From 8495eb70fa91926c8d1ad79d2651ef1f8dd9634d Mon Sep 17 00:00:00 2001 From: lelaboratoire Date: Thu, 3 Jun 2021 14:46:46 -0400 Subject: [PATCH 1/2] revised code --- 10.visualize-gender.Rmd | 153 ++++++++-------- 11.visualize-name-origins.Rmd | 321 +++++++++++++++++++++++++--------- 14.us-name-origin.Rmd | 150 ++++++++-------- 3 files changed, 394 insertions(+), 230 deletions(-) diff --git a/10.visualize-gender.Rmd b/10.visualize-gender.Rmd index 9210729..9e520c6 100644 --- a/10.visualize-gender.Rmd +++ b/10.visualize-gender.Rmd @@ -9,7 +9,7 @@ library(tidyverse) library(lubridate) library(rnaturalearth) library(wru) -source('utils/r-utils.R') +source("utils/r-utils.R") theme_set(theme_bw() + theme(legend.title = element_blank())) ``` @@ -19,17 +19,17 @@ Only keep articles from 2002 because few authors had gender predictions before 2 See [093.summary-stats](docs/093.summary-stats.html) for more details. ```{r} -load('Rdata/raws.Rdata') +load("Rdata/raws.Rdata") alpha_threshold <- qnorm(0.975) -gender_df <- read_tsv('data/gender/genderize.tsv') +gender_df <- read_tsv("data/gender/genderize.tsv") pubmed_gender_df <- corr_authors %>% - filter(year(year) >= 2002) %>% - left_join(gender_df, by = 'fore_name_simple') + filter(year(year) >= 2002) %>% + left_join(gender_df, by = "fore_name_simple") -iscb_gender_df <- keynotes %>% - left_join(gender_df, by = 'fore_name_simple') +iscb_gender_df <- keynotes %>% + left_join(gender_df, by = "fore_name_simple") start_year <- 1993 end_year <- 2019 @@ -48,38 +48,41 @@ n_confs <- length(my_confs) ```{r} iscb_pubmed <- iscb_gender_df %>% - rename('journal' = conference) %>% + rename("journal" = conference) %>% select(year, journal, probability_male, publication_date) %>% - mutate(type = 'Keynote speakers/Fellows', - adjusted_citations = 1) %>% + mutate( + type = "Keynote speakers/Fellows", + adjusted_citations = 1 + ) %>% bind_rows( pubmed_gender_df %>% select(year, journal, probability_male, publication_date, adjusted_citations) %>% - mutate(type = 'Pubmed authors') + mutate(type = "Pubmed authors") + ) %>% + mutate(probability_female = 1 - probability_male) %>% + pivot_longer(contains("probability"), + names_to = "gender", + values_to = "probabilities" ) %>% - mutate(probability_female = 1 - probability_male) %>% - pivot_longer(contains('probability'), - names_to = 'gender', - values_to = 'probabilities') %>% - filter(!is.na(probabilities)) %>% + filter(!is.na(probabilities)) %>% group_by(type, year, gender) %>% mutate( pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations/pmc_citations_year, - weighted_probs = probabilities*weight + weight = adjusted_citations / pmc_citations_year, + weighted_probs = probabilities * weight # weight = 1 - ) + ) -iscb_pubmed_sum <- iscb_pubmed %>% +iscb_pubmed_sum <- iscb_pubmed %>% summarise( # n = n(), mean_prob = mean(weighted_probs), # mean_prob = mean(probabilities, na.rm = T), # sd_prob = sd(probabilities, na.rm = T), - se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)), + se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), # n = mean(n), me_prob = alpha_threshold * se_prob, - .groups = 'drop' + .groups = "drop" ) # https://stats.stackexchange.com/questions/25895/computing-standard-error-in-weighted-mean-estimation ``` @@ -97,18 +100,18 @@ iscb_pubmed_sum <- iscb_pubmed %>% ```{r fig.height=3} fig_1 <- iscb_pubmed_sum %>% # group_by(year, type, gender) %>% - gender_breakdown('main', fct_rev(type)) + gender_breakdown("main", fct_rev(type)) fig_1 -ggsave('figs/gender_breakdown.png', fig_1, width = 5, height = 2.5) -ggsave('figs/gender_breakdown.svg', fig_1, width = 5, height = 2.5) +ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5) +ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5) ``` ```{r echo=FALSE} iscb_pubmed_sum %>% # group_by(year, type, gender) %>% - # summarise(mean_prob = mean(probabilities, na.rm = T), .groups = 'drop') %>% - filter(year(year) > 2016, grepl('female', gender)) %>% - group_by(type) %>% + # summarise(mean_prob = mean(probabilities, na.rm = T), .groups = 'drop') %>% + filter(year(year) > 2016, grepl("female", gender)) %>% + group_by(type) %>% summarise(prob_female_avg = mean(mean_prob)) ``` @@ -121,22 +124,23 @@ fig_1d <- iscb_pubmed %>% ungroup() %>% mutate( type2 = case_when( - type == 'Pubmed authors' ~ 'Pubmed authors', - journal == 'ISCB Fellow' ~ 'ISCB Fellows', - type == 'Keynote speakers/Fellows' ~ 'Keynote speakers' + type == "Pubmed authors" ~ "Pubmed authors", + journal == "ISCB Fellow" ~ "ISCB Fellows", + type == "Keynote speakers/Fellows" ~ "Keynote speakers" ) ) %>% group_by(type2, year, gender) %>% summarise( mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight ^ 2) / (sum(weight) ^ - 2)), + se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), me_prob = alpha_threshold * se_prob, - .groups = 'drop' + .groups = "drop" ) %>% - gender_breakdown('main', fct_rev(type2)) + - scale_x_date(labels = scales::date_format("'%y"), - expand = c(0, 0)) + gender_breakdown("main", fct_rev(type2)) + + scale_x_date( + labels = scales::date_format("'%y"), + expand = c(0, 0) + ) ``` @@ -148,71 +152,70 @@ fig_1d <- iscb_pubmed %>% # theme(legend.position = 'bottom') # fig_1d -ggsave('figs/fig_s1.png', fig_1d, width = 7, height = 3) -ggsave('figs/fig_s1.svg', fig_1d, width = 7, height = 3) +ggsave("figs/fig_s1.png", fig_1d, width = 7, height = 3) +ggsave("figs/fig_s1.svg", fig_1d, width = 7, height = 3) ``` ## Mean and standard deviation of predicted probabilities ```{r} -iscb_pubmed_sum %>% filter(gender == 'probability_male') %>% - gam_and_ci(df2 = iscb_pubmed %>% filter(gender == 'probability_male'), - start_y = start_year, end_y = end_year) + +iscb_pubmed_sum %>% + filter(gender == "probability_male") %>% + gam_and_ci( + df2 = iscb_pubmed %>% filter(gender == "probability_male"), + start_y = start_year, end_y = end_year + ) + theme(legend.position = c(0.88, 0.2)) ``` ## Hypothesis testing ```{r echo = F} -get_p <- function(inte, colu){ - broom::tidy(inte) %>% - filter(term == 'weighted_probs') %>% - pull(colu) %>% +get_p <- function(inte, colu) { + broom::tidy(inte) %>% + filter(term == "weighted_probs") %>% + pull(colu) %>% sprintf("%0.5g", .) } ``` ```{r} -iscb_lm <- iscb_pubmed %>% - filter(gender == 'probability_female', !is.na(weighted_probs)) %>% - mutate(type = as.factor(type)) %>% - mutate(type = relevel(type, ref = 'Pubmed authors'), - year = as.factor(year)) - -main_lm <- glm(type ~ year + weighted_probs, - data = iscb_lm, family = 'binomial') - -summary(main_lm) +iscb_lm <- iscb_pubmed %>% + filter(gender == "probability_female", !is.na(weighted_probs)) %>% + mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors")) ``` -The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`). -Interaction terms do not predict `type` over and above the main effect of gender probability and year. - ```{r} -scaled_iscb <- iscb_lm +scaled_iscb <- iscb_lm %>% + filter(year(year) >= 2002) # scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F) # scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F) -main_lm <- glm(type ~ year + weighted_probs, - data = scaled_iscb %>% mutate(year = as.factor(year)), - family = 'binomial') -summary(main_lm) +main_lm <- glm(type ~ year + weighted_probs, + data = scaled_iscb, # %>% mutate(year = as.factor(year)) + family = "binomial" +) + +broom::tidy(main_lm) inte_lm <- glm( # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F), - # type ~ s_year * s_prob, + # type ~ s_year * s_prob, type ~ year * weighted_probs, - data = scaled_iscb %>% mutate(year = as.factor(year)) - , - family = 'binomial') -summary(inte_lm) -anova(main_lm, inte_lm, test = 'Chisq') -mean(scaled_iscb$year) -mean(scaled_iscb$weighted_probs) + data = scaled_iscb, # %>% mutate(year = as.factor(year)) + family = "binomial" +) +broom::tidy(inte_lm) +anova(main_lm, inte_lm, test = "Chisq") +# mean(scaled_iscb$year) +# mean(scaled_iscb$weighted_probs) ``` -```{r} -# inte_lm <- glm(type ~ (year * weighted_probs), -# data = iscb_lm, +The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`). +Interaction terms do not predict `type` over and above the main effect of gender probability and year. + +```{r include=FALSE, eval=FALSE} +# inte_lm <- glm(type ~ (year * weighted_probs), +# data = iscb_lm, # family = 'binomial') ``` diff --git a/11.visualize-name-origins.Rmd b/11.visualize-name-origins.Rmd index 4286182..719ef2f 100644 --- a/11.visualize-name-origins.Rmd +++ b/11.visualize-name-origins.Rmd @@ -6,7 +6,7 @@ title: "Representation analysis of name origins" library(tidyverse) library(lubridate) library(rnaturalearth) -source('utils/r-utils.R') +source("utils/r-utils.R") theme_set(theme_bw() + theme(legend.title = element_blank())) ``` @@ -15,17 +15,17 @@ See [093.summary-stats](docs/093.summary-stats.html) for more details. ```{r} alpha_threshold <- qnorm(0.975) -load('Rdata/raws.Rdata') +load("Rdata/raws.Rdata") pubmed_nat_df <- corr_authors %>% - filter(year(year) >= 2002) %>% - left_join(nationalize_df, by = c('fore_name', 'last_name')) %>% + filter(year(year) >= 2002) %>% + left_join(nationalize_df, by = c("fore_name", "last_name")) %>% group_by(pmid, journal, publication_date, year, adjusted_citations) %>% summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>% ungroup() iscb_nat_df <- keynotes %>% - left_join(nationalize_df, by = c('fore_name', 'last_name')) + left_join(nationalize_df, by = c("fore_name", "last_name")) start_year <- 1992 end_year <- 2019 @@ -34,34 +34,38 @@ my_jours <- unique(pubmed_nat_df$journal) my_confs <- unique(iscb_nat_df$conference) n_jours <- length(my_jours) n_confs <- length(my_confs) -region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names') +region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names") region_levels_let <- toupper(letters[1:8]) -region_cols <- c('#ffffb3', '#fccde5', '#b3de69', '#fdb462', '#80b1d3', '#8dd3c7', '#bebada', '#fb8072', '#bc80bd', '#ccebc5') +region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5") ``` Names grouping: ```{r warning=FALSE, fig.height = 3} -our_country_map <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv') %>% - rename('region' = Region) %>% +our_country_map <- read_tsv("https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv") %>% + rename("region" = Region) %>% recode_region() my_world <- world %>% - select(- geometry) %>% - rename(Country = 'name') %>% - left_join(our_country_map, by = 'Country') + select(-geometry) %>% + rename(Country = "name") %>% + left_join(our_country_map, by = "Country") (gworld <- ggplot(data = my_world) + - geom_sf(aes(fill = fct_relevel(region, region_levels))) + - coord_sf(crs = "+proj=eqearth +wktext") + - scale_fill_manual(values = region_cols, - na.translate = FALSE) + - theme(panel.background = element_rect(fill = "azure"), - legend.title = element_blank(), - legend.position = 'bottom', - panel.border = element_rect(fill = NA))) - -ggsave('figs/2020-01-31_groupings.png', gworld, width = 7.2, height = 4.3) -ggsave('figs/2020-01-31_groupings.svg', gworld, width = 7.2, height = 4.3) + geom_sf(aes(fill = fct_relevel(region, region_levels))) + + coord_sf(crs = "+proj=eqearth +wktext") + + scale_fill_manual( + values = region_cols, + na.translate = FALSE + ) + + theme( + panel.background = element_rect(fill = "azure"), + legend.title = element_blank(), + legend.position = "bottom", + panel.border = element_rect(fill = NA) + )) + +ggsave("figs/2020-01-31_groupings.png", gworld, width = 7.2, height = 4.3) +ggsave("figs/2020-01-31_groupings.svg", gworld, width = 7.2, height = 4.3) ``` @@ -74,38 +78,41 @@ Prepare data frames for later analyses: ```{r} iscb_pubmed_oth <- iscb_nat_df %>% - rename('journal' = conference) %>% + rename("journal" = conference) %>% select(year, journal, African:SouthAsian, publication_date) %>% - mutate(type = 'Keynote speakers/Fellows', - adjusted_citations = 1, - pmid = -9999) %>% + mutate( + type = "Keynote speakers/Fellows", + adjusted_citations = 1, + pmid = -9999 + ) %>% bind_rows( pubmed_nat_df %>% select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>% - mutate(type = 'Pubmed authors') + mutate(type = "Pubmed authors") ) %>% mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>% pivot_longer(c(African:SouthAsian, OtherCategories), - names_to = 'region', - values_to = 'probabilities') %>% - filter(!is.na(probabilities)) %>% + names_to = "region", + values_to = "probabilities" + ) %>% + filter(!is.na(probabilities)) %>% group_by(type, year, region) %>% mutate( pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations/pmc_citations_year, - weighted_probs = probabilities*weight - ) + weight = adjusted_citations / pmc_citations_year, + weighted_probs = probabilities * weight + ) -iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% +iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% summarise( mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)), + se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), me_prob = alpha_threshold * se_prob, - .groups = 'drop' + .groups = "drop" ) iscb_pubmed_sum <- iscb_pubmed_sum_oth %>% - filter(region != 'OtherCategories') + filter(region != "OtherCategories") ``` ## Prepare data frames for analysis @@ -113,20 +120,19 @@ iscb_pubmed_sum <- iscb_pubmed_sum_oth %>% ### By conference keynotes/fellows ```{r fig.height=6} i <- 0 -iscb_nat <- vector('list', length = n_confs) +iscb_nat <- vector("list", length = n_confs) -for (conf in my_confs){ +for (conf in my_confs) { i <- i + 1 iscb_nat[[i]] <- iscb_pubmed_oth %>% - filter(region != 'OtherCategories', type != 'Pubmed authors' & journal == conf) %>% + filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>% group_by(type, year, region, journal) %>% - summarise(mean_prob = mean(weighted_probs), .groups = 'drop') %>% - {.} + summarise(mean_prob = mean(weighted_probs), .groups = "drop") } ``` ```{r} -save(my_world, iscb_pubmed_oth, iscb_nat, file = 'Rdata/iscb-pubmed_nat.Rdata') +save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata") ``` ## Figures for paper @@ -137,74 +143,80 @@ Compared to the name collection of Pubmed authors, honorees with Celtic/English ```{r fig.height=7, fig.width=9, warning=FALSE} fig_4a <- iscb_pubmed_sum %>% - filter(year < '2020-01-01') %>% - region_breakdown('main', region_levels, fct_rev(type)) + + filter(year < "2020-01-01") %>% + region_breakdown("main", region_levels, fct_rev(type)) + guides(fill = guide_legend(nrow = 2)) + - theme(legend.position = 'bottom') + theme(legend.position = "bottom") -large_regions <- c('CelticEnglish', 'EastAsian', 'European', 'OtherCategories') +large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories") ## Mean and standard deviation of predicted probabilities: fig_4b <- iscb_pubmed_sum_oth %>% filter(region %in% large_regions) %>% recode_region() %>% gam_and_ci( - df2 = iscb_pubmed_oth %>% + df2 = iscb_pubmed_oth %>% filter(region %in% large_regions) %>% recode_region(), - start_y = start_year, end_y = end_year) + - theme(legend.position = c(0.88, 0.83), - panel.grid.minor = element_blank(), - legend.margin = margin(-0.5, 0, 0, 0, unit='cm'), - legend.text = element_text(size = 7)) + + start_y = start_year, end_y = end_year + ) + + theme( + legend.position = c(0.88, 0.83), + panel.grid.minor = element_blank(), + legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"), + legend.text = element_text(size = 7) + ) + facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1) -fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = 'AUTO', ncol = 1, rel_heights = c(1.3,1)) +fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1)) fig_4 -ggsave('figs/region_breakdown.png', fig_4, width = 6.7, height = 5.5) -ggsave('figs/region_breakdown.svg', fig_4, width = 6.7, height = 5.5) +ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5) +ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5) ``` ## Hypothesis testing ```{r} -iscb_lm <- iscb_pubmed_oth %>% - ungroup() %>% +iscb_lm <- iscb_pubmed_oth %>% + ungroup() %>% mutate( # year = c(scale(year(year))), - year = as.factor(year), - type = as.factor(type) %>% relevel(ref = 'Pubmed authors')) -main_lm <- function(regioni){ - glm(type ~ year + weighted_probs, - data = iscb_lm %>% - filter(region == regioni, !is.na(probabilities)) , - family = 'binomial') + # year = as.factor(year), + type = as.factor(type) %>% relevel(ref = "Pubmed authors") + ) +main_lm <- function(regioni) { + glm(type ~ year + weighted_probs, + data = iscb_lm %>% + filter(region == regioni, !is.na(probabilities), year(year) >= 2002), + family = "binomial" + ) } -inte_lm <- function(regioni){ - glm(type ~ year * weighted_probs, - data = iscb_lm %>% - filter(region == regioni, !is.na(weighted_probs)), - family = 'binomial') +inte_lm <- function(regioni) { + glm(type ~ year * weighted_probs, + data = iscb_lm %>% + filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002), + family = "binomial" + ) } main_list <- lapply(large_regions, main_lm) names(main_list) <- large_regions -lapply(main_list, summary) +lapply(main_list, broom::tidy) inte_list <- lapply(large_regions, inte_lm) -lapply(inte_list, summary) -for (i in 1:4){ - print(anova(main_list[[i]], inte_list[[i]], test = 'Chisq')) +lapply(inte_list, broom::tidy) +for (i in 1:4) { + print(anova(main_list[[i]], inte_list[[i]], test = "Chisq")) } ``` Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01). ```{r echo = F} -get_p <- function(i, colu){ - broom::tidy(main_list[[i]]) %>% - filter(term == 'weighted_probs') %>% - pull(colu) +get_p <- function(i, colu) { + broom::tidy(main_list[[i]]) %>% + filter(term == "weighted_probs") %>% + pull(colu) } print_p <- function(x) sprintf("%0.5g", x) @@ -215,25 +227,162 @@ A Celtic/English name has `r exp(get_p(1, 'estimate'))` the odds of being select An East Asian name has `r exp(get_p(2, 'estimate'))` the odds of being selected as an honoree, significantly lower than to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_p(2, 'estimate'))`, _P_ = `r print_p(get_p(2, 'p.value'))`). The two groups of scientists did not have a significant association with names predicted to be European (_P_ = `r print_p(get_p(3, 'p.value'))`) or in Other categories (_P_ = `r print_p(get_p(4, 'p.value'))`). -### Supplementary Figure S5 {#sup_fig_s5} + +## Alternative approach +_Sincere thanks to the reviewers, Byron Smith and Katie Pollard, for their detailed suggestion with code._ + +The question of what unit one should use to perform this type of analyses is a difficult one. +We present here an alternative analysis that treats _names_ as units instead of _honors_ and _authorships_. +We caution that this approach does not distinguish scientists who were honored 4 times vs. one time +and hence may yield a conservative estimate of disparity. +Further, different authors may have the same names, +and to sum `adjusted_citations` across them may not be optimal. + +Nonetheless, the finding here is consistent with what we have seen above +where East Asian names are underrepresented in the honoree group. + +```{r} + +keynotes_post_2002 <- keynotes %>% + filter(year(year) >= 2002) %>% + separate_rows(afflcountries, sep = ",") %>% + filter(afflcountries == "United States") %>% + group_by(fore_name_simple, last_name_simple) %>% + summarise_at("year", n_distinct, na.rm = T) + +# nationalize_df was not unique, so the left join to corr_authors resulted +# in (mostly) duplicate rows. +# FIXME: I was getting occasional crashes on this line, and it's slow. +# TRANG: fixed on June 3, 2021 using distinct(). +# Also, the duplication was intentional. +# Please see our extensive discussion on the merge/join on full names vs +# fore_name and last_name here: +# + +distinct_nationalize_df <- nationalize_df %>% + distinct(fore_name_simple, last_name_simple, .keep_all = TRUE) + +# Calculate sum of adjusted citations for all publications for a first-name/ +# last-name pair in db since 2002 +# where the author countries include US. +authors <- corr_authors %>% + filter(year(year) >= 2002) %>% + separate_rows(countries, sep = ",") %>% + filter(countries == "US") %>% + group_by(fore_name_simple, last_name_simple) %>% + summarise_at(vars(adjusted_citations), sum, na.rm = T) %>% + left_join( + keynotes_post_2002[c("fore_name_simple", "last_name_simple", "year")], + by = c("fore_name_simple", "last_name_simple") + ) %>% + left_join(distinct_nationalize_df, by = c("fore_name_simple", "last_name_simple")) %>% + mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) + +for (large_region in large_regions) { + glm( + as.formula(paste("honoree ~ adjusted_citations +", large_region)), + data = authors %>% mutate(honoree = !is.na(year)), + family = "binomial", + control = list(epsilon = 1e-12, maxit = 55, trace = FALSE) + ) %>% + broom::tidy() %>% + print() +} +``` + + + + + + + + + +## Time lag + +In this section, we show that a 10-year lag model results in a similar result for East Asian scientists' names, +even though the effect size is less striking. +For example, if we assume that honors accrue 10 years after their most prolific year with respect to authorships, +the proportion of honor associated with East Asian name origins in 2019 is still substantially less than the proportion of senior authorships associated with East Asian names in 2009. + +```{r} +year_lag <- period(10, "years") +iscb_pubmed_oth_lag <- iscb_nat_df %>% + rename("journal" = conference) %>% + select(year, journal, African:SouthAsian, publication_date) %>% + mutate( + type = "Keynote speakers/Fellows", + adjusted_citations = 1, + pmid = -9999 + ) %>% + bind_rows( + pubmed_nat_df %>% + select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>% + mutate(type = "Pubmed authors", year = year + year_lag) + ) %>% + mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>% + pivot_longer(c(African:SouthAsian, OtherCategories), + names_to = "region", + values_to = "probabilities" + ) %>% + filter(!is.na(probabilities), year(year) >= 2002) %>% + group_by(type, year, region) %>% + mutate( + pmc_citations_year = mean(adjusted_citations), + weight = adjusted_citations / pmc_citations_year, + weighted_probs = probabilities * weight + ) + +iscb_lm_lag <- iscb_pubmed_oth_lag %>% + ungroup() %>% + mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors")) + +main_lm <- function(regioni) { + glm(type ~ year + weighted_probs, + data = iscb_lm_lag %>% + filter(region == regioni, !is.na(weighted_probs)), + family = "binomial" + ) +} + +main_list <- lapply(large_regions, main_lm) +names(main_list) <- large_regions +lapply(main_list, tidy::broom) +``` + +```{r include=FALSE, eval = FALSE} +checkdf <- iscb_lm %>% + filter( + year(year) == 2010, + adjusted_citations > 3.1, + adjusted_citations < 3.32, + region == "EastAsian", + probabilities > 0.5 + ) +``` + + +## Supplementary Figure S5 {#sup_fig_s5} It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction. There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field. However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented. ```{r fig.height=6, warning=FALSE} -df2 <- iscb_pubmed_oth %>% - filter(region != 'OtherCategories') %>% +df2 <- iscb_pubmed_oth %>% + filter(region != "OtherCategories") %>% recode_region() fig_s5 <- iscb_pubmed_sum %>% recode_region() %>% - gam_and_ci(df2 = df2, - start_y = start_year, end_y = end_year) + + gam_and_ci( + df2 = df2, + start_y = start_year, end_y = end_year + ) + theme(legend.position = c(0.8, 0.1)) + facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3) fig_s5 -ggsave('figs/fig_s5.png', fig_s5, width = 6, height = 6) -ggsave('figs/fig_s5.svg', fig_s5, width = 6, height = 6) +ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6) +ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6) ``` diff --git a/14.us-name-origin.Rmd b/14.us-name-origin.Rmd index 7e6b080..06ca206 100644 --- a/14.us-name-origin.Rmd +++ b/14.us-name-origin.Rmd @@ -5,7 +5,7 @@ title: "Representation analysis of name origin in the US" ```{r setup, include=FALSE} library(tidyverse) library(lubridate) -source('utils/r-utils.R') +source("utils/r-utils.R") theme_set(theme_bw() + theme(legend.title = element_blank())) ``` @@ -13,23 +13,23 @@ Only keep articles from 2002 because few authors had nationality predictions bef See [093.summary-stats](docs/093.summary-stats.html) for more details. ```{r} -load('Rdata/raws.Rdata') +load("Rdata/raws.Rdata") alpha_threshold <- qnorm(0.975) -pubmed_nat_df <- corr_authors %>% - filter(year(year) >= 2002) %>% - separate_rows(countries, sep = ',') %>% - filter(countries == 'US') %>% - left_join(nationalize_df, by = c('fore_name', 'last_name')) %>% +pubmed_nat_df <- corr_authors %>% + filter(year(year) >= 2002) %>% + separate_rows(countries, sep = ",") %>% + filter(countries == "US") %>% + left_join(nationalize_df, by = c("fore_name", "last_name")) %>% group_by(pmid, journal, publication_date, year, adjusted_citations) %>% summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>% ungroup() iscb_nat_df <- keynotes %>% - separate_rows(afflcountries, sep = '\\|') %>% - filter(afflcountries == 'United States') %>% - left_join(nationalize_df, by = c('fore_name', 'last_name')) + separate_rows(afflcountries, sep = "\\|") %>% + filter(afflcountries == "United States") %>% + left_join(nationalize_df, by = c("fore_name", "last_name")) start_year <- 1992 end_year <- 2019 @@ -38,9 +38,9 @@ my_jours <- unique(pubmed_nat_df$journal) my_confs <- unique(iscb_nat_df$conference) n_jours <- length(my_jours) n_confs <- length(my_confs) -region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names') +region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names") -region_cols <- c('#ffffb3', '#fccde5', '#b3de69', '#fdb462', '#80b1d3', '#8dd3c7', '#bebada', '#fb8072', '#bc80bd', '#ccebc5') +region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5") ``` ## Organize data @@ -53,110 +53,120 @@ Prepare data frames for later analyses: ```{r} iscb_pubmed_oth <- iscb_nat_df %>% - rename('journal' = conference) %>% + rename("journal" = conference) %>% select(year, journal, African:SouthAsian, publication_date) %>% - mutate(type = 'Keynote speakers/Fellows', - adjusted_citations = 1) %>% + mutate( + type = "Keynote speakers/Fellows", + adjusted_citations = 1 + ) %>% bind_rows( pubmed_nat_df %>% select(year, journal, African:SouthAsian, publication_date, adjusted_citations) %>% - mutate(type = 'Pubmed authors') + mutate(type = "Pubmed authors") ) %>% mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>% pivot_longer(c(African:SouthAsian, OtherCategories), - names_to = 'region', - values_to = 'probabilities') %>% - filter(!is.na(probabilities)) %>% + names_to = "region", + values_to = "probabilities" + ) %>% + filter(!is.na(probabilities)) %>% group_by(type, year, region) %>% mutate( pmc_citations_year = mean(adjusted_citations), - weight = adjusted_citations/pmc_citations_year, - weighted_probs = probabilities*weight - ) + weight = adjusted_citations / pmc_citations_year, + weighted_probs = probabilities * weight + ) -iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% +iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% summarise( mean_prob = mean(weighted_probs), - se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)), + se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)), me_prob = alpha_threshold * se_prob, - .groups = 'drop' + .groups = "drop" ) iscb_pubmed_sum <- iscb_pubmed_sum_oth %>% - filter(region != 'OtherCategories') + filter(region != "OtherCategories") ``` ## Figures for paper ```{r fig.height=7, fig.width=9, warning=FALSE} fig_us_name_origina <- iscb_pubmed_sum %>% - filter(year < '2020-01-01') %>% - region_breakdown('main', region_levels, fct_rev(type)) + + filter(year < "2020-01-01") %>% + region_breakdown("main", region_levels, fct_rev(type)) + guides(fill = guide_legend(nrow = 2)) -large_regions <- c('CelticEnglish', 'EastAsian', 'European', 'OtherCategories') +large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories") ## Mean and standard deviation of predicted probabilities: fig_us_name_originb <- iscb_pubmed_sum_oth %>% filter(region %in% large_regions) %>% recode_region() %>% - gam_and_ci(df2 = iscb_pubmed_oth %>% - filter(region %in% large_regions) %>% - recode_region(), - start_y = start_year, end_y = end_year) + - theme(legend.position = c(0.88, 0.83), - panel.grid.minor = element_blank(), - legend.margin = margin(-0.5, 0, 0, 0, unit='cm'), - legend.text = element_text(size = 6)) + + gam_and_ci( + df2 = iscb_pubmed_oth %>% + filter(region %in% large_regions) %>% + recode_region(), + start_y = start_year, end_y = end_year + ) + + theme( + legend.position = c(0.88, 0.83), + panel.grid.minor = element_blank(), + legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"), + legend.text = element_text(size = 6) + ) + facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1) -fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = 'AUTO', ncol = 1, rel_heights = c(1.3,1)) +fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1)) fig_us_name_origin -ggsave('figs/us_name_origin.png', fig_us_name_origin, width = 6.5, height = 5.5) -ggsave('figs/us_name_origin.svg', fig_us_name_origin, width = 6.5, height = 5.5) +ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5) +ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5) ``` ## Hypothesis testing ```{r} -iscb_lm <- iscb_pubmed_oth %>% - ungroup() %>% +iscb_lm <- iscb_pubmed_oth %>% + ungroup() %>% mutate( # year = c(scale(year)), - year = as.factor(year), - type = relevel(as.factor(type), ref = 'Pubmed authors')) -main_lm <- function(regioni){ - glm(type ~ year + weighted_probs, - data = iscb_lm %>% - filter(region == regioni, !is.na(weighted_probs)), - family = 'binomial') + # year = as.factor(year), + type = relevel(as.factor(type), ref = "Pubmed authors") + ) +main_lm <- function(regioni) { + glm(type ~ year + weighted_probs, + data = iscb_lm %>% + filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002), + family = "binomial" + ) } -inte_lm <- function(regioni){ - glm(type ~ weighted_probs*year, - data = iscb_lm %>% - filter(region == regioni, !is.na(weighted_probs)), - family = 'binomial') +inte_lm <- function(regioni) { + glm(type ~ weighted_probs * year, + data = iscb_lm %>% + filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002), + family = "binomial" + ) } main_list <- lapply(large_regions, main_lm) names(main_list) <- large_regions -lapply(main_list, summary) +lapply(main_list, broom::tidy) inte_list <- lapply(large_regions, inte_lm) -lapply(inte_list, summary) -for (i in 1:4){ - print(anova(main_list[[i]], inte_list[[i]], test = 'Chisq')) +lapply(inte_list, broom::tidy) +for (i in 1:4) { + print(anova(main_list[[i]], inte_list[[i]], test = "Chisq")) } ``` Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01). ```{r echo = F} -get_exp <- function(i, colu){ - broom::tidy(main_list[[i]]) %>% - filter(term == 'weighted_probs') %>% - pull(colu) +get_exp <- function(i, colu) { + broom::tidy(main_list[[i]]) %>% + filter(term == "weighted_probs") %>% + pull(colu) } print_p <- function(x) sprintf("%0.5g", x) @@ -175,20 +185,22 @@ There seems to be little difference between the proportion of keynote speakers o However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented. ```{r fig.height=6, warning=FALSE} -df2 <- iscb_pubmed_oth %>% - filter(region != 'OtherCategories') %>% +df2 <- iscb_pubmed_oth %>% + filter(region != "OtherCategories") %>% recode_region() fig_s7 <- iscb_pubmed_sum %>% recode_region() %>% - gam_and_ci(df2 = df2, - start_y = start_year, end_y = end_year) + + gam_and_ci( + df2 = df2, + start_y = start_year, end_y = end_year + ) + theme(legend.position = c(0.8, 0.1)) + facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3) - + fig_s7 -ggsave('figs/fig_s7.png', fig_s7, width = 6, height = 6) -ggsave('figs/fig_s7.svg', fig_s7, width = 6, height = 6) +ggsave("figs/fig_s7.png", fig_s7, width = 6, height = 6) +ggsave("figs/fig_s7.svg", fig_s7, width = 6, height = 6) ``` From 7a4ec7ce451a54807668fa1197e37063c2348c3d Mon Sep 17 00:00:00 2001 From: lelaboratoire Date: Thu, 3 Jun 2021 16:21:13 -0400 Subject: [PATCH 2/2] revised htmls --- 10.visualize-gender.Rmd | 3 +- 11.visualize-name-origins.Rmd | 32 +- docs/091.draw-roc.html | 44 +- docs/092.save-raws-to-Rdata.html | 19 +- docs/093.summary-stats.html | 13 +- docs/10.visualize-gender.html | 422 +++---- docs/11.visualize-name-origins.html | 1002 ++++++--------- docs/12.analyze-affiliation.html | 87 +- docs/13.us-race-analysis.html | 6 +- docs/14.us-name-origin.html | 785 +++--------- docs/15.analyze-2020.html | 82 +- figs/fig_s5.png | Bin 207401 -> 0 bytes figs/fig_s5.svg | 1768 +++++++++++++-------------- 13 files changed, 1744 insertions(+), 2519 deletions(-) diff --git a/10.visualize-gender.Rmd b/10.visualize-gender.Rmd index 9e520c6..a005ea7 100644 --- a/10.visualize-gender.Rmd +++ b/10.visualize-gender.Rmd @@ -182,7 +182,8 @@ get_p <- function(inte, colu) { ```{r} iscb_lm <- iscb_pubmed %>% filter(gender == "probability_female", !is.na(weighted_probs)) %>% - mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors")) + mutate(type = as.factor(type)) %>% + mutate(type = type %>% relevel(ref = "Pubmed authors")) ``` ```{r} diff --git a/11.visualize-name-origins.Rmd b/11.visualize-name-origins.Rmd index 719ef2f..2b9de29 100644 --- a/11.visualize-name-origins.Rmd +++ b/11.visualize-name-origins.Rmd @@ -347,7 +347,7 @@ main_lm <- function(regioni) { main_list <- lapply(large_regions, main_lm) names(main_list) <- large_regions -lapply(main_list, tidy::broom) +lapply(main_list, broom::tidy) ``` ```{r include=FALSE, eval = FALSE} @@ -368,21 +368,21 @@ There seems to be little difference between the proportion of keynote speakers o However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented. ```{r fig.height=6, warning=FALSE} -df2 <- iscb_pubmed_oth %>% - filter(region != "OtherCategories") %>% - recode_region() - -fig_s5 <- iscb_pubmed_sum %>% - recode_region() %>% - gam_and_ci( - df2 = df2, - start_y = start_year, end_y = end_year - ) + - theme(legend.position = c(0.8, 0.1)) + - facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3) -fig_s5 -ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6) -ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6) +# df2 <- iscb_pubmed_oth %>% +# filter(region != "OtherCategories") %>% +# recode_region() +# +# fig_s5 <- iscb_pubmed_sum %>% +# recode_region() %>% +# gam_and_ci( +# df2 = df2, +# start_y = start_year, end_y = end_year +# ) + +# theme(legend.position = c(0.8, 0.1)) + +# facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3) +# fig_s5 +# ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6) +# ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6) ``` diff --git a/docs/091.draw-roc.html b/docs/091.draw-roc.html index c2826b5..524ce17 100644 --- a/docs/091.draw-roc.html +++ b/docs/091.draw-roc.html @@ -1742,12 +1742,12 @@

