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# Packages used {#sec-annex-a}
## autoplotly
:::::{.my-package}
:::{.my-package-header}
autoplotly: Automatic Generation of Interactive Visualizations for Statistical Results
:::
::::{.my-package-container}
***
::: {#pak-autoplotly}
***
{**autoplotly**}: [Automatic Generation of Interactive Visualizations for Statistical Results](https://github.com/terrytangyuan/autoplotly) [@autoplotly]
Functionalities to automatically generate interactive visualizations for statistical results supported by {**ggfortify**}, such as time series, PCA, clustering and survival analysis, with [plotly.js](https://plotly.com/) and {**ggplot2**} style. The generated visualizations can also be easily extended using {**ggplot2**} and {**plotly**} syntax while staying interactive.
{**autoplotly**}: Automatic Generation of Interactive Visualizations for Statistical Results
:::
***
::::
:::::
## BSDA
:::::{.my-package}
:::{.my-package-header}
BSDA: Basic Statistics and Data Analysis
:::
::::{.my-package-container}
***
::: {#pak-BDSA}
***
{**BSDA**}: [Basic Statistics and Data Analysis](https://alanarnholt.github.io/BSDA/) [@BSDA]
(*There is no hexagon logo for {**BSDA**} available*)
Functions and data sets for the text Basic Statistics and Data Analysis (BSDA) [@kitchens2002].
{**BSDA**}: Basic Statistics and Data Analysis
:::
***
::::
:::::
## broom
:::::{.my-package}
:::{.my-package-header}
broom: Convert Statistical Objects into Tidy Tibbles
:::
::::{.my-package-container}
***
::: {#pak-broom}
***
{**broom**}: [Convert Statistical Objects into Tidy Tibbles](https://broom.tidymodels.org/) [@broom]
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once.
:::
{**broom**} provides three verbs to make it convenient to interact with model objects:
- `tidy()` summarizes information about model components
- `glance()` reports information about the entire model
- `augment()` adds informations about observations to a dataset
For a detailed introduction, please see [Introduction to broom](https://broom.tidymodels.org/articles/broom.html).
{**broom**} tidies 100+ models from popular modelling packages and almost all of the model objects in the stats package that comes with base R.
The vignette [Available methods](https://broom.tidymodels.org/articles/available-methods.html) lists method availability.
{**broom**}: Convert Statistical Objects into Tidy Tibbles
:::
***
::::
:::::
## car
:::::{.my-package}
:::{.my-package-header}
car: Companion to Applied Regression
:::
::::{.my-package-container}
***
::: {#pak-car}
***
{**car**}: [Companion to Applied Regression](https://www.john-fox.ca/Companion/index.html) [@car]
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019. [@fox2018]
:::
An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. The book provides a step-by-step guide to using the free statistical software R, and emphasizes integrating statistical computing in R with the practice of data analysis. The R packages car and effects, written to facilitate the application and interpretation of regression analysis, are extensively covered in the book.
{**car**}: Companion to Applied Regression
:::
***
::::
:::::
## colorblindcheck
:::::{.my-package}
:::{.my-package-header}
colorblindcheck: Check Color Palettes for Problems with Color Vision Deficiency
:::
::::{.my-package-container}
***
::: {#pak-colorblindcheck}
***
{**colorblindcheck**}: [Check Color Palettes for Problems with Color Vision Deficiency](https://jakubnowosad.com/colorblindcheck/) [@colorblindcheck]
{*There is no hexagon sticker available for {**colorblindcheck**}.*}
Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia. It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies.
Deciding if a color palette is a colorblind friendly is a hard task. This cannot be done in an entirely automatic fashion, as the decision needs to be confirmed by visual judgments. The goal of {**colorblindcheck**} is to provide tools to decide if the selected color palette is colorblind friendly, including:
- `palette_dist()` - Calculation of the distances between the colors in the input palette and between the colors in simulations of the color vision deficiencies: deuteranopia, protanopia, and tritanopia.
- `palette_plot()` - Plotting of the original input palette and simulations of color vision deficiencies: deuteranopia, protanopia, and tritanopia.
- `palette_check()` - Creating summary statistics comparing the original input palette and simulations of color vision deficiencies: deuteranopia, protanopia, and tritanopia.
