You can use renderImage() if you want to display existing images (not plots).For example, you might have a directory of photographs that you want shown to the user.The following app illustrates the basics of renderImage() by showing cute puppy photos.The photos come from , my favourite source of royalty free stock photographs.
You must also supply the deleteFile argument.Unfortunately renderImage() was originally designed to work with temporary files, so it automatically deleted images after rendering them.This was obviously very dangerous, so the behaviour changed in Shiny 1.5.0.Now shiny no longer deletes the images, but instead forces you to explicitly choose which behaviour you want.
R Shiny Download Graph
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There are a number of packages that we can use to make interactive plots in Shiny. Personally I use a lot of plotly (by that I mean their original syntax, not ggploty() which is nice but not sufficient), RStudio seems to favour dygraphs (at least for time series) and I've seen people use both highcharts and anycharts.
What is interactive? By a broad definition, ggplot and ggvis can be considered to be interactive when incorporated into an interactive shiny app. However, when I say interactive I mainly refer to the ability of hoover-functions.
I am currently working on a Shiny application that has a customizable interactive graph, and I have chosen to use ggvis due to the speed at which it can re-make the graph. A user can make make a change to the graph, and see the result almost instantly. It also helps that is integrates so well with dplyr.
Interesting. I really like ggvis. However, the lack of hoovering is a problem for me. Not that the hoovering itself necessarily improves the analysis but as soon as I see a graph online I want to be able to see what's in it.
Complete reactives can return their values immediately; invalidated reactives will kick off their own execution graph.This cycle will repeat until every invalidated output enters the complete (green) state.
The code doesn't throw any error. It works fine. Issue is only with displaying plotly graph in the browser. I tried it on all browser, graph is displayed on viewer pane instead of the browser, although If I plot other chart like barplot it easily get displayed on the browser. I am not able to resolve the issue!! Help me out!! Thanks in advance!!!!
I wish to have a shiny app created where I will plot two dataframes in ggplot, then do some simple stats calcs on a variable and plot a summary table underneath it. If I leave the table calculations out then I can plot the line plots just fine and have them change based on the $input function. The problem comes when I try to have the table update based on the $input value from the ui.
yes, you need chains of reactivity so your app runs reactively... thats largely the concept of shiny.
why would copy and pasting be easier, if they have the same content then make a single reactive that is their shared content, and call it twice in the two places its needed.
So my understanding it that if I want both the plot and table to react to the input, all expressions that generate the ggplot line graph and the summary statistics will have to be reactive. In theory this isn't too bad but I have some while loops and some dataframe creations that would have to be reactive, not just simple assignments, which is beyond my knowledge of shiny at the moment.
I have a shinyscript prepared where i want to show a graph based on two widgets.The first widget(selectInput) controls for the area i want to show a diagnostic plot for.The second widget (checkboxGroupInput) controls for the amount of data i want to show for the area selected with the first widget. So, the options for the checkboxes depend on what is selected with the selectInput.
This only shows when all the checkboxes are unchecked and no graph is visible. I Can plot the graph that corresponds with the checkboxes, but it only shows 5 barcharts (when for example ten are to be plotted) and the error is given.
I'm new to Shiny, so apologies if I'm way off on this. I'm trying to make an interactive line graph through Shiny so a country can be chosen through drop down list, and then an accompanying line graph can show how suicides/100k people within that country have changed from 1987 to 2015. It looks like I have the Input portion correct, but I am struggling with coding the appropriate output plot. More specifically, I am unsure of where to introduce the reactivity portion of this (where do I put the input$country?).
I've always loved statistics and messing with numbers and so following my interests, I made a cumulative probability graph of encountering at least 1 Shiny Pokemon for all the Shiny Hunting Methods in Pokemon Sun and Moon based on rates/mechanics given by Bulbapedia which I believe to be accurate. This includes SR/RE, Masuda Method, and SOS Chaining, all with and without Shiny Charm (if I missed a method please let me know!)