Plotting ROC curves

Name origin prediction method performance

library(tidyverse)
-
## ── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
+
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
 ## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
 ## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
 ## ✓ readr   1.4.0     ✓ forcats 0.5.0
-
## ── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
 ## x dplyr::filter() masks stats::filter()
 ## x dplyr::lag()    masks stats::lag()

 
# still need to install caret for the calibration function because tidymodels's 
@@ -1769,7 +1769,7 @@ 
Name origin prediction method performance

   mutate(Sensitivity = tpr, Specificity = 1-fpr, dSens = c(abs(diff(1-tpr)), 0)) %>%
   ungroup()

 
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   fpr = col_double(),
 ##   tpr = col_double(),
@@ -1811,7 +1811,7 @@ 
Name origin prediction method performance

   mutate(y_true = as.factor(truth)) %>%
   select(-truth)
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   African = col_double(),
 ##   CelticEnglish = col_double(),
@@ -1844,18 +1844,30 @@ 
Name origin prediction method performance

 ## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
 ## This message is displayed once per session.
cal_dfs$EastAsian
-
##    calibModelVar            bin   Percent      Lower     Upper Count  midpoint    region
-## 1           prob     [0,0.0909]  0.973038  0.9061138  1.043559   777  4.545455 EastAsian
-## 2           prob (0.0909,0.182] 12.715105 10.7555376 14.887108   133 13.636364 EastAsian
-## 3           prob  (0.182,0.273] 20.620843 16.9791523 24.652952    93 22.727273 EastAsian
-## 4           prob  (0.273,0.364] 29.924242 24.4643714 35.841394    79 31.818182 EastAsian
-## 5           prob  (0.364,0.455] 35.897436 29.7515540 42.405681    84 40.909091 EastAsian
-## 6           prob  (0.455,0.545] 38.536585 31.8402892 45.569554    79 50.000000 EastAsian
-## 7           prob  (0.545,0.636] 45.637584 37.4635833 53.988516    68 59.090909 EastAsian
-## 8           prob  (0.636,0.727] 56.953642 48.6544756 64.974492    86 68.181818 EastAsian
-## 9           prob  (0.727,0.818] 61.421320 54.2394760 68.253900   121 77.272727 EastAsian
-## 10          prob  (0.818,0.909] 71.764706 66.6571343 76.488532   244 86.363636 EastAsian
-## 11          prob      (0.909,1] 97.209555 96.8524649 97.536348  8953 95.454545 EastAsian
+
##    calibModelVar            bin   Percent      Lower     Upper Count  midpoint
+## 1           prob     [0,0.0909]  0.973038  0.9061138  1.043559   777  4.545455
+## 2           prob (0.0909,0.182] 12.715105 10.7555376 14.887108   133 13.636364
+## 3           prob  (0.182,0.273] 20.620843 16.9791523 24.652952    93 22.727273
+## 4           prob  (0.273,0.364] 29.924242 24.4643714 35.841394    79 31.818182
+## 5           prob  (0.364,0.455] 35.897436 29.7515540 42.405681    84 40.909091
+## 6           prob  (0.455,0.545] 38.536585 31.8402892 45.569554    79 50.000000
+## 7           prob  (0.545,0.636] 45.637584 37.4635833 53.988516    68 59.090909
+## 8           prob  (0.636,0.727] 56.953642 48.6544756 64.974492    86 68.181818
+## 9           prob  (0.727,0.818] 61.421320 54.2394760 68.253900   121 77.272727
+## 10          prob  (0.818,0.909] 71.764706 66.6571343 76.488532   244 86.363636
+## 11          prob      (0.909,1] 97.209555 96.8524649 97.536348  8953 95.454545
+##       region
+## 1  EastAsian
+## 2  EastAsian
+## 3  EastAsian
+## 4  EastAsian
+## 5  EastAsian
+## 6  EastAsian
+## 7  EastAsian
+## 8  EastAsian
+## 9  EastAsian
+## 10 EastAsian
+## 11 EastAsian
fig_3b <- bind_rows(cal_dfs) %>%
   recode_region() %>%
   ggplot(aes(x = midpoint/100, y = Percent/100, color = fct_relevel(region, as.character(auc_df$region)))) +
diff --git a/docs/092.save-raws-to-Rdata.html b/docs/092.save-raws-to-Rdata.html
index aad4c8d..6a6034e 100644
--- a/docs/092.save-raws-to-Rdata.html
+++ b/docs/092.save-raws-to-Rdata.html
@@ -4300,7 +4300,7 @@ 
Save raw data into .Rdata to be loaded in by analys
 
General data read-in

 
all_full_names <- readr::read_tsv('data/names/full-names.tsv.xz') %>% distinct()

 
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   fore_name = col_character(),
 ##   last_name = col_character(),
@@ -4334,7 +4334,7 @@ 
General data read-in

          publication_date = ymd(publication_date, truncated = 2)) %>% 
   filter(year(publication_date) < 2020)
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   pmid = col_double(),
 ##   pmcid = col_character(),
@@ -4358,7 +4358,7 @@ 
General data read-in

   left_join(select(all_full_names, - full_name), by = c('fore_name', 'last_name')) %>% 
   filter(year(year) < 2020, conference != 'PSB') # remove PSB, exclude ISCB Fellows and ISMB speakers in 2020 for now
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   year = col_double(),
 ##   full_name = col_character(),
@@ -4371,9 +4371,10 @@ 
General data read-in

 ## )
keynotes %>% filter(is.na(fore_name_simple))
## # A tibble: 0 x 11
-## # … with 11 variables: year <date>, full_name <chr>, fore_name <chr>, last_name <chr>,
-## #   conference <chr>, source <chr>, affiliations <chr>, afflcountries <chr>, publication_date <date>,
-## #   fore_name_simple <chr>, last_name_simple <chr>
+## # … with 11 variables: year <date>, full_name <chr>, fore_name <chr>, +## # last_name <chr>, conference <chr>, source <chr>, affiliations <chr>, +## # afflcountries <chr>, publication_date <date>, fore_name_simple <chr>, +## # last_name_simple <chr> 
large_jours <- articles %>%
   count(journal, sort = T) %>% 
   head(10)
@@ -4384,7 +4385,7 @@ 
General data read-in

   left_join(all_full_names, by = 'full_name')
## Warning: Missing column names filled in: 'X1' [1]
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   X1 = col_character(),
 ##   African = col_double(),
@@ -4405,8 +4406,8 @@ 
General data read-in

 
corr_authors %>% 
   count(year, name = 'Number of articles with last authors') %>% 
   DT::datatable(rownames = F)

-

-
+

+
 
If we set a threshold at least 200 articles a year, we should only consider articles from 1998 on.

 
corr_authors <- corr_authors %>% 
   add_count(year, name = 'n_aut_yr') %>% 
diff --git a/docs/093.summary-stats.html b/docs/093.summary-stats.html
index 20df43e..54e91c6 100644
--- a/docs/093.summary-stats.html
+++ b/docs/093.summary-stats.html
@@ -4317,8 +4317,8 @@ 
Honorees

   count(fore_name, last_name) %>% 
   arrange(desc(n)) %>% 
   DT::datatable()

-

-
+

+
 
Number of keynote speakers/fellows across years:

 
keynotes %>%
   select(year, conference) %>% 
@@ -4364,7 +4364,7 @@ 
Authors

 
Gender analysis

 
gender_df <- read_tsv('data/gender/genderize.tsv')

 
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   fore_name_simple = col_character(),
 ##   n_authors = col_double(),
@@ -4412,7 +4412,8 @@ 
Gender analysis

   pull(pred.asi) %>% 
   mean(na.rm = T)

 
## [1] "Proceeding with surname-only predictions..."

-
## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 1305 surnames were not matched.

