Having trouble with isolate() function

[elphlord]

Like the title says, I'm having a problem using the isolate function. I have drop down menus where i only want it to update the map when the action button is pressed; however, it's still updating whenever any variable (input) is changed. If anybody out here can help me, I would really appreciate that! Here is some sample code:
UI

tabPanel(span("Interactive map", style="color:olive; font-family: Calibri"),
fluidRow(
column(3,
# input: select variable to map
selectInput("map.var", span("Variable:", style="color:brown; font-family: Calibri"),
c( "Precipiation" = "_precip",
"Evapotranspiration" = "_et",
"Soil water content" = "_sw",
"Groundwater Recharge" = "_perc",
"Baseflow" = "_gw_q",
"Streamflow" = "_flow_out",
"Water Yield" = "_wyld"))
),
My action button code:
actionButton('goMap', 'Go Map',icon("refresh"))

Server

output$map <- renderLeaflet({
input$goMap # Re-run when button is clicked
isolate(input$map.var)

[cpsievert]

Are you sure there aren't other input variables outside of the isolate() (but inside your renderLeaflet() statement)?

[elphlord]

Here's my entire code for the leaflet section:
activeSubbasin <- reactiveVal()

output$map <- renderLeaflet({
input$goMap # Re-run when button is clicked
isolate(input$map.var)
# Create 0-row data frame which will be used to store data
dat <- data.frame(x = numeric(0), y = numeric(0))

withProgress(message = 'Creating Map', value = 0, {
  # Number of times we'll go through the loop
  n <- 10
  
  for (i in 1:n) {
    # Each time through the loop, add another row of data. This is
    # a stand-in for a long-running computation.
    dat <- rbind(dat, data.frame(x = rnorm(1), y = rnorm(1)))
    
    # Increment the progress bar, and update the detail text.
    incProgress(1/n, detail = paste( i))
    
    # Pause for 0.1 seconds to simulate a long computation.
    Sys.sleep(0.1)
  }
})
# build the SQL query from the user selections
if (input$map.stype == "ann") {
  col.name <- columns.annual[grep(input$map.var, columns.annual)]
  proj.query <- paste0("SELECT ", col.name, ", subbasin FROM hydro_ann WHERE (period = ", input$map.period,
                       ") AND (rcp = ", input$map.rcp, ")")
  hist.query <- paste0("SELECT ", col.name, ", subbasin FROM hydro_ann WHERE (period = 1980)")
} else {
  col.name <- columns.month[grep(input$map.var, columns.month)]
  proj.query <- paste0("SELECT ", col.name, ", subbasin FROM hydro_month WHERE (period = ", input$map.period,
                       ") AND (rcp = ", input$map.rcp, ") AND (calendar_month = ", 
                       input$map.stype, ")")
  hist.query <- paste0("SELECT ", col.name, ", subbasin FROM hydro_month WHERE (period = 1980) AND (calendar_month = ", 
                       input$map.stype, ")")
}

# query the database
dat.proj <- dbGetQuery(db, proj.query)
dat.hist <- dbGetQuery(db, hist.query)

# rename the columns for use with different variables - enables the following code to be generic (for any variable)
colnames(dat.proj) <- c("value", "subbasin")
colnames(dat.hist) <- c("value", "subbasin")

# calculate the mean value by subbasin for the 10-member gcm ensemble
dat.proj.mean <- tapply(dat.proj$value, dat.proj$subbasin, mean)

# calculate the percent change relative to historical
pct.change <- ((dat.proj.mean - dat.hist$value) / dat.hist$value) * 100

# generate a color pallette from reactive expression output
mbreaks <- c(0, quantile(abs(pct.change), c(0.20, 0.4, 0.6, 0.8), na.rm = T), max(abs(pct.change), na.rm = T))
mbreaks <- ceiling(mbreaks)
mbreaks <- unique(c(rev(-1 * mbreaks), mbreaks))
pal <- colorBin(palette = "RdBu", domain = pct.change, bins = mbreaks)

id <- as.vector(basins$id)

leaflet() %>% 
  addProviderTiles("Stamen.TonerLite", group = "Toner Lite") %>% 
  addPolygons(data = basins, layerId = id, stroke = T, color = "black", 
              smoothFactor = 0.2, weight = 1, 
              fillColor = ~pal(pct.change), fillOpacity = 0.75, 
              popup = paste0(labels$label[grep(input$map.var, labels$input.var)], ": ", round(pct.change, 1)), 
              highlight = highlightOptions(weight = 2.5, fillOpacity = 1, bringToFront = T)) %>% 
  addLegend("bottomleft", pal = pal, values = pct.change,
            title = labels$label[grep(input$map.var, labels$input.var)],
            opacity = 0.75)

})

There's an if statement that contains input variables.