ESTC/BNF analysis

library(assertthat)
library(here)

here() starts at /Users/jiemakel/tyo/estc_bnf_analysis

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.6     ✓ dplyr   1.0.8
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1
── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter()    masks stats::filter()
x tibble::has_name() masks assertthat::has_name()
x dplyr::lag()       masks stats::lag()

library(glue)
# Install with devtools::install_github("hsci-r/gghsci") / pak::pkg_install("hsci-r/gghsci")
library(gghsci)

# Requires estcr and bnfr. See https://github.com/COMHIS/estcr/blob/main/README.md#installand https://github.com/COMHIS/bnfr/blob/main/README.md#installation for installing them

source(here("code/analysis/load_data_all_bnf_matches.R"))

Warning: One or more parsing issues, see `problems()` for details

Joining, by = "bnf_author_id"
Rows: 5186 Columns: 9
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): actor_id, actor_name_primary, estc_id, publoc, work_id
dbl (4): pubyear, ncbel2_5_iv_b_French, ncbel2_4_iv_5_children, ncbel2_4_iii_minor

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Joining, by = "actor_id"
Joining, by = "actor_id"
Joining, by = "actor_id"
Joining, by = "actor_id"
Joining, by = "actor_id"
Joining, by = "actor_id"
Joining, by = "actor_id"
Joining, by = "bnf_author_id"
Joining, by = "bnf_author_id"
Joining, by = "bnf_record_id"
Joining, by = "bnf_author_id"
Joining, by = "bnf_record_id"
Joining, by = "bnf_record_id"

Proportion of ESTC that are French translations

estc_core %>% 
  filter(!uncertain,publication_year>1550,publication_year<=1800) %>% 
  group_by(publication_year) %>% 
  summarize(prop=sum(french)/n()) %>%
  ggplot(aes(x=publication_year,y=prop)) + 
  scale_y_continuous(labels = scales::percent) +
  geom_line() + 
  theme_hsci()

Reprint count comparison

estc_core %>%
  filter(!is.na(work_id), work_id != "no_work_id") %>%
  count(french, work_id, name = "editions") %>%
  filter(editions < 100) %>%
  count(french, editions) %>%
  group_by(french) %>%
  mutate(prop = n / sum(n)) %>%
  ggplot(aes(x = editions, y = prop, color = french)) +
  geom_line() +
  scale_y_log10(labels = scales::percent) +
  theme_hsci_discrete()

Document type comparison

estc_core %>%
  count(french, document_type) %>%
  group_by(french) %>%
  mutate(prop = n / sum(n)) %>%
  ggplot(aes(x = document_type, y = prop, fill = french)) +
  geom_bar(stat = 'identity', position = 'dodge') +
  scale_y_continuous(labels = scales::percent) +
  theme_hsci_discrete()

Subject topic comparison

estc_core %>%
  left_join(estc_projected_ecco_modules %>% filter(max_prop > 0.5),
            by = c("work_id")) %>%
  count(french, projected_ecco_module) %>%
  group_by(french) %>%
  mutate(prop = n / sum(n)) %>%
  ggplot(aes(x = projected_ecco_module, y = prop, fill = french)) +
  geom_bar(stat = 'identity', position = 'dodge') +
  scale_y_continuous(labels = scales::percent) +
  coord_flip() +
  theme_hsci_discrete()

Subject topic and document type comparison

estc_core %>%
  left_join(estc_projected_ecco_modules %>% filter(max_prop > 0.5),
            by = c("work_id")) %>%
  count(french, document_type,projected_ecco_module) %>%
  group_by(document_type, french) %>%
  mutate(prop = n / sum(n)) %>%
  ggplot(aes(x = projected_ecco_module, y = prop, fill = french)) +
  geom_bar(stat = 'identity', position = 'dodge') +
  scale_y_continuous(labels = scales::percent) +
  coord_flip() +
  theme_hsci_discrete() +
  facet_wrap(~ document_type)

Subject topics through time

estc_core %>% 
  left_join(estc_projected_ecco_modules %>% filter(max_prop > 0.5),by=c("work_id")) %>%
  left_join(estc_actor_links %>% filter(actor_role_author),by=c("estc_id")) %>%
  filter(!is.na(actor_id)) %>%
  group_by(projected_ecco_module,actor_id) %>% 
  summarize(max_prop=max(max_prop),.groups="drop") %>%
  filter(max_prop > 0.5) %>%
  distinct(projected_ecco_module,actor_id,max_prop) %>%
  left_join(estc_actor_links,by=c("actor_id")) %>%
  full_join(estc_core,by=c("estc_id")) %>%
  filter(!uncertain, publication_year >= 1600,publication_year <= 1800) %>%
  group_by(projected_ecco_module,publication_decade) %>%
  summarize(frenchprop=sum(french)/n(),.groups="drop") %>%
  ggplot(aes(x=publication_decade,y=frenchprop,color=projected_ecco_module)) +
  scale_y_continuous(labels = scales::percent) +
  geom_line(show.legend = F) +
  theme_hsci_discrete() + 
  facet_wrap(~projected_ecco_module,scales="free_y")

Warning: Removed 21 row(s) containing missing values (geom_path).

estc_core %>%
  filter(!uncertain, publication_year >= 1600, publication_year <= 1800) %>%
  left_join(estc_projected_ecco_modules %>% filter(max_prop > 0.5),
            by = c("work_id")) %>%
  count(french, publication_decade, projected_ecco_module) %>%
  group_by(publication_decade, french) %>%
  mutate(prop = n / sum(n)) %>%
  ggplot(aes(x = publication_decade, y = prop, color = french)) +
  geom_line() +
  scale_y_continuous(labels = scales::percent) +
  theme_hsci_discrete() +
  facet_wrap( ~ projected_ecco_module,scales="free_y")