This set of functions inspect a data frame to anticipate problems before writing with REDCap's API.

validate_for_write( d, convert_logical_to_integer, record_id_name )

validate_data_frame_inherits( d )

validate_no_logical( d, stop_on_error = FALSE )

validate_field_names( d, stop_on_error = FALSE )

validate_record_id_name( d, record_id_name = "record_id", stop_on_error = FALSE )

validate_repeat_instance( d, stop_on_error = FALSE )

validate_uniqueness( d, record_id_name, stop_on_error = FALSE)



The base::data.frame() or tibble::tibble() containing the dataset used to update the REDCap project.


The name of the field that represents one record. The default name in REDCap is "record_id".


If TRUE, an error is thrown for violations. Otherwise, a dataset summarizing the problems is returned.


This mimics the convert_logical_to_integer parameter in redcap_write() when checking for potential importing problems. Defaults to FALSE.


A tibble::tibble(), where each potential violation is a row. The two columns are:

  • field_name: The name of the field/column/variable that might cause problems during the upload.

  • field_index: The position of the field. (For example, a value of '1' indicates the first column, while a '3' indicates the third column.)

  • concern: A description of the problem potentially caused by the field.

  • suggestion: A potential solution to the concern.


All functions listed in the Usage section above inspect a specific aspect of the dataset. The validate_for_write() function executes all these individual validation checks. It allows the client to check everything with one call.

Currently, the individual checks include:

  1. validate_data_frame_inherits(d): d inherits from base::data.frame()

  2. validate_field_names(d): The columns of d start with a lowercase letter, and subsequent optional characters are a sequence of (a) lowercase letters, (b) digits 0-9, and/or (c) underscores. (The exact regex is ^[a-z][0-9a-z_]*$.)

  3. validate_record_id_name(d): d contains a field called "record_id", or whatever value was passed to record_id_name.

  4. validate_no_logical(d) (unless convert_logical_to_integer is TRUE): d does not contain logical values (because REDCap typically wants 0/1 values instead of FALSE/TRUE).

  5. validate_repeat_instance(d): d has an integer for redcap_repeat_instance, if the column is present.

  6. validate_uniqueness(d, record_id_name = record_id_name): d does not contain multiple rows with duplicate values of record_id, redcap_event_name, redcap_repeat_instrument, and redcap_repeat_instance (depending on the longitudinal & repeating structure of the project).

    Technically duplicate rows are not errors, but we feel that this will almost always be unintentional, and lead to an irrecoverable corruption of the data.

If you encounter additional types of problems when attempting to write to REDCap, please tell us by creating a new issue, and we'll incorporate a new validation check into this function.


The official documentation can be found on the 'API Help Page' and 'API Examples' pages on the REDCap wiki (i.e., and If you do not have an account for the wiki, please ask your campus REDCap administrator to send you the static material.


Will Beasley


d1 <- data.frame(
  record_id      = 1:4,
  flag_logical   = c(TRUE, TRUE, FALSE, TRUE),
  flag_Uppercase = c(4, 6, 8, 2)
REDCapR::validate_for_write(d = d1)
#> # A tibble: 2 × 4
#>   field_name     field_index concern                                  suggestion
#>   <chr>          <chr>       <chr>                                    <chr>     
#> 1 flag_Uppercase 3           A REDCap project does not allow field n… Change th…
#> 2 flag_logical   2           The REDCap API does not automatically c… Convert t…

REDCapR::validate_for_write(d = d1, convert_logical_to_integer = TRUE)
#> # A tibble: 1 × 4
#>   field_name     field_index concern                                  suggestion
#>   <chr>          <chr>       <chr>                                    <chr>     
#> 1 flag_Uppercase 3           A REDCap project does not allow field n… Change th…

# If `d1` is not a data.frame, the remaining validation checks are skipped:
# REDCapR::validate_for_write(as.matrix(mtcars))
# REDCapR::validate_for_write(c(mtcars, iris))

d2 <- tibble::tribble(
  ~record_id, ~redcap_event_name, ~redcap_repeat_instrument, ~redcap_repeat_instance,
  1L, "e1", "i1", 1L,
  1L, "e1", "i1", 2L,
  1L, "e1", "i1", 3L,
  1L, "e1", "i1", 4L,
  1L, "e1", "i2", 1L,
  1L, "e1", "i2", 2L,
  1L, "e1", "i2", 3L,
  1L, "e1", "i2", 4L,
  2L, "e1", "i1", 1L,
  2L, "e1", "i1", 2L,
  2L, "e1", "i1", 3L,
  2L, "e1", "i1", 4L,
#> # A tibble: 0 × 4
#> # ℹ 4 variables: field_name <chr>, field_index <chr>, concern <chr>,
#> #   suggestion <chr>
#> # A tibble: 0 × 4
#> # ℹ 4 variables: field_name <chr>, field_index <chr>, concern <chr>,
#> #   suggestion <chr>

d3 <- tibble::tribble(
  ~record_id, ~redcap_event_name, ~redcap_repeat_instrument, ~redcap_repeat_instance,
  1L, "e1", "i1", 1L,
  1L, "e1", "i1", 3L,
  1L, "e1", "i1", 3L, # Notice this duplicates the row above
# validate_uniqueness(d3)
# Throws error:
# validate_uniqueness(d3, stop_on_error = TRUE)