rancors_builder() generates multiple random corpus (rancor) based on a user
defined term probabilities and vocabulary. sers can set the number of
documents, as well as the mean, standard deviation, minimum, and maximum
document lengths (i.e., number of tokens) of the parent normal distribution
from which the document lengths are randomly sampled. The output is a list of
document-term matrices. To produce a single random corpus, use
rancor_builder() (note the singular).
rancors_builder(
data,
vocab,
probs,
n_cors,
n_docs,
len_mean,
len_var,
len_min,
len_max,
seed = NULL
)Data.frame containing vocabulary and probabilities
Name of the column containing vocabulary
Name of the column containing probabilities
Integer indicating the number of corpora to build
Integer(s) indicating the number of documents to be returned If two numbers are provide, number will be randomly sampled within the range for each corpora.
Integer(s) indicating the mean of the document lengths in the parent normal sampling distribution. If two numbers are provided, number will be randomly sampled within the range for each corpora.
Integer(s) indicating the standard deviation of the document lengths in the parent normal sampling distribution. If two numbers are provided, number will be randomly sampled within the range for each corpora.
Integer(s) indicating the minimum of the document lengths in the parent normal sampling distribution. If two numbers are provided, number will be randomly sampled within the range for each corpora.
Integer(s) indicating the maximum of the document lengths in the parent normal sampling distribution. If two numbers are provided, number will be randomly sampled within the range for each corpora.
Optional seed for reproducibility
# \donttest{
# create corpus and DTM
my_corpus <- data.frame(
text = c(
"I hear babies crying I watch them grow",
"They'll learn much more than I'll ever know",
"And I think to myself",
"What a wonderful world",
"Yes I think to myself",
"What a wonderful world"
),
line_id = paste0("line", seq_len(6))
)
## some text preprocessing
my_corpus$clean_text <- tolower(gsub("'", "", my_corpus$text))
dtm <- dtm_builder(
data = my_corpus,
text = clean_text,
doc_id = line_id
)
# use colSums to get term frequencies
df <- data.frame(
vocab = colnames(dtm),
freqs = colSums(dtm)
)
# convert to probabilities
df$probs <- df$freqs / sum(df$freqs)
# create random DTM
ls_dtms <- df |>
rancors_builder(vocab,
probs,
n_cors = 20,
n_docs = 100,
len_mean = c(50, 200),
len_var = 5,
len_min = 20,
len_max = 1000,
seed = 59801
)
length(ls_dtms)
#> [1] 20
# }