How to group by a custom date (month, day, year) range and create seasons across the time group and get summary?

Question

I am looking for an easy way to group by a specific date range (i.e., from a specific month, day, and year to the following month, day, year). I also need that same date range to repeat every year (e.g., 1980-06-01 to 1981-05-31 then 1981-06-01 to 1982-05-31, etc.). Further, I want to define seasons for each year, where winter will occur across years (e.g., December 1980 to February 1981). Here is an example:

Data

df <- structure(list(date = structure(c(3652, 3653, 3654, 3655, 3656, 
3657, 3658, 3659, 3660, 3661, 3662, 3663, 3664, 3665, 3666, 3667, 
3668, 3669, 3670, 3671, 3672, 3673, 3674, 3675, 3676, 3677, 3678, 
3679, 3680, 3681, 3682, 3683, 3684, 3685, 3686, 3687, 3688, 3689, 
3690, 3691, 3692, 3693, 3694, 3695, 3696, 3697, 3698, 3699, 3700, 
3701, 3702, 3703, 3704, 3705, 3706, 3707, 3708, 3709, 3710, 3711, 
3712, 3713, 3714, 3715, 3716, 3717, 3718, 3719, 3720, 3721, 3722, 
3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 
3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741, 3742, 3743, 3744, 
3745, 3746, 3747, 3748, 3749, 3750, 3751, 3752, 3753, 3754, 3755, 
3756, 3757, 3758, 3759, 3760, 3761, 3762, 3763, 3764, 3765, 3766, 
3767, 3768, 3769, 3770, 3771, 3772, 3773, 3774, 3775, 3776, 3777, 
3778, 3779, 3780, 3781, 3782, 3783, 3784, 3785, 3786, 3787, 3788, 
3789, 3790, 3791, 3792, 3793, 3794, 3795, 3796, 3797, 3798, 3799, 
3800, 3801, 3802, 3803, 3804, 3805, 3806, 3807, 3808, 3809, 3810, 
3811, 3812, 3813, 3814, 3815, 3816, 3817, 3818, 3819, 3820, 3821, 
3822, 3823, 3824, 3825, 3826, 3827, 3828, 3829, 3830, 3831, 3832, 
3833, 3834, 3835, 3836, 3837, 3838, 3839, 3840, 3841, 3842, 3843, 
3844, 3845, 3846, 3847, 3848, 3849, 3850, 3851, 3852, 3853, 3854, 
3855, 3856, 3857, 3858, 3859, 3860, 3861, 3862, 3863, 3864, 3865, 
3866, 3867, 3868, 3869, 3870, 3871, 3872, 3873, 3874, 3875, 3876, 
3877, 3878, 3879, 3880, 3881, 3882, 3883, 3884, 3885, 3886, 3887, 
3888, 3889, 3890, 3891, 3892, 3893, 3894, 3895, 3896, 3897, 3898, 
3899, 3900, 3901, 3902, 3903, 3904, 3905, 3906, 3907, 3908, 3909, 
3910, 3911, 3912, 3913, 3914, 3915, 3916, 3917, 3918, 3919, 3920, 
3921, 3922, 3923, 3924, 3925, 3926, 3927, 3928, 3929, 3930, 3931, 
3932, 3933, 3934, 3935, 3936, 3937, 3938, 3939, 3940, 3941, 3942, 
3943, 3944, 3945, 3946, 3947, 3948, 3949, 3950, 3951, 3952, 3953, 
3954, 3955, 3956, 3957, 3958, 3959, 3960, 3961, 3962, 3963, 3964, 
3965, 3966, 3967, 3968, 3969, 3970, 3971, 3972, 3973, 3974, 3975, 
