Category: Data analysis

Here’s a function that generates a specified number of unique random IDs, of a certain length, in a reproducible way.

There are many reasons you might want a vector of unique random IDs.  In this case, I embed my unique IDs in SurveyMonkey links that I send via mail merge. This way I can control the emailing process, rather than having messages come from SurveyMonkey, but I can still identify the respondents.  If you are doing this for the same purpose, note that you first need to enable a custom variable in SurveyMonkey!  I call mine a for simplicity.

The function

create_unique_ids <- function(n, seed_no = 1, char_len = 5){
  set.seed(seed_no)
  pool <- c(letters, LETTERS, 0:9)
  
  res <- character(n) # pre-allocating vector is much faster than growing it
  for(i in seq(n)){
    this_res <- paste0(sample(pool, char_len, replace = TRUE), collapse = "")
    while(this_res %in% res){ # if there was a duplicate, redo
      this_res <- paste0(sample(pool, char_len, replace = TRUE), collapse = "")
    }
      res[i] <- this_res
  }
  res
}

Here’s what you get:

> create_unique_ids(10)
 [1] "qxJ4m" "36ONd" "mkQxV" "ES9xW" "5nOhq" "xax1v" "DLElZ" "PXgSz" "YOWIG" "WbDTQ"

This function could get stuck in the while-loop if your N exceeds the number of unique permutations of alphanumeric strings of length char_len.  There are length(pool) ^ char_len permutations available.  Under the default value of char_len = 5, that’s 62^5 combinations or 916,132,832.  This should not be a problem for most users.

On reproducible randomization

The ability to set the randomization seed is to aid in reproducing the ID vector.  If you’re careful, and using version control, you should be able to retrace what you did even without setting seed.  There are downsides to setting the same seed each time too, for instance, if your input list gets shuffled and you’re now assigning already-used codes to different users.

No matter how you use this function, think carefully about how to record and reuse values such that IDs stay consistent over time.

Exporting results for mail merging

Here’s what this might look like in practice if you want to generate these IDs, then merge them into SurveyMonkey links and export for sending in a mail merge.  In the example below, I generate both English- and Spanish-language links.

roster$id <- create_unique_ids(nrow(roster), seed = 23)
roster$link_en <- paste0("https://www.research.net/r/YourSurveyName?a=", roster$id, "&lang=en")
roster$link_es <- paste0("https://www.research.net/r/YourSurveyName?a=", roster$id, "&lang=es")
readr::write_csv(roster, "data/clean/roster_to_mail.csv", na = "")

Note that I have created the custom variable a in SurveyMonkey, which is why I can say a= in the URL.

(Or, “how to teach professionals to teach themselves R”).

Background: I taught myself R in 2014 from public web resources, and since then have steered several cohorts of data analysts at my organization through various R curricula, adapting based on their feedback.

This is geared toward people teaching themselves R outside of graduate school (I perceive graduate students to have more built-in applications and more time for learning, though I don’t speak from experience).  I say “students” below but I am referring to professionals.  This advice assumes little or no programming experience in other languages, e.g., people making the shift from Excel to R (I maintain that Excel is one of R’s chief competitors).  If you already work in say, Stata, you may face fewer frustrations (and might consider DataCamp’s modules geared specifically to folks in your situation). 

I’ve tried combinations of Coursera’s Data Science Specialization, DataCamp’s R courses, and the “R for Data Science” textbook.  Here’s what I’ve learned about learning and teaching R and what I recommend.

I see three big things that will help you learn R:

1. A problem you really want to solve
2. A self-study resource
3. A coach/community to help you

Summary: in 2015 I created a Twitter bot, @AnnArborVotes (code on GitHub).  (2018 Sam says: after this project ceased I gave the Twitter handle to local civics hero Mary Morgan at A2CivCity).  I searched Twitter for 52,000 unique voter names, matching names from the Ann Arbor, MI voter rolls to Twitter accounts based nearby.  The bot then tweeted messages to a randomly-selected half of those 2,091 matched individuals, encouraging them to vote in a local primary election that is ordinarily very low-turnout.

I then examined who actually voted (a matter of public record).  There was no overall difference between the treatment and control groups. I observed a promising difference in the voting rate when looking only at active Twitter users, i.e., those who had tweeted in the month before I visited their profile. These active users only comprised 7% of my matched voters, however, and the difference in this small subgroup was not statistically significant (n = 150, voting rates of 23% vs 15%, p = 0.28).

I gave a talk summarizing the experiment at Nerd Nite Ann Arbor that is accessible to laypeople (it was at a bar and meant to be entertainment):

This video is hosted by the amazing Ann Arbor District Library – here is their page with multiple formats of this video and a summary of the talk.  Here are the slides from the talk (PDF), but they’ll make more sense with the video’s voiceover.

The full write-up:

I love the R programming language (#rstats) and wanted a side project.  I’d been curious about Twitter bots.  And I’m vexed by how low voter turnout is in local elections.  Thus, this experiment.