statsExpressions

statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details - Published in JOSS (2021)

https://github.com/indrajeetpatil/statsexpressions

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Keywords

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

Keywords from Contributors

blackhole gravitational-lenses meshes bayes-factors statistical-analysis pypi annotations simulations hydrology stellar

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Repository

Tidy data frames and expressions with statistical summaries 📜

Basic Info

Host: GitHub
Owner: IndrajeetPatil
License: other
Language: R
Default Branch: main
Homepage: https://indrajeetpatil.github.io/statsExpressions/
Size: 39.7 MB

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Stars: 314
Watchers: 7
Forks: 20
Open Issues: 17
Releases: 41

Topics

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

Created over 6 years ago · Last pushed 5 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Support Codemeta

README.Rmd

---
output: github_document
---

  

```{r}
#| echo = FALSE
options(pillar.width = Inf, pillar.bold = TRUE, pillar.subtle_num = TRUE)

knitr::opts_chunk$set(
  collapse  = TRUE,
  dpi       = 300,
  out.width = "100%",
  comment   = "#>",
  warning   = FALSE,
  message   = FALSE,
  fig.path  = "man/figures/README-"
)

set.seed(123)
suppressPackageStartupMessages(library(statsExpressions))
```

# `{statsExpressions}`: Tidy dataframes and expressions with statistical details

Status | Usage | Miscellaneous
----------------- | ----------------- | ----------------- 
[![R build status](https://github.com/IndrajeetPatil/statsExpressions/workflows/R-CMD-check/badge.svg)](https://github.com/IndrajeetPatil/statsExpressions/actions) | [![Total downloads](https://cranlogs.r-pkg.org/badges/grand-total/statsExpressions?color=blue)](https://CRAN.R-project.org/package=statsExpressions) | [![Codecov](https://codecov.io/gh/IndrajeetPatil/statsExpressions/branch/main/graph/badge.svg)](https://app.codecov.io/gh/IndrajeetPatil/statsExpressions?branch=main)
[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html) | [![Daily downloads](https://cranlogs.r-pkg.org/badges/last-day/statsExpressions?color=blue)](https://CRAN.R-project.org/package=statsExpressions) | [![DOI](https://joss.theoj.org/papers/10.21105/joss.03236/status.svg)](https://doi.org/10.21105/joss.03236) 
 
# Introduction 

```{r, child = "man/rmd-fragments/statsExpressions-package.Rmd"}
```

# Installation

| Type        | Command                                       |
| :---------- | :-------------------------------------------- |
| Release     | `install.packages("statsExpressions")`        |
| Development | `pak::pak("IndrajeetPatil/statsExpressions")` |

On Linux, `{statsExpressions}` installation may require additional system dependencies, which can be checked using:

```{r, eval=FALSE}
pak::pkg_sysreqs("statsExpressions")
```

# Citation

The package can be cited as:

```{r}
#| label = "citation",
#| comment = ""
citation("statsExpressions")
```

# General Workflow

```{r}
#| echo = FALSE,
#| out.width = "80%"
knitr::include_graphics("man/figures/card.png")
```

# Summary of functionality

```{r, child = "man/rmd-fragments/functionality.Rmd"}
```

# Tidy dataframes from statistical analysis

To illustrate the simplicity of this syntax, let's say we want to run a one-way
ANOVA. If we first run a non-parametric ANOVA and then decide to run a robust
ANOVA instead, the syntax remains the same and the statistical approach can be
modified by changing a single argument:

```{r}
#| label = "df"

mtcars %>% oneway_anova(cyl, wt, type = "nonparametric")

mtcars %>% oneway_anova(cyl, wt, type = "robust")
```

All possible output dataframes from functions are tabulated here:


Needless to say this will also work with the `kable` function to generate a
table:

```{r}
#| label = "kable"

set.seed(123)

# one-sample robust t-test
# we will leave `expression` column out; it's not needed for using only the dataframe
mtcars %>%
  one_sample_test(wt, test.value = 3, type = "robust") %>%
  dplyr::select(-expression) %>%
  knitr::kable()
```

These functions are also compatible with other popular data manipulation
packages. 

