tests/testthat/_snaps/source_data.md
In phuongquan/daiquiri: Data Quality Reporting for Temporal Datasets
prepare_data() creates source_data object correctly
Code
source_data$validation_warnings
Output
field_name message
<char> <char>
1: col_timepoint Missing or invalid value in Timepoint field
2: col_timepoint expected valid date, but got '2021-06-31'
3: col_timepoint expected valid date, but got '2021-06-01 33:00:00'
4: col_timepoint expected valid date, but got '2021-06-31 02:00:00'
5: col_date_time expected valid date, but got '2021-06-31 02:00:00'
6: col_date_time expected valid date, but got '2021-06-01 33:00:00'
7: col_date_time expected valid date, but got '2021-06-02 24:00:00'
8: col_date_time expected date like , but got '2021-06 05:00:00'
9: col_date_time expected date like , but got '21-06-03 06:00:00'
10: col_date_time expected date like , but got '2021-06-3 07:00:00'
11: col_date_only expected valid date, but got '2021-06-31'
12: col_date_only expected date like , but got '2021-06'
13: col_date_only expected date like , but got '02/06/2021'
14: col_date_only expected date like , but got '2021-06-2'
15: col_date_only expected date like , but got '21-06-03'
16: col_date_uk expected valid date, but got '31/06/2021'
17: col_date_uk expected date like %d/%m/%Y, but got '02/06/21'
18: col_date_uk expected date like %d/%m/%Y, but got '2021-06-03'
19: col_numeric_dirty expected no trailing characters, but got '3.02*'
20: col_numeric_dirty expected a double, but got 'other'
21: col_numeric_dirty expected no trailing characters, but got '5,04'
22: col_numeric_dirty expected no trailing characters, but got '6.05g'
23: col_numeric_dirty expected a double, but got '*'
24: col_numeric_dirty expected a double, but got '"9.08"'
25: col_numeric_dirty expected a double, but got ''10.09''
26: col_numeric_dirty expected no trailing characters, but got '11.1''
27: col_numeric_dirty expected a double, but got '`12.11''
28: col_numeric_missing expected a double, but got 'not a number'
field_name message
instances
<int>
1: 6
2: 1
3: 1
4: 1
5: 1
6: 1
7: 1
8: 1
9: 1
10: 1
11: 1
12: 1
13: 1
14: 1
15: 1
16: 1
17: 1
18: 1
19: 2
20: 1
21: 1
22: 1
23: 1
24: 1
25: 1
26: 1
27: 1
28: 4
instances
source_data object prints to console ok
Dataset: completetestset
Overall:
Columns in source: 26
Columns imported: 13
Rows in source: 900
Duplicate rows removed: 4
Rows imported: 890
Column used for timepoint: col_timepoint
Min timepoint value: 2021-06-01 03:00:00
Max timepoint value: 2022-03-26
Rows missing timepoint values removed: 6
Strings interpreted as missing values: "","NA","NULL"
Total validation warnings: 37
Datafields:
field_name field_type datatype count missing
1 col_timepoint_err ignore NA NA NA
2 col_timepoint timepoint double 890 0 (0%)
3 col_date_time_err ignore NA NA NA
4 col_date_time datetime double 878 12 (1%)
5 col_date_only_err ignore NA NA NA
6 col_date_only datetime double 879 11 (1%)
7 col_date_uk_err ignore NA NA NA
8 col_date_uk datetime double 881 9 (1%)
9 col_id_num_err ignore NA NA NA
10 col_id_num uniqueidentifier character 883 7 (0.8%)
11 col_id_string_err ignore NA NA NA
12 col_id_string uniqueidentifier character 883 7 (0.8%)
13 col_numeric_clean_err ignore NA NA NA
14 col_numeric_clean numeric double 883 7 (0.8%)
15 col_numeric_dirty_err ignore NA NA NA
16 col_numeric_dirty numeric double 874 16 (2%)
17 col_categorical_small_err ignore NA NA NA
18 col_categorical_small categorical character 883 7 (0.8%)
19 col_categorical_large_err ignore NA NA NA
20 col_categorical_large categorical character 883 7 (0.8%)
21 col_freetext_err ignore NA NA NA
22 col_freetext freetext character 883 7 (0.8%)
23 col_simple_err ignore NA NA NA
24 col_simple simple character 883 7 (0.8%)
25 col_numeric_missing_err ignore NA NA NA
26 col_numeric_missing numeric double 0 890 (100%)
