'R--Filter Dynamically for 10 Quarters

I'd like to filter the following dataset for 10 quarters starting at Q1 2021:

structure(list(Year = c(2002, 2002, 2002, 2002, 2002, 2002, 2002, 
2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 
2002, 2002, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 
2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 
2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 
2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2006, 2006, 2006, 2006, 
2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 
2006, 2006, 2006, 2006, 2006, 2007, 2007, 2007, 2007, 2007, 2007, 
2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 
2007, 2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 
2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 
2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 
2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 
2011, 2011, 2011, 2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 
2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 
2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 
2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 
2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 
2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2021, 2021, 
2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 
2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 
2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 
2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2022, 
2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 
2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 
2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 
2022, 2022, 2022, 2022, 2022, 2022, 2023, 2023, 2023, 2023, 2023, 
2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 
2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 
2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 
2023, 2023, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 
2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024, 2024), Quarter = c(1, 
1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 
1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 
1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 
1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 
2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 
2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 
2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 
2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 
2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 
3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 
3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 
3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 
3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 
3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 
4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 
4, 4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 
4, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 
4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 
2, 2, 2, 2, 2, 2, 2), Name = c("Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_4", "Name_5", "Name_1", 
"Name_2", "Name_3", "Name_4", "Name_5", 
"Name_1", "Name_2", "Name_3", 
"Name_4", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_5", "Name_1", "Name_2", 
"Name_3", "Name_5"), Interest = c(47998744, 
212537486, 64814280, 37572307, 109034562, 49556524, 216656962, 
67422378, 36462961, 109164006, 51507223, 223656450, 69358647, 
36788852, 108511260, 52623907, 227577079, 69263733, 35549413, 
106889709, 50295319, 229185571, 67091600, 34344915, 102522629, 
49971127, 225826369, 66495405, 33535801, 97460593, 50274064, 
226202834, 67190263, 32864610, 96632152, 47551747, 227438418, 
69107153, 33197895, 98853009, 47453558, 228391530, 69060457, 
33177927, 100324962, 48794328, 230840882, 70831022, 34994359, 
102252595, 52438823, 234118523, 74822023, 39033041, 107501508, 
55637302, 238558134, 79151465, 41176714, 110639481, 56838457, 
241091961, 82827744, 42764095, 112471888, 59896375, 249465650, 
86522724, 45693800, 118124916, 64424080, 264794055, 92730606, 
47813490, 126504673, 69621133, 276596719, 99449241, 53535772, 
132391845, 72625569, 289197793, 104812463, 56042668, 136252107, 
78624737, 310106248, 111466739, 61412122, 141782095, 84700269, 
336264984, 121214593, 68733885, 148840778, 91162214, 390085351, 
127134416, 74268369, 152103378, 93241404, 371995994, 129980452, 
76582659, 152821426, 97429725, 397721755, 138700731, 80481391, 
155849329, 103178535, 433151976, 150828708, 83984431, 161570721, 
106680012, 548728287, 157751362, 85890641, 165503764, 107059280, 
507568721, 161144237, 85209212, 164820263, 106560673, 525164898, 
164692016, 83126116, 161238801, 110270377, 541545719, 172978580, 
82493141, 178355555, 109313348, 532993455, 178379372, 82320780, 
182809646, 105357214, 537594410, 177219066, 75627303, 182059782, 
105367952, 535560462, 180519003, 71966173, 177554530, 99747003, 
546456050, 182817902, 69973109, 177555524, 100715828, 547184844, 
182940718, 69789453, 178012144, 95956574, 549336925, 173509891, 
66534437, 160354331, 96190771, 543232004, 169483267, 64884308, 
160881737, 95134642, 546463420, 168842248, 62749109, 161903566, 
92749555, 551732301, 166355764, 60010334, 157190919, 89533742, 
544960916, 160594132, 58165245, 143482598, 87158613, 545410994, 
161260757, 57144888, 159214245, 87648238, 546060788, 159496467, 
57265616, 164614674, 86697821, 547763698, 156585801, 56590640, 
164179764, 85003197, 546144679, 150163953, 56684250, 163324519, 
83624129, 550320919, 145741132, 56114920, 161319060, 84163890, 
560860777, 142068134, 55822533, 161524303, 83239461, 585336903, 
141165446, 54914806, 161632246, 79744782, 582252621, 132130313, 
53225913, 161417403, 82403949, 590039449, 129564763, 52776014, 
162661393, 81562433, 617692070, 130694750, 53956405, 166072140, 
82588700, 643354560, 132674028, 55234558, 169936271, 81707450, 
655269505, 132345649, 55488142, 173090418, 82045816, 677897278, 
136925462, 56317096, 178501279, 83916966, 716102802, 143639461, 
58760485, 185896142, 86874529, 748760759, 145622820, 61046304, 
191893202, 87392290, 766689144, 142172063, 61964297, 195009870, 
89140238, 798531675, 143142507, 62500892, 199339368, 92075059, 
838676574, 150617311, 64686207, 206231765, 95540407, 860373206, 
154030578, 66809360, 212321830, 97194055, 885988466, 156253436, 
68624143, 214676929, 96694854, 904886941, 160716741, 69409108, 
217874049, 102181929, 944422431, 166327020, 71992965, 224043376, 
104722463, 970520293, 173068148, 73286680, 229298476, 107043192, 
997946123, 181255627, 74891148, 233062712, 112248704, 1027741773, 
193133252, 78176911, 238941791, 118250258, 1085842722, 201636182, 
82731718, 248285391, 122401490, 1127438539, 202227834, 85984563, 
253962329, 126410678, 1156040869, 208064951, 88510665, 258676271, 
131236379, 1195206293, 214386947, 92425388, 264956579, 133919705, 
1270131528, 224782680, 99110667, 273186639, 145402910, 1317693906, 
230569868, 104595945, 279817998, 148602044, 1358739813, 232185680, 
107810710, 286191496, 153122947, 1399286409, 234690849, 111956925, 
293194461, 159946667, 1454752604, 239544457, 116886300, 301794643, 
159270183, 1476517177, 243117648, 120249984, 305060190, 158312179, 
1497511830, 250034915, 142187491, 305642292, 146456219, 1465748312, 
239411402, 134415282, 292374098, 139703152, 1443930398, 237160902, 
130707028, 300831349, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 134551883, 
1357118303, 240093771, 127742162, 299126264, 125634812, 1422191515, 
230671670, 121656245, 293007695, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0)), row.names = c(NA, -533L), class = c("tbl_df", "tbl", 
"data.frame"))
rdplyr


