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6-3-analysis.R
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6-3-analysis.R
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# Kode pemrograman untuk analisis kuantitatif pada makalah
# "Afiksasi Verba dalam Bahasa Indonesia"
# penulis: Gede Primahadi Wijaya Rajeg & Karlina Denistia (2022)
# Tautan repositori: https://doi.org/10.17605/OSF.IO/NUXD4 -- NB: reader can skip codes from lines 14 - 24, and line 58. Run the codes from line 6-10, line 13, then jump to lines 28-45, and finally run all codes from lines 61 until the end.
library(tidyverse)
library(gridExtra)
library(ggrepel)
library(RColorBrewer)
library(effsize)
# verbs <- readr::read_tsv("verbs_main.txt")
verbs <- readr::read_rds("verbs_main.rds")
genre_levels <- c("Surat_Resmi", "Perundang_undangan", "Jurnal",
"Disertasi_Tesis_Skripsi", "Buku_Teks", "Laman_Resmi",
"Populer", "Biografi", "Majalah",
"Koran", "Cerpen", "Novel")
verbs <- verbs %>%
mutate(genres = factor(genres, levels = genre_levels))
genressizes <- readr::read_tsv("genressizes.txt") %>%
mutate(genres = factor(genres, levels = genre_levels))
yearsizes <- readr::read_tsv("yearsizes.txt") %>%
mutate(year = as.character(year))
corpussizes_all <- as.numeric(readr::read_lines("corpussizes_all.txt"))
# Deriving the sizes of the verb database ======
## tokens
verbs_tokens_dbase_total <- sum(verbs$n)
verbs_tokens_dbase_total
prettyNum(verbs_tokens_dbase_total, big.mark = ".", decimal.mark = ",")
## types
verbs_types_dbase_total <- verbs %>%
select(pref_morphind, root_morphind, suff_morphind) %>%
distinct() %>%
nrow()
verbs_types_dbase_total
prettyNum(verbs_types_dbase_total, big.mark = ".", decimal.mark = ",")
## hapax legomena
verbs_hapax_dbase_total <- verbs %>%
group_by(pref_morphind, root_morphind, suff_morphind) %>%
summarise(n = sum(n), .groups = "drop") %>%
filter(n == 1) %>%
nrow()
verbs_hapax_dbase_total
prettyNum(verbs_hapax_dbase_total, big.mark = ".", decimal.mark = ",")
# Create the content of "Tabel 1." ========
# samples_of_database <- verbs %>%
# group_by(dbase) %>%
# slice_sample(n = 1) %>%
# ungroup() %>%
# select(tahun=year, `bentukan kata`=word_form, token = n,
# genre = genres, morphind, `akar kata`=root_morphind,
# `kelas kata akar` = root_pos_morphind)
# samples_of_database %>%
# write_rds('samples_of_database.rds')
samples_of_database <- read_rds('samples_of_database.rds')
# Analysis ============
### 1. number of tokens, types, and hapaxes for me, di, ter, and ber verbs (prefix only) ======
prefix_overall <- verbs %>%
group_by(dbase) %>%
summarise(tokens = sum(n),
types = n_distinct(word_form)) %>% # by word form %>%
# join the type freq analysis by unique combination of root and affixes
left_join(verbs %>%
group_by(dbase, affix_morphind, root_morphind) %>%
summarise(n = sum(n), .groups = 'drop') %>%
group_by(dbase) %>%
summarise(types_root = n(), .groups = "drop")) %>%
# join the hapax analysis by word_form
left_join(verbs %>%
group_by(dbase, word_form) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(dbase) %>%
summarise(hapaxes = sum(is_hapax)) %>%
arrange(desc(hapaxes))) %>%
# join the hapax analysis by root by unique combination of root and affixes
left_join(verbs %>%
group_by(dbase, affix_morphind, root_morphind) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(dbase) %>%
summarise(hapaxes_root = sum(is_hapax)) %>%
arrange(desc(hapaxes_root))) %>%
arrange(desc(tokens)) %>%
mutate(dbase = factor(dbase, levels = .$dbase)) %>%
pivot_longer(-dbase, names_to = "analyses", values_to = "values") %>%
mutate(corpussize = corpussizes_all,
pmw = (values/corpussize) * 1000000)
#### 1.1 visualisation ======
##### 1.1.1 tokens plot =======
toks_fig <- prefix_overall %>%
filter(analyses == "tokens") %>%
mutate(dbase = paste(toupper(dbase), "-", sep = ""),
dbase = factor(dbase, levels = c("ME-", "DI-", "BER-", "TER-"))) %>%
ggplot(aes(x = dbase, y = pmw, fill = dbase)) + # `pmw` = RELATIVE PER MILLION count
# ggplot(aes(x = dbase, y = values, fill = dbase)) + # `values` = RAW count
geom_col(position = 'dodge') +
geom_text(aes(label = prettyNum(round(pmw, 2), big.mark = ".", decimal.mark = ",")),
position = position_dodge(.9), vjust = -.5, size = 2.5) +
labs(x = "",
title = "Kekerapan/kemunculan relatif\n(frekuensi-token relatif per 1 juta kemunculan kata)",
y = "") +
theme_light() +
theme(legend.position = "none",
plot.title = element_text(size = 8.75)) +
scale_fill_brewer(type = "qual", palette = "Set2")
##### 1.1.2 types plot ========
types_fig <- prefix_overall %>%
filter(analyses == "types_root") %>%
mutate(dbase = paste(toupper(dbase), "-", sep = ""),
dbase = factor(dbase, levels = c("ME-", "DI-", "BER-", "TER-"))) %>%
ggplot(aes(x = dbase, y = pmw, fill = dbase)) + # `pmw` = RELATIVE PER MILLION count
# ggplot(aes(x = dbase, y = values, fill = dbase)) + # `values` = RAW count
geom_col(position = 'dodge') +
geom_text(aes(label = prettyNum(round(pmw, 2), big.mark = ".", decimal.mark = ",")),
position = position_dodge(.9), vjust = -.5, size = 2.5) +
labs(x = "",
title = "Jumlah bentukan kata relatif\n(frekuensi-tipe relatif per 1 juta kemunculan kata)",
y = "") +
theme_light() +
theme(legend.position = "none",