min max validation_warnings
1 NA NA NA
2 2021-06-01 03:00:00 2022-03-26 9
3 NA NA NA
4 2021-06-03 08:00:00 2022-05-02 08:00:00 6
5 NA NA NA
6 2021-06-03 2022-03-26 5
7 NA NA NA
8 2021-06-02 2022-03-26 3
9 NA NA NA
10 10 99 0
11 NA NA NA
12 A0126 A1019 0
13 NA NA NA
14 0 30.23 0
15 NA NA NA
16 0 15.14 10
17 NA NA NA
18 cat1 cat7 0
19 NA NA NA
20 cat1 cat9 0
21 NA NA NA
22 " some, text 0
23 NA NA NA
24 some text some text 0
25 NA NA NA
26 NA NA 4
Validation warnings:
field_name message
<char> <char>
1: col_timepoint Missing or invalid value in Timepoint field
2: col_timepoint expected valid date, but got '2021-06-31'
3: col_timepoint expected valid date, but got '2021-06-01 33:00:00'
4: col_timepoint expected valid date, but got '2021-06-31 02:00:00'
5: col_date_time expected valid date, but got '2021-06-31 02:00:00'
6: col_date_time expected valid date, but got '2021-06-01 33:00:00'
7: col_date_time expected valid date, but got '2021-06-02 24:00:00'
8: col_date_time expected date like , but got '2021-06 05:00:00'
9: col_date_time expected date like , but got '21-06-03 06:00:00'
10: col_date_time expected date like , but got '2021-06-3 07:00:00'
11: col_date_only expected valid date, but got '2021-06-31'
12: col_date_only expected date like , but got '2021-06'
13: col_date_only expected date like , but got '02/06/2021'
14: col_date_only expected date like , but got '2021-06-2'
15: col_date_only expected date like , but got '21-06-03'
16: col_date_uk expected valid date, but got '31/06/2021'
17: col_date_uk expected date like %d/%m/%Y, but got '02/06/21'
18: col_date_uk expected date like %d/%m/%Y, but got '2021-06-03'
19: col_numeric_dirty expected no trailing characters, but got '3.02*'
20: col_numeric_dirty expected a double, but got 'other'
21: col_numeric_dirty expected no trailing characters, but got '5,04'
22: col_numeric_dirty expected no trailing characters, but got '6.05g'
23: col_numeric_dirty expected a double, but got '*'
24: col_numeric_dirty expected a double, but got '"9.08"'
25: col_numeric_dirty expected a double, but got ''10.09''
26: col_numeric_dirty expected no trailing characters, but got '11.1''
27: col_numeric_dirty expected a double, but got '`12.11''
28: col_numeric_missing expected a double, but got 'not a number'
field_name message
instances
<int>
1: 6
2: 1
3: 1
4: 1
5: 1
6: 1
7: 1
8: 1
9: 1
10: 1
11: 1
12: 1
13: 1
14: 1
15: 1
16: 1
17: 1
18: 1
19: 2
20: 1
21: 1
22: 1
23: 1
24: 1
25: 1
26: 1
27: 1
28: 4
instances
source_data object prints to console ok when there is a strata field
Dataset: df
Overall:
Columns in source: 7
Columns imported: 7
Rows in source: 20
Duplicate rows removed: 0
Rows imported: 20
Column used for timepoint: col_timepoint
Min timepoint value: 2022-01-01
Max timepoint value: 2022-01-05
Rows missing timepoint values removed: 0
Column used for strata: col_stratify
Strata values: SITE1, SITE2, NA
Strings interpreted as missing values: "","NULL"
Total validation warnings: NA
Datafields:
field_name field_type datatype count missing min
1 col_timepoint timepoint double 20 0 (0%) 2022-01-01
2 col_numeric numeric double 20 0 (0%) 2
3 col_datetime datetime double 9 11 (55%) 2022-01-11
4 col_uniqueidentifier uniqueidentifier character 20 0 (0%) 1
5 col_categorical categorical character 20 0 (0%) a
6 col_simple simple character 10 10 (50%) a
7 col_stratify strata character 18 2 (10%) SITE1
max validation_warnings
1 2022-01-05 0
2 3 1
3 2022-01-19 0
4 9 1
5 b 0
6 a 0
7 SITE2 0
Validation warnings:
field_name
<char>
1: col_numeric
2: col_uniqueidentifier
message
<char>
1: Data supplied as double instead of character, non-conformant values will not be identified
2: Data supplied as integer instead of character, non-conformant values will not be identified
instances
<int>
1: NA
2: NA
phuongquan/daiquiri documentation built on April 5, 2024, 10:36 a.m.
