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:scroll: :tada: Automated reporting of objects in R

Readme

*“From R to your manuscript”*

:warning: **report** has been entirely rewritten *(again)*, and won’t be
compatible with your old code. We’ve changed it for the best, and with
your help we’ll continue improving it. You’ll need the latest versions
of the **easystats** packages (you can update them by running

easystats::install_easystats_latest()).

**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
practices** guidelines (*e.g.,* APA’s
style), ensuring **standardization** and **quality** in results
reporting.

library(report)model

# We fitted a linear model (estimated using OLS) to predict Sepal.Length with Species (formula: Sepal.Length ~## Species). 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 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 significantly 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 significantly 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.

## Documentation

The package documentation can be found

here. Check-out these tutorials:

** 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!

Run the following:

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

library("report") # Load the package every time you start R

The

reportpackage works in a two step fashion. First, you create a

reportobject 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 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:

library(dplyr)iris %>% select(-starts_with("Sepal")) %>% group_by(Species) %>% report() %>% summary()

# The data contains 150 observations, grouped by Species, of the following 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 positive, significant and large (difference = 7.24, 95% CI # [-11.28, -3.21], t(18.33) = -3.77, p < .01; Cohen's d = -1.41, 95% CI [-2.17, -0.51])

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() # Parameter1 | Parameter2 | r | 95% CI | t(148) | p | Method # -------------------------------------------------------------------------------------------------------------------- # iris$Sepal.Length | iris$Sepal.Width | -0.12 | [-0.27, 0.04] | -1.44 | 0.152 | Pearson's product-moment correlation

This 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 significant and large (F(2, 147) = 119.26, p < .001; Eta2 = 0.62, 90% CI # [0.54, 0.68]) # # Effect sizes were labelled following Field's (2013) recommendations.

Reports are also compatible with GLMs, such as this **logistic
regression**:

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 non-significantly 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 non-significantly positive (beta = 5.96, 95% CI [-3.75, 16.26], p = 0.205; Std. beta # = -0.36, 95% CI [-1.96, 0.98]) # - The interaction effect of drat on mpg is non-significantly 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## Mixed Models

Mixed models (coming from example from the

lme4package), which popularity and usage is exploding, can also be reported as it should:library(lme4)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 [1.20, 3.81], t(146) = 3.75, p < .001). Within this model:

## - The effect of Petal.Length is significantly 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 the Wald approximation.

## Bayesian Models

Bayesian models can also be reported using the new

SEXITframework, that combines clarity, precision and usefulness.library(rstanarm)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 set as

## normal (mean = 0.00, SD = 8.43) and normal (mean = 0.00, SD = 15.40) distributions. The model's explanatory

## power is substantial (R2 = 0.81, 89% CI [0.73, 0.88], adj. R2 = 0.78). The model's intercept, corresponding

## to qsec = 0 and wt = 0, is at 19.87 (95% CI [9.27, 30.28]). Within this model:

## - The effect of qsec (Median = 0.92, 0.95% CI [0.40, 1.48]) has a 99.80% probability of being positive (> 0),

## 99.00% of being significant (> 0.30), and 0.15% of being large (> 1.81). The estimation successfuly converged

## (Rhat = 1.000) and the indices are reliable (ESS = 1682)

## - The effect of wt (Median = -5.03, 0.95% CI [-6.00, -4.12]) 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 successfuly

## converged (Rhat = 0.999) and the indices are reliable (ESS = 2111)

## 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

One can, for complex reports, directly access the pieces of the reports:

model# linear model (estimated using OLS) to predict Sepal.Length with Species (formula: Sepal.Length ~ Species) # The model explains a significant and substantial proportion of variance (R2 = 0.62, F(2, 147) = 119.26, p < .001, adj. R2 = 0.61) # 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

This can be useful to complete the

Participantsparagraph of your manuscript.data# [1] "Four participants (Mean age = 30.0, SD = 16.0, range: [21, 54]; 50.0% females) were recruited in the study by means of torture and coercion."## Report sample

Report can also help you create sample description table (also referred to as

Table 1).| Variable | setosa (n=50) | versicolor (n=50) | virginica (n=50) | Total | | :--------------------- | :------------ | :---------------- | :--------------- | :---------- | | 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) |

## Report system and packages

Finally,

reportincludes 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 4.0.3; R Core Team, 2020) on macOS Catalina # 10.15.7, using the packages Rcpp (version 1.0.6; Dirk Eddelbuettel and Romain Francois, 2011), Matrix # (version 1.2.18; Douglas Bates and Martin Maechler, 2019), lme4 (version 1.1.26; Douglas Bates et al., 2015), # rstanarm (version 2.21.1; Goodrich B et al., 2020), dplyr (version 1.0.4; Hadley Wickham et al., 2021) and # report (version 0.2.0; Makowski et al., 2020). # # References # ---------- # - Dirk Eddelbuettel and Romain Francois (2011). Rcpp: Seamless R and C++ Integration. Journal of Statistical # Software, 40(8), 1-18. URL https://www.jstatsoft.org/v40/i08/. # - Douglas Bates and Martin Maechler (2019). Matrix: Sparse and Dense Matrix Classes and Methods. R package # version 1.2-18. https://CRAN.R-project.org/package=Matrix # - Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using # lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01. # - Goodrich B, Gabry J, Ali I & Brilleman S. (2020). rstanarm: Bayesian applied regression modeling via Stan. # R package version 2.21.1 https://mc-stan.org/rstanarm. # - Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data # Manipulation. R package version 1.0.4. https://CRAN.R-project.org/package=dplyr # - Makowski, D., Lüdecke, D., & Ben-Shachar, M.S. (2020). Automated reporting as a practical tool to improve # reproducibility and methodological best practices adoption. CRAN. Available from # https://github.com/easystats/report. doi: . # - R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical # Computing, Vienna, Austria. URL https://www.R-project.org/.## Credits

If you like it, you can put a

staron this repo, and cite the package as follows:

- Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2020).
*Automated reporting as a practical tool to improve reproducibility and methodological best practices adoption*. CRAN.