R/soundcomparisons.feature.R
In ropensci/lingtypology: Linguistic Typology and Mapping
Defines functions
soundcomparisons.feature
Documented in
soundcomparisons.feature
#' Download SOUNDCOMPARISONS data
#'
#' This function downloads data from SOUNDCOMPARISONS and changes language names to the names from lingtypology database. You need the internet connection.
#'
#' @param word A character vector that define with a feature ids from SOUNDCOMPARISONS (e. g. "one", "sharp_fem", "near_neut", "on_the_left", " I_will_give", "write_ipv_sg", "your_pl_pl").
#' @seealso \code{\link{abvd.feature}}, \code{\link{afbo.feature}}, \code{\link{autotyp.feature}}, \code{\link{oto_mangueanIC.feature}}, \code{\link{phoible.feature}}, \code{\link{sails.feature}}, \code{\link{uralex.feature}}, \code{\link{valpal.feature}}, \code{\link{vanuatu.feature}}, \code{\link{eurasianphonology.feature}}, \code{\link{eurasianphonology.feature}}
#' @seealso \code{\link{abvd.feature}}, \code{\link{afbo.feature}}, \code{\link{autotyp.feature}}, \code{\link{bivaltyp.feature}}, \code{\link{eurasianphonology.feature}}, \code{\link{oto_mangueanIC.feature}}, \code{\link{phoible.feature}}, \code{\link{sails.feature}}, \code{\link{uralex.feature}}, \code{\link{valpal.feature}}, \code{\link{vanuatu.feature}}, \code{\link{wals.feature}}
#' @author Anna Smirnova
#' @examples
#' # soundcomparisons.feature(c("sun", "house"))
#' @export
#'
#' @importFrom utils read.csv
#'
soundcomparisons.feature <- function(word) {
message(paste0("Don't forget to cite a source (modify in case of using individual chapters):
Heggarty, Paul and Shimelman, Aviva and Abete, Giovanni and Anderson, Cormac and Sadowsky, Scott and Paschen, Ludger and Maguire, Warren and Jocz, Lechoslaw and Aninao, Maria Jose and Wagerle, Laura and Dermaku-Appelganz, Darja and Silva, Ariel Pheula do Couto e and Lawyer, Lewis C. and Michalsky, Jan and Cabral, Ana Suelly Arruda Camara and Walworth, Mary and Koile, Ezequiel and Runge, Jakob and Bibiko, Hans-Jorg (eds.) 2019. New online database and resource for researching phonetic diversity.
(Available online at https://soundcomparisons.com/, Accessed on ",
Sys.Date(),
".)
@misc{heggarty_sound_2019,
title = {Sound {Comparisons}: {Exploring} {Diversity} in {Phonetics} across {Language} {Families}},
url = {https://soundcomparisons.com},
journal = {Sound Comparisons: Exploring Diversity in Phonetics across Language Families},
author = {Heggarty, Paul and Shimelman, Aviva and Abete, Giovanni and Anderson, Cormac and Sadowsky, Scott and Paschen, Ludger and Maguire, Warren and Jocz, Lechoslaw and Aninao, Maria Jose and Wagerle, Laura and Dermaku-Appelganz, Darja and Silva, Ariel Pheula do Couto e and Lawyer, Lewis C. and Michalsky, Jan and Cabral, Ana Suelly Arruda Camara and Walworth, Mary and Koile, Ezequiel and Runge, Jakob and Bibiko, Hans-Jorg},
year = {2019},
keywords = {Dialects, Phonetics, Recordings, Sound Comparisons, Transcriptions}
}
@inproceedings{heggarty_sound_2019,
address = {Canberra, Australia},
title = {Sound {Comparisons}: {A} new online database and resource for researching phonetic diversity},
isbn = {978-0-646-80069-1},
url = {https://icphs2019.org/icphs2019-fullpapers/pdf/full-paper_490.pdf},
booktitle = {Proceedings of the 19th {International} {Congress} of {Phonetic} {Sciences}},
publisher = {Australasian Speech Science and Technology Association},
author = {Heggarty, Paul and Shimelman, Aviva and Abete, Giovanni and Anderson, Cormac and Sadowsky, Scott and Paschen, Ludger and Maguire, Warren and Jocz, Lechoslaw and Aninao, Maria Jose and Wagerle, Laura and Dermaku-Appelganz, Darja and Silva, Ariel Pheula do Couto e and Lawyer, Lewis C. and Michalsky, Jan and Cabral, Ana Suelly Arruda Camara and Walworth, Mary and Koile, Ezequiel and Runge, Jakob and Bibiko, Hans-Jorg},
month = aug,
year = {2019},
keywords = {Dialects, Phonetics, Recordings, Sound Comparisons, Transcriptions},
pages = {280--284}
}"))
# # # in order to recollect word_set_1 and word_set_2
# rom_words <- read.csv("https://soundcomparisons.com/export/csv?study=Romance&languages=12231572409&words=10,20,30,40,50,60,70,80,90,100,110,200,210,7820,1620,1810,7000,7040,7320,7390,7840,7510,7980,7960,6200,7330,7650,7710,7540,7610,7910,7920,2010,2110,2130,2140,2230,2240,2250,2310,2340,2360,2510,2610,2620,2640,2660,2840,4310,4320,4410,4370,4520,4100,3020,3140,3160,3170,3180,3190,3310,3420,3810,3820,3900,5120,5130,5150,5210,5240,5510,5520,6410,5610,6260,6310,6320,5900,5890,5850,5870,6040,6020,6070,6140,8391,8390,6520,6530,6550,6860,6940,6840,8000,8020,8050,8170,8430,8510,8530,8410,8480,8490,8611,8610,8630,8640,8650,8930,9040,9041,9070,710,720,730,740,1120,1240,1260,1410,7340,5040,5020,5090,5070,5080,510,4710", encoding = "UTF-8")
# ger_words <- read.csv("https://soundcomparisons.com/export/csv?study=Germanic&languages=11171246109&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
# eng_words <- read.csv("https://soundcomparisons.com/export/csv?study=Englishes&languages=11111230301&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7650,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2140,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,1440,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8940,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
# slav_words <- read.csv("https://soundcomparisons.com/export/csv?study=Slavic&languages=13111210507&words=10,20,30,40,50,60,70,80,90,100,200,7010,7020,7040,7050,7210,7360,7380,7410,7420,7510,7980,7520,7650,7660,7670,7680,7710,7740,7840,7880,7890,7910,7930,7940,7960,7970,820,2010,2120,2130,2140,2230,2240,2250,2310,2530,2610,2810,4330,4332,4100,4110,3120,3140,3150,3240,3310,3350,3810,3820,3840,3850,3900,5010,5012,5020,5110,5120,5150,4150,5210,5220,5230,5232,5240,5410,5420,5510,5520,5430,5540,5610,6230,6240,6270,6290,6310,6410,5820,5830,5850,5920,6020,6030,6040,6150,6070,6200,6590,6630,6840,6860,6942,6940,8000,8010,8130,8140,8320,8410,8420,8430,8480,8530,8580,8620,8740,8850,8890,8950,8970,8980,9010,9100,1010,710,1030,1040,720,740,1080,780,1230,1330,1410,1350,1120,5060,5080,5052,5050", encoding = "UTF-8")
# celt_words <- read.csv("https://soundcomparisons.com/export/csv?study=Celtic&languages=14111238609&words=10,20,30,40,50,60,70,80,90,100,110,200,7810,1830,7010,7020,7060,7040,7110,7170,7120,7360,7390,7400,7460,7480,7840,7520,7510,7580,7570,7590,7650,7660,7710,7720,7740,7760,810,2140,2230,2320,2530,2630,2640,2670,2600,2740,2840,4010,4020,4420,4470,3120,3190,3200,3330,3840,3910,6420,5110,5140,5240,6460,5430,5620,5630,6470,6340,6360,6310,6320,5900,5850,6010,6950,6150,6040,6070,6300,6820,8040,8070,8300,9090,8480,9070,1010,1040,1080,1090,1490,4610,4620,4910,4930,6190,6191,6192,4100,4101,4102,6450,6451,6452,3300,3301,3302,5410,5411,5412,6510,6511,6512,3110,3111,3112,2810,2811,2812,2010,2011,2012,5520,5521,5522,510,511,512,2370,2371,2372,3280,3281,3282,5100,5101,5102,3950,3951,3952,6990,6991,6992", encoding = "UTF-8")
# ands_words <- read.csv("https://soundcomparisons.com/export/csv?study=Andes&languages=21222210001&words=11,10,20,30,40,50,60,70,80,90,100,200,7011,7010,7020,7110,7120,7840,7860,7880,7510,7980,7960,7710,7750,7970,7971,7910,7930,2010,2050,2110,2111,2120,2130,2210,2230,2240,2310,2410,2510,2611,2610,2620,2630,2640,2710,2811,2810,4010,4030,4101,4100,3120,3430,3820,3830,5110,5120,5140,5170,5210,5230,5410,5510,5430,5520,6410,6220,5850,6030,6060,6040,6020,6120,6510,6520,6810,8021,8020,8030,8050,8110,8311,8310,8370,8420,8421,8430,8440,8510,8550,8580,8590,8410,8411,8270,8600,8610,8620,8740,8910,8940,9040,1011,1010,1020,1040,1060,1070,1120,1210,1220,1230,1260,1310,1320,1360,1910,5010,5020,510,520", encoding = "UTF-8")
# map_words <- read.csv("https://soundcomparisons.com/export/csv?study=Mapudungun&languages=28141005109&words=10,20,30,40,50,60,70,80,90,100,200,210,7010,7020,7030,7050,7110,7840,7860,7880,7980,7960,7710,7750,7910,7930,7170,20680,20690,7720,20790,2010,2050,2110,2120,2130,2210,2230,2240,10560,2410,2310,2510,2530,2610,2620,2630,2640,2710,2810,2840,10050,3010,4010,4030,4100,20170,10370,10620,20240,10740,4320,20190,10790,31180,21410,21400,20090,3120,20070,20030,20910,10200,3210,20010,20020,3420,3430,20100,3820,3830,3810,5110,5120,5130,5140,10230,20980,5170,10710,5210,5220,5230,5240,5410,5510,5430,10120,5520,6410,10760,20480,6220,6210,6240,6260,20130,5850,5810,5840,10810,21530,21520,6030,6060,6040,6020,6120,10730,20550,20610,30380,6510,6520,30500,20700,20660,10720,20890,20670,6810,8020,8030,8050,8110,8310,8370,8420,8430,8440,8550,8580,8590,8410,21450,31390,10550,8270,21510,21440,8510,21090,8600,8610,8620,8740,8830,8910,8940,9040,1010,1040,1060,1023,1070,1024,1210,1220,1230,1260,1360,1910,20830,10450,21050,5010,5020,510,20560,21000,20620,10770,20630,10800,10540,10650,7520,10350,10340,10490,10480,10040,10030,10640,10630,6320,10430,10290,10280,10100,10090,10130,10140,10260,10270,10300,10310,10010,10020,10460,10470,10500,10510,10400,10410,10670,10680,10700,10380,21100,10070,10080,10590,10600", encoding = "UTF-8")
# braz_words <- read.csv("https://soundcomparisons.com/export/csv?study=Brazil&languages=31111102309&words=10,20,30,40,50,7820,7830,1610,1620,7010,7020,7030,7040,7050,7110,7170,7210,7220,7310,7320,7350,7360,7370,7380,7410,7420,7470,7450,7840,7860,820,810,7890,7880,7520,7980,7960,7430,7710,7720,7770,7800,7910,7920,7930,7940,7990,6060,2010,2050,2110,2120,2130,2210,2230,2240,2310,2340,2410,2510,2520,2530,2550,2610,2620,2630,2640,2710,2720,2730,2740,2810,2840,4010,4020,4030,4050,4310,4350,4360,4100,3010,3120,3210,3410,3420,3430,3500,3240,3810,3820,3830,3840,3850,4320,5850,6080,6090,5110,5120,5130,5150,5170,5210,5220,5230,5240,5310,5410,5420,5510,5540,5430,5530,5520,6810,6210,6220,6230,6240,6260,6280,6310,6320,5810,5830,5860,8980,6030,6040,6510,6620,8010,8020,8030,8040,8050,8060,8250,8260,8210,8220,8110,8130,8140,8150,8310,8330,8320,8340,8370,8420,8430,8440,8510,8520,8540,8560,8550,8580,8590,8410,8270,8600,8610,8620,8120,8670,8680,8690,8700,8720,8740,8810,8830,8840,8850,8870,8880,8890,8920,8930,8940,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9070,1010,1020,1040,1050,1060,1070,1120,1210,1220,1230,1250,1310,1320,1330,1340,1360,1410,1350,2420,5010,5020,5050", encoding = "UTF-8")
# pap_words <- read.csv("https://soundcomparisons.com/export/csv?study=WestPapua&languages=53121400009&words=10,20,30,40,50,60,70,80,90,100,110,150,200,210,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1021,1022,1040,1050,1052,1060,1070,1072,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,1710,5010,5020,510", encoding = "UTF-8")
# mix_words <- read.csv("https://soundcomparisons.com/export/csv?study=MixeZoque&languages=61051523409&words=10,20,30,40,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1040,1060,1070,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,5010,5020,510", encoding = "UTF-8")
# words <- rbind(rom_words, ger_words, eng_words, slav_words, celt_words,
# ands_words, map_words, braz_words, pap_words, mix_words)
# words <- words[,c("WordId", "WordModernName1", "WordModernName2")]
# word_set_1 <- paste0('"', unique(str_remove_all(words$WordModernName1, "[^a-z_]")), '",')
# str_remove_all(words$WordModernName1, "[^a-z_]")
#
#
# word_set_2 <- paste0('"', unique(words$WordModernName2), '",')
# cat(word_set_1)
# cat(word_set_2)
word_set_1 <- c("one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "twenty", "hundred", "thousand", "all_fem", "few", "part", "colour", "green", "straight", "broad", "full", "young", "new", "sweet", "raw_fem", "fast", "alive", "good", "sad", "strong", "hot", "cold", "head", "eye", "ear", "tear", "tongue", "tooth", "beard", "hand", "arm", "finger", "foot", "blood", "bone", "nail", "cough", "liver", "mother", "father", "daughter", "widow", "neighbour_fem", "name", "beast", "wolf", "deer", "donkey", "bear", "goat", "cow", "worm", "egg", "horn", "wool", "moon", "star", "sky", "water", "sea", "stone", "earth", "field", "gold", "leaf", "grass", "flower", "weather", "it_rains", "wind", "heat", "salt", "bread", "corn", "wine", "taste", "to_taste", "door", "entrance", "oven", "game", "castle", "wheel", "it_is", "come_ipv_pl", "carry_you_pl", "let_us_go_in", "dead_fem", "_see", "listen_ipv_sg", "he_sleeps", "he_believes_subj", "let_us_hope", "done", "_do", "serve_ipv_pl", "to_sell", "it_lasts", "he_holds", "he_says_subj", "said", "we_sing", "my", "your_sg", "his_fem", "your_pl_fem", "not", "when", "which", "in", "ly_adv_sfx", "hour", "night", "week", "autumn", "winter", "year", "sin", "all", "white", "red", "long", "sharp", "naked", "warm", "better", "right", "mouth", "knee", "toe", "heart", "brother", "word", "hound", "mouse", "goose", "lamb", "calf", "fish", "north", "ash", "thorn", "rain", "storm", "thunder", "snow", "ice", "hunger", "milk", "honey", "house", "home", "yard", "stool", "thing", "needle", "ring", "is", "eat", "bite", "drink", "hear", "open", "sit", "wash", "swear", "", "what", "out", "over", "day", "quick", "sore", "holy", "oak", "bath", "top", "see", "fight", "mid", "black_fem", "yellow", "thin", "wide", "sharp_fem", "blunt", "old", "dear", "good_neut", "true_neut", "full_fem", "far_neut", "near_neut", "hot_fem", "dry", "wet_neut", "happy", "on_the_left", "nose", "tears", "teeth", "hands", "brothers", "words", "dog", "fish_pl", "sheep", "eggs", "wing", "feather", "days", "sun", "world", "rivers", "lake", "lakes", "fire", "smoke", "sand", "bark", "root", "oaks", "fields", "meat", "grain", "straw", "table