Get started excluding data

The {excluder} package facilitates marking, checking for, and excluding rows of data frames¹ for common online participant exclusion criteria. This package applies to online data with a focus on data collected from Qualtrics surveys, and default column names come from importing data with the {qualtRics} package. This may be most useful for Mechanical Turk data to screen for duplicate entries from the same location/IP address or entries from locations outside of the United States. However, the package can be used for data from other sources that need to be screened for IP address, location, duplicated locations, participant progress, survey completion time, and/or screen resolution.

Usage

The package has three core verbs:

mark_*() functions add a new column to the original data frame that labels the rows meeting the exclusion criteria. This is useful to label the potential exclusions for future processing without changing the original data frame.
check_*() functions search for the exclusion criteria and output a message with the number of rows meeting the criteria and a data frame of the rows meeting the criteria. This is useful for viewing the potential exclusions.
exclude_*() functions remove rows meeting the exclusion criteria. This is safest to do after checking the rows to ensure the exclusions are correct.

The check_*() and exclude_*() functions call the mark_*() function internally and then filter either the excluded or non-excluded rows. So avoid combining different verbs to sidestep unnecessary passes through the data.

The package provides seven types of exclusions based on Qualtrics metadata:

duplicates works with rows that have duplicate IP addresses and/or locations (latitude/longitude), using janitor::get_dupes().
duration works with rows whose survey completion time is too short and/or too long.
ip works with rows whose IP addresses are not found in the specified country (note: this exclusion type requires an internet connection to download the country’s IP ranges), using package {iptools}.
location works with rows whose latitude and longitude are not found in the United States.
preview works with rows that are survey previews.
progress works with rows in which the survey was not complete.
resolution works with rows whose screen resolution is not acceptable.

The verbs combine with the exclusion types to generate functions. For instance, mark_duplicates() will mark duplicate rows and exclude_preview() will exclude preview rows.

There are also helper functions:

unite_exclusions() unites all of the columns marked by mark functions into a single column (each use of a mark function creates a new column).
deidentify() removes standard Qualtrics columns with identifiable information.
remove_label_rows() removes the first two rows of labels and convert date and numeric columns in the metadata.

Preparing your data

If you use the fetch_survey() from the {qualtRics} package to import your Qualtrics data, it will automatically remove the first two label rows from the data set. However, if you directly download your data from Qualtrics, it will include two rows in your data set that include label information. This has two side effects: (1) there are non-data rows that need to be removed from your data set, and (2) all of your columns will be imported as character data types.

The remove_label_rows() function will remove these two label rows. Also, by default, it will coerce the Qualtrics metadata columns from character types to the correct formats (e.g., StartDate is coerced to a date, Progress is coerced to numeric). So if you download your data from Qualtrics, you will need to run this function on your data before proceeding.

