Extract Different Values in Table Format Function to extract rows with different values between two data frames.
extract_diff_table.RdExtract Different Values in Table Format Function to extract rows with different values between two data frames.
Arguments
- dfx
a non-empty data.frame.
- dfy
a non-empty data.frame.
- myrror_object
myrror object from create_myrror_object
- by
character, key to be used for dfx and dfy.
- by.x
character, key to be used for dfx.
- by.y
character, key to be used for dfy.
- output
character: one of "full", "simple", "silent".
- tolerance
numeric, default to 1e-7.
- verbose
logical: If
TRUEadditional information will be displayed.- interactive
logical: If
TRUE, print S3 method for myrror objects displays by chunks. IfFALSE, everything will be printed at once.
Examples
# 1. Standard report, after running myrror() or compare_values():
myrror(survey_data, survey_data_2, by=c('country', 'year'))
#>
#> ── Myrror Report ───────────────────────────────────────────────────────────────
#>
#> ── General Information: ──
#>
#> dfx: survey_data with 16 rows and 6 columns.
#> dfy: survey_data_2 with 16 rows and 6 columns.
#> keys: country and year.
#>
#> ── Note: comparison is done for shared columns and rows. ──
#>
#> ✔ Total shared columns (no keys): 4
#> ! Non-shared columns in survey_data: 0 ()
#> ! Non-shared columns in survey_data_2: 0 ()
#>
#> ✔ Total shared rows: 16
#> ! Non-shared rows in survey_data: 0.
#> ! Non-shared rows in survey_data_2: 0.
#>
#> ✔ There are no missing or new rows.
#>
#> ── 1. Shared Columns Class Comparison ──────────────────────────────────────────
#>
#> ✔ All shared columns have the same class.
#>
#>
#> ── 2. Shared Columns Values Comparison ─────────────────────────────────────────
#>
#> ! 1 shared column(s) have different value(s):
#> ℹ Note: character-numeric comparison is allowed.
#>
#>
#> ── Overview: ──
#>
#> # A tibble: 1 × 4
#> variable change_in_value na_to_value value_to_na
#> <fct> <int> <int> <int>
#> 1 variable2 16 0 0
#>
#>
#>
#> ── Value comparison: ──
#>
#> ! 1 shared column(s) have different value(s):
#> ℹ Note: Only first 5 rows shown for each variable.
#>
#> ── "variable2"
#> diff indexes country year variable2.x variable2.y
#> <char> <char> <char> <int> <num> <num>
#> 1: change_in_value 1 A 2010 0.4978505 -1.07179123
#> 2: change_in_value 2 A 2011 -1.9666172 0.30352864
#> 3: change_in_value 3 A 2012 0.7013559 0.44820978
#> 4: change_in_value 4 A 2013 -0.4727914 0.05300423
#> 5: change_in_value 5 A 2014 -1.0678237 0.92226747
#> ...
#>
#> ℹ Note: run `extract_diff_values()` or `extract_diff_table()` to access the results in list or table format.
#>
#> ✔ End of Myrror Report.
extract_diff_table()
#> Last myrror object used for comparison.
#> diff variable indexes country year variable1.x variable1.y
#> <char> <fctr> <char> <char> <int> <num> <num>
#> 1: change_in_value variable2 1 A 2010 -0.56047565 -0.56047565
#> 2: change_in_value variable2 2 A 2011 -0.23017749 -0.23017749
#> 3: change_in_value variable2 3 A 2012 1.55870831 1.55870831
#> 4: change_in_value variable2 4 A 2013 0.07050839 0.07050839
#> 5: change_in_value variable2 5 A 2014 0.12928774 0.12928774
#> 6: change_in_value variable2 6 A 2015 1.71506499 1.71506499
#> 7: change_in_value variable2 7 A 2016 0.46091621 0.46091621
#> 8: change_in_value variable2 8 A 2017 -1.26506123 -1.26506123
#> 9: change_in_value variable2 9 B 2010 -0.68685285 -0.68685285
