diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 00000000..d72255cd --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,30 @@ +# TODO: delete this file once the paper is published +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + + - name: Upload + uses: actions/upload-artifact@v4 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. 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logreq Info: Writing requests to 'paper.run.xml'. -\openout1 = `paper.run.xml'. - - ) -Here is how much of TeX's memory you used: - 37240 strings out of 474924 - 763296 string characters out of 5755242 - 1961186 words of memory out of 5000000 - 58678 multiletter control sequences out of 15000+600000 - 564941 words of font info for 84 fonts, out of 8000000 for 9000 - 1348 hyphenation exceptions out of 8191 - 84i,12n,87p,1194b,1159s stack positions out of 10000i,1000n,20000p,200000b,200000s - -Output written on paper.pdf (6 pages). diff --git a/paper/paper.md b/paper/paper.md index 77d7b4e5..1c9945aa 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -40,40 +40,72 @@ affiliations: - index: 6 name: Université du Québec à Montréal, Montréal, Canada -date: "2024-04-02" -bibliography: paper.bib +date: "2024-05-26" + output: - rticles::joss_article: - includes: - in_header: preamble.tex + md_document: + variant: "markdown" + preserve_yaml: true + standalone: true csl: apa.csl +bibliography: paper.bib link-citations: yes --- - - # Summary -The `{report}` package is a powerful tool for researchers who use R to analyze data and create reports for publication. It bridges the gap between R's output and the formatted results to be included in a manuscript, allowing users to easily convert statistical models and data frames into textual reports that are suitable for publication. This paper provides an overview of this package, including its features, advantages, and ways to use it. We also provide examples of how the package can be used to standardize and improve results reporting in R. +The `{report}` package is a powerful tool for researchers who use R to +analyze data and create reports for publication. It bridges the gap +between R's output and the formatted results to be included in a +manuscript, allowing users to easily convert statistical models and data +frames into textual reports that are suitable for publication. This +paper provides an overview of this package, including its features, +advantages, and ways to use it. We also provide examples of how the +package can be used to standardize and improve results reporting in R. # Statement of Need -Reporting results is a crucial part of scientific research. Researchers need to ensure that their results are reported accurately and clearly, so that other researchers can understand and reproduce their work. However, the process of results reporting can be time-consuming and error-prone, particularly when it comes to formatting data for publication [@nuijten2016prevalence]. The `{report}` package was developed to address this problem, providing researchers with a tool that can help standardize and improve reporting results from their workflows in R. `{report}` is part of the *easystats* ecosystem of packages that build an R framework for easy statistical modeling, visualization, and reporting [@easystatspackage]. +Reporting results is a crucial part of scientific research. Researchers +need to ensure that their results are reported accurately and clearly, +so that other researchers can understand and reproduce their work. +However, the process of results reporting can be time-consuming and +error-prone, particularly when it comes to formatting data for +publication [@nuijten2016prevalence]. The `{report}` package was +developed to address this problem, providing researchers with a tool +that can help standardize and improve reporting results from their +workflows in R. `{report}` is part of the *easystats* ecosystem of +packages that build an R framework for easy statistical modeling, +visualization, and reporting [@easystatspackage]. # Features -The package includes several features that make it a valuable tool for researchers. It can convert statistical models and data frames into textual reports, which can be easily customized and formatted to meet the requirements of a particular journal or publication. It can also automatically report demographics information from column names. The package supports a wide range of output formats, including HTML, LaTeX, and Microsoft Word, via RMarkdown. - -Using this package in an analytic workflow has several advantages. First, it can help standardize results reporting across a research team or project, ensuring that