tests/testthat/_snaps/execute-checks.md
In OuhscBbmc/trawler: Inspect a dataset
execute-checks-biochemical
Code
checks$record_id_name
Output
[1] "record_id"
Code
checks$baseline_date_name
Output
[1] "date_enrolled"
Code
checks$record_id_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
checks$github_file_prefix
Output
[1] "https://github.com/OuhscBbmc/validator-1/tree/main"
Code
checks$redcap_project_id
Output
[1] 1612
Code
checks$redcap_version
Output
[1] "10.5.1"
Code
checks$redcap_default_arm
Output
[1] 1
Code
checks$redcap_codebook
Output
[1] "https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/Design/data_dictionary_codebook.php?pid=1612"
Code
checks$redcap_record_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
result$smells
Output
# A tibble: 11 x 12
check_name pass description priority debug bound_lower bound_upper
<chr> <lgl> <chr> <int> <lgl> <dbl> <dbl>
1 females TRUE Proportion~ 2 FALSE 0.4 0.6
2 males TRUE Proportion~ 2 FALSE 0.4 0.6
3 age TRUE Mean parti~ 2 FALSE 20 60
4 serum_prealbumin_le~ FALSE Mean serum~ 2 FALSE 32 39
5 serum_creatinine_le~ FALSE Mean serum~ 2 FALSE 3 15
6 bmi_at_baseline FALSE Mean BMI i~ 2 FALSE 18 24
7 serum_cholesterol_l~ TRUE Mean chole~ 1 FALSE 100 140
8 dialysis_adequacy TRUE Mean Kt/V ~ 1 FALSE 1.2 5
9 nutritional_counsel~ TRUE Most patie~ 2 FALSE 0.85 0.99
10 definitive_diagnosis TRUE The propor~ 1 FALSE 1 1
11 normalized_protein_~ TRUE Mean Norma~ 1 FALSE 0 1.2
# i 5 more variables: bounds_template <chr>, value_template <chr>,
# equation <chr>, boundaries <chr>, value <dbl>
Code
as.data.frame(result$smells)
Output
check_name pass
1 females TRUE
2 males TRUE
3 age TRUE
4 serum_prealbumin_level_at_baseline FALSE
5 serum_creatinine_level_at_baseline FALSE
6 bmi_at_baseline FALSE
7 serum_cholesterol_levels_at_baseline TRUE
8 dialysis_adequacy TRUE
9 nutritional_counseling TRUE
10 definitive_diagnosis TRUE
11 normalized_protein_catabolic_rate TRUE
description
1 Proportion of female participants is about half the sample
2 Proportion of male participants is about half the sample
3 Mean participant age is between 20 and 60 years
4 Mean serum pre-albumin level is between 31mg/dl and 39mg/dl at baseline
5 Mean serum creatinine level is between 3mg/dl and 15mg/dl at baseline
6 Mean BMI is between 18 and 24
7 Mean cholesterol levels range is between 100 and 140mg/dl at baseline
8 Mean Kt/V value is within the normal range (*i.e.*, between 1.2 and 5)
9 Most patients agreed to receiving nutritional counseling
10 The proportion of study participants diagnosed with malnutrition associated with Chronic Renal Disease is 1.0 (*i.e.*, everyone)
11 Mean Normalized Protein Catabolic Rate (nPCR) is <= 1.2g/kg/day
priority debug bound_lower bound_upper bounds_template value_template
1 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
2 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
3 2 FALSE 20.00 60.00 [%.0f, %.0f] %.1f
4 2 FALSE 32.00 39.00 [%.0f, %.0f] %.1f
5 2 FALSE 3.00 15.00 [%.0f, %.0f] %.1f
6 2 FALSE 18.00 24.00 [%.0f, %.0f] %.1f
7 1 FALSE 100.00 140.00 [%.0f, %.0f] %.1f
8 1 FALSE 1.20 5.00 [%.1f, %.1f] %.2f
9 2 FALSE 0.85 0.99 [%.2f, %.2f] %.3f
10 1 FALSE 1.00 1.00 [%.1f, %.1f] %.2f
11 1 FALSE 0.00 1.20 [%.1f, %.1f] %.3f
equation
1 function (d) {\n mean(d$sex == "female", na.rm = TRUE)\n}\n
2 function (d) {\n mean(d$sex == "male", na.rm = TRUE)\n}\n
3 function (d) {\n mean(d$age, na.rm = TRUE)\n}\n
4 function (d) {\n mean(d$baseline_prealbumin_level, na.rm = TRUE)\n}\n
5 function (d) {\n mean(d$baseline_creatinine_level, na.rm = TRUE)\n}\n
6 function (d) {\n mean(d$baseline_bmi, na.rm = TRUE)\n}\n
7 function (d) {\n mean(d$baseline_cholesterol, na.rm = TRUE)\n}\n
8 function (d) {\n mean(d$completion_project_questionnaire_ktv_value, na.rm = TRUE)\n}\n
9 function (d) {\n mean(d$nutritional_counseling, na.rm = TRUE)\n}\n
10 function (d) {\n mean(d$differential_diagnoses_malnutrition, na.rm = TRUE)\n}\n
11 function (d) {\n mean(d$baseline_normalized_protein_catabolic_rate, na.rm = TRUE)\n}\n
boundaries value
1 [0.40, 0.60] 0.4444444
2 [0.40, 0.60] 0.5555556
3 [20, 60] 44.3888889
4 [32, 39] 23.6666667
5 [3, 15] 23.1687500
6 [18, 24] 24.8500000
7 [100, 140] 134.1176471
8 [1.2, 5.0] 1.9166667
9 [0.85, 0.99] 0.8888889
10 [1.0, 1.0] 1.0000000
11 [0.0, 1.2] 0.9312500
Code
result$smell_status
Output
[1] "11 smells have been sniffed. 3 violation(s) were found."