If you want to see how many encounters or eggs you need for you to get at least 1 shiny 50% or 90% or even 99% of the time, check out the graph. There are instructions on how to use it within the graph.
I have a input where the user can select which type of graph they want to see. But my problem is I can't figure out to get the graph to change to based off the input. Here a screenshot of what I am working with.
Hello everyone , i would like to have a Shiny application which could print an interactive graph (thanks to the package plotly on Rstudio) with the selection of the x and y parameters with this function : selectInput()
The data come from a database already import in Rstudio named table31.
Here is my code :
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I would like to adjust my generated graph. Looking at it, it looks like a stretched graphic (I've inserted an image for you to see). I could centralize it more, make it more "beautiful", let's put it that way.
While that is the best citation and archival location of the dataset, using Shiny, one can also provide an easy-to-use exploratory web application that you use to make your point that directly loads the data from the archival site. For example, the Board of Fisheries application above lets people who are not inherently familiar with the data to generate graphs showing the relationships between the variables in the dataset.
To extend the layout, we will first nest the existing sidebarLayout in a newverticalLayout, which simply flows components down the page vertically. Thenwe will add a new sidebarLayout to contain the bottom controls and graph.
The easiest path is to create an account on shinyapps.io, and then configure RStudio touse that account for publishing. Instructions for enabling your local RStudio to publishto your account are displayed when you first log into shinyapps.io:
In this article we walk you through the steps of building a shiny app for visualizing disease occurrence in the United States from 1928-2011. The finished app is available at the following link. Take a look at the app, play with it, and then come back and build it yourself!
Inputs and outputs are the key components of the ui. Inputs are the features of the app that users interact with, such as a numeric slider bar, date range, check boxes, text input, or a drop down menu. You can see all the options included in the standard shiny package at -gallery/.
Often when building a shiny app, you will be working with a dataset that you will want to change in some way to reflect user inputs. To do this, you can create a reactive expression in the server object that will make those changes to the data while the app is running.
Before we add the interactive customizations to the app, we will add a line graph plotting disease frequencies over time in the main panel. As we add our customizations, the user will be able to manipulate the data displayed in this graph.
At this point your app.R file should match the code below. If you run the app as is, you get a very busy graph, with the aggregated frequencies across all states displayed for every disease. (Try it!) Allowing the user to select the data they are most interested in will result in a much more visually appealing and readable graph.
Finally, our last output will be a second graph, a box plot, this time using plotly instead of ggplot. Because plotly graphs are interactive, we need to use different output and render functions. Instead of the plotOutput() and renderPlot() functions we used to create the line graph, we will use plotlyOutput() and renderPlotly().
Shiny is an R package from RStudio that makes it incredibly easy to build interactive web applications with R. Behind the scenes, Shiny builds a reactive graph that can quickly become intertwined and difficult to debug. reactlog provides a visual insight into that black box of Shiny reactivity.
Problem Summary: ggplot2 graphs seem to be of poor quality in shiny, when hosted on shinyapps.io. Graph elements contain visible defects, e.g. dots of geom_point() are not round, some seem like triangles, other like rectangles etc. Legend looks as if it has been blown up from a low-res jpg. My example is here.
Detailed description: Developing a simple Shiny application, I have noticed that ggplot graphs are a bit rough around the edges, and was unable to improve the situation. The example app demonstrates this well enough, notice how the dots seem to be of irregular shape. Their shape improves, approximating circularity, when I increase zoom (in Chrome) to 200% or more. On 100% zoom the whole graph looks weird and not smooth. As if when an image resolution is decreased by the factor of 0.77 or something like that, you get minor distortion artefacts; the similar effect is here. The easiest way to see it, is to download the image at 100% zoom (right click, save as), and then increase zoom on the local copy. You will see how dots are far from being round. The code for server.R and ui.R is very simple: e24fc04721
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