Rearranging dataset to make plot with ggplot for specific time-period

Question

I have a long data.frame with daily precipitation values. What I want to do is:

1. Select a specific time-frame
2. Group and plot the average values by day and month

To select a specific time-period I've used the openair package:

library(openair)
filtered <- selectByDate(mydata, year = 2017:2020, month = c(9, 10, 11, 12, 1, 2, 3))

Which gives this:

   
           date value
  1: 2017-01-01  0.04
  2: 2017-01-02  0.08
  3: 2017-01-03  8.83
  4: 2017-01-04  1.51
  5: 2017-01-05  7.71
 ---                 
845: 2020-12-27  0.82
846: 2020-12-28  2.29
847: 2020-12-29  2.29
848: 2020-12-30  0.03
849: 2020-12-31  2.36
         

However, I need dates in Day-Month format, so I did:

dm.date <- format(filtered$date, "%d-%m")
filtered$dm <- factor(dm.date) 

And got:

         date value    dm
1: 2017-01-01  0.04 01-01
2: 2017-01-02  0.08 02-01
3: 2017-01-03  8.83 03-01
4: 2017-01-04  1.51 04-01
5: 2017-01-05  7.71 05-01
6: 2017-01-06  1.62 06-01 

What I want to do is summarize the information for each day and month (DD-MM), the dm column in my new dataset. So I've used dplyr's group_by:

library(dplyr)
data.new <- filtered %>% group_by(dm) %>% summarise(avg = mean(value))
data.new