For example, let's say we want to run a one-sample *t*-test for all levels of a
certain grouping variable. We can use `dplyr` to do so:

```{r}
#| label = "grouped_df"
# for reproducibility
set.seed(123)
library(dplyr)

# grouped operation
# running one-sample test for all levels of grouping variable `cyl`
mtcars %>%
  group_by(cyl) %>%
  group_modify(~ one_sample_test(.x, wt, test.value = 3), .keep = TRUE) %>%
  ungroup()
```

# Using expressions in custom plots

Note that *expression* here means **a pre-formatted in-text statistical result**.
In addition to other details contained in the dataframe, there is also a column
titled `expression`, which contains expression with statistical details and can
be displayed in a plot.

For **all** statistical test expressions, the default template attempt to follow
the gold standard for statistical reporting.

For example, here are results from Welch's *t*-test:



Let's load the needed library for visualization:

```{r}
library(ggplot2)
```

## Expressions for centrality measure

**Note that when used in a geometric layer, the expression need to be parsed.**

```{r}
#| label = "centrality"

# displaying mean for each level of `cyl`
centrality_description(mtcars, cyl, wt) |>
  ggplot(aes(cyl, wt)) +
  geom_point() +
  geom_label(aes(label = expression), parse = TRUE)
```

Here are a few examples for supported analyses.

## Expressions for one-way ANOVAs

The returned data frame will always have a column called `expression`. 

Assuming there is only a single result you need to display in a plot, to use it in a plot, you have two options:

- extract the expression from the list column (`results_data$expression[[1]]`) without parsing
- use the list column as is, in which case you will need to parse it (`parse(text = results_data$expression)`)

If you want to display more than one expression in a plot, you will *have to* parse them.

### Between-subjects design

```{r}
#| label = "anova_rob1"

set.seed(123)
library(ggridges)

results_data <- oneway_anova(iris, Species, Sepal.Length, type = "robust")

# create a ridgeplot
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges() +
  labs(
    title = "A heteroscedastic one-way ANOVA for trimmed means",
    subtitle = results_data$expression[[1]]
  )
```

### Within-subjects design

```{r}
#| label = "anova_parametric2"

set.seed(123)
library(WRS2)
library(ggbeeswarm)

results_data <- oneway_anova(
  WineTasting,
  Wine,
  Taste,
  paired = TRUE,
  subject.id = Taster,
  type = "np"
)

ggplot2::ggplot(WineTasting, aes(Wine, Taste, color = Wine)) +
  geom_quasirandom() +
  labs(
    title = "Friedman's rank sum test",
    subtitle = parse(text = results_data$expression)
  )
```

## Expressions for two-sample tests

### Between-subjects design

```{r}
#| label = "t_two"

set.seed(123)
library(gghalves)

results_data <- two_sample_test(ToothGrowth, supp, len)

ggplot(ToothGrowth, aes(supp, len)) +
  geom_half_dotplot() +
  labs(
    title = "Two-Sample Welch's t-test",
    subtitle = parse(text = results_data$expression)
  )
```

### Within-subjects design

```{r}
#| label = "t_two_paired1"

set.seed(123)
library(tidyr)
library(PairedData)
data(PrisonStress)

# get data in tidy format
df <- pivot_longer(PrisonStress, starts_with("PSS"), names_to = "PSS", values_to = "stress")

results_data <- two_sample_test(
  data = df,
  x = PSS,
  y = stress,
  paired = TRUE,
  subject.id = Subject,
  type = "np"
)

# plot
paired.plotProfiles(PrisonStress, "PSSbefore", "PSSafter", subjects = "Subject") +
  labs(
    title = "Two-sample Wilcoxon paired test",
    subtitle = parse(text = results_data$expression)
  )
```