", "place", "citadel", "there_is", "they_were", "you_are_falling", "are_you_lying_down", "we_drink", "she_was_asleep", "she_lives", "you_believe", "he_is_laughing", "_will_give", "she_is_sewing", "he_rubbed", "we_are_digging", "we_are_afraid", "breathe_ipv_sg", "it_froze", "he_is_tied_up", "write_ipv_sg", "us", "you_sg", "your_sg_fem_sg", "your_pl_pl", "him_his", "own_nt", "where", "there", "at_sea", "and", "summer", "uno_no_mas", "uno", "dos", "tres", "cuatro", "cinco", "seis", "siete", "ocho", "nueve", "diez", "cien", "se_ha_vuelto_negro", "negro", "blanco", "grande", "arriba", "lleno", "pesado", "lejos", "joven_todavia", "nuevo", "dulce", "bueno", "sabio", "feliz", "feliz_ppp", "caliente", "seco", "cabeza", "cabello", "ojo", "que_tiene_ojos", "nariz", "oreja", "boca", "lengua", "diente", "mano", "cuello", "pie", "que_tiene_sangre", "sangre", "hueso", "piel", "unya", "barriga", "con_mi_corazon", "corazon", "persona", "mujer", "y_tu_nombre", "nombre", "perro", "piojo", "cuerno", "cola", "sol", "luna", "mundo", "sombra", "agua", "laguna", "fuego", "piedra", "ceniza", "tierra", "campo", "lenya", "viento", "carne", "grasa", "sal", "pan", "choclo", "casa", "puerta", "camino", "si_el_viene", "venir", "caminar", "llevar", "estar_en_pie", "para_comer", "comer", "escupir", "vivir", "vive_todavia", "morir", "matar", "ver", "saber", "reir", "llorar", "dormir",
"yo_dormiria", "esconder_se", "jugar", "hacer", "dar", "coser", "querer", "pelear", "decir", "nosotros_incl", "tu", "el", "ellos", "no", "que", "quien", "donde", "cual", "este", "ese", "aqui", "ahora", "dia", "noche", "anyo", "vez", "mil", "rojo", "amarillo", "pequenyo", "flaco", "gordo", "malo", "noble", "mandibula", "rodilla", "higado", "nervio", "animal", "vieja", "abuela_paterno", "esposa", "hermana", "hombre", "padre", "amigo", "persona_que_ensenya", "duenyo", "duenyo_tierra", "duenyo_casa", "buey", "zorro", "gato", "tigre", "liebre", "pajaro", "pollo", "culebra", "gusano", "pescado", "huevo", "estrella", "norte", "luz", "neblina", "rio", "barro", "cerro", "volcan", "arbol", "raiz", "hoja", "lluvia", "nube", "cielo_truenos", "hace_calor", "hace_frio", "papa", "harina_tostada", "vino", "chicha", "pueblo", "cama", "piedra_para_moler", "poncho", "arado", "canoa", "bajar", "pisar", "machacar", "tengo_hambre", "tomar_agua", "olvidar", "empujar", "yo", "nosotros_dos", "nosotros_pl", "solamente", "demasiado", "manyana", "rogativa", "ceremonia", "tambor", "cinta", "vestido_lana", "marido", "viejo", "toser", "quebrar", "sanadora", "sangre_cuajada", "primo_paterno", "basta", "nido", "chancho", "flor", "cascada", "gargajo", "otro_canto", "cadaver", "plano", "parte_cadera", "hija_del_hombre", "por_todas_partes", "otro_lugar", "tio_paterno", "greda_blanca", "acertar", "nacer", "suegra", "olla", "visita", "telar", "ajo", "suave", "soga", "joyeria", "dejar_algo", "pasto", "mojarse", "estrujar", "verde", "cazuela", "dois", "sangue")
word_set_2 <- c("odin", "dva", "tri", "chetyre", "pyat_", "shest_", "sem_", "vosem_", "devyat_", "desyat_", "sto", "chyornaya", "belyj", "zelyonyj", "zhyoltyj", "dolgij", "tonkij", "shirokij", "ostraya", "tupoj", "molodoj", "novyj", "staryj", "zhivoj", "svyatoj", "golyj", "milyj", "dobro_", "vernoe", "polnaya", "daleko", "blizko", "tyoplaya", "suxoj", "mokroe", "sladkij", "blago_", "na_levo_", "golova", "nos", "uxo", "slyozy", "yazyk", "zuby", "boroda", "ruki", "koleno", "krov_", "serdce", "brat", "brat_ya", "imya", "slova", "pyos", "volk", "mysh_", "ryby", "korova", "ovca", "yajca", "rog", "krylo", "pero", "volna", "den_", "dni", "noch_", "solnce", "mesyac", "nebo", "svet", "voda", "reki", "ozero", "ozera", "more", "ogon_", "dym", "kamen_", "zemlya", "pepel", "pesok", "zoloto", "kora", "koren_", "tyorn", "duby", "trava", "polya", "sneg", "lyod", "veter", "grom", "xleb", "myaso", "sol_", "myod", "zerno", "soloma", "stol", "igla", "koleso", "igra", "mesto", "gorod", "est_", "byli", "padaesh_", "lezhish_", "p_yom", "spala", "zhivyot", "mertva", "verish_", "slushaj", "smeyotsya", "dam", "sh_yot", "tyor", "kopaem", "boimsya", "dyshi", "zamyorzlo", "svyazan", "pishi", "ya", "moj", "nas", "ty", "tvoya", "vashi", "ego", "svoyo", "gde", "tam", "v_more", "i", "ne", "leto", "zima", "god", "chas", "only_one", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "hundred", "black", "white", "big", "high_up", "full", "heavy", "far", "still_young", "new", "sweet", "good", "wise", "be_happy", "happy_pppl", "warm", "dry", "head", "hair", "eye", "having_eyes", "nose", "ear", "mouth", "tongue", "tooth", "hand", "neck", "foot", "having_blood", "blood", "bone", "skin", "fingernail", "belly", "with_my_heart", "heart", "person", "woman", "as_for_your_name", "name", "dog", "louse", "horn", "tail", "sun", "moon", "world", "shadow", "water", "lake", "fire", "stone", "ash", "earth", "field", "fire_wood", "wind", "meat", "fat", "salt", "bread", "cob_of_corn", "house", "door", "road", "if_he_comes", "come", "walk", "carry", "be_standing", "for_eating", "eat", "spit", "live", "still_lives", "die", "kill", "see", "know", "laugh", "cry", "sleep", "I_would_sleep", "hide", "play", "do", "give", "sew", "want", "fight", "say", "we incl", "you_sg", "he_she", "they", "not", "what", "who", "where", "which", "this", "that", "here", "now", "day", "night", "year", "time", "thousand", "red", "yellow", "small", "skinny", "bad", "rich", "jaw", "knee", "liver", "nerve", "animal", "old_woman", "grandmother", "wife", "sister", "man", "father", "friend", "teacher", "owner", "landowner", "house_owner", "ox", "fox", "cat", "puma", "hare", "bird", "chicken", "snake", "worm", "fish", "egg", "star", "north", "light", "fog", "river", "mud", "hill", "volcano", "tree", "root", "leaf", "rain", "cloud", "thundery_sky", "it_is_hot", "it_is_cold", "potato", "toasted_flour", "wine", "chicha", "town", "bed", "mortar", "poncho", "plough", "canoe", "go_down", "step_on", "smash", "I_am_hungry", "drink", "forget", "push", "I", "we_dual", "we_plur", "only", "alone", "too_much", "morning", "ceremony", "healing", "drum", "headband", "dress", "husband", "old_man", "cough", "shatter", "healer", "blood_dish", "cousin", "enough", "nest", "pig", "flower", "waterfall", "phlegm", "song", "corpse", "spread_out", "hip", "daughter_of_a_man", "everywhere", "elsewhere", "uncle", "clay", "get_right", "be_born", "mother_in_law", "pot", "visitor", "loom", "garlic", "smooth", "rope", "jewellery", "leave", "grass", "get_wet", "wring", "green", "soup")
if (sum(!((word %in% word_set_1)|(word %in% word_set_2))) > 0){
stop(paste(
"There is no features",
paste0("'", word[!((word %in% word_set_1)|(word %in% word_set_2))], "'",
collapse = ", "),
"in our version of the soundcomparisons database."
