R convert character string to a dataframe - r

Question

Here is a small sample of a larger character string that I have (no whitespaces). It contains fictional details of individuals.
Each individual is separated by a . There are 10 attributes for each individual.
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."
I'd like to parse this into a dataframe, with as many observations as there are individuals and 10 columns for each variable.
I've tried using regex and looking at other text extraction solutions on stackoverflow, but haven't been able to reach the output I want.
This is the final dataframe I have in mind, based on the character string input -
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'))
Any help would be much appreciated

Answer 1

Here's a tidy solution with tidyr's functions separate_rows and extract:
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>

Answer 2

Here is a solution using the tidyverse which pipes together different stringr functions to clean the string, before having readr read it, basically as a CSV:
library(dplyr, warn.conflicts = FALSE) # for pipes
df <-
txt %>%
# Replace "." sep with newline
stringr::str_replace_all(
"\\.[A-Z]",
function(x) stringr::str_replace(x, "\\.", "\n")
) %>%
# Replace all commas in (First[,Middle1,Middle2,...]) with space
stringr::str_replace_all(
# Match anything inside brackets, but as few times as possible, so we don't
# match multiple brackets
"\\(.*?\\)",
# Inside the regex that was matched, replace comma with space
function(x) stringr::str_replace_all(x, ",", " ")
) %>%
# Replace ( with ,
stringr::str_replace_all("\\(", ",") %>%
# Remove )
stringr::str_remove_all("\\)") %>%
# Replace # with ,
stringr::str_replace_all("#", ",") %>%
# Remove the last "."
stringr::str_replace_all("\\.$", "\n") %>%
# Add , after female/male
stringr::str_replace_all("male", "male,") %>%
# Read as comma delimited file (works since string contains \n)
readr::read_delim(
file = .,
delim = ",",
col_names = FALSE,
show_col_types = FALSE
)
# Add names (could also be done directly in read_delim with col_names argument)
names(df) <- c(
"family_name",
"first_names",
"gender",
"birthday",
"birth_city",
"birth_country",
"acc_type",
"acc_num",
"district",
"code"
)
df
#> # A tibble: 4 × 10
#> family_name first_names gender birthday birth_city birth_country acc_type
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 EREKSON Andrew Hélène female 10/06/2… Geneva Switzerland PPF
#> 2 BOUKAR Mohamed El-Hadi male 04/12/1… London England PPF
#> 3 HARIMA Olak N’nassik G… female 25/06/2… Paris France PPF
#> 4 THOMAS Hajil Pau Joëli female 03/03/1… Berlin Germany VAT
#> # … with 3 more variables: acc_num <chr>, district <chr>, code <chr>
Created on 2022-03-20 by the reprex package (v2.0.1)
Note that there probably exists more efficient regex'es one could use, but I believe this is simpler and easier to change later.