Code
result$rules
Output
# A tibble: 14 x 8
check_name violation_count error_message priority debug redcap_instrument
<chr> <int> <chr> <int> <lgl> <chr>
1 baseline_prea~ 10 Serum pre-al~ 1 FALSE baseline_data
2 missing_serum~ 3 Relevant nut~ 1 FALSE baseline_data
3 serum_prealbu~ 15 Baseline pre~ 2 FALSE baseline_data, v~
4 serum_prealbu~ 0 Baseline pre~ 2 FALSE baseline_data, v~
5 serum_prealbu~ 0 Baseline pre~ 2 FALSE baseline_data, v~
6 serum_prealbu~ 0 serum prealb~ 3 FALSE baseline_data, v~
7 baseline_firs~ 0 Serum prealb~ 3 FALSE baseline_data, v~
8 daily_first_v~ 0 In-addition ~ 3 FALSE baseline_data, p~
9 daily_protein~ 0 npcr levels ~ 3 FALSE baseline_data, p~
10 hospitalizati~ 2 Patient was ~ 1 FALSE completion_proje~
11 optimal_daily~ 7 Daily protei~ 2 FALSE completion_proje~
12 recommended_n~ 10 NPCR values ~ 2 FALSE completion_data
13 npcr 1 NPCR at comp~ 2 FALSE completion_data
14 npcr_comparis~ 2 NPCR at comp~ 3 FALSE completion_data
# i 2 more variables: passing_test <chr>, results <list>
Code
as.data.frame(dplyr::select(result$rules, !tidyselect::contains("results")))
Output
check_name violation_count
1 baseline_prealbumin_levels 10
2 missing_serum_marker_levels 3
3 serum_prealbumin_levels_1 15
4 serum_prealbumin_levels_2 0
5 serum_prealbumin_levels_completion_data 0
6 serum_prealbumin_levels_expectations 0
7 baseline_first_visit_lab_parameters 0
8 daily_first_visit_lab_and_workup_parameters 0
9 daily_protein_intake 0
10 hospitalization_reason 2
11 optimal_daily_protein_intake 7
12 recommended_npcr_range 10
13 npcr 1
14 npcr_comparison 2
error_message
1 Serum pre-albumin level of all enrolled patients do not meet the study criterion
2 Relevant nutritional serum markers are missing
3 Baseline prealbumin levels are not missing however levels were not carefully monitored in the subsequent visit
4 Baseline prealbumin levels and pre-albumin levels during the 1st visit are not missing however levels in the next reading are missing
5 Baseline prealbumin levels are not missing however readings are not carefully monitored at completion
6 serum prealbumin levels are not missing however subsequent readings did not come as expected
7 Serum prealbumin levels are low and protein intake at baseline is less than optimal
8 In-addition to baseline & visit lab protein parameters, blood work-up npcr levels are also low
9 npcr levels in study have not improved as intended
10 Patient was hospitalized but reason and the date of hospitalization is missing
11 Daily protein intake is optimal but one of the nutritional marker is not within the normal range
12 NPCR values are not within the recommended range at completion
13 NPCR at completion is missing
14 NPCR at completion is not greater than npcr at baseline
priority debug
1 1 FALSE
2 1 FALSE
3 2 FALSE
4 2 FALSE
5 2 FALSE
6 3 FALSE
7 3 FALSE
8 3 FALSE
9 3 FALSE
10 1 FALSE
11 2 FALSE
12 2 FALSE
13 2 FALSE
14 3 FALSE
redcap_instrument
1 baseline_data
2 baseline_data
3 baseline_data, visit_lab_date
4 baseline_data, visit_lab_date, visit_blood_workup
5 baseline_data, visit_lab_date, visit_blood_workup, completion_date
6 baseline_data, visit_lab_date, visit_blood_workup, completion_date
7 baseline_data, visit_lab_data
8 baseline_data, patient_morale_questionnaire
9 baseline_data, patient_morale_questionnaire
10 completion_project_questionnaire
11 completion_project_questionnaire, completion_data
12 completion_data
13 completion_data
14 completion_data
passing_test
1 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n dplyr::between(d$baseline_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
2 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n (\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$baseline_creatinine_level) &\n !is.na(d$baseline_transferrin_level)\n ),\n TRUE\n )\n}\n
3 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level)\n ),\n !is.na(d$visit_lab_prealbumin_level),\n TRUE\n )\n}\n
4 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level)\n ),\n !is.na(d$visit_blood_workup_prealbumin_level),\n TRUE\n )\n}\n
5 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level)\n ),\n !is.na(d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
6 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level) &\n !is.na(d$completion_data_prealbumin_level)\n ),\n (d$visit_lab_prealbumin_level < d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
7 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2)\n ),\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr),\n TRUE\n )\n}\n
8 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr)\n ),\n d$visit_lab_npcr <= d$visit_blood_workup_npcr,\n TRUE\n )\n}\n
9 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr) &\n (d$visit_lab_npcr <= d$visit_blood_workup_npcr)\n ),\n d$visit_blood_workup_npcr < d$completion_data_npcr,\n TRUE\n )\n}\n