dplyr::glimpse(qualtrics_raw)
#> Rows: 102
#> Columns: 16
#> $ StartDate               <chr> "Start Date", "{\"ImportId\":\"startDate\",\"t…
#> $ EndDate                 <chr> "End Date", "{\"ImportId\":\"endDate\",\"timeZ…
#> $ Status                  <chr> "Response Type", "{\"ImportId\":\"status\"}", …
#> $ IPAddress               <chr> "IP Address", "{\"ImportId\":\"ipAddress\"}", …
#> $ Progress                <chr> "Progress", "{\"ImportId\":\"progress\"}", "10…
#> $ `Duration (in seconds)` <chr> "Duration (in seconds)", "{\"ImportId\":\"dura…
#> $ Finished                <chr> "Finished", "{\"ImportId\":\"finished\"}", "TR…
#> $ RecordedDate            <chr> "Recorded Date", "{\"ImportId\":\"recordedDate…
#> $ ResponseId              <chr> "Response ID", "{\"ImportId\":\"_recordId\"}",…
#> $ LocationLatitude        <chr> "Location Latitude", "{\"ImportId\":\"location…
#> $ LocationLongitude       <chr> "Location Longitude", "{\"ImportId\":\"locatio…
#> $ UserLanguage            <chr> "User Language", "{\"ImportId\":\"userLanguage…
#> $ Browser                 <chr> "Browser Meta Info - Browser", "{\"ImportId\":…
#> $ Version                 <chr> "Browser Meta Info - Version", "{\"ImportId\":…
#> $ `Operating System`      <chr> "Browser Meta Info - Operating System", "{\"Im…
#> $ Resolution              <chr> "Browser Meta Info - Resolution", "{\"ImportId…
#
# Remove label rows and coerce metadata columns
df <- remove_label_rows(qualtrics_raw) %>% 
  dplyr::glimpse()
#> Rows: 100
#> Columns: 16
#> $ StartDate               <dttm> 2020-12-11 12:06:52, 2020-12-11 12:06:43, 202…
#> $ EndDate                 <dttm> 2020-12-11 12:10:30, 2020-12-11 12:11:27, 202…
#> $ Status                  <chr> "Survey Preview", "Survey Preview", "IP Addres…
#> $ IPAddress               <chr> NA, NA, "73.23.43.0", "16.140.105.0", "107.57.…
#> $ Progress                <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…
#> $ `Duration (in seconds)` <dbl> 465, 545, 651, 409, 140, 213, 177, 662, 296, 2…
#> $ Finished                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
#> $ RecordedDate            <dttm> 2020-12-11 12:10:30, 2020-12-11 12:11:27, 202…
#> $ ResponseId              <chr> "R_xLWiuPaNuURSFXY", "R_Q5lqYw6emJQZx2o", "R_f…
#> $ LocationLatitude        <dbl> 29.73694, 39.74107, 34.03852, 44.96581, 27.980…
#> $ LocationLongitude       <dbl> -94.97599, -121.82490, -118.25739, -93.07187, …
#> $ UserLanguage            <chr> "EN", "EN", "EN", "EN", "EN", "EN", "EN", "EN"…
#> $ Browser                 <chr> "Chrome", "Chrome", "Chrome", "Chrome", "Chrom…
#> $ Version                 <chr> "88.0.4324.41", "88.0.4324.50", "87.0.4280.88"…
#> $ `Operating System`      <chr> "Windows NT 10.0", "Macintosh", "Windows NT 10…
#> $ Resolution              <chr> "1366x768", "1680x1050", "1366x768", "1536x864…

Marking observations

The core verbs in this package mark them for future processing. The mark_*() suite of functions creates a new column for each mark function used that marks which observations violate the exclusion criterion. They print a message about the number of observations meeting each exclusion criteria. Mark functions return a data frame identical to the original with additional columns marking exclusions.

# Mark observations run as preview
df %>% 
  mark_preview() %>% 
  dplyr::glimpse()
#> ℹ 2 rows were collected as previews. It is highly recommended to exclude these rows before further processing.
#> Rows: 100
#> Columns: 17
#> $ StartDate               <dttm> 2020-12-11 12:06:52, 2020-12-11 12:06:43, 202…
#> $ EndDate                 <dttm> 2020-12-11 12:10:30, 2020-12-11 12:11:27, 202…
#> $ Status                  <chr> "Survey Preview", "Survey Preview", "IP Addres…
#> $ IPAddress               <chr> NA, NA, "73.23.43.0", "16.140.105.0", "107.57.…
#> $ Progress                <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…
#> $ `Duration (in seconds)` <dbl> 465, 545, 651, 409, 140, 213, 177, 662, 296, 2…
#> $ Finished                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
#> $ RecordedDate            <dttm> 2020-12-11 12:10:30, 2020-12-11 12:11:27, 202…
#> $ ResponseId              <chr> "R_xLWiuPaNuURSFXY", "R_Q5lqYw6emJQZx2o", "R_f…
#> $ LocationLatitude        <dbl> 29.73694, 39.74107, 34.03852, 44.96581, 27.980…
#> $ LocationLongitude       <dbl> -94.97599, -121.82490, -118.25739, -93.07187, …
#> $ UserLanguage            <chr> "EN", "EN", "EN", "EN", "EN", "EN", "EN", "EN"…
#> $ Browser                 <chr> "Chrome", "Chrome", "Chrome", "Chrome", "Chrom…
#> $ Version                 <chr> "88.0.4324.41", "88.0.4324.50", "87.0.4280.88"…
#> $ `Operating System`      <chr> "Windows NT 10.0", "Macintosh", "Windows NT 10…
#> $ Resolution              <chr> "1366x768", "1680x1050", "1366x768", "1536x864…
#> $ exclusion_preview       <chr> "preview", "preview", "", "", "", "", "", "", …

Notice the new exclusion_preview column at the end of the data frame. It has marked the first two observations as preview.

