Say we have an iris dataset and we want to make some subset on it.
iris$Species # We can also use `with` for that with(iris, Species) # We are interested in more complicated subsetting though. Want to have all rows # with 'setosa' with(iris, Species %in% 'setosa') iris[with(iris, Species %in% 'setosa'), ] # Now 'setosa' with some more condition iris[with(iris, Species %in% 'setosa' & Sepal.Length > 5.3), ] # That works perfectly. There is, however, an another way doing the exact thing in r. # We can input the subsetting condition as a character string, then change it to # the `expression` and `eval`uate it. cond_str <- paste0("with(iris, Species %in% 'setosa' & Sepal.Length > 5.3)") cond_str # which is the same as cond_str <- paste0("with(iris, ", "Species %in% ", "'", "setosa", "'", " & ", "Sepal.Length > ", "5.3", ")") cond_str # This second approach will prove very powerful since we will replace "setosa" # with, say, `input$species` later on. cond <- parse(text = cond_str) cond eval(cond) iris[eval(cond), ] # √
This will be a bit more complicated, because input$species can be a vector, and as a result we can get a few lines of characters as output. For instance:
Spec <- c("setosa", "virginica") # ~ input$species paste0("with(iris, Species %in% ", Spec, ")") # We want only one character string! So, we'll have to collapse the vector Spec paste0("with(iris, Species %in% ", paste0(Spec, collapse = " "), ")") # This is still not what we wanted. We have to wrap the entries into "c()" # and add quote marks. So, it going to be pretty technical: paste0("with(iris, Species %in% ", "c(", paste0("'", Spec, collapse = "',"), "'))") # Now, this is what we wanted :) Let check it check <- eval(parse(text = paste0("with(iris, Species %in% ", "c(", paste0("'", Spec, collapse = "',"), "'))"))) iris[check, ] # √
Now go to the brilliant app. Since I do not know where I can find the baseball dataset that will match your variables, I am going to use the diamonds dataset from the ggplot2 package and will not use dplyr .
I changed your application a bit - changed the variable names, and then used the trick described above for a subset. It will be easy for you to match my example to your problem.
library(shiny) library(DT) # data("diamonds") don't know where I can find this dataset, hence I'll use # diamond dataset library(ggplot2) # for diamonds dataset cut <- unique(as.character(diamonds$cut)) # or just levels(diamonds$cut) color <- unique(as.character(diamonds$color)) clarity <- unique(as.character(diamonds$clarity)) runApp(list(ui = fluidPage( titlePanel("Summary"), sidebarLayout( sidebarPanel( # changed names of inputs selectInput("cut", label = "Cut", choices = cut, selected = NULL, multiple = T), selectInput("filter_join1", label = "", choices = c("OR","AND")), selectInput("color", label = "Color", choices = color, selected = NULL, multiple = T), selectInput("filter_join2", label = "", choices = c("OR","AND")), selectInput("clarity", label = "Clarity", choices = clarity, selected = NULL, multiple = T) ), mainPanel( DT::dataTableOutput("table") ) ) ), server = function(input, output, session) { WorkingDataset <- reactive({ req(input$cut, input$color, input$clarity) # show table only if all three inputs are available # depending on filter_join inputs return "OR" or "AND" join1 <- ifelse(test = input$filter_join1 == "OR", yes = "| ", no = "& ") join2 <- ifelse(test = input$filter_join2 == "OR", yes = "| ", no = "& ") # You could do this differently: just set choices = c("OR" = "|", "AND" = "&")) # in the selectInput widget. # Similar as in the example above with the iris dataset. cond_str <- paste0( "with(diamonds, ", paste0("cut %in% ", "c(", paste0("'", input$cut, collapse = "',"), "')", colapse = " "), join1, paste0("color %in% ", "c(", paste0("'", input$color, collapse = "',"), "')", colapse = " "), join2, paste0("clarity %in% ", "c(", paste0("'", input$clarity, collapse = "',"), "')", colapse = " "), ")") print(cond_str) # print the result to the console cond <- parse(text = cond_str) df <- as.data.frame(diamonds)[eval(cond), ] df }) output$table <- DT::renderDataTable({ WorkingDataset() }) }) )