fix the null check for group values generation

R/build_group_variables.R

@@ -44,13 +44,11 @@ build_group_variables <- function(table_schema,
                             #"/model_id=", model_id,
                             "?endpoint_override=",config$endpoint)
 
-  group_var_vector <- paste0('"', paste(group_var_vector, collapse='", "'), '"')
-
-
   if (is.null(group_var_vector)){
     aws_asset_description <- paste0("Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(",aws_asset_link,")\ndf <- all_results |> dplyr::collect()\n\n```
        \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n")
   }else{
+  group_var_vector <- paste0('"', paste(group_var_vector, collapse='", "'), '"')
   aws_asset_description <- paste0("Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(",aws_asset_link,")\ndf <- all_results |>\n dplyr::filter(variable %in% c(", group_var_vector,")) |>\n dplyr::collect()\n\n```
        \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n")
   }