Регулярное выражение для извлечения одной строки без строки префикса или суффикса в R-tidyverse-stringr - PullRequest
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Регулярное выражение для извлечения одной строки без строки префикса или суффикса в R-tidyverse-stringr

0 голосов
/ 04 июня 2018

У меня есть список строк, который состоит из четырех компонентов:

a_b_c_d, где:

  • a имеет 3 шаблона строк: str, jtpи mdl
  • b имеет 5 шаблонов строк: HBW, HBS, HBO, NHBB и NHBO
  • c имеет 4 шаблонастроки: L, M, H и ALL
  • d имеет 4 шаблона строк: NMT, MC, CAR и PT
  • a, b, c и d связаны друг с другом _ в порядке
  • Некоторые записи не имеют b, c и d

Мне нужно извлечь c из списков как новое поле income.Если c не существует, его следует заменить на NA.Ниже приводится фактический фрейм данных, который я использую: 

df <- c(
"str_HBW_L_NMT" ,"str_HBW_M_NMT" ,"str_HBW_H_NMT" ,"str_HBW_L_MC" ,"str_HBW_M_MC" ,"str_HBW_H_MC" ,
"str_HBW_L_CAR" ,"str_HBW_M_CAR" ,"str_HBW_H_CAR" ,"str_HBW_L_PT" ,"str_HBW_M_PT" ,"str_HBW_H_PT" ,
"str_HBS_L_NMT" ,"str_HBS_M_NMT" ,"str_HBS_H_NMT" ,"str_HBS_L_MC" ,"str_HBS_M_MC" ,"str_HBS_H_MC" ,
"str_HBS_L_CAR" ,"str_HBS_M_CAR" ,"str_HBS_H_CAR" ,"str_HBS_L_PT" ,"str_HBS_M_PT" ,"str_HBS_H_PT" ,
"str_HBO_L_NMT" ,"str_HBO_M_NMT" ,"str_HBO_H_NMT" ,"str_HBO_L_MC" ,"str_HBO_M_MC" ,"str_HBO_H_MC" ,
"str_HBO_L_CAR" ,"str_HBO_M_CAR" ,"str_HBO_H_CAR" ,"str_HBO_L_PT" ,"str_HBO_M_PT" ,"str_HBO_H_PT" ,
"str_NHBB_L_NMT","str_NHBB_M_NMT","str_NHBB_H_NMT","str_NHBB_L_MC","str_NHBB_M_MC","str_NHBB_H_MC",
"str_NHBB_L_CAR","str_NHBB_M_CAR","str_NHBB_H_CAR","str_NHBB_L_PT","str_NHBB_M_PT","str_NHBB_H_PT",
"str_NHBO_L_NMT","str_NHBO_M_NMT","str_NHBO_H_NMT","str_NHBO_L_MC","str_NHBO_M_MC","str_NHBO_H_MC",
"str_NHBO_L_CAR","str_NHBO_M_CAR","str_NHBO_H_CAR","str_NHBO_L_PT","str_NHBO_M_PT","str_NHBO_H_PT",
"str_HBW_L"     ,"str_HBW_M"     ,"str_HBW_H"     ,"str_HBS_L"    ,"str_HBS_M"    ,"str_HBS_H"    ,
"str_HBO_L"     ,"str_HBO_M"     ,"str_HBO_H"     ,"str_NHBB_L"   ,"str_NHBB_M"   ,"str_NHBB_H"   ,
"str_NHBO_L"    ,"str_NHBO_M"    ,"str_NHBO_H"    ,"str_HBW"      ,"str_HBS"      ,"str_HBO"      ,
