Create a table of monthly frequency analysis — monthly_frequency

The table will accept daily, discrete, or a both types of data. The median of each year/month is calculated. Then using that median, monthly stats are calculated. Percentiles are calculated using the quantile function with "type=6".

Usage

monthly_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 monthly groundwater level statistics including the 5th, 10th, 25th, 75th, 90th, and 95th percentiles; the number of years of data; and the lowest monthly median and the highest monthly median.

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$Daily
monthly_frequency <- monthly_frequency_table(gw_level_dv,
                                             NULL,
                                             parameter_cd = "62610")
head(monthly_frequency)
#> # A tibble: 6 × 11
#>   month   p05   p10   p25   p50   p75   p90    p95 nYears minMed maxMed
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <int>  <dbl>  <dbl>
#> 1     1 -38.8 -32.3 -29.5 -21.5 -17.8 -12.2  -6.88     42  -41.8  -5.68
#> 2     2 -41.2 -32.6 -29.1 -22.1 -19.0 -12.6  -7.53     43  -42.8  -7.25
#> 3     3 -41.4 -36.6 -30.0 -24.3 -20.4 -10.2  -7.65     42  -45.1  -6.87
#> 4     4 -45.6 -39.2 -32.2 -26.4 -22.4 -16.1 -10.1      42  -48.5  -9.34
#> 5     5 -46.5 -40.0 -34.3 -29.2 -23.5 -18.2 -14.2      42  -49.4 -10.0 
#> 6     6 -42.0 -39.4 -32.4 -28.3 -21.3 -17.0 -13.6      41  -42.8 -11.0 
gwl_data <- L2701_example_data$Discrete

monthly_frequency_combo <- monthly_frequency_table(gw_level_dv,
                                             gwl_data,
                                             parameter_cd = "62610")
head(monthly_frequency_combo)
#> # A tibble: 6 × 11
#>   month   p05   p10   p25   p50   p75   p90    p95 nYears minMed maxMed
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <int>  <dbl>  <dbl>
#> 1     1 -38.6 -32.3 -29.5 -21.6 -17.9 -12.6  -6.95     43  -41.8  -5.68
#> 2     2 -41.2 -32.6 -29.1 -22.1 -19.0 -12.5  -7.53     43  -42.9  -7.25
#> 3     3 -41.5 -36.4 -30.0 -24   -19.8 -10.6  -7.68     43  -45.2  -6.87
#> 4     4 -45.7 -39.2 -32.2 -26.4 -22.6 -16.1 -10.1      42  -48.4  -9.36
#> 5     5 -46.5 -40.0 -34.3 -29.4 -23.5 -18.2 -14.2      42  -49.4 -10.0 
#> 6     6 -42.0 -39.4 -32.5 -28.4 -21.7 -17.0 -13.6      41  -42.8 -11.0 
monthly_flip <- monthly_frequency_table(gw_level_dv,
                                        gwl_data,
                                        parameter_cd = "62610",
                                        flip = TRUE)
head(monthly_flip)
#> # A tibble: 6 × 11
#>   month    p05   p10   p25   p50   p75   p90   p95 nYears minMed maxMed
#>   <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <int>  <dbl>  <dbl>
#> 1     1  -6.95 -12.6 -17.9 -21.6 -29.5 -32.3 -38.6     43  -5.68  -41.8
#> 2     2  -7.53 -12.5 -19.0 -22.1 -29.1 -32.6 -41.2     43  -7.25  -42.9
#> 3     3  -7.68 -10.6 -19.8 -24   -30.0 -36.4 -41.5     43  -6.87  -45.2
#> 4     4 -10.1  -16.1 -22.6 -26.4 -32.2 -39.2 -45.7     42  -9.36  -48.4
#> 5     5 -14.2  -18.2 -23.5 -29.4 -34.3 -40.0 -46.5     42 -10.0   -49.4
#> 6     6 -13.6  -17.0 -21.7 -28.4 -32.5 -39.4 -42.0     41 -11.0   -42.8