#> 10: change_in_value variable2 10 B 2011 -0.44566197 -0.44566197
#> 11: change_in_value variable2 11 B 2012 1.22408180 1.22408180
#> 12: change_in_value variable2 12 B 2013 0.35981383 0.35981383
#> 13: change_in_value variable2 13 B 2014 0.40077145 0.40077145
#> 14: change_in_value variable2 14 B 2015 0.11068272 0.11068272
#> 15: change_in_value variable2 15 B 2016 -0.55584113 -0.55584113
#> 16: change_in_value variable2 16 B 2017 1.78691314 1.78691314
#> variable2.x variable2.y variable3.x variable3.y variable4.x variable4.y
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.4978505 -1.07179123 0.89512566 0.89512566 0.77996512 0.77996512
#> 2: -1.9666172 0.30352864 0.87813349 0.87813349 -0.08336907 -0.08336907
#> 3: 0.7013559 0.44820978 0.82158108 0.82158108 0.25331851 0.25331851
#> 4: -0.4727914 0.05300423 0.68864025 0.68864025 -0.02854676 -0.02854676
#> 5: -1.0678237 0.92226747 0.55391765 0.55391765 -0.04287046 -0.04287046
#> 6: -0.2179749 2.05008469 -0.06191171 -0.06191171 1.36860228 1.36860228
#> 7: -1.0260044 -0.49103117 -0.30596266 -0.30596266 -0.22577099 -0.22577099
#> 8: -0.7288912 -2.30916888 -0.38047100 -0.38047100 1.51647060 1.51647060
#> 9: -0.6250393 1.00573852 -0.69470698 -0.69470698 -1.54875280 -1.54875280
#> 10: -1.6866933 -0.70920076 -0.20791728 -0.20791728 0.58461375 0.58461375
#> 11: 0.8377870 -0.68800862 -1.26539635 -1.26539635 0.12385424 0.12385424
#> 12: 0.1533731 1.02557137 2.16895597 2.16895597 0.21594157 0.21594157
#> 13: -1.1381369 -0.28477301 1.20796200 1.20796200 0.37963948 0.37963948
#> 14: 1.2538149 -1.22071771 -1.12310858 -1.12310858 -0.50232345 -0.50232345
#> 15: 0.4264642 0.18130348 -0.40288484 -0.40288484 -0.33320738 -0.33320738
#> 16: -0.2950715 -0.13889136 -0.46665535 -0.46665535 -1.01857538 -1.01857538
# 2. Standard report, with new data:
extract_diff_table(survey_data, survey_data_2, by=c('country', 'year'))
#> diff variable indexes country year variable1.x variable1.y
#> <char> <fctr> <char> <char> <int> <num> <num>
#> 1: change_in_value variable2 1 A 2010 -0.56047565 -0.56047565
#> 2: change_in_value variable2 2 A 2011 -0.23017749 -0.23017749
#> 3: change_in_value variable2 3 A 2012 1.55870831 1.55870831
#> 4: change_in_value variable2 4 A 2013 0.07050839 0.07050839
#> 5: change_in_value variable2 5 A 2014 0.12928774 0.12928774
#> 6: change_in_value variable2 6 A 2015 1.71506499 1.71506499
#> 7: change_in_value variable2 7 A 2016 0.46091621 0.46091621
#> 8: change_in_value variable2 8 A 2017 -1.26506123 -1.26506123
#> 9: change_in_value variable2 9 B 2010 -0.68685285 -0.68685285
#> 10: change_in_value variable2 10 B 2011 -0.44566197 -0.44566197
#> 11: change_in_value variable2 11 B 2012 1.22408180 1.22408180
#> 12: change_in_value variable2 12 B 2013 0.35981383 0.35981383
#> 13: change_in_value variable2 13 B 2014 0.40077145 0.40077145
#> 14: change_in_value variable2 14 B 2015 0.11068272 0.11068272
#> 15: change_in_value variable2 15 B 2016 -0.55584113 -0.55584113
#> 16: change_in_value variable2 16 B 2017 1.78691314 1.78691314
#> variable2.x variable2.y variable3.x variable3.y variable4.x variable4.y
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.4978505 -1.07179123 0.89512566 0.89512566 0.77996512 0.77996512
#> 2: -1.9666172 0.30352864 0.87813349 0.87813349 -0.08336907 -0.08336907
#> 3: 0.7013559 0.44820978 0.82158108 0.82158108 0.25331851 0.25331851
#> 4: -0.4727914 0.05300423 0.68864025 0.68864025 -0.02854676 -0.02854676
