User can access actual columns and path variables by creating a view using a SELECT query with each column from JSON or CSV data projected from foreign table. Each column must be typecast to a valid type and then aliased to the appropriate column name. This allows users to access actual columns and keys in the JSON or CSV data. It is up to the user on what must be selected in SELECT query: columns, attributes, keys from JSON or CSV data and path variables.
Example for JSON data
Create a view.
# Create a select statement with each column typecast to a valid type and then aliased to the required column name. # View is created on top of this select statement. # Note that we are selecting each attribute including the path variables. # Following is the VIEW created at the backend: """ REPLACE VIEW riverflowview AS ( SELECT CAST($path.$siteno AS CHAR(10)) TheSite, CAST($path.$year AS CHAR(4)) TheYear, CAST($path.$month AS CHAR(2)) TheMonth, CAST($path.$day AS CHAR(2)) TheDay, CAST(payload.site_no AS CHAR(8)) Site_no, CAST(payload.Flow AS FLOAT) Flow, CAST(payload.GageHeight AS FLOAT) GageHeight1, CAST(payload.Precipitation AS FLOAT) Precipitation, CAST(payload.Temp AS FLOAT) Temperature, CAST(payload.Velocity AS FLOAT) Velocity, CAST(payload.BatteryVoltage AS FLOAT) BatteryVoltage, CAST(payload.GageHeight2 AS FLOAT) GageHeight2 FROM riverflow); """
Create a tbl_teradata object on the view and display the head of the tbl_teradata object.
# Create object(s) of class "tbl_teradata" on a view > wrk2dfview <- tbl(con, "riverflowview") > as.data.frame(head(wrk2dfview))
A data.frame: 6 × 12 TheSite TheYear TheMonth TheDay Site_no Flow GageHeight1 Precipitation Temperature Velocity BatteryVoltage GageHeight2 <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 09423560 2018 06 29 09423560 122 8.05 NA 71.2 NA NA NA 09423560 2018 06 29 09423560 125 8.09 NA 71.0 NA NA NA 09423560 2018 06 29 09423560 128 8.14 NA 70.8 NA NA NA 09423560 2018 06 29 09423560 122 8.08 NA 71.1 NA NA NA 09423560 2018 06 29 09423560 119 8.01 NA 71.3 NA NA NA 09423560 2018 06 29 09423560 115 7.98 NA 71.4 NA NA NA
Example for CSV data
Create a view.
# Create a select statement with each column typecast to a valid type and then aliased to the required column name. # View is created on top of this select statement. # Note that we are selecting each attribute including the path variables. # Following is the VIEW created at the backend: """ REPLACE VIEW riverflowcsvview AS ( SELECT CAST($path.$siteno AS CHAR(10)) TheSite, CAST($path.$year AS CHAR(4)) TheYear, CAST($path.$month AS CHAR(2)) TheMonth, CAST($path.$day AS CHAR(2)) TheDay, CAST(payload..site_no AS CHAR(8)) Site_no, CAST(payload..Flow AS FLOAT) Flow, CAST(payload..GageHeight AS FLOAT) GageHeight1, CAST(payload..Precipitation AS FLOAT) Precipitation, CAST(payload..Temp AS FLOAT) Temperature, CAST(payload..Velocity AS FLOAT) Velocity, CAST(payload..BatteryVoltage AS FLOAT) BatteryVoltage, CAST(payload..GageHeight2 AS FLOAT) GageHeight2 FROM riverflowcsv); """
Create a tbl_teradata object on the view and display the head of the tbl_teradata object.
# Create object(s) of class "tbl_teradata" on a view > wrk2dfview <- tbl(con, "riverflowcsvview") > as.data.frame(head(wrk2dfview))
A data.frame: 6 × 12 TheSite TheYear TheMonth TheDay Site_no Flow GageHeight1 Precipitation Temperature Velocity BatteryVoltage GageHeight2 <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 09396100 2018 07 16 09396100 24.4 1.33 0 NA NA NA 1.33 09396100 2018 07 16 09396100 113.0 1.83 0 NA NA NA 1.83 09396100 2018 07 16 09396100 105.0 1.80 0 NA NA NA 1.80 09396100 2018 07 16 09396100 16.1 1.23 0 NA NA NA 1.23 09396100 2018 07 16 09396100 34.1 1.42 0 NA NA NA 1.42 09396100 2018 07 16 09396100 44.7 1.50 0 NA NA NA 1.50