+
## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 1305
+## surnames were not matched.

 
## [1] 0.8174599

 
Honorees that didn’t receive a gender prediction: Chung-I Wu.

 
In summary, the NA predictions mostly include initials only, hyphenated names and perhaps names with accent marks.

@@ -4491,8 +4492,8 @@ 
Name origin analysis

 ## ℹ Unknown levels in `f`: OtherCategories
 ## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
- +
+ 
 pubmed_nat_pmids %>% count(!is.na(African), is.na(fore_name_simple.x))
## # A tibble: 2 x 3
 ##   `!is.na(African)` `is.na(fore_name_simple.x)`      n
diff --git a/docs/10.visualize-gender.html b/docs/10.visualize-gender.html
index c8c3c08..1f524c3 100644
--- a/docs/10.visualize-gender.html
+++ b/docs/10.visualize-gender.html
@@ -1671,18 +1671,18 @@ 
Setups

 library(lubridate)
 library(rnaturalearth)
 library(wru)
-source('utils/r-utils.R')
+source("utils/r-utils.R")
 theme_set(theme_bw() + theme(legend.title = element_blank()))

Load data

Only keep articles from 2002 because few authors had gender predictions before 2002. See 093.summary-stats for more details.

-
load('Rdata/raws.Rdata')
+
load("Rdata/raws.Rdata")
 
 alpha_threshold <- qnorm(0.975)
-gender_df <- read_tsv('data/gender/genderize.tsv')

+gender_df <- read_tsv("data/gender/genderize.tsv")
## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   fore_name_simple = col_character(),
 ##   n_authors = col_double(),
@@ -1691,11 +1691,11 @@ 
Load data

 ##   probability_male = col_double()
 ## )
pubmed_gender_df <- corr_authors %>%
-  filter(year(year) >= 2002) %>% 
-  left_join(gender_df, by = 'fore_name_simple')
+  filter(year(year) >= 2002) %>%
+  left_join(gender_df, by = "fore_name_simple")
 
-iscb_gender_df <- keynotes %>% 
-  left_join(gender_df, by = 'fore_name_simple')
+iscb_gender_df <- keynotes %>%
+  left_join(gender_df, by = "fore_name_simple")
 
 start_year <- 1993
 end_year <- 2019
@@ -1713,53 +1713,56 @@ 
Prepare data frames for later analyses

 
  • compute mean, sd, marginal error
    • 
 
 
    iscb_pubmed <- iscb_gender_df %>%
-  rename('journal' = conference) %>%
+  rename("journal" = conference) %>%
   select(year, journal, probability_male, publication_date) %>%
-  mutate(type = 'Keynote speakers/Fellows',
-         adjusted_citations = 1) %>%
+  mutate(
+    type = "Keynote speakers/Fellows",
+    adjusted_citations = 1
+  ) %>%
   bind_rows(
     pubmed_gender_df %>%
       select(year, journal, probability_male, publication_date, adjusted_citations) %>%
-      mutate(type = 'Pubmed authors')
+      mutate(type = "Pubmed authors")
   ) %>%
-  mutate(probability_female = 1 - probability_male) %>% 
-  pivot_longer(contains('probability'),
-               names_to = 'gender',
-               values_to = 'probabilities') %>%
-  filter(!is.na(probabilities)) %>% 
+  mutate(probability_female = 1 - probability_male) %>%
+  pivot_longer(contains("probability"),
+    names_to = "gender",
+    values_to = "probabilities"
+  ) %>%
+  filter(!is.na(probabilities)) %>%
   group_by(type, year, gender) %>%
   mutate(
     pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations/pmc_citations_year,
-    weighted_probs = probabilities*weight
+    weight = adjusted_citations / pmc_citations_year,
+    weighted_probs = probabilities * weight
     # weight = 1
-    )
+  )
 
-iscb_pubmed_sum <- iscb_pubmed %>% 
+iscb_pubmed_sum <- iscb_pubmed %>%
   summarise(
     # n = n(),
     mean_prob = mean(weighted_probs),
     # mean_prob = mean(probabilities, na.rm = T),
     # sd_prob = sd(probabilities, na.rm = T),
-    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
+    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
     # n = mean(n),
     me_prob = alpha_threshold * se_prob,
-    .groups = 'drop'
+    .groups = "drop"
   )
 # https://stats.stackexchange.com/questions/25895/computing-standard-error-in-weighted-mean-estimation
    
 
    # save(iscb_pubmed, file = 'Rdata/iscb-pubmed_gender.Rdata')

    Figures for paper

    -
    -

    Figure 1: ISCB Fellows and keynote speakers appear more evenly split between men and women than PubMed authors, but the proportion has not reached parity.

    +
    +

    Figure 2: ISCB Fellows and keynote speakers appear more evenly split between men and women than PubMed authors, but the proportion has not reached parity.

    fig_1 <- iscb_pubmed_sum %>%
   # group_by(year, type, gender) %>%
-  gender_breakdown('main', fct_rev(type))
+  gender_breakdown("main", fct_rev(type))
 fig_1

    -
    ggsave('figs/gender_breakdown.png', fig_1, width = 5, height = 2.5)
-ggsave('figs/gender_breakdown.svg', fig_1, width = 5, height = 2.5)
    +
    ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5)
+ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5)
    ## `summarise()` ungrouping output (override with `.groups` argument)
    ## # A tibble: 2 x 2
 ##   type                     prob_female_avg
@@ -1768,32 +1771,42 @@ 
    Figure 1: ISCB Fellows and keynote speakers appear more evenly split between
 ## 2 Pubmed authors                     0.268
    -

    Supplementary Figure S1

    +

    Supplementary Figure S2

    Additional fig. 1 with separated keynote speakers and fellows

    -
    iscb_pubmed %>%
-  ungroup() %>% 
-  mutate(type2 = case_when(
-    journal == 'ISCB Fellow' ~ 'ISCB Fellows',
-    type == 'Keynote speakers/Fellows' ~ 'Keynote speakers',
-    TRUE ~ 'Pubmed authors'
-  )) %>% 
-  group_by(type2, year, gender) %>% 
+
    fig_1d <- iscb_pubmed %>%
+  ungroup() %>%
+  mutate(
+    type2 = case_when(
+      type == "Pubmed authors" ~ "Pubmed authors",
+      journal == "ISCB Fellow" ~ "ISCB Fellows",
+      type == "Keynote speakers/Fellows" ~ "Keynote speakers"
+    )
+  ) %>%
+  group_by(type2, year, gender) %>%
   summarise(
     mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
+    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
     me_prob = alpha_threshold * se_prob,
-    .groups = 'drop'
-  ) %>% 
-  gender_breakdown('main', fct_rev(type2))
    
-
    
+    .groups = "drop"
+  ) %>%
+  gender_breakdown("main", fct_rev(type2)) +
+  scale_x_date(
+    labels = scales::date_format("'%y"),
+    expand = c(0, 0)
+  )
    +
    ## Scale for 'x' is already present. Adding another scale for 'x', which will
+## replace the existing scale.

    Mean and standard deviation of predicted probabilities

    -
    iscb_pubmed_sum %>% filter(gender == 'probability_male') %>% 
-  gam_and_ci(df2 = iscb_pubmed %>% filter(gender == 'probability_male'),
-             start_y = start_year, end_y = end_year) +
+
    iscb_pubmed_sum %>%
+  filter(gender == "probability_male") %>%
+  gam_and_ci(
+    df2 = iscb_pubmed %>% filter(gender == "probability_male"),
+    start_y = start_year, end_y = end_year
+  ) +
   theme(legend.position = c(0.88, 0.2))
    
 
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    
 
    ## Warning: Removed 13171 rows containing non-finite values (stat_smooth).
    
@@ -1801,220 +1814,53 @@ 
    Mean and standard deviation of predicted probabilities

    Hypothesis testing

    -
    iscb_lm <- iscb_pubmed %>% 
-       filter(gender == 'probability_female', !is.na(weighted_probs)) %>% 
+
    iscb_lm <- iscb_pubmed %>%
+  filter(gender == "probability_female", !is.na(weighted_probs)) %>%
   mutate(type = as.factor(type)) %>% 
-  mutate(type = relevel(type, ref = 'Pubmed authors'),
-         year = as.factor(year))
-
-main_lm <- glm(type ~ year + weighted_probs, 
-     data = iscb_lm, family = 'binomial')
-
-summary(main_lm)
    
-
    ## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm)
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.2521  -0.0675  -0.0602  -0.0536   3.7156  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)  
-## (Intercept)     1.655e+01  1.385e+03   0.012   0.9905  
-## year1994-01-01  8.750e-03  1.959e+03   0.000   1.0000  
-## year1995-01-01 -3.840e-02  1.959e+03   0.000   1.0000  
-## year1996-01-01  5.925e-03  1.833e+03   0.000   1.0000  
-## year1997-01-01 -6.766e-03  1.537e+03   0.000   1.0000  
-## year1998-01-01 -1.777e-03  1.549e+03   0.000   1.0000  
-## year1999-01-01  8.430e-03  1.510e+03   0.000   1.0000  
-## year2000-01-01 -1.929e-03  1.510e+03   0.000   1.0000  
-## year2001-01-01 -1.254e-03  1.510e+03   0.000   1.0000  
-## year2002-01-01 -2.118e+01  1.385e+03  -0.015   0.9878  
-## year2003-01-01 -2.150e+01  1.385e+03  -0.016   0.9876  
-## year2004-01-01 -2.191e+01  1.385e+03  -0.016   0.9874  
-## year2005-01-01 -2.227e+01  1.385e+03  -0.016   0.9872  
-## year2006-01-01 -2.244e+01  1.385e+03  -0.016   0.9871  
-## year2007-01-01 -2.231e+01  1.385e+03  -0.016   0.9872  
-## year2008-01-01 -2.264e+01  1.385e+03  -0.016   0.9870  
-## year2009-01-01 -2.241e+01  1.385e+03  -0.016   0.9871  
-## year2010-01-01 -2.277e+01  1.385e+03  -0.016   0.9869  
-## year2011-01-01 -2.287e+01  1.385e+03  -0.017   0.9868  
-## year2012-01-01 -2.287e+01  1.385e+03  -0.017   0.9868  
-## year2013-01-01 -2.291e+01  1.385e+03  -0.017   0.9868  
-## year2014-01-01 -2.310e+01  1.385e+03  -0.017   0.9867  
-## year2015-01-01 -2.303e+01  1.385e+03  -0.017   0.9867  
-## year2016-01-01 -2.280e+01  1.385e+03  -0.016   0.9869  
-## year2017-01-01 -2.321e+01  1.385e+03  -0.017   0.9866  
-## year2018-01-01 -2.321e+01  1.385e+03  -0.017   0.9866  
-## year2019-01-01 -2.346e+01  1.385e+03  -0.017   0.9865  
-## weighted_probs  1.548e-01  9.295e-02   1.665   0.0958 .
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5692.3  on 154030  degrees of freedom
-## Residual deviance: 4566.8  on 154003  degrees of freedom
-## AIC: 4622.8
-## 
-## Number of Fisher Scoring iterations: 15
    
-
    The two groups of scientists did not have a significant association with the gender predicted from fore names (P = 0.095815). Interaction terms do not predict type over and above the main effect of gender probability and year.
    
-
    scaled_iscb <- iscb_lm
+  mutate(type = type %>% relevel(ref = "Pubmed authors"))
    
+
    scaled_iscb <- iscb_lm %>%
+  filter(year(year) >= 2002)
 # scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
 # scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)
-main_lm <- glm(type ~ year + weighted_probs, 
-     data = scaled_iscb %>% mutate(year = as.factor(year)), 
-     family = 'binomial')
 
-summary(main_lm)
    
-
    ## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = scaled_iscb %>% mutate(year = as.factor(year)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.2521  -0.0675  -0.0602  -0.0536   3.7156  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)  
-## (Intercept)     1.655e+01  1.385e+03   0.012   0.9905  
-## year1994-01-01  8.750e-03  1.959e+03   0.000   1.0000  
-## year1995-01-01 -3.840e-02  1.959e+03   0.000   1.0000  
-## year1996-01-01  5.925e-03  1.833e+03   0.000   1.0000  
-## year1997-01-01 -6.766e-03  1.537e+03   0.000   1.0000  
-## year1998-01-01 -1.777e-03  1.549e+03   0.000   1.0000  
-## year1999-01-01  8.430e-03  1.510e+03   0.000   1.0000  
-## year2000-01-01 -1.929e-03  1.510e+03   0.000   1.0000  
-## year2001-01-01 -1.254e-03  1.510e+03   0.000   1.0000  
-## year2002-01-01 -2.118e+01  1.385e+03  -0.015   0.9878  
-## year2003-01-01 -2.150e+01  1.385e+03  -0.016   0.9876  
-## year2004-01-01 -2.191e+01  1.385e+03  -0.016   0.9874  
-## year2005-01-01 -2.227e+01  1.385e+03  -0.016   0.9872  
-## year2006-01-01 -2.244e+01  1.385e+03  -0.016   0.9871  
-## year2007-01-01 -2.231e+01  1.385e+03  -0.016   0.9872  
-## year2008-01-01 -2.264e+01  1.385e+03  -0.016   0.9870  
-## year2009-01-01 -2.241e+01  1.385e+03  -0.016   0.9871  
-## year2010-01-01 -2.277e+01  1.385e+03  -0.016   0.9869  
-## year2011-01-01 -2.287e+01  1.385e+03  -0.017   0.9868  
-## year2012-01-01 -2.287e+01  1.385e+03  -0.017   0.9868  
-## year2013-01-01 -2.291e+01  1.385e+03  -0.017   0.9868  
-## year2014-01-01 -2.310e+01  1.385e+03  -0.017   0.9867  
-## year2015-01-01 -2.303e+01  1.385e+03  -0.017   0.9867  
-## year2016-01-01 -2.280e+01  1.385e+03  -0.016   0.9869  
-## year2017-01-01 -2.321e+01  1.385e+03  -0.017   0.9866  
-## year2018-01-01 -2.321e+01  1.385e+03  -0.017   0.9866  
-## year2019-01-01 -2.346e+01  1.385e+03  -0.017   0.9865  
-## weighted_probs  1.548e-01  9.295e-02   1.665   0.0958 .
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5692.3  on 154030  degrees of freedom
-## Residual deviance: 4566.8  on 154003  degrees of freedom
-## AIC: 4622.8
-## 
-## Number of Fisher Scoring iterations: 15
    
+main_lm <- glm(type ~ year + weighted_probs,
+  data = scaled_iscb, # %>% mutate(year = as.factor(year))
+  family = "binomial"
+)
+
+broom::tidy(main_lm)
    +
    ## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    -2.06     0.478         -4.30 1.67e- 5
+## 2 year           -0.000271 0.0000320     -8.47 2.42e-17
+## 3 weighted_probs  0.155    0.0921         1.69 9.19e- 2
    inte_lm <- glm(
   # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
-  # type ~ s_year * s_prob, 
+  # type ~ s_year * s_prob,
   type ~ year * weighted_probs,
-   data = scaled_iscb %>% mutate(year = as.factor(year))
-   , 
-   family = 'binomial')
-summary(inte_lm)
    -
    ## 
-## Call:
-## glm(formula = type ~ year * weighted_probs, family = "binomial", 
-##     data = scaled_iscb %>% mutate(year = as.factor(year)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.4974  -0.0666  -0.0600  -0.0544   3.7730  
-## 
-## Coefficients:
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  2.486e+03   0.007    0.995
-## year1994-01-01                 1.785e-05  5.665e+03   0.000    1.000
-## year1995-01-01                 1.785e-05  3.020e+03   0.000    1.000
-## year1996-01-01                 1.785e-05  2.976e+03   0.000    1.000
-## year1997-01-01                 1.785e-05  2.596e+03   0.000    1.000
-## year1998-01-01                 1.785e-05  2.592e+03   0.000    1.000
-## year1999-01-01                 1.785e-05  2.646e+03   0.000    1.000
-## year2000-01-01                 1.785e-05  2.568e+03   0.000    1.000
-## year2001-01-01                 1.785e-05  2.567e+03   0.000    1.000
-## year2002-01-01                -2.101e+01  2.486e+03  -0.008    0.993
-## year2003-01-01                -2.147e+01  2.486e+03  -0.009    0.993
-## year2004-01-01                -2.195e+01  2.486e+03  -0.009    0.993
-## year2005-01-01                -2.227e+01  2.486e+03  -0.009    0.993
-## year2006-01-01                -2.237e+01  2.486e+03  -0.009    0.993
-## year2007-01-01                -2.244e+01  2.486e+03  -0.009    0.993
-## year2008-01-01                -2.268e+01  2.486e+03  -0.009    0.993
-## year2009-01-01                -2.241e+01  2.486e+03  -0.009    0.993
-## year2010-01-01                -2.274e+01  2.486e+03  -0.009    0.993
-## year2011-01-01                -2.295e+01  2.486e+03  -0.009    0.993
-## year2012-01-01                -2.288e+01  2.486e+03  -0.009    0.993
-## year2013-01-01                -2.288e+01  2.486e+03  -0.009    0.993
-## year2014-01-01                -2.294e+01  2.486e+03  -0.009    0.993
-## year2015-01-01                -2.291e+01  2.486e+03  -0.009    0.993
-## year2016-01-01                -2.288e+01  2.486e+03  -0.009    0.993
-## year2017-01-01                -2.307e+01  2.486e+03  -0.009    0.993
-## year2018-01-01                -2.332e+01  2.486e+03  -0.009    0.993
-## year2019-01-01                -2.352e+01  2.486e+03  -0.009    0.992
-## weighted_probs                 2.470e-04  2.815e+04   0.000    1.000
-## year1994-01-01:weighted_probs -2.470e-04  2.952e+05   0.000    1.000
-## year1995-01-01:weighted_probs -2.470e-04  2.831e+04   0.000    1.000
-## year1996-01-01:weighted_probs -2.470e-04  4.246e+04   0.000    1.000
-## year1997-01-01:weighted_probs -2.470e-04  2.829e+04   0.000    1.000
-## year1998-01-01:weighted_probs -2.470e-04  2.828e+04   0.000    1.000
-## year1999-01-01:weighted_probs -2.470e-04  4.592e+04   0.000    1.000
-## year2000-01-01:weighted_probs -2.470e-04  2.827e+04   0.000    1.000
-## year2001-01-01:weighted_probs -2.470e-04  2.827e+04   0.000    1.000
-## year2002-01-01:weighted_probs -1.987e+00  2.815e+04   0.000    1.000
-## year2003-01-01:weighted_probs -1.160e-01  2.815e+04   0.000    1.000
-## year2004-01-01:weighted_probs  2.712e-01  2.815e+04   0.000    1.000
-## year2005-01-01:weighted_probs  8.687e-02  2.815e+04   0.000    1.000
-## year2006-01-01:weighted_probs -2.989e-01  2.815e+04   0.000    1.000
-## year2007-01-01:weighted_probs  6.183e-01  2.815e+04   0.000    1.000
-## year2008-01-01:weighted_probs  2.592e-01  2.815e+04   0.000    1.000
-## year2009-01-01:weighted_probs  1.071e-01  2.815e+04   0.000    1.000
-## year2010-01-01:weighted_probs -5.974e-02  2.815e+04   0.000    1.000
-## year2011-01-01:weighted_probs  3.763e-01  2.815e+04   0.000    1.000
-## year2012-01-01:weighted_probs  1.388e-01  2.815e+04   0.000    1.000
-## year2013-01-01:weighted_probs  2.515e-02  2.815e+04   0.000    1.000
-## year2014-01-01:weighted_probs -7.596e-01  2.815e+04   0.000    1.000
-## year2015-01-01:weighted_probs -4.676e-01  2.815e+04   0.000    1.000
-## year2016-01-01:weighted_probs  3.395e-01  2.815e+04   0.000    1.000
-## year2017-01-01:weighted_probs -5.012e-01  2.815e+04   0.000    1.000
-## year2018-01-01:weighted_probs  4.535e-01  2.815e+04   0.000    1.000
-## year2019-01-01:weighted_probs  3.118e-01  2.815e+04   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5692.3  on 154030  degrees of freedom
-## Residual deviance: 4555.1  on 153977  degrees of freedom
-## AIC: 4663.1
-## 
-## Number of Fisher Scoring iterations: 15
    -
    anova(main_lm, inte_lm, test = 'Chisq')
    + data = scaled_iscb, # %>% mutate(year = as.factor(year)) + family = "binomial" +) +broom::tidy(inte_lm)     +
    ## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         -1.91      0.523        -3.65  2.59e- 4
+## 2 year                -0.000281  0.0000351    -7.99  1.30e-15
+## 3 weighted_probs      -0.445     0.901        -0.494 6.21e- 1
+## 4 year:weighted_probs  0.0000402 0.0000593     0.679 4.97e- 1
    +
    anova(main_lm, inte_lm, test = "Chisq")
    ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ year * weighted_probs
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    154003     4566.8                     
-## 2    153977     4555.1 26   11.719   0.9926
    -
    mean(scaled_iscb$year)
    -
    ## Warning in mean.default(scaled_iscb$year): argument is not numeric or logical: returning NA
    -
    ## [1] NA
    -
    mean(scaled_iscb$weighted_probs)
    -
    ## [1] 0.2260815
    -
    # inte_lm <- glm(type ~ (year * weighted_probs), 
-#    data = iscb_lm, 
-#    family = 'binomial')
    +## 1 153942 4582.3 +## 2 153941 4581.8 1 0.47034 0.4928 +
    # mean(scaled_iscb$year)
+# mean(scaled_iscb$weighted_probs)
    +

