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Each of the mark_*() functions appends a new column to the data. The unite_exclusions() function unites all of those columns in a single column that can be used to filter any or all exclusions downstream. Rows with multiple exclusions are concatenated with commas.


  exclusion_types = c("duplicates", "duration", "ip", "location", "preview",
    "progress", "resolution"),
  separator = ",",
  remove = TRUE



Data frame or tibble (preferably exported from Qualtrics).


Vector of types of exclusions to unite.


Character string specifying what character to use to separate multiple exclusion types


Logical specifying whether to remove united columns (default = TRUE) or leave them in the data frame (FALSE)


An object of the same type as x that includes the all of the same rows but with a single exclusion column replacing all of the specified exclusion_* columns.


# Unite all exclusion types
df <- qualtrics_text %>%
  mark_duplicates() %>%
  mark_duration(min_duration = 100) %>%
  mark_ip() %>%
  mark_location() %>%
  mark_preview() %>%
  mark_progress() %>%
#>  2 NAs were found in IP addresses.
#>  7 out of 7 rows had duplicate IP addresses.
#>  1 NA was found in location.
#>  10 out of 10 rows had duplicate locations.
#>  4 out of 100 rows took less time than 100.
#>  2 out of 100 rows had NA values for IP addresses (check for preview data with 'check_preview()').
#>  4 out of 100 rows had IP address outside of US.
#>  1 out of 100 rows had no information on location.
#>  5 out of 100 rows were located outside of the US.
#>  2 rows were collected as previews. It is highly recommended to exclude these rows before further processing.
#>  6 out of 100 rows did not complete the study.
#>  3 out of 100 rows had screen resolutions less than 0 or height less than 0.
df2 <- df %>%

# Unite subset of exclusion types
df2 <- df %>%
  unite_exclusions(exclusion_types = c("duplicates", "duration", "ip"))