))
}
rom_words <- read.csv("https://soundcomparisons.com/export/csv?study=Romance&languages=12231572409&words=10,20,30,40,50,60,70,80,90,100,110,200,210,7820,1620,1810,7000,7040,7320,7390,7840,7510,7980,7960,6200,7330,7650,7710,7540,7610,7910,7920,2010,2110,2130,2140,2230,2240,2250,2310,2340,2360,2510,2610,2620,2640,2660,2840,4310,4320,4410,4370,4520,4100,3020,3140,3160,3170,3180,3190,3310,3420,3810,3820,3900,5120,5130,5150,5210,5240,5510,5520,6410,5610,6260,6310,6320,5900,5890,5850,5870,6040,6020,6070,6140,8391,8390,6520,6530,6550,6860,6940,6840,8000,8020,8050,8170,8430,8510,8530,8410,8480,8490,8611,8610,8630,8640,8650,8930,9040,9041,9070,710,720,730,740,1120,1240,1260,1410,7340,5040,5020,5090,5070,5080,510,4710", encoding = "UTF-8")
ger_words <- read.csv("https://soundcomparisons.com/export/csv?study=Germanic&languages=11171246109&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
eng_words <- read.csv("https://soundcomparisons.com/export/csv?study=Englishes&languages=11111230301&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7650,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2140,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,1440,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8940,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
slav_words <- read.csv("https://soundcomparisons.com/export/csv?study=Slavic&languages=13111210507&words=10,20,30,40,50,60,70,80,90,100,200,7010,7020,7040,7050,7210,7360,7380,7410,7420,7510,7980,7520,7650,7660,7670,7680,7710,7740,7840,7880,7890,7910,7930,7940,7960,7970,820,2010,2120,2130,2140,2230,2240,2250,2310,2530,2610,2810,4330,4332,4100,4110,3120,3140,3150,3240,3310,3350,3810,3820,3840,3850,3900,5010,5012,5020,5110,5120,5150,4150,5210,5220,5230,5232,5240,5410,5420,5510,5520,5430,5540,5610,6230,6240,6270,6290,6310,6410,5820,5830,5850,5920,6020,6030,6040,6150,6070,6200,6590,6630,6840,6860,6942,6940,8000,8010,8130,8140,8320,8410,8420,8430,8480,8530,8580,8620,8740,8850,8890,8950,8970,8980,9010,9100,1010,710,1030,1040,720,740,1080,780,1230,1330,1410,1350,1120,5060,5080,5052,5050", encoding = "UTF-8")
celt_words <- read.csv("https://soundcomparisons.com/export/csv?study=Celtic&languages=14111238609&words=10,20,30,40,50,60,70,80,90,100,110,200,7810,1830,7010,7020,7060,7040,7110,7170,7120,7360,7390,7400,7460,7480,7840,7520,7510,7580,7570,7590,7650,7660,7710,7720,7740,7760,810,2140,2230,2320,2530,2630,2640,2670,2600,2740,2840,4010,4020,4420,4470,3120,3190,3200,3330,3840,3910,6420,5110,5140,5240,6460,5430,5620,5630,6470,6340,6360,6310,6320,5900,5850,6010,6950,6150,6040,6070,6300,6820,8040,8070,8300,9090,8480,9070,1010,1040,1080,1090,1490,4610,4620,4910,4930,6190,6191,6192,4100,4101,4102,6450,6451,6452,3300,3301,3302,5410,5411,5412,6510,6511,6512,3110,3111,3112,2810,2811,2812,2010,2011,2012,5520,5521,5522,510,511,512,2370,2371,2372,3280,3281,3282,5100,5101,5102,3950,3951,3952,6990,6991,6992", encoding = "UTF-8")
ands_words <- read.csv("https://soundcomparisons.com/export/csv?study=Andes&languages=21222210001&words=11,10,20,30,40,50,60,70,80,90,100,200,7011,7010,7020,7110,7120,7840,7860,7880,7510,7980,7960,7710,7750,7970,7971,7910,7930,2010,2050,2110,2111,2120,2130,2210,2230,2240,2310,2410,2510,2611,2610,2620,2630,2640,2710,2811,2810,4010,4030,4101,4100,3120,3430,3820,3830,5110,5120,5140,5170,5210,5230,5410,5510,5430,5520,6410,6220,5850,6030,6060,6040,6020,6120,6510,6520,6810,8021,8020,8030,8050,8110,8311,8310,8370,8420,8421,8430,8440,8510,8550,8580,8590,8410,8411,8270,8600,8610,8620,8740,8910,8940,9040,1011,1010,1020,1040,1060,1070,1120,1210,1220,1230,1260,1310,1320,1360,1910,5010,5020,510,520", encoding = "UTF-8")
map_words <- read.csv("https://soundcomparisons.com/export/csv?study=Mapudungun&languages=28141005109&words=10,20,30,40,50,60,70,80,90,100,200,210,7010,7020,7030,7050,7110,7840,7860,7880,7980,7960,7710,7750,7910,7930,7170,20680,20690,7720,20790,2010,2050,2110,2120,2130,2210,2230,2240,10560,2410,2310,2510,2530,2610,2620,2630,2640,2710,2810,2840,10050,3010,4010,4030,4100,20170,10370,10620,20240,10740,4320,20190,10790,31180,21410,21400,20090,3120,20070,20030,20910,10200,3210,20010,20020,3420,3430,20100,3820,3830,3810,5110,5120,5130,5140,10230,20980,5170,10710,5210,5220,5230,5240,5410,5510,5430,10120,5520,6410,10760,20480,6220,6210,6240,6260,20130,5850,5810,5840,10810,21530,21520,6030,6060,6040,6020,6120,10730,20550,20610,30380,6510,6520,30500,20700,20660,10720,20890,20670,6810,8020,8030,8050,8110,8310,8370,8420,8430,8440,8550,8580,8590,8410,21450,31390,10550,8270,21510,21440,8510,21090,8600,8610,8620,8740,8830,8910,8940,9040,1010,1040,1060,1023,1070,1024,1210,1220,1230,1260,1360,1910,20830,10450,21050,5010,5020,510,20560,21000,20620,10770,20630,10800,10540,10650,7520,10350,10340,10490,10480,10040,10030,10640,10630,6320,10430,10290,10280,10100,10090,10130,10140,10260,10270,10300,10310,10010,10020,10460,10470,10500,10510,10400,10410,10670,10680,10700,10380,21100,10070,10080,10590,10600", encoding = "UTF-8")
braz_words <- read.csv("https://soundcomparisons.com/export/csv?study=Brazil&languages=31111102309&words=10,20,30,40,50,7820,7830,1610,1620,7010,7020,7030,7040,7050,7110,7170,7210,7220,7310,7320,7350,7360,7370,7380,7410,7420,7470,7450,7840,7860,820,810,7890,7880,7520,7980,7960,7430,7710,7720,7770,7800,7910,7920,7930,7940,7990,6060,2010,2050,2110,2120,2130,2210,2230,2240,2310,2340,2410,2510,2520,2530,2550,2610,2620,2630,2640,2710,2720,2730,2740,2810,2840,4010,4020,4030,4050,4310,4350,4360,4100,3010,3120,3210,3410,3420,3430,3500,3240,3810,3820,3830,3840,3850,4320,5850,6080,6090,5110,5120,5130,5150,5170,5210,5220,5230,5240,5310,5410,5420,5510,5540,5430,5530,5520,6810,6210,6220,6230,6240,6260,6280,6310,6320,5810,5830,5860,8980,6030,6040,6510,6620,8010,8020,8030,8040,8050,8060,8250,8260,8210,8220,8110,8130,8140,8150,8310,8330,8320,8340,8370,8420,8430,8440,8510,8520,8540,8560,8550,8580,8590,8410,8270,8600,8610,8620,8120,8670,8680,8690,8700,8720,8740,8810,8830,8840,8850,8870,8880,8890,8920,8930,8940,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9070,1010,1020,1040,1050,1060,1070,1120,1210,1220,1230,1250,1310,1320,1330,1340,1360,1410,1350,2420,5010,5020,5050", encoding = "UTF-8")
pap_words <- read.csv("https://soundcomparisons.com/export/csv?study=WestPapua&languages=53121400009&words=10,20,30,40,50,60,70,80,90,100,110,150,200,210,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1021,1022,1040,1050,1052,1060,1070,1072,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,1710,5010,5020,510", encoding = "UTF-8")
mix_words <- read.csv("https://soundcomparisons.com/export/csv?study=MixeZoque&languages=61051523409&words=10,20,30,40,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1040,1060,1070,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,5010,5020,510", encoding = "UTF-8")
words <- rbind(rom_words, ger_words, eng_words, slav_words, celt_words,
ands_words, map_words, braz_words, pap_words, mix_words)
words <- words[,c("WordId", "WordModernName1", "WordModernName2")]
my_data <- lapply(word, function(x) {
if ((x %in% word_set_1)|(x %in% word_set_2)){
z <- (words[words$WordModernName1 == x,])
y <- (words[words$WordModernName2 == x,])
new <- rbind(z,y)
new <- new[rowSums(is.na(new)) != ncol(new),]
word_id <- new$WordId[1]
families <- c("Romance", "Germanic", "Englishes", "Slavic", "Celtic",
"Andes", "Mapudungun", "Brazil", "WestPapua", "MixeZoque")
links_set <- c()
langs <- paste(lingtypology::soundcomparisons$LanguageId, collapse=",")
myword <- words[which(words$WordModernName1 == x), ]
my_links <- lapply(families, function(link) {
links <- paste0("https://soundcomparisons.com/export/csv?study="
,link,"&languages=", langs,"&words=", word_id)
links_set <- append(links_set, links)}) ##получили ссылки, по которым скачали данные
data <-lapply(my_links, function(l) {
datalist <- read.csv(l, encoding = "UTF-8")
datalist <- datalist[c("LanguageId", "LanguageName", "Latitude",
"Longitude", "WordId", "WordModernName1",
"WordProtoName1", "Phonetic")]
})
final_df <- Reduce(function(first, second) {
merge(
first,
second,
all = TRUE,
by = c(
"LanguageId",
"LanguageName",
"Latitude",
"Longitude",
"WordId",
"WordModernName1",
"WordProtoName1",
"Phonetic"
)
)
}, data)
} else {
}})
my_df <- do.call("rbind", my_data)
my_df <- merge(my_df, lingtypology::soundcomparisons, by = "LanguageId")
my_df$LanguageName.y <- NULL
#my_df <- subset(my_df, select = -c(LanguageName.y))
colnames(my_df) <- c("lang_id", "language", "latitude", "longitude",
"word_id", "word", "proto_word", "phonetic",
"glottocode")
return(my_df)
}
ropensci/lingtypology documentation built on March 17, 2024, 11:13 p.m.
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Defines functions soundcomparisons.feature
Documented in soundcomparisons.feature
#' Download SOUNDCOMPARISONS data
#'
#' This function downloads data from SOUNDCOMPARISONS and changes language names to the names from lingtypology database. You need the internet connection.
#'
#' @param word A character vector that define with a feature ids from SOUNDCOMPARISONS (e. g. "one", "sharp_fem", "near_neut", "on_the_left", " I_will_give", "write_ipv_sg", "your_pl_pl").
