How to filter tables with advanced methods for Shiny Apps?

EkremBayar · May 23, 2023, 8:02am

Thanks for your response @BLukomski,

I may not have been clear when I asked the question. I am sorry about that.

As you gave that example, we can add some inputs to filter the data. However, it is a specific solution, not a general solution. Especially, if the number of columns in the data is large, we can't add inputs one by one for filtering. We have to design a general and advanced filtering method about that.

While I was researching advanced filtering, I saw a great package called shinyDataFilter created by @dgkf . In my opinion, it needs some developments such as working with all data types, preventing some bugs and so on. That's why, I'm afraid of using the package at the moment.

In addition, we can design a filtering method like the following topic. It needs to generalize for all data types too.

Use Shiny to choose column, equality, and value to filter by conditions shiny

I'm creating a Shiny app where I'd like the user to be able to select a column and condition, resulting in the input$COLUMN input$CONDITION input$VALUE which can be used to filter a dataframe. [enter image description here] Desired Output iris %>% filter(input$COLUMN input$CONDITION input$VALUE) == iris %>% filter(Sepal.Length > 4.7) For this to work I need to use rlang for the input$COLUMN , I need to eval the input$CONDITION and I need the input$VALUE to be converted to a numeric when appropriate. (I'm attempting this in my verbatimTextOutput ) What is the best approach for achieving this? I thought making the whole expression a string to be parsed within a tidy pipeline ma…

I'm trying to create a filtering method by looking at the examples and their logic I mentioned above. It would be difficult to generalize a filtering method likewise.