{**colorblindcheck**}: A Package for Check Color Palettes for Problems with Color Vision Deficiency
:::
***
::::
:::::
## colorblindr
:::::{.my-package}
:::{.my-package-header}
colorblindr: Simulate colorblindness in R figures
:::
::::{.my-package-container}
***
::: {#pak-colorblindr}
***
{**colorblindr**}: [Simulate colorblindness in R figures](https://github.com/clauswilke/colorblindr) [@colorblindr]
(*There is no hexagon logo for {**colorblindr**} available*)
Provides a variety of functions that are helpful to simulate the effects of colorblindness in R figures. Complete figures can be modified to simulate the effects of various types of colorblindness. The resulting figures are standard grid objects and can be further manipulated or outputted as usual.
{**colorblindr**}: A Package for Simulate colorblindness in R figures
:::
***
::::
:::::
## colorspace
:::::{.my-package}
:::{.my-package-header}
colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes
:::
::::{.my-package-container}
***
::: {#pak-colorspace}
***
{**colorspace**}: [A Toolbox for Manipulating and Assessing Colors and Palettes](URL to package) [biblio]
(*There is no hexagon logo for {**colorspace**} available*)
The colorspace package provides a broad toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in various kinds of visualizations.
At the core of the package there are various utilities for computing with color spaces (as the name of the package conveys). Thus, the package helps to map various three-dimensional representations of color to each other. A particularly important mapping is the one from the perceptually-based and device-independent color model HCL (Hue-Chroma-Luminance) to standard Red-Green-Blue (sRGB) which is the basis for color specifications in many systems based on the corresponding hex codes (e.g., in HTML but also in R). For completeness further standard color models are included as well in the package: polarLUV() (= HCL), LUV(), polarLAB(), LAB(), XYZ(), RGB(), sRGB(), HLS(), HSV().
The HCL space (= polar coordinates in CIELUV) is particularly useful for specifying individual colors and color palettes as its three axes match those of the human visual system very well: Hue (= type of color, dominant wavelength), chroma (= colorfulness), luminance (= brightness).
There is extensive documentation available. See also the website on [HCL Color Space](https://hclwizard.org/):
> The hclwizard provides tools for manipulating and assessing colors and palettes based on the underlying colorspace software (available in R and Python). It leverages the HCL color space: a color model that is based on human color perception and thus makes it easy to choose good color palettes by varying three color properties: Hue (= type of color, dominant wavelength) - Chroma (= colorfulness) - Luminance (= brightness). As shown in the color swatches below each property can be varied while keeping the other two properties fixed.
{**colorspace**}: A Toolbox for Manipulating and Assessing Colors and Palettes
:::
This toolbox package is very important: All of the other color palette related package uses {**colorspace**} as a bases for their functionality.
***
::::
:::::
## cowplot
::: {.my-package}
::: {.my-package-header}
cowplot: Streamlined Plot Theme and Plot Annotations for 'ggplot2'
:::
::: {.my-package-container}
------------------------------------------------------------------------
::: {#pak-cowplot}
------------------------------------------------------------------------
{**cowplot**}: [Streamlined Plot Theme and Plot Annotations for
'ggplot2'](https://wilkelab.org/cowplot/)
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
The {**cowplot**} package provides various features that help with
creating publication-quality figures, such as a set of themes, functions
to align plots and arrange them into complex compound figures, and
functions that make it easy to annotate plots and or mix plots with
images. The package was originally written for internal use in the Wilke
lab, hence the name (Claus O. Wilke’s plot package). It has also been
used extensively in the book [Fundamentals of Data
Visualization](https://www.amazon.com/gp/product/1492031089).
:::
There are several packages that can be used to align plots. The most
widely used ones beside {**cowplot**} are {**egg**} and {**patchwork**}
(see @pak-patchwork). All these packages use slightly different
approaches to plot alignment, and the respective approaches have
different strengths and weaknesses. If you cannot achieve your desired
result with one of these packages try another one.
Most importantly, while {**egg**} and {**patchwork**} align and arrange
plots at the same time, {**cowplot**} aligns plots independently of how
they are arranged. This makes it possible to align plots and then
reproduce them separately, or even overlay them on top of each other.
The {**cowplot**} package now provides a set of complementary themes
with different features. I now believe that there isn’t one single theme
that works for all figures, and therefore I recommend that you always
explicitly set a theme for every plot you make.
