Download Ggplot2 Shiny
Elvira Mu
This app is for reseachers, students or professors who want to learn how to make a plot with ggplot2. With this app, you can make your plot step by step without coding. You can obtain beautiful plots in png or pdf format. You can also download the powerpoint file with or without r code by just a click.
I thought that my syntax is wrong, and when I asked somebody for advice, he said that it works fine for him (he uses R version 3.5.0 , plotly_4.7.1.9000 ggplot2_2.2.1.9000 while I have R version 3.5.1., plotly v.4.8.0 and ggplot2 v 3.0.0.).
I immediately tried alot of options to fix this: installed plotly v4.7.1. and ggplot2 v.2.2.1. from CRAN (this worked for a bit. After i ran ggplotly, it showed the map both in the viewer and in shiny but shortly after something went wrong, i was asked to update shiny package and to use the dev version of ggplot2), i updated to dev versions to both ggplot & plotly, i tried to use R version 3.5.0 but there were alot of incompatibilities with other packages and now I am back to where I started.
Simplified theming of ggplot2, lattice, and base R graphics. In addition to providing a centralized approach to styling R graphics, thematic also enables automatic styling of R plots in Shiny, R Markdown, and RStudio.
Call thematic_rmd() before generating plots inside R Markdown to have all subsequent plots within the document reflect the relevant theme. In a static (i.e., non-runtime: shiny) R Markdown context, auto-theming only works with bslib-powered rmarkdown::html_document() (as in the example below), but in other situations you may also provide colors and fonts explicitly to thematic_rmd().
By default, thematic attempts to detect the relevant background, foreground, and accent colors. However, you may also specify these settings more directly by providing relevant color and fonts directly to thematic_on() (or thematic_shiny()/thematic_rmd()).
Para dar a atenção que eles merecem, farei aqui uma série de posts para apresentar as principais alternativas para construção de gráficos dentro do Shiny. Falaremos do plotly, do echarts4r, do highcharter, começando neste post pelo nosso fiel companheiro: o ggplot2.
O pacote ggplot2 é uma ferramenta maravilhosa para produzirmos gráficos no R. Quando entendemos a sua sintaxe, nos tornamos capazes de fazer uma variedade enorme de gráficos e dar a eles a cara que quisermos.
We will show how this can be achieved with static plots generated by ggplot2 and also with two JavaScript libraries, Plotly and HighCharts, which allow for even more interactivity including zooming and tooltips.
It is possible when using ggplot2 (and base) graphics to handle mouse click events within a Shiny application. This can be very useful for allowing a user to select data points of interest and display more detailed information about the items selected.
Note that if you are using base graphics the nearPoints function needs to be provided with details of what columns were used for the x- and y-axes using the xvar and yvar arguments; for ggplot2 Shiny was able to infer these.
ggiraph is a tool that allows you to create dynamic ggplot graphs. This allows you to add tooltips, hover effects and JavaScript actions to the graphics. The package also allows the selection of graphical elements when used in shiny applications.
For plots created with ggplot2, it should not be necessary to specify thecolumn names; that information will already be contained in the brush,provided that variables are in the original data, and not computed. Forexample, with ggplot(cars, aes(x=speed, y=dist)) + geom_point(), youcould use brushedPoints(cars, input$cars_brush). If, however, you usea computed column, like ggplot(cars, aes(x=speed/2, y=dist)) +geom_point(), then it will not be able to automatically extract column namesand filter on them. If you want to use this function to filter data, it isrecommended that you not use computed columns; instead, modify the datafirst, and then make the plot with "raw" columns in the modified data.
Alternate text for the HTML tag if it cannot be displayedor viewed (i.e., the user uses a screen reader). In addition to a characterstring, the value may be a reactive expression (or a function referencingreactive values) that returns a character string. If the value is NA (thedefault), then ggplot2::get_alt_text() is used to extract alt text fromggplot objects; for other plots, NA results in alt text of "Plot object".NULL or "" is not recommended because those should be limited todecorative images.
With ggplot2 graphics, the code in renderPlot should return a ggplotobject; if instead the code prints the ggplot2 object with something likeprint(p), then the coordinates for interactive graphics will not beproperly scaled to the data space.
