col_schema function

Generate a table column schema manually or with a reference table

A table column schema object, as can be created by col_schema(), is necessary when using the col_schema_match() validation function (which checks whether the table object under study matches a known column schema). The col_schema object can be made by carefully supplying the column names and their types as a set of named arguments, or, we could provide a table object, which could be of the data.frame, tbl_df, tbl_dbi, or tbl_spark varieties. There's an additional option, which is just for validating the schema of a tbl_dbi or tbl_spark object: we can validate the schema based on R column types (e.g., "numeric", "character", etc.), SQL column types (e.g., "double", "varchar", etc.), or Spark SQL column types ("DoubleType", "StringType", etc.). This is great if we want to validate table column schemas both on the server side and when tabular data is collected and loaded into R.

col_schema(..., .tbl = NULL, .db_col_types = c("r", "sql"))

Arguments

• ...: Column-by-column schema definition

  <multiple expressions> // required (or, use .tbl)

  A set of named arguments where the names refer to column names and the values are one or more column types.

• .tbl: A data table for defining a schema

  obj:<tbl_*> // optional

  An option to use a table object to define the schema. If this is provided then any values provided to ... will be ignored. This can either be a table object, a table-prep formula.This can be a table object such as a data frame, a tibble, a tbl_dbi object, or a tbl_spark object. Alternatively, a table-prep formula (~ <tbl reading code>) or a function (function() <tbl reading code>) can be used to lazily read in the table at interrogation time.

• .db_col_types: Use R column types or database column types?

  singl-kw:[r|sql] // default: "r"

  Determines whether the column types refer to R column types ("r") or SQL column types ("sql").

Examples

Create a simple table with two columns: one integer and the other character.

tbl <- 
  dplyr::tibble(
    a = 1:5,
    b = letters[1:5]
  )

tbl
#> # A tibble: 5 x 2
#>       a b    
#>   <int> <chr>
#> 1     1 a    
#> 2     2 b    
#> 3     3 c    
#> 4     4 d    
#> 5     5 e

Create a column schema object that describes the columns and their types (in the expected order).

schema_obj <- 
  col_schema(
    a = "integer",
    b = "character"
  )

schema_obj
#> $a
#> [1] "integer"
#> 
#> $b
#> [1] "character"
#> 
#> attr(,"class")
#> [1] "r_type"     "col_schema"

Validate that the schema object schema_obj exactly defines the column names and column types of the tbl table.

agent <-
  create_agent(tbl = tbl) %>%
  col_schema_match(schema_obj) %>%
  interrogate()

Determine if this validation step passed by using all_passed().

all_passed(agent)

## [1] TRUE

We can alternatively create a column schema object from a tbl_df object.

schema_obj <-
  col_schema(
    .tbl = dplyr::tibble(
 a = integer(0),
 b = character(0)
    )
  )

This should provide the same interrogation results as in the previous example.

create_agent(tbl = tbl) %>%
  col_schema_match(schema_obj) %>%
  interrogate() %>%
  all_passed()

## [1] TRUE

Function ID

13-1

See Also

Other Utility and Helper Functions: affix_date(), affix_datetime(), from_github(), has_columns(), stop_if_not()