York. doi:10.1007/978-1-4614-6868-4
# <https://doi.org/10.1007/978-1-4614-6868-4>, ISBN 978-1-4614-6867-7.
# Eddelbuettel D, Balamuta J (2018). "Extending R with C++: A Brief Introduction
# to Rcpp." _The American Statistician_, *72*(1), 28-36.
# doi:10.1080/00031305.2017.1375990
# <https://doi.org/10.1080/00031305.2017.1375990>.
# - Goodrich B, Gabry J, Ali I, Brilleman S (2024). "rstanarm: Bayesian applied
# regression modeling via Stan." R package version 2.32.1,
# <https://mc-stan.org/rstanarm/>. Brilleman S, Crowther M, Moreno-Betancur M,
# Buros Novik J, Wolfe R (2018). "Joint longitudinal and time-to-event models via
# Stan." StanCon 2018. 10-12 Jan 2018. Pacific Grove, CA, USA.,
# <https://github.com/stan-dev/stancon_talks/>.
# - Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B (2023).
# "Automated Results Reporting as a Practical Tool to Improve Reproducibility and
# Methodological Best Practices Adoption." _CRAN_.
# <https://easystats.github.io/report/>.
# - R Core Team (2024). _R: A Language and Environment for Statistical
# Computing_. R Foundation for Statistical Computing, Vienna, Austria.
# <https://www.R-project.org/>.
# - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar
# of Data Manipulation_. R package version 1.1.4,
# <https://CRAN.R-project.org/package=dplyr>.
If you like it, you can put a star on this repo, and cite the package as follows:
citation("report")
To cite in publications use:
Makowski, D., Lüdecke, D., Patil, I., Thériault, R., Ben-Shachar,
M.S., & Wiernik, B.M. (2023). Automated Results Reporting as a
Practical Tool to Improve Reproducibility and Methodological Best
Practices Adoption. CRAN. Available from
https://easystats.github.io/report/ doi: .
A BibTeX entry for LaTeX users is
@Article{,
title = {Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption},
author = {Dominique Makowski and Daniel Lüdecke and Indrajeet Patil and Rémi Thériault and Mattan S. Ben-Shachar and Brenton M. Wiernik},
year = {2023},
journal = {CRAN},
url = {https://easystats.github.io/report/},
}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!
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.
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