    The two groups of scientists did not have a significant association with the gender predicted from fore names (P = 0.091898). Interaction terms do not predict type over and above the main effect of gender probability and year.

    sessionInfo()
    ## R version 4.0.3 (2020-10-10)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
@@ -2024,51 +1870,63 @@ 
    Hypothesis testing
    
 ## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
-##  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
-## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
+##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
+##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
+##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
+## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-##  [1] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0 lubridate_1.7.9.2  
-##  [5] caret_6.0-86        lattice_0.20-41     forcats_0.5.0       stringr_1.4.0      
-##  [9] dplyr_1.0.2         purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
+##  [1] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0
+##  [4] lubridate_1.7.9.2   caret_6.0-86        lattice_0.20-41    
+##  [7] forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
+## [10] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
 ## [13] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0    
 ## 
 ## loaded via a namespace (and not attached):
-##   [1] colorspace_2.0-0        ellipsis_0.3.1          class_7.3-17            rprojroot_1.3-2        
-##   [5] fs_1.5.0                rstudioapi_0.12         farver_2.0.3            remotes_2.2.0          
-##   [9] DT_0.16                 prodlim_2019.11.13      fansi_0.4.1             xml2_1.3.2             
-##  [13] codetools_0.2-16        splines_4.0.3           knitr_1.30              pkgload_1.1.0          
-##  [17] jsonlite_1.7.1          pROC_1.16.2             broom_0.7.2             dbplyr_2.0.0           
-##  [21] rgeos_0.5-5             compiler_4.0.3          httr_1.4.2              backports_1.2.0        
-##  [25] assertthat_0.2.1        Matrix_1.2-18           cli_2.1.0               htmltools_0.5.0        
-##  [29] prettyunits_1.1.1       tools_4.0.3             gtable_0.3.0            glue_1.4.2             
-##  [33] rnaturalearthdata_0.1.0 reshape2_1.4.4          Rcpp_1.0.5              cellranger_1.1.0       
-##  [37] vctrs_0.3.4             svglite_1.2.3.2         nlme_3.1-149            iterators_1.0.13       
-##  [41] crosstalk_1.1.0.1       timeDate_3043.102       gower_0.2.2             xfun_0.19              
-##  [45] ps_1.4.0                testthat_3.0.0          rvest_0.3.6             lifecycle_0.2.0        
-##  [49] devtools_2.3.2          MASS_7.3-53             scales_1.1.1            ipred_0.9-9            
-##  [53] hms_0.5.3               RColorBrewer_1.1-2      yaml_2.2.1              curl_4.3               
-##  [57] memoise_1.1.0           rpart_4.1-15            stringi_1.5.3           desc_1.2.0             
-##  [61] foreach_1.5.1           e1071_1.7-4             pkgbuild_1.1.0          lava_1.6.8.1           
-##  [65] systemfonts_0.3.2       rlang_0.4.8             pkgconfig_2.0.3         evaluate_0.14          
-##  [69] sf_0.9-6                recipes_0.1.15          htmlwidgets_1.5.2       labeling_0.4.2         
-##  [73] cowplot_1.1.0           tidyselect_1.1.0        processx_3.4.4          plyr_1.8.6             
-##  [77] magrittr_1.5            R6_2.5.0                generics_0.1.0          DBI_1.1.0              
-##  [81] mgcv_1.8-33             pillar_1.4.6            haven_2.3.1             withr_2.3.0            
-##  [85] units_0.6-7             survival_3.2-7          sp_1.4-4                nnet_7.3-14            
-##  [89] modelr_0.1.8            crayon_1.3.4            KernSmooth_2.23-17      utf8_1.1.4             
-##  [93] rmarkdown_2.5           usethis_1.6.3           grid_4.0.3              readxl_1.3.1           
-##  [97] data.table_1.13.2       callr_3.5.1             ModelMetrics_1.2.2.2    reprex_0.3.0           
-## [101] digest_0.6.27           classInt_0.4-3          stats4_4.0.3            munsell_0.5.0          
-## [105] viridisLite_0.3.0       sessioninfo_1.1.1
    +## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 +## [4] rprojroot_1.3-2 fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 DT_0.16 +## [10] prodlim_2019.11.13 fansi_0.4.1 xml2_1.3.2 +## [13] codetools_0.2-16 splines_4.0.3 knitr_1.30 +## [16] pkgload_1.1.0 jsonlite_1.7.1 pROC_1.16.2 +## [19] broom_0.7.2 dbplyr_2.0.0 rgeos_0.5-5 +## [22] compiler_4.0.3 httr_1.4.2 backports_1.2.0 +## [25] assertthat_0.2.1 Matrix_1.2-18 cli_2.1.0 +## [28] htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.3 +## [31] gtable_0.3.0 glue_1.4.2 rnaturalearthdata_0.1.0 +## [34] reshape2_1.4.4 Rcpp_1.0.5 cellranger_1.1.0 +## [37] vctrs_0.3.4 svglite_1.2.3.2 nlme_3.1-149 +## [40] iterators_1.0.13 crosstalk_1.1.0.1 timeDate_3043.102 +## [43] gower_0.2.2 xfun_0.19 ps_1.4.0 +## [46] testthat_3.0.0 rvest_0.3.6 lifecycle_0.2.0 +## [49] devtools_2.3.2 MASS_7.3-53 scales_1.1.1 +## [52] ipred_0.9-9 hms_0.5.3 RColorBrewer_1.1-2 +## [55] yaml_2.2.1 curl_4.3 memoise_1.1.0 +## [58] rpart_4.1-15 stringi_1.5.3 desc_1.2.0 +## [61] foreach_1.5.1 e1071_1.7-4 pkgbuild_1.1.0 +## [64] lava_1.6.8.1 systemfonts_0.3.2 rlang_0.4.8 +## [67] pkgconfig_2.0.3 evaluate_0.14 sf_0.9-6 +## [70] recipes_0.1.15 htmlwidgets_1.5.2 labeling_0.4.2 +## [73] cowplot_1.1.0 tidyselect_1.1.0 processx_3.4.4 +## [76] plyr_1.8.6 magrittr_1.5 R6_2.5.0 +## [79] generics_0.1.0 DBI_1.1.0 mgcv_1.8-33 +## [82] pillar_1.4.6 haven_2.3.1 withr_2.3.0 +## [85] units_0.6-7 survival_3.2-7 sp_1.4-4 +## [88] nnet_7.3-14 modelr_0.1.8 crayon_1.3.4 +## [91] KernSmooth_2.23-17 utf8_1.1.4 rmarkdown_2.5 +## [94] usethis_1.6.3 grid_4.0.3 readxl_1.3.1 +## [97] data.table_1.13.2 callr_3.5.1 ModelMetrics_1.2.2.2 +## [100] reprex_0.3.0 digest_0.6.27 classInt_0.4-3 +## [103] stats4_4.0.3 munsell_0.5.0 viridisLite_0.3.0 +## [106] sessioninfo_1.1.1
    -
    ---
title: "Representation analysis of gender"
---

## Setups

```{r message = F, warning=F}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
library(wru)
source('utils/r-utils.R')
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

## Load data

Only keep articles from 2002 because few authors had gender predictions before 2002.
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load('Rdata/raws.Rdata')

alpha_threshold <- qnorm(0.975)
gender_df <- read_tsv('data/gender/genderize.tsv')

pubmed_gender_df <- corr_authors %>%
  filter(year(year) >= 2002) %>% 
  left_join(gender_df, by = 'fore_name_simple')

iscb_gender_df <- keynotes %>% 
  left_join(gender_df, by = 'fore_name_simple')

start_year <- 1993
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_gender_df$journal)
my_confs <- unique(iscb_gender_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
```

## Prepare data frames for later analyses

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed <- iscb_gender_df %>%
  rename('journal' = conference) %>%
  select(year, journal, probability_male, publication_date) %>%
  mutate(type = 'Keynote speakers/Fellows',
         adjusted_citations = 1) %>%
  bind_rows(
    pubmed_gender_df %>%
      select(year, journal, probability_male, publication_date, adjusted_citations) %>%
      mutate(type = 'Pubmed authors')
  ) %>%
  mutate(probability_female = 1 - probability_male) %>% 
  pivot_longer(contains('probability'),
               names_to = 'gender',
               values_to = 'probabilities') %>%
  filter(!is.na(probabilities)) %>% 
  group_by(type, year, gender) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations/pmc_citations_year,
    weighted_probs = probabilities*weight
    # weight = 1
    )

iscb_pubmed_sum <- iscb_pubmed %>% 
  summarise(
    # n = n(),
    mean_prob = mean(weighted_probs),
    # mean_prob = mean(probabilities, na.rm = T),
    # sd_prob = sd(probabilities, na.rm = T),
    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
    # n = mean(n),
    me_prob = alpha_threshold * se_prob,
    .groups = 'drop'
  )
# https://stats.stackexchange.com/questions/25895/computing-standard-error-in-weighted-mean-estimation
```


```{r}
# save(iscb_pubmed, file = 'Rdata/iscb-pubmed_gender.Rdata')
```


## Figures for paper

### Figure 1: ISCB Fellows and keynote speakers appear more evenly split between men and women than PubMed authors, but the proportion has not reached parity.

```{r fig.height=3}
fig_1 <- iscb_pubmed_sum %>%
  # group_by(year, type, gender) %>%
  gender_breakdown('main', fct_rev(type))
fig_1
ggsave('figs/gender_breakdown.png', fig_1, width = 5, height = 2.5)
ggsave('figs/gender_breakdown.svg', fig_1, width = 5, height = 2.5)
```

```{r echo=FALSE}
iscb_pubmed_sum %>%
  # group_by(year, type, gender) %>%
  # summarise(mean_prob = mean(probabilities, na.rm = T), .groups = 'drop') %>% 
  filter(year(year) > 2016, grepl('female', gender)) %>% 
  group_by(type) %>% 
  summarise(prob_female_avg = mean(mean_prob))
```

### Supplementary Figure S1 {#sup_fig_s1}

Additional fig. 1 with separated keynote speakers and fellows

```{r}
iscb_pubmed %>%
  ungroup() %>% 
  mutate(type2 = case_when(
    journal == 'ISCB Fellow' ~ 'ISCB Fellows',
    type == 'Keynote speakers/Fellows' ~ 'Keynote speakers',
    TRUE ~ 'Pubmed authors'
  )) %>% 
  group_by(type2, year, gender) %>% 
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = 'drop'
  ) %>% 
  gender_breakdown('main', fct_rev(type2))
```

<!-- Increasing trend of honorees who were women in each honor category, especially in the group of ISCB Fellows, which markedly increased after 2015.  -->

```{r eval=FALSE, include=FALSE}
# By conference:
# fig_1d <- bind_rows(iscb_gender) %>%
#   gender_breakdown(category = 'sub', journal) +
#   theme(legend.position = 'bottom')

# fig_1d
# ggsave('figs/fig_s1.png', fig_1d, width = 6, height = 2)
# ggsave('figs/fig_s1.svg', fig_1d, width = 6, height = 2)
```

## Mean and standard deviation of predicted probabilities

```{r}
iscb_pubmed_sum %>% filter(gender == 'probability_male') %>% 
  gam_and_ci(df2 = iscb_pubmed %>% filter(gender == 'probability_male'),
             start_y = start_year, end_y = end_year) +
  theme(legend.position = c(0.88, 0.2))
```

## Hypothesis testing

```{r echo = F}
get_p <- function(inte, colu){
  broom::tidy(inte) %>% 
    filter(term == 'weighted_probs') %>% 
    pull(colu) %>% 
    sprintf("%0.5g", .)
}
```

```{r}
iscb_lm <- iscb_pubmed %>% 
       filter(gender == 'probability_female', !is.na(weighted_probs)) %>% 
  mutate(type = as.factor(type)) %>% 
  mutate(type = relevel(type, ref = 'Pubmed authors'),
         year = as.factor(year))

main_lm <- glm(type ~ year + weighted_probs, 
     data = iscb_lm, family = 'binomial')

summary(main_lm)
```

The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`).
Interaction terms do not predict `type` over and above the main effect of gender probability and year.

```{r}
scaled_iscb <- iscb_lm
# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
# scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)
main_lm <- glm(type ~ year + weighted_probs, 
     data = scaled_iscb %>% mutate(year = as.factor(year)), 
     family = 'binomial')

summary(main_lm)
inte_lm <- glm(
  # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
  # type ~ s_year * s_prob, 
  type ~ year * weighted_probs,
   data = scaled_iscb %>% mutate(year = as.factor(year))
   , 
   family = 'binomial')
summary(inte_lm)
anova(main_lm, inte_lm, test = 'Chisq')
mean(scaled_iscb$year)
mean(scaled_iscb$weighted_probs)
```

```{r}
# inte_lm <- glm(type ~ (year * weighted_probs), 
#    data = iscb_lm, 
#    family = 'binomial')
```

```{r}
sessionInfo()
```

    +
    ---
title: "Representation analysis of gender"
---

## Setups

```{r message = F, warning=F}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
library(wru)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

## Load data

Only keep articles from 2002 because few authors had gender predictions before 2002.
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load("Rdata/raws.Rdata")

alpha_threshold <- qnorm(0.975)
gender_df <- read_tsv("data/gender/genderize.tsv")

pubmed_gender_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  left_join(gender_df, by = "fore_name_simple")

iscb_gender_df <- keynotes %>%
  left_join(gender_df, by = "fore_name_simple")

start_year <- 1993
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_gender_df$journal)
my_confs <- unique(iscb_gender_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
```

## Prepare data frames for later analyses

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed <- iscb_gender_df %>%
  rename("journal" = conference) %>%
  select(year, journal, probability_male, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1
  ) %>%
  bind_rows(
    pubmed_gender_df %>%
      select(year, journal, probability_male, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(probability_female = 1 - probability_male) %>%
  pivot_longer(contains("probability"),
    names_to = "gender",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, gender) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
    # weight = 1
  )

iscb_pubmed_sum <- iscb_pubmed %>%
  summarise(
    # n = n(),
    mean_prob = mean(weighted_probs),
    # mean_prob = mean(probabilities, na.rm = T),
    # sd_prob = sd(probabilities, na.rm = T),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    # n = mean(n),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )
# https://stats.stackexchange.com/questions/25895/computing-standard-error-in-weighted-mean-estimation
```


```{r}
# save(iscb_pubmed, file = 'Rdata/iscb-pubmed_gender.Rdata')
```


## Figures for paper

### Figure 2: ISCB Fellows and keynote speakers appear more evenly split between men and women than PubMed authors, but the proportion has not reached parity.

```{r fig.height=3}
fig_1 <- iscb_pubmed_sum %>%
  # group_by(year, type, gender) %>%
  gender_breakdown("main", fct_rev(type))
fig_1
ggsave("figs/gender_breakdown.png", fig_1, width = 5, height = 2.5)
ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5)
```

```{r echo=FALSE}
iscb_pubmed_sum %>%
  # group_by(year, type, gender) %>%
  # summarise(mean_prob = mean(probabilities, na.rm = T), .groups = 'drop') %>%
  filter(year(year) > 2016, grepl("female", gender)) %>%
  group_by(type) %>%
  summarise(prob_female_avg = mean(mean_prob))
```

### Supplementary Figure S2 {#sup_fig_s1}

Additional fig. 1 with separated keynote speakers and fellows

```{r}
fig_1d <- iscb_pubmed %>%
  ungroup() %>%
  mutate(
    type2 = case_when(
      type == "Pubmed authors" ~ "Pubmed authors",
      journal == "ISCB Fellow" ~ "ISCB Fellows",
      type == "Keynote speakers/Fellows" ~ "Keynote speakers"
    )
  ) %>%
  group_by(type2, year, gender) %>%
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  ) %>%
  gender_breakdown("main", fct_rev(type2)) +
  scale_x_date(
    labels = scales::date_format("'%y"),
    expand = c(0, 0)
  )
```

<!-- Increasing trend of honorees who were women in each honor category, especially in the group of ISCB Fellows, which markedly increased after 2015.  -->

```{r eval=FALSE, include=FALSE}
# By conference:
# fig_1d <- bind_rows(iscb_gender) %>%
#   gender_breakdown(category = 'sub', journal) +
#   theme(legend.position = 'bottom')

# fig_1d
ggsave("figs/fig_s1.png", fig_1d, width = 7, height = 3)
ggsave("figs/fig_s1.svg", fig_1d, width = 7, height = 3)
```

## Mean and standard deviation of predicted probabilities

```{r}
iscb_pubmed_sum %>%
  filter(gender == "probability_male") %>%
  gam_and_ci(
    df2 = iscb_pubmed %>% filter(gender == "probability_male"),
    start_y = start_year, end_y = end_year
  ) +
  theme(legend.position = c(0.88, 0.2))
```

## Hypothesis testing

```{r echo = F}
get_p <- function(inte, colu) {
  broom::tidy(inte) %>%
    filter(term == "weighted_probs") %>%
    pull(colu) %>%
    sprintf("%0.5g", .)
}
```

```{r}
iscb_lm <- iscb_pubmed %>%
  filter(gender == "probability_female", !is.na(weighted_probs)) %>%
  mutate(type = as.factor(type)) %>% 
  mutate(type = type %>% relevel(ref = "Pubmed authors"))
```

```{r}
scaled_iscb <- iscb_lm %>%
  filter(year(year) >= 2002)
# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
# scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)

main_lm <- glm(type ~ year + weighted_probs,
  data = scaled_iscb, # %>% mutate(year = as.factor(year))
  family = "binomial"
)

broom::tidy(main_lm)
inte_lm <- glm(
  # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
  # type ~ s_year * s_prob,
  type ~ year * weighted_probs,
  data = scaled_iscb, # %>% mutate(year = as.factor(year))
  family = "binomial"
)
broom::tidy(inte_lm)
anova(main_lm, inte_lm, test = "Chisq")
# mean(scaled_iscb$year)
# mean(scaled_iscb$weighted_probs)
```

The two groups of scientists did not have a significant association with the gender predicted from fore names (_P_ = `r get_p(main_lm, 'p.value')`).
Interaction terms do not predict `type` over and above the main effect of gender probability and year.

```{r include=FALSE, eval=FALSE}
# inte_lm <- glm(type ~ (year * weighted_probs),
#    data = iscb_lm,
#    family = 'binomial')
```

```{r}
sessionInfo()
```

    diff --git a/docs/11.visualize-name-origins.html b/docs/11.visualize-name-origins.html index 7f90e1d..34d453b 100644 --- a/docs/11.visualize-name-origins.html +++ b/docs/11.visualize-name-origins.html @@ -1667,17 +1667,17 @@

    Representation analysis of name origins

    Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata). See 093.summary-stats for more details.

    alpha_threshold <- qnorm(0.975)
-load('Rdata/raws.Rdata')
+load("Rdata/raws.Rdata")
 
 pubmed_nat_df <- corr_authors %>%
-  filter(year(year) >= 2002) %>% 
-  left_join(nationalize_df, by = c('fore_name', 'last_name')) %>%
+  filter(year(year) >= 2002) %>%
+  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
   group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
   summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
   ungroup()
 
 iscb_nat_df <- keynotes %>%
-  left_join(nationalize_df, by = c('fore_name', 'last_name'))
+  left_join(nationalize_df, by = c("fore_name", "last_name"))
 
 start_year <- 1992
 end_year <- 2019
@@ -1686,36 +1686,40 @@ 
    Representation analysis of name origins
    
 my_confs <- unique(iscb_nat_df$conference)
 n_jours <- length(my_jours)
 n_confs <- length(my_confs)
-region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names')
+region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")
 region_levels_let <- toupper(letters[1:8])
-region_cols <- c('#ffffb3', '#fccde5', '#b3de69', '#fdb462', '#80b1d3', '#8dd3c7', '#bebada', '#fb8072', '#bc80bd', '#ccebc5')
    +region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")