#' @seealso \code{\link{abvd.feature}}, \code{\link{afbo.feature}}, \code{\link{autotyp.feature}}, \code{\link{oto_mangueanIC.feature}}, \code{\link{phoible.feature}}, \code{\link{sails.feature}}, \code{\link{uralex.feature}}, \code{\link{valpal.feature}}, \code{\link{vanuatu.feature}}, \code{\link{eurasianphonology.feature}}, \code{\link{eurasianphonology.feature}}
#' @seealso \code{\link{abvd.feature}}, \code{\link{afbo.feature}}, \code{\link{autotyp.feature}}, \code{\link{bivaltyp.feature}}, \code{\link{eurasianphonology.feature}}, \code{\link{oto_mangueanIC.feature}}, \code{\link{phoible.feature}}, \code{\link{sails.feature}}, \code{\link{uralex.feature}}, \code{\link{valpal.feature}}, \code{\link{vanuatu.feature}}, \code{\link{wals.feature}}
#' @author Anna Smirnova
#' @examples
#' # soundcomparisons.feature(c("sun", "house"))
#' @export
#'
#' @importFrom utils read.csv
#'
soundcomparisons.feature <- function(word) {
message(paste0("Don't forget to cite a source (modify in case of using individual chapters):
Heggarty, Paul and Shimelman, Aviva and Abete, Giovanni and Anderson, Cormac and Sadowsky, Scott and Paschen, Ludger and Maguire, Warren and Jocz, Lechoslaw and Aninao, Maria Jose and Wagerle, Laura and Dermaku-Appelganz, Darja and Silva, Ariel Pheula do Couto e and Lawyer, Lewis C. and Michalsky, Jan and Cabral, Ana Suelly Arruda Camara and Walworth, Mary and Koile, Ezequiel and Runge, Jakob and Bibiko, Hans-Jorg (eds.) 2019. New online database and resource for researching phonetic diversity.
(Available online at https://soundcomparisons.com/, Accessed on ",
Sys.Date(),
".)
@misc{heggarty_sound_2019,
title = {Sound {Comparisons}: {Exploring} {Diversity} in {Phonetics} across {Language} {Families}},
url = {https://soundcomparisons.com},
journal = {Sound Comparisons: Exploring Diversity in Phonetics across Language Families},
author = {Heggarty, Paul and Shimelman, Aviva and Abete, Giovanni and Anderson, Cormac and Sadowsky, Scott and Paschen, Ludger and Maguire, Warren and Jocz, Lechoslaw and Aninao, Maria Jose and Wagerle, Laura and Dermaku-Appelganz, Darja and Silva, Ariel Pheula do Couto e and Lawyer, Lewis C. and Michalsky, Jan and Cabral, Ana Suelly Arruda Camara and Walworth, Mary and Koile, Ezequiel and Runge, Jakob and Bibiko, Hans-Jorg},
year = {2019},
keywords = {Dialects, Phonetics, Recordings, Sound Comparisons, Transcriptions}
}
@inproceedings{heggarty_sound_2019,
address = {Canberra, Australia},
title = {Sound {Comparisons}: {A} new online database and resource for researching phonetic diversity},
isbn = {978-0-646-80069-1},
url = {https://icphs2019.org/icphs2019-fullpapers/pdf/full-paper_490.pdf},
booktitle = {Proceedings of the 19th {International} {Congress} of {Phonetic} {Sciences}},
publisher = {Australasian Speech Science and Technology Association},
author = {Heggarty, Paul and Shimelman, Aviva and Abete, Giovanni and Anderson, Cormac and Sadowsky, Scott and Paschen, Ludger and Maguire, Warren and Jocz, Lechoslaw and Aninao, Maria Jose and Wagerle, Laura and Dermaku-Appelganz, Darja and Silva, Ariel Pheula do Couto e and Lawyer, Lewis C. and Michalsky, Jan and Cabral, Ana Suelly Arruda Camara and Walworth, Mary and Koile, Ezequiel and Runge, Jakob and Bibiko, Hans-Jorg},
month = aug,
year = {2019},
keywords = {Dialects, Phonetics, Recordings, Sound Comparisons, Transcriptions},
pages = {280--284}
}"))
# # # in order to recollect word_set_1 and word_set_2
# rom_words <- read.csv("https://soundcomparisons.com/export/csv?study=Romance&languages=12231572409&words=10,20,30,40,50,60,70,80,90,100,110,200,210,7820,1620,1810,7000,7040,7320,7390,7840,7510,7980,7960,6200,7330,7650,7710,7540,7610,7910,7920,2010,2110,2130,2140,2230,2240,2250,2310,2340,2360,2510,2610,2620,2640,2660,2840,4310,4320,4410,4370,4520,4100,3020,3140,3160,3170,3180,3190,3310,3420,3810,3820,3900,5120,5130,5150,5210,5240,5510,5520,6410,5610,6260,6310,6320,5900,5890,5850,5870,6040,6020,6070,6140,8391,8390,6520,6530,6550,6860,6940,6840,8000,8020,8050,8170,8430,8510,8530,8410,8480,8490,8611,8610,8630,8640,8650,8930,9040,9041,9070,710,720,730,740,1120,1240,1260,1410,7340,5040,5020,5090,5070,5080,510,4710", encoding = "UTF-8")
# ger_words <- read.csv("https://soundcomparisons.com/export/csv?study=Germanic&languages=11171246109&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
# eng_words <- read.csv("https://soundcomparisons.com/export/csv?study=Englishes&languages=11111230301&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7650,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2140,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,1440,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8940,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
# slav_words <- read.csv("https://soundcomparisons.com/export/csv?study=Slavic&languages=13111210507&words=10,20,30,40,50,60,70,80,90,100,200,7010,7020,7040,7050,7210,7360,7380,7410,7420,7510,7980,7520,7650,7660,7670,7680,7710,7740,7840,7880,7890,7910,7930,7940,7960,7970,820,2010,2120,2130,2140,2230,2240,2250,2310,2530,2610,2810,4330,4332,4100,4110,3120,3140,3150,3240,3310,3350,3810,3820,3840,3850,3900,5010,5012,5020,5110,5120,5150,4150,5210,5220,5230,5232,5240,5410,5420,5510,5520,5430,5540,5610,6230,6240,6270,6290,6310,6410,5820,5830,5850,5920,6020,6030,6040,6150,6070,6200,6590,6630,6840,6860,6942,6940,8000,8010,8130,8140,8320,8410,8420,8430,8480,8530,8580,8620,8740,8850,8890,8950,8970,8980,9010,9100,1010,710,1030,1040,720,740,1080,780,1230,1330,1410,1350,1120,5060,5080,5052,5050", encoding = "UTF-8")
# celt_words <- read.csv("https://soundcomparisons.com/export/csv?study=Celtic&languages=14111238609&words=10,20,30,40,50,60,70,80,90,100,110,200,7810,1830,7010,7020,7060,7040,7110,7170,7120,7360,7390,7400,7460,7480,7840,7520,7510,7580,7570,7590,7650,7660,7710,7720,7740,7760,810,2140,2230,2320,2530,2630,2640,2670,2600,2740,2840,4010,4020,4420,4470,3120,3190,3200,3330,3840,3910,6420,5110,5140,5240,6460,5430,5620,5630,6470,6340,6360,6310,6320,5900,5850,6010,6950,6150,6040,6070,6300,6820,8040,8070,8300,9090,8480,9070,1010,1040,1080,1090,1490,4610,4620,4910,4930,6190,6191,6192,4100,4101,4102,6450,6451,6452,3300,3301,3302,5410,5411,5412,6510,6511,6512,3110,3111,3112,2810,2811,2812,2010,2011,2012,5520,5521,5522,510,511,512,2370,2371,2372,3280,3281,3282,5100,5101,5102,3950,3951,3952,6990,6991,6992", encoding = "UTF-8")
# ands_words <- read.csv("https://soundcomparisons.com/export/csv?study=Andes&languages=21222210001&words=11,10,20,30,40,50,60,70,80,90,100,200,7011,7010,7020,7110,7120,7840,7860,7880,7510,7980,7960,7710,7750,7970,7971,7910,7930,2010,2050,2110,2111,2120,2130,2210,2230,2240,2310,2410,2510,2611,2610,2620,2630,2640,2710,2811,2810,4010,4030,4101,4100,3120,3430,3820,3830,5110,5120,5140,5170,5210,5230,5410,5510,5430,5520,6410,6220,5850,6030,6060,6040,6020,6120,6510,6520,6810,8021,8020,8030,8050,8110,8311,8310,8370,8420,8421,8430,8440,8510,8550,8580,8590,8410,8411,8270,8600,8610,8620,8740,8910,8940,9040,1011,1010,1020,1040,1060,1070,1120,1210,1220,1230,1260,1310,1320,1360,1910,5010,5020,510,520", encoding = "UTF-8")
# map_words <- read.csv("https://soundcomparisons.com/export/csv?study=Mapudungun&languages=28141005109&words=10,20,30,40,50,60,70,80,90,100,200,210,7010,7020,7030,7050,7110,7840,7860,7880,7980,7960,7710,7750,7910,7930,7170,20680,20690,7720,20790,2010,2050,2110,2120,2130,2210,2230,2240,10560,2410,2310,2510,2530,2610,2620,2630,2640,2710,2810,2840,10050,3010,4010,4030,4100,20170,10370,10620,20240,10740,4320,20190,10790,31180,21410,21400,20090,3120,20070,20030,20910,10200,3210,20010,20020,3420,3430,20100,3820,3830,3810,5110,5120,5130,5140,10230,20980,5170,10710,5210,5220,5230,5240,5410,5510,5430,10120,5520,6410,10760,20480,6220,6210,6240,6260,20130,5850,5810,5840,10810,21530,21520,6030,6060,6040,6020,6120,10730,20550,20610,30380,6510,6520,30500,20700,20660,10720,20890,20670,6810,8020,8030,8050,8110,8310,8370,8420,8430,8440,8550,8580,8590,8410,21450,31390,10550,8270,21510,21440,8510,21090,8600,8610,8620,8740,8830,8910,8940,9040,1010,1040,1060,1023,1070,1024,1210,1220,1230,1260,1360,1910,20830,10450,21050,5010,5020,510,20560,21000,20620,10770,20630,10800,10540,10650,7520,10350,10340,10490,10480,10040,10030,10640,10630,6320,10430,10290,10280,10100,10090,10130,10140,10260,10270,10300,10310,10010,10020,10460,10470,10500,10510,10400,10410,10670,10680,10700,10380,21100,10070,10080,10590,10600", encoding = "UTF-8")
# braz_words <- read.csv("https://soundcomparisons.com/export/csv?study=Brazil&languages=31111102309&words=10,20,30,40,50,7820,7830,1610,1620,7010,7020,7030,7040,7050,7110,7170,7210,7220,7310,7320,7350,7360,7370,7380,7410,7420,7470,7450,7840,7860,820,810,7890,7880,7520,7980,7960,7430,7710,7720,7770,7800,7910,7920,7930,7940,7990,6060,2010,2050,2110,2120,2130,2210,2230,2240,2310,2340,2410,2510,2520,2530,2550,2610,2620,2630,2640,2710,2720,2730,2740,2810,2840,4010,4020,4030,4050,4310,4350,4360,4100,3010,3120,3210,3410,3420,3430,3500,3240,3810,3820,3830,3840,3850,4320,5850,6080,6090,5110,5120,5130,5150,5170,5210,5220,5230,5240,5310,5410,5420,5510,5540,5430,5530,5520,6810,6210,6220,6230,6240,6260,6280,6310,6320,5810,5830,5860,8980,6030,6040,6510,6620,8010,8020,8030,8040,8050,8060,8250,8260,8210,8220,8110,8130,8140,8150,8310,8330,8320,8340,8370,8420,8430,8440,8510,8520,8540,8560,8550,8580,8590,8410,8270,8600,8610,8620,8120,8670,8680,8690,8700,8720,8740,8810,8830,8840,8850,8870,8880,8890,8920,8930,8940,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9070,1010,1020,1040,1050,1060,1070,1120,1210,1220,1230,1250,1310,1320,1330,1340,1360,1410,1350,2420,5010,5020,5050", encoding = "UTF-8")
# pap_words <- read.csv("https://soundcomparisons.com/export/csv?study=WestPapua&languages=53121400009&words=10,20,30,40,50,60,70,80,90,100,110,150,200,210,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1021,1022,1040,1050,1052,1060,1070,1072,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,1710,5010,5020,510", encoding = "UTF-8")
# mix_words <- read.csv("https://soundcomparisons.com/export/csv?study=MixeZoque&languages=61051523409&words=10,20,30,40,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1040,1060,1070,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,5010,5020,510", encoding = "UTF-8")
# words <- rbind(rom_words, ger_words, eng_words, slav_words, celt_words,
# ands_words, map_words, braz_words, pap_words, mix_words)
# words <- words[,c("WordId", "WordModernName1", "WordModernName2")]
# word_set_1 <- paste0('"', unique(str_remove_all(words$WordModernName1, "[^a-z_]")), '",')
# str_remove_all(words$WordModernName1, "[^a-z_]")
#
#
# word_set_2 <- paste0('"', unique(words$WordModernName2), '",')
# cat(word_set_1)
# cat(word_set_2)
word_set_1 <- c("one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "twenty", "hundred", "thousand", "all_fem", "few", "part", "colour", "green", "straight", "broad", "full", "young", "new", "sweet", "raw_fem", "fast", "alive", "good", "sad", "strong", "hot", "cold", "head", "eye", "ear", "tear", "tongue", "tooth", "beard", "hand", "arm", "finger", "foot", "blood", "bone", "nail", "cough", "liver", "mother", "father", "daughter", "widow", "neighbour_fem", "name", "beast", "wolf", "deer", "donkey", "bear", "goat", "cow", "worm", "egg", "horn", "wool", "moon", "star", "sky", "water", "sea", "stone", "earth", "field", "gold", "leaf", "grass", "flower", "weather", "it_rains", "wind", "heat", "salt", "bread", "corn", "wine", "taste", "to_taste", "door", "entrance", "oven", "game", "castle", "wheel", "it_is", "come_ipv_pl", "carry_you_pl", "let_us_go_in", "dead_fem", "_see", "listen_ipv_sg", "he_sleeps", "he_believes_subj", "let_us_hope", "done", "_do", "serve_ipv_pl", "to_sell", "it_lasts", "he_holds", "he_says_subj", "said", "we_sing", "my", "your_sg", "his_fem", "your_pl_fem", "not", "when", "which", "in", "ly_adv_sfx", "hour", "night", "week", "autumn", "winter", "year", "sin", "all", "white", "red", "long", "sharp", "naked", "warm", "better", "right", "mouth", "knee", "toe", "heart", "brother", "word", "hound", "mouse", "goose", "lamb", "calf", "fish", "north", "ash", "thorn", "rain", "storm", "thunder", "snow", "ice", "hunger", "milk", "honey", "house", "home", "yard", "stool", "thing", "needle", "ring", "is", "eat", "bite", "drink", "hear", "open", "sit", "wash", "swear", "", "what", "out", "over", "day", "quick", "sore", "holy", "oak", "bath", "top", "see", "fight", "mid", "black_fem", "yellow", "thin", "wide", "sharp_fem", "blunt", "old", "dear", "good_neut", "true_neut", "full_fem", "far_neut", "near_neut", "hot_fem", "dry", "wet_neut", "happy", "on_the_left", "nose", "tears", "teeth", "hands", "brothers", "words", "dog", "fish_pl", "sheep", "eggs", "wing", "feather", "days", "sun", "world", "rivers", "lake", "lakes", "fire", "smoke", "sand", "bark", "root", "oaks", "fields", "meat", "grain", "straw", "table", "place", "citadel", "there_is", "they_were", "you_are_falling", "are_you_lying_down", "we_drink", "she_was_asleep", "she_lives", "you_believe", "he_is_laughing", "_will_give", "she_is_sewing", "he_rubbed", "we_are_digging", "we_are_afraid", "breathe_ipv_sg", "it_froze", "he_is_tied_up", "write_ipv_sg", "us", "you_sg", "your_sg_fem_sg", "your_pl_pl", "him_his", "own_nt", "where", "there", "at_sea", "and", "summer", "uno_no_mas", "uno", "dos", "tres", "cuatro", "cinco", "seis", "siete", "ocho", "nueve", "diez", "cien", "se_ha_vuelto_negro", "negro", "blanco", "grande", "arriba", "lleno", "pesado", "lejos", "joven_todavia", "nuevo", "dulce", "bueno", "sabio", "feliz", "feliz_ppp", "caliente", "seco", "cabeza", "cabello", "ojo", "que_tiene_ojos", "nariz", "oreja", "boca", "lengua", "diente", "mano", "cuello", "pie", "que_tiene_sangre", "sangre", "hueso", "piel", "unya", "barriga", "con_mi_corazon", "corazon", "persona", "mujer", "y_tu_nombre", "nombre", "perro", "piojo", "cuerno", "cola", "sol", "luna", "mundo", "sombra", "agua", "laguna", "fuego", "piedra", "ceniza", "tierra", "campo", "lenya", "viento", "carne", "grasa", "sal", "pan", "choclo", "casa", "puerta", "camino", "si_el_viene", "venir", "caminar", "llevar", "estar_en_pie", "para_comer", "comer", "escupir", "vivir", "vive_todavia", "morir", "matar", "ver", "saber", "reir", "llorar", "dormir",
