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## 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## dplyr lme4
## TRUE TRUEFirst, 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 onceGreat! 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 | 0report 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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.