#RSTUDIO IF STATEMENT HOW TO#
#RSTUDIO IF STATEMENT DRIVER#
#RSTUDIO IF STATEMENT PORTABLE#

If(input$month != 99) # Matches the current month selection tab_list % # Reuses the db_flights dbplyr variableįilter(dest_name = airport, # Uses the value for the filterĬarrier = input$airline) # Matches the current airline filter

#RSTUDIO IF STATEMENT HOW TO#

Comments are added to the code below to cover more aspects of how to use these features. The observeEvent() function is the one that “catches” the event executed by the JavaScript, because it monitors the bar_clicked Shiny input. The updateTabsetPanel() function is used to switch to the newly or previously created tab. A simple vector, called tab_list, is used to track all existing detail tabs. The new, and really cool, appendTab() function is used to dynamically create a new Shiny tab with a DataTable that contains the first 100 rows of the selection. To prevent pulling the same data unnecessarily, the code will be smart enough to simply switch the focus to an existing tab if the same bar has been clicked on before. The plan is to display a new drill-down report every time the end user clicks on a bar. Using appendTab() to create the drill-down report js_bar_clicked %Įvents = list(click = js_bar_clicked)) %>% # The JavaScript variable is called hereĬategories = result$dest_name, # Value in The variable name and the input name ( bar_clicked) would be the only two statements that would have to be changed to match your chart. The example below could be used in most cases, so you can copy and paste it as-is into your code. To capture a bar-click event in highcharter, a small JavaScript needs to be written. The highcharter package will be used in this example. When a bar is clicked, the desired result is for the plot to activate a drill-down. The idea is to display the top airport destinations for a given airline in a bar plot. The following is one way that capturing a click event is possible.

ValueBox(value = prettyNum(result, big.mark = ','), Pull %>% # Use pull to get the total count as a vector output$total_flights % # Use the db_flights variable A second example is in the code used to build the highcharter plot below. The dplyr variable can then be used in more than one Shiny output. Left_join(tbl(con, 'airlines'), by = 'carrier') %>% A base query can be built because dplyr translates into SQL under the covers and, due to “laziness”, doesn’t evaluate the query until something is requested from it. # This code runs in uiĬhoices = airline_list) # Use airline_list as the choices argument valueĭashboards normally have a common data theme, which is sourced with a common data set. The selectInput() drop-down menu is able to read the resulting airline_list list variable. Map(~.$carrier) # Place here the field that will be used for keys Split(.$name) %>% # Place here the field that will be used for the labels In the example below, all of the records in the airlines table are collected, and a list of names is created, map() is then used to insert the carrier codes into each name node.

To separate the keys from the values, the map() function in the purrr package can be used. Because other queries in the dashboard will use the selected input to filter accordingly, the value required to pass to the other queries is normally an identification code, and not the label displayed in the drop down. It is very common for Shiny inputs to retrieve their values from a table or a query. The default setting will be automatically used when development, and RStudio Connect will use the rsconnect values when executing this code: dw <- config::get('mssql')

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It has one driver name for local development, and a different name for use during deployment: default: This code snippet is an example YAML file that config is able to read.

#RSTUDIO IF STATEMENT PORTABLE#

A more detailed write-up is available in the Make scripts portable article. Another advantage of using config, in lieu of Kerberos or DSN, is that the credentials used will not appear in the plain text of the R code. The RStudio Connect product supports the use of the config package out of the box. If it is not possible to set up DSNs, then the config package can be used to make the switch between credentials used in the different environments invisible. For databases, the best way to accommodate for this is to have a Data Source Name (DSN) with the same alias name set up on both environments. It is very common that credentials used during development will not be the same ones used for publishing.

Rstudio if statement