"str_NHBB"      ,"str_NHBO"      ,"str_L"         ,"str_M"        ,"str_H"        ,"str_ALL"      ,
"jtp_HBW_L_NMT" ,"jtp_HBW_M_NMT" ,"jtp_HBW_H_NMT" ,"jtp_HBW_L_MC" ,"jtp_HBW_M_MC" ,"jtp_HBW_H_MC" ,
"jtp_HBW_L_CAR" ,"jtp_HBW_M_CAR" ,"jtp_HBW_H_CAR" ,"jtp_HBW_L_PT" ,"jtp_HBW_M_PT" ,"jtp_HBW_H_PT" ,
"jtp_HBS_L_NMT" ,"jtp_HBS_M_NMT" ,"jtp_HBS_H_NMT" ,"jtp_HBS_L_MC" ,"jtp_HBS_M_MC" ,"jtp_HBS_H_MC" ,
"jtp_HBS_L_CAR" ,"jtp_HBS_M_CAR" ,"jtp_HBS_H_CAR" ,"jtp_HBS_L_PT" ,"jtp_HBS_M_PT" ,"jtp_HBS_H_PT" ,
"jtp_HBW_L"     ,"jtp_HBW_M"     ,"jtp_HBW_H"     ,"jtp_HBS_L"    ,"jtp_HBS_M"    ,"jtp_HBS_H"    ,
"jtp_HBW"       ,"jtp_HBS"       ,"jtp_L"         ,"jtp_M"        ,"jtp_H"        ,"jtp_ALL"      ,
"mdl_HBW_L_NMT" ,"mdl_HBW_M_NMT" ,"mdl_HBW_H_NMT" ,"mdl_HBW_L_MC" ,"mdl_HBW_M_MC" ,"mdl_HBW_H_MC" ,
"mdl_HBW_L_CAR" ,"mdl_HBW_M_CAR" ,"mdl_HBW_H_CAR" ,"mdl_HBW_L_PT" ,"mdl_HBW_M_PT" ,"mdl_HBW_H_PT" ,
"mdl_HBS_L_NMT" ,"mdl_HBS_M_NMT" ,"mdl_HBS_H_NMT" ,"mdl_HBS_L_MC" ,"mdl_HBS_M_MC" ,"mdl_HBS_H_MC" ,
"mdl_HBS_L_CAR" ,"mdl_HBS_M_CAR" ,"mdl_HBS_H_CAR" ,"mdl_HBS_L_PT" ,"mdl_HBS_M_PT" ,"mdl_HBS_H_PT" ,
"mdl_HBO_L_NMT" ,"mdl_HBO_M_NMT" ,"mdl_HBO_H_NMT" ,"mdl_HBO_L_MC" ,"mdl_HBO_M_MC" ,"mdl_HBO_H_MC" ,
"mdl_HBO_L_CAR" ,"mdl_HBO_M_CAR" ,"mdl_HBO_H_CAR" ,"mdl_HBO_L_PT" ,"mdl_HBO_M_PT" ,"mdl_HBO_H_PT" ,
"mdl_NHBB_L_NMT","mdl_NHBB_M_NMT","mdl_NHBB_H_NMT","mdl_NHBB_L_MC","mdl_NHBB_M_MC","mdl_NHBB_H_MC",
"mdl_NHBB_L_CAR","mdl_NHBB_M_CAR","mdl_NHBB_H_CAR","mdl_NHBB_L_PT","mdl_NHBB_M_PT","mdl_NHBB_H_PT",
"mdl_NHBO_L_NMT","mdl_NHBO_M_NMT","mdl_NHBO_H_NMT","mdl_NHBO_L_MC","mdl_NHBO_M_MC","mdl_NHBO_H_MC",
"mdl_NHBO_L_CAR","mdl_NHBO_M_CAR","mdl_NHBO_H_CAR","mdl_NHBO_L_PT","mdl_NHBO_M_PT","mdl_NHBO_H_PT",
"mdl_HBW_L"     ,"mdl_HBW_M"     ,"mdl_HBW_H"     ,"mdl_HBS_L"    ,"mdl_HBS_M"    ,"mdl_HBS_H"    ,
"mdl_HBO_L"     ,"mdl_HBO_M"     ,"mdl_HBO_H"     ,"mdl_NHBB_L"   ,"mdl_NHBB_M"   ,"mdl_NHBB_H"   ,
"mdl_NHBO_L"    ,"mdl_NHBO_M"    ,"mdl_NHBO_H"    ,"mdl_HBW"      ,"mdl_HBS"      ,"mdl_HBO"      ,
"mdl_NHBB"      ,"mdl_NHBO"      ,"mdl_L"         ,"mdl_M"        ,"mdl_H"        ,"mdl_ALL"
)