    Names grouping:

    -
    our_country_map <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv') %>%
-  rename('region' = Region) %>%
+
    our_country_map <- read_tsv("https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv") %>%
+  rename("region" = Region) %>%
   recode_region()
    
 
    ## 
-## ── Column specification ───────────────────────────────────────────────────────────────────────────────
+## ── Column specification ────────────────────────────────────────────────────────
 ## cols(
 ##   Country = col_character(),
 ##   Region = col_character()
 ## )
    
 
    my_world <- world %>%
-  select(- geometry) %>% 
-  rename(Country = 'name') %>%
-  left_join(our_country_map, by = 'Country')
+  select(-geometry) %>%
+  rename(Country = "name") %>%
+  left_join(our_country_map, by = "Country")
 
 (gworld <- ggplot(data = my_world) +
-    geom_sf(aes(fill = fct_relevel(region, region_levels))) +
-    coord_sf(crs = "+proj=eqearth +wktext") +
-    scale_fill_manual(values = region_cols,
-                      na.translate = FALSE) +
-    theme(panel.background = element_rect(fill = "azure"),
-          legend.title = element_blank(),
-          legend.position = 'bottom',
-          panel.border = element_rect(fill = NA)))
    
+  geom_sf(aes(fill = fct_relevel(region, region_levels))) +
+  coord_sf(crs = "+proj=eqearth +wktext") +
+  scale_fill_manual(
+    values = region_cols,
+    na.translate = FALSE
+  ) +
+  theme(
+    panel.background = element_rect(fill = "azure"),
+    legend.title = element_blank(),
+    legend.position = "bottom",
+    panel.border = element_rect(fill = NA)
+  ))

    -
    ggsave('figs/2020-01-31_groupings.png', gworld, width = 7.2, height = 4.3)
-ggsave('figs/2020-01-31_groupings.svg', gworld, width = 7.2, height = 4.3)
    +
    ggsave("figs/2020-01-31_groupings.png", gworld, width = 7.2, height = 4.3)
+ggsave("figs/2020-01-31_groupings.svg", gworld, width = 7.2, height = 4.3)

    Descriptive statistics

    Prepare data frames for later analyses:

    @@ -1725,55 +1729,57 @@

    Descriptive statistics

  • compute mean, sd, marginal error
    • iscb_pubmed_oth <- iscb_nat_df %>%
-  rename('journal' = conference) %>%
+  rename("journal" = conference) %>%
   select(year, journal, African:SouthAsian, publication_date) %>%
-  mutate(type = 'Keynote speakers/Fellows',
-         adjusted_citations = 1,
-         pmid = -9999) %>%
+  mutate(
+    type = "Keynote speakers/Fellows",
+    adjusted_citations = 1,
+    pmid = -9999
+  ) %>%
   bind_rows(
     pubmed_nat_df %>%
       select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
-      mutate(type = 'Pubmed authors')
+      mutate(type = "Pubmed authors")
   ) %>%
   mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
   pivot_longer(c(African:SouthAsian, OtherCategories),
-               names_to = 'region',
-               values_to = 'probabilities') %>%
-  filter(!is.na(probabilities)) %>% 
+    names_to = "region",
+    values_to = "probabilities"
+  ) %>%
+  filter(!is.na(probabilities)) %>%
   group_by(type, year, region) %>%
   mutate(
     pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations/pmc_citations_year,
-    weighted_probs = probabilities*weight
-  ) 
+    weight = adjusted_citations / pmc_citations_year,
+    weighted_probs = probabilities * weight
+  )
 
-iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% 
+iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
   summarise(
     mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
+    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
     me_prob = alpha_threshold * se_prob,
-    .groups = 'drop'
+    .groups = "drop"
   )
 
 iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
-  filter(region != 'OtherCategories')
    + filter(region != "OtherCategories")

    Prepare data frames for analysis

    By conference keynotes/fellows

    i <- 0
-iscb_nat <- vector('list', length = n_confs)
+iscb_nat <- vector("list", length = n_confs)
 
-for (conf in my_confs){
+for (conf in my_confs) {
   i <- i + 1
   iscb_nat[[i]] <- iscb_pubmed_oth %>%
-    filter(region != 'OtherCategories', type != 'Pubmed authors' & journal == conf) %>%
+    filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>%
     group_by(type, year, region, journal) %>%
-    summarise(mean_prob = mean(weighted_probs), .groups = 'drop') %>%
-    {.}
+    summarise(mean_prob = mean(weighted_probs), .groups = "drop")
 }
    -
    save(my_world, iscb_pubmed_oth, iscb_nat, file = 'Rdata/iscb-pubmed_nat.Rdata')
    +
    save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata")
    @@ -1782,626 +1788,349 @@

    Figures for paper

    Figure 4

    Compared to the name collection of Pubmed authors, honorees with Celtic/English names are overrepresented while honorees with East Asian names are underrepresented.

    fig_4a <- iscb_pubmed_sum %>%
-  filter(year < '2020-01-01') %>%
-  region_breakdown('main', region_levels, fct_rev(type)) +
+  filter(year < "2020-01-01") %>%
+  region_breakdown("main", region_levels, fct_rev(type)) +
   guides(fill = guide_legend(nrow = 2)) +
-  theme(legend.position = 'bottom')
+  theme(legend.position = "bottom")
 
-large_regions <- c('CelticEnglish', 'EastAsian', 'European', 'OtherCategories')
+large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")
 ## Mean and standard deviation of predicted probabilities:
 fig_4b <- iscb_pubmed_sum_oth %>%
   filter(region %in% large_regions) %>%
   recode_region() %>%
   gam_and_ci(
-    df2 = iscb_pubmed_oth %>% 
+    df2 = iscb_pubmed_oth %>%
       filter(region %in% large_regions) %>%
       recode_region(),
-    start_y = start_year, end_y = end_year) + 
-  theme(legend.position = c(0.88, 0.83),
-        panel.grid.minor = element_blank(),
-        legend.margin = margin(-0.5, 0, 0, 0, unit='cm'),
-        legend.text = element_text(size = 7)) +
+    start_y = start_year, end_y = end_year
+  ) +
+  theme(
+    legend.position = c(0.88, 0.83),
+    panel.grid.minor = element_blank(),
+    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
+    legend.text = element_text(size = 7)
+  ) +
   facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)
 
-fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = 'AUTO', ncol = 1, rel_heights = c(1.3,1))
    +fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1)) 
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    fig_4

    -
    ggsave('figs/region_breakdown.png', fig_4, width = 6.7, height = 5.5)
-ggsave('figs/region_breakdown.svg', fig_4, width = 6.7, height = 5.5)
    +
    ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5)
+ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)

    Hypothesis testing

    -
    iscb_lm <- iscb_pubmed_oth %>% 
-  ungroup() %>% 
+
    iscb_lm <- iscb_pubmed_oth %>%
+  ungroup() %>%
   mutate(
     # year = c(scale(year(year))),
-    year = as.factor(year),
-    type = as.factor(type) %>% relevel(ref = 'Pubmed authors'))
-main_lm <- function(regioni){
-  glm(type ~ year + weighted_probs, 
-      data = iscb_lm %>% 
-        filter(region == regioni, !is.na(probabilities)) ,
-      family = 'binomial')
+    # year = as.factor(year),
+    type = as.factor(type) %>% relevel(ref = "Pubmed authors")
+  )
+main_lm <- function(regioni) {
+  glm(type ~ year + weighted_probs,
+    data = iscb_lm %>%
+      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
+    family = "binomial"
+  )
 }
 
-inte_lm <- function(regioni){
-  glm(type ~ year * weighted_probs, 
-      data = iscb_lm %>% 
-        filter(region == regioni, !is.na(weighted_probs)),
-      family = 'binomial')
+inte_lm <- function(regioni) {
+  glm(type ~ year * weighted_probs,
+    data = iscb_lm %>%
+      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+    family = "binomial"
+  )
 }
 
 main_list <- lapply(large_regions, main_lm)
    
 
    ## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    
 
    names(main_list) <- large_regions
-lapply(main_list, summary)
    