3976, 3977, 3978, 3979, 3980, 3981, 3982, 3983, 3984, 3985, 3986, 
3987, 3988, 3989, 3990, 3991, 3992, 3993, 3994, 3995, 3996, 3997, 
3998, 3999, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 
4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 
4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 
4031, 4032, 4033, 4034, 4035, 4036, 4037, 4038, 4039, 4040, 4041, 
4042, 4043, 4044, 4045, 4046, 4047, 4048, 4049, 4050, 4051, 4052, 
4053, 4054, 4055, 4056, 4057, 4058, 4059, 4060, 4061, 4062, 4063, 
4064, 4065, 4066, 4067, 4068, 4069, 4070, 4071, 4072, 4073, 4074, 
4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082, 4083, 4084, 4085, 
4086, 4087, 4088, 4089, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 
4097, 4098, 4099, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 
4108, 4109, 4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 
4119, 4120, 4121, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 
4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 
4141, 4142, 4143, 4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 
4152, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 
4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 4172, 4173, 
4174, 4175, 4176, 4177, 4178, 4179, 4180, 4181, 4182, 4183, 4184, 
4185, 4186, 4187, 4188, 4189, 4190, 4191, 4192, 4193, 4194, 4195, 
4196, 4197, 4198, 4199, 4200, 4201, 4202, 4203, 4204, 4205, 4206, 
4207, 4208, 4209, 4210, 4211, 4212, 4213, 4214, 4215, 4216, 4217, 
4218, 4219, 4220, 4221, 4222, 4223, 4224, 4225, 4226, 4227, 4228, 
4229, 4230, 4231, 4232, 4233, 4234, 4235, 4236, 4237, 4238, 4239, 
4240, 4241, 4242, 4243, 4244, 4245, 4246, 4247, 4248, 4249, 4250, 
4251, 4252, 4253, 4254, 4255, 4256, 4257, 4258, 4259, 4260, 4261, 
4262, 4263, 4264, 4265, 4266, 4267, 4268, 4269, 4270, 4271, 4272, 
4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281, 4282, 4283, 
4284, 4285, 4286, 4287, 4288, 4289, 4290, 4291, 4292, 4293, 4294, 
4295, 4296, 4297, 4298, 4299, 4300, 4301, 4302, 4303, 4304, 4305, 
4306, 4307, 4308, 4309, 4310, 4311, 4312, 4313, 4314, 4315, 4316, 
4317, 4318, 4319, 4320, 4321, 4322, 4323, 4324, 4325, 4326, 4327, 
4328, 4329, 4330, 4331, 4332, 4333, 4334, 4335, 4336, 4337, 4338, 
4339, 4340, 4341, 4342, 4343, 4344, 4345, 4346, 4347, 4348, 4349, 
4350, 4351, 4352, 4353, 4354, 4355, 4356, 4357, 4358, 4359, 4360, 
4361, 4362, 4363, 4364, 4365, 4366, 4367, 4368, 4369, 4370, 4371, 
4372, 4373, 4374, 4375, 4376, 4377, 4378, 4379, 4380, 4381, 4382, 
4383, 4384, 4385, 4386, 4387, 4388, 4389, 4390, 4391, 4392, 4393, 
