realtime_ws() server side daily averages to speed up download for Shiny App

I'm working on a Shiny App that displays realtime data. The realtime_ws() function in the httr2 branch is fabulous.

In this context, I am only interested in daily averages. The app is a little slow because the realtime_ws() functions downloads 5 minute data. Is there a way to get daily data from the server and not require downloading the 5 min data, then calculate daily averages?

(I really don't know the mechanics of the realtime_ws() function, so no idea if server side calculations are doable!)

library(tidyhydat)
library(dplyr)

# Works, but I do not require 5 minute data ##########################################

rt <- tidyhydat::realtime_ws("07EC003",
                             parameter = 47,
                             start_date = as.Date("2023-01-01"),
                             end_date = Sys.Date())
#> All station successfully retrieved
#> All parameters successfully retrieved
print(head(rt))
#> # A tibble: 6 × 10
#>   STATION_NUMBER Date                Name_En   Value Unit  Grade Symbol Approval
#>   <chr>          <dttm>              <chr>     <dbl> <chr> <chr> <chr>  <chr>   
#> 1 07EC003        2023-01-01 00:00:00 Discharg…  6.14 m3/s  10    <NA>   Final/F…
#> 2 07EC003        2023-01-01 00:05:00 Discharg…  6.14 m3/s  10    <NA>   Final/F…
#> 3 07EC003        2023-01-01 00:10:00 Discharg…  6.14 m3/s  10    <NA>   Final/F…
#> 4 07EC003        2023-01-01 00:15:00 Discharg…  6.14 m3/s  10    <NA>   Final/F…
#> 5 07EC003        2023-01-01 00:20:00 Discharg…  6.14 m3/s  10    <NA>   Final/F…
#> 6 07EC003        2023-01-01 00:25:00 Discharg…  6.14 m3/s  10    <NA>   Final/F…
#> # ℹ 2 more variables: Parameter <dbl>, Code <chr>

# Does not work ##########################################
rt_daily <- rt %>% 
  tidyhydat::realtime_daily_mean(na.rm = T)
#> Error in `dplyr::group_by()`:
#> ! Must group by variables found in `.data`.
#> ✖ Column `PROV_TERR_STATE_LOC` is not found.
#> Backtrace:
#>     ▆
#>  1. ├─rt %>% tidyhydat::realtime_daily_mean(na.rm = T)
#>  2. └─tidyhydat::realtime_daily_mean(., na.rm = T)
#>  3.   ├─dplyr::group_by(...)
#>  4.   └─dplyr:::group_by.data.frame(df_mean, STATION_NUMBER, PROV_TERR_STATE_LOC, Date, Parameter)
#>  5.     └─dplyr::group_by_prepare(.data, ..., .add = .add, error_call = current_env())
#>  6.       └─rlang::abort(bullets, call = error_call)

# My work arround ##########################################
rt_daily <- rt %>% 
  mutate(Date = as.Date(Date)) %>% 
  group_by(Date) %>% 
  summarise(Value = mean(Value, na.rm = T))

print(head(rt_daily))
#> # A tibble: 6 × 2
#>   Date       Value
#>   <date>     <dbl>
#> 1 2023-01-01  6.10
#> 2 2023-01-02  6.04
#> 3 2023-01-03  5.99
#> 4 2023-01-04  5.95
#> 5 2023-01-05  5.90
#> 6 2023-01-06  5.85

^{Created on 2023-07-28 with reprex v2.0.2}

ropensci / tidyhydat

realtime_ws() server side daily averages to speed up download for Shiny App #199