Report R environment (packages, system, etc.)
Usage
# S3 method for sessionInfo
report(x, ...)
report_packages(session = NULL, include_R = TRUE, ...)
cite_packages(session = NULL, include_R = TRUE, ...)
report_system(session = NULL)Arguments
- x
The R object that you want to report (see list of of supported objects above).
- ...
Arguments passed to or from other methods.
- session
A sessionInfo object.
- include_R
Include R in the citations.
Value
For report_packages, a data frame of class with information on package
name, version and citation.
An object of class report().
Examples
library(report)
session <- sessionInfo()
r <- report(session)
r
#> Analyses were conducted using the R Statistical language (version 4.3.0; R Core
#> Team, 2023) on Ubuntu 22.04.2 LTS, using the packages Matrix (version 1.5.4.1;
#> Bates D et al., 2023), lme4 (version 1.1.33; Bates D et al., 2015), brms
#> (version 2.19.0; Bürkner P, 2017), Rcpp (version 1.0.10; Eddelbuettel D,
#> François R, 2011), performance (version 0.10.3; Lüdecke D et al., 2021),
#> bayestestR (version 0.13.1; Makowski D et al., 2019), report (version 0.5.7.4;
#> Makowski D et al., 2023), lavaan (version 0.6.15; Rosseel Y, 2012) and dplyr
#> (version 1.1.2; Wickham H et al., 2023).
#>
#> References
#> ----------
#> - Bates D, Maechler M, Jagan M (2023). _Matrix: Sparse and Dense Matrix Classes
#> and Methods_. R package version 1.5-4.1,
#> <https://Matrix.R-forge.R-project.org>.
#> - 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>.
#> - Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using
#> Stan.” _Journal of Statistical Software_, *80*(1), 1-28.
#> doi:10.18637/jss.v080.i01 <https://doi.org/10.18637/jss.v080.i01>. Bürkner P
#> (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” _The R
#> Journal_, *10*(1), 395-411. doi:10.32614/RJ-2018-017
#> <https://doi.org/10.32614/RJ-2018-017>. Bürkner P (2021). “Bayesian Item
#> Response Modeling in R with brms and Stan.” _Journal of Statistical Software_,
#> *100*(5), 1-54. doi:10.18637/jss.v100.i05
#> <https://doi.org/10.18637/jss.v100.i05>.
#> - 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 JJ (2018). “Extending extitR with extitC++: A Brief
#> Introduction to extitRcpp.” _The American Statistician_, *72*(1), 28-36.
#> doi:10.1080/00031305.2017.1375990
#> <https://doi.org/10.1080/00031305.2017.1375990>.
#> - Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021).
#> “performance: An R Package for Assessment, Comparison and Testing of
#> Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139.
#> doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#> - Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects
#> and their Uncertainty, Existence and Significance within the Bayesian
#> Framework.” _Journal of Open Source Software_, *4*(40), 1541.
#> doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>,
#> <https://joss.theoj.org/papers/10.21105/joss.01541>.
#> - 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 (2023). _R: A Language and Environment for Statistical
#> Computing_. R Foundation for Statistical Computing, Vienna, Austria.
#> <https://www.R-project.org/>.
#> - Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.”
#> _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02
#> <https://doi.org/10.18637/jss.v048.i02>.
#> - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar
#> of Data Manipulation_. https://dplyr.tidyverse.org,
#> https://github.com/tidyverse/dplyr.
summary(r)
#> The analysis was done using the R Statistical language (v4.3.0; R Core Team,
#> 2023) on Ubuntu 22.04.2 LTS, using the packages Matrix (v1.5.4.1), lme4
#> (v1.1.33), brms (v2.19.0), Rcpp (v1.0.10), performance (v0.10.3), bayestestR
#> (v0.13.1), report (v0.5.7.4), lavaan (v0.6.15) and dplyr (v1.1.2).
as.data.frame(r)
#> Package | Version | Reference
#> ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#> Matrix | 1.5.4.1 | Bates D, Maechler M, Jagan M (2023). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.5-4.1, <https://Matrix.R-forge.R-project.org>.
