`## Warning: package 'dplyr' was built under R version 4.2.3`

```
##
## Attaching package: 'dplyr'
```

```
## The following objects are masked from 'package:stats':
##
## filter, lag
```

```
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
```

`## Warning: package 'lme4' was built under R version 4.2.3`

`## Warning: package 'Matrix' was built under R version 4.2.3`

```
## dplyr lme4
## TRUE TRUE
```

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

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

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}`

package), 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:

```
iris %>%
group_by(Species) %>%
report_table()
# Group | Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | n_Missing
# ---------------------------------------------------------------------------------------------------------------
# setosa | Sepal.Length | 50 | 5.01 | 0.35 | 5.00 | 0.30 | 4.30 | 5.80 | 0.12 | -0.25 | 0
# setosa | Sepal.Width | 50 | 3.43 | 0.38 | 3.40 | 0.37 | 2.30 | 4.40 | 0.04 | 0.95 | 0
# setosa | Petal.Length | 50 | 1.46 | 0.17 | 1.50 | 0.15 | 1.00 | 1.90 | 0.11 | 1.02 | 0
# setosa | Petal.Width | 50 | 0.25 | 0.11 | 0.20 | 0.00 | 0.10 | 0.60 | 1.25 | 1.72 | 0
# 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,
Wilcoxon tests, etc.

```
report(t.test(formula = mtcars$wt ~ mtcars$am))
# Effect sizes were labelled following Cohen's (1988)
# recommendations.
#
# The Welch Two Sample t-test testing the difference of
# mtcars$wt by mtcars$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 =
# 1.93, 95% CI [1.08, 2.77])
```

`lm`

)We will start out simple: a simple linear regression

```
model <- lm(wt ~ am + mpg, data = mtcars)
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. 95% Confidence
# Intervals (CIs) and p-values were computed using a Wald
# t-distribution approximation.
```

`aov`

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

:

```
model <- aov(wt ~ am + mpg, data = mtcars)
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`

)```
model <- glm(vs ~ mpg + cyl, data = mtcars, family = "binomial")
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 a Wald
# z-distribution approximation.
```

`merMod`

)```
library(lme4)
model <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)
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 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 a Wald
# t-distribution approximation.
```