# Interpret the rows of a contingency table Describes the rows of a contingency table. For each row, this description is based on the columns of the contingency table that are significantly related to it. ```r nail_descfreq( dataset, introduction = "", request = NULL, model = "llama3", isolate.groups = FALSE, by.quali = NULL, proba = 0.05, generate = TRUE ) ``` ## Arguments - `dataset`: a data frame corresponding to a contingency table. - `introduction`: the introduction for the LLM prompt. - `request`: the request made to the LLM. - `model`: the model name ('llama3' by default). - `isolate.groups`: a boolean that indicates whether to give the LLM a single prompt, or one prompt per row. Recommended if the contingency table has a great number of rows. - `by.quali`: a factor used to merge the data from different rows of the contingency table; by default NULL and each row is characterized. - `proba`: the significance threshold considered to characterize the category (by default 0.05). - `generate`: a boolean that indicates whether to generate the LLM response. If FALSE, the function only returns the prompt. ## Returns A data frame, or a list of data frames, containing the LLM's prompt and response (if generate = TRUE). ## Details This function directly sends a prompt to an LLM. Therefore, to get a consistent answer, we highly recommend to customize the parameters introduction and request and add all relevant information on your data for the LLM. Additionally, if isolate.groups = TRUE, you will need an introduction and a request that take into account the fact that only one group is analyzed at a time. ## Examples ```r # Processing time is often longer than ten seconds # because the function uses a large language model. ### Example 1: beard dataset ### data(beard_cont) intro_beard_iso <- 'A survey was conducted about beards and 8 types of beards were described. I will give you the results for one type of beard.' intro_beard_iso <- gsub('\n', ' ', intro_beard_iso) |> stringr::str_squish() req_beard_iso <- 'Please give a name to this beard and summarize what makes this beard unique.' req_beard_iso <- gsub('\n', ' ', req_beard_iso) |> stringr::str_squish() res_beard <- nail_descfreq(beard_cont, introduction = intro_beard_iso, request = req_beard_iso, isolate.groups = TRUE, generate = FALSE) res_beard$prompt[1] res_beard$prompt[2] intro_beard <- 'A survey was conducted about beards and 8 types of beards were described. In the data that follow, beards are named B1 to B8.' intro_beard <- gsub('\n', ' ', intro_beard) |> stringr::str_squish() req_beard <- 'Please give a name to each beard and summarize what makes this beard unique.' req_beard <- gsub('\n', ' ', req_beard) |> stringr::str_squish() res_beard <- nail_descfreq(beard_cont, introduction = intro_beard, request = req_beard) cat(res_beard$response) text <- res_beard$response titles <- stringr::str_extract_all(text, "\\*\\*B[0-9]+: [^\\*\\*]+\\*\\*")[[1]] titles # for the following code to work, the response must have the beards' # new names with this format: **B1: The Nice beard**, etc. titles <- stringr::str_replace_all(titles, "\\*\\*", "") # remove asterisks names <- stringr::str_extract(titles, ": .+") names <- stringr::str_replace_all(names, ": ", "") # remove the colon and space rownames(beard_cont) <- names library(FactoMineR) res_ca_beard <- CA(beard_cont, graph = F) plot.CA(res_ca_beard, invisible = "col") ### Example 2: children dataset ### data(children) children <- children[1:14, 1:5] |> t() |> as.data.frame() rownames(children) <- c('No education', 'Elementary school', 'Middle school', 'High school', 'University') intro_children <- 'The data used here is a contingency table that summarizes the answers given by different categories of people to the following question: "according to you, what are the reasons that can make a woman of a couple hesitate to have children?". Each row corresponds to a level of education, and columns are reasons.' intro_children <- gsub('\n', ' ', intro_children) |> stringr::str_squish() req_children <- "Please explain the main differences between more educated and less educated couples, when it comes to hesitating to have children." req_children <- gsub('\n', ' ', req_children) |> stringr::str_squish() res_children <- nail_descfreq(children, introduction = intro_children, request = req_children) cat(res_children$response) ```

nail_descfreq function