Piping multiple mark functions will create multiple rows marking observations for exclusion.

# Mark preview and incomplete observations
df %>% 
  mark_preview() %>% 
  mark_progress() %>% 
  dplyr::glimpse()
#> ℹ 2 rows were collected as previews. It is highly recommended to exclude these rows before further processing.
#> ℹ 6 out of 100 rows did not complete the study.
#> Rows: 100
#> Columns: 18
#> $ StartDate               <dttm> 2020-12-11 12:06:52, 2020-12-11 12:06:43, 202…
#> $ EndDate                 <dttm> 2020-12-11 12:10:30, 2020-12-11 12:11:27, 202…
#> $ Status                  <chr> "Survey Preview", "Survey Preview", "IP Addres…
#> $ IPAddress               <chr> NA, NA, "73.23.43.0", "16.140.105.0", "107.57.…
#> $ Progress                <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…
#> $ `Duration (in seconds)` <dbl> 465, 545, 651, 409, 140, 213, 177, 662, 296, 2…
#> $ Finished                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
#> $ RecordedDate            <dttm> 2020-12-11 12:10:30, 2020-12-11 12:11:27, 202…
#> $ ResponseId              <chr> "R_xLWiuPaNuURSFXY", "R_Q5lqYw6emJQZx2o", "R_f…
#> $ LocationLatitude        <dbl> 29.73694, 39.74107, 34.03852, 44.96581, 27.980…
#> $ LocationLongitude       <dbl> -94.97599, -121.82490, -118.25739, -93.07187, …
#> $ UserLanguage            <chr> "EN", "EN", "EN", "EN", "EN", "EN", "EN", "EN"…
#> $ Browser                 <chr> "Chrome", "Chrome", "Chrome", "Chrome", "Chrom…
#> $ Version                 <chr> "88.0.4324.41", "88.0.4324.50", "87.0.4280.88"…
#> $ `Operating System`      <chr> "Windows NT 10.0", "Macintosh", "Windows NT 10…
#> $ Resolution              <chr> "1366x768", "1680x1050", "1366x768", "1536x864…
#> $ exclusion_preview       <chr> "preview", "preview", "", "", "", "", "", "", …
#> $ exclusion_progress      <chr> "", "", "", "", "", "", "", "", "", "", "", ""…

To unite all of the marked columns into a single column, use the unite_exclusions() function. This will create a new exclusions columns that will unite all exclusions in each observation into a single column. Here we move the combined exclusions column to the beginning of the data frame to view it.

