First, install R and R studio. Then, copy and paste the following lines in the console:

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

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

Great! The `report`

package is now installed and loaded in your session.

The `report`

package works in a two step fashion: - First, you create a `report`

object with the `report()`

function. - Second, 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 compact version of the report using `summary()`

on the report object.

If an entire dataframe is supplied, `report`

will provide descriptive statistics for all columns:

```
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%)
```

The dataframe can also be a *grouped* dataframe (from `dplyr`

), in which case `report`

would return a separate report for each level of the grouping variable. Additionally, instead of textual summary, `report`

also allows one to return a tabular summary using the `report_table()`

function:

```
library(dplyr)
%>%
iris group_by(Species) %>%
report_table()
# Group | Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | n_Missing
# ---------------------------------------------------------------------------------------------------------------
# versicolor | Sepal.Length | 50 | 5.94 | 0.52 | 5.90 | 0.52 | 4.90 | 7.00 | 0.11 | -0.53 | 0
# versicolor | Sepal.Width | 50 | 2.77 | 0.31 | 2.80 | 0.30 | 2.00 | 3.40 | -0.36 | -0.37 | 0
# versicolor | Petal.Length | 50 | 4.26 | 0.47 | 4.35 | 0.52 | 3.00 | 5.10 | -0.61 | 0.05 | 0
# versicolor | Petal.Width | 50 | 1.33 | 0.20 | 1.30 | 0.22 | 1.00 | 1.80 | -0.03 | -0.41 | 0
# virginica | Sepal.Length | 50 | 6.59 | 0.64 | 6.50 | 0.59 | 4.90 | 7.90 | 0.12 | 0.03 | 0
# virginica | Sepal.Width | 50 | 2.97 | 0.32 | 3.00 | 0.30 | 2.20 | 3.80 | 0.37 | 0.71 | 0
# virginica | Petal.Length | 50 | 5.55 | 0.55 | 5.55 | 0.67 | 4.50 | 6.90 | 0.55 | -0.15 | 0
# virginica | Petal.Width | 50 | 2.03 | 0.27 | 2.00 | 0.30 | 1.40 | 2.50 | -0.13 | -0.60 | 0
```

`report`

can also be used to provide automated summaries for statistical model objects from correlation, t-tests, etc.

```
report(t.test(formula = wt ~ am, data = mtcars))
# Effect sizes were labelled following Cohen's (1988) recommendations.
#
# The Welch Two Sample t-test testing the difference of wt by am (mean in group 0 = 3.77, mean in group 1 = 2.41) suggests that the effect is positive, statistically significant, and large (difference = 1.36, 95% CI [0.85, 1.86], t(29.23) = 5.49, p < .001; Cohen's d = 2.03, 95% CI [1.13, 2.91])
```

```
<- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
x <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
y
report(wilcox.test(x, y, paired = TRUE))
```

`lm`

)We will start out simple: a simple linear regression

```
<- lm(wt ~ am + mpg, data = mtcars)
model
report(model)
# We fitted a linear model (estimated using OLS) to predict wt with am and mpg (formula: wt ~ am + mpg). The model explains a statistically significant and substantial proportion of variance (R2 = 0.80, F(2, 29) = 57.66, p < .001, adj. R2 = 0.79). The model's intercept, corresponding to am = 0 and mpg = 0, is at 5.74 (95% CI [5.11, 6.36], t(29) = 18.64, p < .001). Within this model:
#
# - The effect of am is statistically significant and negative (beta = -0.53, 95% CI [-0.94, -0.11], t(29) = -2.58, p = 0.015; Std. beta = -0.27, 95% CI [-0.48, -0.06])
# - The effect of mpg is statistically significant and negative (beta = -0.11, 95% CI [-0.15, -0.08], t(29) = -6.79, p < .001; Std. beta = -0.71, 95% CI [-0.92, -0.49])
#
# Standardized parameters were obtained by fitting the model on a standardized version of the dataset.
```

`aov`

)And its close cousin ANOVA is also covered by `report`

:

```
<- aov(wt ~ am + mpg, data = mtcars)
model
report(model)
# The ANOVA (formula: wt ~ am + mpg) suggests that:
#
# - The main effect of am is statistically significant and large (F(1, 29) = 69.21, p < .001; Eta2 (partial) = 0.70, 95% CI [0.54, 1.00])
# - The main effect of mpg is statistically significant and large (F(1, 29) = 46.12, p < .001; Eta2 (partial) = 0.61, 95% CI [0.42, 1.00])
#
# Effect sizes were labelled following Field's (2013) recommendations.
```

`glm`

)```
<- glm(vs ~ mpg + cyl, data = mtcars, family = "binomial")
model
report(model)
# We fitted a logistic model (estimated using ML) to predict vs with mpg and cyl (formula: vs ~ mpg + cyl). The model's explanatory power is substantial (Tjur's R2 = 0.67). The model's intercept, corresponding to mpg = 0 and cyl = 0, is at 15.97 (95% CI [-2.71, 44.69], p = 0.147). Within this model:
#
# - The effect of mpg is statistically non-significant and negative (beta = -0.16, 95% CI [-0.71, 0.34], p = 0.496; Std. beta = -0.98, 95% CI [-4.28, 2.03])
# - The effect of cyl is statistically significant and negative (beta = -2.15, 95% CI [-5.19, -0.54], p = 0.047; Std. beta = -3.84, 95% CI [-9.26, -0.97])
#
# 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
```

`merMod`

)```
library(lme4)
<- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)
model
report(model)
# We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict Reaction with Days (formula: Reaction ~ Days). The model included Days and Subject as random effects (formula: ~Days | Subject). The model's total explanatory power is substantial (conditional R2 = 0.80) and the part related to the fixed effects alone (marginal R2) is of 0.28. The model's intercept, corresponding to Days = 0, is at 251.41 (95% CI [237.94, 264.87], t(174) = 36.84, p < .001). Within this model:
#
# - The effect of Days is statistically significant and positive (beta = 10.47, 95% CI [7.42, 13.52], t(174) = 6.77, p < .001; Std. beta = 0.54, 95% CI [0.38, 0.69])
#
# 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
```