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prepare_data() creates source_data object correctly
Code
source_data$validation_warnings
Output
field_name message
<char> <char>
1: col_timepoint Missing or invalid value in Timepoint field
2: col_timepoint expected valid date, but got '2021-06-31'
3: col_timepoint expected valid date, but got '2021-06-01 33:00:00'
4: col_timepoint expected valid date, but got '2021-06-31 02:00:00'
5: col_date_time expected valid date, but got '2021-06-31 02:00:00'
6: col_date_time expected valid date, but got '2021-06-01 33:00:00'
7: col_date_time expected valid date, but got '2021-06-02 24:00:00'
8: col_date_time expected date like , but got '2021-06 05:00:00'
9: col_date_time expected date like , but got '21-06-03 06:00:00'
10: col_date_time expected date like , but got '2021-06-3 07:00:00'
11: col_date_only expected valid date, but got '2021-06-31'
12: col_date_only expected date like , but got '2021-06'
13: col_date_only expected date like , but got '02/06/2021'
14: col_date_only expected date like , but got '2021-06-2'
15: col_date_only expected date like , but got '21-06-03'
16: col_date_uk expected valid date, but got '31/06/2021'
17: col_date_uk expected date like %d/%m/%Y, but got '02/06/21'
18: col_date_uk expected date like %d/%m/%Y, but got '2021-06-03'
19: col_numeric_dirty expected no trailing characters, but got '3.02*'
20: col_numeric_dirty expected a double, but got 'other'
21: col_numeric_dirty expected no trailing characters, but got '5,04'
22: col_numeric_dirty expected no trailing characters, but got '6.05g'
23: col_numeric_dirty expected a double, but got '*'
24: col_numeric_dirty expected a double, but got '"9.08"'
25: col_numeric_dirty expected a double, but got ''10.09''
26: col_numeric_dirty expected no trailing characters, but got '11.1''
27: col_numeric_dirty expected a double, but got '`12.11''
28: col_numeric_missing expected a double, but got 'not a number'
field_name message
instances
<int>
1: 6
2: 1
3: 1
4: 1
5: 1
6: 1
7: 1
8: 1
9: 1
10: 1
11: 1
12: 1
13: 1
14: 1
15: 1
16: 1
17: 1
18: 1
19: 2
20: 1
21: 1
22: 1
23: 1
24: 1
25: 1
26: 1
27: 1
28: 4
instances
source_data object prints to console ok
Dataset: completetestset
Overall:
Columns in source: 26
Columns imported: 13
Rows in source: 900
Duplicate rows removed: 4
Rows imported: 890
Column used for timepoint: col_timepoint
Min timepoint value: 2021-06-01 03:00:00
Max timepoint value: 2022-03-26
Rows missing timepoint values removed: 6
Strings interpreted as missing values: "","NA","NULL"
Total validation warnings: 37
Datafields:
field_name field_type datatype count missing
1 col_timepoint_err ignore NA NA NA
2 col_timepoint timepoint double 890 0 (0%)
3 col_date_time_err ignore NA NA NA
4 col_date_time datetime double 878 12 (1%)
5 col_date_only_err ignore NA NA NA
6 col_date_only datetime double 879 11 (1%)
7 col_date_uk_err ignore NA NA NA
8 col_date_uk datetime double 881 9 (1%)
9 col_id_num_err ignore NA NA NA
10 col_id_num uniqueidentifier character 883 7 (0.8%)
11 col_id_string_err ignore NA NA NA
12 col_id_string uniqueidentifier character 883 7 (0.8%)
13 col_numeric_clean_err ignore NA NA NA
14 col_numeric_clean numeric double 883 7 (0.8%)
15 col_numeric_dirty_err ignore NA NA NA
16 col_numeric_dirty numeric double 874 16 (2%)
17 col_categorical_small_err ignore NA NA NA
18 col_categorical_small categorical character 883 7 (0.8%)
19 col_categorical_large_err ignore NA NA NA
20 col_categorical_large categorical character 883 7 (0.8%)
21 col_freetext_err ignore NA NA NA
22 col_freetext freetext character 883 7 (0.8%)
23 col_simple_err ignore NA NA NA
24 col_simple simple character 883 7 (0.8%)
25 col_numeric_missing_err ignore NA NA NA
26 col_numeric_missing numeric double 0 890 (100%)
min max validation_warnings
