在R语言绘图中，x轴双因子绘制
类似上图中，x轴上有两种因子，请问该用R语言该如何绘制
数据如下：
Pathway Location Fermentation_time
1 Cellular Processes T 0
2 Cellular Processes M 0
3 Cellular Processes B 0
4 Environmental Information Processing T 0
5 Environmental Information Processing M 0
6 Environmental Information Processing B 0
7 Genetic Information Processing T 0
8 Genetic Information Processing M 0
9 Genetic Information Processing B 0
10 Human Diseases T 0
11 Human Diseases M 0
12 Human Diseases B 0
13 Metabolism T 0
14 Metabolism M 0
15 Metabolism B 0
16 Organismal Systems T 0
17 Organismal Systems M 0
18 Organismal Systems B 0
19 Cellular Processes T 7
20 Cellular Processes M 7
21 Cellular Processes B 7
22 Environmental Information Processing T 7
23 Environmental Information Processing M 7
24 Environmental Information Processing B 7
25 Genetic Information Processing T 7
26 Genetic Information Processing M 7
27 Genetic Information Processing B 7
28 Human Diseases T 7
29 Human Diseases M 7
30 Human Diseases B 7
31 Metabolism T 7
32 Metabolism M 7
33 Metabolism B 7
34 Organismal Systems T 7
35 Organismal Systems M 7
36 Organismal Systems B 7
37 Cellular Processes T 14
38 Cellular Processes M 14
39 Cellular Processes B 14
40 Environmental Information Processing T 14
41 Environmental Information Processing M 14
42 Environmental Information Processing B 14
43 Genetic Information Processing T 14
44 Genetic Information Processing M 14
45 Genetic Information Processing B 14
46 Human Diseases T 14
47 Human Diseases M 14
48 Human Diseases B 14
49 Metabolism T 14
50 Metabolism M 14
51 Metabolism B 14
52 Organismal Systems T 14
53 Organismal Systems M 14
54 Organismal Systems B 14
55 Cellular Processes T 21
56 Cellular Processes M 21
57 Cellular Processes B 21
58 Environmental Information Processing T 21
59 Environmental Information Processing M 21
60 Environmental Information Processing B 21
61 Genetic Information Processing T 21
62 Genetic Information Processing M 21
63 Genetic Information Processing B 21
64 Human Diseases T 21
65 Human Diseases M 21
66 Human Diseases B 21
67 Metabolism T 21
68 Metabolism M 21
69 Metabolism B 21
70 Organismal Systems T 21
71 Organismal Systems M 21
72 Organismal Systems B 21
73 Cellular Processes T 28
74 Cellular Processes M 28
75 Cellular Processes B 28
76 Environmental Information Processing T 28
77 Environmental Information Processing M 28
78 Environmental Information Processing B 28
79 Genetic Information Processing T 28
80 Genetic Information Processing M 28
81 Genetic Information Processing B 28
82 Human Diseases T 28
83 Human Diseases M 28
84 Human Diseases B 28
85 Metabolism T 28
86 Metabolism M 28
87 Metabolism B 28
88 Organismal Systems T 28
89 Organismal Systems M 28
90 Organismal Systems B 28
91 Cellular Processes T 35
92 Cellular Processes M 35
93 Cellular Processes B 35
94 Environmental Information Processing T 35
95 Environmental Information Processing M 35
96 Environmental Information Processing B 35
97 Genetic Information Processing T 35
98 Genetic Information Processing M 35
99 Genetic Information Processing B 35
100 Human Diseases T 35
101 Human Diseases M 35
102 Human Diseases B 35
103 Metabolism T 35
104 Metabolism M 35
105 Metabolism B 35
106 Organismal Systems T 35
107 Organismal Systems M 35
108 Organismal Systems B 35
Value
1 0.02373511
2 0.02581420
3 0.03072140
4 0.04865264
5 0.05043712
6 0.06028976
7 0.07193867
8 0.07096576
9 0.07009559
10 0.04468496
11 0.04257613
12 0.03625124
13 0.79296499
14 0.79222115
15 0.78551891
16 0.01802363
17 0.01798565
18 0.01712310
19 0.03496092
20 0.01944159
21 0.02938003
22 0.05920001
23 0.05572461
24 0.05646940
25 0.07477080
26 0.10072181
27 0.08788174
28 0.03284002
29 0.03626260
30 0.03448715
31 0.78103544
32 0.77240186
33 0.77545875
34 0.01719281
35 0.01544754
36 0.01632292
37 0.02095560
38 0.01599157
39 0.01608294
40 0.05668599
41 0.05624718
42 0.05624750
43 0.09738202
44 0.10892417
45 0.10868347
46 0.03429298
47 0.03493579
48 0.03490331
49 0.77498270
50 0.76909849
51 0.76925950
52 0.01570070
53 0.01480280
54 0.01482328
55 0.01699935
56 0.01615236
57 0.01836526
58 0.05612544
59 0.05617380
60 0.05612747
61 0.10653672
62 0.10858544
63 0.10737192
64 0.03456613
65 0.03485986
66 0.03447533
67 0.77075098
68 0.76937131
69 0.76886890
70 0.01502139
71 0.01485723
72 0.01479112
73 0.01624415
74 0.01599640
75 0.01663936
76 0.05611752
77 0.05625212
78 0.05614491
79 0.10808014
80 0.10892875
81 0.10748703
82 0.03481902
83 0.03493154
84 0.03462397
85 0.76983280
86 0.76908896
87 0.77014965
88 0.01490637
89 0.01480224
90 0.01495508
91 0.01639599
92 0.01610071
93 0.01896354
94 0.05631049
95 0.05628908
96 0.05564710
97 0.10806663
98 0.10878130
99 0.10642341
100 0.03506288
101 0.03493755
102 0.03426395
103 0.76928840
104 0.76907444
105 0.76974069
106 0.01487561
107 0.01481693
108 0.01496130