R将字符串转换为数据框

Question

这是我拥有的较大字符串的一小部分示例(无空格)。它包含个人的虚构细节。

每个个体由分隔。每个个体有10个属性。

txt = "EREKSON(Andrew,Hélène),female10/06/2011@Geneva(Switzerland),PPF,2000X007707,dist.093,Dt.043/996.BOUKAR(Mohamed,El-Hadi),male04/12/1956@London(England),PPF,2001X005729,dist.097,Dt.043/997.HARIMA(Olak,N’nassik,Gerad,Elisa,Jeremie),female25/06/2013@Paris(France),PPF,2009X005729,dist.088,Dt.043/998.THOMAS(Hajil,Pau,Joëli),female03/03/1980@Berlin(Germany),VAT,2010X006016,dist.078,Dt.043/999."

我想将其解析为一个数据框，其中包含与个体一样多的观察结果，每个变量有 10 列。

我试过使用正则表达式并查看了 stackoverflow 上的其他文本提取解决方案，但无法获得我想要的输出。

这是我想到的最终数据框，基于字符串输入——

result = data.frame(first_names = c('Hélène Andrew','Mohamed El-Hadi','Olak N’nassik Gerad Elisa Jeremie','Joëli Pau Hajil'),
                    family_name = c('EREKSON','BOUKAR','HARIMA','THOMAS'),
                    gender = c('male','male','female','female'),
                    birthday = c('10/06/2011','04/12/1956','25/06/2013','03/03/1980'),
                    birth_city = c('Geneva','London','Paris','Berlin'),
                    birth_country = c('Switzerland','England','France','Germany'),
                    acc_type = c('PPF','PPF','PPF','VAT'),
                    acc_num = c('2000X007707','2001X005729','2009X005729','2010X006016'),
                    district = c('dist.093','dist.097','dist.088','dist.078'),
                    code = c('Dt.043/996','Dt.043/997','Dt.043/998','Dt.043/999'))

任何帮助将不胜感激

Answer 1

这是一个使用 tidyr 函数 separate_rows 和 extract 的简洁解决方案:

library(tidyr)
data.frame(txt) %>%
  # separate `txt` into rows using the dot `.` *if* 
  # preceded by `Dt\\.\\d{3}/\\d{3}` as splitting pattern:
  separate_rows(txt, sep = "(?<=Dt\\.\\d{3}/\\d{3})\\.(?!$)") %>%
  extract(
          # select column from which to extract:
          txt,
          # define column names into which to extract:
          into = c("family_name","first_names","gender",
                   "birthday","birth_city","birth_country",
                   "acc_type","acc_num","district","code"),
          # describe the string exhaustively using capturing groups
          # `(...)` to delimit what's to be extracted:
          regex = "([A-Z]+)\\(([\\w,]+)\\),([a-z]+)([\\d/]+)@(\\w+)\\((\\w+)\\),([A-Z]+),(\\w+),dist.(\\d+),Dt\\.([\\d/]+)")
# A tibble: 4 × 10
  family_name first_names    gender birthday   birth_city birth_country acc_type acc_num  
  <chr>       <chr>          <chr>  <chr>      <chr>      <chr>         <chr>    <chr>    
1 EREKSON     Andrew,Peter   male   10/06/2011 Geneva     Switzerland   PPF      2000X007…
2 OBAMA       Barack,Hussian male   04/12/1956 London     England       PPF      2001X005…
3 CLINTON     Hillary        female 25/06/2013 Paris      France        PPF      2009X005…
4 GATES       Melinda        female 03/03/1980 Berlin     Germany       VAT      2010X006…
# … with 2 more variables: district <chr>, code <chr>