pointblank (version 0.7.0)

has_columns: Determine if one or more columns exist in a table.

Description

This utility function can help you easily determine whether a column of a specified name is present in a table object. This function works well enough on a table object but it can also be used as part of a formula in any validation function's active argument. Using active = ~ . %>% has_columns("column_1") means that the validation step will be inactive if the target table doesn't contain a column named column_1. We can also use multiple columns in vars() so having active = ~ . %>% has_columns(vars(column_1, column_2)) in a validation step will make it inactive at interrogate() time unless the columns column_1 and column_2 are both present.

Usage

has_columns(x, columns)

Arguments

x

The table object.

columns

One or more column names that are to be checked for existence in the table x.

Value

A length-1 logical vector.

Function ID

12-2

See Also

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

Examples

# NOT RUN {
# The `small_table` dataset in the
# package has the columns `date_time`,
# `date`, and the `a` through `f`
# columns
small_table

# With `has_columns()` we can check for
# column existence by using it directly
# with the table; a column name can be
# verified as present by using it in
# double quotes
small_table %>% has_columns("date")

# Multiple column names can be supplied;
# this is `TRUE` because both columns are
# present in `small_table`
small_table %>% has_columns(c("a", "b"))

# It's possible to supply column names
# in `vars()` as well
small_table %>% has_columns(vars(a, b))

# Because column `h` isn't present, this
# returns `FALSE` (all specified columns
# need to be present to obtain `TRUE`)
small_table %>% has_columns(vars(a, h))

# The `has_columns()` function can be
# useful in expressions that involve the
# target table, especially if it is
# uncertain that the table will contain
# a column that's involved in a validation

# In the following agent-based validation,
# the first two steps will be 'active'
# because all columns checked for in the
# expressions are present; the third step
# is inactive because column `j` isn't
# there (without the `active` statement we
# would get an evaluation failure in the
# agent report)
agent <- 
  create_agent(
    read_fn = ~ small_table,
    tbl_name = "small_table"
  ) %>%
  col_vals_gt(
    vars(c), value = vars(a),
    active = ~ . %>% has_columns(vars(a, c))
  ) %>%
  col_vals_lt(
    vars(h), value = vars(d),
    preconditions = ~ . %>% dplyr::mutate(h = d - a),
    active = ~ . %>% has_columns(vars(a, d))
  ) %>%
  col_is_character(
    vars(j),
    active = ~ . %>% has_columns("j")
  ) %>%
  interrogate() 

# }