report

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.

Installation

install.packages("report")

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

library("report")

Documentation

Report all the things

General Workflow

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%)

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]

t-tests and 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])

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.sided

ANOVAs

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.

Generalized Linear Models (GLMs)

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

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

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).

Other types of reports

Specific parts

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]

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.")

# 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 sample

report_sample(iris,by="Species")

Report system and packages

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>.

Credits

citation("report")Tociteinpublicationsuse:Makowski,D.,Lüdecke,D.,Patil,I.,Thériault,R.,Ben-Shachar,M.S.,&Wiernik, B.M. (2023).AutomatedResultsReportingasaPracticalTooltoImproveReproducibilityandMethodologicalBestPracticesAdoption.CRAN.Availablefromhttps://easystats.github.io/report/doi:.ABibTeXentryforLaTeXusersis@Article{,title= {AutomatedResultsReportingasaPracticalTooltoImproveReproducibilityandMethodologicalBestPracticesAdoption},author= {DominiqueMakowskiandDanielLüdeckeandIndrajeetPatilandRémiThériaultandMattanS.Ben-ShacharandBrentonM.Wiernik},year= {2023},journal= {CRAN},url= {https://easystats.github.io/report/},  }

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