I am looking for an easier way to group a specific date range together (i.e., from a specific month, day, and year to the following month, day, year), but I 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 also want to break up the years by seasons, 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")
I know that for my minimal example, if I was just focusing on annual ranges and summaries, then I could use findInterval in group_by (borrowed from here) to summarize for each year. But I generally have around 100 years of data, so I obviously do not want to type 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
However, I also want to be able to get summaries for each season. 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, one main issue is still with the interval of years, as I don't want to have to specify every year. 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, it is just the first winter that is an 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
CodePudding user response：
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
CodePudding user response：
I figured out one possibility by creating a new corr.year column, which essentially takes the date to the beginning of a season, then I simply pull the year. I use the corr.year column to group rather than using findInterval to create a group column. However, I still 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