1 NA NA NA
2 2021-06-01 03:00:00 2022-03-26 9
3 NA NA NA
4 2021-06-03 08:00:00 2022-05-02 08:00:00 6
5 NA NA NA
6 2021-06-03 2022-03-26 5
7 NA NA NA
8 2021-06-02 2022-03-26 3
9 NA NA NA
10 10 99 0
11 NA NA NA
12 A0126 A1019 0
13 NA NA NA
14 0 30.23 0
15 NA NA NA
16 0 15.14 10
17 NA NA NA
18 cat1 cat7 0
19 NA NA NA
20 cat1 cat9 0
21 NA NA NA
22 " some, text 0
23 NA NA NA
24 some text some text 0
25 NA NA NA
26 NA NA 4
Validation warnings:
field_name message
<char> <char>
1: col_timepoint Missing or invalid value in Timepoint field
2: col_timepoint expected valid date, but got '2021-06-31'
3: col_timepoint expected valid date, but got '2021-06-01 33:00:00'
4: col_timepoint expected valid date, but got '2021-06-31 02:00:00'
5: col_date_time expected valid date, but got '2021-06-31 02:00:00'
6: col_date_time expected valid date, but got '2021-06-01 33:00:00'
7: col_date_time expected valid date, but got '2021-06-02 24:00:00'
8: col_date_time expected date like , but got '2021-06 05:00:00'
9: col_date_time expected date like , but got '21-06-03 06:00:00'
10: col_date_time expected date like , but got '2021-06-3 07:00:00'
11: col_date_only expected valid date, but got '2021-06-31'
12: col_date_only expected date like , but got '2021-06'
13: col_date_only expected date like , but got '02/06/2021'
14: col_date_only expected date like , but got '2021-06-2'
15: col_date_only expected date like , but got '21-06-03'
16: col_date_uk expected valid date, but got '31/06/2021'
17: col_date_uk expected date like %d/%m/%Y, but got '02/06/21'
18: col_date_uk expected date like %d/%m/%Y, but got '2021-06-03'
19: col_numeric_dirty expected no trailing characters, but got '3.02*'
20: col_numeric_dirty expected a double, but got 'other'
21: col_numeric_dirty expected no trailing characters, but got '5,04'
22: col_numeric_dirty expected no trailing characters, but got '6.05g'
23: col_numeric_dirty expected a double, but got '*'
24: col_numeric_dirty expected a double, but got '"9.08"'
25: col_numeric_dirty expected a double, but got ''10.09''
26: col_numeric_dirty expected no trailing characters, but got '11.1''
27: col_numeric_dirty expected a double, but got '`12.11''
28: col_numeric_missing expected a double, but got 'not a number'
field_name message
instances
<int>
1: 6
2: 1
3: 1
4: 1
5: 1
6: 1
7: 1
8: 1
9: 1
10: 1
11: 1
12: 1
13: 1
14: 1
15: 1
16: 1
17: 1
18: 1
19: 2
20: 1
21: 1
22: 1
23: 1
24: 1
25: 1
26: 1
27: 1
28: 4
instances
source_data object prints to console ok when there is a strata field
Dataset: df
Overall:
Columns in source: 7
Columns imported: 7
Rows in source: 20
Duplicate rows removed: 0
Rows imported: 20
Column used for timepoint: col_timepoint
Min timepoint value: 2022-01-01
Max timepoint value: 2022-01-05
Rows missing timepoint values removed: 0
Column used for strata: col_stratify
Strata values: SITE1, SITE2, NA
Strings interpreted as missing values: "","NULL"
Total validation warnings: NA
Datafields:
field_name field_type datatype count missing min
1 col_timepoint timepoint double 20 0 (0%) 2022-01-01
2 col_numeric numeric double 20 0 (0%) 2
3 col_datetime datetime double 9 11 (55%) 2022-01-11
4 col_uniqueidentifier uniqueidentifier character 20 0 (0%) 1
5 col_categorical categorical character 20 0 (0%) a
6 col_simple simple character 10 10 (50%) a
7 col_stratify strata character 18 2 (10%) SITE1
max validation_warnings
1 2022-01-05 0
2 3 1
3 2022-01-19 0
4 9 1
5 b 0
6 a 0
7 SITE2 0
Validation warnings:
field_name
<char>
1: col_numeric
2: col_uniqueidentifier
message
<char>
1: Data supplied as double instead of character, non-conformant values will not be identified
2: Data supplied as integer instead of character, non-conformant values will not be identified
instances
<int>
1: NA
2: NA
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