Я провел много испытаний, но не смог извлечь его правильно.Ниже приведен пример моего сценария: 

df %>% mutate(income=str_extract_all(string=name,
        pattern="(?!str|jtp|mdl|HBW|HBS|HBO|NHBB|NHBO|_)[L|M|H|(ALL)](?!NMT|MC|CAR|PT|_)"))

Есть ли у вас какие-либо предложения для извлечения желаемого результата, как показано ниже?Я предпочитаю использовать tidyverse и stringr, но функция base также применима вместо stringr.

      name          income
1    str_HBW_L_NMT  L
2    str_HBW_M_NMT  M
3    str_HBW_H_NMT  H
4     str_HBW_L_MC  L
5     str_HBW_M_MC  M
6     str_HBW_H_MC  H
7    str_HBW_L_CAR  L
8    str_HBW_M_CAR  M
9    str_HBW_H_CAR  H
10    str_HBW_L_PT  L
11    str_HBW_M_PT  M
12    str_HBW_H_PT  H
13   str_HBS_L_NMT  L
14   str_HBS_M_NMT  M
15   str_HBS_H_NMT  H
16    str_HBS_L_MC  L
17    str_HBS_M_MC  M
18    str_HBS_H_MC  H
19   str_HBS_L_CAR  L
20   str_HBS_M_CAR  M
21   str_HBS_H_CAR  H
22    str_HBS_L_PT  L
23    str_HBS_M_PT  M
24    str_HBS_H_PT  H
25   str_HBO_L_NMT  L
26   str_HBO_M_NMT  M
27   str_HBO_H_NMT  H
28    str_HBO_L_MC  L
29    str_HBO_M_MC  M
30    str_HBO_H_MC  H
31   str_HBO_L_CAR  L
32   str_HBO_M_CAR  M
33   str_HBO_H_CAR  H
34    str_HBO_L_PT  L
35    str_HBO_M_PT  M
36    str_HBO_H_PT  H
37  str_NHBB_L_NMT  L
38  str_NHBB_M_NMT  M
39  str_NHBB_H_NMT  H
40   str_NHBB_L_MC  L
41   str_NHBB_M_MC  M
42   str_NHBB_H_MC  H
43  str_NHBB_L_CAR  L
44  str_NHBB_M_CAR  M
45  str_NHBB_H_CAR  H
46   str_NHBB_L_PT  L
47   str_NHBB_M_PT  M
48   str_NHBB_H_PT  H
49  str_NHBO_L_NMT  L
50  str_NHBO_M_NMT  M
51  str_NHBO_H_NMT  H
52   str_NHBO_L_MC  L
53   str_NHBO_M_MC  M
54   str_NHBO_H_MC  H
55  str_NHBO_L_CAR  L
56  str_NHBO_M_CAR  M
57  str_NHBO_H_CAR  H
58   str_NHBO_L_PT  L
59   str_NHBO_M_PT  M
60   str_NHBO_H_PT  H
61       str_HBW_L  L
62       str_HBW_M  M
63       str_HBW_H  H
64       str_HBS_L  L
65       str_HBS_M  M
66       str_HBS_H  H
67       str_HBO_L  L
68       str_HBO_M  M
69       str_HBO_H  H
70      str_NHBB_L  L
71      str_NHBB_M  M
72      str_NHBB_H  H
73      str_NHBO_L  L
74      str_NHBO_M  M
75      str_NHBO_H  H
76         str_HBW  <N/A>
77         str_HBS  <N/A>
78         str_HBO  <N/A>
79        str_NHBB  <N/A>
80        str_NHBO  <N/A>
81           str_L  L
82           str_M  M
83           str_H  H
84         str_ALL  ALL
85   jtp_HBW_L_NMT  L
86   jtp_HBW_M_NMT  M
87   jtp_HBW_H_NMT  H
88    jtp_HBW_L_MC  L
89    jtp_HBW_M_MC  M
90    jtp_HBW_H_MC  H
91   jtp_HBW_L_CAR  L
92   jtp_HBW_M_CAR  M
