No Clocks

No Clocks

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Buildpacks | Heroku Dev Center
Buildpacks | Heroku Dev Center
An overview of buildpacks, which are responsible for transforming deployed code into a slug, which can then be executed on a dyno
·devcenter.heroku.com·
Buildpacks | Heroku Dev Center
Repeated Evaluation • retry
Repeated Evaluation • retry
Provide simple mechanism to repeatedly evaluate an expression until either it succeeds or timeout exceeded. It is useful in situations that random failures could happen.
·randy3k.github.io·
Repeated Evaluation • retry
css - Rshiny Table collapse text overflow - Stack Overflow
css - Rshiny Table collapse text overflow - Stack Overflow
How do we pass CSS arguments text-overflow: ellipsis or other arguments to renderDataTable in R shiny ? I have uneven text description in columns, by Autowidth the rows and columns are expanded bas...
·stackoverflow.com·
css - Rshiny Table collapse text overflow - Stack Overflow
ThinkR
ThinkR
R Engineering, training, and consulting. ThinkR has 87 repositories available. Follow their code on GitHub.
·github.com·
ThinkR
Parameterize an R Markdown report using Shiny components — yml_params • ymlthis
Parameterize an R Markdown report using Shiny components — yml_params • ymlthis
R Markdown lets you add dynamic parameters to your report using the params YAML field (see the R Markdown book for examples); parameterized reports are also used in RStudio Connect. The values of these variables can be called inside your R Markdown document using params$field_name. There are several ways to change the values of the parameters: manually change the YAML, use the params argument in rmarkdown::render(), or knit with parameters, which launches a Shiny app to select values for each. yml_params() accepts any number of named R objects to set as YAML fields. You can also pass argume...
·ymlthis.r-lib.org·
Parameterize an R Markdown report using Shiny components — yml_params • ymlthis
TnV Blog
TnV Blog
Docking or containerization is a new method of distribute a software/tool. Beside providing only the source code for installing, we give the users the so-called container, which contains the whole environment to run the program, including the tool and its dependencies with the exact version and all the needed configurations. By delivering such a “container”, users are always able to “reuse” the tool and reproduce the results as we did.
·trvinh.github.io·
TnV Blog