{**cowplot**}: A Package for Streamlined Plot Themes and Plot
Annotations for {**ggplot2**}
:::
:::
:::
## cranlogs
:::::{.my-package}
:::{.my-package-header}
cranlogs: Download Logs from the 'RStudio' 'CRAN' Mirror
:::
::::{.my-package-container}
***
::: {#pak-cranlogs}
***
{**cranlogs**}: [Download Logs from the RStudio CRAN Mirror](https://r-hub.github.io/cranlogs/) [@cranlogs]
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
`r glossary("APIx", "API")` to the database of `r glossary("CRAN")` package downloads from the RStudio CRAN mirror. The database itself is at <http://cranlogs.r-pkg.org>,
see <https://github.com/r-hub/cranlogs.app> for the raw API.
:::
RStudio publishes the download logs from their CRAN package mirror daily at http://cran-logs.rstudio.com.
This R package queries a web API maintained by R-hub that contains the daily download numbers for each package.
The RStudio CRAN mirror is not the only CRAN mirror, but it’s a popular one: it’s the default choice for RStudio users. The actual number of downloads over all CRAN mirrors is unknown.
{**cranlogs**}: Download Logs from the 'RStudio' 'CRAN' Mirror
:::
***
::::
:::::
## crosstable
:::::{.my-package}
:::{.my-package-header}
crosstable: Crosstables for Descriptive Analyses
:::
::::{.my-package-container}
***
::: {#pak-crosstable}
***
{**crosstable**}: [Crosstables for Descriptive Analyses](https://danchaltiel.github.io/crosstable/) [@crosstable]
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
Crosstable is a package centered on a single function, crosstable, which easily computes descriptive statistics on datasets. It can use the {**tidyverse**} syntax and is interfaced with the package {**officer**} to create automatized reports.
:::
Create descriptive tables for continuous and categorical variables. Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using {**rmarkdown**} or {**officer**}. You can also compute effect sizes and statistical tests if needed.
{**crosstable**}: Crosstables for Descriptive Analyses
:::
I believe that the main usage for this package is to prepare ready-to-print tables. Similar like {**gtsummary**} (see @pak-gtsummary) it provides some descriptive statistics with many display options. But I got the impression that analysis of data is not the main usage of these packages.
For instance you could use `crosstable::display_test(chisq.test(x, y))` to get as result a string, for instance: p value: <0.0001 \n(Pearson's Chi-squared test)". This is nice to include for a table, but for the analysis one would also need the values of the different cells.
***
::::
:::::
## curl
:::::{.my-package}
:::{.my-package-header}
curl: A Modern and Flexible Web Client for R
:::
::::{.my-package-container}
***
::: {#pak-curl}
***
{**curl**}: [A Modern and Flexible Web Client for R](https://cran.r-project.org/web/packages/curl/vignettes/intro.html) [@curl]
(*There is no hexagon logo for {**curl**} available*)
The `curl()` and `curl_download(`) functions provide highly configurable drop-in replacements for base `url()` and `download.file()` with better performance, support for encryption (https, ftps), gzip compression, authentication, and other 'libcurl' goodies.
The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly web client see the 'httr' package which builds on this package with http specific tools and logic.
{**curl**}: A Modern and Flexible Web Client for R
:::
***
::::
:::::
## data.table
:::::{.my-package}
:::{.my-package-header}
data.table: Extension of `data.frame`
:::
::::{.my-package-container}
***
::: {#pak-data-table}
***
{**data.table**}: [Extension of `data.frame`](URL to package) [biblio]
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
{**data.table**} provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.
:::
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
**Features**
- fast and friendly delimited file reader: `data.table::fread()`, see also convenience features for small data
- fast and feature rich delimited file writer: `data.table::fwrite()`
- low-level parallelism: many common operations are internally parallelized to use multiple CPU threads
- fast and scalable aggregations; e.g. 100GB in RAM (see benchmarks on up to two billion rows)
- fast and feature rich joins: ordered joins (e.g. rolling forwards, backwards, nearest and limited staleness), overlapping range joins (similar to IRanges::findOverlaps), non-equi joins (i.e. joins using operators >, >=, <, <=), aggregate on join (by=.EACHI), update on join
- fast add/update/delete columns by reference by group using no copies at all
- fast and feature rich reshaping data: `data.table::dcast()` (pivot/wider/spread) and `data.table::melt()` (unpivot/longer/gather)
- any R function from any R package can be used in queries not just the subset of functions made available by a database backend, also columns of type list are supported
- has no dependencies at all other than base R itself, for simpler production/maintenance
- the R dependency is as old as possible for as long as possible, dated April 2014, and we continuously test against that version; e.g. v1.11.0 released on 5 May 2018 bumped the dependency up from 5 year old R 3.0.0 to 4 year old R 3.1.0
{**data.table**}: A Package for the Extension of `data.frame`
:::
I believe the most important application of {**data.table**} is working with huge amount of data (several GB). In the book SwR it is used in this first chapter with the `data.table::fread()` function. I have used here the `readr::read_csv()` as part of the {**tidyverse**} collection, because the dataset is very small (29 kB).