## Expressions for one-sample tests

```{r}
#| label = "t_one"

set.seed(123)

# dataframe with results
results_data <- one_sample_test(mtcars, wt, test.value = 3, type = "bayes")

# creating a histogram plot
ggplot(mtcars, aes(wt)) +
  geom_histogram(alpha = 0.5) +
  geom_vline(xintercept = mean(mtcars$wt), color = "red") +
  labs(subtitle = parse(text = results_data$expression))
```

## Expressions for correlation analysis

Let's look at another example where we want to run correlation analysis:

```{r}
#| label = "corr"

set.seed(123)

# dataframe with results
results_data <- corr_test(mtcars, mpg, wt, type = "nonparametric")

# create a scatter plot
ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  geom_smooth(method = "lm", formula = y ~ x) +
  labs(
    title = "Spearman's rank correlation coefficient",
    subtitle = parse(text = results_data$expression)
  )
```

## Expressions for contingency table analysis

For categorical/nominal data - one-sample:

```{r}
#| label = "gof"

set.seed(123)

# dataframe with results
results_data <- contingency_table(
  as.data.frame(table(mpg$class)),
  Var1,
  counts = Freq,
  type = "bayes"
)

# create a pie chart
ggplot(as.data.frame(table(mpg$class)), aes(x = "", y = Freq, fill = factor(Var1))) +
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank()) +
  # cleaning up the chart and adding results from one-sample proportion test
  coord_polar(theta = "y", start = 0) +
  labs(
    fill = "Class",
    x = NULL,
    y = NULL,
    title = "Pie Chart of class (type of car)",
    caption = parse(text = results_data$expression)
  )
```

You can also use these function to get the expression in return without having
to display them in plots:

```{r}
#| label = "expr_output"

set.seed(123)

# Pearson's chi-squared test of independence
contingency_table(mtcars, am, vs)$expression[[1]]
```

## Expressions for meta-analysis

```{r}
#| label = "metaanalysis",
#| fig.height = 14,
#| fig.width = 12

set.seed(123)
library(metaviz)

# dataframe with results
results_data <- meta_analysis(dplyr::rename(mozart, estimate = d, std.error = se))

# meta-analysis forest plot with results random-effects meta-analysis
viz_forest(
  x = mozart[, c("d", "se")],
  study_labels = mozart[, "study_name"],
  xlab = "Cohen's d",
  variant = "thick",
  type = "cumulative"
) +
  labs(
    title = "Meta-analysis of Pietschnig, Voracek, and Formann (2010) on the Mozart effect",
    subtitle = parse(text = results_data$expression)
  ) +
  theme(text = element_text(size = 12))
```

# Customizing details to your liking

Sometimes you may not wish include so many details in the subtitle. In that
case, you can extract the expression and copy-paste only the part you wish to
include. For example, here only statistic and *p*-values are included:

```{r}
#| label = "custom_expr"

set.seed(123)

# extracting detailed expression
(res_expr <- oneway_anova(iris, Species, Sepal.Length, var.equal = TRUE)$expression[[1]])

# adapting the details to your liking
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot() +
  labs(subtitle = ggplot2::expr(paste(
    NULL, italic("F"), "(", "2", ",", "147", ") = ", "119.26", ", ",
    italic("p"), " = ", "1.67e-31"
  )))
```

# Summary of tests and effect sizes

Here a go-to summary about statistical test carried out and the returned effect
size for each function is provided. This should be useful if one needs to find
out more information about how an argument is resolved in the underlying package
or if one wishes to browse the source code. So, for example, if you want to know
more about how one-way (between-subjects) ANOVA, you can run
`?stats::oneway.test` in your R console.