+lapply(main_list, broom::tidy)
    ## $CelticEnglish
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(probabilities)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.6215  -0.0651  -0.0580  -0.0513   3.7315  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)    
-## (Intercept)     1.648e+01  1.385e+03   0.012 0.990508    
-## year1994-01-01 -6.727e-02  1.959e+03   0.000 0.999973    
-## year1995-01-01 -9.883e-02  1.959e+03   0.000 0.999960    
-## year1996-01-01 -1.957e-02  1.832e+03   0.000 0.999991    
-## year1997-01-01  7.983e-03  1.536e+03   0.000 0.999996    
-## year1998-01-01  2.415e-02  1.548e+03   0.000 0.999988    
-## year1999-01-01  1.437e-02  1.509e+03   0.000 0.999992    
-## year2000-01-01  3.791e-02  1.509e+03   0.000 0.999980    
-## year2001-01-01  1.267e-02  1.509e+03   0.000 0.999993    
-## year2002-01-01 -2.118e+01  1.385e+03  -0.015 0.987800    
-## year2003-01-01 -2.150e+01  1.385e+03  -0.016 0.987612    
-## year2004-01-01 -2.191e+01  1.385e+03  -0.016 0.987380    
-## year2005-01-01 -2.227e+01  1.385e+03  -0.016 0.987172    
-## year2006-01-01 -2.244e+01  1.385e+03  -0.016 0.987073    
-## year2007-01-01 -2.231e+01  1.385e+03  -0.016 0.987147    
-## year2008-01-01 -2.263e+01  1.385e+03  -0.016 0.986961    
-## year2009-01-01 -2.240e+01  1.385e+03  -0.016 0.987095    
-## year2010-01-01 -2.277e+01  1.385e+03  -0.016 0.986881    
-## year2011-01-01 -2.287e+01  1.385e+03  -0.017 0.986825    
-## year2012-01-01 -2.287e+01  1.385e+03  -0.017 0.986825    
-## year2013-01-01 -2.285e+01  1.385e+03  -0.017 0.986835    
-## year2014-01-01 -2.310e+01  1.385e+03  -0.017 0.986692    
-## year2015-01-01 -2.303e+01  1.385e+03  -0.017 0.986735    
-## year2016-01-01 -2.279e+01  1.385e+03  -0.016 0.986870    
-## year2017-01-01 -2.320e+01  1.385e+03  -0.017 0.986635    
-## year2018-01-01 -2.319e+01  1.385e+03  -0.017 0.986640    
-## year2019-01-01 -2.344e+01  1.385e+03  -0.017 0.986498    
-## weighted_probs  1.921e-01  5.650e-02   3.400 0.000673 ***
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4611.5  on 163947  degrees of freedom
-## AIC: 4667.5
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    -2.17     0.481         -4.52 6.06e- 6
+## 2 year           -0.000268 0.0000320     -8.37 5.64e-17
+## 3 weighted_probs  0.194    0.0561         3.46 5.46e- 4
 ## 
 ## $EastAsian
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(probabilities)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.1463  -0.0682  -0.0594  -0.0507   4.0267  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)    
-## (Intercept)     1.658e+01  1.385e+03   0.012    0.990    
-## year1994-01-01  6.665e-02  1.959e+03   0.000    1.000    
-## year1995-01-01 -1.257e-02  1.959e+03   0.000    1.000    
-## year1996-01-01 -2.152e-03  1.833e+03   0.000    1.000    
-## year1997-01-01  6.364e-03  1.537e+03   0.000    1.000    
-## year1998-01-01 -4.470e-03  1.549e+03   0.000    1.000    
-## year1999-01-01  7.432e-03  1.510e+03   0.000    1.000    
-## year2000-01-01  6.429e-01  1.502e+03   0.000    1.000    
-## year2001-01-01 -5.036e-03  1.510e+03   0.000    1.000    
-## year2002-01-01 -2.111e+01  1.385e+03  -0.015    0.988    
-## year2003-01-01 -2.142e+01  1.385e+03  -0.015    0.988    
-## year2004-01-01 -2.181e+01  1.385e+03  -0.016    0.987    
-## year2005-01-01 -2.217e+01  1.385e+03  -0.016    0.987    
-## year2006-01-01 -2.233e+01  1.385e+03  -0.016    0.987    
-## year2007-01-01 -2.221e+01  1.385e+03  -0.016    0.987    
-## year2008-01-01 -2.252e+01  1.385e+03  -0.016    0.987    
-## year2009-01-01 -2.228e+01  1.385e+03  -0.016    0.987    
-## year2010-01-01 -2.264e+01  1.385e+03  -0.016    0.987    
-## year2011-01-01 -2.273e+01  1.385e+03  -0.016    0.987    
-## year2012-01-01 -2.272e+01  1.385e+03  -0.016    0.987    
-## year2013-01-01 -2.268e+01  1.385e+03  -0.016    0.987    
-## year2014-01-01 -2.292e+01  1.385e+03  -0.017    0.987    
-## year2015-01-01 -2.285e+01  1.385e+03  -0.016    0.987    
-## year2016-01-01 -2.260e+01  1.385e+03  -0.016    0.987    
-## year2017-01-01 -2.299e+01  1.385e+03  -0.017    0.987    
-## year2018-01-01 -2.297e+01  1.385e+03  -0.017    0.987    
-## year2019-01-01 -2.320e+01  1.385e+03  -0.017    0.987    
-## weighted_probs -1.665e+00  2.861e-01  -5.820 5.89e-09 ***
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4560.8  on 163947  degrees of freedom
-## AIC: 4616.8
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    -2.30     0.480         -4.80 1.58e- 6
+## 2 year           -0.000242 0.0000321     -7.54 4.82e-14
+## 3 weighted_probs -1.67     0.286         -5.82 6.02e- 9
 ## 
 ## $European
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(probabilities)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.1960  -0.0638  -0.0588  -0.0509   3.7279  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)
-## (Intercept)     1.655e+01  1.385e+03   0.012    0.990
-## year1994-01-01  1.671e-02  1.959e+03   0.000    1.000
-## year1995-01-01  1.751e-02  1.959e+03   0.000    1.000
-## year1996-01-01 -5.959e-03  1.833e+03   0.000    1.000
-## year1997-01-01 -1.572e-03  1.537e+03   0.000    1.000
-## year1998-01-01 -4.096e-03  1.549e+03   0.000    1.000
-## year1999-01-01 -5.232e-03  1.510e+03   0.000    1.000
-## year2000-01-01 -1.138e-03  1.510e+03   0.000    1.000
-## year2001-01-01 -1.393e-02  1.510e+03   0.000    1.000
-## year2002-01-01 -2.120e+01  1.385e+03  -0.015    0.988
-## year2003-01-01 -2.153e+01  1.385e+03  -0.016    0.988
-## year2004-01-01 -2.194e+01  1.385e+03  -0.016    0.987
-## year2005-01-01 -2.230e+01  1.385e+03  -0.016    0.987
-## year2006-01-01 -2.248e+01  1.385e+03  -0.016    0.987
-## year2007-01-01 -2.235e+01  1.385e+03  -0.016    0.987
-## year2008-01-01 -2.267e+01  1.385e+03  -0.016    0.987
-## year2009-01-01 -2.244e+01  1.385e+03  -0.016    0.987
-## year2010-01-01 -2.281e+01  1.385e+03  -0.016    0.987
-## year2011-01-01 -2.291e+01  1.385e+03  -0.017    0.987
-## year2012-01-01 -2.291e+01  1.385e+03  -0.017    0.987
-## year2013-01-01 -2.290e+01  1.385e+03  -0.017    0.987
-## year2014-01-01 -2.315e+01  1.385e+03  -0.017    0.987
-## year2015-01-01 -2.307e+01  1.385e+03  -0.017    0.987
-## year2016-01-01 -2.284e+01  1.385e+03  -0.016    0.987
-## year2017-01-01 -2.325e+01  1.385e+03  -0.017    0.987
-## year2018-01-01 -2.325e+01  1.385e+03  -0.017    0.987
-## year2019-01-01 -2.350e+01  1.385e+03  -0.017    0.986
-## weighted_probs  7.241e-02  8.237e-02   0.879    0.379
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4618.3  on 163947  degrees of freedom
-## AIC: 4674.3
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)    -2.08     0.480        -4.32  1.53e- 5
+## 2 year           -0.000272 0.0000319    -8.53  1.46e-17
+## 3 weighted_probs  0.0713   0.0822        0.867 3.86e- 1
 ## 
 ## $OtherCategories
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(probabilities)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.1628  -0.0636  -0.0589  -0.0508   3.7283  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)
-## (Intercept)     1.655e+01  1.385e+03   0.012    0.990
-## year1994-01-01  1.077e-02  1.959e+03   0.000    1.000
-## year1995-01-01  1.868e-02  1.959e+03   0.000    1.000
-## year1996-01-01  1.373e-02  1.833e+03   0.000    1.000
-## year1997-01-01 -1.182e-03  1.537e+03   0.000    1.000
-## year1998-01-01 -5.156e-03  1.549e+03   0.000    1.000
-## year1999-01-01  3.844e-04  1.510e+03   0.000    1.000
-## year2000-01-01  4.700e-03  1.510e+03   0.000    1.000
-## year2001-01-01  9.411e-03  1.510e+03   0.000    1.000
-## year2002-01-01 -2.119e+01  1.385e+03  -0.015    0.988
-## year2003-01-01 -2.152e+01  1.385e+03  -0.016    0.988
-## year2004-01-01 -2.193e+01  1.385e+03  -0.016    0.987
-## year2005-01-01 -2.230e+01  1.385e+03  -0.016    0.987
-## year2006-01-01 -2.247e+01  1.385e+03  -0.016    0.987
-## year2007-01-01 -2.234e+01  1.385e+03  -0.016    0.987
-## year2008-01-01 -2.267e+01  1.385e+03  -0.016    0.987
-## year2009-01-01 -2.244e+01  1.385e+03  -0.016    0.987
-## year2010-01-01 -2.280e+01  1.385e+03  -0.016    0.987
-## year2011-01-01 -2.291e+01  1.385e+03  -0.017    0.987
-## year2012-01-01 -2.291e+01  1.385e+03  -0.017    0.987
-## year2013-01-01 -2.290e+01  1.385e+03  -0.017    0.987
-## year2014-01-01 -2.314e+01  1.385e+03  -0.017    0.987
-## year2015-01-01 -2.307e+01  1.385e+03  -0.017    0.987
-## year2016-01-01 -2.284e+01  1.385e+03  -0.016    0.987
-## year2017-01-01 -2.325e+01  1.385e+03  -0.017    0.987
-## year2018-01-01 -2.325e+01  1.385e+03  -0.017    0.987
-## year2019-01-01 -2.350e+01  1.385e+03  -0.017    0.986
-## weighted_probs  7.448e-02  9.548e-02   0.780    0.435
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4618.4  on 163947  degrees of freedom
-## AIC: 4674.4
-## 
-## Number of Fisher Scoring iterations: 15
    +## # A tibble: 3 x 5 +## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) -2.06 0.479 -4.30 1.73e- 5 +## 2 year -0.000273 0.0000319 -8.57 1.03e-17 +## 3 weighted_probs 0.0724 0.0948 0.763 4.45e- 1 
    inte_list <- lapply(large_regions, inte_lm)
    ## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    -
    lapply(inte_list, summary)
    +
    lapply(inte_list, broom::tidy)
    ## [[1]]
-## 
-## Call:
-## glm(formula = type ~ year * weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.5406  -0.0661  -0.0589  -0.0514   3.7625  
-## 
-## Coefficients:
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  2.261e+03   0.007    0.994
-## year1994-01-01                 1.551e-07  1.105e+04   0.000    1.000
-## year1995-01-01                 1.703e-05  3.833e+05   0.000    1.000
-## year1996-01-01                 1.549e-07  3.070e+03   0.000    1.000
-## year1997-01-01                 1.551e-07  2.483e+03   0.000    1.000
-## year1998-01-01                 1.547e-07  2.445e+03   0.000    1.000
-## year1999-01-01                 1.550e-07  2.418e+03   0.000    1.000
-## year2000-01-01                 1.547e-07  2.391e+03   0.000    1.000
-## year2001-01-01                 1.548e-07  2.417e+03   0.000    1.000
-## year2002-01-01                -2.120e+01  2.261e+03  -0.009    0.993
-## year2003-01-01                -2.155e+01  2.261e+03  -0.010    0.992
-## year2004-01-01                -2.202e+01  2.261e+03  -0.010    0.992
-## year2005-01-01                -2.241e+01  2.261e+03  -0.010    0.992
-## year2006-01-01                -2.251e+01  2.261e+03  -0.010    0.992
-## year2007-01-01                -2.248e+01  2.261e+03  -0.010    0.992
-## year2008-01-01                -2.269e+01  2.261e+03  -0.010    0.992
-## year2009-01-01                -2.251e+01  2.261e+03  -0.010    0.992
-## year2010-01-01                -2.283e+01  2.261e+03  -0.010    0.992
-## year2011-01-01                -2.295e+01  2.261e+03  -0.010    0.992
-## year2012-01-01                -2.290e+01  2.261e+03  -0.010    0.992
-## year2013-01-01                -2.292e+01  2.261e+03  -0.010    0.992
-## year2014-01-01                -2.320e+01  2.261e+03  -0.010    0.992
-## year2015-01-01                -2.315e+01  2.261e+03  -0.010    0.992
-## year2016-01-01                -2.290e+01  2.261e+03  -0.010    0.992
-## year2017-01-01                -2.333e+01  2.261e+03  -0.010    0.992
-## year2018-01-01                -2.335e+01  2.261e+03  -0.010    0.992
-## year2019-01-01                -2.347e+01  2.261e+03  -0.010    0.992
-## weighted_probs                 6.439e-07  3.794e+03   0.000    1.000
-## year1994-01-01:weighted_probs -6.445e-07  1.374e+04   0.000    1.000
-## year1995-01-01:weighted_probs -1.794e-05  3.928e+05   0.000    1.000
-## year1996-01-01:weighted_probs -6.439e-07  4.804e+03   0.000    1.000
-## year1997-01-01:weighted_probs -6.445e-07  4.207e+03   0.000    1.000
-## year1998-01-01:weighted_probs -6.440e-07  4.197e+03   0.000    1.000
-## year1999-01-01:weighted_probs -6.441e-07  4.092e+03   0.000    1.000
-## year2000-01-01:weighted_probs -6.438e-07  4.207e+03   0.000    1.000
-## year2001-01-01:weighted_probs -6.437e-07  4.076e+03   0.000    1.000
-## year2002-01-01:weighted_probs  1.041e-02  3.794e+03   0.000    1.000
-## year2003-01-01:weighted_probs  8.070e-02  3.794e+03   0.000    1.000
-## year2004-01-01:weighted_probs  2.619e-01  3.794e+03   0.000    1.000
-## year2005-01-01:weighted_probs  3.303e-01  3.794e+03   0.000    1.000
-## year2006-01-01:weighted_probs  1.454e-01  3.794e+03   0.000    1.000
-## year2007-01-01:weighted_probs  3.719e-01  3.794e+03   0.000    1.000
-## year2008-01-01:weighted_probs  9.020e-02  3.794e+03   0.000    1.000
-## year2009-01-01:weighted_probs  2.450e-01  3.794e+03   0.000    1.000
-## year2010-01-01:weighted_probs  1.140e-01  3.794e+03   0.000    1.000
-## year2011-01-01:weighted_probs  1.726e-01  3.794e+03   0.000    1.000
-## year2012-01-01:weighted_probs -5.235e-02  3.794e+03   0.000    1.000
-## year2013-01-01:weighted_probs  1.263e-01  3.794e+03   0.000    1.000
-## year2014-01-01:weighted_probs  2.076e-01  3.794e+03   0.000    1.000
-## year2015-01-01:weighted_probs  3.125e-01  3.794e+03   0.000    1.000
-## year2016-01-01:weighted_probs  2.667e-01  3.794e+03   0.000    1.000
-## year2017-01-01:weighted_probs  3.387e-01  3.794e+03   0.000    1.000
-## year2018-01-01:weighted_probs  4.747e-01  3.794e+03   0.000    1.000
-## year2019-01-01:weighted_probs -1.915e-01  3.794e+03   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4607.9  on 163921  degrees of freedom
-## AIC: 4715.9
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         -2.11      0.507      -4.17    3.06e- 5
+## 2 year                -0.000272  0.0000338  -8.05    8.48e-16
+## 3 weighted_probs       0.00264   0.525       0.00502 9.96e- 1
+## 4 year:weighted_probs  0.0000131 0.0000353   0.370   7.11e- 1
 ## 
 ## [[2]]
-## 
-## Call:
-## glm(formula = type ~ year * weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.1634  -0.0704  -0.0618  -0.0518   4.1800  
-## 
-## Coefficients:
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  1.839e+03   0.009    0.993
-## year1994-01-01                 5.534e-06  2.678e+03   0.000    1.000
-## year1995-01-01                 5.534e-06  3.525e+03   0.000    1.000
-## year1996-01-01                 5.534e-06  2.371e+03   0.000    1.000
-## year1997-01-01                 5.534e-06  2.011e+03   0.000    1.000
-## year1998-01-01                 5.534e-06  2.058e+03   0.000    1.000
-## year1999-01-01                 5.534e-06  1.966e+03   0.000    1.000
-## year2000-01-01                 5.534e-06  1.967e+03   0.000    1.000
-## year2001-01-01                 5.534e-06  2.002e+03   0.000    1.000
-## year2002-01-01                -2.113e+01  1.839e+03  -0.011    0.991
-## year2003-01-01                -2.147e+01  1.839e+03  -0.012    0.991
-## year2004-01-01                -2.165e+01  1.839e+03  -0.012    0.991
-## year2005-01-01                -2.218e+01  1.839e+03  -0.012    0.990
-## year2006-01-01                -2.222e+01  1.839e+03  -0.012    0.990
-## year2007-01-01                -2.229e+01  1.839e+03  -0.012    0.990
-## year2008-01-01                -2.272e+01  1.839e+03  -0.012    0.990
-## year2009-01-01                -2.210e+01  1.839e+03  -0.012    0.990
-## year2010-01-01                -2.249e+01  1.839e+03  -0.012    0.990
-## year2011-01-01                -2.272e+01  1.839e+03  -0.012    0.990
-## year2012-01-01                -2.258e+01  1.839e+03  -0.012    0.990
-## year2013-01-01                -2.291e+01  1.839e+03  -0.012    0.990
-## year2014-01-01                -2.265e+01  1.839e+03  -0.012    0.990
-## year2015-01-01                -2.271e+01  1.839e+03  -0.012    0.990
-## year2016-01-01                -2.250e+01  1.839e+03  -0.012    0.990
-## year2017-01-01                -2.304e+01  1.839e+03  -0.013    0.990
-## year2018-01-01                -2.283e+01  1.839e+03  -0.012    0.990
-## year2019-01-01                -2.312e+01  1.839e+03  -0.013    0.990
-## weighted_probs                 5.664e-04  1.271e+05   0.000    1.000
-## year1994-01-01:weighted_probs -5.664e-04  1.303e+05   0.000    1.000
-## year1995-01-01:weighted_probs -5.665e-04  1.316e+06   0.000    1.000
-## year1996-01-01:weighted_probs -5.664e-04  1.673e+05   0.000    1.000
-## year1997-01-01:weighted_probs -5.664e-04  1.320e+05   0.000    1.000
-## year1998-01-01:weighted_probs -5.664e-04  1.552e+05   0.000    1.000
-## year1999-01-01:weighted_probs -5.664e-04  1.297e+05   0.000    1.000
-## year2000-01-01:weighted_probs -5.664e-04  1.271e+05   0.000    1.000
-## year2001-01-01:weighted_probs -5.664e-04  1.496e+05   0.000    1.000
-## year2002-01-01:weighted_probs -9.616e-01  1.271e+05   0.000    1.000
-## year2003-01-01:weighted_probs -4.927e-01  1.271e+05   0.000    1.000
-## year2004-01-01:weighted_probs -1.039e+01  1.271e+05   0.000    1.000
-## year2005-01-01:weighted_probs -1.171e+00  1.271e+05   0.000    1.000
-## year2006-01-01:weighted_probs -4.484e+00  1.271e+05   0.000    1.000
-## year2007-01-01:weighted_probs -4.192e-01  1.271e+05   0.000    1.000
-## year2008-01-01:weighted_probs  2.439e-01  1.271e+05   0.000    1.000
-## year2009-01-01:weighted_probs -1.025e+01  1.271e+05   0.000    1.000
-## year2010-01-01:weighted_probs -7.067e+00  1.271e+05   0.000    1.000
-## year2011-01-01:weighted_probs -1.600e+00  1.271e+05   0.000    1.000
-## year2012-01-01:weighted_probs -5.715e+00  1.271e+05   0.000    1.000
-## year2013-01-01:weighted_probs  7.159e-02  1.271e+05   0.000    1.000
-## year2014-01-01:weighted_probs -2.094e+01  1.271e+05   0.000    1.000
-## year2015-01-01:weighted_probs -4.384e+00  1.271e+05   0.000    1.000
-## year2016-01-01:weighted_probs -3.331e+00  1.271e+05   0.000    1.000
-## year2017-01-01:weighted_probs -1.043e+00  1.271e+05   0.000    1.000
-## year2018-01-01:weighted_probs -4.340e+00  1.271e+05   0.000    1.000
-## year2019-01-01:weighted_probs -2.436e+00  1.271e+05   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4516.4  on 163921  degrees of freedom
-## AIC: 4624.4
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 4 x 5
+##   term                 estimate std.error statistic  p.value
+##   <chr>                   <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         -2.53     0.506        -5.01  5.54e- 7
+## 2 year                -0.000227 0.0000338    -6.71  1.94e-11
+## 3 weighted_probs       1.96     2.45          0.800 4.24e- 1
+## 4 year:weighted_probs -0.000239 0.000164     -1.46  1.44e- 1
 ## 
 ## [[3]]
-## 
-## Call:
-## glm(formula = type ~ year * weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.4562  -0.0634  -0.0575  -0.0511   3.7671  
-## 
-## Coefficients:
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  1.727e+03   0.010    0.992
-## year1994-01-01                 7.478e-08  2.308e+04   0.000    1.000
-## year1995-01-01                -5.512e-08  5.655e+04   0.000    1.000
-## year1996-01-01                 8.130e-08  2.388e+03   0.000    1.000
-## year1997-01-01                 8.129e-08  1.961e+03   0.000    1.000
-## year1998-01-01                 8.110e-08  1.966e+03   0.000    1.000
-## year1999-01-01                 8.127e-08  1.905e+03   0.000    1.000
-## year2000-01-01                 8.109e-08  1.885e+03   0.000    1.000
-## year2001-01-01                 8.096e-08  1.945e+03   0.000    1.000
-## year2002-01-01                -2.120e+01  1.727e+03  -0.012    0.990
-## year2003-01-01                -2.155e+01  1.727e+03  -0.012    0.990
-## year2004-01-01                -2.190e+01  1.727e+03  -0.013    0.990
-## year2005-01-01                -2.232e+01  1.727e+03  -0.013    0.990
-## year2006-01-01                -2.258e+01  1.727e+03  -0.013    0.990
-## year2007-01-01                -2.217e+01  1.727e+03  -0.013    0.990
-## year2008-01-01                -2.247e+01  1.727e+03  -0.013    0.990
-## year2009-01-01                -2.245e+01  1.727e+03  -0.013    0.990
-## year2010-01-01                -2.278e+01  1.727e+03  -0.013    0.989
-## year2011-01-01                -2.297e+01  1.727e+03  -0.013    0.989
-## year2012-01-01                -2.298e+01  1.727e+03  -0.013    0.989
-## year2013-01-01                -2.286e+01  1.727e+03  -0.013    0.989
-## year2014-01-01                -2.304e+01  1.727e+03  -0.013    0.989
-## year2015-01-01                -2.312e+01  1.727e+03  -0.013    0.989
-## year2016-01-01                -2.291e+01  1.727e+03  -0.013    0.989
-## year2017-01-01                -2.321e+01  1.727e+03  -0.013    0.989
-## year2018-01-01                -2.332e+01  1.727e+03  -0.014    0.989
-## year2019-01-01                -2.366e+01  1.727e+03  -0.014    0.989
-## weighted_probs                 1.938e-06  4.121e+03   0.000    1.000
-## year1994-01-01:weighted_probs -1.570e-06  1.402e+06   0.000    1.000
-## year1995-01-01:weighted_probs  2.381e-05  1.069e+07   0.000    1.000
-## year1996-01-01:weighted_probs -1.939e-06  5.341e+03   0.000    1.000
-## year1997-01-01:weighted_probs -1.938e-06  4.763e+03   0.000    1.000
-## year1998-01-01:weighted_probs -1.938e-06  4.612e+03   0.000    1.000
-## year1999-01-01:weighted_probs -1.938e-06  4.443e+03   0.000    1.000
-## year2000-01-01:weighted_probs -1.938e-06  4.469e+03   0.000    1.000
-## year2001-01-01:weighted_probs -1.938e-06  4.384e+03   0.000    1.000
-## year2002-01-01:weighted_probs  3.218e-02  4.121e+03   0.000    1.000
-## year2003-01-01:weighted_probs  8.794e-02  4.121e+03   0.000    1.000
-## year2004-01-01:weighted_probs -9.221e-02  4.121e+03   0.000    1.000
-## year2005-01-01:weighted_probs  5.542e-02  4.121e+03   0.000    1.000
-## year2006-01-01:weighted_probs  2.826e-01  4.121e+03   0.000    1.000
-## year2007-01-01:weighted_probs -7.673e-01  4.121e+03   0.000    1.000
-## year2008-01-01:weighted_probs -8.470e-01  4.121e+03   0.000    1.000
-## year2009-01-01:weighted_probs  3.223e-02  4.121e+03   0.000    1.000
-## year2010-01-01:weighted_probs -8.737e-02  4.121e+03   0.000    1.000
-## year2011-01-01:weighted_probs  1.924e-01  4.121e+03   0.000    1.000
-## year2012-01-01:weighted_probs  2.004e-01  4.121e+03   0.000    1.000
-## year2013-01-01:weighted_probs -1.344e-01  4.121e+03   0.000    1.000
-## year2014-01-01:weighted_probs -4.055e-01  4.121e+03   0.000    1.000
-## year2015-01-01:weighted_probs  1.523e-01  4.121e+03   0.000    1.000
-## year2016-01-01:weighted_probs  2.242e-01  4.121e+03   0.000    1.000
-## year2017-01-01:weighted_probs -1.849e-01  4.121e+03   0.000    1.000
-## year2018-01-01:weighted_probs  2.411e-01  4.121e+03   0.000    1.000
-## year2019-01-01:weighted_probs  4.947e-01  4.121e+03   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4609.4  on 163921  degrees of freedom
-## AIC: 4717.4
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         -1.87      0.538        -3.47  5.20e- 4
+## 2 year                -0.000286  0.0000358    -8.01  1.19e-15
+## 3 weighted_probs      -0.587     0.779        -0.753 4.51e- 1
+## 4 year:weighted_probs  0.0000441 0.0000511     0.862 3.89e- 1
 ## 
 ## [[4]]
-## 
-## Call:
-## glm(formula = type ~ year * weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.3124  -0.0638  -0.0592  -0.0506   3.7349  
-## 
-## Coefficients:
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  2.111e+03   0.008    0.994
-## year1994-01-01                 7.554e-07  3.745e+03   0.000    1.000
-## year1995-01-01                 7.562e-07  6.822e+03   0.000    1.000
-## year1996-01-01                 7.560e-07  2.790e+03   0.000    1.000
-## year1997-01-01                 7.560e-07  2.296e+03   0.000    1.000
-## year1998-01-01                 7.562e-07  2.334e+03   0.000    1.000
-## year1999-01-01                 7.563e-07  2.246e+03   0.000    1.000
-## year2000-01-01                 7.562e-07  2.256e+03   0.000    1.000
-## year2001-01-01                 7.561e-07  2.238e+03   0.000    1.000
-## year2002-01-01                -2.124e+01  2.111e+03  -0.010    0.992
-## year2003-01-01                -2.147e+01  2.111e+03  -0.010    0.992
-## year2004-01-01                -2.195e+01  2.111e+03  -0.010    0.992
-## year2005-01-01                -2.220e+01  2.111e+03  -0.011    0.992
-## year2006-01-01                -2.232e+01  2.111e+03  -0.011    0.992
-## year2007-01-01                -2.224e+01  2.111e+03  -0.011    0.992
-## year2008-01-01                -2.272e+01  2.111e+03  -0.011    0.991
-## year2009-01-01                -2.248e+01  2.111e+03  -0.011    0.992
-## year2010-01-01                -2.289e+01  2.111e+03  -0.011    0.991
-## year2011-01-01                -2.288e+01  2.111e+03  -0.011    0.991
-## year2012-01-01                -2.293e+01  2.111e+03  -0.011    0.991
-## year2013-01-01                -2.287e+01  2.111e+03  -0.011    0.991
-## year2014-01-01                -2.322e+01  2.111e+03  -0.011    0.991
-## year2015-01-01                -2.311e+01  2.111e+03  -0.011    0.991
-## year2016-01-01                -2.286e+01  2.111e+03  -0.011    0.991
-## year2017-01-01                -2.333e+01  2.111e+03  -0.011    0.991
-## year2018-01-01                -2.335e+01  2.111e+03  -0.011    0.991
-## year2019-01-01                -2.354e+01  2.111e+03  -0.011    0.991
-## weighted_probs                 2.123e-06  5.917e+03   0.000    1.000
-## year1994-01-01:weighted_probs -2.119e-06  2.323e+04   0.000    1.000
-## year1995-01-01:weighted_probs -2.134e-06  3.764e+05   0.000    1.000
-## year1996-01-01:weighted_probs -2.121e-06  1.749e+04   0.000    1.000
-## year1997-01-01:weighted_probs -2.123e-06  6.287e+03   0.000    1.000
-## year1998-01-01:weighted_probs -2.124e-06  6.281e+03   0.000    1.000
-## year1999-01-01:weighted_probs -2.124e-06  6.185e+03   0.000    1.000
-## year2000-01-01:weighted_probs -2.124e-06  6.436e+03   0.000    1.000
-## year2001-01-01:weighted_probs -2.123e-06  6.679e+03   0.000    1.000
-## year2002-01-01:weighted_probs  1.885e-01  5.917e+03   0.000    1.000
-## year2003-01-01:weighted_probs -2.208e-01  5.917e+03   0.000    1.000
-## year2004-01-01:weighted_probs  6.477e-02  5.917e+03   0.000    1.000
-## year2005-01-01:weighted_probs -4.335e-01  5.917e+03   0.000    1.000
-## year2006-01-01:weighted_probs -7.937e-01  5.917e+03   0.000    1.000
-## year2007-01-01:weighted_probs -5.026e-01  5.917e+03   0.000    1.000
-## year2008-01-01:weighted_probs  1.947e-01  5.917e+03   0.000    1.000
-## year2009-01-01:weighted_probs  1.551e-01  5.917e+03   0.000    1.000
-## year2010-01-01:weighted_probs  2.730e-01  5.917e+03   0.000    1.000
-## year2011-01-01:weighted_probs -9.967e-02  5.917e+03   0.000    1.000
-## year2012-01-01:weighted_probs  6.868e-02  5.917e+03   0.000    1.000
-## year2013-01-01:weighted_probs -1.147e-01  5.917e+03   0.000    1.000
-## year2014-01-01:weighted_probs  2.779e-01  5.917e+03   0.000    1.000
-## year2015-01-01:weighted_probs  1.308e-01  5.917e+03   0.000    1.000
-## year2016-01-01:weighted_probs  9.547e-02  5.917e+03   0.000    1.000
-## year2017-01-01:weighted_probs  2.577e-01  5.917e+03   0.000    1.000
-## year2018-01-01:weighted_probs  3.356e-01  5.917e+03   0.000    1.000
-## year2019-01-01:weighted_probs  1.585e-01  5.917e+03   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 5755.8  on 163974  degrees of freedom
-## Residual deviance: 4613.5  on 163921  degrees of freedom
-## AIC: 4721.5
-## 
-## Number of Fisher Scoring iterations: 15
    -
    for (i in 1:4){
-  print(anova(main_list[[i]], inte_list[[i]], test = 'Chisq'))
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         -1.80      0.527         -3.42 6.25e- 4
+## 2 year                -0.000290  0.0000351     -8.29 1.17e-16
+## 3 weighted_probs      -0.880     0.849         -1.04 3.00e- 1
+## 4 year:weighted_probs  0.0000628 0.0000544      1.15 2.48e- 1
    +
    for (i in 1:4) {
+  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
 }
    ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ year * weighted_probs
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163947     4611.5                     
-## 2    163921     4607.9 26   3.6735        1
+## 1    163886     4627.2                     
+## 2    163885     4627.1  1  0.14187   0.7064
 ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ year * weighted_probs
-##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)  
-## 1    163947     4560.8                       
-## 2    163921     4516.4 26   44.388  0.01373 *
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
+## 1    163886     4576.6                     
+## 2    163885     4574.6  1   2.0829    0.149
 ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ year * weighted_probs
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163947     4618.3                     
-## 2    163921     4609.4 26   8.8572   0.9993
+## 1    163886     4634.1                     
+## 2    163885     4633.4  1  0.74565   0.3879
 ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ year * weighted_probs
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1    163947     4618.4                     
-## 2    163921     4613.5 26   4.9708        1
    +## 1 163886 4634.3 +## 2 163885 4633.1 1 1.2103 0.2713

    Interaction terms do not predict type over and above the main effect of name origin probability and year (p > 0.01).

    Conclusion

    -

    A Celtic/English name has 1.2117973 the odds of being selected as an honoree, significantly higher compared to other names (\(\beta_\textrm{Celtic/English} =\) 0.1921, P = 0.00067307). An East Asian name has 0.1891745 the odds of being selected as an honoree, significantly lower than to other names (\(\beta_\textrm{East Asian} =\) -1.6651, P = 5.89e-09). The two groups of scientists did not have a significant association with names predicted to be European (P = 0.37934) or in Other categories (P = 0.43538).

    -
    -

    Supplementary Figure S5

    -

    It’s difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction. There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field. However, just because a nationality isn’t underrepresented against the field doesn’t mean scientists from that nationality are appropriately represented.