plot.title = element_text(size = 8.75)) +
scale_fill_brewer(type = "qual", palette = "Set2")
##### 1.1.3 hapax plot ==========
hapax_fig <- prefix_overall %>%
filter(analyses == "hapaxes_root") %>%
mutate(dbase = paste(toupper(dbase), "-", sep = ""),
dbase = factor(dbase, levels = c("ME-", "DI-", "BER-", "TER-"))) %>%
ggplot(aes(x = dbase, y = pmw, fill = dbase)) + # `pmw` = RELATIVE PER MILLION count
# ggplot(aes(x = dbase, y = values, fill = dbase)) + # `values` = RAW count
geom_col(position = 'dodge') +
geom_text(aes(label = prettyNum(round(pmw, 2), big.mark = ".", decimal.mark = ",")),
position = position_dodge(.9), vjust = -.5, size = 2.5) +
labs(x = "",
title = "Jumlah bentukan kata berkekerapan 1\n(hapax per 1 juta kemunculan kata)",
y = "") +
theme_light() +
theme(legend.position = "none",
plot.title = element_text(size = 8.75)) +
scale_fill_brewer(type = "qual", palette = "Set2")
##### 1.1.4 combined plot ==========
prefix_overall_plot <- grid.arrange(toks_fig, types_fig, hapax_fig, nrow = 1, ncol = 3)
##### 1.1.5 save the plot ==========
ggsave(plot = prefix_overall_plot,
filename = "figures/1-prefix-overall-freq-profiles.png",
width = 11, height = 4,
dpi = 300, units = "in")
### 3. types, tokens, and hapax for each affix SUB-PATTERN in the WHOLE CORPUS ======
overall_prods <- verbs %>%
group_by(dbase, affix_morphind) %>%
summarise(types_root = n_distinct(root_morphind),
types_wordform = n_distinct(word_form),
tokens = sum(n),
.groups = "drop") %>%
mutate(perc_tokens = round(tokens/sum(tokens) * 100, 2),
perc_type_root = round(types_root/sum(types_root) * 100, 2),
perc_type_wordform = round(types_wordform/sum(types_wordform) * 100, 2)) %>%
arrange(dbase, desc(types_root)) %>%
# join the hapax analysis by root
left_join(verbs %>%
group_by(dbase, affix_morphind, root_morphind) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(affix_morphind) %>%
summarise(hapaxes_root = sum(is_hapax)) %>%
arrange(desc(hapaxes_root))) %>%
# join the hapax analysis by word_form
left_join(verbs %>%
group_by(dbase, affix_morphind, word_form) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(affix_morphind) %>%
summarise(hapaxes_wordform = sum(is_hapax)) %>%
arrange(desc(hapaxes_wordform))) %>%
arrange(dbase, desc(types_root), desc(hapaxes_root)) %>%
mutate(htr_root = round(hapaxes_root/tokens, 6),
htr_wordform = round(hapaxes_wordform/tokens, 6)) %>%
group_by(dbase) %>%
mutate(focused = if_else(types_root > 5,
TRUE, # mark the focused affixes with type freq. > 5
FALSE)) %>%
ungroup() %>%
pivot_longer(cols = -c(dbase, affix_morphind, focused),
names_to = "freq_profile",
values_to = "values") %>%
mutate(corpussize = corpussizes_all,
pmw = 0,
pmw = if_else(str_detect(freq_profile, "^(perc|htr)", negate = TRUE),
((values/corpussize) * 1000000),
pmw))
#### 3.0 Get the values of the focused affixes =======
focused_affixes <- overall_prods %>%
filter(focused) %>%
pull(affix_morphind) %>%
unique()
focused_affixes
#### 3.1 visualisation for each affix sub-pattern BY FREQUENCY PROFILE =======
# unitising the colouring for ME, DI, BER, and TER
colour_values <- data.frame(dbase = c("me", "di", "ber", "ter"),
colvals = c("#66C2A5", "#FC8D62",
"#8DA0CB", "#E78AC3"))
overall_prods <- overall_prods %>%
left_join(colour_values)
##### 3.1.1 token frequency ==========
token_plots <- overall_prods %>%
filter(focused, freq_profile == "tokens") %>%
mutate(dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
mutate(affix_morphind = str_replace_all(affix_morphind, "_0", ""),
affix_morphind = str_replace_all(affix_morphind, "_([a-z])", "/-\\1"),
affix_morphind = str_replace_all(affix_morphind, "-(\\+)", "\\1")) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values, fill = colvals)) + # `values` = RAW count
~ggplot(., aes(x = reorder(affix_morphind, pmw), y = pmw, fill = colvals)) +
geom_col() +
coord_flip() +
theme_light() +
geom_text(data = filter(., pmw < 10000 & affix_morphind != "ter-"),
aes(label = prettyNum(round(pmw, 1), big.mark = ".", decimal.mark = ",")),
hjust = -.2, size = 2.2) +
geom_text(data = filter(., pmw > 10000 | affix_morphind == "ter-"),
aes(label = prettyNum(round(pmw, 1), big.mark = ".", decimal.mark = ",")),
hjust = 1.5, size = 2.2) +
scale_fill_manual(values = .$colvals) +
labs(x = "", y = "Kekerapan relatif per 1 juta kata", fill = "") +
theme(axis.title.x = element_text(size = 7),
axis.text.x = element_text(size = 6.1),
legend.position = "none"))
token_plots1 <- grid.arrange(grobs = token_plots, ncol = 2, nrow = 2)
ggsave(plot = token_plots1,
filename = "figures/2-sub-pattern-tokens.png",
width = 7.85, height = 5.5,
dpi = 300, units = "in")
##### 3.1.2 type frequency ==========
type_plots <- overall_prods %>%
filter(focused, freq_profile == "types_root") %>%
mutate(dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
mutate(affix_morphind = str_replace_all(affix_morphind, "_0", ""),
affix_morphind = str_replace_all(affix_morphind, "_([a-z])", "/-\\1"),
affix_morphind = str_replace_all(affix_morphind, "-(\\+)", "\\1")) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values, fill = colvals)) + # `values` = RAW count
~ggplot(., aes(x = reorder(affix_morphind, pmw), y = pmw, fill = colvals)) +
