Create a table of weekly frequency analysis — weekly_frequency

The weekly frequency analysis is based on daily, discrete, or both types of data. The median of each year/week combo is calculated, then overall weekly statistics are calculated off of that median.

Usage

weekly_frequency_table(
  gw_level_dv,
  gwl_data,
  parameter_cd = NA,
  date_col = NA,
  value_col = NA,
  approved_col = NA,
  stat_cd = NA,
  flip = FALSE
)

Arguments

gw_level_dv: data frame, daily groundwater level data. Often obtained from readNWISdv. Use NULL for no daily data.
gwl_data: data frame returned from readNWISgwl, or data frame with a date, value, and approval columns. Using the convention: lev_dt (representing date), lev_age_cd (representing approval code), and lev_va or sl_lev_va (representing value) will allow defaults to work. Use NULL for no discrete data.
parameter_cd: If data in gw_level_dv comes from NWIS, the parameter_cd can be used to define the value_col. If the data doesn't come directly from NWIS services, this can be set to NA,and this argument will be ignored.
date_col: the name of the date column. The default is NA, in which case, the code will try to get the column name automatically based on NWIS naming conventions. If both gw_level_dv and gwl_data data frames require custom column names, the first value of this input defines the date column for gw_level_dv, and the second defines gwl_data.
value_col: the name of the value column. The default is NA, in which case, the code will try to get the column name automatically based on NWIS naming conventions. If both gw_level_dv and gwl_data data frames require custom column names, the first value of this input defines the value column for gw_level_dv, and the second defines gwl_data.
approved_col: the name of the column to get provisional/approved status. The default is NA, in which case, the code will try to get the column name automatically based on NWIS naming conventions. If both gw_level_dv and gwl_data data frames require custom column names, the first value of this input defines the approval column for gw_level_dv, and the second defines gwl_data.
stat_cd: If data in gw_level_dv comes from NWIS, the stat_cd can be used to help define the value_col.
flip: logical. If TRUE, flips labels so that the lower numbers are in the higher percentages. Default is TRUE.

Value

a data frame of weekly frequency analysis

Examples


# site <- "263819081585801"
p_code_dv <- "62610"
statCd <- "00001"
# gw_level_dv <- dataRetrieval::readNWISdv(site, p_code_dv, statCd = statCd)
gw_level_dv <- L2701_example_data$Daily
weekly_frequency <- weekly_frequency_table(gw_level_dv,
                                           NULL,
                                           parameter_cd = "62610")
head(weekly_frequency)
#> # A tibble: 6 × 12
#>    week   p05   p10   p25   p50   p75   p90   p95 nYears minMed maxMed
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <int>  <dbl>  <dbl>
#> 1     1 -38.5 -33.1 -28.8 -21.4 -17.5 -12.3 -8.59     41  -40.6  -5.48
#> 2     2 -39.3 -32.8 -29.3 -21.4 -17.5 -12.3 -7.38     42  -41.4  -5.52
#> 3     3 -38.8 -32.6 -29.2 -21.2 -17.7 -12.2 -6.11     42  -42.1  -5.68
#> 4     4 -39.4 -32.0 -29.7 -21.4 -18.3 -12.0 -6.83     42  -41.9  -6.12
#> 5     5 -40.4 -32.1 -29.7 -21.7 -18.6 -12.4 -7.01     43  -41.6  -6.8 
#> 6     6 -41.0 -32.3 -29.0 -21.9 -19.0 -12.4 -7.89     43  -42.1  -7.02
#> # ℹ 1 more variable: week_start <chr>

gwl_data <- L2701_example_data$Discrete

weekly_frequency <- weekly_frequency_table(gw_level_dv,
                                           gwl_data,
                                           parameter_cd = "62610")
weekly_frequency
#> # A tibble: 53 × 12
#>     week   p05   p10   p25   p50   p75    p90   p95 nYears minMed maxMed
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>  <int>  <dbl>  <dbl>
#>  1     1 -38.5 -33.1 -28.9 -21.4 -17.5 -12.3  -8.59     41  -40.6  -5.48
#>  2     2 -39.3 -32.8 -29.3 -21.4 -17.5 -12.3  -7.38     42  -41.4  -5.52
#>  3     3 -38.8 -32.6 -29.2 -21.2 -17.7 -12.2  -6.11     42  -42.1  -5.68
#>  4     4 -39.5 -32.1 -29.8 -21.5 -18.3 -12.1  -6.83     42  -42.4  -6.12
#>  5     5 -40.4 -32.1 -29.7 -21.7 -18.6 -12.4  -7.01     43  -41.6  -6.8 
#>  6     6 -41.0 -32.3 -29.0 -21.9 -19.0 -12.4  -7.89     43  -42.1  -7.02
#>  7     7 -40.8 -32.6 -29.2 -22.2 -19   -12.8  -7.69     43  -43.4  -7.45
#>  8     8 -41.2 -33.6 -29.3 -22.2 -18.6 -11.2  -7.80     43  -45.0  -6.07
#>  9     9 -41.7 -34.8 -29.7 -22.4 -19.3 -10.3  -7.88     43  -42.6  -6.15
#> 10    10 -41.1 -35.3 -30.0 -23.3 -19.9  -9.29 -7.64     42  -43.3  -6.26
#> # ℹ 43 more rows
#> # ℹ 1 more variable: week_start <chr>
weekly_flip <- weekly_frequency_table(gw_level_dv,
                                      gwl_data,
                                      parameter_cd = "62610",
                                      flip = TRUE)
weekly_flip
#> # A tibble: 53 × 12
#>     week   p05    p10   p25   p50   p75   p90   p95 nYears minMed maxMed
#>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <int>  <dbl>  <dbl>
#>  1     1 -8.59 -12.3  -17.5 -21.4 -28.9 -33.1 -38.5     41  -5.48  -40.6
#>  2     2 -7.38 -12.3  -17.5 -21.4 -29.3 -32.8 -39.3     42  -5.52  -41.4
#>  3     3 -6.11 -12.2  -17.7 -21.2 -29.2 -32.6 -38.8     42  -5.68  -42.1
#>  4     4 -6.83 -12.1  -18.3 -21.5 -29.8 -32.1 -39.5     42  -6.12  -42.4
#>  5     5 -7.01 -12.4  -18.6 -21.7 -29.7 -32.1 -40.4     43  -6.8   -41.6
#>  6     6 -7.89 -12.4  -19.0 -21.9 -29.0 -32.3 -41.0     43  -7.02  -42.1
#>  7     7 -7.69 -12.8  -19   -22.2 -29.2 -32.6 -40.8     43  -7.45  -43.4
#>  8     8 -7.80 -11.2  -18.6 -22.2 -29.3 -33.6 -41.2     43  -6.07  -45.0
#>  9     9 -7.88 -10.3  -19.3 -22.4 -29.7 -34.8 -41.7     43  -6.15  -42.6
#> 10    10 -7.64  -9.29 -19.9 -23.3 -30.0 -35.3 -41.1     42  -6.26  -43.3
#> # ℹ 43 more rows
#> # ℹ 1 more variable: week_start <chr>