df %>% 
  mark_preview() %>% 
  mark_duration(min = 500) %>% 
  unite_exclusions() %>% 
  dplyr::relocate(exclusions, .before = StartDate)
#> ℹ 2 rows were collected as previews. It is highly recommended to exclude these rows before further processing.
#> ℹ 82 out of 100 rows took less time than 500.
#> # A tibble: 100 × 17
#>    exclusions  StartDate           EndDate             Status IPAddress Progress
#>    <chr>       <dttm>              <dttm>              <chr>  <chr>        <dbl>
#>  1 "preview,d… 2020-12-11 12:06:52 2020-12-11 12:10:30 Surve… <NA>           100
#>  2 "preview"   2020-12-11 12:06:43 2020-12-11 12:11:27 Surve… <NA>           100
#>  3 ""          2020-12-11 12:17:22 2020-12-11 12:21:41 IP Ad… 73.23.43…      100
#>  4 "duration_… 2020-12-11 12:17:41 2020-12-11 12:22:07 IP Ad… 16.140.1…      100
#>  5 "duration_… 2020-12-11 12:19:45 2020-12-11 12:23:16 IP Ad… 107.57.2…      100
#>  6 "duration_… 2020-12-11 12:37:51 2020-12-11 12:43:09 IP Ad… 15.232.1…      100
#>  7 "duration_… 2020-12-11 12:41:23 2020-12-11 12:44:37 IP Ad… 24.195.9…      100
#>  8 ""          2020-12-11 12:37:04 2020-12-11 12:45:50 IP Ad… 98.75.20…      100
#>  9 "duration_… 2020-12-11 12:55:35 2020-12-11 12:59:24 IP Ad… 51.113.1…      100
#> 10 "duration_… 2020-12-11 13:22:34 2020-12-11 13:35:19 IP Ad… 17.163.1…      100
#> # … with 90 more rows, and 11 more variables: `Duration (in seconds)` <dbl>,
#> #   Finished <lgl>, RecordedDate <dttm>, ResponseId <chr>,
#> #   LocationLatitude <dbl>, LocationLongitude <dbl>, UserLanguage <chr>,
#> #   Browser <chr>, Version <chr>, `Operating System` <chr>, Resolution <chr>

Multiple exclusions are separated by , by default, but the separating character can be controlled by the separator argument. By default, the multiple exclusion columns are removed from the final data frame, but this can be turned off by setting the remove argument to FALSE.

df %>% 
  mark_preview() %>% 
  mark_duration(min = 500) %>% 
  unite_exclusions(separator = ";", remove = FALSE) %>% 
  dplyr::relocate(exclusions, .before = StartDate)
#> ℹ 2 rows were collected as previews. It is highly recommended to exclude these rows before further processing.
#> ℹ 82 out of 100 rows took less time than 500.
#> # A tibble: 100 × 19
#>    exclusions  StartDate           EndDate             Status IPAddress Progress
#>    <chr>       <dttm>              <dttm>              <chr>  <chr>        <dbl>
#>  1 "preview;d… 2020-12-11 12:06:52 2020-12-11 12:10:30 Surve… <NA>           100
#>  2 "preview"   2020-12-11 12:06:43 2020-12-11 12:11:27 Surve… <NA>           100
#>  3 ""          2020-12-11 12:17:22 2020-12-11 12:21:41 IP Ad… 73.23.43…      100
#>  4 "duration_… 2020-12-11 12:17:41 2020-12-11 12:22:07 IP Ad… 16.140.1…      100
#>  5 "duration_… 2020-12-11 12:19:45 2020-12-11 12:23:16 IP Ad… 107.57.2…      100
#>  6 "duration_… 2020-12-11 12:37:51 2020-12-11 12:43:09 IP Ad… 15.232.1…      100
#>  7 "duration_… 2020-12-11 12:41:23 2020-12-11 12:44:37 IP Ad… 24.195.9…      100
#>  8 ""          2020-12-11 12:37:04 2020-12-11 12:45:50 IP Ad… 98.75.20…      100
#>  9 "duration_… 2020-12-11 12:55:35 2020-12-11 12:59:24 IP Ad… 51.113.1…      100
#> 10 "duration_… 2020-12-11 13:22:34 2020-12-11 13:35:19 IP Ad… 17.163.1…      100
#> # … with 90 more rows, and 13 more variables: `Duration (in seconds)` <dbl>,
#> #   Finished <lgl>, RecordedDate <dttm>, ResponseId <chr>,
#> #   LocationLatitude <dbl>, LocationLongitude <dbl>, UserLanguage <chr>,
#> #   Browser <chr>, Version <chr>, `Operating System` <chr>, Resolution <chr>,
#> #   exclusion_preview <chr>, exclusion_duration <chr>

Checking observations

The check_*() suite of functions return a data frame that includes only the observations that meet the criterion. Since these functions first run the appropriate mark_*() function, they also print a message about the number of observations that meet the exclusion criterion.