geom_col() +
coord_flip() +
theme_light() +
geom_text(data = filter(., pmw < 50),
aes(label = prettyNum(round(pmw, 1), big.mark = ".", decimal.mark = ",")),
hjust = -.2, size = 2.2) +
geom_text(data = filter(., pmw > 50),
aes(label = prettyNum(round(pmw, 1), big.mark = ".", decimal.mark = ",")),
hjust = 1.5, size = 2.2) +
scale_fill_manual(values = .$colvals) +
labs(x = "",
y = "Jumlah bentukan kata (frekuensi tipe)\n(relatif per 1 juta kata)",
fill = "") +
theme(axis.title.x = element_text(size = 7),
axis.text.x = element_text(size = 6.1),
legend.position = "none"))
type_plots1 <- grid.arrange(grobs = type_plots, ncol = 2, nrow = 2)
ggsave(plot = type_plots1,
filename = "figures/3-sub-pattern-types.png",
width = 7.85, height = 5.5,
dpi = 300, units = "in")
##### 3.1.3 hapax ==========
hapax_plots <- overall_prods %>%
filter(focused, freq_profile == "hapaxes_root") %>%
mutate(dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
mutate(affix_morphind = str_replace_all(affix_morphind, "_0", ""),
affix_morphind = str_replace_all(affix_morphind, "_([a-z])", "/-\\1"),
affix_morphind = str_replace_all(affix_morphind, "-(\\+)", "\\1")) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values, fill = colvals)) + # `values` = RAW count
~ggplot(., aes(x = reorder(affix_morphind, pmw), y = pmw, fill = colvals)) +
geom_col() +
coord_flip() +
theme_light() +
geom_text(data = filter(., pmw < 10),
aes(label = prettyNum(round(pmw, 1), big.mark = ".", decimal.mark = ",")),
hjust = -.2, size = 2.2) +
geom_text(data = filter(., pmw > 10),
aes(label = prettyNum(round(pmw, 1), big.mark = ".", decimal.mark = ",")),
hjust = 1.5, size = 2.2) +
scale_fill_manual(values = .$colvals) +
labs(x = "",
y = "Jumlah bentukan kata berkekerapan 1 (hapax)\n(relatif per 1 juta kata)",
fill = "") +
theme(axis.title.x = element_text(size = 7),
axis.text.x = element_text(size = 6.1),
legend.position = "none"))
hapax_plots1 <- grid.arrange(grobs = hapax_plots, ncol = 2, nrow = 2)
ggsave(plot = hapax_plots1,
filename = "figures/4-sub-pattern-hapax.png",
width = 7.85, height = 5.5,
dpi = 300, units = "in")
#### 3.3 Correlation analysis of sub-patterns productivity measures =============
correlation_analysis_df <- overall_prods %>%
filter(str_detect(freq_profile, '(perc|htr_|wordform)', TRUE)) %>%
select(dbase, affix_morphind, freq_profile, values) %>%
pivot_wider(names_from = "freq_profile", values_from = "values")
correlation_analysis_df
##### 3.3.1 cor.test ME- and DI- subtypes========
me_df <- correlation_analysis_df %>%
filter(dbase %in% c("me", "di")) %>%
mutate(affix_morphind = str_replace(affix_morphind, "^(meN-|di-)", ""),
affix_morphind = str_replace(affix_morphind, "^[+]", ""))
intersecting_affixes <- intersect(pull(filter(me_df, dbase=='me'), affix_morphind),
pull(filter(me_df, dbase=='di'), affix_morphind))
me_df <- me_df %>%
filter(affix_morphind %in% intersecting_affixes) %>%
mutate(affix_morphind = factor(affix_morphind, levels = intersecting_affixes)) %>%
arrange(dbase, affix_morphind)
###### correlation tests (Kendall)
####### 3.3.1.1 tokens
cortest_me_di_tokens <- cor.test(pull(filter(me_df, dbase == "me"), tokens),
pull(filter(me_df, dbase == "di"), tokens),
method = "kendall",
exact = FALSE) # Warning message: Cannot compute exact p-value with ties' hence exact = FALSE
cortest_me_di_tokens
####### 3.3.1.2 types
cortest_me_di_types <- cor.test(pull(filter(me_df, dbase == "me"), types_root),
pull(filter(me_df, dbase == "di"), types_root),
method = "kendall",
exact = FALSE) # Warning message: Cannot compute exact p-value with ties' hence exact = FALSE
cortest_me_di_types
####### 3.3.1.3 hapaxes
cortest_me_di_hapaxes <- cor.test(pull(filter(me_df, dbase == "me"), hapaxes_root),
pull(filter(me_df, dbase == "di"), hapaxes_root),
method = "kendall",
exact = FALSE) # Warning message: Cannot compute exact p-value with ties' hence exact = FALSE
cortest_me_di_hapaxes
##### 3.3.2 pair-wise correlation between values for each of the four affix sub-patterns========
cor(filter(correlation_analysis_df, dbase=='me')[, 3:5], method = "kendall") # ME-
cor(filter(correlation_analysis_df, dbase=='di')[, 3:5], method = "kendall") # DI-
cor(filter(correlation_analysis_df, dbase=='ber')[, 3:5], method = "kendall") # BER-
cor(filter(correlation_analysis_df, dbase=='ter')[, 3:5], method = "kendall") # TER-
### 4. types, tokens, and hapax for each focused affix sub-patterns by YEAR ======
year_prods <- verbs %>%
group_by(dbase, affix_morphind, year) %>%
summarise(types_root = n_distinct(root_morphind),
types_wordform = n_distinct(word_form),
tokens = sum(n),
.groups = "drop") %>%
mutate(perc_tokens = round(tokens/sum(tokens) * 100, 2),
perc_type_root = round(types_root/sum(types_root) * 100, 2),
perc_type_wordform = round(types_wordform/sum(types_wordform) * 100, 2)) %>%
arrange(dbase, desc(types_root)) %>%
# join the hapax analysis by root
left_join(verbs %>%
group_by(dbase, affix_morphind, root_morphind, year) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(affix_morphind, year) %>%
summarise(hapaxes_root = sum(is_hapax)) %>%
arrange(desc(hapaxes_root))) %>%
# join the hapax analysis by word_form
left_join(verbs %>%
