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Create a frequency analysis based on custom interval

stats_by_interval.Rd

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

stats_by_interval(
  interval,
  gw_level_dv,
  gwl_data,
  parameter_cd = NA,
  date_col = NA,
  value_col = NA,
  approved_col = NA,
  flip = FALSE
)

Arguments

gw_level_dv

data frame, daily groundwater level data. Often obtained from read_waterdata_daily. Use NULL for no daily data.

gwl_data

data frame returned from read_waterdata_field_measurements, or data frame with a date, value, and approval columns. Use NULL for no discrete data.

parameter_cd

Can be used to filter data if the data frame has a "parameter_code" column. The default is NA, which will not do any filtering. If the gwl_data and gw_level_dv need different parameter code filtering, use a vector of 2 parameter codes. The first one will filter the gw_level_dv data frame, the second will filter the gwl_data data frame.

date_col

the name of the time columns. The first value is associated with the gw_level_dv input, and the second value is associated with the gwl_data input. The default is c("time", "time").

value_col

the name of the value columns. The first value is associated with the gw_level_dv input, and the second value is associated with the gwl_data input. The default is c("value", "value").

approved_col

the name of the column to get provisional/approved status. The first value is associated with the gw_level_dv input, and the second value is associated with the gwl_data input. The default is c("approval_status", "approval_status"). It is expected that these columns will have only "Approved" or "Provisional".

flip

logical. If TRUE, flips labels so that the lower numbers are in the higher percentages. Default is TRUE.

Examples


site <- "USGS-263819081585801"
p_code_dv <- "62610"
statCd <- "00001"
# gw_level_dv <- dataRetrieval::read_waterdata_daily(monitoring_location_id = site,
#                                                    parameter_code = p_code_dv,
#                                                    statistic_id = statCd)
                                                    
gw_level_dv <- L2701_example_data$Daily
monthly_frequency <- stats_by_interval("month",
                                       gw_level_dv,
                                       NULL)
head(monthly_frequency)
#> # A tibble: 6 × 11
#>   month   p05   p10   p25   p50   p75   p90    p95 nYears minMed maxMed
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <int>  <dbl>  <dbl>
#> 1     1 -37.8 -32.2 -29.1 -20.2 -16.9 -10.9  -7.15     46  -41.8  -5.68
#> 2     2 -40.4 -32.5 -28.8 -21.4 -17.7 -12.3  -7.64     47  -42.8  -7.25
#> 3     3 -40.8 -35.5 -29.9 -22.8 -18.8 -12.1  -7.81     47  -45.1  -6.87
#> 4     4 -44.6 -39.0 -31.3 -25.3 -21.7 -15.0 -10.4      46  -48.5  -9.34
#> 5     5 -45.7 -39.8 -33.8 -27.0 -23.2 -17.6 -14.2      46  -49.4 -10.0 
#> 6     6 -41.6 -37.7 -32.1 -26.6 -20.3 -15.6 -13.7      45  -42.8 -11.0 
# gwl_data <- dataRetrieval::read_waterdata_field_measurements(monitoring_location_id = site)
gwl_data <- L2701_example_data$Discrete
gwl_data <- gwl_data[gwl_data$parameter_code == p_code_dv, ]

monthly_frequency2 <- stats_by_interval("month",
                                       gw_level_dv,
                                       gwl_data) 

annual_frequency2 <- stats_by_interval("year",
                                       gw_level_dv,
                                       gwl_data) 
                                       
week_frequency2 <- stats_by_interval("week",
                                     gw_level_dv,
                                     gwl_data)