Here is another approach, just for fun:
library(tidyverse)
library(tokenizers)
anyall <- function(x, # a character vector
terms # a list of character vectors
){
any(map_lgl(terms, function(term) {
all(term %in% x)
}))
}
mutate(th,
flag = map_lgl(tokenize_tweets(text),
anyall,
terms = tokenize_words(st$terms)))
Best,
Ista
On Tue, Oct 16, 2018 at 5:39 PM Nathan Parsons
<nathan.f.pars...@gmail.com> wrote:
>
> Thanks all for your patience. Here’s a second go that is perhaps more
> explicative of what it is I am trying to accomplish (and hopefully in plain
> text form)...
>
>
> I’m using the following packages: tidyverse, purrr, tidytext
>
>
> I have a number of tweets in the following form:
>
>
> th <- structure(list(status_id = c("x1047841705729306624",
> "x1046966595610927105",
>
> "x1047094786610552832", "x1046988542818308097", "x1046934493553221632",
>
> "x1047227442899775488"), created_at = c("2018-10-04T13:31:45Z",
>
> "2018-10-02T03:34:22Z", "2018-10-02T12:03:45Z", "2018-10-02T05:01:35Z",
>
> "2018-10-02T01:26:49Z", "2018-10-02T20:50:53Z"), text = c("Technique is
> everything with olympic lifts ! @ Body By John https://t.co/UsfR6DafZt";,
>
> "@Subtronics just went back and rewatched ur FBlice with ur CDJs and let me
> tell you man. You are the fucking messiah",
>
> "@ic4rus1 Opportunistic means short-game. As in getting drunk now vs. not
> being hung over tomorrow vs. not fucking up your life ten years later.",
>
> "I tend to think about my dreams before I sleep.", "@MichaelAvenatti
> @SenatorCollins So, if your client was in her 20s, attending parties with
> teenagers, doesn't that make her at the least immature as hell, or at the
> worst, a pedophile and a person contributing to the delinquency of minors?",
>
> "i wish i could take credit for this"), lat = c(43.6835853, 40.284123,
>
> 37.7706565, 40.431389, 31.1688935, 33.9376735), lng = c(-70.3284118,
>
> -83.078589, -122.4359785, -79.9806895, -100.0768885, -118.130426
>
> ), county_name = c("Cumberland County", "Delaware County", "San Francisco
> County",
>
> "Allegheny County", "Concho County", "Los Angeles County"), fips = c(23005L,
>
> 39041L, 6075L, 42003L, 48095L, 6037L), state_name = c("Maine",
>
> "Ohio", "California", "Pennsylvania", "Texas", "California"),
>
> state_abb = c("ME", "OH", "CA", "PA", "TX", "CA"), urban_level = c("Medium
> Metro",
>
> "Large Fringe Metro", "Large Central Metro", "Large Central Metro",
>
> "NonCore (Nonmetro)", "Large Central Metro"), urban_code = c(3L,
>
> 2L, 1L, 1L, 6L, 1L), population = c(277308L, 184029L, 830781L,
>
> 1160433L, 4160L, 9509611L)), class = c("data.table", "data.frame"
>
> ), row.names = c(NA, -6L), .internal.selfref = )
>
>
> I also have a number of search terms in the following form:
>
>
> st <- structure(list(terms = c("me abused depressed", "me hurt depressed",
>
> "feel hopeless depressed", "feel alone depressed", "i feel helpless",
>
> "i feel worthless")), row.names = c(NA, -6L), class = c("tbl_df",
>
> "tbl", "data.frame”))
>
>
> I am trying to isolate the tweets that contain all of the words in each of
> the search terms, i.e “me” “abused” and “depressed” from the first example
> search term, but they do not have to be in order or even next to one
> another.
>
>
> I am familiar with the dplyr suite of tools and have been attempting to
> generate some sort of ‘filter()’ to do this. I am not very familiar with
> purrr, but there may be a solution using the map function? I have also
> explored the tidytext ‘unnest_tokens’ function which transforms the ’th’
> data in the following way:
>
>
> > tidytext::unnest_tokens(th, word, text, token = "tweets") -> tt
>
> > head(tt)
>
> status_id created_at lat lng
>
> 1: x1047841705729306624 2018-10-04T13:31:45Z 43.68359 -70.32841
>
> 2: x1047841705729306624 2018-10-04T13:31:45Z 43.68359 -70.32841
>
> 3: x1047841705729306624 2018-10-04T13:31:45Z 43.68359 -70.32841
>
> 4: x1047841705729306624 2018-10-04T13:31:45Z 43.68359 -70.32841
>
> 5: x1047841705729306624 2018-10-04T13:31:45Z 43.68359 -70.32841
>
> 6: x1047841705729306624 2018-10-04T13:31:45Z 43.68359 -70.32841
>
> county_name fips state_name state_abb urban_level urban_code
>
> 1: Cumberland County 23005 Maine ME Medium Metro 3
>
> 2: Cumberland County 23005 Maine ME Medium Metro 3
>
> 3: Cumberland County 23005 Maine ME Medium Metro 3
>
> 4: Cumberland County 23005 Maine ME Medium Metro 3
>
> 5: Cumberland County 23005 Maine ME Medium Metro 3
>
> 6: Cumberland County 23005 Maine ME Medium Metro 3
>
> population word
>
> 1: 277308 technique
>
> 2: 277308 is
>
> 3: 277308 everything
>
> 4: 277308 with
>
> 5: 277308 olympic
>
> 6: 277308 lifts
>
>
> but once I have unnested the tokens, I am unable to recombine them back
> into tweets.
>
>
> Ideally the end result would append a new column to the ‘th’ data that
> would flag a tweet that contained all of the search words for any of the
> search terms; so the work flow would look like
>
> 1) look for all search words for one search term in a tweet
>
> 2) if all of the search words in the search term are found, create a flag
> (mutate(flag = 1) or some such)
>
> 3) do this for all of the tweets
>
> 4) move on the next search term and repeat
>
>
> Again, my thanks for your patience.
>
>
> --
>
>
> Nate Parsons
>
> Pronouns: He, Him, His
>
> Graduate Teaching Assistant
>
> Department of Sociology
>
> Portland State University
>
> Portland, Oregon
>
>
> 503-725-9025
>
> 503-725-3957 FAX
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