group_by(dbase, affix_morphind, word_form, year) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(affix_morphind, year) %>%
summarise(hapaxes_wordform = sum(is_hapax)) %>%
arrange(desc(hapaxes_wordform))) %>%
arrange(dbase, desc(hapaxes_root), desc(types_root)) %>%
mutate(htr_root = round(hapaxes_root/tokens, 6),
htr_wordform = round(hapaxes_wordform/tokens, 6)) %>%
group_by(dbase) %>%
mutate(focused = if_else(affix_morphind %in% focused_affixes, TRUE, FALSE)) %>%
ungroup() %>%
arrange(dbase, year) %>%
pivot_longer(cols = -c(dbase, affix_morphind, focused, year),
names_to = "freq_profile", values_to = "values") %>%
left_join(yearsizes, by = 'year') %>%
mutate(pmw = 0,
pmw = if_else(str_detect(freq_profile, "^(perc|htr)", negate = TRUE),
((values/year_size) * 1000000),
pmw))
#### 4.x visualisation for each affix patterns ========
# focused_affixes %>% factor(levels = focused_affixes) %>% tibble(affix_morphind_colour = ., affix_morphind = .) -> focused_affix_colour
focused_affix_colour <- focused_affixes %>%
factor(levels = focused_affixes) %>%
tibble(affix_morphind_colour = ., affix_morphind = .) %>%
mutate(affix_morphind_colour = str_replace_all(affix_morphind_colour, "_0", ""),
affix_morphind_colour = str_replace_all(affix_morphind_colour, "_([a-z])", "/-\\1"),
affix_morphind_colour = str_replace_all(affix_morphind_colour, "-(\\+)", "\\1"),
affix_morphind_colour = factor(affix_morphind_colour,
levels = c("ber-", "ber-/-an", "ber-/-kan", "ber+ke-/-an",
"ber+peN-", "ber+peN-/-an",
"ber+si-", "ber+per-/-an", "ber-/-i", "di-",
"di-/-kan", "di-/-i", "di+per-",
"di+per-/-kan", "di+per-/-i", "di-/-in",
"meN-", "meN-/-kan", "meN-/-i",
"meN+per-", "meN+per-/-kan", "meN+per-/-i",
"meN-/-in", "meN+ber-/-kan", "ter-",
"ter-/-kan", "ter-/-i", "ter-/-an")))
##### 4.1 token frequency ==========
token_plots <- year_prods %>%
filter(focused, freq_profile == "tokens") %>%
mutate(year = as.numeric(year),
dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
left_join(focused_affix_colour) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values)) + # `values` = RAW count
~ggplot(., aes(x = year, y = pmw,
fill = affix_morphind_colour,
colour = affix_morphind_colour,
group = affix_morphind_colour)) +
geom_line() +
geom_point(size = 2, shape = 22, alpha = .7) +
theme_light() +
labs(x = NULL, colour = NULL, y = NULL, fill = paste(toupper(.$dbase), "-", sep = "")) +
geom_text_repel(data = filter(., year == '2020'),
aes(label = affix_morphind_colour),
direction = "y",
hjust = 0,
segment.size = .7,
segment.alpha = .5,
segment.linetype = "dotted",
box.padding = .4,
xlim = c(2020.8, NA),
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
size = 2, fontface = 'bold') +
guides(colour = "none") +
scale_x_continuous(expand = c(0, 0),
limits = c(2010, 2022),
breaks = seq(2011, 2020, by = 1)) +
theme(axis.title.y = element_text(size = 8),
axis.text.y = element_text(size = 7),
axis.text.x = element_text(size = 7),
legend.text = element_text(size = 8)))
token_plots1 <- grid.arrange(grobs = token_plots, ncol = 2, nrow = 2)
ggsave(plot = token_plots1,
filename = "figures/12-sub-pattern-tokens-by-year.png",
width = 11.5, height = 6,
dpi = 300, units = "in")
###### 4.1.1 correlation test for token frequency of some afix subtypes ==========
men <- filter(year_prods, affix_morphind == "meN-_0", freq_profile == "tokens")
cor.test(as.numeric(men$year), men$pmw, method = "kendall")
di <- filter(year_prods, affix_morphind == "di-_0", freq_profile == "tokens")
cor.test(as.numeric(di$year), di$pmw, method = "kendall")
men_kan <- filter(year_prods, affix_morphind == "meN-_kan", freq_profile == "tokens")
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")
di_kan <- filter(year_prods, affix_morphind == "di-_kan", freq_profile == "tokens")
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")
men_i <- filter(year_prods, affix_morphind == "meN-_i", freq_profile == "tokens")
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")
di_i <- filter(year_prods, affix_morphind == "di-_i", freq_profile == "tokens")
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")
ber <- filter(year_prods, affix_morphind == "ber-_0", freq_profile == "tokens")
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")
ter <- filter(year_prods, affix_morphind == "ter-_0", freq_profile == "tokens")
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")
###### 4.1.2 T-test for relative token frequency of di vs. di-/-kan ==========
di_di_kan_token_ttest_df <- bind_rows(di, di_kan) %>%
mutate(affix_morphind = factor(affix_morphind, levels = c("di-_0", "di-_kan"))) %>%
select(affix_morphind, pmw)
di_di_kan_token_ttest_df
di_di_kan_token_ttest <- t.test(di_di_kan_token_ttest_df$pmw~di_di_kan_token_ttest_df$affix_morphind, alternative = "two.sided", paired = FALSE)
di_di_kan_token_ttest # significantly different
cohen_d <- effsize::cohen.d(di$pmw, di_kan$pmw)
cohen_d # large effect size (large frequency different)
##### 4.2 type frequency ==========
type_plots <- year_prods %>%
filter(focused, freq_profile == "types_root") %>%
mutate(year = as.numeric(year),
dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
left_join(focused_affix_colour) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values)) + # `values` = RAW count
