DataTables from the DT package as a Shiny CRUD app interface

Shiny Scorekeeper is a basketball scorekeeper app built with the Shiny web framework for R. I’ve written about the motivation for creating Shiny Scorekeeper on my projects page. The short version is that I needed a new app for scoring video of my son’s basketball games and I decided it would be a good learning experience to try to build my own. In this post, I describe using DataTables from the DT package as the interface to the CRUD (create-read-update-delete) features in Shiny Scorekeeper. The post assumes familiarity with features of Shiny apps, particularly reactiveValues(), observe(), and observeEvent().

As with many decisions when building Shiny Scorekeeper, I decided to bumble through creating my own CRUD components rather than follow existing examples. Eventually, I settled on a spreadsheet interface as a familiar, intuitive, and compact approach for creating teams and rosters in Shiny Scorekeeper. I had some previous experience using rhandsontables as spreadsheets in Shiny apps but the DT package also recently added the option to edit DataTables and I decided it would be fun to learn more about the capabilities of the DT package.1

Shiny Scorekeeper uses a homemade database comprised of a set of CSV files linked with ID columns. The teams table includes columns for TeamID, Season, League, and Coach; the players table includes PlayerID, FirstName, and LastName; and, the rosters table includes TeamID, PlayerID, and Number (i.e., jersey number). The goal is to manage data for multiple players across many different teams and seasons.

Teams

Teams.csv is read from file, stored as a reactive value (rv[["teams"]]), and rendered as a DataTable with custom options to simplify the display. Importantly, the TeamID column is hidden to make it inaccessible for editing. The proxy object (proxyTeams) allows for manipulation of the DataTable.

output$teamsTable = renderDT(
  rv[["teams"]], selection = "single", editable = TRUE, 
  style = "bootstrap", rownames = FALSE,
  options = list(searching = FALSE, bPaginate = FALSE, info = FALSE,
                 columnDefs = list(list(visible = FALSE, targets = 0)))) # hide TeamID column; first column indexed with 0

proxyTeams = dataTableProxy("teamsTable")

When the teamsTable is edited, the edited value replaces the previous value in rv[["teams"]] and the new rv[["teams"]] object replaces proxyTeams. The coerceValue() function from the DT package coerces the edited value (passed as character string) to the type of the value that it is replacing.

observeEvent(input$teamsTable_cell_edit, {
  info = input$teamsTable_cell_edit
  i = info$row
  j = info$col + 1L  # column index offset by 1 b/c TeamID column hidden
  v = info$value
  rv[["teams"]][i, j] = coerceValue(v, rv[["teams"]][i, j])
  replaceData(proxyTeams, rv[["teams"]], resetPaging = FALSE, rownames = FALSE) 
})

A button to delete a row in the teamsTable is conditionally shown and hidden with toggle() from the shinyjs package.

observe({
  toggle("delete_teams_row", 
         condition = nrow(rv[["teams"]]) > 0 & !is.null(input$teamsTable_rows_selected))
})

Deleting a row in the teamsTable also deletes the roster for that team and the players on that roster that are not on rosters for any other teams. When all the reactive values are updated, proxyTeams is also updated.

observeEvent(input$delete_teams_row,{
  req(input$teamsTable_rows_selected)
  i = input$teamsTable_rows_selected
  
  rv[["rosters"]] = rv[["rosters"]] %>% 
    filter(TeamID != rv[["teams"]]$TeamID[i])  # drop old roster
  rv[["players"]] = rv[["players"]] %>% 
    filter(PlayerID %in% rv[["rosters"]][["PlayerID"]]) # drop players not on any rosters
  rv[["teams"]] <- rv[["teams"]][-i,]  # needs to come last b/c updating rv$rosters requires rv$teams
  
  replaceData(proxyTeams, rv[["teams"]], resetPaging = FALSE, rownames = FALSE)  
})

The add row button is not shown conditionally. Adding a new row is always an option. The DT package includes a function (addRow()) for adding rows to a DataTable but it only works for client-side tables.

In my homemade database, I include single-column CSV files for tracking unique IDs for teams and players (IDs are integers). Initially, I created an ID by finding the max ID in either the teams or players tables, but I started to worry that I could get unexpected behavior with lots of row addition and deletion. Rather than thinking through whether that was a legitimate concern, I added the clunky solution of single-column CSV files for ID tracking. Those “tables” (teamIDs and playerIDs) are not stored in reactive values; the tables are updated in memory with the super assignment operator (<<-) and updated on disk with write.csv().