Shinylive deployment was relatively seamless - it integrated well with GitHub Pages and I was able to automate the deployment so any changes to main on GitHub trigger a re-deploy. The official documentation was enough to guide me through the process. I did have a look at a few other blog posts on shinylive, but some of the early tutorials were a little out of date since Shiny for Python is still in active development and changing. If you are browsing for blog posts, look for one that gets updated, or has a recent date. I particularly like this guide from Rami Krispin which talks about deploying an app with shinylive to GitHub Pages.
If you want to explore the Shiny apps mentioned in this post, the Python version is deployed on GitHub Pages, and the source code can be found on GitHub. The R version is deployed on shinyapps.io, and the source code can be found on GitHub.
It is super simple to combine shiny and ggiraph. All you have to do is to drop a girafeOutput() in the UI and a renderGirafe() in the server function. For example, let us embed our interactive boxplot into a Shiny app.
A shiny app-based GUI wrapper for ggplot2 with built-in statisticalanalysis. Import data from file and use dropdown menus and checkboxes tospecify the plotting variables, graph type, and look of your plots. Oncecreated, plots can be saved independently or stored in a report that canbe saved as a pdf. If new data are added to the file, the report can berefreshed to include new data. Statistical tests can be selected andadded to the graphs.
When you are building a shiny (Chang et al. 2020) application, you will have to deal with dependencies.Well, at least with one dependency, shiny.But chances are that you will not only be using shiny inside your application: you will probably call functions from other packages, for example, from ggplot2 (Wickham, Chang, et al. 2021) for plotting, or any other package that is necessary for your application to work.
If you are building your application using golem, you will have 3 default dependencies: golem itself, shiny, and config.If you call golem::use_recommended_deps() in the first workflow script, you will also have shiny, DT, attempt, glue, htmltools, and golem as dependencies to your package.39But what about other dependencies like ggplot2? These ones need to be added by hand.
The DESCRIPTION file dictates which packages have to be installed when your application is installed.For example, when you install golem on your machine, you will also need other packages that are internally used by golem.And your application will also have dependencies: at the very least shiny, golem, and config.When building your application, you have to list these requirements somewhere, and the standard way to do that is by using the DESCRIPTION file.41
For our example, we will use the reactive_demo app. It shows three select inputs that allow the user to choose values from the cut, color, and clarity columns of the diamonds dataset from ggplot2, then draws a plot of the relationship between carat and price for the selected subset.
You will grouping and summarising the msleep data table from ggplot2 by calculating the mean or standard deviation for all (or some) of the numeric columns grouped by the categorical columns vore, order, or conservation. If you're not sure how to create such a summary table with dplyr, look at the following code for a concrete example.
If you attach and run the notebook hosting the Shiny app on a different cluster, the Shiny URL changes. Also, if you restart the app on the same cluster, Shiny might pick a different random port. To ensure a stable URL, you can set the shiny.port option, or, when restarting the app on the same cluster, you can specify the port argument.
This code would create a scatter plot with body mass on the x-axis and flipper length on the y-axis. You can customize the appearance of the plot by adding additional layers, such as a smoother line or different color schemes, using the geom_ functions provided by ggplot2. For more information and examples, you can refer to the ggplot2 documentation and tutorials online.
Where ChatGPT is likely to prove beneficial is for R learners (a group, to be clear, that includes everyone who ever Googles an R question; i.e. all of us). My question about and the tweet by Marek Rogala below show how ChatGPT can teach you how ggplot2 (or any R package) works.
What makes plotly especially attractive is that it can convert any ggplot2 object into a plotly object using the ggplotly() function.This enables immediate interactivity for existing data graphics. Features like brushing (where selected points are marked) and mouse-over annotations (where points display additional information when the mouse hovers over them) are automatic.For example, in Figure 14.1 we display a static plot of the frequency of the names of births in the United States of the four members of the Beatles overtime (using data from the babynames package).
The code shown in Figure 14.12 also includes a call to the add_busy_spinner() function from the shinybusy package.It takes time to render the various reactive objects, and the spinner shows up to alert the user that there will be a slight delay.
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