In rich-iannone/pointblank: Data Validation and Organization of Metadata for Local and Remote Tables

knitr::opts_chunk$set(echo = TRUE)

library(pointblank)
# Use `validate_rmd()` here to set options for the
# pointblank validation workflow within R Markdown documents

We can validate data inside of specialized code chunks. The pointblank makes this possible by loading it, as above. We'll perform some simple validations using small_table.

small_table

We can perform validation checks on that table with pointblank step functions (inside code chunks where validate = TRUE). The results will be initially hidden in the rendered HTML document but can be revealed.

col_exists(small_table, columns = vars(a, b, c, d, e, f))
rows_distinct(small_table, vars(d, e))
col_vals_gt(small_table, vars(d), 1000)

We could also use pointblank's stop_if_not() function to generate some predicate-based validation statements.

stop_if_not(nrow(small_table) > 10)
stop_if_not("time" %in% colnames(small_table))

Note that with multiple pointblank step functions chained together, only the first error encountered will be reported.

small_table %>% 
  col_exists(columns = vars(a, b, c, d, e, f)) %>% # this passes validation
  rows_distinct() %>%                              # this step fails (showing us the error message)
  col_vals_gt(vars(d), 5000)                       # this also fails (we don't see its message)

If all validations in a chunk do not fail, we can still inspect the validation code.

small_table %>% 
  col_is_date("date") %>%
  col_vals_gt(vars(d), vars(c), na_pass = TRUE)

rich-iannone/pointblank documentation built on June 29, 2024, 4:09 p.m.