Solution 1:[1]

Three ways come to mind.

  1. Simply filter.

    library(dplyr)
filtered_data <- filter(zz, Year %in% 2021:2022 | (Year == 2023 & Quarter %in% 1:2))

    Demonstration that it limited to just 10 rows:

    filtered_data <- dplyr::filter(zz, Year %in% 2021:2022 | (Year == 2023 & Quarter %in% 1:2))
count(filtered_data, Year, Quarter)
# # A tibble: 10 x 3
#     Year Quarter     n
#    <dbl>   <dbl> <int>
#  1  2021       1    15
#  2  2021       2    10
#  3  2021       3    10
#  4  2021       4    10
#  5  2022       1    10
#  6  2022       2    10
#  7  2022       3    10
#  8  2022       4    10
#  9  2023       1    10
# 10  2023       2    10

  2. Left-join with a frame that includes just the years/quarters you want. This method works better than above if you have non-continuous filtering requirements.

    I_need <- tibble(Year=c(rep(2021,4),rep(2022,4),rep(2023,2)), Quarter=c(1:4,1:4,1:2))
I_need
# # A tibble: 10 x 2
#     Year Quarter
#    <dbl>   <int>
#  1  2021       1
#  2  2021       2
#  3  2021       3
#  4  2021       4
#  5  2022       1
#  6  2022       2
#  7  2022       3
#  8  2022       4
#  9  2023       1
# 10  2023       2
filtered_data2 <- left_join(I_need, zz, by = c("Year", "Quarter"))

  3. Math wizardry, prone to all sorts of problems/mistakes/missed-assumptions.

    filtered_data2 <- filter(zz, between(Year + (Quarter-1)/4, 2021, 2023.25))

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution Source
Solution 1

Related Questions

R - Gamma distribution - Linear Mixed-Effects Model - lmer/glmer errors

R - Gamma distribution - Linear Mixed-Effects Model - lmer/glmer errors

R - Gamma distribution - Linear Mixed-Effects Model - lmer/glmer errors

R - Gamma distribution - Linear Mixed-Effects Model - lmer/glmer errors

Error when using DiceKriging function km: 'Error in t.default(T) : argument is not a matrix'

How to connect SQL Server with Rstudio?

[Shiny]: Add link to another tabPanel in another tabPanel

Calculate correlation with cor(), only for numerical columns

Trying to be benchmark dplyr vs data.table

Does a plug-in selector bivariate kernel density estimator with weights exist for python?

predict() and resid() for lavaan.survey

python(or numpy) equivalent of match in R

ggplot2: fill color behaviour of geom_ribbon

Grouped bar plot in ggplot

How to reactively change title of ShinyDashboard box in R?