# Check for rows with incomplete data
df %>%
  check_progress()
#> ℹ 6 out of 100 rows did not complete the study.
#> # A tibble: 6 × 16
#>   StartDate           EndDate             Status     IPAddress    Progress
#>   <dttm>              <dttm>              <chr>      <chr>           <dbl>
#> 1 2020-12-17 15:40:53 2020-12-17 15:43:25 IP Address 22.51.31.0         99
#> 2 2020-12-17 15:40:56 2020-12-17 15:46:23 IP Address 71.146.112.0        1
#> 3 2020-12-17 15:41:52 2020-12-17 15:46:37 IP Address 15.223.0.0         13
#> 4 2020-12-17 15:41:27 2020-12-17 15:46:45 IP Address 19.127.87.0        48
#> 5 2020-12-17 15:49:42 2020-12-17 15:51:50 IP Address 40.146.247.0        5
#> 6 2020-12-17 15:49:28 2020-12-17 15:55:06 IP Address 2.246.67.0         44
#> # … with 11 more variables: `Duration (in seconds)` <dbl>, Finished <lgl>,
#> #   RecordedDate <dttm>, ResponseId <chr>, LocationLatitude <dbl>,
#> #   LocationLongitude <dbl>, UserLanguage <chr>, Browser <chr>, Version <chr>,
#> #   `Operating System` <chr>, Resolution <chr>

# Check for rows with durations less than 100 seconds
df %>% 
  check_duration(min_duration = 100)
#> ℹ 4 out of 100 rows took less time than 100.
#> # A tibble: 4 × 16
#>   StartDate           EndDate             Status     IPAddress    Progress
#>   <dttm>              <dttm>              <chr>      <chr>           <dbl>
#> 1 2020-12-11 16:59:08 2020-12-11 17:02:05 IP Address 84.56.189.0       100
#> 2 2020-12-17 15:41:52 2020-12-17 15:46:37 IP Address 15.223.0.0         13
#> 3 2020-12-17 15:41:27 2020-12-17 15:46:45 IP Address 19.127.87.0        48
#> 4 2020-12-17 15:46:46 2020-12-17 15:49:02 IP Address 21.134.217.0      100
#> # … with 11 more variables: `Duration (in seconds)` <dbl>, Finished <lgl>,
#> #   RecordedDate <dttm>, ResponseId <chr>, LocationLatitude <dbl>,
#> #   LocationLongitude <dbl>, UserLanguage <chr>, Browser <chr>, Version <chr>,
#> #   `Operating System` <chr>, Resolution <chr>

Because checks return only the rows meeting the criteria, they should not be connected via pipes unless you want to subset the second check criterion within the rows that meet the first criterion.

# Check for rows with durations less than 100 seconds in rows that did not complete the survey
df %>%
  check_progress() %>%
  check_duration(min_duration = 100)
#> ℹ 6 out of 100 rows did not complete the study.
#> ℹ 2 out of 6 rows took less time than 100.
#> # A tibble: 2 × 16
#>   StartDate           EndDate             Status     IPAddress   Progress
#>   <dttm>              <dttm>              <chr>      <chr>          <dbl>
#> 1 2020-12-17 15:41:52 2020-12-17 15:46:37 IP Address 15.223.0.0        13
#> 2 2020-12-17 15:41:27 2020-12-17 15:46:45 IP Address 19.127.87.0       48
#> # … with 11 more variables: `Duration (in seconds)` <dbl>, Finished <lgl>,
#> #   RecordedDate <dttm>, ResponseId <chr>, LocationLatitude <dbl>,
#> #   LocationLongitude <dbl>, UserLanguage <chr>, Browser <chr>, Version <chr>,
#> #   `Operating System` <chr>, Resolution <chr>

To check all data for multiple criteria, use the mark_*() functions followed by a filter.

