agricolamz

embrase <- function(string){
  if(is.na(string)){
    string
  } else {
    string |> 
      str_remove_all("[\\{\\}]") |> 
      str_replace_all("((?<=[ABCDEFGHIJKLMNOPQRSTUVWXYZАБВГДЕËЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ]))",
                      "\\}\\1") |> 
      str_replace_all("((?=[ABCDEFGHIJKLMNOPQRSTUVWXYZАБВГДЕËЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ]))",
                      "\\1\\{")

agricolamz

library(ellmer)
chat <- chat_ollama(model = "gemma3n")

type_dictionary_entry <- type_object(
    lemma = type_string("Begining of the string before parenthesis or list of meanings."),
    morphology = type_string("Data provided in parenthesis", required = FALSE),
    meanings = type_array(
      type_string(),
      "List of translation into Russian and examples. In most cases I expect just a sing meaning after parenthesis. If there are multiple meanings they will be listed using roman numerals like 1) 2) 3) or 1. 2. 3. The number of meanings should be exactly as much as there are items in the enumerated list. There are also examples of word usage that are listed after the colon. For each meaning there could be multiple examples",
    )

agricolamz

# This code is licensed under the terms of the MIT license
# Author: George Moroz
# Date: 2025-08-14
# Update: 2025-09-16
# see the oficial docs: https://ruscorpora.github.io/public-api/

library(tidyverse)
my_token <- "put your token here"
lemma_for_search <- "накачать"

agricolamz

library(tidyverse)

readxl::read_xlsx("data.xlsx") |> 
  filter(corpus == "Dagestan.xml",
         !is.na(string_id)) ->
  df

df |> 
  select(where(function(x) sum(is.na(x)) != nrow(df))) |> 
  select(-person_id, -corpus) |> 

agricolamz

agricolamz

library(tidyverse)
read_csv("https://raw.githubusercontent.com/agricolamz/2024_HSE_b_da4l/master/data/Coretta_2017_icelandic.csv") |> 
  filter(speaker == "tt01") ->
  vowels

mean_data <- mean(vowels$vowel.dur)
sd_data <- sd(vowels$vowel.dur)

m1 <- function(x) dnorm(x, mean = mean_data, sd = sd_data) * 
  dnorm(x, mean = 87, sd = 25)

agricolamz

library(tidyverse)
read_csv("https://raw.githubusercontent.com/agricolamz/2025_HSE_b_da4l/refs/heads/main/data/Coretta_2017_icelandic.csv") |> 
  filter(speaker == "tt01") ->
  vowels

sd_prior <- 25
sd_data <- sd(vowels$vowel.dur)
sd_post <- 1/sqrt(1/sd_prior^2 + 1/sd_data^2)
mean_prior <- 87
mean_data <- mean(vowels$vowel.dur)

agricolamz

library(tidyverse)
library(lingtypology)
df <- bivaltyp.feature()


df |> 
  filter(family_WALS == "Nakh-Daghestanian") |> 
  writexl::write_xlsx("~/Desktop/daghestan_bivaltyp.xlsx")

df |> 

agricolamz

library(tidyverse)
phonfieldwork::read_from_folder("...") |> 
  filter(tier == 2,
         content != "") |> 
  mutate(dur = time_end-time_start) |> 
  summarise(duration_minutes = sum(dur)/60)

agricolamz

library(tidyverse)
khi <- read_csv("russian_spoken_corpora_analysis/dialect_khislavichi_udpiped_mystemed.csv")
spi <- read_csv("russian_spoken_corpora_analysis/dialect_spiridonova_buda_udpiped_mystemed.csv")

khi |> 
  bind_rows(spi) |> 
  filter(mystem_pos == "S",
         str_detect(mystem_feats, "nom.pl"),
         str_detect(mystem_feats, "^[mn],")) |> 
  writexl::write_xlsx("~/Desktop/4Sveta_N_mn_nompl.xlsx")