Matching two strings and extracting the matched character in R - r

Question

Consider I have the below mentioned input character;
text_input <- c("ADOPT", "A", "FAIL", "FAST")
test <- c("TEST", "INPUT", "FAIL", "FAST")
I would like to match both the inputs and extract the words which occurred in common in text_input, I would like to something similar to str_extract.
I do understand that str_extract uses a matching pattern or word to do it, but,my test data consists of around 500,000 words. Any inputs would be really helpful.
Expected Outcome:
"FAIL", "FAST"
EDIT
Just adding one more question here... What happens when the Input is a pure string, like, the one provided below;
text_input <- c("‘Data Scientist’ has been named the sexiest job of the 21st century by Harvard Business Review. The same article tells us that “demand has raced ahead of supply” and that the lack of data scientists “is becoming a serious constraint in some sectors.” A 2011 study by McKinsey Global Institute found that “there will be a shortage of talent necessary for organizations to take advantage of big data” – a shortage to the tune of 140,000 to 190,000 in the United States alone by 2018.")
test <- c("Data Scientist", "McKinsey", "ORGANIZATIONS", "FAST")
Is it possible to perform string match even in this case, as mentioned above.
Note: Changed the input and testing string.

Answer 1

If we need the characters to be extracted
library(stringr)
str_extract(text_input, paste0("[", test, "]+"))
If we are looking for full string match
library(data.table)
fintersect(data.table(col1 = text_input), data.table(col1 = test))

Answer 2

For the easy example you may use intersect() as already was stated in the comments.
text_input1 <- c("ADOPT", "A", "FAIL", "FAST")
test1 <- c("TEST", "INPUT", "FAIL", "FAST")
intersect(text_input1, test1)
# [1] "FAIL" "FAST"
The long example is a little more complicated.
text_input2 <- c("‘Data Scientist’ has been named the sexiest job of the 21st century by Harvard Business Review. The same article tells us that “demand has raced ahead of supply” and that the lack of data scientists “is becoming a serious constraint in some sectors.” A 2011 study by McKinsey Global Institute found that “there will be a shortage of talent necessary for organizations to take advantage of big data” – a shortage to the tune of 140,000 to 190,000 in the United States alone by 2018.")
phrases <- c("Data Scientist", "McKinsey", "ORGANIZATIONS", "FAST")
The test string vector you've defined - I'll call it phrases contains compound terms of two (or probably more) words i.e. containing spaces. Therefore, we need a regular expression rx1 that can handle it. It is not clear if you want case sensitive matches or not, you'd need tolower() both the phrases and the text for the latter. Next we test whether there's a match or not. If so we extend the regex to rx2 so that we can use it well with gsub() replacement functionality. We Vectorize() our function that it can handle vectors of phrases.
matchPhrase <- Vectorize(function(phr, txt, tol=FALSE) {
rx1 <- gsub(" ", "\\\\s", phr) # handle spaces
if (tol) { # optional tolower
rx1 <- tolower(rx1)
txt <- tolower(txt)
}
if (regexpr(rx1, txt) > 0) { # test for matches
rx2 <- paste0(".*(", rx1, ").*")
return(gsub(rx2, "\\1", txt)) # gsub extraction
} else {
return(NA) # we want NA for no matches
}
})
Default without case-sensitivity.
matchPhrase(phrases, text_input2, tol=FALSE)
# Data Scientist McKinsey ORGANIZATIONS FAST
# "Data Scientist" "McKinsey" NA NA
Non-case-sensitive also finds "organizations".
matchPhrase(phrases, text_input2, tol=TRUE)
# Data Scientist McKinsey ORGANIZATIONS FAST
# "data scientist" "mckinsey" "organizations" NA
For a clean output just do:
as.character(na.omit(matchPhrase(phrases, text_input2, tol=TRUE)))
# [1] "data scientist" "mckinsey" "organizations"
Note: Probably you need to adapt the function several times for your specific needs/desired outputs. Actually the quanteda package is quite sophisticated in doing this kind of stuff.

Answer 3

This can also be achieved using the package fuzzyjoin, which contains a way to join df's based on regex.
text_input <- c("ADOPT", "A", "FAIL", "FAST")
regex <- c("TEST", "INPUT", "FAIL", "FAST")
library(fuzzyjoin)
library(dplyr)
df <- tibble( text = text_input )
df.regex <- tibble( regex_name = regex )
# now we can regex match them
df %>%
regex_left_join( df.regex, by = c( text = "regex_name" ) )
# # A tibble: 4 x 2
# text regex_name
# <chr> <chr>
# 1 ADOPT NA
# 2 A NA
# 3 FAIL FAIL
# 4 FAST FAST
#or only regex 'hits'
df %>%
regex_inner_join( df.regex, by = c( text = "regex_name" ) )
# # A tibble: 2 x 2
# text regex_name
# <chr> <chr>
# 1 FAIL FAIL
# 2 FAST FAST