# Check for multiple criteria
df %>% 
  mark_preview() %>% 
  mark_duration(min = 500) %>% 
  unite_exclusions() %>% 
  dplyr::filter(exclusions != "")
#> ℹ 2 rows were collected as previews. It is highly recommended to exclude these rows before further processing.
#> ℹ 82 out of 100 rows took less time than 500.
#> # A tibble: 83 × 17
#>    StartDate           EndDate             Status         IPAddress    Progress
#>    <dttm>              <dttm>              <chr>          <chr>           <dbl>
#>  1 2020-12-11 12:06:52 2020-12-11 12:10:30 Survey Preview <NA>              100
#>  2 2020-12-11 12:06:43 2020-12-11 12:11:27 Survey Preview <NA>              100
#>  3 2020-12-11 12:17:41 2020-12-11 12:22:07 IP Address     16.140.105.0      100
#>  4 2020-12-11 12:19:45 2020-12-11 12:23:16 IP Address     107.57.244.0      100
#>  5 2020-12-11 12:37:51 2020-12-11 12:43:09 IP Address     15.232.167.0      100
#>  6 2020-12-11 12:41:23 2020-12-11 12:44:37 IP Address     24.195.91.0       100
#>  7 2020-12-11 12:55:35 2020-12-11 12:59:24 IP Address     51.113.171.0      100
#>  8 2020-12-11 13:22:34 2020-12-11 13:35:19 IP Address     17.163.199.0      100
#>  9 2020-12-11 16:59:08 2020-12-11 17:02:05 IP Address     84.56.189.0       100
#> 10 2020-12-11 17:02:00 2020-12-11 17:03:30 IP Address     70.203.63.0       100
#> # … with 73 more rows, and 12 more variables: `Duration (in seconds)` <dbl>,
#> #   Finished <lgl>, RecordedDate <dttm>, ResponseId <chr>,
#> #   LocationLatitude <dbl>, LocationLongitude <dbl>, UserLanguage <chr>,
#> #   Browser <chr>, Version <chr>, `Operating System` <chr>, Resolution <chr>,
#> #   exclusions <chr>

Excluding observations

The exclude_*() suite of function will return a data frame that has removed observations that match the exclusion criteria. Exclude functions print their own messages about the number of observations excluded.

# Exclude survey responses used to preview the survey
df %>% 
  exclude_preview() %>% 
  dplyr::glimpse()
#> ℹ 2 out of 100 preview rows were excluded, leaving 98 rows.
#> Rows: 98
#> Columns: 16
#> $ StartDate               <dttm> 2020-12-11 12:17:22, 2020-12-11 12:17:41, 202…
#> $ EndDate                 <dttm> 2020-12-11 12:21:41, 2020-12-11 12:22:07, 202…
#> $ Status                  <chr> "IP Address", "IP Address", "IP Address", "IP …
#> $ IPAddress               <chr> "73.23.43.0", "16.140.105.0", "107.57.244.0", …
#> $ Progress                <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…
#> $ `Duration (in seconds)` <dbl> 651, 409, 140, 213, 177, 662, 296, 277, 54, 43…
#> $ Finished                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
#> $ RecordedDate            <dttm> 2020-12-11 12:21:42, 2020-12-11 12:22:07, 202…
#> $ ResponseId              <chr> "R_fbYBeNscosfzN9L", "R_yyG1HGXOMNPfWDn", "R_9…
#> $ LocationLatitude        <dbl> 34.03852, 44.96581, 27.98064, 29.76499, 40.335…
#> $ LocationLongitude       <dbl> -118.25739, -93.07187, -82.78531, -95.36156, -…
#> $ UserLanguage            <chr> "EN", "EN", "EN", "EN", "EN", "EN", "EN", "EN"…
#> $ Browser                 <chr> "Chrome", "Chrome", "Chrome", "Chrome", "Chrom…
#> $ Version                 <chr> "87.0.4280.88", "87.0.4280.88", "87.0.4280.88"…
#> $ `Operating System`      <chr> "Windows NT 10.0", "Windows NT 10.0", "Windows…
#> $ Resolution              <chr> "1366x768", "1536x864", "1536x864", "1366x768"…

Piping will apply subsequent excludes to the data frames with the previous excludes already applied. Therefore, it often makes sense to remove the preview surveys and incomplete surveys before checking other exclusion types to speed processing.