10 function (d) {\n dplyr::if_else(\n d$completion_project_questionnaire_hospitalization == 1L,\n !is.na(d$completion_project_questionnaire_hospitalization_cause) & !is.na(d$completion_project_questionnaire_hospitalization_date),\n TRUE\n )\n}\n
11 function (d) {\n dplyr::if_else(\n d$completion_data_npcr >= 1.2,\n dplyr::between(d$completion_data_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
12 function (d) {\n dplyr::between(d$completion_data_npcr, 1.2, 1.4)\n}\n
13 function (d) {\n events_to_check <- c("final_visit_arm_1")\n dplyr::if_else(\n d$redcap_event_name %in% events_to_check,\n !is.na(d$completion_data_npcr), # If this row exists in the desired event, then check for nonmissingness.\n TRUE # Otherwise, the test passes for rows associated with all other events.\n )\n}\n
14 function (d) {\n dplyr::if_else(\n !is.na(d$completion_data_npcr),\n (d$npcr_at_baseline < d$completion_data_npcr),\n TRUE\n )\n}
Code
ds_result_unnested
Output
# A tibble: 50 x 5
check_name record_id data_collector baseline_date record_id_linked
<chr> <int> <int> <date> <chr>
1 baseline_prealbumin_~ 1 1 2015-01-02 "<a href=\"http~
2 baseline_prealbumin_~ 2 2 2015-01-02 "<a href=\"http~
3 baseline_prealbumin_~ 3 3 2015-01-05 "<a href=\"http~
4 baseline_prealbumin_~ 8 1 2015-02-03 "<a href=\"http~
5 baseline_prealbumin_~ 9 3 2015-02-08 "<a href=\"http~
6 baseline_prealbumin_~ 12 3 2015-03-06 "<a href=\"http~
7 baseline_prealbumin_~ 13 1 2015-03-15 "<a href=\"http~
8 baseline_prealbumin_~ 14 1 2015-03-10 "<a href=\"http~
9 baseline_prealbumin_~ 15 3 2015-03-03 "<a href=\"http~
10 baseline_prealbumin_~ 100 1 2015-04-02 "<a href=\"http~
# i 40 more rows
Code
as.data.frame(ds_result_unnested)
Output
check_name record_id data_collector baseline_date
1 baseline_prealbumin_levels 1 1 2015-01-02
2 baseline_prealbumin_levels 2 2 2015-01-02
3 baseline_prealbumin_levels 3 3 2015-01-05
4 baseline_prealbumin_levels 8 1 2015-02-03
5 baseline_prealbumin_levels 9 3 2015-02-08
6 baseline_prealbumin_levels 12 3 2015-03-06
7 baseline_prealbumin_levels 13 1 2015-03-15
8 baseline_prealbumin_levels 14 1 2015-03-10
9 baseline_prealbumin_levels 15 3 2015-03-03
10 baseline_prealbumin_levels 100 1 2015-04-02
11 missing_serum_marker_levels 7 2 2015-01-27
12 missing_serum_marker_levels 10 255 2015-02-13
13 missing_serum_marker_levels 11 2 2015-02-19
14 serum_prealbumin_levels_1 1 1 2015-01-02
15 serum_prealbumin_levels_1 2 2 2015-01-02
16 serum_prealbumin_levels_1 3 3 2015-01-05
17 serum_prealbumin_levels_1 4 255 2015-01-10
18 serum_prealbumin_levels_1 5 1 2015-01-13
19 serum_prealbumin_levels_1 6 3 2015-01-16
20 serum_prealbumin_levels_1 8 1 2015-02-03
21 serum_prealbumin_levels_1 9 3 2015-02-08
22 serum_prealbumin_levels_1 12 3 2015-03-06
23 serum_prealbumin_levels_1 13 1 2015-03-15
24 serum_prealbumin_levels_1 14 1 2015-03-10
25 serum_prealbumin_levels_1 15 3 2015-03-03
26 serum_prealbumin_levels_1 16 2 2015-03-09
27 serum_prealbumin_levels_1 100 1 2015-04-02
28 serum_prealbumin_levels_1 220 1 2015-04-02
29 hospitalization_reason 8 NA <NA>
30 hospitalization_reason 14 NA <NA>
31 optimal_daily_protein_intake 5 NA <NA>
32 optimal_daily_protein_intake 6 NA <NA>
33 optimal_daily_protein_intake 7 NA <NA>
34 optimal_daily_protein_intake 8 NA <NA>
35 optimal_daily_protein_intake 11 NA <NA>
36 optimal_daily_protein_intake 15 NA <NA>
37 optimal_daily_protein_intake 100 NA <NA>
38 recommended_npcr_range 1 NA <NA>
39 recommended_npcr_range 6 NA <NA>
40 recommended_npcr_range 7 NA <NA>
41 recommended_npcr_range 8 NA <NA>
42 recommended_npcr_range 12 NA <NA>
43 recommended_npcr_range 13 NA <NA>
44 recommended_npcr_range 14 NA <NA>
45 recommended_npcr_range 16 NA <NA>
46 recommended_npcr_range 100 NA <NA>
47 recommended_npcr_range 220 NA <NA>
48 npcr 10 NA <NA>
49 npcr_comparison 1 NA <NA>
50 npcr_comparison 12 NA <NA>
record_id_linked
1 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=baseline_data" target="_blank">1</a>
2 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=2&page=baseline_data" target="_blank">2</a>
3 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=3&page=baseline_data" target="_blank">3</a>
4 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=baseline_data" target="_blank">8</a>
5 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=9&page=baseline_data" target="_blank">9</a>
6 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=baseline_data" target="_blank">12</a>
7 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=13&page=baseline_data" target="_blank">13</a>
8 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=baseline_data" target="_blank">14</a>
9 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=15&page=baseline_data" target="_blank">15</a>
