Slack and Plumber, Part One

Filters

The first filter is responsible for parsing the incoming request and ensuring it is assigned to the appropriate endpoint. This is done because when a slash command is created in Slack, there is only one endpoint defined for requests made from the command. This filter enables several endpoints to be utilized by the same slash command by parsing the incoming text of the command and treating the first value of that command as the endpoint to which the request should be routed.

#* Parse the incoming request and route it to the appropriate endpoint
#* @filter route-endpoint
function(req, text = "") {
  # Identify endpoint
  split_text <- urltools::url_decode(text) %>%
    strsplit(" ") %>%
    unlist()
  
  if (length(split_text) >= 1) {
    endpoint <- split_text[[1]]
    
    # Modify request with updated endpoint
    req$PATH_INFO <- paste0("/", endpoint)
    
    # Modify request with remaining commands from text
    req$ARGS <- split_text[-1] %>% 
      paste0(collapse = " ")
  }
  
  # Forward request 
  forward()
}

This filter requires an understanding of the req object. It’s important to note that a few things happen in this filter. First, we parse the text argument and use the first part of text as the req$PATH_INFO, which tells plumber where to route the request. Second, we take anything remaining from text and attach it to the request in req$ARGS. This means that any downstream filters or endpoints will have access to req$ARGS. The second filter is taken straight from the plumber documentation and simply logs details about the incoming request.

#* Log information about the incoming request
#* @filter logger
function(req){
  cat(as.character(Sys.time()), "-", 
      req$REQUEST_METHOD, req$PATH_INFO, "-", 
      req$HTTP_USER_AGENT, "@", req$REMOTE_ADDR, "\n")
  
  # Forward request
  forward()
}

Endpoints

There are a few endpoints we need to define. First, we need to define an endpoint that provides a response Slack can understand and interpret into a message. In this case, we’re going to return a JSON object that Slack interprets into a message with attachments. Slack provides detailed documentation on what fields it accepts in a response. Also note that Slack expects unboxed JSON, while the plumber default is to return boxed JSON. In order to ensure that Slack understands the response, we set the serializer for this response to be unboxedJSON.

#* Return a message containing status details about the customer
#* @serializer unboxedJSON
#* @post /status
function(req, res) {
  # Check req$ARGS and match to customer - if no customer match is found, return
  # an error
  
  customer_ids <- unique(sim_data$id)
  customer_names <- unique(sim_data$name)
  
  if (!as.numeric(req$ARGS) %in% customer_ids & !req$ARGS %in% customer_names) {
    res$status <- 400
    return(
      list(
        response_type = "ephemeral",
        text = paste("Error: No customer found matching", req$ARGS)
      )
    )
  }
  
  # Filter data to customer data based on provided id / name
  if (as.numeric(req$ARGS) %in% customer_ids) {
    customer_id <- as.numeric(req$ARGS)
    customer_data <- dplyr::filter(sim_data, id == customer_id)
    customer_name <- unique(customer_data$name)
  } else {
    customer_name <- req$ARGS
    customer_data <- dplyr::filter(sim_data, name == customer_name)
    customer_id <- unique(customer_data$id)
  }
  
  # Simple heuristics for customer status
  total_customer_calls <- sum(customer_data$calls)
  
  customer_status <- dplyr::case_when(total_customer_calls > 250 ~ "danger",
                                      total_customer_calls > 130 ~ "warning",
                                      TRUE ~ "good")
  
  # Build response
  list(
    # response type - ephemeral indicates the response will only be seen by the
    # user who invoked the slash command as opposed to the entire channel
    response_type = "ephemeral",
    # attachments is expected to be an array, hence the list within a list
    attachments = list(
      list(
        color = customer_status,
        title = paste0("Status update for ", customer_name, " (", customer_id, ")"),
        fallback = paste0("Status update for ", customer_name, " (", customer_id, ")"),
        # History plot
        image_url = paste0("localhost:5762/plot/history/", customer_id),
        # Fields provide a way of communicating semi-tabular data in Slack
        fields = list(
          list(
            title = "Total Calls",
            value = sum(customer_data$calls),
            short = TRUE
          ),
          list(
            title = "DoB",
            value = unique(customer_data$dob),
            short = TRUE
          )
        )
      )
    )
  )
}

There are three main things that happen in this endpoint. First, we check to ensure that the provided customer name or ID appear in the dataset. Next, we create a subset of the data for only the identified customer and use a simple heuristic to determine the customer’s status. Finally, we put a list together that will be serialized into JSON in response to requests made to this endpoint. This list conforms to the standards outlined by Slack.

The second endpoint is used to provide the history plot that is referenced in the first endpoint. When an image_url is provided in a Slack attachment, Slack uses a GET request to fetch the image from the URL. So, this endpoint responds to incoming GET requests with an image.

#* Plot customer weekly calls
#* @png
#* @param cust_id ID of the customer
#* @get /plot/history/<cust_id:int>
function(cust_id, res) {
  # Throw error if cust_id doesn't exist in data
  if (!cust_id %in% sim_data$id) {
    res$status <- 400
    stop("Customer id" , cust_id, " not found.")
  }
  
  # Filter data to customer id provided
  plot_data <- dplyr::filter(sim_data, id == cust_id)
  
  # Customer name (id)
  customer_name <- paste0(unique(plot_data$name), " (", unique(plot_data$id), ")")
  
  # Create plot
  history_plot <- plot_data %>%
    ggplot(aes(x = time, y = calls, col = calls)) +
    ggalt::geom_lollipop(show.legend = FALSE) +
    theme_light() +
    labs(
      title = paste("Weekly calls for", customer_name),
      x = "Week",
      y = "Calls"
    )
  
  # print() is necessary to render plot properly
  print(history_plot)
}

Once these pieces are together, you can run the API either through the UI as described in the previous post, or by running plumber::plumb("plumber.R")$run(port = 5762) from the directory containing the API.