93   jtp_HBW_H_CAR  H
94    jtp_HBW_L_PT  L
95    jtp_HBW_M_PT  M
96    jtp_HBW_H_PT  H
97   jtp_HBS_L_NMT  L
98   jtp_HBS_M_NMT  M
99   jtp_HBS_H_NMT  H
100   jtp_HBS_L_MC  L
101   jtp_HBS_M_MC  M
102   jtp_HBS_H_MC  H
103  jtp_HBS_L_CAR  L
104  jtp_HBS_M_CAR  M
105  jtp_HBS_H_CAR  H
106   jtp_HBS_L_PT  L
107   jtp_HBS_M_PT  M
108   jtp_HBS_H_PT  H
109      jtp_HBW_L  L
110      jtp_HBW_M  M
111      jtp_HBW_H  H
112      jtp_HBS_L  L
113      jtp_HBS_M  M
114      jtp_HBS_H  H
115        jtp_HBW  <N/A>
116        jtp_HBS  <N/A>
117          jtp_L  L
118          jtp_M  M
119          jtp_H  H
120        jtp_ALL  ALL
121  mdl_HBW_L_NMT  L
122  mdl_HBW_M_NMT  M
123  mdl_HBW_H_NMT  H
124   mdl_HBW_L_MC  L
125   mdl_HBW_M_MC  M
126   mdl_HBW_H_MC  H
127  mdl_HBW_L_CAR  L
128  mdl_HBW_M_CAR  M
129  mdl_HBW_H_CAR  H
130   mdl_HBW_L_PT  L
131   mdl_HBW_M_PT  M
132   mdl_HBW_H_PT  H
133  mdl_HBS_L_NMT  L
134  mdl_HBS_M_NMT  M
135  mdl_HBS_H_NMT  H
136   mdl_HBS_L_MC  L
137   mdl_HBS_M_MC  M
138   mdl_HBS_H_MC  H
139  mdl_HBS_L_CAR  L
140  mdl_HBS_M_CAR  M
141  mdl_HBS_H_CAR  H
142   mdl_HBS_L_PT  L
143   mdl_HBS_M_PT  M
144   mdl_HBS_H_PT  H
145  mdl_HBO_L_NMT  L
146  mdl_HBO_M_NMT  M
147  mdl_HBO_H_NMT  H
148   mdl_HBO_L_MC  L
149   mdl_HBO_M_MC  M
150   mdl_HBO_H_MC  H
151  mdl_HBO_L_CAR  L
152  mdl_HBO_M_CAR  M
153  mdl_HBO_H_CAR  H
154   mdl_HBO_L_PT  L
155   mdl_HBO_M_PT  M
156   mdl_HBO_H_PT  H
157 mdl_NHBB_L_NMT  L
158 mdl_NHBB_M_NMT  M
159 mdl_NHBB_H_NMT  H
160  mdl_NHBB_L_MC  L
161  mdl_NHBB_M_MC  M
162  mdl_NHBB_H_MC  H
163 mdl_NHBB_L_CAR  L
164 mdl_NHBB_M_CAR  M
165 mdl_NHBB_H_CAR  H
166  mdl_NHBB_L_PT  L
167  mdl_NHBB_M_PT  M
168  mdl_NHBB_H_PT  H
169 mdl_NHBO_L_NMT  L
170 mdl_NHBO_M_NMT  M
171 mdl_NHBO_H_NMT  H
172  mdl_NHBO_L_MC  L
173  mdl_NHBO_M_MC  M
174  mdl_NHBO_H_MC  H
175 mdl_NHBO_L_CAR  L
176 mdl_NHBO_M_CAR  M
177 mdl_NHBO_H_CAR  H
178  mdl_NHBO_L_PT  L
179  mdl_NHBO_M_PT  M
180  mdl_NHBO_H_PT  H
181      mdl_HBW_L  L
182      mdl_HBW_M  M
183      mdl_HBW_H  H
184      mdl_HBS_L  L
185      mdl_HBS_M  M
186      mdl_HBS_H  H
187      mdl_HBO_L  L
188      mdl_HBO_M  M
189      mdl_HBO_H  H
190     mdl_NHBB_L  L
191     mdl_NHBB_M  M
192     mdl_NHBB_H  H
193     mdl_NHBO_L  L
194     mdl_NHBO_M  M
195     mdl_NHBO_H  H
196        mdl_HBW  <N/A>
197        mdl_HBS  <N/A>
198        mdl_HBO  <N/A>
199       mdl_NHBB  <N/A>
200       mdl_NHBO  <N/A>
201          mdl_L  L
202          mdl_M  M
203          mdl_H  H
204        mdl_ALL  ALL