"yo_dormiria", "esconder_se", "jugar", "hacer", "dar", "coser", "querer", "pelear", "decir", "nosotros_incl", "tu", "el", "ellos", "no", "que", "quien", "donde", "cual", "este", "ese", "aqui", "ahora", "dia", "noche", "anyo", "vez", "mil", "rojo", "amarillo", "pequenyo", "flaco", "gordo", "malo", "noble", "mandibula", "rodilla", "higado", "nervio", "animal", "vieja", "abuela_paterno", "esposa", "hermana", "hombre", "padre", "amigo", "persona_que_ensenya", "duenyo", "duenyo_tierra", "duenyo_casa", "buey", "zorro", "gato", "tigre", "liebre", "pajaro", "pollo", "culebra", "gusano", "pescado", "huevo", "estrella", "norte", "luz", "neblina", "rio", "barro", "cerro", "volcan", "arbol", "raiz", "hoja", "lluvia", "nube", "cielo_truenos", "hace_calor", "hace_frio", "papa", "harina_tostada", "vino", "chicha", "pueblo", "cama", "piedra_para_moler", "poncho", "arado", "canoa", "bajar", "pisar", "machacar", "tengo_hambre", "tomar_agua", "olvidar", "empujar", "yo", "nosotros_dos", "nosotros_pl", "solamente", "demasiado", "manyana", "rogativa", "ceremonia", "tambor", "cinta", "vestido_lana", "marido", "viejo", "toser", "quebrar", "sanadora", "sangre_cuajada", "primo_paterno", "basta", "nido", "chancho", "flor", "cascada", "gargajo", "otro_canto", "cadaver", "plano", "parte_cadera", "hija_del_hombre", "por_todas_partes", "otro_lugar", "tio_paterno", "greda_blanca", "acertar", "nacer", "suegra", "olla", "visita", "telar", "ajo", "suave", "soga", "joyeria", "dejar_algo", "pasto", "mojarse", "estrujar", "verde", "cazuela", "dois", "sangue")
word_set_2 <- c("odin", "dva", "tri", "chetyre", "pyat_", "shest_", "sem_", "vosem_", "devyat_", "desyat_", "sto", "chyornaya", "belyj", "zelyonyj", "zhyoltyj", "dolgij", "tonkij", "shirokij", "ostraya", "tupoj", "molodoj", "novyj", "staryj", "zhivoj", "svyatoj", "golyj", "milyj", "dobro_", "vernoe", "polnaya", "daleko", "blizko", "tyoplaya", "suxoj", "mokroe", "sladkij", "blago_", "na_levo_", "golova", "nos", "uxo", "slyozy", "yazyk", "zuby", "boroda", "ruki", "koleno", "krov_", "serdce", "brat", "brat_ya", "imya", "slova", "pyos", "volk", "mysh_", "ryby", "korova", "ovca", "yajca", "rog", "krylo", "pero", "volna", "den_", "dni", "noch_", "solnce", "mesyac", "nebo", "svet", "voda", "reki", "ozero", "ozera", "more", "ogon_", "dym", "kamen_", "zemlya", "pepel", "pesok", "zoloto", "kora", "koren_", "tyorn", "duby", "trava", "polya", "sneg", "lyod", "veter", "grom", "xleb", "myaso", "sol_", "myod", "zerno", "soloma", "stol", "igla", "koleso", "igra", "mesto", "gorod", "est_", "byli", "padaesh_", "lezhish_", "p_yom", "spala", "zhivyot", "mertva", "verish_", "slushaj", "smeyotsya", "dam", "sh_yot", "tyor", "kopaem", "boimsya", "dyshi", "zamyorzlo", "svyazan", "pishi", "ya", "moj", "nas", "ty", "tvoya", "vashi", "ego", "svoyo", "gde", "tam", "v_more", "i", "ne", "leto", "zima", "god", "chas", "only_one", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "hundred", "black", "white", "big", "high_up", "full", "heavy", "far", "still_young", "new", "sweet", "good", "wise", "be_happy", "happy_pppl", "warm", "dry", "head", "hair", "eye", "having_eyes", "nose", "ear", "mouth", "tongue", "tooth", "hand", "neck", "foot", "having_blood", "blood", "bone", "skin", "fingernail", "belly", "with_my_heart", "heart", "person", "woman", "as_for_your_name", "name", "dog", "louse", "horn", "tail", "sun", "moon", "world", "shadow", "water", "lake", "fire", "stone", "ash", "earth", "field", "fire_wood", "wind", "meat", "fat", "salt", "bread", "cob_of_corn", "house", "door", "road", "if_he_comes", "come", "walk", "carry", "be_standing", "for_eating", "eat", "spit", "live", "still_lives", "die", "kill", "see", "know", "laugh", "cry", "sleep", "I_would_sleep", "hide", "play", "do", "give", "sew", "want", "fight", "say", "we incl", "you_sg", "he_she", "they", "not", "what", "who", "where", "which", "this", "that", "here", "now", "day", "night", "year", "time", "thousand", "red", "yellow", "small", "skinny", "bad", "rich", "jaw", "knee", "liver", "nerve", "animal", "old_woman", "grandmother", "wife", "sister", "man", "father", "friend", "teacher", "owner", "landowner", "house_owner", "ox", "fox", "cat", "puma", "hare", "bird", "chicken", "snake", "worm", "fish", "egg", "star", "north", "light", "fog", "river", "mud", "hill", "volcano", "tree", "root", "leaf", "rain", "cloud", "thundery_sky", "it_is_hot", "it_is_cold", "potato", "toasted_flour", "wine", "chicha", "town", "bed", "mortar", "poncho", "plough", "canoe", "go_down", "step_on", "smash", "I_am_hungry", "drink", "forget", "push", "I", "we_dual", "we_plur", "only", "alone", "too_much", "morning", "ceremony", "healing", "drum", "headband", "dress", "husband", "old_man", "cough", "shatter", "healer", "blood_dish", "cousin", "enough", "nest", "pig", "flower", "waterfall", "phlegm", "song", "corpse", "spread_out", "hip", "daughter_of_a_man", "everywhere", "elsewhere", "uncle", "clay", "get_right", "be_born", "mother_in_law", "pot", "visitor", "loom", "garlic", "smooth", "rope", "jewellery", "leave", "grass", "get_wet", "wring", "green", "soup")
if (sum(!((word %in% word_set_1)|(word %in% word_set_2))) > 0){
stop(paste(
"There is no features",
paste0("'", word[!((word %in% word_set_1)|(word %in% word_set_2))], "'",
collapse = ", "),
"in our version of the soundcomparisons database."
))
}
rom_words <- read.csv("https://soundcomparisons.com/export/csv?study=Romance&languages=12231572409&words=10,20,30,40,50,60,70,80,90,100,110,200,210,7820,1620,1810,7000,7040,7320,7390,7840,7510,7980,7960,6200,7330,7650,7710,7540,7610,7910,7920,2010,2110,2130,2140,2230,2240,2250,2310,2340,2360,2510,2610,2620,2640,2660,2840,4310,4320,4410,4370,4520,4100,3020,3140,3160,3170,3180,3190,3310,3420,3810,3820,3900,5120,5130,5150,5210,5240,5510,5520,6410,5610,6260,6310,6320,5900,5890,5850,5870,6040,6020,6070,6140,8391,8390,6520,6530,6550,6860,6940,6840,8000,8020,8050,8170,8430,8510,8530,8410,8480,8490,8611,8610,8630,8640,8650,8930,9040,9041,9070,710,720,730,740,1120,1240,1260,1410,7340,5040,5020,5090,5070,5080,510,4710", encoding = "UTF-8")
ger_words <- read.csv("https://soundcomparisons.com/export/csv?study=Germanic&languages=11171246109&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
eng_words <- read.csv("https://soundcomparisons.com/export/csv?study=Englishes&languages=11111230301&words=10,20,30,40,50,60,70,80,90,100,200,7820,7020,7030,7040,7210,7410,7840,7510,7980,7670,7650,7660,7710,7910,7920,7560,7680,7700,810,2010,2110,2130,2140,2210,2230,2240,2310,2510,2530,2540,2610,2620,2640,2810,2840,4310,4320,4410,4330,4100,4110,3120,3150,3230,3310,3320,3340,3240,3820,3900,5120,5260,5510,5430,5520,6260,6270,6290,5810,5910,5920,5820,5830,5850,6000,6050,6150,6040,6070,6510,6560,6540,6550,6570,6580,6600,1440,6630,6640,8000,8310,8330,8320,8510,8520,9620,8120,8720,8940,8990,1010,1210,1410,1430,1460,1470,5010,5020,510", encoding = "UTF-8")
slav_words <- read.csv("https://soundcomparisons.com/export/csv?study=Slavic&languages=13111210507&words=10,20,30,40,50,60,70,80,90,100,200,7010,7020,7040,7050,7210,7360,7380,7410,7420,7510,7980,7520,7650,7660,7670,7680,7710,7740,7840,7880,7890,7910,7930,7940,7960,7970,820,2010,2120,2130,2140,2230,2240,2250,2310,2530,2610,2810,4330,4332,4100,4110,3120,3140,3150,3240,3310,3350,3810,3820,3840,3850,3900,5010,5012,5020,5110,5120,5150,4150,5210,5220,5230,5232,5240,5410,5420,5510,5520,5430,5540,5610,6230,6240,6270,6290,6310,6410,5820,5830,5850,5920,6020,6030,6040,6150,6070,6200,6590,6630,6840,6860,6942,6940,8000,8010,8130,8140,8320,8410,8420,8430,8480,8530,8580,8620,8740,8850,8890,8950,8970,8980,9010,9100,1010,710,1030,1040,720,740,1080,780,1230,1330,1410,1350,1120,5060,5080,5052,5050", encoding = "UTF-8")
