我使用以下代码将数据加载到R：
filelist <- list.files(pattern = "^KB.*.txt")
sorted <- mixedsort(sort(filelist))
#sorts the data in numerical order (i.e. c1-c11, fixed)
data_list = lapply(sorted, read.table, sep = "")
#loads all the .txt files into R
这是已排序的
的输出[1] "KB_5223_LLM1_rotated_1.jpg_c1.txt"
[2] "KB_5223_LLM1_rotated_1.jpg_c2.txt"
[3] "KB_5223_LLM1_rotated_1.jpg_c3.txt"
[4] "KB_5223_LLM1_rotated_1.jpg_c4.txt"
[5] "KB_5223_LLM1_rotated_1.jpg_c5.txt"
[6] "KB_5223_LLM1_rotated_1.jpg_c6.txt"
[7] "KB_5223_LLM1_rotated_1.jpg_c7.txt"
[8] "KB_5223_LLM1_rotated_1.jpg_c8.txt"
[9] "KB_5223_LLM1_rotated_1.jpg_c9.txt"
[10] "KB_5223_LLM1_rotated_1.jpg_c10.txt"
[11] "KB_5223_LLM1_rotated_1.jpg_c11.txt"
[12] "KB_5223_LLM1_rotated_1.jpg_fixed.txt"
然而，数据看起来像（这些只是12个表中的2个，因为数据非常大）
[[10]]
V1 V2
1 12.1153 6.3112
2 12.0841 6.2956
3 12.0529 6.2800
4 12.0217 6.2644
5 11.9906 6.2488
6 11.9595 6.2332
7 11.9285 6.2176
8 11.8976 6.2019
9 11.8668 6.1862
10 11.8362 6.1705
11 11.8056 6.1547
12 11.7753 6.1389
13 11.7451 6.1231
14 11.7151 6.1071
15 11.6854 6.0912
16 11.6558 6.0751
17 11.6265 6.0590
18 11.5974 6.0428
19 11.5686 6.0266
20 11.5401 6.0103
21 11.5118 5.9940
22 11.4837 5.9776
23 11.4558 5.9613
24 11.4282 5.9450
25 11.4008 5.9288
26 11.3736 5.9127
27 11.3465 5.8966
28 11.3197 5.8807
29 11.2930 5.8649
30 11.2664 5.8493
31 11.2400 5.8338
32 11.2138 5.8186
33 11.1877 5.8036
34 11.1617 5.7888
35 11.1358 5.7741
36 11.1101 5.7596
37 11.0844 5.7450
38 11.0588 5.7305
39 11.0334 5.7158
40 11.0080 5.7009
41 10.9827 5.6857
42 10.9574 5.6702
43 10.9323 5.6542
44 10.9071 5.6378
45 10.8821 5.6208
46 10.8570 5.6031
47 10.8320 5.5848
48 10.8071 5.5657
49 10.7822 5.5458
50 10.7572 5.5252
51 10.7323 5.5041
52 10.7074 5.4824
53 10.6825 5.4604
54 10.6576 5.4380
55 10.6327 5.4154
56 10.6077 5.3927
57 10.5827 5.3699
58 10.5577 5.3472
59 10.5326 5.3246
60 10.5075 5.3022
61 10.4823 5.2801
62 10.4570 5.2584
63 10.4317 5.2372
64 10.4063 5.2166
65 10.3808 5.1966
66 10.3552 5.1772
67 10.3296 5.1583
68 10.3039 5.1401
69 10.2781 5.1224
70 10.2523 5.1053
71 10.2264 5.0887
72 10.2004 5.0726
73 10.1744 5.0571
74 10.1484 5.0421
75 10.1223 5.0275
76 10.0961 5.0135
77 10.0699 4.9999
78 10.0437 4.9868
79 10.0175 4.9741
80 9.9912 4.9619
81 9.9648 4.9500
82 9.9383 4.9385
83 9.9116 4.9272
84 9.8847 4.9163
85 9.8575 4.9055
86 9.8301 4.8950
87 9.8023 4.8846
88 9.7741 4.8743
89 9.7456 4.8640
90 9.7166 4.8538
91 9.6871 4.8436
92 9.6571 4.8334
93 9.6265 4.8230
94 9.5953 4.8126
95 9.5636 4.8021