I wasn’t able to figure out how to add an empty, editable row to a DataTable. Thus, the new row, added as an unnamed list, includes placeholder values. The app includes a few vectors (e.g., ex_names, ex_nums, ex_coaches, etc.) with example values (not shown in this post) for the different fields, which are sampled to create different combinations when a new row is added.

observeEvent(input$add_teams_row,{
  # addRow() only works when server = FALSE
  req(rv[["teams"]])
  
  # update master list of team IDs
  tid = nrow(teamIDs) + 1L # ID and row number are the same
  teamIDs[tid,] <<- tid
  write.csv(teamIDs, paste0(data_fldr, "TeamIDs.csv"), row.names = FALSE)
  
  # update master list of player IDs
  pid = nrow(playerIDs) + 1L # ID and row number are the same
  playerIDs[pid,] <<- pid
  write.csv(playerIDs, paste0(data_fldr, "PlayerIDs.csv"), row.names = FALSE)
  
  # update all of the relevant tables
  ti = nrow(rv[["teams"]]) + 1L
  rv[["teams"]][ti,] = list(tid, sample(ex_seasons, 1), sample(ex_leagues, 1), sample(ex_coaches, 1))
  ri = nrow(rv[["rosters"]]) + 1L
  rv[["rosters"]][ri,] = list(tid, pid, sample(ex_nums, 1))
  pi = nrow(rv[["players"]]) + 1L
  rv[["players"]][pi,] = list(pid, sample(ex_names, 1), "")
  
  replaceData(proxyTeams, rv[["teams"]], resetPaging = FALSE, rownames = FALSE) 
})

Rosters

Creating the rosterTable is similar to the teamsTable. In this case, I am hiding the first two columns, which are indexed with c(0,1). Also, Rosters.csv contains rosters for all teams and is stored in rv[["rosters"]] whereas rv[["roster"]]2 holds only the roster for the team selected in the teamsTable.

output$rosterTable = renderDT(
  rv[["roster"]], selection = "single", editable = TRUE, 
  style = "bootstrap", rownames = FALSE,
  options = list(searching = FALSE, bPaginate = FALSE, info = FALSE,
                 columnDefs = list(list(visible = FALSE, targets = c(0,1))))) # hide TeamID and PlayerID columns

proxyRoster = dataTableProxy("rosterTable")

observeEvent(input$teamsTable_rows_selected,{
  req(input$teamsTable_rows_selected)
  ti = rv[["teams"]]$TeamID[input$teamsTable_rows_selected]
  rv[["roster"]] <- rv[["rosters"]] %>% 
    filter(TeamID == ti) %>% 
    left_join(rv[["players"]], by = "PlayerID")
  replaceData(proxyRoster, rv[["roster"]], resetPaging = FALSE, rownames = FALSE) 
})

Editing a cell in the rosterTable involves jumping through a few extra hoops because two tables are being edited. As a quick reminder, the players table includes PlayerID, FirstName, and LastName and the rosters table includes TeamID, PlayerID, and Number. Because TeamID and PlayerID are hidden columns, only FirstName, LastName, and Number are editable. If first or last name are edited, then rv[["players""]] is updated. If jersey number is edited, then rv[["rosters"]] is updated.

observeEvent(input$rosterTable_cell_edit, {
  info = input$rosterTable_cell_edit
  i = info$row
  j = info$col + 1L  # column index offset by 1
  v = info$value
  
  # get IDs for row where change was made
  tid = rv[["roster"]][["TeamID"]][i]
  pid = rv[["roster"]][["PlayerID"]][i]
  
  # find row indices
  ri = which(rv[["rosters"]][["TeamID"]] == tid & rv[["rosters"]][["PlayerID"]] == pid)
  pi = which(rv[["players"]][["PlayerID"]] == pid)
  
  # find colunm name
  cn = names(rv[["roster"]])[j]
  
  # update values
  if (cn == "Number"){ 
    rv[["rosters"]][ri, cn] = coerceValue(v, rv[["rosters"]][ri, cn])
  }else{
    rv[["players"]][pi, cn] = coerceValue(v, rv[["players"]][pi, cn])
  }
  
  rv[["roster"]] = rv[["rosters"]] %>% # rebuild rv$roster
    filter(TeamID == tid) %>% 
    left_join(rv[["players"]], by = "PlayerID")
  
  replaceData(proxyRoster, rv[["roster"]], resetPaging = FALSE, rownames = FALSE)  # important
})

The code for adding and deleting rows in the rosterTable is very similar to the code for the teamsTable. Interested readers can find that code in the server.R file in the GitHub repository.