celt_words <- read.csv("https://soundcomparisons.com/export/csv?study=Celtic&languages=14111238609&words=10,20,30,40,50,60,70,80,90,100,110,200,7810,1830,7010,7020,7060,7040,7110,7170,7120,7360,7390,7400,7460,7480,7840,7520,7510,7580,7570,7590,7650,7660,7710,7720,7740,7760,810,2140,2230,2320,2530,2630,2640,2670,2600,2740,2840,4010,4020,4420,4470,3120,3190,3200,3330,3840,3910,6420,5110,5140,5240,6460,5430,5620,5630,6470,6340,6360,6310,6320,5900,5850,6010,6950,6150,6040,6070,6300,6820,8040,8070,8300,9090,8480,9070,1010,1040,1080,1090,1490,4610,4620,4910,4930,6190,6191,6192,4100,4101,4102,6450,6451,6452,3300,3301,3302,5410,5411,5412,6510,6511,6512,3110,3111,3112,2810,2811,2812,2010,2011,2012,5520,5521,5522,510,511,512,2370,2371,2372,3280,3281,3282,5100,5101,5102,3950,3951,3952,6990,6991,6992", encoding = "UTF-8")
ands_words <- read.csv("https://soundcomparisons.com/export/csv?study=Andes&languages=21222210001&words=11,10,20,30,40,50,60,70,80,90,100,200,7011,7010,7020,7110,7120,7840,7860,7880,7510,7980,7960,7710,7750,7970,7971,7910,7930,2010,2050,2110,2111,2120,2130,2210,2230,2240,2310,2410,2510,2611,2610,2620,2630,2640,2710,2811,2810,4010,4030,4101,4100,3120,3430,3820,3830,5110,5120,5140,5170,5210,5230,5410,5510,5430,5520,6410,6220,5850,6030,6060,6040,6020,6120,6510,6520,6810,8021,8020,8030,8050,8110,8311,8310,8370,8420,8421,8430,8440,8510,8550,8580,8590,8410,8411,8270,8600,8610,8620,8740,8910,8940,9040,1011,1010,1020,1040,1060,1070,1120,1210,1220,1230,1260,1310,1320,1360,1910,5010,5020,510,520", encoding = "UTF-8")
map_words <- read.csv("https://soundcomparisons.com/export/csv?study=Mapudungun&languages=28141005109&words=10,20,30,40,50,60,70,80,90,100,200,210,7010,7020,7030,7050,7110,7840,7860,7880,7980,7960,7710,7750,7910,7930,7170,20680,20690,7720,20790,2010,2050,2110,2120,2130,2210,2230,2240,10560,2410,2310,2510,2530,2610,2620,2630,2640,2710,2810,2840,10050,3010,4010,4030,4100,20170,10370,10620,20240,10740,4320,20190,10790,31180,21410,21400,20090,3120,20070,20030,20910,10200,3210,20010,20020,3420,3430,20100,3820,3830,3810,5110,5120,5130,5140,10230,20980,5170,10710,5210,5220,5230,5240,5410,5510,5430,10120,5520,6410,10760,20480,6220,6210,6240,6260,20130,5850,5810,5840,10810,21530,21520,6030,6060,6040,6020,6120,10730,20550,20610,30380,6510,6520,30500,20700,20660,10720,20890,20670,6810,8020,8030,8050,8110,8310,8370,8420,8430,8440,8550,8580,8590,8410,21450,31390,10550,8270,21510,21440,8510,21090,8600,8610,8620,8740,8830,8910,8940,9040,1010,1040,1060,1023,1070,1024,1210,1220,1230,1260,1360,1910,20830,10450,21050,5010,5020,510,20560,21000,20620,10770,20630,10800,10540,10650,7520,10350,10340,10490,10480,10040,10030,10640,10630,6320,10430,10290,10280,10100,10090,10130,10140,10260,10270,10300,10310,10010,10020,10460,10470,10500,10510,10400,10410,10670,10680,10700,10380,21100,10070,10080,10590,10600", encoding = "UTF-8")
braz_words <- read.csv("https://soundcomparisons.com/export/csv?study=Brazil&languages=31111102309&words=10,20,30,40,50,7820,7830,1610,1620,7010,7020,7030,7040,7050,7110,7170,7210,7220,7310,7320,7350,7360,7370,7380,7410,7420,7470,7450,7840,7860,820,810,7890,7880,7520,7980,7960,7430,7710,7720,7770,7800,7910,7920,7930,7940,7990,6060,2010,2050,2110,2120,2130,2210,2230,2240,2310,2340,2410,2510,2520,2530,2550,2610,2620,2630,2640,2710,2720,2730,2740,2810,2840,4010,4020,4030,4050,4310,4350,4360,4100,3010,3120,3210,3410,3420,3430,3500,3240,3810,3820,3830,3840,3850,4320,5850,6080,6090,5110,5120,5130,5150,5170,5210,5220,5230,5240,5310,5410,5420,5510,5540,5430,5530,5520,6810,6210,6220,6230,6240,6260,6280,6310,6320,5810,5830,5860,8980,6030,6040,6510,6620,8010,8020,8030,8040,8050,8060,8250,8260,8210,8220,8110,8130,8140,8150,8310,8330,8320,8340,8370,8420,8430,8440,8510,8520,8540,8560,8550,8580,8590,8410,8270,8600,8610,8620,8120,8670,8680,8690,8700,8720,8740,8810,8830,8840,8850,8870,8880,8890,8920,8930,8940,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9070,1010,1020,1040,1050,1060,1070,1120,1210,1220,1230,1250,1310,1320,1330,1340,1360,1410,1350,2420,5010,5020,5050", encoding = "UTF-8")
pap_words <- read.csv("https://soundcomparisons.com/export/csv?study=WestPapua&languages=53121400009&words=10,20,30,40,50,60,70,80,90,100,110,150,200,210,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1021,1022,1040,1050,1052,1060,1070,1072,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,1710,5010,5020,510", encoding = "UTF-8")
mix_words <- read.csv("https://soundcomparisons.com/export/csv?study=MixeZoque&languages=61051523409&words=10,20,30,40,7010,7020,7030,7040,7050,810,820,7110,7170,7210,7220,7350,7360,7370,7380,7410,7420,7490,7520,7630,7710,7720,7730,7770,7800,7820,7860,7880,7890,7910,7920,7930,7940,7980,7990,2010,2050,2110,2120,2130,2210,2230,2240,2310,2330,2410,2420,2520,2610,2620,2630,2710,2720,2740,2840,4010,4020,4030,4050,4100,4310,4320,4350,4360,3120,3210,3240,3410,3420,3430,3450,3490,3520,3810,3830,3840,3850,5120,5130,5150,5210,6220,5230,5240,6240,6250,6260,6280,6310,6320,5410,5420,5430,5510,5520,5530,5540,5810,5840,5850,5860,5920,5930,6030,6040,6060,6090,6330,6510,6620,6630,6810,8020,8030,8090,8110,8120,8130,8140,8150,8180,8210,8220,8260,8270,8290,8310,8320,8330,8340,8360,8370,8400,8410,8420,8430,8440,8510,8520,8540,8550,8560,8580,8590,8670,8680,8690,8740,8800,8810,8870,8880,8890,8920,8930,8950,8960,8970,9000,9010,9020,9030,9040,9050,9060,9120,9130,9150,9180,9220,9400,9500,9620,1010,1023,1020,1040,1060,1070,1120,1210,1220,1230,1240,1250,1310,1320,1340,1380,1390,1410,1480,5010,5020,510", encoding = "UTF-8")
words <- rbind(rom_words, ger_words, eng_words, slav_words, celt_words,
ands_words, map_words, braz_words, pap_words, mix_words)
words <- words[,c("WordId", "WordModernName1", "WordModernName2")]
my_data <- lapply(word, function(x) {
if ((x %in% word_set_1)|(x %in% word_set_2)){
z <- (words[words$WordModernName1 == x,])
y <- (words[words$WordModernName2 == x,])
new <- rbind(z,y)
new <- new[rowSums(is.na(new)) != ncol(new),]
word_id <- new$WordId[1]
families <- c("Romance", "Germanic", "Englishes", "Slavic", "Celtic",
"Andes", "Mapudungun", "Brazil", "WestPapua", "MixeZoque")
links_set <- c()
langs <- paste(lingtypology::soundcomparisons$LanguageId, collapse=",")
myword <- words[which(words$WordModernName1 == x), ]
my_links <- lapply(families, function(link) {
links <- paste0("https://soundcomparisons.com/export/csv?study="
,link,"&languages=", langs,"&words=", word_id)
links_set <- append(links_set, links)}) ##получили ссылки, по которым скачали данные
data <-lapply(my_links, function(l) {
datalist <- read.csv(l, encoding = "UTF-8")
datalist <- datalist[c("LanguageId", "LanguageName", "Latitude",
"Longitude", "WordId", "WordModernName1",
"WordProtoName1", "Phonetic")]
})
final_df <- Reduce(function(first, second) {
merge(
first,
second,
all = TRUE,
by = c(
"LanguageId",
"LanguageName",
"Latitude",
"Longitude",
"WordId",
"WordModernName1",
"WordProtoName1",
"Phonetic"
)
)
}, data)
} else {
}})
my_df <- do.call("rbind", my_data)
my_df <- merge(my_df, lingtypology::soundcomparisons, by = "LanguageId")
my_df$LanguageName.y <- NULL
#my_df <- subset(my_df, select = -c(LanguageName.y))
colnames(my_df) <- c("lang_id", "language", "latitude", "longitude",
"word_id", "word", "proto_word", "phonetic",
"glottocode")
return(my_df)
}
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