# Exclude preview then incomplete progress rows then duplicate locations and IP addresses
df %>%
  exclude_preview() %>%
  exclude_progress() %>%
  exclude_duplicates(print = FALSE)
#> ℹ 2 out of 100 preview rows were excluded, leaving 98 rows.
#> ℹ 6 out of 98 rows with incomplete progress were excluded, leaving 92 rows.
#> ℹ 9 out of 92 duplicate rows were excluded, leaving 83 rows.

Messages and console output

Messages about the number of rows meeting the exclusion criteria are printed to the console by default. These messages are generated by the mark_*() functions and carry over to check_*() functions. They can be turn off by setting quiet to TRUE.

# Turn off marking/checking messages with quiet = TRUE
df %>%
  check_progress(quiet = TRUE)
#> # A tibble: 6 × 16
#>   StartDate           EndDate             Status     IPAddress    Progress
#>   <dttm>              <dttm>              <chr>      <chr>           <dbl>
#> 1 2020-12-17 15:40:53 2020-12-17 15:43:25 IP Address 22.51.31.0         99
#> 2 2020-12-17 15:40:56 2020-12-17 15:46:23 IP Address 71.146.112.0        1
#> 3 2020-12-17 15:41:52 2020-12-17 15:46:37 IP Address 15.223.0.0         13
#> 4 2020-12-17 15:41:27 2020-12-17 15:46:45 IP Address 19.127.87.0        48
#> 5 2020-12-17 15:49:42 2020-12-17 15:51:50 IP Address 40.146.247.0        5
#> 6 2020-12-17 15:49:28 2020-12-17 15:55:06 IP Address 2.246.67.0         44
#> # … with 11 more variables: `Duration (in seconds)` <dbl>, Finished <lgl>,
#> #   RecordedDate <dttm>, ResponseId <chr>, LocationLatitude <dbl>,
#> #   LocationLongitude <dbl>, UserLanguage <chr>, Browser <chr>, Version <chr>,
#> #   `Operating System` <chr>, Resolution <chr>

Note that exclude_*() functions have the mark_*() messages turned off by default and produce their own messages about exclusions. To silence these messages, set silent to TRUE.

# Turn off exclusion messages with silent = TRUE
df %>%
  exclude_preview(silent = TRUE) %>%
  exclude_progress(silent = TRUE) %>%
  exclude_duplicates(silent = TRUE)
#> # A tibble: 83 × 16
#>    StartDate           EndDate             Status     IPAddress    Progress
#>    <dttm>              <dttm>              <chr>      <chr>           <dbl>
#>  1 2020-12-11 12:17:22 2020-12-11 12:21:41 IP Address 73.23.43.0        100
#>  2 2020-12-11 12:17:41 2020-12-11 12:22:07 IP Address 16.140.105.0      100
#>  3 2020-12-11 12:19:45 2020-12-11 12:23:16 IP Address 107.57.244.0      100
#>  4 2020-12-11 12:37:51 2020-12-11 12:43:09 IP Address 15.232.167.0      100
#>  5 2020-12-11 12:37:04 2020-12-11 12:45:50 IP Address 98.75.201.0       100
#>  6 2020-12-11 12:55:35 2020-12-11 12:59:24 IP Address 51.113.171.0      100
#>  7 2020-12-11 13:22:34 2020-12-11 13:35:19 IP Address 17.163.199.0      100
#>  8 2020-12-11 16:59:08 2020-12-11 17:02:05 IP Address 84.56.189.0       100
#>  9 2020-12-11 17:02:00 2020-12-11 17:03:30 IP Address 70.203.63.0       100
#> 10 2020-12-11 17:01:32 2020-12-11 17:09:41 IP Address 33.185.89.0       100
#> # … with 73 more rows, and 11 more variables: `Duration (in seconds)` <dbl>,
#> #   Finished <lgl>, RecordedDate <dttm>, ResponseId <chr>,
#> #   LocationLatitude <dbl>, LocationLongitude <dbl>, UserLanguage <chr>,
#> #   Browser <chr>, Version <chr>, `Operating System` <chr>, Resolution <chr>

Though exclude_*() functions do not print the data frame to the console, mark_*() and check_*() do. To avoid printing to the console, set print = FALSE.