4394, 4395, 4396, 4397, 4398, 4399, 4400, 4401, 4402, 4403, 4404, 
4405, 4406, 4407, 4408, 4409, 4410, 4411, 4412, 4413, 4414, 4415, 
4416, 4417, 4418, 4419, 4420, 4421, 4422, 4423, 4424, 4425, 4426, 
4427, 4428, 4429, 4430, 4431, 4432, 4433, 4434, 4435, 4436, 4437, 
4438, 4439, 4440, 4441, 4442, 4443, 4444, 4445, 4446, 4447, 4448, 
4449, 4450, 4451, 4452, 4453, 4454, 4455, 4456, 4457, 4458, 4459, 
4460, 4461, 4462, 4463, 4464, 4465, 4466, 4467, 4468, 4469, 4470, 
4471, 4472, 4473, 4474, 4475, 4476, 4477, 4478, 4479, 4480, 4481, 
4482, 4483, 4484, 4485, 4486, 4487, 4488, 4489, 4490, 4491, 4492, 
4493, 4494, 4495, 4496, 4497, 4498, 4499, 4500, 4501, 4502, 4503, 
4504, 4505, 4506, 4507, 4508, 4509, 4510, 4511, 4512, 4513, 4514, 
4515, 4516, 4517, 4518, 4519, 4520, 4521, 4522, 4523, 4524, 4525, 
4526, 4527, 4528, 4529, 4530, 4531, 4532, 4533, 4534, 4535, 4536, 
4537, 4538, 4539, 4540, 4541, 4542, 4543, 4544, 4545, 4546, 4547, 
4548, 4549, 4550, 4551, 4552, 4553, 4554, 4555, 4556, 4557, 4558, 
4559, 4560, 4561, 4562, 4563, 4564, 4565, 4566, 4567, 4568, 4569, 
4570, 4571, 4572, 4573, 4574, 4575, 4576, 4577, 4578, 4579, 4580, 
4581, 4582, 4583, 4584, 4585, 4586, 4587, 4588, 4589, 4590, 4591, 
4592, 4593, 4594, 4595, 4596, 4597, 4598, 4599, 4600, 4601, 4602, 
4603, 4604, 4605, 4606, 4607, 4608, 4609, 4610, 4611, 4612, 4613, 
4614, 4615, 4616, 4617, 4618, 4619, 4620, 4621, 4622, 4623, 4624, 
4625, 4626, 4627, 4628, 4629, 4630, 4631, 4632, 4633, 4634, 4635, 
4636, 4637, 4638, 4639, 4640, 4641, 4642, 4643, 4644, 4645, 4646, 
4647, 4648, 4649, 4650, 4651, 4652, 4653, 4654, 4655, 4656, 4657, 
4658, 4659, 4660, 4661, 4662, 4663, 4664, 4665, 4666, 4667, 4668, 
4669, 4670, 4671, 4672, 4673, 4674, 4675, 4676, 4677, 4678, 4679, 
4680, 4681, 4682, 4683, 4684, 4685, 4686, 4687, 4688, 4689, 4690, 
4691, 4692, 4693, 4694, 4695, 4696, 4697, 4698, 4699, 4700, 4701, 
4702, 4703, 4704, 4705, 4706, 4707, 4708, 4709, 4710, 4711, 4712, 
4713, 4714, 4715, 4716, 4717, 4718, 4719, 4720, 4721, 4722, 4723, 
4724, 4725, 4726, 4727, 4728, 4729, 4730, 4731, 4732, 4733, 4734, 
4735, 4736, 4737, 4738, 4739, 4740, 4741, 4742, 4743, 4744, 4745, 
4746, 4747), class = "Date"), climate = c(39L, 4L, 7L, 8L, 87L, 
8L, 12L, 9L, 26L, 69L, 18L, 89L, 53L, 47L, 70L, 94L, 26L, 51L, 
93L, 91L, 71L, 32L, 21L, 35L, 2L, 12L, 97L, 50L, 58L, 55L, 93L, 
8L, 99L, 97L, 25L, 44L, 60L, 81L, 3L, 66L, 64L, 26L, 61L, 53L, 
97L, 7L, 54L, 78L, 62L, 30L, 28L, 82L, 23L, 81L, 39L, 66L, 62L, 
13L, 77L, 8L, 22L, 17L, 89L, 6L, 16L, 6L, 3L, 99L, 57L, 23L, 
15L, 14L, 7L, 78L, 24L, 50L, 83L, 86L, 44L, 25L, 92L, 63L, 87L, 