96 9.5314 4.7916
97 9.4988 4.7813
98 9.4658 4.7711
99 9.4324 4.7611
100 9.3987 4.7513
101 9.3648 4.7420
102 9.3307 4.7330
103 9.2965 4.7245
104 9.2621 4.7166
105 9.2277 4.7092
106 9.1933 4.7025
107 9.1589 4.6966
108 9.1246 4.6914
109 9.0905 4.6871
110 9.0566 4.6837
111 9.0228 4.6812
112 8.9892 4.6795
113 8.9558 4.6784
114 8.9225 4.6778
115 8.8893 4.6777
116 8.8561 4.6778
117 8.8230 4.6780
118 8.7898 4.6783
119 8.7567 4.6785
120 8.7234 4.6784
121 8.6901 4.6780
122 8.6567 4.6772
123 8.6232 4.6757
124 8.5894 4.6735
125 8.5555 4.6705
126 8.5214 4.6665
127 8.4870 4.6615
128 8.4525 4.6556
129 8.4178 4.6488
130 8.3831 4.6414
131 8.3484 4.6332
132 8.3138 4.6246
133 8.2793 4.6156
134 8.2450 4.6062
135 8.2110 4.5966
136 8.1772 4.5868
137 8.1438 4.5770
138 8.1109 4.5673
139 8.0784 4.5578
140 8.0465 4.5486
141 8.0152 4.5397
142 7.9845 4.5313
143 7.9546 4.5234
144 7.9253 4.5161
145 7.8965 4.5093
146 7.8681 4.5028
147 7.8398 4.4966
148 7.8116 4.4906
149 7.7832 4.4846
150 7.7546 4.4786
151 7.7256 4.4724
152 7.6959 4.4661
153 7.6655 4.4594
154 7.6343 4.4522
155 7.6019 4.4446
156 7.5684 4.4363
157 7.5334 4.4273
158 7.4970 4.4175
159 7.4590 4.4068
160 7.4195 4.3954
161 7.3785 4.3831
162 7.3362 4.3702
163 7.2926 4.3565
164 7.2477 4.3421
165 7.2017 4.3271
166 7.1545 4.3115
167 7.1063 4.2953
168 7.0571 4.2786
169 7.0069 4.2614
170 6.9558 4.2437
171 6.9040 4.2255
172 6.8514 4.2070
173 6.7981 4.1881
174 6.7442 4.1688
175 6.6897 4.1493
176 6.6347 4.1295
177 6.5793 4.1094
178 6.5236 4.0892
179 6.4675 4.0687
180 6.4111 4.0482
181 6.3546 4.0275
182 6.2979 4.0068
183 6.2412 3.9860
[[11]]
V1 V2
1 12.1153 6.3112
2 12.1376 6.2977
3 12.1599 6.2842
4 12.1821 6.2705
5 12.2041 6.2567
6 12.2259 6.2426
7 12.2476 6.2281
8 12.2689 6.2133
9 12.2900 6.1980
10 12.3107 6.1822
11 12.3309 6.1658
12 12.3508 6.1488
13 12.3701 6.1311
14 12.3889 6.1125
15 12.4071 6.0932
16 12.4247 6.0729
17 12.4416 6.0517
18 12.4578 6.0295
19 12.4734 6.0064
20 12.4885 5.9825
21 12.5030 5.9579
22 12.5170 5.9326
23 12.5305 5.9067
24 12.5437 5.8802
25 12.5565 5.8533
26 12.5689 5.8260
27 12.5811 5.7983
28 12.5930 5.7703
29 12.6047 5.7421
30 12.6163 5.7138
31 12.6278 5.6855
32 12.6391 5.6571
33 12.6505 5.6287
34 12.6618 5.6006
35 12.6732 5.5725
36 12.6846 5.5447
37 12.6961 5.5170
38 12.7077 5.4895
39 12.7194 5.4622
40 12.7311 5.4351
41 12.7430 5.4082
42 12.7550 5.3815
43 12.7671 5.3551
44 12.7794 5.3288
45 12.7918 5.3028
46 12.8044 5.2769
47 12.8172 5.2513
48 12.8302 5.2260