When filling out a new roster, a dropdown menu allows for selecting from previously entered players. The dropdown is dynamically created with renderUI() because the contents of the dropdown depend on previous selections.

output$previousPlayers <- renderUI({
  req(rv[["roster"]], rv[["players"]])
  
  sel_ids = rv[["roster"]][["PlayerID"]]
  all_ids = rv[["players"]][["PlayerID"]]
  ids = all_ids[!(all_ids %in% sel_ids)] # find PlayerIDs that haven't been added to roster
  
  req(length(ids) > 0) # at least one player that could be selected
  
  d = rv[["players"]] %>% 
    filter(PlayerID %in% ids) %>%
    mutate(PlayerName = paste(FirstName, LastName)) %>% 
    arrange(FirstName)
  
  picker.ids = d[["PlayerID"]]
  names(picker.ids) = d[["PlayerName"]]
  pickerInput("selected_players", "Select previous players", choices = picker.ids, multiple = TRUE)
})

Players selected in the dropdown are added to the roster with a button (add_selected_players) that is conditionally shown or hidden based on the existence of the dropdown menu.

observe({
  toggle("add_selected_players", condition = !is.null(input$selected_players))
})

The players selected from the dropdown menu are appended to the bottom of rv[["rosters"]] and rv[["roster"]] is rebuilt.

observeEvent(input$add_selected_players,{
  req(rv[["roster"]], rv[["players"]]) # probably not necessary b/c handled upstream
  
  tid = rv[["roster"]]$TeamID[1] # all rows in rv[["roster"]] have same TeamID
  
  rv[["rosters"]] = bind_rows(rv[["rosters"]],
                              data.frame(TeamID = tid, 
                                         PlayerID = as.integer(input$selected_players), 
                                         Number = sample(ex_nums, length(input$selected_players)),
                                         stringsAsFactors = FALSE))
  
  rv[["roster"]] = rv[["rosters"]] %>% # rebuild rv$roster
    filter(TeamID == tid) %>% 
    left_join(rv[["players"]], by = "PlayerID")
  replaceData(proxyRoster, rv[["roster"]], resetPaging = FALSE, rownames = FALSE)
})

Saving Data

A save button is shown when changes are made to the tables and hidden when the save button is clicked. Clicking the save button updates the files on disk and the objects in the global environment. Comparing reactive values to objects in the global environment is used to conditionally show/hide the save button.

observe({
  input$save_teams_roster_changes # take dependency on save button to hide button after saving
  toggle("save_teams_roster_changes", condition = !isTRUE(all_equal(players, rv[["players"]])) | !isTRUE(all_equal(rosters, rv[["rosters"]])) | !isTRUE(all_equal(teams, rv[["teams"]])))
})

observeEvent(input$save_teams_roster_changes,{
  # write teams, rosters, & players from memory to disk
  write.csv(rv[["teams"]], paste0(data_fldr, "Teams.csv"), row.names = FALSE)
  write.csv(rv[["rosters"]], paste0(data_fldr, "Rosters.csv"), row.names = FALSE)
  write.csv(rv[["players"]], paste0(data_fldr, "Players.csv"), row.names = FALSE)
  # update non-reactive versions to keep track of changes
  teams <<- rv[["teams"]]
  rosters <<- rv[["rosters"]]
  players <<- rv[["players"]]
})

Conclusions

I’m satisfied with both the appearance and functionality of DataTables as a CRUD app interface for this hobby project. In fact, in a different part of the app, I even use DataTables in place of a selectInput because I wanted the option to sort by different fields when selecting records for display. I’m less confident that my homemade database will hold up well with increasing amounts of data collected.


  1. For a fancier interface to editable DataTables, check out the DTEdit package.↩︎

  2. I should have chosen better names. I found the plural/singular distinction intuitive but it does make a small typo more likely to create a problem than if the names were longer.↩︎

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Travis Hinkelman
Ecological Modeler