10 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=baseline_data" target="_blank">100</a>
11 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=7&page=baseline_data" target="_blank">7</a>
12 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=10&page=baseline_data" target="_blank">10</a>
13 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=11&page=baseline_data" target="_blank">11</a>
14 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=baseline_data, visit_lab_date" target="_blank">1</a>
15 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=2&page=baseline_data, visit_lab_date" target="_blank">2</a>
16 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=3&page=baseline_data, visit_lab_date" target="_blank">3</a>
17 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=4&page=baseline_data, visit_lab_date" target="_blank">4</a>
18 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=5&page=baseline_data, visit_lab_date" target="_blank">5</a>
19 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=6&page=baseline_data, visit_lab_date" target="_blank">6</a>
20 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=baseline_data, visit_lab_date" target="_blank">8</a>
21 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=9&page=baseline_data, visit_lab_date" target="_blank">9</a>
22 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=baseline_data, visit_lab_date" target="_blank">12</a>
23 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=13&page=baseline_data, visit_lab_date" target="_blank">13</a>
24 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=baseline_data, visit_lab_date" target="_blank">14</a>
25 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=15&page=baseline_data, visit_lab_date" target="_blank">15</a>
26 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=16&page=baseline_data, visit_lab_date" target="_blank">16</a>
27 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=baseline_data, visit_lab_date" target="_blank">100</a>
28 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=220&page=baseline_data, visit_lab_date" target="_blank">220</a>
29 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=completion_project_questionnaire" target="_blank">8</a>
30 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=completion_project_questionnaire" target="_blank">14</a>
31 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=5&page=completion_project_questionnaire, completion_data" target="_blank">5</a>
32 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=6&page=completion_project_questionnaire, completion_data" target="_blank">6</a>
33 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=7&page=completion_project_questionnaire, completion_data" target="_blank">7</a>
34 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=completion_project_questionnaire, completion_data" target="_blank">8</a>
35 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=11&page=completion_project_questionnaire, completion_data" target="_blank">11</a>
36 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=15&page=completion_project_questionnaire, completion_data" target="_blank">15</a>
37 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=completion_project_questionnaire, completion_data" target="_blank">100</a>
38 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=completion_data" target="_blank">1</a>
39 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=6&page=completion_data" target="_blank">6</a>
40 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=7&page=completion_data" target="_blank">7</a>
41 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=completion_data" target="_blank">8</a>
42 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=completion_data" target="_blank">12</a>
43 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=13&page=completion_data" target="_blank">13</a>
44 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=completion_data" target="_blank">14</a>
45 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=16&page=completion_data" target="_blank">16</a>
46 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=completion_data" target="_blank">100</a>
47 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=220&page=completion_data" target="_blank">220</a>
48 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=10&page=completion_data" target="_blank">10</a>
49 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=completion_data" target="_blank">1</a>
50 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=completion_data" target="_blank">12</a>
Code
result$rule_status
Output
[1] "14 rules were examined. 8 rule(s) had at least 1 violation. 50 total violation(s) were found."