49 12.8434 5.2008
50 12.8568 5.1760
51 12.8704 5.1513
52 12.8843 5.1269
53 12.8983 5.1027
54 12.9126 5.0787
55 12.9270 5.0547
56 12.9415 5.0308
57 12.9561 5.0068
58 12.9709 4.9828
59 12.9856 4.9587
60 13.0004 4.9344
61 13.0152 4.9099
62 13.0300 4.8851
63 13.0447 4.8601
64 13.0593 4.8346
65 13.0739 4.8088
66 13.0883 4.7825
67 13.1025 4.7557
68 13.1166 4.7283
69 13.1305 4.7003
70 13.1442 4.6719
71 13.1576 4.6430
72 13.1709 4.6137
73 13.1840 4.5842
74 13.1969 4.5544
75 13.2096 4.5244
76 13.2221 4.4943
77 13.2344 4.4642
78 13.2465 4.4341
79 13.2584 4.4041
80 13.2702 4.3743
81 13.2817 4.3447
82 13.2930 4.3154
83 13.3042 4.2864
84 13.3151 4.2579
85 13.3259 4.2299
86 13.3365 4.2025
87 13.3469 4.1757
88 13.3571 4.1494
89 13.3671 4.1238
90 13.3769 4.0986
91 13.3866 4.0739
92 13.3961 4.0495
93 13.4055 4.0255
94 13.4147 4.0018
95 13.4237 3.9783
96 13.4326 3.9550
97 13.4414 3.9318
98 13.4501 3.9087
99 13.4586 3.8856
100 13.4670 3.8624
101 13.4753 3.8391
102 13.4834 3.8157
103 13.4916 3.7920
104 13.4996 3.7681
105 13.5077 3.7438
106 13.5159 3.7191
107 13.5242 3.6940
108 13.5326 3.6682
109 13.5413 3.6419
110 13.5503 3.6148
111 13.5595 3.5870
112 13.5691 3.5584
113 13.5792 3.5289
114 13.5897 3.4984
115 13.6007 3.4669
116 13.6123 3.4343
117 13.6245 3.4006
118 13.6373 3.3656
119 13.6509 3.3293
120 13.6652 3.2917
121 13.6803 3.2527
122 13.6961 3.2124
123 13.7126 3.1708
124 13.7297 3.1280
125 13.7476 3.0841
126 13.7661 3.0390
127 13.7852 2.9928
128 13.8049 2.9455
129 13.8251 2.8972
130 13.8459 2.8480
131 13.8673 2.7978
132 13.8891 2.7467
133 13.9114 2.6947
134 13.9342 2.6419
135 13.9574 2.5884
136 13.9810 2.5341
137 14.0050 2.4791
138 14.0293 2.4235
139 14.0540 2.3672
140 14.0791 2.3104
141 14.1044 2.2531
142 14.1300 2.1953
143 14.1558 2.1370
144 14.1819 2.0783
145 14.2081 2.0192
146 14.2346 1.9598
147 14.2612 1.9002
148 14.2880 1.8403
149 14.3148 1.7801
150 14.3418 1.7199
151 14.3688 1.6595
152 14.3959 1.5990
153 14.4230 1.5385
加载后，但我需要它是一个矩阵，看起来像
V1 V2
[1,] 12.1153 6.3112
[2,] 12.0841 6.2956
[3,] 12.0529 6.2800
[4,] 12.0217 6.2644
[5,] 11.9906 6.2488
[6,] 11.9595 6.2332
[7,] 11.9285 6.2176
[8,] 11.8976 6.2019
[9,] 11.8668 6.1862
[10,] 11.8362 6.1705
[11,] 11.8056 6.1547
[12,] 11.7753 6.1389
[13,] 11.7451 6.1231
[14,] 11.7151 6.1071
[15,] 11.6854 6.0912
[16,] 11.6558 6.0751
[17,] 11.6265 6.0590
[18,] 11.5974 6.0428
[19,] 11.5686 6.0266
[20,] 11.5401 6.0103
[21,] 11.5118 5.9940
[22,] 11.4837 5.9776
[23,] 11.4558 5.9613
[24,] 11.4282 5.9450