With {**DT**} there is another package that seems important. It is a wrapper of the JavaScript library 'DataTables' (See @pak-DT). I was using already {**DT**} to display interactive tables on websites, but I do not understand completely the difference between {**data.table**} and {**DT**}.
As far as I understood:
- Using {**data.table**} is very fast but you will loose the inutitive function of the tidyverse.
- {**DT**} is for the presentation of (large) interactive tables whereas {**data.table**} is for the fast manipulation of huge data sets.
***
::::
:::::
## datawizard
:::::{.my-package}
:::{.my-package-header}
datawizard: Easy Data Wrangling and Statistical Transformations
:::
::::{.my-package-container}
***
::: {#pak-datawizard}
***
{**datawizard**}: [Easy Data Wrangling and Statistical Transformations](https://easystats.github.io/datawizard/) [@datawizard]
::: {layout="[10, 30]" layout-valign="center"}
{width="176"}
{**datawizard**} is a lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. It is part of the {**easystats**} ecosystem, a suite of R packages to deal with your entire statistical analysis, from cleaning the data to reporting the results.
:::
{**datawizard**} covers two aspects of data preparation:
- **Data manipulation**: datawizard offers a very similar set of functions to that of the tidyverse packages, such as a {**dplyr**} and {**tidyr**}, to select, filter and reshape data, with a few key differences.
1) All data manipulation functions start with the prefix `data_*` (which makes them easy to identify).
2) Although most functions can be used exactly as their tidyverse equivalents, they are also string-friendly (which makes them easy to program with and use inside functions).
3) Finally, datawizard is super lightweight (no dependencies, similar to {**poorman**}), which makes it awesome for developers to use in their packages.
- Statistical transformations: {**datawizard**} also has powerful functions to easily apply common data transformations, including standardization, normalization, rescaling, rank-transformation, scale reversing, recoding, binning, etc.
{**datawizard**}: Easy Data Wrangling and Statistical Transformations
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***
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## descr
:::::{.my-package}
:::{.my-package-header}
descr: Descriptive Statistics
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***
::: {#pak-descr}
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{**descr**}: [Descriptive Statistics](https://github.com/jalvesaq/descr)
There is no logo available for {**descr**}
Weighted frequency and contingency tables of categorical variables and of the comparison of the mean value of a numerical variable by the levels of a factor, and methods to produce xtable objects of the tables and to plot them. There are also functions to facilitate the character encoding conversion of objects, to quickly convert fixed width files into csv ones, and to export a data.frame to a text file with the necessary R and SPSS codes to reread the data. [@descr]
Package for Descriptive Statistics
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***
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## DescTools
:::::{.my-package}
:::{.my-package-header}
DescTools: Tools for Descriptive Statistics
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***
::: {#pak-DescTools}
***
{**DescTools**}: [Tools for Descriptive Statistics](https://andrisignorell.github.io/DescTools/) [@DescTools]
(*There is no hexagon logo for {**DescTools**} available*)
:::
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results.
The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.
{**DescTools**}: Tools for Descriptive Statistics
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***
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## dfidx
:::::{.my-package}
:::{.my-package-header}
dfidx: Indexed Data Frames
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***
::: {#pak-dfidx}
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{**dfidx**}: [Indexed Data Frames](https://cran.r-project.org/package=dfidx) [@dfidx]
(*There is no hexagon logo for {**dfidx**} available*)
Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.
{**dfidx**}: Indexed Data Frames
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***
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## dichromat
:::::{.my-package}
:::{.my-package-header}
dichromat: Color Schemes for Dichromats
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::::{.my-package-container}
***
::: {#pak-dichromat}
***
{**dichromats**}: [Color Schemes for Dichromats](https://cran.r-project.org/package=dichromat) [@dichromat]
Collapse red-green or green-blue distinctions to simulate the effects of different types of color-blindness.