68L, 42L, 5L, 24L, 61L, 22L, 79L, 44L, 92L, 74L, 48L, 90L, 1L, 
96L, 28L, 97L, 88L, 56L, 83L, 92L, 66L, 76L, 23L, 95L, 26L, 34L, 
93L, 8L, 24L, 40L, 90L, 85L, 18L, 55L, 71L, 81L, 12L, 97L, 35L, 
69L, 55L, 53L, 94L, 26L, 85L, 22L, 87L, 25L, 31L, 8L, 1L, 7L, 
72L, 65L, 62L, 5L, 49L, 75L, 40L, 38L, 22L, 57L, 94L, 13L, 55L, 
99L, 64L, 90L, 56L, 65L, 83L, 73L, 69L, 9L, 14L, 28L, 49L, 1L, 
38L, 55L, 78L, 58L, 48L, 55L, 65L, 37L, 85L, 75L, 84L, 84L, 59L, 
19L, 62L, 37L, 23L, 74L, 94L, 51L, 31L, 64L, 24L, 42L, 24L, 76L, 
71L, 95L, 21L, 18L, 67L, 53L, 38L, 60L, 97L, 8L, 80L, 67L, 1L, 
28L, 91L, 73L, 99L, 60L, 68L, 12L, 52L, 28L, 32L, 82L, 10L, 58L, 
4L, 56L, 49L, 72L, 90L, 16L, 92L, 38L, 32L, 77L, 69L, 18L, 68L, 
55L, 78L, 56L, 36L, 21L, 21L, 91L, 63L, 10L, 2L, 97L, 16L, 56L, 
89L, 9L, 53L, 23L, 28L, 28L, 41L, 72L, 42L, 10L, 97L, 100L, 79L, 
62L, 28L, 64L, 15L, 25L, 42L, 29L, 26L, 70L, 12L, 98L, 93L, 92L, 
12L, 38L, 57L, 37L, 51L, 8L, 38L, 71L, 42L, 37L, 76L, 21L, 2L, 
3L, 30L, 76L, 78L, 48L, 46L, 22L, 84L, 36L, 9L, 65L, 7L, 62L, 
51L, 38L, 31L, 18L, 53L, 1L, 89L, 57L, 19L, 37L, 57L, 66L, 65L, 
39L, 46L, 70L, 86L, 50L, 92L, 99L, 10L, 74L, 33L, 52L, 37L, 45L, 
85L, 6L, 77L, 54L, 97L, 98L, 98L, 65L, 39L, 3L, 6L, 58L, 13L, 
29L, 69L, 32L, 59L, 8L, 12L, 18L, 14L, 39L, 73L, 42L, 62L, 70L, 
75L, 57L, 65L, 77L, 88L, 40L, 91L, 6L, 20L, 27L, 67L, 45L, 14L, 
57L, 1L, 73L, 40L, 14L, 40L, 95L, 46L, 78L, 11L, 69L, 22L, 41L, 
49L, 47L, 99L, 2L, 20L, 8L, 91L, 11L, 85L, 18L, 73L, 22L, 2L, 
64L, 12L, 43L, 34L, 8L, 38L, 30L, 55L, 31L, 49L, 1L, 66L, 76L, 
49L, 89L, 74L, 62L, 70L, 23L, 26L, 42L, 81L, 49L, 26L, 70L, 75L, 
78L, 18L, 51L, 84L, 47L, 39L, 24L, 31L, 51L, 11L, 96L, 46L, 46L, 
78L, 53L, 82L, 5L, 77L, 18L, 6L, 45L, 92L, 22L, 72L, 100L, 6L, 
41L, 1L, 9L, 84L, 19L, 31L, 8L, 96L, 24L, 98L, 60L, 43L, 50L, 
27L, 4L, 75L, 83L, 48L, 10L, 8L, 85L, 39L, 50L, 27L, 43L, 8L, 
83L, 92L, 70L, 84L, 78L, 51L, 67L, 26L, 63L, 43L, 90L, 60L, 6L, 
27L, 9L, 58L, 63L, 29L, 5L, 77L, 99L, 72L, 17L, 22L, 40L, 92L, 
22L, 11L, 70L, 94L, 31L, 93L, 6L, 93L, 62L, 84L, 62L, 21L, 62L, 
13L, 35L, 47L, 29L, 12L, 57L, 100L, 6L, 30L, 14L, 23L, 78L, 72L, 
39L, 55L, 71L, 56L, 76L, 37L, 54L, 19L, 58L, 94L, 78L, 64L, 100L, 
44L, 46L, 40L, 47L, 23L, 88L, 70L, 24L, 38L, 27L, 35L, 53L, 64L, 
31L, 50L, 16L, 76L, 94L, 73L, 10L, 73L, 78L, 96L, 42L, 5L, 87L, 
64L, 14L, 86L, 9L, 59L, 27L, 48L, 24L, 79L, 77L, 25L, 19L, 43L, 
46L, 14L, 39L, 48L, 28L, 29L, 73L, 4L, 79L, 58L, 92L, 30L, 30L, 
51L, 35L, 11L, 36L, 33L, 35L, 43L, 66L, 19L, 66L, 48L, 44L, 90L, 