OuhscBbmc/trawler documentation built on Oct. 8, 2024, 11:44 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
execute-checks-biochemical
Code
checks$record_id_name
Output
[1] "record_id"
Code
checks$baseline_date_name
Output
[1] "date_enrolled"
Code
checks$record_id_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
checks$github_file_prefix
Output
[1] "https://github.com/OuhscBbmc/validator-1/tree/main"
Code
checks$redcap_project_id
Output
[1] 1612
Code
checks$redcap_version
Output
[1] "10.5.1"
Code
checks$redcap_default_arm
Output
[1] 1
Code
checks$redcap_codebook
Output
[1] "https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/Design/data_dictionary_codebook.php?pid=1612"
Code
checks$redcap_record_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
result$smells
Output
# A tibble: 11 x 12
check_name pass description priority debug bound_lower bound_upper
<chr> <lgl> <chr> <int> <lgl> <dbl> <dbl>
1 females TRUE Proportion~ 2 FALSE 0.4 0.6
2 males TRUE Proportion~ 2 FALSE 0.4 0.6
3 age TRUE Mean parti~ 2 FALSE 20 60
4 serum_prealbumin_le~ FALSE Mean serum~ 2 FALSE 32 39
5 serum_creatinine_le~ FALSE Mean serum~ 2 FALSE 3 15
6 bmi_at_baseline FALSE Mean BMI i~ 2 FALSE 18 24
7 serum_cholesterol_l~ TRUE Mean chole~ 1 FALSE 100 140
8 dialysis_adequacy TRUE Mean Kt/V ~ 1 FALSE 1.2 5
9 nutritional_counsel~ TRUE Most patie~ 2 FALSE 0.85 0.99
10 definitive_diagnosis TRUE The propor~ 1 FALSE 1 1
11 normalized_protein_~ TRUE Mean Norma~ 1 FALSE 0 1.2
# i 5 more variables: bounds_template <chr>, value_template <chr>,
# equation <chr>, boundaries <chr>, value <dbl>
Code
as.data.frame(result$smells)
Output
check_name pass
1 females TRUE
2 males TRUE
3 age TRUE
4 serum_prealbumin_level_at_baseline FALSE
5 serum_creatinine_level_at_baseline FALSE
6 bmi_at_baseline FALSE
7 serum_cholesterol_levels_at_baseline TRUE
8 dialysis_adequacy TRUE
9 nutritional_counseling TRUE
10 definitive_diagnosis TRUE
11 normalized_protein_catabolic_rate TRUE
description
1 Proportion of female participants is about half the sample
2 Proportion of male participants is about half the sample
3 Mean participant age is between 20 and 60 years
4 Mean serum pre-albumin level is between 31mg/dl and 39mg/dl at baseline
5 Mean serum creatinine level is between 3mg/dl and 15mg/dl at baseline
6 Mean BMI is between 18 and 24
7 Mean cholesterol levels range is between 100 and 140mg/dl at baseline
8 Mean Kt/V value is within the normal range (*i.e.*, between 1.2 and 5)
9 Most patients agreed to receiving nutritional counseling
10 The proportion of study participants diagnosed with malnutrition associated with Chronic Renal Disease is 1.0 (*i.e.*, everyone)
11 Mean Normalized Protein Catabolic Rate (nPCR) is <= 1.2g/kg/day
priority debug bound_lower bound_upper bounds_template value_template
1 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
2 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
3 2 FALSE 20.00 60.00 [%.0f, %.0f] %.1f
4 2 FALSE 32.00 39.00 [%.0f, %.0f] %.1f
5 2 FALSE 3.00 15.00 [%.0f, %.0f] %.1f
6 2 FALSE 18.00 24.00 [%.0f, %.0f] %.1f
7 1 FALSE 100.00 140.00 [%.0f, %.0f] %.1f
8 1 FALSE 1.20 5.00 [%.1f, %.1f] %.2f
9 2 FALSE 0.85 0.99 [%.2f, %.2f] %.3f
10 1 FALSE 1.00 1.00 [%.1f, %.1f] %.2f
11 1 FALSE 0.00 1.20 [%.1f, %.1f] %.3f
equation
1 function (d) {\n mean(d$sex == "female", na.rm = TRUE)\n}\n
2 function (d) {\n mean(d$sex == "male", na.rm = TRUE)\n}\n
3 function (d) {\n mean(d$age, na.rm = TRUE)\n}\n
4 function (d) {\n mean(d$baseline_prealbumin_level, na.rm = TRUE)\n}\n
5 function (d) {\n mean(d$baseline_creatinine_level, na.rm = TRUE)\n}\n
6 function (d) {\n mean(d$baseline_bmi, na.rm = TRUE)\n}\n
7 function (d) {\n mean(d$baseline_cholesterol, na.rm = TRUE)\n}\n
8 function (d) {\n mean(d$completion_project_questionnaire_ktv_value, na.rm = TRUE)\n}\n
9 function (d) {\n mean(d$nutritional_counseling, na.rm = TRUE)\n}\n
10 function (d) {\n mean(d$differential_diagnoses_malnutrition, na.rm = TRUE)\n}\n
11 function (d) {\n mean(d$baseline_normalized_protein_catabolic_rate, na.rm = TRUE)\n}\n
boundaries value
1 [0.40, 0.60] 0.4444444
2 [0.40, 0.60] 0.5555556
3 [20, 60] 44.3888889
4 [32, 39] 23.6666667
5 [3, 15] 23.1687500
6 [18, 24] 24.8500000
7 [100, 140] 134.1176471
8 [1.2, 5.0] 1.9166667
9 [0.85, 0.99] 0.8888889
10 [1.0, 1.0] 1.0000000
11 [0.0, 1.2] 0.9312500
Code
result$smell_status
Output
[1] "11 smells have been sniffed. 3 violation(s) were found."