    -
    df2 <- iscb_pubmed_oth %>% 
-  filter(region != 'OtherCategories') %>% 
-  recode_region()
+
    A Celtic/English name has 1.2141832 the odds of being selected as an honoree, significantly higher compared to other names (\(\beta_\textrm{Celtic/English} =\) 0.19407, P = 0.00054646). An East Asian name has 0.1889996 the odds of being selected as an honoree, significantly lower than to other names (\(\beta_\textrm{East Asian} =\) -1.666, P = 6.0164e-09). The two groups of scientists did not have a significant association with names predicted to be European (P = 0.38583) or in Other categories (P = 0.44544).
    
+
    +
    +

    Alternative approach

    +

    Sincere thanks to the reviewers, Byron Smith and Katie Pollard, for their detailed suggestion with code.

    +

    The question of what unit one should use to perform this type of analyses is a difficult one. We present here an alternative analysis that treats names as units instead of honors and authorships. We caution that this approach does not distinguish scientists who were honored 4 times vs. one time and hence may yield a conservative estimate of disparity. Further, different authors may have the same names, and to sum adjusted_citations across them may not be optimal.

    +

    Nonetheless, the finding here is consistent with what we have seen above where East Asian names are underrepresented in the honoree group.

    +
    keynotes_post_2002 <- keynotes %>%
+  filter(year(year) >= 2002) %>%
+  separate_rows(afflcountries, sep = ",") %>%
+  filter(afflcountries == "United States") %>%
+  group_by(fore_name_simple, last_name_simple) %>%
+  summarise_at("year", n_distinct, na.rm = T)
+
+# nationalize_df was not unique, so the left join to corr_authors resulted
+# in (mostly) duplicate rows.
+# FIXME: I was getting occasional crashes on this line, and it's slow.
+# TRANG: fixed on June 3, 2021 using distinct().
+# Also, the duplication was intentional.
+# Please see our extensive discussion on the merge/join on full names vs
+# fore_name and last_name here:
+# <https://github.com/greenelab/iscb-diversity/issues/6>
+
+distinct_nationalize_df <- nationalize_df %>%
+  distinct(fore_name_simple, last_name_simple, .keep_all = TRUE)
+
+# Calculate sum of adjusted citations for all publications for a first-name/
+# last-name pair in db since 2002
+# where the author countries include US.
+authors <- corr_authors %>%
+  filter(year(year) >= 2002) %>%
+  separate_rows(countries, sep = ",") %>%
+  filter(countries == "US") %>%
+  group_by(fore_name_simple, last_name_simple) %>%
+  summarise_at(vars(adjusted_citations), sum, na.rm = T) %>%
+  left_join(
+    keynotes_post_2002[c("fore_name_simple", "last_name_simple", "year")],
+    by = c("fore_name_simple", "last_name_simple")
+  ) %>%
+  left_join(distinct_nationalize_df, by = c("fore_name_simple", "last_name_simple")) %>%
+  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African)
+
+for (large_region in large_regions) {
+  glm(
+    as.formula(paste("honoree ~ adjusted_citations +", large_region)),
+    data = authors %>% mutate(honoree = !is.na(year)),
+    family = "binomial",
+    control = list(epsilon = 1e-12, maxit = 55, trace = FALSE)
+  ) %>%
+    broom::tidy() %>%
+    print()
+}
    +
    ## # A tibble: 3 x 5
+##   term               estimate std.error statistic   p.value
+##   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)         -5.79     0.160      -36.2  2.15e-286
+## 2 adjusted_citations   0.0555   0.00426     13.0  8.68e- 39
+## 3 CelticEnglish        0.453    0.272        1.66 9.65e-  2
+## # A tibble: 3 x 5
+##   term               estimate std.error statistic  p.value
+##   <chr>                 <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)         -5.42     0.130      -41.5  0.      
+## 2 adjusted_citations   0.0566   0.00446     12.7  7.36e-37
+## 3 EastAsian           -3.18     1.02        -3.10 1.91e- 3
+## # A tibble: 3 x 5
+##   term               estimate std.error statistic   p.value
+##   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)         -5.75     0.154      -37.3  6.30e-304
+## 2 adjusted_citations   0.0560   0.00423     13.2  5.74e- 40
+## 3 European             0.393    0.294        1.33 1.82e-  1
+## # A tibble: 3 x 5
+##   term               estimate std.error statistic   p.value
+##   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)         -5.76     0.163      -35.2  6.52e-272
+## 2 adjusted_citations   0.0563   0.00425     13.3  3.70e- 40
+## 3 OtherCategories      0.357    0.299        1.19 2.33e-  1
    + + + + + + +
    +
    +

    Time lag

    +

    In this section, we show that a 10-year lag model results in a similar result for East Asian scientists’ names, even though the effect size is less striking. For example, if we assume that honors accrue 10 years after their most prolific year with respect to authorships, the proportion of honor associated with East Asian name origins in 2019 is still substantially less than the proportion of senior authorships associated with East Asian names in 2009.

    +
    year_lag <- period(10, "years")
+iscb_pubmed_oth_lag <- iscb_nat_df %>%
+  rename("journal" = conference) %>%
+  select(year, journal, African:SouthAsian, publication_date) %>%
+  mutate(
+    type = "Keynote speakers/Fellows",
+    adjusted_citations = 1,
+    pmid = -9999
+  ) %>%
+  bind_rows(
+    pubmed_nat_df %>%
+      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
+      mutate(type = "Pubmed authors", year = year + year_lag)
+  ) %>%
+  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
+  pivot_longer(c(African:SouthAsian, OtherCategories),
+    names_to = "region",
+    values_to = "probabilities"
+  ) %>%
+  filter(!is.na(probabilities), year(year) >= 2002) %>%
+  group_by(type, year, region) %>%
+  mutate(
+    pmc_citations_year = mean(adjusted_citations),
+    weight = adjusted_citations / pmc_citations_year,
+    weighted_probs = probabilities * weight
+  )
 
-fig_s5 <- iscb_pubmed_sum %>%
-  recode_region() %>%
-  gam_and_ci(df2 = df2,
-             start_y = start_year, end_y = end_year) +
-  theme(legend.position = c(0.8, 0.1)) +
-  facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
-fig_s5
    -
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    -

    -
    ggsave('figs/fig_s5.png', fig_s5, width = 6, height = 6)
    -
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    -
    ggsave('figs/fig_s5.svg', fig_s5, width = 6, height = 6)
    -
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    +iscb_lm_lag <- iscb_pubmed_oth_lag %>% + ungroup() %>% + mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors")) + +main_lm <- function(regioni) { + glm(type ~ year + weighted_probs, + data = iscb_lm_lag %>% + filter(region == regioni, !is.na(weighted_probs)), + family = "binomial" + ) +} + +main_list <- lapply(large_regions, main_lm) +names(main_list) <- large_regions +lapply(main_list, broom::tidy)     +
    ## $CelticEnglish
+## # A tibble: 3 x 5
+##   term           estimate std.error statistic   p.value
+##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)    29.1     1.18         24.7   2.46e-134
+## 2 year           -0.00210 0.0000749   -28.0   2.09e-172
+## 3 weighted_probs  0.0199  0.102         0.196 8.45e-  1
+## 
+## $EastAsian
+## # A tibble: 3 x 5
+##   term           estimate std.error statistic   p.value
+##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)    29.0     1.18          24.6  5.89e-134
+## 2 year           -0.00209 0.0000749    -27.8  1.41e-170
+## 3 weighted_probs -0.708   0.292         -2.43 1.52e-  2
+## 
+## $European
+## # A tibble: 3 x 5
+##   term           estimate std.error statistic   p.value
+##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)    29.1     1.18        24.7    4.84e-135
+## 2 year           -0.00210 0.0000748  -28.0    7.61e-173
+## 3 weighted_probs  0.00832 0.106        0.0788 9.37e-  1
+## 
+## $OtherCategories
+## # A tibble: 3 x 5
+##   term           estimate std.error statistic   p.value
+##   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+## 1 (Intercept)    29.1     1.18          24.7  3.41e-135
+## 2 year           -0.00210 0.0000748    -28.1  3.25e-173
+## 3 weighted_probs  0.170   0.106          1.60 1.09e-  1
    +
    +
    +

    Supplementary Figure S5

    +

    It’s difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction. There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field. However, just because a nationality isn’t underrepresented against the field doesn’t mean scientists from that nationality are appropriately represented.

    +
    # df2 <- iscb_pubmed_oth %>%
+#   filter(region != "OtherCategories") %>%
+#   recode_region()
+# 
+# fig_s5 <- iscb_pubmed_sum %>%
+#   recode_region() %>%
+#   gam_and_ci(
+#     df2 = df2,
+#     start_y = start_year, end_y = end_year
+#   ) +
+#   theme(legend.position = c(0.8, 0.1)) +
+#   facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
+# fig_s5
+# ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6)
+# ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6)
    sessionInfo()
    ## R version 4.0.3 (2020-10-10)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
@@ -2411,52 +2140,63 @@ 
    Supplementary Figure S5
    
 ## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
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+##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
+##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
+##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
+##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
+## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-##  [1] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0 lubridate_1.7.9.2  
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    -
    ---
title: "Representation analysis of name origins"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
source('utils/r-utils.R')
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
alpha_threshold <- qnorm(0.975)
load('Rdata/raws.Rdata')

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>% 
  left_join(nationalize_df, by = c('fore_name', 'last_name')) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  left_join(nationalize_df, by = c('fore_name', 'last_name'))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names')
region_levels_let <- toupper(letters[1:8])
region_cols <- c('#ffffb3', '#fccde5', '#b3de69', '#fdb462', '#80b1d3', '#8dd3c7', '#bebada', '#fb8072', '#bc80bd', '#ccebc5')
```

Names grouping:
```{r warning=FALSE, fig.height = 3}
our_country_map <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv') %>%
  rename('region' = Region) %>%
  recode_region()

my_world <- world %>%
  select(- geometry) %>% 
  rename(Country = 'name') %>%
  left_join(our_country_map, by = 'Country')

(gworld <- ggplot(data = my_world) +
    geom_sf(aes(fill = fct_relevel(region, region_levels))) +
    coord_sf(crs = "+proj=eqearth +wktext") +
    scale_fill_manual(values = region_cols,
                      na.translate = FALSE) +
    theme(panel.background = element_rect(fill = "azure"),
          legend.title = element_blank(),
          legend.position = 'bottom',
          panel.border = element_rect(fill = NA)))

ggsave('figs/2020-01-31_groupings.png', gworld, width = 7.2, height = 4.3)
ggsave('figs/2020-01-31_groupings.svg', gworld, width = 7.2, height = 4.3)
```


## Descriptive statistics
Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename('journal' = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(type = 'Keynote speakers/Fellows',
         adjusted_citations = 1,
         pmid = -9999) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = 'Pubmed authors')
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
               names_to = 'region',
               values_to = 'probabilities') %>%
  filter(!is.na(probabilities)) %>% 
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations/pmc_citations_year,
    weighted_probs = probabilities*weight
  ) 

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% 
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = 'drop'
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != 'OtherCategories')
```

## Prepare data frames for analysis

### By conference keynotes/fellows
```{r fig.height=6}
i <- 0
iscb_nat <- vector('list', length = n_confs)

for (conf in my_confs){
  i <- i + 1
  iscb_nat[[i]] <- iscb_pubmed_oth %>%
    filter(region != 'OtherCategories', type != 'Pubmed authors' & journal == conf) %>%
    group_by(type, year, region, journal) %>%
    summarise(mean_prob = mean(weighted_probs), .groups = 'drop') %>%
    {.}
}
```

```{r}
save(my_world, iscb_pubmed_oth, iscb_nat, file = 'Rdata/iscb-pubmed_nat.Rdata')
```

## Figures for paper

### Figure 4

Compared to the name collection of Pubmed authors, honorees with Celtic/English names are overrepresented while honorees with East Asian names are underrepresented.

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_4a <- iscb_pubmed_sum %>%
  filter(year < '2020-01-01') %>%
  region_breakdown('main', region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2)) +
  theme(legend.position = 'bottom')

large_regions <- c('CelticEnglish', 'EastAsian', 'European', 'OtherCategories')
## Mean and standard deviation of predicted probabilities:
fig_4b <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>% 
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year) + 
  theme(legend.position = c(0.88, 0.83),
        panel.grid.minor = element_blank(),
        legend.margin = margin(-0.5, 0, 0, 0, unit='cm'),
        legend.text = element_text(size = 7)) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = 'AUTO', ncol = 1, rel_heights = c(1.3,1))
fig_4
ggsave('figs/region_breakdown.png', fig_4, width = 6.7, height = 5.5)
ggsave('figs/region_breakdown.svg', fig_4, width = 6.7, height = 5.5)
```


## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>% 
  ungroup() %>% 
  mutate(
    # year = c(scale(year(year))),
    year = as.factor(year),
    type = as.factor(type) %>% relevel(ref = 'Pubmed authors'))
main_lm <- function(regioni){
  glm(type ~ year + weighted_probs, 
      data = iscb_lm %>% 
        filter(region == regioni, !is.na(probabilities)) ,
      family = 'binomial')
}

inte_lm <- function(regioni){
  glm(type ~ year * weighted_probs, 
      data = iscb_lm %>% 
        filter(region == regioni, !is.na(weighted_probs)),
      family = 'binomial')
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, summary)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, summary)
for (i in 1:4){
  print(anova(main_list[[i]], inte_list[[i]], test = 'Chisq'))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_p <- function(i, colu){
  broom::tidy(main_list[[i]]) %>% 
    filter(term == 'weighted_probs') %>% 
    pull(colu) 
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion
A Celtic/English name has `r exp(get_p(1, 'estimate'))` the odds of being selected as an honoree, significantly higher compared to other names ($\beta_\textrm{Celtic/English} =$ `r print_p(get_p(1, 'estimate'))`, _P_ = `r print_p(get_p(1, 'p.value'))`).
An East Asian name has `r exp(get_p(2, 'estimate'))` the odds of being selected as an honoree, significantly lower than to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_p(2, 'estimate'))`, _P_ = `r print_p(get_p(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be European (_P_ = `r print_p(get_p(3, 'p.value'))`) or in Other categories (_P_ = `r print_p(get_p(4, 'p.value'))`).

### Supplementary Figure S5 {#sup_fig_s5}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
df2 <- iscb_pubmed_oth %>% 
  filter(region != 'OtherCategories') %>% 
  recode_region()

fig_s5 <- iscb_pubmed_sum %>%
  recode_region() %>%
  gam_and_ci(df2 = df2,
             start_y = start_year, end_y = end_year) +
  theme(legend.position = c(0.8, 0.1)) +
  facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
fig_s5
ggsave('figs/fig_s5.png', fig_s5, width = 6, height = 6)
ggsave('figs/fig_s5.svg', fig_s5, width = 6, height = 6)
```


```{r}
sessionInfo()
```

    +
    ---
title: "Representation analysis of name origins"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(rnaturalearth)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
alpha_threshold <- qnorm(0.975)
load("Rdata/raws.Rdata")

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  left_join(nationalize_df, by = c("fore_name", "last_name"))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")
region_levels_let <- toupper(letters[1:8])
region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
```

Names grouping:
```{r warning=FALSE, fig.height = 3}
our_country_map <- read_tsv("https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/country_to_region_NamePrism.tsv") %>%
  rename("region" = Region) %>%
  recode_region()

my_world <- world %>%
  select(-geometry) %>%
  rename(Country = "name") %>%
  left_join(our_country_map, by = "Country")

(gworld <- ggplot(data = my_world) +
  geom_sf(aes(fill = fct_relevel(region, region_levels))) +
  coord_sf(crs = "+proj=eqearth +wktext") +
  scale_fill_manual(
    values = region_cols,
    na.translate = FALSE
  ) +
  theme(
    panel.background = element_rect(fill = "azure"),
    legend.title = element_blank(),
    legend.position = "bottom",
    panel.border = element_rect(fill = NA)
  ))

ggsave("figs/2020-01-31_groupings.png", gworld, width = 7.2, height = 4.3)
ggsave("figs/2020-01-31_groupings.svg", gworld, width = 7.2, height = 4.3)
```


## Descriptive statistics
Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1,
    pmid = -9999
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
  )

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != "OtherCategories")
```

## Prepare data frames for analysis

### By conference keynotes/fellows
```{r fig.height=6}
i <- 0
iscb_nat <- vector("list", length = n_confs)

for (conf in my_confs) {
  i <- i + 1
  iscb_nat[[i]] <- iscb_pubmed_oth %>%
    filter(region != "OtherCategories", type != "Pubmed authors" & journal == conf) %>%
    group_by(type, year, region, journal) %>%
    summarise(mean_prob = mean(weighted_probs), .groups = "drop")
}
```

```{r}
save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata")
```

## Figures for paper

### Figure 4

Compared to the name collection of Pubmed authors, honorees with Celtic/English names are overrepresented while honorees with East Asian names are underrepresented.

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_4a <- iscb_pubmed_sum %>%
  filter(year < "2020-01-01") %>%
  region_breakdown("main", region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2)) +
  theme(legend.position = "bottom")

large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")
## Mean and standard deviation of predicted probabilities:
fig_4b <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>%
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year
  ) +
  theme(
    legend.position = c(0.88, 0.83),
    panel.grid.minor = element_blank(),
    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
    legend.text = element_text(size = 7)
  ) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_4 <- cowplot::plot_grid(fig_4a, fig_4b, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
fig_4
ggsave("figs/region_breakdown.png", fig_4, width = 6.7, height = 5.5)
ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)
```


## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>%
  ungroup() %>%
  mutate(
    # year = c(scale(year(year))),
    # year = as.factor(year),
    type = as.factor(type) %>% relevel(ref = "Pubmed authors")
  )
main_lm <- function(regioni) {
  glm(type ~ year + weighted_probs,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
    family = "binomial"
  )
}

inte_lm <- function(regioni) {
  glm(type ~ year * weighted_probs,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, broom::tidy)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
for (i in 1:4) {
  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_p <- function(i, colu) {
  broom::tidy(main_list[[i]]) %>%
    filter(term == "weighted_probs") %>%
    pull(colu)
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion
A Celtic/English name has `r exp(get_p(1, 'estimate'))` the odds of being selected as an honoree, significantly higher compared to other names ($\beta_\textrm{Celtic/English} =$ `r print_p(get_p(1, 'estimate'))`, _P_ = `r print_p(get_p(1, 'p.value'))`).
An East Asian name has `r exp(get_p(2, 'estimate'))` the odds of being selected as an honoree, significantly lower than to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_p(2, 'estimate'))`, _P_ = `r print_p(get_p(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be European (_P_ = `r print_p(get_p(3, 'p.value'))`) or in Other categories (_P_ = `r print_p(get_p(4, 'p.value'))`).


## Alternative approach
_Sincere thanks to the reviewers, Byron Smith and Katie Pollard, for their detailed suggestion with code._

The question of what unit one should use to perform this type of analyses is a difficult one.
We present here an alternative analysis that treats _names_ as units instead of _honors_ and _authorships_.
We caution that this approach does not distinguish scientists who were honored 4 times vs. one time
and hence may yield a conservative estimate of disparity.
Further, different authors may have the same names, 
and to sum `adjusted_citations` across them may not be optimal.

Nonetheless, the finding here is consistent with what we have seen above
where East Asian names are underrepresented in the honoree group.

```{r}

keynotes_post_2002 <- keynotes %>%
  filter(year(year) >= 2002) %>%
  separate_rows(afflcountries, sep = ",") %>%
  filter(afflcountries == "United States") %>%
  group_by(fore_name_simple, last_name_simple) %>%
  summarise_at("year", n_distinct, na.rm = T)

# nationalize_df was not unique, so the left join to corr_authors resulted
# in (mostly) duplicate rows.
# FIXME: I was getting occasional crashes on this line, and it's slow.
# TRANG: fixed on June 3, 2021 using distinct().
# Also, the duplication was intentional.
# Please see our extensive discussion on the merge/join on full names vs
# fore_name and last_name here:
# <https://github.com/greenelab/iscb-diversity/issues/6>

distinct_nationalize_df <- nationalize_df %>%
  distinct(fore_name_simple, last_name_simple, .keep_all = TRUE)

# Calculate sum of adjusted citations for all publications for a first-name/
# last-name pair in db since 2002
# where the author countries include US.
authors <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  separate_rows(countries, sep = ",") %>%
  filter(countries == "US") %>%
  group_by(fore_name_simple, last_name_simple) %>%
  summarise_at(vars(adjusted_citations), sum, na.rm = T) %>%
  left_join(
    keynotes_post_2002[c("fore_name_simple", "last_name_simple", "year")],
    by = c("fore_name_simple", "last_name_simple")
  ) %>%
  left_join(distinct_nationalize_df, by = c("fore_name_simple", "last_name_simple")) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African)

for (large_region in large_regions) {
  glm(
    as.formula(paste("honoree ~ adjusted_citations +", large_region)),
    data = authors %>% mutate(honoree = !is.na(year)),
    family = "binomial",
    control = list(epsilon = 1e-12, maxit = 55, trace = FALSE)
  ) %>%
    broom::tidy() %>%
    print()
}
```

<!-- We argue that honors and authorships are the appropriate units. -->
<!-- Although this approach may not satisfy the independent and identically distributed assumption -->

<!-- honor vs not honored. -->
<!-- But then this is considering scientists who have 4 rows of honorees as one  -->
<!-- Is this fair? -->
<!-- 195 rows/honors of keynotes post 2002 to 145 names/scientists. -->

## Time lag

In this section, we show that a 10-year lag model results in a similar result for East Asian scientists' names,
even though the effect size is less striking.
For example, if we assume that honors accrue 10 years after their most prolific year with respect to authorships, 
the proportion of honor associated with East Asian name origins in 2019 is still substantially less than the proportion of senior authorships associated with East Asian names in 2009.