~ggplot(., aes(x = year, y = pmw,
fill = affix_morphind_colour,
colour = affix_morphind_colour,
group = affix_morphind_colour)) +
geom_line() +
geom_point(size = 2, shape = 22, alpha = .7) +
theme_light() +
labs(x = NULL, colour = NULL, y = NULL, fill = paste(toupper(.$dbase), "-", sep = "")) +
geom_text_repel(data = filter(., year == '2020'),
aes(label = affix_morphind_colour),
direction = "y",
hjust = 0,
segment.size = .7,
segment.alpha = .5,
segment.linetype = "dotted",
box.padding = .4,
xlim = c(2020.8, NA),
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
size = 2,
fontface = 'bold') +
guides(colour = "none") +
scale_x_continuous(expand = c(0, 0),
limits = c(2010, 2022),
breaks = seq(2011, 2020, by = 1)) +
theme(axis.title.y = element_text(size = 8),
axis.text.y = element_text(size = 7),
axis.text.x = element_text(size = 7),
legend.text = element_text(size = 8)))
type_plots1 <- grid.arrange(grobs = type_plots, ncol = 2, nrow = 2)
ggsave(plot = type_plots1,
filename = "figures/13-sub-pattern-types-by-year.png",
width = 11.5, height = 6,
dpi = 300, units = "in")
###### 4.2.1 correlation test for relative type frequency of some subtypes ==========
men <- filter(year_prods, affix_morphind == "meN-_0", freq_profile == "types_root")
cor.test(as.numeric(men$year), men$pmw, method = "kendall")
cor.test(as.numeric(men$year), men$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(men$year), men$pmw, method = "kendall")$estimate < -0.5
di <- filter(year_prods, affix_morphind == "di-_0", freq_profile == "types_root")
cor.test(as.numeric(di$year), di$pmw, method = "kendall")
cor.test(as.numeric(di$year), di$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(di$year), di$pmw, method = "kendall")$estimate < -0.5
men_kan <- filter(year_prods, affix_morphind == "meN-_kan", freq_profile == "types_root")
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")$estimate < -0.5
di_kan <- filter(year_prods, affix_morphind == "di-_kan", freq_profile == "types_root")
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")$estimate < -0.5
men_i <- filter(year_prods, affix_morphind == "meN-_i", freq_profile == "types_root")
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")$estimate < -0.5
di_i <- filter(year_prods, affix_morphind == "di-_i", freq_profile == "types_root")
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")$estimate < -0.5
ber <- filter(year_prods, affix_morphind == "ber-_0", freq_profile == "types_root")
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")$estimate < -0.5
ter <- filter(year_prods, affix_morphind == "ter-_0", freq_profile == "types_root")
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")$estimate < -0.5
###### 4.2.2 T-test for relative type frequency of di vs. di-/-kan ==========
di_di_kan_type_ttest_df <- bind_rows(di, di_kan) %>%
mutate(affix_morphind = factor(affix_morphind, levels = c("di-_0", "di-_kan"))) %>%
select(affix_morphind, pmw)
di_di_kan_type_ttest <- t.test(di_di_kan_type_ttest_df$pmw~di_di_kan_type_ttest_df$affix_morphind, alternative = "two.sided", paired = FALSE)
di_di_kan_type_ttest # not significantly different
cohen_d <- effsize::cohen.d(di$pmw, di_kan$pmw)
cohen_d # large effect size (large frequency different)
##### 4.3 hapax frequency ==========
hapax_plots <- year_prods %>%
filter(focused, freq_profile == "hapaxes_root") %>%
mutate(year = as.numeric(year),
dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
left_join(focused_affix_colour) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values)) + # `values` = RAW count
~ggplot(., aes(x = year, y = pmw,
fill = affix_morphind_colour,
colour = affix_morphind_colour,
group = affix_morphind_colour)) +
geom_line() +
geom_point(size = 2, shape = 22, alpha = .7) +
theme_light() +
labs(x = NULL, colour = NULL, y = NULL, fill = paste(toupper(.$dbase), "-", sep = "")) +
geom_text_repel(data = filter(., year == '2020'),
aes(label = affix_morphind_colour),
direction = "y",
hjust = 0,
segment.size = .7,
segment.alpha = .5,
segment.linetype = "dotted",
box.padding = .4,
xlim = c(2020.8, NA),
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
size = 2,
fontface = 'bold') +
guides(colour = "none") +
scale_x_continuous(expand = c(0, 0),
limits = c(2010, 2022),
breaks = seq(2011, 2020, by = 1)) +
theme(axis.title.y = element_text(size = 8),
axis.text.y = element_text(size = 7),
axis.text.x = element_text(size = 7),
legend.text = element_text(size = 8)))
hapax_plots1 <- grid.arrange(grobs = hapax_plots, ncol = 2, nrow = 2)
ggsave(plot = hapax_plots1,
filename = "figures/14-sub-pattern-hapax-by-year.png",
width = 11.5, height = 6,
dpi = 300, units = "in")
###### 4.3.1 correlation test for relative hapax of some subtypes ==========
men <- filter(year_prods, affix_morphind == "meN-_0", freq_profile == "hapaxes_root")
cor.test(as.numeric(men$year), men$pmw, method = "kendall")
cor.test(as.numeric(men$year), men$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(men$year), men$pmw, method = "kendall")$estimate < -0.5
di <- filter(year_prods, affix_morphind == "di-_0", freq_profile == "hapaxes_root")
cor.test(as.numeric(di$year), di$pmw, method = "kendall")