75L, 32L, 2L, 64L, 46L, 86L, 55L, 16L, 35L, 59L, 51L, 56L, 9L, 
84L, 90L, 23L, 17L, 45L, 20L, 3L, 7L, 43L, 46L, 26L, 52L, 75L, 
33L, 22L, 96L, 14L, 45L, 1L, 4L, 92L, 69L, 85L, 21L, 20L, 100L, 
28L, 8L, 77L, 29L, 77L, 55L, 72L, 42L, 52L, 79L, 51L, 46L, 21L, 
61L, 19L, 21L, 49L, 96L, 33L, 61L, 33L, 69L, 62L, 91L, 58L, 3L, 
11L, 66L, 89L, 59L, 22L, 50L, 75L, 3L, 82L, 1L, 26L, 53L, 47L, 
94L, 57L, 10L, 81L, 98L, 19L, 38L, 92L, 52L, 91L, 6L, 42L, 76L, 
39L, 21L, 89L, 19L, 3L, 37L, 93L, 61L, 35L, 18L, 90L, 25L, 48L, 
73L, 24L, 51L, 58L, 1L, 58L, 8L, 8L, 92L, 51L, 85L, 17L, 68L, 
98L, 64L, 60L, 64L, 65L, 3L, 23L, 76L, 84L, 24L, 59L, 40L, 58L, 
43L, 21L, 3L, 17L, 36L, 90L, 86L, 41L, 65L, 99L, 12L, 11L, 64L, 
63L, 24L, 46L, 79L, 97L, 70L, 12L, 1L, 77L, 46L, 17L, 15L, 86L, 
4L, 32L, 10L, 67L, 95L, 68L, 67L, 21L, 69L, 2L, 66L, 29L, 80L, 
42L, 76L, 85L, 62L, 56L, 4L, 36L, 42L, 73L, 100L, 90L, 99L, 15L, 
46L, 37L, 78L, 29L, 35L, 14L, 27L, 23L, 16L, 56L, 57L, 87L, 57L, 
80L, 89L, 63L, 63L, 37L, 80L, 71L, 43L, 12L, 39L, 51L, 4L, 66L, 
91L, 84L, 8L, 88L, 33L, 24L, 87L, 54L, 65L, 5L, 31L, 61L, 86L, 
1L, 2L, 85L, 66L, 82L, 24L, 80L, 78L, 22L, 54L, 72L, 64L, 61L, 
91L, 64L, 16L, 38L, 50L, 27L, 47L, 43L, 54L, 79L, 64L, 37L, 6L, 
32L, 94L, 32L, 43L, 99L, 13L, 86L, 32L, 85L, 94L, 99L, 26L, 19L, 
11L, 15L, 20L, 38L, 58L, 73L, 6L, 7L, 93L, 56L, 48L, 28L, 70L, 
8L, 53L, 90L, 36L, 95L, 96L, 98L, 98L, 57L, 40L, 47L, 26L, 50L, 
82L, 98L, 69L, 22L, 52L, 86L, 15L, 99L, 76L, 35L, 46L, 87L, 14L, 
40L, 68L, 29L, 58L, 32L, 26L, 68L, 68L, 62L, 95L, 6L, 65L, 16L, 
64L, 88L, 52L, 89L, 55L, 86L, 86L, 41L, 39L, 62L, 65L, 36L, 40L, 
87L, 46L, 66L, 88L, 25L, 49L, 58L, 44L, 1L, 36L, 48L, 56L, 64L, 
92L, 94L, 8L, 61L, 84L, 90L, 38L, 42L, 1L, 69L, 98L, 51L, 64L, 
80L, 79L, 7L, 33L, 33L, 20L, 35L, 63L, 56L, 96L, 1L, 93L, 30L, 
39L, 86L, 78L, 18L, 20L, 59L, 80L, 100L, 100L, 11L, 39L, 72L, 
55L, 4L, 42L, 50L, 77L, 64L, 67L, 40L, 100L, 6L, 9L, 67L, 67L, 
41L, 57L, 68L, 90L, 38L, 63L, 53L, 85L, 52L, 86L, 49L, 43L, 17L, 
35L, 45L, 66L, 5L, 100L, 51L, 30L, 95L, 43L, 94L, 87L, 88L, 23L, 
49L, 67L, 5L, 78L, 4L, 8L, 30L, 42L, 18L, 38L, 13L, 1L, 68L, 
23L, 27L, 86L, 72L, 39L, 80L, 5L, 87L, 12L, 40L, 43L, 65L, 33L, 
42L, 23L, 87L, 19L, 44L, 84L, 58L, 18L, 20L, 49L, 50L, 70L, 99L, 
92L, 91L, 25L, 62L, 73L, 43L, 83L, 47L, 38L, 86L, 58L, 7L, 15L, 
85L, 38L, 87L, 97L, 69L, 59L, 9L, 9L, 29L, 99L, 35L, 87L, 19L, 
2L, 52L, 44L, 81L, 73L, 10L, 83L, 89L, 17L, 58L, 57L, 14L, 42L, 
77L, 73L, 47L, 71L, 25L, 96L, 11L, 31L, 6L, 47L, 15L, 71L, 75L
)), row.names = c(NA, -1096L), class = "data.frame")