{**dichromat**}: A Package for Color Schemes for Dichromats
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<Other text not included in the package reference>
***
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## dplyr
:::::{.my-package}
:::{.my-package-header}
dplyr:: A Grammar of Data Manipulation
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***
::: {#pak-dplyr}
***
{**package**}: [A Grammar of Data Manipulation](https://dplyr.tidyverse.org/)
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{**dplyr**} is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:
- mutate() adds new variables that are functions of existing variables
- select() picks variables based on their names.
- filter() picks cases based on their values.
- summarise() reduces multiple values down to a single summary.
- arrange() changes the ordering of the rows. [@dplyr]
:::
These all combine naturally with group_by() which allows you to perform any operation “by group”. You can learn more about them in [vignette("dplyr")](https://dplyr.tidyverse.org/articles/dplyr.html). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in [vignette("two-table")](https://dplyr.tidyverse.org/articles/two-table.html). [@dplyr]
Package for Data Manipulation
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## DT
:::::{.my-package}
:::{.my-package-header}
DT: A Wrapper of the JavaScript Library 'DataTables'
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***
::: {#pak-DT}
***
{**DT**}: [https://github.com/rstudio/DT](https://rstudio.github.io/DT/)
(*There is no hexagon icon available for {**DT**}*)
Data objects in R can be rendered as HTML tables using the JavaScript library [DataTables](https://datatables.net/) (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name {**DT**} is an abbreviation of 'DataTables'.
{**DT**}: A Package as Wrapper of the JavaScript Library 'DataTables'
:::
What I do not understand: What is the relationship of {**DT**} to the {**data.table**} package?
***
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## dunn.test
:::::{.my-package}
:::{.my-package-header}
dunn.test: Dunn's Test of Multiple Comparisons Using Rank Sums
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::::{.my-package-container}
***
::: {#pak-dunn.test}
***
{**dunn.test**}: [Dunn's Test of Multiple Comparisons Using Rank Sums](https://cran.r-project.org/package=dunn.test) [@dunn.test]
Computes Dunn's test [@dunn1964] for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups [@kruskal1952. The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other.
{**dunn.test**} makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the `r glossary("Mann-Whitney", "Wilcoxon-Mann-Whitney rank-sum test")`. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. {**dunn.test**} accounts for tied ranks.
{**dunn.test**}: Dunn's Test of Multiple Comparisons Using Rank Sums
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***
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## e1071
:::::{.my-package}
:::{.my-package-header}
e1071: Misc. functions
:::
::::{.my-package-container}
***
::: {#pak-e1071}
***
{**e1071**}: [Misc. functions](https://cran.r-project.org/web/packages/e1071/index.html) [@e1071]
(*There is no hexagon logo for {**e1071**} available*)
Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour ...
{**e1071**}: Misc. functions
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***
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## effectsize
:::::{.my-package}
:::{.my-package-header}
effectsize: Indices of Effect Size
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***
::: {#pak-effectsize}
***
{**effectsize**}: [Indices of Effect Size](https://easystats.github.io/effectsize/) [@effectsize]
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The goal of this package is to provide utilities to work with indices of effect size and standardized parameters, allowing computation and conversion of indices such as Cohen’s d, r, odds-ratios, etc.
:::
Provide utilities to work with indices of effect size for a wide variety of models and hypothesis tests (see list of supported models using the function 'insight::supported_models()'), allowing computation of and conversion between indices such as Cohen's d, r, odds, etc.
{**effectsize**}: Indices of Effect Size
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***
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## fmsb
:::::{.my-package}
:::{.my-package-header}
fmsb: Functions for Medical Statistics Book with some Demographic Data
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::::{.my-package-container}
***
::: {#pak-fmsb}
***
{**fmsb**}: [Functions for Medical Statistics Book with some Demographic Data](https://cran.r-project.org/package=fmsb) [@fmsb]
(*There is no hexagon logo for {**fmsb**} available*)
Several utility functions for the book entitled "Practices of Medical and Health Data Analysis using R" (Pearson Education Japan, 2007) with Japanese demographic data and some demographic analysis related functions.
{**fmsb**}: Functions for Medical Statistics Book with some Demographic Data
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***
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## forcats
:::::{.my-package}
:::{.my-package-header}
forcats: Tools for Working with Categorical Variables
:::
::::{.my-package-container}
***
::: {#pak-forcats}
***
{**forcats**}: [Tools for Working with Categorical Variables (Factors)](https://forcats.tidyverse.org/)
{**forcats**} provide a suite of useful tools that solve common problems with factors.
