findGivenNames: Getting gender prediction data for a given text vector.
In genderizeR: Gender Prediction Based on First Names

Description Usage Arguments Value Examples

findGivenNames extracts from text unique terms and predicts gender for them.

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findGivenNames(x, textPrepare = TRUE, country = NULL,
  language = NULL, apikey = NULL, queryLength = 10,
  progress = TRUE, ssl.verifypeer = TRUE)

`x`	A text vector or a character vector of unique terms pre-processed earlier manually or by the `textPrepare` function.
`textPrepare`	If TRUE (default) the `textPrepare` function will be used on the `x` vector. Set it to FALSE if you already have prepared a character vector of cleaned up and deduplicated terms that you want to send to the API for gender checking.
`country`	A character string with a country code for localized search of names. Country codes follow the ISO_3166-1 alpha-2 standard https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2.
`language`	A character string with a language code for localized search of names. Language codes follow the ISO_639-1 standard: https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes
`apikey`	A character string with the API key obtained via https://store.genderize.io. A default is NULL, which uses the free API plan. If you reached the limit of the API you can start from the last checked term next time.
`queryLength`	How much terms can be checked in a one single query.
`progress`	If TRUE (default) progress bar is displayed in the console.
`ssl.verifypeer`	Checks the SSL Certificate. Default is TRUE. You may set it to FALSE if you encounter some errors that break the connection with the API (though it is not recommended).

A data table with given names found in database, gender predictions, probabilities of gender predictions, and counts how many people with a given name is recorded in the database.

x = "Tom did play hookey, and he had a very good time. He got back home 
     barely in season to help Jim, the small colored boy, saw next-day's wood 
     and split the kindlings before supper-at least he was there in time 
     to tell his adventures to Jim while Jim did three-fourths of the work. 
     Tom's younger brother (or rather half-brother) Sid was already through 
     with his part of the work (picking up chips), for he was a quiet boy, 
     and had no adventurous, trouble-some ways. While Tom was eating his
     supper, and stealing sugar as opportunity offered, Aunt Polly asked 
     him questions that were full of guile, and very deep-for she wanted 
     to trap him into damaging revealments. Like many other simple-hearted
     souls, it was her pet vanity to believe she was endowed with a talent 
     for dark and mysterious diplomacy, and she loved to contemplate her 
     most transparent devices as marvels of low cunning. 
     (from 'Tom Sawyer' by Mark Twain)"

xProcessed = textPrepare(x)

foundNames = findGivenNames(xProcessed, textPrepare = FALSE, 
                            ssl.verifypeer = FALSE)
foundNames[count > 100]

# (the results can differ due to new, updated data pulled from the API)
#    name gender probability count
# 1:   jim   male        1.00  2291
# 2:  mark   male        1.00  6178
# 3: polly female        0.99   191
# 4:   tom   male        1.00  3736


# localization
findGivenNames("andrea", country = "us", ssl.verifypeer = FALSE)
#      name gender probability count
# 1: andrea female        0.97  2308

findGivenNames("andrea", country = "it", ssl.verifypeer = FALSE)
#      name gender probability count
# 1: andrea  male         0.99  1070