[25,] 11.4008 5.9288
[26,] 11.3736 5.9127
[27,] 11.3465 5.8966
[28,] 11.3197 5.8807
[29,] 11.2930 5.8649
[30,] 11.2664 5.8493
[31,] 11.2400 5.8338
[32,] 11.2138 5.8186
[33,] 11.1877 5.8036
[34,] 11.1617 5.7888
[35,] 11.1358 5.7741
[36,] 11.1101 5.7596
[37,] 11.0844 5.7450
[38,] 11.0588 5.7305
[39,] 11.0334 5.7158
[40,] 11.0080 5.7009
[41,] 10.9827 5.6857
[42,] 10.9574 5.6702
[43,] 10.9323 5.6542
[44,] 10.9071 5.6378
[45,] 10.8821 5.6208
[46,] 10.8570 5.6031
[47,] 10.8320 5.5848
[48,] 10.8071 5.5657
[49,] 10.7822 5.5458
[50,] 10.7572 5.5252
[51,] 10.7323 5.5041
[52,] 10.7074 5.4824
[53,] 10.6825 5.4604
[54,] 10.6576 5.4380
[55,] 10.6327 5.4154
[56,] 10.6077 5.3927
[57,] 10.5827 5.3699
[58,] 10.5577 5.3472
[59,] 10.5326 5.3246
[60,] 10.5075 5.3022
[61,] 10.4823 5.2801
[62,] 10.4570 5.2584
[63,] 10.4317 5.2372
[64,] 10.4063 5.2166
[65,] 10.3808 5.1966
[66,] 10.3552 5.1772
[67,] 10.3296 5.1583
[68,] 10.3039 5.1401
[69,] 10.2781 5.1224
[70,] 10.2523 5.1053
[71,] 10.2264 5.0887
[72,] 10.2004 5.0726
[73,] 10.1744 5.0571
[74,] 10.1484 5.0421
[75,] 10.1223 5.0275
[76,] 10.0961 5.0135
[77,] 10.0699 4.9999
[78,] 10.0437 4.9868
[79,] 10.0175 4.9741
[80,] 9.9912 4.9619
[81,] 9.9648 4.9500
[82,] 9.9383 4.9385
[83,] 9.9116 4.9272
[84,] 9.8847 4.9163
[85,] 9.8575 4.9055
[86,] 9.8301 4.8950
[87,] 9.8023 4.8846
[88,] 9.7741 4.8743
[89,] 9.7456 4.8640
[90,] 9.7166 4.8538
[91,] 9.6871 4.8436
[92,] 9.6571 4.8334
[93,] 9.6265 4.8230
[94,] 9.5953 4.8126
[95,] 9.5636 4.8021
[96,] 9.5314 4.7916
[97,] 9.4988 4.7813
[98,] 9.4658 4.7711
[99,] 9.4324 4.7611
[100,] 9.3987 4.7513
[101,] 9.3648 4.7420
[102,] 9.3307 4.7330
[103,] 9.2965 4.7245
[104,] 9.2621 4.7166
[105,] 9.2277 4.7092
[106,] 9.1933 4.7025
[107,] 9.1589 4.6966
[108,] 9.1246 4.6914
[109,] 9.0905 4.6871
[110,] 9.0566 4.6837
[111,] 9.0228 4.6812
[112,] 8.9892 4.6795
[113,] 8.9558 4.6784
[114,] 8.9225 4.6778
[115,] 8.8893 4.6777
[116,] 8.8561 4.6778
[117,] 8.8230 4.6780
[118,] 8.7898 4.6783
[119,] 8.7567 4.6785
[120,] 8.7234 4.6784
[121,] 8.6901 4.6780
[122,] 8.6567 4.6772
[123,] 8.6232 4.6757
[124,] 8.5894 4.6735
[125,] 8.5555 4.6705
[126,] 8.5214 4.6665
[127,] 8.4870 4.6615
[128,] 8.4525 4.6556
[129,] 8.4178 4.6488
[130,] 8.3831 4.6414
[131,] 8.3484 4.6332
[132,] 8.3138 4.6246
[133,] 8.2793 4.6156
[134,] 8.2450 4.6062
[135,] 8.2110 4.5966
[136,] 8.1772 4.5868
[137,] 8.1438 4.5770
[138,] 8.1109 4.5673
[139,] 8.0784 4.5578
[140,] 8.0465 4.5486
[141,] 8.0152 4.5397
[142,] 7.9845 4.5313
