column selection when multiple sensors at site

[limnoliver]

Currently we choose the column (or sensor) with the most data, but some hints from the DRB pipeline suggest that we should check the output of this exercise. See the code below from the DRB NGWOS pull:

  # retrieve remaining sites from NWISuv
  new_ngwos_uv <- dataRetrieval::readNWISuv(siteNumbers = missing_sites, parameterCd = '00010')

  uv_long <- select(new_ngwos_uv, site_no, dateTime, ends_with('00010_00000')) %>%
    tidyr::gather(key = 'temp_column', value = 'temp_c', - site_no, -dateTime)

  uv_site_col <- filter(uv_long, !is.na(temp_c)) %>%
    group_by(site_no, temp_column) %>%
    summarize(n_vals = n(),
              n_dates = length(unique(as.Date(dateTime)))) %>%
    filter(!grepl('piezometer', temp_column, ignore.case = TRUE))

  # always choose the standard temp column. In cases where that is missing, choose the one on that day
  # with the most data
  # first take day-temp type means
  uv_long_dailies <- filter(uv_long, !is.na(temp_c)) %>%
    filter(!grepl('piezometer', temp_column, ignore.case = TRUE)) %>%
    group_by(site_no, date = as.Date(dateTime), temp_column) %>%
    summarize(temp_c = mean(temp_c),
              n_obs = n()) %>%
    left_join(select(uv_site_col, site_no, temp_column, n_dates))

  # find the temperature for each site-day
  # first choose standard temp column, then choose one with most data when available
  uv_dat <- uv_long_dailies %>%
    group_by(site_no, date) %>%
    summarize(temp_c = ifelse(grepl('X_00010_00000', paste0(temp_column, collapse = ', ')),
                              temp_c[which(temp_column %in% 'X_00010_00000')], temp_c[which.max(n_dates)]),
              temp_column = ifelse(grepl('X_00010_00000', paste0(temp_column, collapse = ', ')),
                                   'X_00010_00000', temp_column[which.max(n_dates)]),
              n_obs = ifelse(grepl('X_00010_00000', paste0(temp_column, collapse = ', ')),
                             n_obs[which(temp_column %in% 'X_00010_00000')], n_obs[which.max(n_dates)])) %>%
    mutate(source = 'nwis_uv') %>%
    select(site_id = site_no, date, temp_c, n_obs, source)