# Turn off marking/checking printing data frame with print = FALSE
df %>%
  check_progress(print = FALSE)
#> ℹ 6 out of 100 rows did not complete the study.

Deidentifying data

By default, Qualtrics records participant IP address and location.² You can also record properties of the participants’ computers such as operating system, web browser type and version, and screen resolution.³ While these pieces of information can be useful for excluding observations, they carry potentially identifiable information. Therefore, you may want to remove them from data frame before saving or processing it. The deidentify() function removes potentially identifiable data columns collected by Qualtrics. By default, the function uses a strict rule to remove IP address, location, and computer information (browser type and version, operating system, and screen resolution).

# Exclude preview then incomplete progress rows
df %>%
  exclude_preview() %>%
  exclude_progress() %>%
  exclude_duplicates() %>%
  deidentify() %>%
  dplyr::glimpse()
#> ℹ 2 out of 100 preview rows were excluded, leaving 98 rows.
#> ℹ 6 out of 98 rows with incomplete progress were excluded, leaving 92 rows.
#> ℹ 9 out of 92 duplicate rows were excluded, leaving 83 rows.
#> Rows: 83
#> Columns: 8
#> $ StartDate               <dttm> 2020-12-11 12:17:22, 2020-12-11 12:17:41, 202…
#> $ EndDate                 <dttm> 2020-12-11 12:21:41, 2020-12-11 12:22:07, 202…
#> $ Status                  <chr> "IP Address", "IP Address", "IP Address", "IP …
#> $ Progress                <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…
#> $ `Duration (in seconds)` <dbl> 651, 409, 140, 213, 662, 296, 277, 54, 432, 10…
#> $ Finished                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
#> $ RecordedDate            <dttm> 2020-12-11 12:21:42, 2020-12-11 12:22:07, 202…
#> $ ResponseId              <chr> "R_fbYBeNscosfzN9L", "R_yyG1HGXOMNPfWDn", "R_9…

If the computer information is not considered sensitive, it can be kept by setting the strict argument to FALSE, thereby only removing IP address and location.

# Exclude preview then incomplete progress rows
df %>%
  exclude_preview() %>%
  exclude_progress() %>%
  exclude_duplicates() %>%
  deidentify(strict = FALSE) %>%
  dplyr::glimpse()
#> ℹ 2 out of 100 preview rows were excluded, leaving 98 rows.
#> ℹ 6 out of 98 rows with incomplete progress were excluded, leaving 92 rows.
#> ℹ 9 out of 92 duplicate rows were excluded, leaving 83 rows.
#> Rows: 83
#> Columns: 12
#> $ StartDate               <dttm> 2020-12-11 12:17:22, 2020-12-11 12:17:41, 202…
#> $ EndDate                 <dttm> 2020-12-11 12:21:41, 2020-12-11 12:22:07, 202…
#> $ Status                  <chr> "IP Address", "IP Address", "IP Address", "IP …
#> $ Progress                <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 1…
#> $ `Duration (in seconds)` <dbl> 651, 409, 140, 213, 662, 296, 277, 54, 432, 10…
#> $ Finished                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
#> $ RecordedDate            <dttm> 2020-12-11 12:21:42, 2020-12-11 12:22:07, 202…
#> $ ResponseId              <chr> "R_fbYBeNscosfzN9L", "R_yyG1HGXOMNPfWDn", "R_9…
#> $ Browser                 <chr> "Chrome", "Chrome", "Chrome", "Chrome", "Edge"…
#> $ Version                 <chr> "87.0.4280.88", "87.0.4280.88", "87.0.4280.88"…
#> $ `Operating System`      <chr> "Windows NT 10.0", "Windows NT 10.0", "Windows…
#> $ Resolution              <chr> "1366x768", "1536x864", "1536x864", "1366x768"…

Though the functions take data frames input, they produce tibbles.↩︎
To avoid recording this potentially identifiable information, go to Survey options > Security > Anonymize responses.↩︎
You can turn these on by adding a new question (usually to the beginning of your survey) and choosing Meta info as the question type.↩︎