cor.test(as.numeric(di$year), di$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(di$year), di$pmw, method = "kendall")$estimate < -0.5
men_kan <- filter(year_prods, affix_morphind == "meN-_kan", freq_profile == "hapaxes_root")
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(men_kan$year), men_kan$pmw, method = "kendall")$estimate < -0.5
di_kan <- filter(year_prods, affix_morphind == "di-_kan", freq_profile == "hapaxes_root")
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(di_kan$year), di_kan$pmw, method = "kendall")$estimate < -0.5
men_i <- filter(year_prods, affix_morphind == "meN-_i", freq_profile == "hapaxes_root")
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(men_i$year), men_i$pmw, method = "kendall")$estimate < -0.5
di_i <- filter(year_prods, affix_morphind == "di-_i", freq_profile == "hapaxes_root")
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(di_i$year), di_i$pmw, method = "kendall")$estimate < -0.5
ber <- filter(year_prods, affix_morphind == "ber-_0", freq_profile == "hapaxes_root")
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(ber$year), ber$pmw, method = "kendall")$estimate < -0.5
ter <- filter(year_prods, affix_morphind == "ter-_0", freq_profile == "hapaxes_root")
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")$p.value < 0.05
cor.test(as.numeric(ter$year), ter$pmw, method = "kendall")$estimate < -0.5
### 5. types, tokens, and hapax for each focused affix sub-patterns by GENRE ======
genre_prods <- verbs %>%
group_by(dbase, affix_morphind, genres) %>%
summarise(types_root = n_distinct(root_morphind),
types_wordform = n_distinct(word_form),
tokens = sum(n),
.groups = "drop") %>%
mutate(perc_tokens = round(tokens/sum(tokens) * 100, 2),
perc_type_root = round(types_root/sum(types_root) * 100, 2),
perc_type_wordform = round(types_wordform/sum(types_wordform) * 100, 2)) %>%
arrange(dbase, desc(types_root)) %>%
# join the hapax analysis by root
left_join(verbs %>%
group_by(dbase, affix_morphind, root_morphind, genres) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(affix_morphind, genres) %>%
summarise(hapaxes_root = sum(is_hapax)) %>%
arrange(desc(hapaxes_root))) %>%
# join the hapax analysis by word_form
left_join(verbs %>%
group_by(dbase, affix_morphind, word_form, genres) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(affix_morphind, genres) %>%
summarise(hapaxes_wordform = sum(is_hapax)) %>%
arrange(desc(hapaxes_wordform))) %>%
arrange(dbase, desc(hapaxes_root), desc(types_root)) %>%
mutate(htr_root = round(hapaxes_root/tokens, 6),
htr_wordform = round(hapaxes_wordform/tokens, 6)) %>%
group_by(dbase) %>%
mutate(focused = if_else(affix_morphind %in% focused_affixes, TRUE, FALSE)) %>%
ungroup() %>%
arrange(dbase, genres) %>%
pivot_longer(cols = -c(dbase, affix_morphind, focused, genres),
names_to = "freq_profile", values_to = "values") %>%
left_join(genressizes, by = 'genres') %>%
mutate(pmw = 0,
pmw = if_else(str_detect(freq_profile, "^(perc|htr)", negate = TRUE),
((values/genre_size) * 1000000),
pmw))
#### 5.x visualisation for each affix patterns
focused_affix_colour <- focused_affixes %>%
factor(levels = focused_affixes) %>%
tibble(affix_morphind_colour = ., affix_morphind = .) %>%
mutate(affix_morphind_colour = str_replace_all(affix_morphind_colour, "_0", ""),
affix_morphind_colour = str_replace_all(affix_morphind_colour, "_([a-z])", "/-\\1"),
affix_morphind_colour = str_replace_all(affix_morphind_colour, "-(\\+)", "\\1"),
affix_morphind_colour = factor(affix_morphind_colour,
levels = c("ber-", "ber-/-an", "ber-/-kan",
"ber+ke-/-an", "ber+peN-",
"ber+peN-/-an", "ber+si-", "ber+per-/-an", "ber-/-i",
"di-", "di-/-kan", "di-/-i",
"di+per-", "di+per-/-kan", "di+per-/-i",
"di-/-in", "meN-", "meN-/-kan",
"meN-/-i", "meN+per-", "meN+per-/-kan",
"meN+per-/-i", "meN-/-in", "meN+ber-/-kan",
"ter-", "ter-/-kan", "ter-/-i", "ter-/-an")))
##### 5.1 token frequency ==========
token_plots <- genre_prods %>%
filter(focused, freq_profile == "tokens") %>%
mutate(dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
left_join(focused_affix_colour) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values)) +
~ggplot(., aes(x = genres, y = pmw, fill = affix_morphind_colour)) +
geom_col() +
theme_light() +
labs(x = NULL,
fill = paste(toupper(.$dbase), "-", sep = ""),
y = "Kekerapan relatif\n(per 1 juta kemunculan kata)") +
theme(axis.title.x = element_text(size = 8),
axis.text.y = element_text(size = 7),
axis.text.x = element_text(size = 7),
legend.text = element_text(size = 8)) +
coord_flip() +
scale_fill_brewer(palette = "Spectral", direction = -1))
token_plots1 <- grid.arrange(grobs = token_plots, ncol = 2, nrow = 2)
ggsave(plot = token_plots1,
filename = "figures/9-sub-pattern-tokens-by-genre.png",
width = 11, height = 6,
dpi = 300, units = "in")
##### 5.2 type frequency ==========
type_plots <- genre_prods %>%
filter(focused, freq_profile == "types_root") %>%
mutate(dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
left_join(focused_affix_colour) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values)) +
~ggplot(., aes(x = genres, y = pmw, fill = affix_morphind_colour)) +
geom_col() +
theme_light() +
labs(x = NULL,
fill = paste(toupper(.$dbase), "-", sep = ""),