#> R | 4.3.0 | R Core Team (2023). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
#> Rcpp | 1.0.10 | 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 JJ (2018). “Extending extitR with extitC++: A Brief Introduction to extitRcpp.” _The American Statistician_, *72*(1), 28-36. doi:10.1080/00031305.2017.1375990 <https://doi.org/10.1080/00031305.2017.1375990>.
#> bayestestR | 0.13.1 | Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework.” _Journal of Open Source Software_, *4*(40), 1541. doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>, <https://joss.theoj.org/papers/10.21105/joss.01541>.
#> brms | 2.19.0 | Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” _Journal of Statistical Software_, *80*(1), 1-28. doi:10.18637/jss.v080.i01 <https://doi.org/10.18637/jss.v080.i01>. Bürkner P (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” _The R Journal_, *10*(1), 395-411. doi:10.32614/RJ-2018-017 <https://doi.org/10.32614/RJ-2018-017>. Bürkner P (2021). “Bayesian Item Response Modeling in R with brms and Stan.” _Journal of Statistical Software_, *100*(5), 1-54. doi:10.18637/jss.v100.i05 <https://doi.org/10.18637/jss.v100.i05>.
#> dplyr | 1.1.2 | Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.
#> lavaan | 0.6.15 | Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02 <https://doi.org/10.18637/jss.v048.i02>.
#> lme4 | 1.1.33 | 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>.
#> performance | 0.10.3 | Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021). “performance: An R Package for Assessment, Comparison and Testing of Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139. doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#> report | 0.5.7.4 | 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/>.
summary(as.data.frame(r))
#> Package | Version
#> ---------------------
#> Matrix | 1.5.4.1
#> R | 4.3.0
#> Rcpp | 1.0.10
#> bayestestR | 0.13.1
#> brms | 2.19.0
#> dplyr | 1.1.2
#> lavaan | 0.6.15
#> lme4 | 1.1.33
#> performance | 0.10.3
#> report | 0.5.7.4
# Convenience functions
report_packages(include_R = FALSE)
#> - Matrix (version 1.5.4.1; Bates D et al., 2023)
#> - lme4 (version 1.1.33; Bates D et al., 2015)
#> - brms (version 2.19.0; Bürkner P, 2017)
#> - Rcpp (version 1.0.10; Eddelbuettel D, François R, 2011)
#> - performance (version 0.10.3; Lüdecke D et al., 2021)
#> - bayestestR (version 0.13.1; Makowski D et al., 2019)
#> - report (version 0.5.7.4; Makowski D et al., 2023)
#> - lavaan (version 0.6.15; Rosseel Y, 2012)
#> - dplyr (version 1.1.2; Wickham H et al., 2023)
cite_packages(prefix = "> ")
#> > Bates D, Maechler M, Jagan M (2023). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.5-4.1, <https://Matrix.R-forge.R-project.org>.
#> > 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>.
#> > Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” _Journal of Statistical Software_, *80*(1), 1-28. doi:10.18637/jss.v080.i01 <https://doi.org/10.18637/jss.v080.i01>. Bürkner P (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” _The R Journal_, *10*(1), 395-411. doi:10.32614/RJ-2018-017 <https://doi.org/10.32614/RJ-2018-017>. Bürkner P (2021). “Bayesian Item Response Modeling in R with brms and Stan.” _Journal of Statistical Software_, *100*(5), 1-54. doi:10.18637/jss.v100.i05 <https://doi.org/10.18637/jss.v100.i05>.
#> > 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 JJ (2018). “Extending extitR with extitC++: A Brief Introduction to extitRcpp.” _The American Statistician_, *72*(1), 28-36. doi:10.1080/00031305.2017.1375990 <https://doi.org/10.1080/00031305.2017.1375990>.
#> > Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021). “performance: An R Package for Assessment, Comparison and Testing of Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139. doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#> > Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework.” _Journal of Open Source Software_, *4*(40), 1541. doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>, <https://joss.theoj.org/papers/10.21105/joss.01541>.
#> > 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 (2023). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
#> > Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02 <https://doi.org/10.18637/jss.v048.i02>.
#> > Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.
report_system()
#> Analyses were conducted using the R Statistical language (version 4.3.0; R Core
#> Team, 2023) on Ubuntu 22.04.2 LTS