============== НОВЫЙ ПРИМЕР КАДРА ДАННЫХ ========== Добавлены записи, содержащие только c или dв верхней части оригинального df. 

df <- c(
"NMT","MC","CAR","PT","L","M","H","ALL",
"str_HBW_L_NMT" ,"str_HBW_M_NMT" ,"str_HBW_H_NMT" ,"str_HBW_L_MC" ,"str_HBW_M_MC" ,"str_HBW_H_MC" ,
"str_HBW_L_CAR" ,"str_HBW_M_CAR" ,"str_HBW_H_CAR" ,"str_HBW_L_PT" ,"str_HBW_M_PT" ,"str_HBW_H_PT" ,
"str_HBS_L_NMT" ,"str_HBS_M_NMT" ,"str_HBS_H_NMT" ,"str_HBS_L_MC" ,"str_HBS_M_MC" ,"str_HBS_H_MC" ,
"str_HBS_L_CAR" ,"str_HBS_M_CAR" ,"str_HBS_H_CAR" ,"str_HBS_L_PT" ,"str_HBS_M_PT" ,"str_HBS_H_PT" ,
"str_HBO_L_NMT" ,"str_HBO_M_NMT" ,"str_HBO_H_NMT" ,"str_HBO_L_MC" ,"str_HBO_M_MC" ,"str_HBO_H_MC" ,
"str_HBO_L_CAR" ,"str_HBO_M_CAR" ,"str_HBO_H_CAR" ,"str_HBO_L_PT" ,"str_HBO_M_PT" ,"str_HBO_H_PT" ,
"str_NHBB_L_NMT","str_NHBB_M_NMT","str_NHBB_H_NMT","str_NHBB_L_MC","str_NHBB_M_MC","str_NHBB_H_MC",
"str_NHBB_L_CAR","str_NHBB_M_CAR","str_NHBB_H_CAR","str_NHBB_L_PT","str_NHBB_M_PT","str_NHBB_H_PT",
"str_NHBO_L_NMT","str_NHBO_M_NMT","str_NHBO_H_NMT","str_NHBO_L_MC","str_NHBO_M_MC","str_NHBO_H_MC",
"str_NHBO_L_CAR","str_NHBO_M_CAR","str_NHBO_H_CAR","str_NHBO_L_PT","str_NHBO_M_PT","str_NHBO_H_PT",
"str_HBW_L"     ,"str_HBW_M"     ,"str_HBW_H"     ,"str_HBS_L"    ,"str_HBS_M"    ,"str_HBS_H"    ,
"str_HBO_L"     ,"str_HBO_M"     ,"str_HBO_H"     ,"str_NHBB_L"   ,"str_NHBB_M"   ,"str_NHBB_H"   ,
"str_NHBO_L"    ,"str_NHBO_M"    ,"str_NHBO_H"    ,"str_HBW"      ,"str_HBS"      ,"str_HBO"      ,
"str_NHBB"      ,"str_NHBO"      ,"str_L"         ,"str_M"        ,"str_H"        ,"str_ALL"      ,
"jtp_HBW_L_NMT" ,"jtp_HBW_M_NMT" ,"jtp_HBW_H_NMT" ,"jtp_HBW_L_MC" ,"jtp_HBW_M_MC" ,"jtp_HBW_H_MC" ,
"jtp_HBW_L_CAR" ,"jtp_HBW_M_CAR" ,"jtp_HBW_H_CAR" ,"jtp_HBW_L_PT" ,"jtp_HBW_M_PT" ,"jtp_HBW_H_PT" ,