[143,] 7.9546 4.5234
[144,] 7.9253 4.5161
[145,] 7.8965 4.5093
[146,] 7.8681 4.5028
[147,] 7.8398 4.4966
[148,] 7.8116 4.4906
[149,] 7.7832 4.4846
[150,] 7.7546 4.4786
[151,] 7.7256 4.4724
[152,] 7.6959 4.4661
[153,] 7.6655 4.4594
[154,] 7.6343 4.4522
[155,] 7.6019 4.4446
[156,] 7.5684 4.4363
[157,] 7.5334 4.4273
[158,] 7.4970 4.4175
[159,] 7.4590 4.4068
[160,] 7.4195 4.3954
[161,] 7.3785 4.3831
[162,] 7.3362 4.3702
[163,] 7.2926 4.3565
[164,] 7.2477 4.3421
[165,] 7.2017 4.3271
[166,] 7.1545 4.3115
[167,] 7.1063 4.2953
[168,] 7.0571 4.2786
[169,] 7.0069 4.2614
[170,] 6.9558 4.2437
[171,] 6.9040 4.2255
[172,] 6.8514 4.2070
[173,] 6.7981 4.1881
[174,] 6.7442 4.1688
[175,] 6.6897 4.1493
[176,] 6.6347 4.1295
[177,] 6.5793 4.1094
[178,] 6.5236 4.0892
[179,] 6.4675 4.0687
[180,] 6.4111 4.0482
[181,] 6.3546 4.0275
[182,] 6.2979 4.0068
[183,] 6.2412 3.9860
V1 V2
[1,] 12.1153 6.3112
[2,] 12.1376 6.2977
[3,] 12.1599 6.2842
[4,] 12.1821 6.2705
[5,] 12.2041 6.2567
[6,] 12.2259 6.2426
[7,] 12.2476 6.2281
[8,] 12.2689 6.2133
[9,] 12.2900 6.1980
[10,] 12.3107 6.1822
[11,] 12.3309 6.1658
[12,] 12.3508 6.1488
[13,] 12.3701 6.1311
[14,] 12.3889 6.1125
[15,] 12.4071 6.0932
[16,] 12.4247 6.0729
[17,] 12.4416 6.0517
[18,] 12.4578 6.0295
[19,] 12.4734 6.0064
[20,] 12.4885 5.9825
[21,] 12.5030 5.9579
[22,] 12.5170 5.9326
[23,] 12.5305 5.9067
[24,] 12.5437 5.8802
[25,] 12.5565 5.8533
[26,] 12.5689 5.8260
[27,] 12.5811 5.7983
[28,] 12.5930 5.7703
[29,] 12.6047 5.7421
[30,] 12.6163 5.7138
[31,] 12.6278 5.6855
[32,] 12.6391 5.6571
[33,] 12.6505 5.6287
[34,] 12.6618 5.6006
[35,] 12.6732 5.5725
[36,] 12.6846 5.5447
[37,] 12.6961 5.5170
[38,] 12.7077 5.4895
[39,] 12.7194 5.4622
[40,] 12.7311 5.4351
[41,] 12.7430 5.4082
[42,] 12.7550 5.3815
[43,] 12.7671 5.3551
[44,] 12.7794 5.3288
[45,] 12.7918 5.3028
[46,] 12.8044 5.2769
[47,] 12.8172 5.2513
[48,] 12.8302 5.2260
[49,] 12.8434 5.2008
[50,] 12.8568 5.1760
[51,] 12.8704 5.1513
[52,] 12.8843 5.1269
[53,] 12.8983 5.1027
[54,] 12.9126 5.0787
[55,] 12.9270 5.0547
[56,] 12.9415 5.0308
[57,] 12.9561 5.0068
[58,] 12.9709 4.9828
[59,] 12.9856 4.9587
[60,] 13.0004 4.9344
[61,] 13.0152 4.9099
[62,] 13.0300 4.8851
[63,] 13.0447 4.8601
[64,] 13.0593 4.8346
[65,] 13.0739 4.8088
[66,] 13.0883 4.7825
[67,] 13.1025 4.7557
[68,] 13.1166 4.7283
[69,] 13.1305 4.7003
[70,] 13.1442 4.6719
[71,] 13.1576 4.6430
[72,] 13.1709 4.6137
[73,] 13.1840 4.5842
[74,] 13.1969 4.5544
[75,] 13.2096 4.5244
[76,] 13.2221 4.4943