Code
result$rules
Output
# A tibble: 14 x 8
check_name violation_count error_message priority debug redcap_instrument
<chr> <int> <chr> <int> <lgl> <chr>
1 baseline_prea~ 10 Serum pre-al~ 1 FALSE baseline_data
2 missing_serum~ 3 Relevant nut~ 1 FALSE baseline_data
3 serum_prealbu~ 15 Baseline pre~ 2 FALSE baseline_data, v~
4 serum_prealbu~ 0 Baseline pre~ 2 FALSE baseline_data, v~
5 serum_prealbu~ 0 Baseline pre~ 2 FALSE baseline_data, v~
6 serum_prealbu~ 0 serum prealb~ 3 FALSE baseline_data, v~
7 baseline_firs~ 0 Serum prealb~ 3 FALSE baseline_data, v~
8 daily_first_v~ 0 In-addition ~ 3 FALSE baseline_data, p~
9 daily_protein~ 0 npcr levels ~ 3 FALSE baseline_data, p~
10 hospitalizati~ 2 Patient was ~ 1 FALSE completion_proje~
11 optimal_daily~ 7 Daily protei~ 2 FALSE completion_proje~
12 recommended_n~ 10 NPCR values ~ 2 FALSE completion_data
13 npcr 1 NPCR at comp~ 2 FALSE completion_data
14 npcr_comparis~ 2 NPCR at comp~ 3 FALSE completion_data
# i 2 more variables: passing_test <chr>, results <list>
Code
as.data.frame(dplyr::select(result$rules, !tidyselect::contains("results")))
Output
check_name violation_count
1 baseline_prealbumin_levels 10
2 missing_serum_marker_levels 3
3 serum_prealbumin_levels_1 15
4 serum_prealbumin_levels_2 0
5 serum_prealbumin_levels_completion_data 0
6 serum_prealbumin_levels_expectations 0
7 baseline_first_visit_lab_parameters 0
8 daily_first_visit_lab_and_workup_parameters 0
9 daily_protein_intake 0
10 hospitalization_reason 2
11 optimal_daily_protein_intake 7
12 recommended_npcr_range 10
13 npcr 1
14 npcr_comparison 2
error_message
1 Serum pre-albumin level of all enrolled patients do not meet the study criterion
2 Relevant nutritional serum markers are missing
3 Baseline prealbumin levels are not missing however levels were not carefully monitored in the subsequent visit
4 Baseline prealbumin levels and pre-albumin levels during the 1st visit are not missing however levels in the next reading are missing
5 Baseline prealbumin levels are not missing however readings are not carefully monitored at completion
6 serum prealbumin levels are not missing however subsequent readings did not come as expected
7 Serum prealbumin levels are low and protein intake at baseline is less than optimal
8 In-addition to baseline & visit lab protein parameters, blood work-up npcr levels are also low
9 npcr levels in study have not improved as intended
10 Patient was hospitalized but reason and the date of hospitalization is missing
11 Daily protein intake is optimal but one of the nutritional marker is not within the normal range
12 NPCR values are not within the recommended range at completion
13 NPCR at completion is missing
14 NPCR at completion is not greater than npcr at baseline
priority debug
1 1 FALSE
2 1 FALSE
3 2 FALSE
4 2 FALSE
5 2 FALSE
6 3 FALSE
7 3 FALSE
8 3 FALSE
9 3 FALSE
10 1 FALSE
11 2 FALSE
12 2 FALSE
13 2 FALSE
14 3 FALSE
redcap_instrument
1 baseline_data
2 baseline_data
3 baseline_data, visit_lab_date
4 baseline_data, visit_lab_date, visit_blood_workup
5 baseline_data, visit_lab_date, visit_blood_workup, completion_date
6 baseline_data, visit_lab_date, visit_blood_workup, completion_date
7 baseline_data, visit_lab_data
8 baseline_data, patient_morale_questionnaire
9 baseline_data, patient_morale_questionnaire
10 completion_project_questionnaire
11 completion_project_questionnaire, completion_data
12 completion_data
13 completion_data
14 completion_data
passing_test
1 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n dplyr::between(d$baseline_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
2 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n (\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$baseline_creatinine_level) &\n !is.na(d$baseline_transferrin_level)\n ),\n TRUE\n )\n}\n
3 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level)\n ),\n !is.na(d$visit_lab_prealbumin_level),\n TRUE\n )\n}\n
4 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level)\n ),\n !is.na(d$visit_blood_workup_prealbumin_level),\n TRUE\n )\n}\n
5 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level)\n ),\n !is.na(d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
6 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level) &\n !is.na(d$completion_data_prealbumin_level)\n ),\n (d$visit_lab_prealbumin_level < d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
7 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2)\n ),\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr),\n TRUE\n )\n}\n
8 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr)\n ),\n d$visit_lab_npcr <= d$visit_blood_workup_npcr,\n TRUE\n )\n}\n
9 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr) &\n (d$visit_lab_npcr <= d$visit_blood_workup_npcr)\n ),\n d$visit_blood_workup_npcr < d$completion_data_npcr,\n TRUE\n )\n}\n