"jtp_HBS_L_NMT" ,"jtp_HBS_M_NMT" ,"jtp_HBS_H_NMT" ,"jtp_HBS_L_MC" ,"jtp_HBS_M_MC" ,"jtp_HBS_H_MC" ,
"jtp_HBS_L_CAR" ,"jtp_HBS_M_CAR" ,"jtp_HBS_H_CAR" ,"jtp_HBS_L_PT" ,"jtp_HBS_M_PT" ,"jtp_HBS_H_PT" ,
"jtp_HBW_L"     ,"jtp_HBW_M"     ,"jtp_HBW_H"     ,"jtp_HBS_L"    ,"jtp_HBS_M"    ,"jtp_HBS_H"    ,
"jtp_HBW"       ,"jtp_HBS"       ,"jtp_L"         ,"jtp_M"        ,"jtp_H"        ,"jtp_ALL"      ,
"mdl_HBW_L_NMT" ,"mdl_HBW_M_NMT" ,"mdl_HBW_H_NMT" ,"mdl_HBW_L_MC" ,"mdl_HBW_M_MC" ,"mdl_HBW_H_MC" ,
"mdl_HBW_L_CAR" ,"mdl_HBW_M_CAR" ,"mdl_HBW_H_CAR" ,"mdl_HBW_L_PT" ,"mdl_HBW_M_PT" ,"mdl_HBW_H_PT" ,
"mdl_HBS_L_NMT" ,"mdl_HBS_M_NMT" ,"mdl_HBS_H_NMT" ,"mdl_HBS_L_MC" ,"mdl_HBS_M_MC" ,"mdl_HBS_H_MC" ,
"mdl_HBS_L_CAR" ,"mdl_HBS_M_CAR" ,"mdl_HBS_H_CAR" ,"mdl_HBS_L_PT" ,"mdl_HBS_M_PT" ,"mdl_HBS_H_PT" ,
"mdl_HBO_L_NMT" ,"mdl_HBO_M_NMT" ,"mdl_HBO_H_NMT" ,"mdl_HBO_L_MC" ,"mdl_HBO_M_MC" ,"mdl_HBO_H_MC" ,
"mdl_HBO_L_CAR" ,"mdl_HBO_M_CAR" ,"mdl_HBO_H_CAR" ,"mdl_HBO_L_PT" ,"mdl_HBO_M_PT" ,"mdl_HBO_H_PT" ,
"mdl_NHBB_L_NMT","mdl_NHBB_M_NMT","mdl_NHBB_H_NMT","mdl_NHBB_L_MC","mdl_NHBB_M_MC","mdl_NHBB_H_MC",
"mdl_NHBB_L_CAR","mdl_NHBB_M_CAR","mdl_NHBB_H_CAR","mdl_NHBB_L_PT","mdl_NHBB_M_PT","mdl_NHBB_H_PT",
"mdl_NHBO_L_NMT","mdl_NHBO_M_NMT","mdl_NHBO_H_NMT","mdl_NHBO_L_MC","mdl_NHBO_M_MC","mdl_NHBO_H_MC",
"mdl_NHBO_L_CAR","mdl_NHBO_M_CAR","mdl_NHBO_H_CAR","mdl_NHBO_L_PT","mdl_NHBO_M_PT","mdl_NHBO_H_PT",
"mdl_HBW_L"     ,"mdl_HBW_M"     ,"mdl_HBW_H"     ,"mdl_HBS_L"    ,"mdl_HBS_M"    ,"mdl_HBS_H"    ,
"mdl_HBO_L"     ,"mdl_HBO_M"     ,"mdl_HBO_H"     ,"mdl_NHBB_L"   ,"mdl_NHBB_M"   ,"mdl_NHBB_H"   ,
"mdl_NHBO_L"    ,"mdl_NHBO_M"    ,"mdl_NHBO_H"    ,"mdl_HBW"      ,"mdl_HBS"      ,"mdl_HBO"      ,
"mdl_NHBB"      ,"mdl_NHBO"      ,"mdl_L"         ,"mdl_M"        ,"mdl_H"        ,"mdl_ALL"

Ответы [ 3 ]