[77,] 13.2344 4.4642
[78,] 13.2465 4.4341
[79,] 13.2584 4.4041
[80,] 13.2702 4.3743
[81,] 13.2817 4.3447
[82,] 13.2930 4.3154
[83,] 13.3042 4.2864
[84,] 13.3151 4.2579
[85,] 13.3259 4.2299
[86,] 13.3365 4.2025
[87,] 13.3469 4.1757
[88,] 13.3571 4.1494
[89,] 13.3671 4.1238
[90,] 13.3769 4.0986
[91,] 13.3866 4.0739
[92,] 13.3961 4.0495
[93,] 13.4055 4.0255
[94,] 13.4147 4.0018
[95,] 13.4237 3.9783
[96,] 13.4326 3.9550
[97,] 13.4414 3.9318
[98,] 13.4501 3.9087
[99,] 13.4586 3.8856
[100,] 13.4670 3.8624
[101,] 13.4753 3.8391
[102,] 13.4834 3.8157
[103,] 13.4916 3.7920
[104,] 13.4996 3.7681
[105,] 13.5077 3.7438
[106,] 13.5159 3.7191
[107,] 13.5242 3.6940
[108,] 13.5326 3.6682
[109,] 13.5413 3.6419
[110,] 13.5503 3.6148
[111,] 13.5595 3.5870
[112,] 13.5691 3.5584
[113,] 13.5792 3.5289
[114,] 13.5897 3.4984
[115,] 13.6007 3.4669
[116,] 13.6123 3.4343
[117,] 13.6245 3.4006
[118,] 13.6373 3.3656
[119,] 13.6509 3.3293
[120,] 13.6652 3.2917
[121,] 13.6803 3.2527
[122,] 13.6961 3.2124
[123,] 13.7126 3.1708
[124,] 13.7297 3.1280
[125,] 13.7476 3.0841
[126,] 13.7661 3.0390
[127,] 13.7852 2.9928
[128,] 13.8049 2.9455
[129,] 13.8251 2.8972
[130,] 13.8459 2.8480
[131,] 13.8673 2.7978
[132,] 13.8891 2.7467
[133,] 13.9114 2.6947
[134,] 13.9342 2.6419
[135,] 13.9574 2.5884
[136,] 13.9810 2.5341
[137,] 14.0050 2.4791
[138,] 14.0293 2.4235
[139,] 14.0540 2.3672
[140,] 14.0791 2.3104
[141,] 14.1044 2.2531
[142,] 14.1300 2.1953
[143,] 14.1558 2.1370
[144,] 14.1819 2.0783
[145,] 14.2081 2.0192
[146,] 14.2346 1.9598
[147,] 14.2612 1.9002
[148,] 14.2880 1.8403
[149,] 14.3148 1.7801
[150,] 14.3418 1.7199
[151,] 14.3688 1.6595
[152,] 14.3959 1.5990
[153,] 14.4230 1.5385
我知道as.matrix(read.land())函数和简单as.matrix()，但我无法使用它来处理我的data_list。我觉得这可能源于我没有完全理解lapply()究竟是做什么以及它产生什么样的数据。
如果有任何帮助，我将不胜感激。
感谢。
答案 0 :(得分：1)
您可以在rbindlist()周围data.table包裹lapply()（与do.call("rbind", data_list)相同，但速度更快）：
library(data.table)
data_list = rbindlist(lapply(sorted, read.table, sep = ""))
如果要为每个文件创建数据框，可以尝试：
lapply(sorted, function(x) {
assign(x, read.table(x, sep = ""),
envir = .GlobalEnv)
})
答案 1 :(得分：1)
如果要将data_list的每个元素存储为全局环境中的对象，可以先将data.frame列表列为名为的列表，然后使用{{ 1}}将元素转换为环境中的list2env：
data.frame根据您的使用情况，您可能希望将data.frame保留在列表中，因为看起来data.frames有些相关。您可以轻松地遍历列表的元素，但不能在全局环境中的单独data.frame对象上进行迭代（特别是如果您的data.frames具有非常不同的名称）。例如，names(data_list) = c(paste0("c", 1:11), "fixed")
list2env(data_list, envir = .GlobalEnv)
的答案会合并@clemens中的所有data.frames。如果所有data.frame都在不同的对象中，那就不那么容易了。