For my example, if I was calculating annual summaries, then I could use findInterval in group_by (borrowed from here) to summarize for each year. However, I generally have around 100 years of data, so I do not want to write out every interval.

library(tidyverse)

df %>%
  group_by(dr = findInterval(date, as.Date(c("1980-06-01", "1981-06-01", "1982-06-01")))) %>% 
  summarise(climate = sum(climate))

     dr climate
  <int>   <int>
1     0    7665
2     1   17734
3     2   18172
4     3   11153

Additionally, I want to get summaries for each season per year. So, I could do something like this:

df %>%
  mutate(dr = findInterval(date, as.Date(
    c("1980-06-01", "1981-06-01", "1982-06-01")
  ))) %>%
  separate("date", c("year", "month", "day"), sep = "-") %>%
  mutate(
    month = str_replace(month, "^0+" , ""),
    season = case_when(
      month %in% c(3:5) ~ "spring",
      month %in% c(6:8) ~ "summer",
      month %in% c(9:11) ~ "fall",
      month %in% c(12, 1:2) ~ "winter"
    )
  ) %>%
  group_by(dr, season) %>%
  summarise(year = first(year), climate = sum(climate)) %>%
  ungroup() %>%
  select(dr, year, season, climate)

# A tibble: 13 × 4
# Groups:   dr [4]
      dr year  season climate
   <int> <chr> <chr>    <int>
 1     0 1980  spring    4744
 2     0 1980  winter    2921
 3     1 1980  fall      4392
 4     1 1981  spring    4456
 5     1 1980  summer    4687
 6     1 1980  winter    4199
 7     2 1981  fall      4330
 8     2 1982  spring    4870
 9     2 1981  summer    4420
10     2 1981  winter    4552
11     3 1982  fall      4622
12     3 1982  summer    4995
13     3 1982  winter    1536

However, the main issue is still with the interval of years. I also want the output to be in the same order as it was in the original dataframe. Note: Since winter is December through February, the year should be assigned according to the year for December. So, in my example data, although the data started in January 1980, I need to actually have it as 1979 since winter started in that year. So, here, the first winter is the only one with that issue.

Expected Output

      dr year  season climate
   <int> <chr> <chr>    <int>
 1     0 1979  winter    2921
 2     0 1980  spring    4744
 3     1 1980  summer    4687
 4     1 1980  fall      4392
 5     1 1980  winter    4199
 6     1 1981  spring    4456
 7     2 1981  summer    4420
 8     2 1981  fall      4330
 9     2 1981  winter    4552
10     2 1982  spring    4870
11     3 1982  summer    4995
12     3 1982  fall      4622
13     3 1982  winter    1536

Answer 1

One possibility is to create a new corr.year column, which determines what year that the beginning of a season is in. I use the corr.year column to group rather than using findInterval to create a group column. However, I feel like there must be a more efficient way.

library(tidyverse)
library(lubridate)

df %>%
  mutate(corr.year=(date - days(59))  %>% floor_date("year")  %>% year()) %>%
  separate("date", c("year", "month", "day"), sep = "-") %>%
  mutate(
    month = str_replace(month, "^0+" , ""),
    season = case_when(
      month %in% c(3:5) ~ "spring",
      month %in% c(6:8) ~ "summer",
      month %in% c(9:11) ~ "fall",
      month %in% c(12, 1:2) ~ "winter"
    )
  ) %>%
  group_by(corr.year, season) %>%
  summarise(corr.year = first(corr.year), climate = sum(climate)) %>%
  ungroup(season) %>% 
  arrange(corr.year, match(season, c("spring", "summer", "fall", "winter")))

Output

# A tibble: 13 × 3
# Groups:   corr.year [4]
   corr.year season climate
       <dbl> <chr>    <int>
 1      1979 winter    2913
 2      1980 spring    4744
 3      1980 summer    4687
 4      1980 fall      4392
 5      1980 winter    4207
 6      1981 spring    4456
 7      1981 summer    4420
 8      1981 fall      4330
 9      1981 winter    4552
10      1982 spring    4870
11      1982 summer    4995
12      1982 fall      4622
13      1982 winter    1536

Answer 2

This first identifies the day 2 months prior, which shifts everything into the year and section of the year it's season begins in. Then it defines an ordered factor with the name of the season in order starting from the spring. count(x, wt = y, name = "name") is a shortcut for group_by(x) %>% summarize(name = sum(y)) so it works here for summing the climate numbers by year & season.

library(lubridate)
df %>%
  mutate(date_2mo_ago = date %m-% months(2),
         season_num = quarter(date_2mo_ago),
         season = c("spring", "summer", "fall", "winter")[season_num] %>%
           forcats::fct_reorder(season_num),
         year   = year(date_2mo_ago)) %>%
  count(year, season, wt = climate, name = "climate")

Result

   year season climate
1  1979 winter    2921
2  1980 spring    4744
3  1980 summer    4687
4  1980   fall    4392
5  1980 winter    4199
6  1981 spring    4456
7  1981 summer    4420
8  1981   fall    4330
9  1981 winter    4552
10 1982 spring    4870
11 1982 summer    4995
12 1982   fall    4622
13 1982 winter    1536