I would like to extract multiple character strings from one line.
suppose I have the following text line (taken with the 'readLines' function form a website):
line <- "abc:city1-street1-long1-lat1,ldjad;skj//abc:city2-street2-long2-lat2,ldjad;skj//abc:city3-street3-long3-lat3,ldjad;skj//abc:city3-street3-long3-lat3,ldjad;skj//"
I would like to extract the following to separate lines:
[1] city1-street1-long1-lat1
[2] city2-street2-long2-lat2
[3] city3-street3-long3-lat3
[4] city4-street4-long4-lat4
I hope someone can give me a hint how to perform this task.
regmatches
to the rescue:
regmatches(line,gregexpr("city\\d+-street\\d+-long\\d+-lat\\d+",line))
#[[1]]
#[1] "city1-street1-long1-lat1"
#[2] "city2-street2-long2-lat2"
#[3] "city3-street3-long3-lat3"
#[4] "city3-street3-long3-lat3"
A solution with the stringi package:
library(stringi)
stri_extract_all_regex(line, "(?<=:).+?(?=,)")[[1]]
## [1] "city1-street1-long1-lat1" "city2-street2-long2-lat2" "city3-street3-long3-lat3" "city3-street3-long3-lat3"
And with the stringr package:
library(stringr)
str_extract_all(line, perl("(?<=:).+?(?=,)"))[[1]]
## [1] "city1-street1-long1-lat1" "city2-street2-long2-lat2" "city3-street3-long3-lat3" "city3-street3-long3-lat3"
In both cases we are using regular expressions.
Here, we are matching all the characters (non-greedily, i.e. with .+?
)
which occur between :
and ,
. (?<=:)
means a positive look-behind: :
will be matched, but not included in the result. On the other hand, (?=,)
is a positive look-ahead: ,
must be matched but will not appear in the output.
Some benchmarks:
lines <- stri_dup(line, 250) # duplicate line 250 times
library(microbenchmark)
microbenchmark(
stri_extract_all_regex(lines, "(?<=:).+?(?=,)")[[1]],
str_extract_all(lines, perl("(?<=:).+?(?=,)"))[[1]],
regmatches(lines, gregexpr("city\\d+-street\\d+-long\\d+-lat\\d+", lines)),
lapply(unlist(strsplit(lines,',')),
function(x)unlist(strsplit(x,':'))[2]),
lapply(strsplit(lines,'//'),
function(x)
sub('.*:(.*),.*','\\1',x))
)
## Unit: milliseconds
## expr min lq median uq max neval
## gagolews-stri_extract_all_regex 4.722515 4.811009 4.835948 4.883854 6.080912 100
## gagolews-str_extract_all 103.514964 103.824223 104.387175 106.246773 117.279208 100
## thelatemail-regmatches 36.049106 36.172549 36.342945 36.967325 47.399339 100
## agstudy-lapply 21.152761 21.500726 21.792979 22.809145 37.273120 100
## agstudy-lapply2 8.763783 8.854666 8.930955 9.128782 10.302468 100
As you see, the stringi
-based solution is the fastest.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With