10 function (d) {\n dplyr::if_else(\n d$completion_project_questionnaire_hospitalization == 1L,\n !is.na(d$completion_project_questionnaire_hospitalization_cause) & !is.na(d$completion_project_questionnaire_hospitalization_date),\n TRUE\n )\n}\n
11 function (d) {\n dplyr::if_else(\n d$completion_data_npcr >= 1.2,\n dplyr::between(d$completion_data_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
12 function (d) {\n dplyr::between(d$completion_data_npcr, 1.2, 1.4)\n}\n
13 function (d) {\n events_to_check <- c("final_visit_arm_1")\n dplyr::if_else(\n d$redcap_event_name %in% events_to_check,\n !is.na(d$completion_data_npcr), # If this row exists in the desired event, then check for nonmissingness.\n TRUE # Otherwise, the test passes for rows associated with all other events.\n )\n}\n
14 function (d) {\n dplyr::if_else(\n !is.na(d$completion_data_npcr),\n (d$npcr_at_baseline < d$completion_data_npcr),\n TRUE\n )\n}
Code
ds_result_unnested
Output
# A tibble: 50 x 5
check_name record_id data_collector baseline_date record_id_linked
<chr> <int> <int> <date> <chr>
1 baseline_prealbumin_~ 1 1 2015-01-02 "<a href=\"http~
2 baseline_prealbumin_~ 2 2 2015-01-02 "<a href=\"http~
3 baseline_prealbumin_~ 3 3 2015-01-05 "<a href=\"http~
4 baseline_prealbumin_~ 8 1 2015-02-03 "<a href=\"http~
5 baseline_prealbumin_~ 9 3 2015-02-08 "<a href=\"http~
6 baseline_prealbumin_~ 12 3 2015-03-06 "<a href=\"http~
7 baseline_prealbumin_~ 13 1 2015-03-15 "<a href=\"http~
8 baseline_prealbumin_~ 14 1 2015-03-10 "<a href=\"http~
9 baseline_prealbumin_~ 15 3 2015-03-03 "<a href=\"http~
10 baseline_prealbumin_~ 100 1 2015-04-02 "<a href=\"http~
# i 40 more rows
Code
as.data.frame(ds_result_unnested)
Output
check_name record_id data_collector baseline_date
1 baseline_prealbumin_levels 1 1 2015-01-02
2 baseline_prealbumin_levels 2 2 2015-01-02
3 baseline_prealbumin_levels 3 3 2015-01-05
4 baseline_prealbumin_levels 8 1 2015-02-03
5 baseline_prealbumin_levels 9 3 2015-02-08
6 baseline_prealbumin_levels 12 3 2015-03-06
7 baseline_prealbumin_levels 13 1 2015-03-15
8 baseline_prealbumin_levels 14 1 2015-03-10
9 baseline_prealbumin_levels 15 3 2015-03-03
10 baseline_prealbumin_levels 100 1 2015-04-02
11 missing_serum_marker_levels 7 2 2015-01-27
12 missing_serum_marker_levels 10 255 2015-02-13
13 missing_serum_marker_levels 11 2 2015-02-19
14 serum_prealbumin_levels_1 1 1 2015-01-02
15 serum_prealbumin_levels_1 2 2 2015-01-02
16 serum_prealbumin_levels_1 3 3 2015-01-05
17 serum_prealbumin_levels_1 4 255 2015-01-10
18 serum_prealbumin_levels_1 5 1 2015-01-13
19 serum_prealbumin_levels_1 6 3 2015-01-16
20 serum_prealbumin_levels_1 8 1 2015-02-03
21 serum_prealbumin_levels_1 9 3 2015-02-08
22 serum_prealbumin_levels_1 12 3 2015-03-06
23 serum_prealbumin_levels_1 13 1 2015-03-15
24 serum_prealbumin_levels_1 14 1 2015-03-10
25 serum_prealbumin_levels_1 15 3 2015-03-03
26 serum_prealbumin_levels_1 16 2 2015-03-09
27 serum_prealbumin_levels_1 100 1 2015-04-02
28 serum_prealbumin_levels_1 220 1 2015-04-02
29 hospitalization_reason 8 NA <NA>
30 hospitalization_reason 14 NA <NA>
31 optimal_daily_protein_intake 5 NA <NA>
32 optimal_daily_protein_intake 6 NA <NA>
33 optimal_daily_protein_intake 7 NA <NA>
34 optimal_daily_protein_intake 8 NA <NA>
35 optimal_daily_protein_intake 11 NA <NA>
36 optimal_daily_protein_intake 15 NA <NA>
37 optimal_daily_protein_intake 100 NA <NA>
38 recommended_npcr_range 1 NA <NA>
39 recommended_npcr_range 6 NA <NA>
40 recommended_npcr_range 7 NA <NA>
41 recommended_npcr_range 8 NA <NA>
42 recommended_npcr_range 12 NA <NA>
43 recommended_npcr_range 13 NA <NA>
44 recommended_npcr_range 14 NA <NA>
45 recommended_npcr_range 16 NA <NA>
46 recommended_npcr_range 100 NA <NA>
47 recommended_npcr_range 220 NA <NA>
48 npcr 10 NA <NA>
49 npcr_comparison 1 NA <NA>
50 npcr_comparison 12 NA <NA>
record_id_linked
1 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=baseline_data" target="_blank">1</a>
2 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=2&page=baseline_data" target="_blank">2</a>
3 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=3&page=baseline_data" target="_blank">3</a>
4 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=baseline_data" target="_blank">8</a>
5 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=9&page=baseline_data" target="_blank">9</a>
6 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=baseline_data" target="_blank">12</a>
7 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=13&page=baseline_data" target="_blank">13</a>
8 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=baseline_data" target="_blank">14</a>
9 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=15&page=baseline_data" target="_blank">15</a>
10 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=baseline_data" target="_blank">100</a>