0 голосов
/ 04 июня 2018

Вы можете создать регулярное выражение, которое будет сопоставлять все ваши входные данные с необязательными группировками для b, c и d, удалять из строки все, кроме части c, а затем заменять пустые элементы на NAs: 

res <- sub("^(?:(?:str|jtp|mdl)(?:_|$))?(?:(?:HB[WSO]|NHB[BO])(?:_|$))?(?:([LMH]|ALL)(?:_|$))?(?:NMT|MC|CAR|PT)?$", "\\1", df)
res[nchar(res)==0] <- NA
> res
  [1] NA    NA    NA    NA    "L"   "M"   "H"   "ALL" "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"  
 [29] "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"  
 [57] "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   NA   
 [85] NA    NA    NA    NA    "L"   "M"   "H"   "ALL" "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"  
[113] "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   NA    NA    "L"   "M"   "H"   "ALL" "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"  
[141] "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"  
[169] "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"   "H"   "L"   "M"  
[197] "H"   "L"   "M"   "H"   "L"   "M"   "H"   NA    NA    NA    NA    NA    "L"   "M"   "H"   "ALL"

См. демоверсию regex .

Как видите, здесь вам не нужен дополнительный пакет.

Детали шаблона

  • ^ - начало строки
  • (?:(?:str|jtp|mdl)(?:_|$))? - a часть (необязательная группа): str, jtp или mdl, за которым следует _ или конец строки ((?:_|$))
  • (?:(?:HB[WSO]|NHB[BO])(?:_|$))? - часть b (необязательная группа): необязательная группа без захвата, соответствующая _, а затем HBW, HBS, HBO, NHBB или NHBO с последующим _ или концом строки ((?:_|$))
  • (?:_([LMH]|ALL))? - часть c: опциональносопоставление группы без захвата _, а затем L, M, H или ALL с последующим _ или концом строки ((?:_|$))
  • (?:NMT|MC|CAR|PT)? - ad часть: необязательное сопоставление группы без захвата NMT, MC, CAR или PT
  • $ - конец строки.

Заполнитель \\1 (строка \1) в шаблоне замены восстанавливаетГруппа 1 (c шаблон).

0 голосов
/ 04 июня 2018

Несколько более простых регулярных выражений 

library(stringr)
library(dplyr)

# Create pattern to look for
patt <- paste0("_", c("L", "M", "H", "ALL"), c(rep("$", 4), rep("_", 4)))
patt <- paste(patt, collapse = "|")
patt
[1] "_L$|_M$|_H$|_ALL$|_L_|_M_|_H_|_ALL_"

# Extract pattern and clean
df$income <- str_extract(df$name, patt) %>% 
  str_replace_all("_", "")

head(df)
         name income
str_HBW_L_NMT      L
str_HBW_M_NMT      M
str_HBW_H_NMT      H
 str_HBW_L_MC      L
 str_HBW_M_MC      M
 str_HBW_H_MC      H

Или вообще избегайте регулярных выражений / dplyr / stringr : 

df$income <- 
  strsplit(df$name, split = "_", fixed = TRUE) %>% 
  lapply(
    function(x) {
      our_el <- x %in% c("L", "M", "H", "ALL")
      ifelse(!any(our_el), NA, x[our_el])
    }
  )
0 голосов
/ 04 июня 2018

Я бы превратил эти данные в фрейм данных, разбив строку на столбцы.С ним гораздо проще работать. 

require(stringr)

l <- str_split(df, pattern = "_") 

df2 <- as.data.frame(do.call(rbind, lapply(l, function(x) {length(x) <- 4
x})))

str (df2)

в результате: 

'data.frame':   204 obs. of  4 variables:
$ V1: Factor w/ 3 levels "jtp","mdl","str": 3 3 3 3 3 3 3 3 3 3 ...
$ V2: Factor w/ 9 levels "ALL","H","HBO",..: 5 5 5 5 5 5 5 5 5 5 ...
$ V3: Factor w/ 3 levels "H","L","M": 2 3 1 2 3 1 2 3 1 2 ...
$ V4: Factor w/ 4 levels "CAR","MC","NMT",..: 3 3 3 2 2 2 1 1 1 4 ...
...