       dm     avg
1   01-01  2.6400
2   01-02  1.9575
3   01-03 11.4300
4   01-09  0.8150
5   01-10  3.0200
6   01-11  4.8975
7   01-12 34.0525
8   02-01  6.2275
9   02-02  8.6775
10  02-03  9.8800
11  02-09  0.1150
12  02-10  2.1925
13  02-11  1.2000
14  02-12 12.2425
15  03-01  6.7125
16  03-02 14.9300
17  03-03  4.8825
18  03-09  0.1250
19  03-10  1.5175
20  03-11  1.5275
21  03-12  5.3400
22  04-01  9.7525
23  04-02 13.5575
24  04-03  3.9450
25  04-09  0.4975
26  04-10  1.4725
27  04-11 11.8100
28  04-12  6.7850
29  05-01  9.2700
30  05-02 16.5575
31  05-03  2.6850
32  05-09  0.3225
33  05-10  0.6150
34  05-11  3.0500
35  05-12  9.5600
36  06-01  2.3375
37  06-02 16.0350
38  06-03  3.7325
39  06-09  0.0825
40  06-10  0.4375
41  06-11  3.4400
42  06-12 11.6975
43  07-01  7.2575
44  07-02  7.1350
45  07-03  3.2650
46  07-09  0.0125
47  07-10  1.1125
48  07-11  6.7450
49  07-12 16.1550
50  08-01  4.9225
51  08-02  2.0500
52  08-03  8.9175
53  08-09  0.0075
54  08-10  3.2425
55  08-11 18.9150
56  08-12  8.2575
57  09-01  0.5700
58  09-02  1.9050
59  09-03  2.3925
60  09-09  0.0000
61  09-10  5.3925
62  09-11 11.2500
63  09-12  4.1200
64  10-01  2.9100
65  10-02  5.6975
66  10-03 11.8350
67  10-09  0.0375
68  10-10  2.5375
69  10-11  7.2300
70  10-12  6.0800
71  11-01  2.0850
72  11-02 13.2200
73  11-03  6.9900
74  11-09  0.0700
75  11-10  0.7775
76  11-11 10.1525
77  11-12  7.2675
78  12-01  4.9575
79  12-02  5.9300
80  12-03  1.7375
81  12-09  0.2600
82  12-10  0.9650
83  12-11  8.5350
84  12-12  5.3500
85  13-01  8.0350
86  13-02 12.4100
87  13-03  2.5500
88  13-09  0.0250
89  13-10  0.9425
90  13-11  5.0075
91  13-12  4.6200
92  14-01  9.2025
93  14-02  1.8525
94  14-03  2.9325
95  14-09  8.0950
96  14-10  5.1650
97  14-11  4.9600
98  14-12  0.8575
99  15-01  5.1700
100 15-02  2.5900
101 15-03  1.9800
102 15-09  2.6225
103 15-10  5.5625
104 15-11  7.4550
105 15-12  2.0725
106 16-01  7.7775
107 16-02 10.3700
108 16-03  5.9400
109 16-09  0.2925
110 16-10  1.1475
111 16-11  9.3025
112 16-12  2.8100
113 17-01  6.5500
114 17-02  9.6000
115 17-03  7.7925
116 17-09  3.4125
117 17-10  2.7100
118 17-11  1.7425
119 17-12  6.2200
120 18-01  4.0150
121 18-02  3.6025
122 18-03  4.8150
123 18-09  1.0450
124 18-10  3.2725
125 18-11 11.6025
126 18-12  2.9650
127 19-01  1.5075
128 19-02  3.2500
129 19-03 12.1250
130 19-09  0.2450
131 19-10  5.6000
132 19-11 16.7350
133 19-12  3.4350
134 20-01  3.6975
135 20-02  2.1825
136 20-03  6.1700
137 20-09  5.0525
138 20-10  0.5350
139 20-11 14.2575
140 20-12  2.5000
141 21-01  1.6850
142 21-02  6.4650
143 21-03  9.8800
144 21-09  3.9850
145 21-10  4.8875
146 21-11  5.6325
147 21-12  6.4300
148 22-01  4.6950
149 22-02  8.7150
150 22-03  5.7250
151 22-09  2.0300
152 22-10  6.4875
153 22-11 11.7175
154 22-12 12.5875
155 23-01  7.8350
156 23-02  5.0875
157 23-03  1.4150
158 23-09  0.4300
159 23-10  3.0675
160 23-11 12.5700
161 23-12 20.4850
162 24-01 23.4325
163 24-02  3.7100
164 24-03  0.3150
165 24-09  0.4125
166 24-10  4.0375
167 24-11  4.2075
168 24-12 16.4575
169 25-01  3.0150
170 25-02  6.2950
171 25-03  0.3500
172 25-09  3.8625
173 25-10  9.7725
174 25-11  4.8350
175 25-12 10.5150
176 26-01  1.1850
177 26-02  7.8750
178 26-03  0.2625
179 26-09  7.8450
180 26-10  6.4100
181 26-11  1.1125
182 26-12  9.3075
183 27-01  0.7025
184 27-02 14.1325
185 27-03  0.3975
186 27-09  1.6325
187 27-10  5.0350
188 27-11  6.5425
189 27-12  7.8400
190 28-01  2.4850
191 28-02 13.2125
192 28-03  1.9425
193 28-09  1.7775
194 28-10  5.6000
195 28-11  4.9150
196 28-12  6.2075
197 29-01  7.4350
198 29-02 15.9200
199 29-03  7.2550
200 29-09  1.4950
201 29-10  6.2625
202 29-11  1.8150
203 29-12  3.1350
204 30-01  6.4025
205 30-03  2.0925
206 30-09  6.9225
207 30-10 11.1275
208 30-11  7.5550
209 30-12  2.5000
210 31-01  1.6925
211 31-03  4.7750
212 31-10  5.4025
213 31-12  7.5950

Here I'm having two big troubles:

1) My approach returns the data.new dataset with dates ordered. For example, all first days of each month, then all second days, and so on... What I want is the dm column to be: 01-01, 02-01, 03-01, ... 30-12, 31-12.

2)

I want to use ggplot2 to make a plot representing the averaged values across a time-specific season, starting from September 1st (01-09) and ending in March 31 (31-03) representing for example spring/summer days, which means the average of values in this time-period, and not starting at January 01 (01-01) and ending at December 31 (31-01) as it would be plotted once this dataset got ordered correctly.

Any thoughts on how to solve that or to make it easier?