Convert data frame or character vector to a ‘corporaexplorerobject’ for subsequent exploration.

prepare_data(dataset, ...)

# S3 method for data.frame
prepare_data(
dataset,
date_based_corpus = TRUE,
grouping_variable = NULL,
within_group_identifier = "Seq",
columns_doc_info = c("Date", "Title", "URL"),
corpus_name = NULL,
use_matrix = TRUE,
matrix_without_punctuation = TRUE,
tile_length_range = c(1, 10),
columns_for_ui_checkboxes = NULL,
...
)

# S3 method for character
prepare_data(
dataset,
corpus_name = NULL,
use_matrix = TRUE,
matrix_without_punctuation = TRUE,
...
)

## Arguments

dataset Object to convert to corporaexplorerobject: A data frame with a column "Text" (class character), and optionally other columns. If date_based_corpus is TRUE (the default), dataset must contain a column "Date" (of class Date). Or a non-empty character vector. Other arguments to be passed to prepare_data. Logical. Set to FALSE if the corpus is not to be organised according to document dates. Character string. If date_based_corpus is TRUE, this argument is ignored. If date_based_corpus is FALSE, this argument can be used to group the documents, e.g. if dataset is organised by chapters belonging to different books. Character string indicating column name in dataset. "Seq", the default, means the rows in each group are assigned a numeric sequence 1:n where n is the number of rows in the group. Used in document tab title in non-date based corpora. If date_based_corpus is TRUE, this argument is ignored. Character vector. The columns from dataset to display in the "document information" tab in the corpus exploration app. By default "Date", "Title" and "URL" will be displayed, if included. If columns_doc_info includes a column which is not present in dataset, it will be ignored. Character string with name of corpus. Logical. Should the function create a document term matrix for fast searching? If TRUE, data preparation will run longer and demand more memory. If FALSE, the returning corporaexplorerobject will be more light-weight, but searching will be slower. Should punctuation and digits be stripped from the text before constructing the document term matrix? If TRUE, the default: The corporaexplorer object will be lighter and most searches in the corpus exploration app will be faster. Searches including punctuation and digits will be carried out in the full text documents. The only "risk" with this strategy is that the corpus exploration app in some cases can produce false positives. E.g. searching for the term "donkey" will also find the term "don%key". This should not be a problem for the vast majority of use cases, but if one so desires, there are three different solutions: set this parameter to FALSE, create a corporaexplorerobject without a matrix by setting the use_matrix parameter to FALSE, or run explore with the use_matrix parameter set to FALSE. If FALSE, the corporaexplorer object will be larger, and most simple searches will be slower. Numeric vector of length two. Fine-tune the tile lengths in document wall and day corpus view. Tile length is calculated by scales::rescale(nchar(dataset\$Text), to = tile_length_range, from = c(0, max(.))) Default is c(1, 10). Character. Character or factor column(s) in dataset. Include sets of checkboxes in the app sidebar for convenient filtering of corpus. Typical useful for columns with a small set of unique (and short) values. Checkboxes will be arranged by sort(), unless columns_for_ui_checkboxes is a vector of factors, in which case the order will be according to factor level order (easy relevelling with forcats::fct_relevel()). To use a different label in the sidebar than the columnn name, simply pass a named character vector to columns_for_ui_checkboxes. If columns_for_ui_checkboxes includes a column which is not present in dataset, it will be ignored.

## Value

A corporaexplorer object to be passed as argument to explore and run_document_extractor.

## Details

For data.frame: Each row in dataset is treated as a base differentiating unit in the corpus, typically chapters in books, or a single document in document collections. The following column names are reserved and cannot be used in dataset: "ID", "Text_original_case", "Tile_length", "Year", "Seq", "Weekday_n", "Day_without_docs", "Invisible_fake_date", "Tile_length".

A character vector will be converted to a simple corporaexplorerobject with no metadata.

## Examples

## From data.frame
# Constructing test data frame:
dates <- as.Date(paste(2011:2020, 1:10, 21:30, sep = "-"))
texts <- paste0(
"This is a document about ", month.name[1:10], ". ",
"This is not a document about ", rev(month.name[1:10]), "."
)
titles <- paste("Text", 1:10)
test_df <- tibble::tibble(Date = dates, Text = texts, Title = titles)

# Converting to corporaexplorerobject:
corpus <- prepare_data(test_df, corpus_name = "Test corpus")
#> Starting.#> Document data frame done.#> Calendar data frame done.#> Document term matrix: text processed.#> Document term matrix: tokenising completed.#> Document term matrix: word list created.#> Document term matrix done.#> Done.
if(interactive()){
# Running exploration app:
explore(corpus)

# Running app to extract documents:
run_document_extractor(corpus)
}

## From character vector
alphabet_corpus <- prepare_data(LETTERS)
#> Starting.#> Document data frame done.#> Corpus is not date based. Calendar data frame skipped.#> Document term matrix: text processed.#> Document term matrix: tokenising completed.#> Document term matrix: word list created.#> Document term matrix done.#> Done.
if(interactive()){
# Running exploration app:
explore(alphabet_corpus)
}