Calculate long-term summary statistics from daily mean flows

Calculates the long-term mean, median, maximum, minimum, and percentiles of daily flow values for over all months and all data (Long-term) from a daily streamflow data set. Calculates statistics from all values, unless specified. Returns a tibble with statistics.

calc_longterm_daily_stats(
  data,
  dates = Date,
  values = Value,
  groups = STATION_NUMBER,
  station_number,
  percentiles = c(10, 90),
  roll_days = 1,
  roll_align = "right",
  water_year_start = 1,
  start_year,
  end_year,
  exclude_years,
  months = 1:12,
  complete_years = FALSE,
  include_longterm = TRUE,
  custom_months,
  custom_months_label,
  transpose = FALSE,
  ignore_missing = FALSE
)

Arguments

data	Data frame of daily data that contains columns of dates, flow values, and (optional) groups (e.g. station numbers). Leave blank if using station_number argument.
dates	Name of column in data that contains dates formatted YYYY-MM-DD. Only required if dates column name is not 'Date' (default). Leave blank if using station_number argument.
values	Name of column in data that contains numeric flow values, in units of cubic metres per second. Only required if values column name is not 'Value' (default). Leave blank if using station_number argument.
groups	Name of column in data that contains unique identifiers for different data sets, if applicable. Only required if groups column name is not 'STATION_NUMBER'. Function will automatically group by a column named 'STATION_NUMBER' if present. Remove the 'STATION_NUMBER' column beforehand to remove this grouping. Leave blank if using station_number argument.
station_number	Character string vector of seven digit Water Survey of Canada station numbers (e.g. "08NM116") of which to extract daily streamflow data from a HYDAT database. Requires tidyhydat package and a HYDAT database. Leave blank if using data argument.
percentiles	Numeric vector of percentiles to calculate. Set to NA if none required. Default c(10,90).
roll_days	Numeric value of the number of days to apply a rolling mean. Default 1.
roll_align	Character string identifying the direction of the rolling mean from the specified date, either by the first ('left'), last ('right'), or middle ('center') day of the rolling n-day group of observations. Default 'right'.
water_year_start	Numeric value indicating the month (1 through 12) of the start of water year for analysis. Default 1.
start_year	Numeric value of the first year to consider for analysis. Leave blank to use the first year of the source data.
end_year	Numeric value of the last year to consider for analysis. Leave blank to use the last year of the source data.
exclude_years	Numeric vector of years to exclude from analysis. Leave blank to include all years.
months	Numeric vector of months to include in analysis (e.g. 6:8 for Jun-Aug). Leave blank to summarize all months (default 1:12).
complete_years	Logical values indicating whether to include only years with complete data in analysis. Default FALSE.
include_longterm	Logical value indicating whether to include long-term calculation of all data. Default TRUE.
custom_months	Numeric vector of months to combine to summarize (ex. 6:8 for Jun-Aug). Adds results to the end of table. If wanting months that overlap calendar years (ex. Oct-Mar), choose water_year_start that begins before the first month listed. Leave blank for no custom month summary.
custom_months_label	Character string to label custom months. For example, if months = 7:9 you may choose "Summer" or "Jul-Sep". Default "Custom-Months".
transpose	Logical value indicating whether to transpose rows and columns of results. Default FALSE.
ignore_missing	Logical value indicating whether dates with missing values should be included in the calculation. If TRUE then a statistic will be calculated regardless of missing dates. If FALSE then only those statistics from time periods with no missing dates will be returned. Default FALSE.

Value

A tibble data frame with the following columns:

Month

month of the year, included 'Long-term' for all months, and 'Custom-Months' if selected

Mean

mean of all daily data for a given month and long-term over all years

Median

median of all daily data for a given month and long-term over all years

Maximum

maximum of all daily data for a given month and long-term over all years

Minimum

minimum of all daily data for a given month and long-term over all years

P'n'

each n-th percentile selected for a given month and long-term over all years

Default percentile columns:

P10

annual 10th percentile selected for a given month and long-term over all years

P90

annual 90th percentile selected for a given month and long-term over all years

Transposing data creates a column of "Statistics" and subsequent columns for each year selected.

Examples

# Run if HYDAT database has been downloaded (using tidyhydat::download_hydat())
if (file.exists(tidyhydat::hy_downloaded_db())) {

# Calculate long-term statistics using data argument with defaults
flow_data <- tidyhydat::hy_daily_flows(station_number = "08NM116")
calc_longterm_daily_stats(data = flow_data,
                          start_year = 1980)

# Calculate long-term statistics using station_number argument with defaults
calc_longterm_daily_stats(station_number = "08NM116",
                          start_year = 1980)

# Calculate long-term statistics regardless if there is missing data for a given year
calc_longterm_daily_stats(station_number = "08NM116",
                          ignore_missing = TRUE)
                  
# Calculate long-term statistics for water years starting in October
calc_longterm_daily_stats(station_number = "08NM116",
                          start_year = 1980,
                          water_year_start = 10)
                  
# Calculate long-term statistics with custom years
calc_longterm_daily_stats(station_number = "08NM116",
                          start_year = 1981,
                          end_year = 2010,
                          exclude_years = c(1991,1993:1995))
                  
# Calculate long-term statistics for 7-day flows for July-September months only, 
# with 25 and 75th percentiles
calc_longterm_daily_stats(station_number = "08NM116",
                          roll_days = 7,
                          months = 7:9,
                          percentiles = c(25,75),
                          ignore_missing = TRUE,
                          include_longterm = FALSE) # removes the Long-term numbers 
                    
# Calculate long-term statistics and add custom stats for July-September
calc_longterm_daily_stats(station_number = "08NM116",
                          start_year = 1980,
                          custom_months = 7:9,
                          custom_months_label = "Summer")                  
                          
}
#> Warning: One or more calculations included missing values and NA's were produced. Filter data for complete years or months, or use to ignore_missing = TRUE to ignore missing values.
#> # A tibble: 14 x 8
#>    STATION_NUMBER Month      Mean Median Maximum Minimum   P10   P90
#>    <chr>          <fct>     <dbl>  <dbl>   <dbl>   <dbl> <dbl> <dbl>
#>  1 08NM116        Jan        1.16  0.940    9.5    0.160 0.570  1.78
#>  2 08NM116        Feb        1.19  0.971    5.81   0.140 0.561  2.00
#>  3 08NM116        Mar        1.89  1.36    17.5    0.380 0.704  3.71
#>  4 08NM116        Apr        8.65  6.51    53.5    0.505 1.45  18.5 
#>  5 08NM116        May       24.6  22.4     80.8    2.55  9.73  42.7 
#>  6 08NM116        Jun       22.0  19.7     86.2    0.450 6.10  39.7 
#>  7 08NM116        Jul        6.28  3.90    76.8    0.332 1.12  14.0 
#>  8 08NM116        Aug        2.03  1.54    13.3    0.427 0.836  3.84
#>  9 08NM116        Sep        2.10  1.58    14.6    0.364 0.770  4.11
#> 10 08NM116        Oct        2.06  1.64    15.2    0.267 0.841  3.82
#> 11 08NM116        Nov        2.01  1.62    11.7    0.260 0.590  3.99
#> 12 08NM116        Dec        1.29  1.06     7.30   0.244 0.528  2.27
#> 13 08NM116        Long-term  6.28  1.83    86.2    0.140 0.705 20   
#> 14 08NM116        Summer     3.48  1.90    76.8    0.332 0.863  7.20