## `centrality_description`

```{r, child = "man/rmd-fragments/centrality_description.Rmd"}
```

## `oneway_anova`

```{r, child = "man/rmd-fragments/oneway_anova.Rmd"}
```

## `two_sample_test` 

```{r, child = "man/rmd-fragments/two_sample_test.Rmd"}
```

## `one_sample_test`

```{r, child = "man/rmd-fragments/one_sample_test.Rmd"}
```

## `corr_test`

```{r, child = "man/rmd-fragments/corr_test.Rmd"}
```

## `contingency_table`

```{r, child = "man/rmd-fragments/contingency_table.Rmd"}
```

## `meta_analysis`

```{r, child = "man/rmd-fragments/meta_analysis.Rmd"}
```

# Usage in `{ggstatsplot}`

Note that these functions were initially written to display results from
statistical tests on ready-made `{ggplot2}` plots implemented in `{ggstatsplot}`.

For detailed documentation, see the package website:


Here is an example from `{ggstatsplot}` of what the plots look like when the
expressions are displayed in the subtitle-



# Acknowledgments

The hexsticker and the schematic illustration of general workflow were
generously designed by Sarah Otterstetter (Max Planck Institute for Human
Development, Berlin).

# Contributing

Bug reports, suggestions, questions, and (most of all)
contributions are welcome.

Please note that this project is released with a 
[Contributor Code of Conduct](https://github.com/IndrajeetPatil/statsExpressions/blob/main/.github/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.

Owner

Name: Indrajeet Patil
Login: IndrajeetPatil
Kind: user
Location: Berlin, Germany

Website: https://sites.google.com/site/indrajeetspatilmorality/
Twitter: patilindrajeets
Repositories: 15
Profile: https://github.com/IndrajeetPatil

Software Engineer || Data Scientist || Former Social Psychologist and Neuroscientist

JOSS Publication

statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details

Published

May 20, 2021

DOI

10.21105/joss.03236

Volume 6, Issue 61, Page 3236

Authors

Indrajeet Patil

Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany

Editor

Mikkel Meyer Andersen

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  "fileSize": "4230.396KB",
  "citation": [
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      "@type": "ScholarlyArticle",
      "datePublished": "2021",
      "author": [
        {
          "@type": "Person",
          "givenName": "Indrajeet",
          "familyName": "Patil"
        }
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      "name": "{statsExpressions: {R} Package for Tidy Dataframes and Expressions with Statistical Details}",
      "identifier": "10.21105/joss.03236",
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  ]
}

Papers & Mentions

Total mentions: 1

Individual cardiovascular responsiveness to work-matched exercise within the moderate- and severe-intensity domains

DOI: 10.1007/s00421-021-04676-7
OpenAlex ID: https://openalex.org/W3147400760
Published: April 2021

Last synced: 3 months ago

GitHub Events

Total

Create event: 23
Issues event: 3
Release event: 4
Watch event: 5
Delete event: 20
Issue comment event: 5
Push event: 79
Pull request review comment event: 2
Pull request review event: 8
Pull request event: 35

Last Year

Create event: 23
Issues event: 3
Release event: 4
Watch event: 5
Delete event: 20
Issue comment event: 5
Push event: 79
Pull request review comment event: 2
Pull request review event: 8
Pull request event: 35

Committers

Last synced: 5 months ago

All Time

Total Commits: 560
Total Committers: 4
Avg Commits per committer: 140.0
Development Distribution Score (DDS): 0.03

Past Year

Commits: 26
Committers: 2
Avg Commits per committer: 13.0
Development Distribution Score (DDS): 0.269

Top Committers

Name	Email	Commits
Indrajeet Patil	i**8@g**m	543
dependabot[bot]	4****]	15
Michael David Wilson	m**1@g**m	1
Arfon Smith	a****n	1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 35
Total pull requests: 154
Average time to close issues: 3 months
Average time to close pull requests: 7 days
Total issue authors: 12
Total pull request authors: 3
Average comments per issue: 0.97
Average comments per pull request: 0.78
Merged pull requests: 128
Bot issues: 0
Bot pull requests: 29