#> 5: -1.0678237 0.92226747 0.55391765 0.55391765 -0.04287046 -0.04287046
#> 6: -0.2179749 2.05008469 -0.06191171 -0.06191171 1.36860228 1.36860228
#> 7: -1.0260044 -0.49103117 -0.30596266 -0.30596266 -0.22577099 -0.22577099
#> 8: -0.7288912 -2.30916888 -0.38047100 -0.38047100 1.51647060 1.51647060
#> 9: -0.6250393 1.00573852 -0.69470698 -0.69470698 -1.54875280 -1.54875280
#> 10: -1.6866933 -0.70920076 -0.20791728 -0.20791728 0.58461375 0.58461375
#> 11: 0.8377870 -0.68800862 -1.26539635 -1.26539635 0.12385424 0.12385424
#> 12: 0.1533731 1.02557137 2.16895597 2.16895597 0.21594157 0.21594157
#> 13: -1.1381369 -0.28477301 1.20796200 1.20796200 0.37963948 0.37963948
#> 14: 1.2538149 -1.22071771 -1.12310858 -1.12310858 -0.50232345 -0.50232345
#> 15: 0.4264642 0.18130348 -0.40288484 -0.40288484 -0.33320738 -0.33320738
#> 16: -0.2950715 -0.13889136 -0.46665535 -0.46665535 -1.01857538 -1.01857538
# 3. Toggle tolerance:
extract_diff_table(survey_data, survey_data_2, by=c('country', 'year'),
tolerance = 1e-5)
#> diff variable indexes country year variable1.x variable1.y
#> <char> <fctr> <char> <char> <int> <num> <num>
#> 1: change_in_value variable2 1 A 2010 -0.56047565 -0.56047565
#> 2: change_in_value variable2 2 A 2011 -0.23017749 -0.23017749
#> 3: change_in_value variable2 3 A 2012 1.55870831 1.55870831
#> 4: change_in_value variable2 4 A 2013 0.07050839 0.07050839
#> 5: change_in_value variable2 5 A 2014 0.12928774 0.12928774
#> 6: change_in_value variable2 6 A 2015 1.71506499 1.71506499
#> 7: change_in_value variable2 7 A 2016 0.46091621 0.46091621
#> 8: change_in_value variable2 8 A 2017 -1.26506123 -1.26506123
#> 9: change_in_value variable2 9 B 2010 -0.68685285 -0.68685285
#> 10: change_in_value variable2 10 B 2011 -0.44566197 -0.44566197
#> 11: change_in_value variable2 11 B 2012 1.22408180 1.22408180
#> 12: change_in_value variable2 12 B 2013 0.35981383 0.35981383
#> 13: change_in_value variable2 13 B 2014 0.40077145 0.40077145
#> 14: change_in_value variable2 14 B 2015 0.11068272 0.11068272
#> 15: change_in_value variable2 15 B 2016 -0.55584113 -0.55584113
#> 16: change_in_value variable2 16 B 2017 1.78691314 1.78691314
#> variable2.x variable2.y variable3.x variable3.y variable4.x variable4.y
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.4978505 -1.07179123 0.89512566 0.89512566 0.77996512 0.77996512
#> 2: -1.9666172 0.30352864 0.87813349 0.87813349 -0.08336907 -0.08336907
#> 3: 0.7013559 0.44820978 0.82158108 0.82158108 0.25331851 0.25331851
#> 4: -0.4727914 0.05300423 0.68864025 0.68864025 -0.02854676 -0.02854676
#> 5: -1.0678237 0.92226747 0.55391765 0.55391765 -0.04287046 -0.04287046
#> 6: -0.2179749 2.05008469 -0.06191171 -0.06191171 1.36860228 1.36860228
#> 7: -1.0260044 -0.49103117 -0.30596266 -0.30596266 -0.22577099 -0.22577099
#> 8: -0.7288912 -2.30916888 -0.38047100 -0.38047100 1.51647060 1.51647060
#> 9: -0.6250393 1.00573852 -0.69470698 -0.69470698 -1.54875280 -1.54875280
#> 10: -1.6866933 -0.70920076 -0.20791728 -0.20791728 0.58461375 0.58461375
#> 11: 0.8377870 -0.68800862 -1.26539635 -1.26539635 0.12385424 0.12385424
#> 12: 0.1533731 1.02557137 2.16895597 2.16895597 0.21594157 0.21594157
#> 13: -1.1381369 -0.28477301 1.20796200 1.20796200 0.37963948 0.37963948
#> 14: 1.2538149 -1.22071771 -1.12310858 -1.12310858 -0.50232345 -0.50232345
#> 15: 0.4264642 0.18130348 -0.40288484 -0.40288484 -0.33320738 -0.33320738
#> 16: -0.2950715 -0.13889136 -0.46665535 -0.46665535 -1.01857538 -1.01857538