y = "Jumlah bentukan kata\n(relatif per 1 juta kata)") +
theme(axis.title.x = element_text(size = 8),
axis.text.y = element_text(size = 7),
axis.text.x = element_text(size = 7),
legend.text = element_text(size = 8)) +
coord_flip() +
scale_fill_brewer(palette = "Spectral", direction = -1))
type_plots1 <- grid.arrange(grobs = type_plots, ncol = 2, nrow = 2)
ggsave(plot = type_plots1,
filename = "figures/10-sub-pattern-types-by-genre.png",
width = 11, height = 6,
dpi = 300, units = "in")
##### 5.3 hapax frequency ==========
hapax_plots <- genre_prods %>%
filter(focused, freq_profile == "hapaxes_root") %>%
mutate(dbase = factor(dbase, levels = c("me", "di", "ber", "ter"))) %>%
left_join(focused_affix_colour) %>%
split(.$dbase) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values)) +
~ggplot(., aes(x = genres, y = pmw, fill = affix_morphind_colour)) +
geom_col() +
theme_light() +
labs(x = NULL,
fill = paste(toupper(.$dbase), "-", sep = ""),
y = "Jumlah bentukan kata berkekerapan 1 (hapax)\n(relatif per 1 juta kata)") +
theme(axis.title.x = element_text(size = 8),
axis.text.y = element_text(size = 7),
axis.text.x = element_text(size = 7),
legend.text = element_text(size = 8)) +
coord_flip() +
scale_fill_brewer(palette = "Spectral", direction = -1))
hapax_plots1 <- grid.arrange(grobs = hapax_plots, ncol = 2, nrow = 2)
ggsave(plot = hapax_plots1,
filename = "figures/11-sub-pattern-hapax-by-genre.png",
width = 11, height = 6,
dpi = 300, units = "in")
### 6. number of tokens, types, and hapaxes for me, di, ter, and ber verbs (prefix only OVERALL) BY GENRES ======
prefix_overall_genres <- verbs %>%
group_by(dbase, genres) %>%
summarise(types_root = n_distinct(root_morphind),
types_wordform = n_distinct(word_form),
tokens = sum(n),
.groups = "drop") %>%
mutate(perc_tokens = round(tokens/sum(tokens) * 100, 2),
perc_type_root = round(types_root/sum(types_root) * 100, 2),
perc_type_wordform = round(types_wordform/sum(types_wordform) * 100, 2)) %>%
arrange(dbase, desc(types_root)) %>%
# join the hapax analysis by root
left_join(verbs %>%
group_by(dbase, affix_morphind, root_morphind, genres) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(dbase, genres) %>%
summarise(hapaxes_root = sum(is_hapax)) %>%
arrange(desc(hapaxes_root))) %>%
# join the hapax analysis by word_form
left_join(verbs %>%
group_by(dbase, affix_morphind, word_form, genres) %>%
summarise(n = sum(n), .groups = 'drop') %>%
mutate(is_hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(dbase, genres) %>%
summarise(hapaxes_wordform = sum(is_hapax)) %>%
arrange(desc(hapaxes_wordform))) %>%
arrange(dbase, desc(hapaxes_root), desc(types_root)) %>%
mutate(htr_root = round(hapaxes_root/tokens, 6),
htr_wordform = round(hapaxes_wordform/tokens, 6)) %>%
group_by(dbase) %>%
ungroup() %>%
arrange(dbase, genres) %>%
pivot_longer(cols = -c(dbase, genres),
names_to = "freq_profile",
values_to = "values") %>%
left_join(genressizes, by = 'genres') %>%
mutate(pmw = 0,
pmw = if_else(str_detect(freq_profile, "^(perc|htr)", negate = TRUE),
((values/genre_size) * 1000000),
pmw))
#### 6.1 visualisation========
toks_fig <- prefix_overall_genres %>%
filter(freq_profile == "tokens") %>%
mutate(dbase = fct_reorder(paste(toupper(dbase), "-", sep =""), pmw)) %>%
ggplot(aes(x = genres, y = pmw, fill = dbase)) +
# ggplot(aes(x = dbase, y = values, fill = dbase)) +
geom_col(position = 'dodge') +
labs(x = "",
title = "Kekerapan/kemunculan relatif\n(frekuensi-token relatif per 1 juta kata)",
y = "",
fill = "awalan") +
theme_light() +
coord_flip() +
theme(legend.position = "none",
plot.title = element_text(size = 8.5),
axis.text.y = element_text(size = 8.35),
axis.text.x = element_text(size = 6)) +
scale_fill_brewer(type = "qual", palette = "Set2", limits = c("ME-", "DI-", "BER-", "TER-"))
types_fig <- prefix_overall_genres %>%
filter(freq_profile == "types_root") %>%
mutate(dbase = fct_reorder(paste(toupper(dbase), "-", sep =""), pmw)) %>%
ggplot(aes(x = genres, y = pmw, fill = dbase)) +
# ggplot(aes(x = dbase, y = values, fill = dbase)) +
geom_col(position = 'dodge') +
labs(x = "",
title = "Jumlah bentukan kata relatif\n(frekuensi-tipe relatif per 1 juta kata)",
y = "",
fill = "awalan") +
theme_light() +
coord_flip() +
theme(legend.position = "none",
plot.title = element_text(size = 8.5),
axis.text.y = element_text(size = 8.35),
axis.text.x = element_text(size = 6)) +
scale_fill_brewer(type = "qual", palette = "Set2", limits = c("ME-", "DI-", "BER-", "TER-"))
hapax_fig <- prefix_overall_genres %>%
filter(freq_profile == "hapaxes_root") %>%
mutate(dbase = fct_reorder(paste(toupper(dbase), "-", sep =""), pmw)) %>%
ggplot(aes(x = genres, y = pmw, fill = dbase)) +
# ggplot(aes(x = dbase, y = values, fill = dbase)) +
geom_col(position = 'dodge') +
labs(x = "",
title = "Jumlah bentukan kata berkekerapan 1\n(hapax per 1 juta kata)",
y = "",
fill = "") +
theme_light() +
coord_flip() +
theme(legend.position = "right",
plot.title = element_text(size = 8.5),
axis.text.y = element_text(size = 8.35),
axis.text.x = element_text(size = 6)) +
scale_fill_brewer(type = "qual", palette = "Set2", limits = c("ME-", "DI-", "BER-", "TER-"))