Past Year

Issues: 1
Pull requests: 37
Average time to close issues: 2 months
Average time to close pull requests: 8 days
Issue authors: 1
Pull request authors: 2
Average comments per issue: 0.0
Average comments per pull request: 0.19
Merged pull requests: 28
Bot issues: 0
Bot pull requests: 17

View more stats

Top Authors

Issue Authors

IndrajeetPatil (23)
strengejacke (4)
xmarti6 (1)
RussellGrayxd (1)
mfansler (1)
madebyafox (1)
Aashilbatavia (1)
altrabio (1)
csrabak (1)
lburleigh (1)
the8thday (1)

Pull Request Authors

IndrajeetPatil (146)
dependabot[bot] (38)
matcasti (1)

Top Labels

Issue Labels

feature :hammer: (6) documentation :books: (3) bug :bug: (3) wont fix :no_entry_sign: (2) consistency :apple: :green_apple: (2) Upkeep 🧹 (1) help wanted :heart: (1)

Pull Request Labels

dependencies (38) github_actions (6) breaking change :skull_and_crossbones: (1)

Packages

Total packages: 2
Total downloads:
- cran 8,591 last-month
Total docker downloads: 763

Total dependent packages: 3
(may contain duplicates)
Total dependent repositories: 4
(may contain duplicates)
Total versions: 66
Total maintainers: 1

cran.r-project.org: statsExpressions

Tidy Dataframes and Expressions with Statistical Details

Homepage: https://indrajeetpatil.github.io/statsExpressions/
Documentation: http://cran.r-project.org/web/packages/statsExpressions/statsExpressions.pdf
License: MIT + file LICENSE
Latest release: 1.7.1
published 6 months ago

Versions: 41
Dependent Packages: 1
Dependent Repositories: 4
Downloads: 8,591 Last month
Docker Downloads: 763

Rankings

Stargazers count: 1.3%

Downloads: 3.6%

Forks count: 4.1%

Average: 8.3%

Dependent repos count: 14.8%

Dependent packages count: 17.6%

Maintainers (1)

patilindrajeet.science@gmail.com

Last synced: 4 months ago

conda-forge.org: r-statsexpressions

Homepage: https://indrajeetpatil.github.io/statsExpressions/
License: MIT
Latest release: 1.3.5
published about 3 years ago

Versions: 25
Dependent Packages: 2
Dependent Repositories: 0

Rankings

Dependent packages count: 19.5%

Stargazers count: 21.0%

Average: 27.5%

Dependent repos count: 34.0%

Forks count: 35.4%

Last synced: 4 months ago

Dependencies

DESCRIPTION cran

R >= 4.0.0 depends
BayesFactor >= 0.9.12 imports
WRS2 >= 1.1 imports
correlation >= 0.8.1 imports
datawizard >= 0.4.1 imports
dplyr * imports
effectsize >= 0.7.0 imports
glue * imports
insight >= 0.18.0 imports
magrittr * imports
parameters >= 0.18.0 imports
performance >= 0.8.0 imports
rlang * imports
stats * imports
tibble * imports
tidyr * imports
zeallot * imports
PMCMRplus * suggests
afex * suggests
ggplot2 * suggests
knitr * suggests
metaBMA * suggests
metafor * suggests
metaplus * suggests
purrr * suggests
rmarkdown * suggests
spelling * suggests
survival * suggests
testthat >= 3.1.4 suggests
utils * suggests
withr * suggests

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statsExpressions

Science Score: 93.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.Rmd

Owner

JOSS Publication

statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details

Authors

Editor

Tags

CodeMeta (codemeta.json)

Papers & Mentions

Individual cardiovascular responsiveness to work-matched exercise within the moderate- and severe-intensity domains

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

cran.r-project.org: statsExpressions

Rankings

Maintainers (1)

conda-forge.org: r-statsexpressions

Rankings

Dependencies