This function creates a new column, TADA.DepthCategory.Flag with values: "No depth info", "Surface", "Bottom", and "Middle" when multiple depths are available. Categories are: less than 2m (or user specified value) depth = "Surface", from bottom up to 2m (or user specified value) from bottom = "Bottom", and all depths in between the Surface and Bottom are assigned to the "Middle" category.
Usage
TADA_FlagDepthCategory(
.data,
bycategory = "no",
bottomvalue = 2,
surfacevalue = 2,
dailyagg = "none",
aggregatedonly = FALSE,
clean = FALSE
)Arguments
- .data
TADA dataframe
- bycategory
character argument with options "no", "all", "surface", "middle", "bottom". The default is bycategory = "no" which means that any aggregate values are based on the entire water column at a Monitoring Location. When bycategory = "all", any aggregate values are determined for each depth category for each Monitoring Location. When bycategory = "surface", "middle", or "bottom", the data frame is filtered only to include results in the selected category and aggregate values are determined ONLY for results with TADA.DepthCategory.Flags "Surface", "Bottom", or "Middle" results respectively.
- bottomvalue
numeric argument. The user enters how many meters from the bottom should be included in the "Bottom" category. Default is bottomvalue = 2. If bottomvalue = "null", "Bottom" and "Middle" results cannot be identified, however TADA.ConsolidatedDepth and TADA.ConsolidatedDepth.Bottom will still be determined.
- surfacevalue
numeric argument. The user enters how many meters from the surface should be included in the "Surface" category. Default is surfacevalue = 2. If surfacevalue = "null", "Surface" and "Middle" results cannot be identified, however TADA.ConsolidatedDepth and TADA.ConsolidatedDepth.Bottom will still be determined.
- dailyagg
Character argument; with options "none", "avg", "min", or "max". The default is dailyagg = "none". When dailyagg = "none", all results will be retained. When dailyagg == "avg", the mean value in each group of results (as determined by the depth category) will be identified or calculated for each MonitoringLocation, ActivityDate, Organization ID, and TADA.CharacteristicName combination. When dailyagg == "min" or when dailyagg == "max", the min or max value in each group of results (as determined by the depth category) will be identified or calculated for each MonitoringLocation, ActivityDate, and TADA.CharacteristicName combination. An additional column, TADA.DepthProfileAggregation.Flag will be added to describe aggregation.
- aggregatedonly
Boolean argument with options "TRUE" or "FALSE". The default is aggregatedonly = "FALSE" which means that all results are returned. When aggregatedonly = "TRUE", only aggregate values are returned.
- clean
Boolean argument with options "TRUE" or "FALSE". The default is clean = "FALSE" which means that all results are returned. When clean = "TRUE", only aggregate results which can be assigned to a depth category are included in the returned dataframe.
Value
The same input TADA dataframe with additional columns TADA.DepthCategory.Flag, TADA.DepthProfileAggregation.Flag, TADA.ConsolidatedDepth, TADA.ConsolidatedDepth.Bottom, and TADA.ConsolidatedDepth.Unit. The consolidated depth fields are created by reviewing multiple WQC columns where users may input depth information. If a daily_agg = "avg", "min", or "max", aggregated values will be identified in the TADA.ResultAggregation.Flag column. In the case of daily_agg = "avg", additional rows to display averages will be added to the data frame. They can be identified by the prefix ("TADA-") of their result identifiers.
Details
When more than one result is available for a MonitoringLocationIdentifier, ActivityStartDate, OrganizationIdentifier, and TADA.CharacteristicName, the user can choose a single result value (average, max, or min value) to use for that day and location. If results vary with depth, the user may also define whether the daily aggregation occurs over each depth category (surface, middle, or bottom) or for the entire depth profile.
Examples
# Load data frame
data(Data_6Tribes_5y)
# assign TADA.DepthCategory.Flag with no aggregation
Data_6Tribs_5y_DepthCat <- TADA_FlagDepthCategory(Data_6Tribes_5y)
#> [1] "TADA_FlagDepthCategory: checking data set for depth values. 57765 results have depth values available."
#> [1] "TADA_FlagDepthCategory: assigning depth categories."
#> [1] "TADA_FlagDepthCategory: Grouping results by MonitoringLocationIdentifier, OrganizationIdentifier, CharacteristicName, and ActivityStartDate for aggregation for entire water column."
#> [1] "TADA_FlagDepthCategory: No aggregation performed."
# assign TADA.DepthCategory.Flag and determine average values by depth category and returning only aggregate values
Data_6Tribs_5y_Mean <- TADA_FlagDepthCategory(Data_6Tribes_5y, bycategory = "all", dailyagg = "avg", aggregatedonly = FALSE)
#> [1] "TADA_FlagDepthCategory: checking data set for depth values. 57765 results have depth values available."
#> [1] "TADA_FlagDepthCategory: assigning depth categories."
#> [1] "TADA_FlagDepthCategory: Grouping results by MonitoringLocationIdentifier, OrganizationIdentifier, CharacteristicName, ActivityStartDate, and TADA.DepthCategory.Flag for aggregation by TADA.DepthCategory.Flag."
#> [1] "TADA_FlagDepthCategory: Calculating mean aggregate value with randomly selected metadata."