prefix_overall_plot_genres <- grid.arrange(toks_fig, types_fig, hapax_fig, nrow = 1, ncol = 3)
ggsave(plot = prefix_overall_plot_genres,
filename = "figures/8-prefix-overall-freq-profiles-genres.png",
width = 11.5, height = 4.5,
dpi = 300, units = "in")
### 7. Word class preference OVERALL CORPUS =======
#### 7.1 TYPES for each affix sub-patterns =====
affix_wclass_types <- verbs %>%
group_by(dbase, affix_morphind, affix_morphind_wclass) %>%
summarise(types_root = n_distinct(root_morphind), .groups = "drop") %>%
arrange(dbase, affix_morphind, desc(types_root)) %>%
group_by(dbase, affix_morphind) %>%
mutate(total_types_by_affix = sum(types_root), perc_types = round((types_root/total_types_by_affix) * 100, 2))
# affix_wclass_types %>% write_excel_csv2(file = "2_affix_wclass_types_stats.txt")
#### 7.2 TOKENS for each affix sub-patterns =====
affix_wclass_tokens <- verbs %>%
group_by(dbase, affix_morphind, affix_morphind_wclass) %>%
summarise(tokens = sum(n), .groups = "drop") %>%
arrange(dbase, affix_morphind, desc(tokens)) %>%
group_by(dbase, affix_morphind) %>%
mutate(total_tokens_by_affix = sum(tokens), perc_tokens = round((tokens/total_tokens_by_affix) * 100, 2))
# affix_wclass_tokens %>% write_excel_csv2(file = "1_affix_wclass_tokens_stats.txt")
#### 7.3 HAPAX for each affix sub-patterns =====
affix_wclass_hapax <- verbs %>%
group_by(dbase, affix_morphind, affix_morphind_wclass, root_morphind) %>%
summarise(n=sum(n), .groups = "drop") %>%
mutate(hapax = if_else(n == 1, TRUE, FALSE)) %>%
group_by(dbase, affix_morphind, affix_morphind_wclass) %>%
summarise(n_hapax = sum(hapax), .groups = "drop") %>%
arrange(dbase, affix_morphind, desc(n_hapax)) %>%
group_by(dbase, affix_morphind) %>%
mutate(total_hapax_by_affix = sum(n_hapax), perc_hapax = round((n_hapax/total_hapax_by_affix) * 100, 2))
# affix_wclass_hapax %>% write_excel_csv2(file = "3_affix_wclass_hapax_stats.txt")
#### 7.4 TYPES, TOKENS, and HAPAX for each affix sub-patterns =====
affix_wclass_prods <- affix_wclass_tokens %>%
select(-perc_tokens) %>%
left_join(affix_wclass_types %>% select(-perc_types)) %>%
left_join(affix_wclass_hapax %>% select(-perc_hapax)) %>%
mutate(htr = n_hapax/tokens) %>%
mutate(corpussize = corpussizes_all,
tokens_pmw = (tokens/corpussize) * 1000000,
types_root_pmw = (types_root/corpussize) * 1000000,
hapax_pmw = (n_hapax/corpussize) * 1000000) %>%
arrange(dbase, affix_morphind, desc(types_root_pmw)) %>%
select(-corpussize)
#### 7.5 For each affix sub-pattern + wordclass group, select the one(s) with types per mill. over 5
affix_wclass_prods %>%
group_by(dbase, affix_morphind) %>%
filter(types_root_pmw > 5) %>%
select(-htr) %>%
arrange(dbase, affix_morphind, desc(n_hapax))
# > brewer.pal(n=11, "BrBG")
# [1] "#543005" "#8C510A" "#BF812D" "#DFC27D" "#F6E8C3" "#F5F5F5" "#C7EAE5" "#80CDC1"
# [9] "#35978F" "#01665E" "#003C30"
wclass_colvals <- data.frame(affix_morphind_wclass = factor(c("n", "v", "a", "f", "pre-cat"),
levels = c("n", "v", "a", "f", "pre-cat")),
colvals = c("#543005", "#BF812D", "#DFC27D", "#80CDC1", "#35978F"))
##### 7.5.1 visualisation: wordclass attached to meN-X (type per mill. > 5) ======
dfs <- affix_wclass_prods %>%
filter(affix_morphind %in% c("meN-_0", "di-_0", "ber-_0", "ter-_0")) %>%
ungroup() %>%
select(matches("pmw|affix_")) %>%
group_by(affix_morphind) %>%
arrange(desc(types_root_pmw)) %>%
mutate(affix_morphind_wclass = str_replace_all(affix_morphind_wclass, "(_0$|^(meN|di|ber|ter)\\-_)", ""),
affix_morphind = str_replace(affix_morphind, "_0", ""),
affix_morphind = factor(affix_morphind, levels = c("meN-", "di-", "ber-", "ter-"))) %>%
filter(types_root_pmw > 1) %>%
mutate(affix_morphind_wclass = factor(affix_morphind_wclass,
levels = c("n", "v", "a", "f", "pre-cat"))) %>%
arrange(affix_morphind, affix_morphind_wclass) %>%
left_join(wclass_colvals)
dfs <- dfs %>%
pivot_longer(cols = -c(affix_morphind_wclass, affix_morphind, colvals),
names_to = "freq_profile",
values_to = "vals")
p <- dfs %>%
mutate(freq_profile = factor(freq_profile,
levels = c("tokens_pmw", "types_root_pmw", "hapax_pmw")),
freq_labels = "Frekuensi token relatif (per 1 juta kata)",
freq_labels = if_else(freq_profile == "types_root_pmw",
"Frekuensi tipe relatif (per 1 juta kata)",
freq_labels),
freq_labels = if_else(freq_profile == "hapax_pmw",
"Hapax relatif (per 1 juta kata)",
freq_labels)) %>%
split(.$freq_profile) %>%
map(# ~ggplot(., aes(x = reorder(affix_morphind, values), y = values, fill = colvals)) +
~ggplot(., aes(x = affix_morphind, y = vals, fill = affix_morphind_wclass)) +
geom_col(position = "dodge") +
theme_light() +
scale_fill_manual(values = .$colvals) +
labs(x=str_replace(.$freq_profile, "(_root)?_pmw", ""),
y = .$freq_labels,
caption = "n=nomina; v=verba; a=ajektiva\nf=asing; pre-cat=tanpa kategori",
fill = "") +
theme(axis.title.y = element_text(size = 7.5),
axis.title.x = element_text(size = 7.5),
axis.text.y = element_text(size = 6),
axis.text.x = element_text(size = 6.5),
plot.caption = element_text(size = 6),
legend.text = element_text(size = 6.25),
legend.key.size = unit(.4, 'cm')))
p <- grid.arrange(grobs = p, ncol = 3, nrow = 1)
ggsave(plot = p,
filename = "figures/5-wclass-dist-me-di-ber-ter.png",
width = 8.5, height = 2.85,
dpi = 300, units = "in")