Function to scrape the data hub and return information on pre-burst rainfall, see https://tonyladson.wordpress.com/2020/03/16/scraping-the-data-hub/

Get_preburst.R

# lat and lon must be latitude and longitude of some point on Australia, latitude must be negative

Get_preburst <- function(percentile = c(50, 10, 25, 75, 90), type = c("depth", "ratio"), lat, lon){
  
    require(tidyverse)
    require(stringr)
    require(glue)
    require(RCurl)
  
    # Check inputs--------------------
  
    if(identical(percentile, c(50, 10, 25, 75, 90))) percentile = 50 # take the first value as the default
    if(!percentile %in% c(50, 10, 25, 75, 90) ) stop("percentile must be one of 10, 25, 50 75, 90")
    
    
    if(identical(type, c("depth", "ratio"))) type = "depth" # take the first value as the default
    if(!type %in% c("depth", "ratio")) stop("type must be either 'depth' or 'ratio'")
    
    # Checking Latitude and Longitude
    # From https://gist.github.com/graydon/11198540 the bounding box for Australia
    # 'AU': ('Australia', (113.338953078, -43.6345972634, 153.569469029, -10.6681857235)),
    
    if(lat > -10.6681857235 | lat < -43.6345972634) stop("Points must be within Australia")
    if(lon < 113.338953078 | lon > 153.569469029) stop("Points must be within Australia")
    
    
    # Functions-------------------------
    
    .Get_start_end <- function(percentile){
      # Given a percentile, get the start and end text to search in the data hub file
      
      if(!percentile %in% c(10, 25, 50, 75, 90)) stop('Function Get_start_end expects percentile be one of 10,25, 50, 75, 90')
      
      search_text <- case_when(percentile == 10 ~ c("PREBURST10","PREBURST10_META") ,
                               percentile == 25 ~ c("PREBURST25","PREBURST25_META"),
                               percentile == 50 ~ c("PREBURST","PREBURST_META"),
                               percentile == 75 ~ c("PREBURST75","PREBURST75_META"),
                               percentile == 90 ~ c("PREBURST90","PREBURST90_META"))
      
      return(search_text)
    }
    
    
    # Scrape the data hub-----------------------
    
    # See details at https://data.arr-software.org/about
    hub_data <- RCurl::getURI(glue("https://data.arr-software.org/?lon_coord={lon}&lat_coord={lat}&type=text&Preburst=1&OtherPreburst=1"))
    
    
    # Get search text
    
    search_text <- .Get_start_end(percentile)
    
    search_pattern = str_c("(?<=\\[", search_text[1],"\\])(.*)(?=\\[", search_text[2],"\\])") # Construct pattern for regex
      
    pb_info <- str_extract(hub_data, regex(search_pattern, dotall = TRUE)) # extract the required pre-burst data from hub_data
   
    if(type == 'depth'){
      x2 <- str_replace_all(pb_info, '\n', ',') # replace all the \n strings with commas so we can split at the commas
      x3 <- str_replace_all(x2, '\\([^)]*\\)', '')  # This removes brackets and everything between brackets
      x3 <- str_split(x3, pattern = ",") # split at commas
      x4 <- unlist(x3) # convert to vector
      x5 <- x4[c(-1,-length(x4))] # remove first and last value
      x5 <- x5[c(-1:-7)] # remove first seven values
      x5 <- as.numeric(x5) 
      x6 <- matrix(x5, ncol = 7, byrow = TRUE)
      colnames(x6) <- c('dur_min', 'AEP_50','AEP_20','AEP_10','AEP_5','AEP_2','AEP_1' )
      pb_depth <- as_tibble(x6, .name_repair = 'check_unique')
      pb_depth <- add_column(pb_depth, dur_hour = pb_depth$dur_min/60, .before = 2)
      return(pb_depth)
    }
    
    if(type == 'ratio'){
      x2 <- str_replace_all(pb_info, '\n', ',') # replace all the \n strings with commas so we can split at the commas
      x3 <- str_extract_all(x2, '\\([^)]*\\)')  # keeps brackets and everything in the brackets
      x3 <- unlist(x3)
      x4 <- str_replace_all(x3, '[()]', '') # remove the brackets
      x5 <- x4[!x4 %in% c("h", "%")] # get rid of other things we don't need
      x5 <- as.numeric(x5)
      x6 <- matrix(x5, ncol = 7, byrow = TRUE) # matrix of rainfall ratios
      colnames(x6) <- c('dur_hour', 'AEP_50','AEP_20','AEP_10','AEP_5','AEP_2','AEP_1' )
      pb_ratio <- as_tibble(x6, .name_repair = 'check_unique') # confert to data frame
      pb_ratio <- add_column(pb_ratio, dur_min = pb_ratio$dur_hour*60, .before = 1) # add a column of minutes for consistency
      
      return(pb_ratio)
    }
    
    stop(str_c("Error in function Get_preburst.  Expecting type = 'depth', or 'ratio'. Type = ", type ))
  
}


# Example
# Get_preburst(percentile = 90, type = 'depth', lat = -36.9, lon = 144.36)

# # A tibble: 11 x 8
#    dur_min dur_hour AEP_50 AEP_20 AEP_10 AEP_5 AEP_2 AEP_1
#      <dbl>    <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
# 1      60      1     22.3   25.2   27.1  29    30.6  31.8
# 2      90      1.5   24.2   25.4   26.1  26.9  31.5  35  
# 3     120      2     27.2   33.4   37.5  41.5  43.6  45.2
# 4     180      3     23.3   27.1   29.6  32    43.4  52  
# 5     360      6     19.5   24.3   27.5  30.6  42.5  51.4
# 6     720     12     12.8   20.3   25.3  30.1  34.4  37.6
# 7    1080     18     15.6   18.1   19.7  21.3  25.7  29  
# 8    1440     24     14.6   18.8   21.6  24.2  24.3  24.4
# 9    2160     36      9     11.2   12.6  14    22.3  28.5
# 10    2880     48      1.1    2.7    3.7   4.7  10.7  15.1
# 11    4320     72      0.2    1.9    3     4    12.5  18.9