11 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=7&page=baseline_data" target="_blank">7</a>
12 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=10&page=baseline_data" target="_blank">10</a>
13 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=11&page=baseline_data" target="_blank">11</a>
14 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=baseline_data, visit_lab_date" target="_blank">1</a>
15 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=2&page=baseline_data, visit_lab_date" target="_blank">2</a>
16 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=3&page=baseline_data, visit_lab_date" target="_blank">3</a>
17 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=4&page=baseline_data, visit_lab_date" target="_blank">4</a>
18 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=5&page=baseline_data, visit_lab_date" target="_blank">5</a>
19 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=6&page=baseline_data, visit_lab_date" target="_blank">6</a>
20 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=baseline_data, visit_lab_date" target="_blank">8</a>
21 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=9&page=baseline_data, visit_lab_date" target="_blank">9</a>
22 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=baseline_data, visit_lab_date" target="_blank">12</a>
23 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=13&page=baseline_data, visit_lab_date" target="_blank">13</a>
24 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=baseline_data, visit_lab_date" target="_blank">14</a>
25 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=15&page=baseline_data, visit_lab_date" target="_blank">15</a>
26 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=16&page=baseline_data, visit_lab_date" target="_blank">16</a>
27 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=baseline_data, visit_lab_date" target="_blank">100</a>
28 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=220&page=baseline_data, visit_lab_date" target="_blank">220</a>
29 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=completion_project_questionnaire" target="_blank">8</a>
30 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=completion_project_questionnaire" target="_blank">14</a>
31 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=5&page=completion_project_questionnaire, completion_data" target="_blank">5</a>
32 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=6&page=completion_project_questionnaire, completion_data" target="_blank">6</a>
33 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=7&page=completion_project_questionnaire, completion_data" target="_blank">7</a>
34 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=completion_project_questionnaire, completion_data" target="_blank">8</a>
35 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=11&page=completion_project_questionnaire, completion_data" target="_blank">11</a>
36 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=15&page=completion_project_questionnaire, completion_data" target="_blank">15</a>
37 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=completion_project_questionnaire, completion_data" target="_blank">100</a>
38 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=completion_data" target="_blank">1</a>
39 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=6&page=completion_data" target="_blank">6</a>
40 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=7&page=completion_data" target="_blank">7</a>
41 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=8&page=completion_data" target="_blank">8</a>
42 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=completion_data" target="_blank">12</a>
43 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=13&page=completion_data" target="_blank">13</a>
44 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=14&page=completion_data" target="_blank">14</a>
45 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=16&page=completion_data" target="_blank">16</a>
46 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=100&page=completion_data" target="_blank">100</a>
47 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=220&page=completion_data" target="_blank">220</a>
48 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=10&page=completion_data" target="_blank">10</a>
49 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=1&page=completion_data" target="_blank">1</a>
50 <a href="https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/DataEntry/index.php?pid=1612&arm=1&id=12&page=completion_data" target="_blank">12</a>
Code
result$rule_status
Output
[1] "14 rules were examined. 8 rule(s) had at least 1 violation. 50 total violation(s) were found."
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.