person(given = "Arsenij", family = "Ustjanzew", email = "arsenij.ustjanzew@mpi-bn.mpg.de", role = c("aut", "cre", "cph")),
person(given = "Arsenij", family = "Ustjanzew", email = "arsenij.ustjanzew@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-1014-4521")),
person(given = "Jens", family = "Preussner", email = "jens.preussner@mpi-bn.mpg.de", role = c("aut", "cph"), comment = c(ORCID = "0000-0003-1927-3458")),
person(given = "Mario", family = "Looso", email = "mario.looso@mpi-bn.mpg.de", role = c("aut", "cph"), comment = c(ORCID = "0000-0003-1495-9530")))
Description: Create customized, web-based dashboards for data presentation, exploration and sharing. 'i2dash' integrates easily into existing data analysis pipelines and can organize scientific findings thematically across different pages and layouts.
Scientific communication and data visualization are important aspects to illustrate complex concepts and results from data analyses. [R Markdown](https://rmarkdown.rstudio.com) enables weaving together narrative text and code into rich and reproducible documents, that can also run interactive widgets from [R Shiny](http://shiny.rstudio.com/).
Scientific communication and data visualization are important aspects to illustrate complex concepts and results from data analyses. [R Markdown](https://rmarkdown.rstudio.com) enables weaving together narrative text and code into rich and reproducible documents, that can also run interactive widgets from [R Shiny](https://shiny.rstudio.com/).
R Studio's [Flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) introduces dashboards to publish a group of related data visualizations, tables, images and narrative text in row and column-based layouts. Both, R Markdown and R Studio's Flexdashboard require authors to manually create Markdown documents and assemble content at the right position in the file.
The R package i2dash provides classes and functionality to programatically create customized, web-based flexdashboards for data presentation, exploration and sharing. Dashboard content is organised in so-called **components** and can be added iteratively to the dashboard, *i.e.* as data or visualizations become available along the analysis pipeline. Components are reusable and can be distributed across different dashboard **pages** to organize scientific findings thematically. Ultimately, i2dash enables xxx and therefore integrates well into existing data analysis pipelines.
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@@ -146,7 +146,7 @@ dashboard %<>%
## Adding content
Content can be added to pages using **components**. A component can be a R object itself (*e.g.* a widget from [htmwlwidgets](http://gallery.htmlwidgets.org/)), a file path (*e.g.* to a markdown or image file) or a function that can be called to generate content. We'll use the `add_component` function to explore several options and fill `page1` iteratively with three R objects:
Content can be added to pages using **components**. A component can be a R object itself (*e.g.* a widget from [htmwlwidgets](https://gallery.htmlwidgets.org/)), a file path (*e.g.* to a markdown or image file) or a function that can be called to generate content. We'll use the `add_component` function to explore several options and fill `page1` iteratively with three R objects:
```{r, eval=TRUE}
library(leaflet)
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@@ -296,7 +296,7 @@ Lets assume we have an experimental factor called year:
To assign each level a color from the [sequential greens](http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=9) palette from the `r BiocStyle::CRANpkg("RColorBrewer")` package, we create a character vector with the colors and assign the levels as names.
To assign each level a color from the [sequential greens](https://colorbrewer2.org/) palette from the `r BiocStyle::CRANpkg("RColorBrewer")` package, we create a character vector with the colors and assign the levels as names.