"Forcats" is an anagram of "factors" and part of the {**tidyverse**} suite of packages.
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(1) reordering factor levels
- moving specified levels to front,
- ordering by first appearance,
- reversing, and
- randomly shuffling
(2) tools for modifying factor levels
- collapsing rare levels into other,
- 'anonymizing', and
- manually 'recoding'
:::
forcats: A Package with Tools for Working with Categorical Variables
:::
***
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## GGally
:::::{.my-package}
:::{.my-package-header}
{**GGally**}: Extension to {**ggplot2**}
:::
::::{.my-package-container}
***
::: {#pak-GGally}
***
{**GGally**}: [Extension to {**ggplot2**}](https://ggobi.github.io/ggally/) [@GGally]
The R package {**ggplot2**} is a plotting system based on the grammar of graphics. {**GGally**} extends {**ggplot2**} by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include
- a pairwise plot matrix,
- a two group pairwise plot matrix,
- a parallel coordinates plot,
- a survival plot,
- and several functions to plot networks.
{**GGally**}: Extension to {**ggplot2**}
:::
***
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## ggfortify
:::::{.my-package}
:::{.my-package-header}
ggfortify: Data Visualization Tools for Statistical Analysis Results
:::
::::{.my-package-container}
***
::: {#pak-ggfortify}
***
{**ggfortify**}: [Data Visualization Tools for Statistical Analysis Results](https://github.com/sinhrks/ggfortify) [@ggfortify]
(*There is no hexagon logo for {**ggfortify**} available*)
Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using {**ggplot2**}.
This package offers `fortify()` and `autoplot()` functions to allow automatic {**ggplot2**} to visualize statistical result of popular R packages. Check out our R Journal paper for more details on the overall architecture design and a gallery of visualizations created with this package. Also check out autoplotly package that could automatically generate interactive visualizations with plotly.js style based on ggfortify. The generated visualizations can also be easily extended using ggplot2 syntax while staying interactive.
{**ggfortify**}: Data Visualization Tools for Statistical Analysis Results
:::
The packages has for this book to be loaded explicitly. Calling functions with the `ggfortify::` syntax will not work, because {**ggfortify**} needs (and loads) {**ggplot**}.
***
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## ggmosaic
::: {.my-package}
::: {.my-package-header}
{**ggmosaic**}: Mosaic Plots in the {**ggplot2**} Framework
:::
::: {.my-package-container}
------------------------------------------------------------------------
::: {#pak-ggmosaic}
------------------------------------------------------------------------
{**ggmosaic**}: [Mosaic Plots in the {**ggplot2**}
Framework](https://haleyjeppson.github.io/ggmosaic/) [@ggmosaic]
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{**ggmosaic**} is designed to create visualizations of categorical data
and is capable of producing bar charts, stacked bar charts, mosaic
plots, and double decker plots and therefore offers a wide range of
potential plots.
:::
Furthermore, {**ggmosaic**} allows various features to be customized:
- the order of the variables,
- the formula setup of the plot,
- faceting,
- the type of partition, and
- the space between the categories.
{**ggmosaic**}: A Package for Mosaic Plots in the {**ggplot2**} Framework
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## ggokabeito
:::::{.my-package}
:::{.my-package-header}
ggokabeito: 'Okabe-Ito' Scales for {**ggplot2**} and {**ggraph**}
:::
::::{.my-package-container}
***
::: {#pak-ggokabeito}
***
{**ggokabeito**}: ['Okabe-Ito' Scales for {**ggplot2**} and {**ggraph**}](https://malcolmbarrett.github.io/ggokabeito/index.html) [@ggokabeito]
(*There is no hexagon logo for {**ggokabeito**} available*)
Discrete scales for the colorblind-friendly `Okabe-Ito` palette, including 'color', 'fill', and 'edge_colour'. {**ggokabeito**} provides {**ggplot2**} and {**ggraph**} scales to easily use the discrete, colorblind-friendly ‘Okabe-Ito’ palette in your data visualizations.
Currently, {**ggokabeito**} provides the following scales:
- `scale_color_okabe_ito(`)/`scale_colour_okabe_ito()`
- `scale_fill_okabe_ito()`
- `scale_edge_color_okabe_ito()`/`scale_edge_colour_okabe_ito()`