everyone is using the same format and style. Second, the package can save researchers time by automating many of the tasks involved in reporting results, such as formatting tables and text. Third, the package can improve the quality of results reporting by providing users with a tool that is specifically designed for this purpose. Additionally, it prevents any copy-and-paste errors. - -The examples below illustrate the ease with which `{report}` can create detailed textual or tabular summaries for statistical objects. +The package includes several features that make it a valuable tool for +researchers. It can convert statistical models and data frames into +textual reports, which can be easily customized and formatted to meet +the requirements of a particular journal or publication. It can also +automatically report demographics information from column names. The +package supports a wide range of output formats, including HTML, LaTeX, +and Microsoft Word, via RMarkdown. + +Using this package in an analytic workflow has several advantages. +First, it can help standardize results reporting across a research team +or project, ensuring that everyone is using the same format and style. +Second, the package can save researchers time by automating many of the +tasks involved in reporting results, such as formatting tables and text. +Third, the package can improve the quality of results reporting by +providing users with a tool that is specifically designed for this +purpose. Additionally, it prevents any copy-and-paste errors. + +The examples below illustrate the ease with which `{report}` can create +detailed textual or tabular summaries for statistical objects. ## Demographics -Note that when using `rmarkdown`, setting a chunk option to `results = "asis"` will print the output as regular text, as below, for a rather seamless reading experience. - +Note that when using `rmarkdown`, setting a chunk option to +`results = "asis"` will print the output as regular text, as below, for +a rather seamless reading experience. -```r +``` r library(report) data <- data.frame( @@ -101,196 +133,216 @@ cat(paste0( )) ``` -This study includes data from 11 participants (Mean age = 28.7, SD = 13.4, range: [8, 54]; Sex: 63.6% females, 27.3% males, 9.1% other; Gender: 63.6% women, 27.3% men, 9.09% non-binary; Education: Bachelor, 36.36%; Highschool, 36.36%; Master, 18.18%; PhD, 9.09%; Country: 45.45% USA, 27.27% Canada, 27.27% other; Race: 27.27% Black, 18.18% Asian, 18.18% White, 36.36% other). +This study includes data from 11 participants (Mean age = 28.7, SD = +13.4, range: \[8, 54\]; Sex: 63.6% females, 27.3% males, 9.1% other; +Gender: 63.6% women, 27.3% men, 9.09% non-binary; Education: Bachelor, +36.36%; Highschool, 36.36%; Master, 18.18%; PhD, 9.09%; Country: 45.45% +USA, 27.27% Canada, 27.27% other; Race: 27.27% Black, 18.18% Asian, +18.18% White, 36.36% other). ## Models - -```r +``` r m_lm <- lm(mpg ~ qsec + wt, data = mtcars) report(m_lm) ``` ::: {.blackbox data-latex=""} - -We fitted a linear model (estimated using OLS) to predict -mpg with qsec and wt (formula: mpg ~ qsec + wt). The model -explains a statistically significant and substantial -proportion of variance (R2 = 0.83, F(2, 29) = 69.03, p < -.001, adj. R2 = 0.81). The model's intercept, corresponding -to qsec = 0 and wt = 0, is at 19.75 (95% CI [9.00, 30.49], -t(29) = 3.76, p < .001). Within this model: - - - The effect of qsec is statistically significant and -positive (beta = 0.93, 95% CI [0.39, 1.47], t(29) = 3.51, p -= 0.001; Std. beta = 0.28, 95% CI [0.11, 0.44]) - - The effect of wt is statistically significant and -negative (beta = -5.05, 95% CI [-6.04, -4.06], t(29) = --10.43, p < .001; Std. beta = -0.82, 95% CI [-0.98, -0.66]) - -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. - +We fitted a linear model (estimated using OLS) to predict mpg with qsec +and wt (formula: mpg \~ qsec + wt). The model explains a statistically +significant and substantial proportion of variance (R2 = 0.83, F(2, 29) += 69.03, p \< .001, adj. R2 = 0.81). The model's intercept, +corresponding to qsec = 0 and wt = 0, is at 19.75 (95% CI \[9.00, +30.49\], t(29) = 3.76, p \< .001). Within this model: + +- The effect of qsec is statistically significant and positive (beta = + 0.93, 95% CI \[0.39, 1.47\], t(29) = 3.51, p = 0.001; Std. beta = + 0.28, 95% CI \[0.11, 0.44\]) +- The effect of wt is statistically significant and negative (beta = + -5.05, 95% CI \[-6.04, -4.06\], t(29) = -10.43, p \< .001; Std. beta + = -0.82, 95% CI \[-0.98, -0.66\]) + +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. ::: - -```r +``` r library(lme4) m_lmer <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris) report(m_lmer) ``` ::: {.blackbox data-latex=""} - -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. - +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. ::: - -```r +``` r library(rstanarm) m_rstan <- stan_glm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 1000) report(m_rstan) ``` ::: {.blackbox data-latex=""} - -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 [0.69, 0.89], adj. R2 = 0.79). The model's -intercept, corresponding to qsec = 0 and wt = 0, is at -19.59 (95% CI [8.71, 30.63]). Within this model: - - - The effect of qsec (Median = 0.93, 95% CI [0.38, 1.50]) -has a 99.85% probability of being positive (> 0), 98.65% of -being significant (> 0.30), and 0.10% of being large (> -1.81). The estimation successfully converged (Rhat = 1.001) -and the indices are reliable (ESS = 2048) - - The effect of wt (Median = -5.05, 95% CI [-6.07, -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 = -0.999) and the indices are reliable (ESS = 1744) - -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). - +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 \[0.69, 0.89\], adj. R2 = 0.79). The model's intercept, +corresponding to qsec = 0 and wt = 0, is at 19.59 (95% CI \[8.71, +30.63\]). Within this model: + +- The effect of qsec (Median = 0.93, 95% CI \[0.38, 1.50\]) has a + 99.85% probability of being positive (\> 0), 98.65% of being + significant (\> 0.30), and 0.10% of being large (\> 1.81). The + estimation successfully converged (Rhat = 1.001) and the indices are + reliable (ESS = 2048) +- The effect of wt (Median = -5.05, 95% CI \[-6.07, -4.05\]) 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 = 0.999) and the indices are + reliable (ESS = 1744) + +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). ::: ## Tables If preferred, you can output the information as a table instead. - -```r +``` r library(insight) export_table(format_table(report_table(m_lm)), format = "markdown") ``` + -------------------------------------------------------------------------------------------- + Parameter Coefficient 95% CI t(29) p Std. Coef. Std. Coef. 95% Fit + CI + ------------- ------------- ----------- -------- ------- ----------- -------------- -------- + (Intercept) 19.75 \[ 9.00, 3.76 \< .001 -1.36e-17 \[-0.16, + 30.49\] 0.16\] + + qsec 0.93 \[ 0.39, 3.51 0.001 0.28 \[ 0.11, + 1.47\] 0.44\] + + wt -5.05 \[-6.04, -10.43 \< .001 -0.82 \[-0.98, + -4.06\] -0.66\] + + + AIC 156.72 -|Parameter | Coefficient| 95% CI | t(29)| p | Std. Coef.| Std. Coef. 95% CI| Fit| -|:-----------|-----------:|:--------------|------:|:------|----------:|-----------------:|------:| -|(Intercept) | 19.75|[ 9.00, 30.49] | 3.76|< .001 | -1.36e-17| [-0.16, 0.16]| | -|qsec | 0.93|[ 0.39, 1.47] | 3.51|0.001 | 0.28| [ 0.11, 0.44]| | -|wt | -5.05|[-6.04, -4.06] | -10.43|< .001 | -0.82| [-0.98, -0.66]| | -| | | | | | | | | -|AIC | | | | | | | 156.72| -|AICc | | | | | | | 158.20| -|BIC | | | | | | | 162.58| -|R2 | | | | | | | 0.83| -|R2 (adj.) | | | | | | | 0.81| -|Sigma | | | | | | | 2.60| + AICc 158.20 + BIC 162.58 + R2 0.83 -```r + R2 (adj.) 0.81 + + Sigma 2.60 + -------------------------------------------------------------------------------------------- + +``` r export_table(format_table(report_table(m_lmer)), format = "markdown") ``` + ------------------------------------------------------------------------------------------------------------- + Parameter Coefficient 95% CI t(146) p Effects Group Std. Coef. Std. Coef. Fit + 95% CI + --------------- ------------- --------- -------- ------ --------- ---------- ----------- ----------- -------- + (Intercept) 2.50 \[1.19, 3.75 \< fixed -4.86e-13 \[-1.49, + 3.82\] .001 1.49\] + + Petal Length 0.89 \[0.76, 13.93 \< fixed 1.89 \[ 1.63, + 1.01\] .001 2.16\] + + 1.08 random Species + + 0.34 random Residual + -|Parameter | Coefficient| 95% CI | t(146)| p | Effects| Group| Std. Coef.| Std. Coef. 95% CI| Fit| -|:----------------|-----------:|:------------|------:|:------|-------:|--------:|----------:|-----------------:|------:| -|(Intercept) | 2.50|[1.19, 3.82] | 3.75|< .001 | fixed| | -5.11e-13| [-1.49, 1.49]| | -|Petal Length | 0.89|[0.76, 1.01] | 13.93|< .001 | fixed| | 1.89| [ 1.63, 2.16]| | -| | 1.08| | | | random| Species| | | | -| | 0.34| | | | random| Residual| | | | -| | | | | | | | | | | -|AIC | | | | | | | | | 127.79| -|AICc | | | | | | | | | 128.07| -|BIC | | | | | | | | | 139.84| -|R2 (conditional) | | | | | | | | | 0.97| -|R2 (marginal) | | | | | | | | | 0.66| -|Sigma | | | | | | | | | 0.34| + AIC 127.79 + AICc 128.07 + BIC 139.84 -```r + R2 0.97 + (conditional) + + R2 (marginal) 0.66 + + Sigma 0.34 + ------------------------------------------------------------------------------------------------------------- + +``` r export_table(format_table(report_table(m_rstan)), format = "markdown") ``` + ------------------------------------------------------------------------------------------------------------ + Parameter Median 95% CI pd Rhat ESS Prior Std. Std_Median Fit + Median 95% CI + ------------- -------- ---------- -------- ------- --------- -------------- ---------- ------------ -------- + (Intercept) 19.59 \[ 8.71, 100% 1.001 1907.00 Normal (20.09 1.52e-03 \[-0.16, + 30.63\] +- 15.07) 0.16\] + + qsec 0.93 \[ 0.38, 99.85% 1.001 2048.00 Normal (0.00 0.28 \[ 0.12, + 1.50\] +- 8.43) 0.44\] + + wt -5.05 \[-6.07, 100% 0.999 1744.00 Normal (0.00 -0.82 \[-0.98, + -4.05\] +- 15.40) -0.65\] + -|Parameter | Median| 95% CI | pd| Rhat | ESS | Prior | Std. Median| Std_Median 95% CI| Fit| -|:-----------|------:|:--------------|------:|:-----|:-------|:-----------------------|-----------:|-----------------:|------:| -|(Intercept) | 19.59|[ 8.71, 30.63] | 100%|1.001 |1907.00 |Normal (20.09 +- 15.07) | 1.52e-03| [-0.16, 0.16]| | -|qsec | 0.93|[ 0.38, 1.50] | 99.85%|1.001 |2048.00 | Normal (0.00 +- 8.43) | 0.28| [ 0.12, 0.44]| | -|wt | -5.05|[-6.07, -4.05] | 100%|0.999 |1744.00 | Normal (0.00 +- 15.40) | -0.82| [-0.98, -0.65]| | -| | | | | | | | | | | -|ELPD | | | | | | | | | -79.23| -|LOOIC | | | | | | | | | 158.46| -|WAIC | | | | | | | | | 158.15| -|R2 | | | | | | | | | 0.81| -|R2 (adj.) | | | | | | | | | 0.79| -|Sigma | | | | | | | | | 2.65| + ELPD -79.23 + LOOIC 158.46 + WAIC 158.15 + + R2 0.81 + + R2 (adj.) 0.79 + + Sigma 2.65 + ------------------------------------------------------------------------------------------------------------ ## Grouped Data You can also seamlessly integrate `{report}` with *tidyverse* workflows: - -```r +``` r library(dplyr) iris %>% @@ -301,34 +353,47 @@ iris %>% ``` ::: {.blackbox data-latex=""} - -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] - +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: \[1.40, 2.50\] ::: # Conclusion -The `{report}` package is a valuable tool for researchers who use R to analyze data and create reports for publication. It can help standardize and improve results reporting while saving researchers time and improving the quality of their work. +The `{report}` package is a valuable tool for researchers who use R to +analyze data and create reports for publication. It can help standardize +and improve results reporting while saving researchers time and +improving the quality of their work. # Licensing and Availability -`{report}` is licensed under the GNU General Public License (v3.0), with all source code openly developed and stored on GitHub (), along with a corresponding issue tracker for bug reporting and feature enhancements. In the spirit of honest and open science, we encourage requests, tips for fixes, feature updates, as well as general questions and concerns via direct interaction with contributors and developers. +`{report}` is licensed under the GNU General Public License (v3.0), with +all source code openly developed and stored on GitHub +(), along with a corresponding +issue tracker for bug reporting and feature enhancements. In the spirit +of honest and open science, we encourage requests, tips for fixes, +feature updates, as well as general questions and concerns via direct +interaction with contributors and developers. # Acknowledgments -`{report}` is part of the collaborative [*easystats*](https://easystats.github.io/easystats/) ecosystem. Thus, we thank the [members of easystats](https://github.com/orgs/easystats/people) as well as the users. +`{report}` is part of the collaborative +[*easystats*](https://easystats.github.io/easystats/) ecosystem. Thus, +we thank the [members of +easystats](https://github.com/orgs/easystats/people) as well as the +users. + +# References {#references .unnumbered} -# References +[^1]: Brenton Wiernik is currently an independent researcher and + Research Scientist at Meta, Demography and Survey Science. The + current work was done in an independent capacity. diff --git a/paper/paper.pdf b/paper/paper.pdf deleted file mode 100644 index cd2096be..00000000 Binary files a/paper/paper.pdf and /dev/null differ diff --git a/paper/preamble.tex b/paper/preamble.tex deleted file mode 100644 index 2e8625a1..00000000 --- a/paper/preamble.tex +++ /dev/null @@ -1,10 +0,0 @@ -\usepackage{color} -\usepackage{framed} -\setlength{\fboxsep}{.8em} -\setlength{\headheight}{63.55022pt} - -\newenvironment{blackbox}{ - \definecolor{shadecolor}{rgb}{0.5, 0.8, 1} % black - \color{black} - \begin{shaded}} - {\end{shaded}} \ No newline at end of file