skipgram_append {eHDPrep} | R Documentation |
Adds new variables to data
which report the presence of skipgrams
(either those specified in skipgrams2append
or, if not specified,
skipgrams with a minimum frequency (min_freq
, default = 1)).
skipgram_append(skipgram_tokens, skipgrams2append, data, id_var, min_freq = 1)
skipgram_tokens |
Output of |
skipgrams2append |
Which skipgrams in |
data |
Data frame to append skipgram variables to. |
id_var |
An unquoted expression which corresponds to a variable in
|
min_freq |
Minimum percentage frequency of skipgram occurrence to return. Default = 1. |
data
with additional variables describing presence of
skipgrams
Guthrie, D., Allison, B., Liu, W., Guthrie, L. & Wilks, Y. A Closer Look at Skip-gram Modelling. in Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06) (European Language Resources Association (ELRA), 2006).
Benoit K, Watanabe K, Wang H, Nulty P, Obeng A, Müller S, Matsuo A (2018). “quanteda: An R package for the quantitative analysis of textual data.” _Journal of Open Source Software_, *3*(30), 774. doi:10.21105/joss.00774 <https://doi.org/10.21105/joss.00774>, <https://quanteda.io>.
Feinerer I, Hornik K (2020). _tm: Text Mining Package_. R package version 0.7-8, <https://CRAN.R-project.org/package=tm>.
Ingo Feinerer, Kurt Hornik, and David Meyer (2008). Text Mining Infrastructure in R. Journal of Statistical Software 25(5): 1-54. URL: https://www.jstatsoft.org/v25/i05/.
Principle underlying function: tokens_ngrams
Other free text functions:
extract_freetext()
,
skipgram_freq()
,
skipgram_identify()
data(example_data)
# identify skipgrams
toks_m <- skipgram_identify(x = example_data$free_text,
ids = example_data$patient_id,
max_interrupt_words = 5)
# add skipgrams by minimum frequency
skipgram_append(toks_m,
id_var = patient_id,
min_freq = 0.6,
data = example_data)
# add specific skipgrams
skipgram_append(toks_m,
id_var = patient_id,
skipgrams2append = c("sixteen_week", "bad_strain"),
data = example_data)