```{r}
year_lag <- period(10, "years")
iscb_pubmed_oth_lag <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1,
    pmid = -9999
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(pmid, year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors", year = year + year_lag)
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities), year(year) >= 2002) %>%
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
  )

iscb_lm_lag <- iscb_pubmed_oth_lag %>%
  ungroup() %>%
  mutate(type = as.factor(type) %>% relevel(ref = "Pubmed authors"))

main_lm <- function(regioni) {
  glm(type ~ year + weighted_probs,
    data = iscb_lm_lag %>%
      filter(region == regioni, !is.na(weighted_probs)),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)
names(main_list) <- large_regions
lapply(main_list, broom::tidy)
```

```{r include=FALSE, eval = FALSE}
checkdf <- iscb_lm %>%
  filter(
    year(year) == 2010,
    adjusted_citations > 3.1, 
    adjusted_citations < 3.32, 
    region == "EastAsian", 
    probabilities > 0.5
  )
```


## Supplementary Figure S5 {#sup_fig_s5}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
# df2 <- iscb_pubmed_oth %>%
#   filter(region != "OtherCategories") %>%
#   recode_region()
# 
# fig_s5 <- iscb_pubmed_sum %>%
#   recode_region() %>%
#   gam_and_ci(
#     df2 = df2,
#     start_y = start_year, end_y = end_year
#   ) +
#   theme(legend.position = c(0.8, 0.1)) +
#   facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
# fig_s5
# ggsave("figs/fig_s5.png", fig_s5, width = 6, height = 6)
# ggsave("figs/fig_s5.svg", fig_s5, width = 6, height = 6)
```


```{r}
sessionInfo()
```

    diff --git a/docs/12.analyze-affiliation.html b/docs/12.analyze-affiliation.html index 10092e1..9f5c206 100644 --- a/docs/12.analyze-affiliation.html +++ b/docs/12.analyze-affiliation.html @@ -4338,8 +4338,8 @@

    Country enrichment table

    formatPercentage('Author proportion', 2) %>% formatRound(c('Observed', 'Expected', 'Observed - Expected', 'Enrichment', 'Log2(enrichment)'), 1)     -
    - +
    +

    Compute enrichment from proportion comparisons

    Adapted from epitools::riskratio().

    @@ -4444,49 +4444,60 @@

    Log enrichment figure

    ## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so ## ## locale: -## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 -## [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C -## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C -## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C +## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C +## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 +## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C +## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C +## [9] LC_ADDRESS=C LC_TELEPHONE=C +## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: -## [1] DT_0.16 epitools_0.5-10.1 gdtools_0.2.2 wru_0.1-10 -## [5] rnaturalearth_0.1.0 lubridate_1.7.9.2 caret_6.0-86 lattice_0.20-41 -## [9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 -## [13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 -## [17] tidyverse_1.3.0 +## [1] DT_0.16 epitools_0.5-10.1 gdtools_0.2.2 +## [4] wru_0.1-10 rnaturalearth_0.1.0 lubridate_1.7.9.2 +## [7] caret_6.0-86 lattice_0.20-41 forcats_0.5.0 +## [10] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 +## [13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 +## [16] ggplot2_3.3.2 tidyverse_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 rprojroot_1.3-2 -## [5] fs_1.5.0 rstudioapi_0.12 farver_2.0.3 remotes_2.2.0 -## [9] prodlim_2019.11.13 fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 -## [13] splines_4.0.3 knitr_1.30 pkgload_1.1.0 jsonlite_1.7.1 -## [17] pROC_1.16.2 broom_0.7.2 dbplyr_2.0.0 rgeos_0.5-5 -## [21] compiler_4.0.3 httr_1.4.2 backports_1.2.0 assertthat_0.2.1 -## [25] Matrix_1.2-18 cli_2.1.0 htmltools_0.5.0 prettyunits_1.1.1 -## [29] tools_4.0.3 gtable_0.3.0 glue_1.4.2 rnaturalearthdata_0.1.0 -## [33] reshape2_1.4.4 Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.4 -## [37] svglite_1.2.3.2 nlme_3.1-149 iterators_1.0.13 crosstalk_1.1.0.1 -## [41] timeDate_3043.102 gower_0.2.2 xfun_0.19 ps_1.4.0 -## [45] testthat_3.0.0 rvest_0.3.6 lifecycle_0.2.0 devtools_2.3.2 -## [49] MASS_7.3-53 scales_1.1.1 ipred_0.9-9 hms_0.5.3 -## [53] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3 memoise_1.1.0 -## [57] rpart_4.1-15 stringi_1.5.3 desc_1.2.0 foreach_1.5.1 -## [61] e1071_1.7-4 pkgbuild_1.1.0 lava_1.6.8.1 systemfonts_0.3.2 -## [65] rlang_0.4.8 pkgconfig_2.0.3 evaluate_0.14 sf_0.9-6 -## [69] recipes_0.1.15 htmlwidgets_1.5.2 labeling_0.4.2 cowplot_1.1.0 -## [73] tidyselect_1.1.0 processx_3.4.4 plyr_1.8.6 magrittr_1.5 -## [77] R6_2.5.0 generics_0.1.0 DBI_1.1.0 mgcv_1.8-33 -## [81] pillar_1.4.6 haven_2.3.1 withr_2.3.0 units_0.6-7 -## [85] survival_3.2-7 sp_1.4-4 nnet_7.3-14 modelr_0.1.8 -## [89] crayon_1.3.4 KernSmooth_2.23-17 utf8_1.1.4 rmarkdown_2.5 -## [93] usethis_1.6.3 grid_4.0.3 readxl_1.3.1 data.table_1.13.2 -## [97] callr_3.5.1 ModelMetrics_1.2.2.2 reprex_0.3.0 digest_0.6.27 -## [101] classInt_0.4-3 stats4_4.0.3 munsell_0.5.0 viridisLite_0.3.0 -## [105] sessioninfo_1.1.1 +## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 +## [4] rprojroot_1.3-2 fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 prodlim_2019.11.13 +## [10] fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 pkgload_1.1.0 +## [16] jsonlite_1.7.1 pROC_1.16.2 broom_0.7.2 +## [19] dbplyr_2.0.0 rgeos_0.5-5 compiler_4.0.3 +## [22] httr_1.4.2 backports_1.2.0 assertthat_0.2.1 +## [25] Matrix_1.2-18 cli_2.1.0 htmltools_0.5.0 +## [28] prettyunits_1.1.1 tools_4.0.3 gtable_0.3.0 +## [31] glue_1.4.2 rnaturalearthdata_0.1.0 reshape2_1.4.4 +## [34] Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.4 +## [37] svglite_1.2.3.2 nlme_3.1-149 iterators_1.0.13 +## [40] crosstalk_1.1.0.1 timeDate_3043.102 gower_0.2.2 +## [43] xfun_0.19 ps_1.4.0 testthat_3.0.0 +## [46] rvest_0.3.6 lifecycle_0.2.0 devtools_2.3.2 +## [49] MASS_7.3-53 scales_1.1.1 ipred_0.9-9 +## [52] hms_0.5.3 RColorBrewer_1.1-2 yaml_2.2.1 +## [55] curl_4.3 memoise_1.1.0 rpart_4.1-15 +## [58] stringi_1.5.3 desc_1.2.0 foreach_1.5.1 +## [61] e1071_1.7-4 pkgbuild_1.1.0 lava_1.6.8.1 +## [64] systemfonts_0.3.2 rlang_0.4.8 pkgconfig_2.0.3 +## [67] evaluate_0.14 sf_0.9-6 recipes_0.1.15 +## [70] htmlwidgets_1.5.2 labeling_0.4.2 cowplot_1.1.0 +## [73] tidyselect_1.1.0 processx_3.4.4 plyr_1.8.6 +## [76] magrittr_1.5 R6_2.5.0 generics_0.1.0 +## [79] DBI_1.1.0 mgcv_1.8-33 pillar_1.4.6 +## [82] haven_2.3.1 withr_2.3.0 units_0.6-7 +## [85] survival_3.2-7 sp_1.4-4 nnet_7.3-14 +## [88] modelr_0.1.8 crayon_1.3.4 KernSmooth_2.23-17 +## [91] utf8_1.1.4 rmarkdown_2.5 usethis_1.6.3 +## [94] grid_4.0.3 readxl_1.3.1 data.table_1.13.2 +## [97] callr_3.5.1 ModelMetrics_1.2.2.2 reprex_0.3.0 +## [100] digest_0.6.27 classInt_0.4-3 stats4_4.0.3 +## [103] munsell_0.5.0 viridisLite_0.3.0 sessioninfo_1.1.1
    diff --git a/docs/13.us-race-analysis.html b/docs/13.us-race-analysis.html index 6c6b8ca..50c5b40 100644 --- a/docs/13.us-race-analysis.html +++ b/docs/13.us-race-analysis.html @@ -1674,7 +1674,8 @@

    Race/ethnicity predictions

    rename('surname' = last_name_simple) %>% predict_race(surname.only = T, impute.missing = F) 
    ## [1] "Proceeding with surname-only predictions..."
    -
    ## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 5166 surnames were not matched.
    +
    ## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 5166
+## surnames were not matched.
    pubmed_us_race <- pubmed_race_pmids %>% 
   group_by(pmid, journal, publication_date, year, adjusted_citations) %>% 
   summarise_at(vars(contains('pred.')), mean, na.rm = T, .groups = 'drop') %>% 
@@ -1684,7 +1685,8 @@ 
    Race/ethnicity predictions
    
   rename('surname' = last_name_simple) %>%
   predict_race(surname.only = T, impute.missing = F)
    ## [1] "Proceeding with surname-only predictions..."
    -
    ## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 100 surnames were not matched.
    +
    ## Warning in merge_surnames(voter.file, impute.missing = impute.missing): 100
+## surnames were not matched.
    my_jours <- unique(pubmed_us_race$journal)
 my_confs <- unique(iscb_us_race$conference)
 n_jours <- length(my_jours)
diff --git a/docs/14.us-name-origin.html b/docs/14.us-name-origin.html
index f88475f..c71358d 100644
--- a/docs/14.us-name-origin.html
+++ b/docs/14.us-name-origin.html
@@ -1666,23 +1666,23 @@ 
    Representation analysis of name origin in the USOnly keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata). See 093.summary-stats for more details.
    
-
    load('Rdata/raws.Rdata')
+
    load("Rdata/raws.Rdata")
 
 alpha_threshold <- qnorm(0.975)
 
-pubmed_nat_df <-  corr_authors %>%
-  filter(year(year) >= 2002) %>% 
-  separate_rows(countries, sep = ',') %>% 
-  filter(countries == 'US') %>% 
-  left_join(nationalize_df, by = c('fore_name', 'last_name')) %>%
+pubmed_nat_df <- corr_authors %>%
+  filter(year(year) >= 2002) %>%
+  separate_rows(countries, sep = ",") %>%
+  filter(countries == "US") %>%
+  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
   group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
   summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
   ungroup()
 
 iscb_nat_df <- keynotes %>%
-  separate_rows(afflcountries, sep = '\\|') %>% 
-  filter(afflcountries == 'United States') %>% 
-  left_join(nationalize_df, by = c('fore_name', 'last_name'))
+  separate_rows(afflcountries, sep = "\\|") %>%
+  filter(afflcountries == "United States") %>%
+  left_join(nationalize_df, by = c("fore_name", "last_name"))
 
 start_year <- 1992
 end_year <- 2019
@@ -1691,9 +1691,9 @@ 
    Representation analysis of name origin in the US
    
+region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
    
 
    
 
    Organize data
    
 
    Prepare data frames for later analyses:
    
@@ -1703,658 +1703,233 @@ 
    Organize data
    
 
  • compute mean, sd, marginal error
    • 
 
 
    iscb_pubmed_oth <- iscb_nat_df %>%
-  rename('journal' = conference) %>%
+  rename("journal" = conference) %>%
   select(year, journal, African:SouthAsian, publication_date) %>%
-  mutate(type = 'Keynote speakers/Fellows',
-         adjusted_citations = 1) %>%
+  mutate(
+    type = "Keynote speakers/Fellows",
+    adjusted_citations = 1
+  ) %>%
   bind_rows(
     pubmed_nat_df %>%
       select(year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
-      mutate(type = 'Pubmed authors')
+      mutate(type = "Pubmed authors")
   ) %>%
   mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
   pivot_longer(c(African:SouthAsian, OtherCategories),
-               names_to = 'region',
-               values_to = 'probabilities') %>%
-  filter(!is.na(probabilities)) %>% 
+    names_to = "region",
+    values_to = "probabilities"
+  ) %>%
+  filter(!is.na(probabilities)) %>%
   group_by(type, year, region) %>%
   mutate(
     pmc_citations_year = mean(adjusted_citations),
-    weight = adjusted_citations/pmc_citations_year,
-    weighted_probs = probabilities*weight
-  ) 
+    weight = adjusted_citations / pmc_citations_year,
+    weighted_probs = probabilities * weight
+  )
 
-iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% 
+iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
   summarise(
     mean_prob = mean(weighted_probs),
-    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
+    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
     me_prob = alpha_threshold * se_prob,
-    .groups = 'drop'
+    .groups = "drop"
   )
 
 iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
-  filter(region != 'OtherCategories')
    
+  filter(region != "OtherCategories")

    Figures for paper

    fig_us_name_origina <- iscb_pubmed_sum %>%
-  filter(year < '2020-01-01') %>%
-  region_breakdown('main', region_levels, fct_rev(type)) +
+  filter(year < "2020-01-01") %>%
+  region_breakdown("main", region_levels, fct_rev(type)) +
   guides(fill = guide_legend(nrow = 2))
 
-large_regions <- c('CelticEnglish', 'EastAsian', 'European', 'OtherCategories')
+large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")
 
 ## Mean and standard deviation of predicted probabilities:
 fig_us_name_originb <- iscb_pubmed_sum_oth %>%
   filter(region %in% large_regions) %>%
   recode_region() %>%
-  gam_and_ci(df2 = iscb_pubmed_oth %>%
-               filter(region %in% large_regions) %>%
-               recode_region(),
-             start_y = start_year, end_y = end_year) + 
-  theme(legend.position = c(0.88, 0.83),
-        panel.grid.minor = element_blank(),
-        legend.margin = margin(-0.5, 0, 0, 0, unit='cm'),
-        legend.text = element_text(size = 6)) +
+  gam_and_ci(
+    df2 = iscb_pubmed_oth %>%
+      filter(region %in% large_regions) %>%
+      recode_region(),
+    start_y = start_year, end_y = end_year
+  ) +
+  theme(
+    legend.position = c(0.88, 0.83),
+    panel.grid.minor = element_blank(),
+    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
+    legend.text = element_text(size = 6)
+  ) +
   facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)
 
-fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = 'AUTO', ncol = 1, rel_heights = c(1.3,1))
    +fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))     
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    fig_us_name_origin

    -
    ggsave('figs/us_name_origin.png', fig_us_name_origin, width = 6.5, height = 5.5)
-ggsave('figs/us_name_origin.svg', fig_us_name_origin, width = 6.5, height = 5.5)
    +
    ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5)
+ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)

    Hypothesis testing

    -
    iscb_lm <- iscb_pubmed_oth %>% 
-  ungroup() %>% 
+
    iscb_lm <- iscb_pubmed_oth %>%
+  ungroup() %>%
   mutate(
     # year = c(scale(year)),
-    year = as.factor(year),
-    type = relevel(as.factor(type), ref = 'Pubmed authors'))
-main_lm <- function(regioni){
-  glm(type ~ year + weighted_probs, 
-      data = iscb_lm %>% 
-        filter(region == regioni, !is.na(weighted_probs)),
-      family = 'binomial')
+    # year = as.factor(year),
+    type = relevel(as.factor(type), ref = "Pubmed authors")
+  )
+main_lm <- function(regioni) {
+  glm(type ~ year + weighted_probs,
+    data = iscb_lm %>%
+      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+    family = "binomial"
+  )
 }
 
-inte_lm <- function(regioni){
-  glm(type ~ weighted_probs*year, 
-      data = iscb_lm %>% 
-  filter(region == regioni, !is.na(weighted_probs)),
-      family = 'binomial')
+inte_lm <- function(regioni) {
+  glm(type ~ weighted_probs * year,
+    data = iscb_lm %>%
+      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+    family = "binomial"
+  )
 }
 
 main_list <- lapply(large_regions, main_lm)
 
 names(main_list) <- large_regions
-lapply(main_list, summary)
    
+lapply(main_list, broom::tidy)
    ## $CelticEnglish
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.5719  -0.0867  -0.0759  -0.0676   3.4967  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)
-## (Intercept)     1.655e+01  1.385e+03   0.012    0.990
-## year1994-01-01 -1.409e-02  1.959e+03   0.000    1.000
-## year1995-01-01 -2.093e-02  2.771e+03   0.000    1.000
-## year1996-01-01 -7.601e-03  1.959e+03   0.000    1.000
-## year1997-01-01  1.080e-02  2.190e+03   0.000    1.000
-## year1998-01-01 -1.349e-02  2.771e+03   0.000    1.000
-## year1999-01-01 -4.707e-03  1.656e+03   0.000    1.000
-## year2000-01-01  1.478e-03  1.580e+03   0.000    1.000
-## year2001-01-01 -1.299e-03  1.563e+03   0.000    1.000
-## year2002-01-01 -1.903e+01  1.385e+03  -0.014    0.989
-## year2003-01-01 -1.992e+01  1.385e+03  -0.014    0.989
-## year2004-01-01 -1.946e+01  1.385e+03  -0.014    0.989
-## year2005-01-01 -1.970e+01  1.385e+03  -0.014    0.989
-## year2006-01-01 -2.004e+01  1.385e+03  -0.014    0.988
-## year2007-01-01 -1.967e+01  1.385e+03  -0.014    0.989
-## year2008-01-01 -2.009e+01  1.385e+03  -0.015    0.988
-## year2009-01-01 -1.939e+01  1.385e+03  -0.014    0.989
-## year2010-01-01 -1.941e+01  1.385e+03  -0.014    0.989
-## year2011-01-01 -2.010e+01  1.385e+03  -0.015    0.988
-## year2012-01-01 -1.993e+01  1.385e+03  -0.014    0.989
-## year2013-01-01 -2.030e+01  1.385e+03  -0.015    0.988
-## year2014-01-01 -2.202e+01  1.385e+03  -0.016    0.987
-## year2015-01-01 -2.266e+01  1.385e+03  -0.016    0.987
-## year2016-01-01 -2.215e+01  1.385e+03  -0.016    0.987
-## year2017-01-01 -2.239e+01  1.385e+03  -0.016    0.987
-## year2018-01-01 -2.263e+01  1.385e+03  -0.016    0.987
-## year2019-01-01 -2.264e+01  1.385e+03  -0.016    0.987
-## weighted_probs  4.104e-02  8.128e-02   0.505    0.614
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1994.0  on 26414  degrees of freedom
-## AIC: 2050
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)     4.91     0.523         9.39  6.14e-21
+## 2 year           -0.000616 0.0000346   -17.8   6.39e-71
+## 3 weighted_probs  0.0434   0.0837        0.519 6.04e- 1
 ## 
 ## $EastAsian
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.4162  -0.0942  -0.0743  -0.0721   3.8535  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)    
-## (Intercept)     1.658e+01  1.385e+03   0.012 0.990450    
-## year1994-01-01  6.836e-02  1.959e+03   0.000 0.999972    
-## year1995-01-01 -1.480e-02  2.771e+03   0.000 0.999996    
-## year1996-01-01 -1.319e-02  1.959e+03   0.000 0.999995    
-## year1997-01-01  3.151e-02  2.190e+03   0.000 0.999989    
-## year1998-01-01  9.420e-03  2.771e+03   0.000 0.999997    
-## year1999-01-01  2.774e-04  1.656e+03   0.000 1.000000    
-## year2000-01-01  1.432e-03  1.580e+03   0.000 0.999999    
-## year2001-01-01 -2.079e-03  1.563e+03   0.000 0.999999    
-## year2002-01-01 -1.899e+01  1.385e+03  -0.014 0.989066    
-## year2003-01-01 -1.988e+01  1.385e+03  -0.014 0.988552    
-## year2004-01-01 -1.941e+01  1.385e+03  -0.014 0.988823    
-## year2005-01-01 -1.961e+01  1.385e+03  -0.014 0.988706    
-## year2006-01-01 -1.995e+01  1.385e+03  -0.014 0.988512    
-## year2007-01-01 -1.960e+01  1.385e+03  -0.014 0.988711    
-## year2008-01-01 -2.000e+01  1.385e+03  -0.014 0.988480    
-## year2009-01-01 -1.931e+01  1.385e+03  -0.014 0.988877    
-## year2010-01-01 -1.932e+01  1.385e+03  -0.014 0.988874    
-## year2011-01-01 -2.003e+01  1.385e+03  -0.014 0.988463    
-## year2012-01-01 -1.982e+01  1.385e+03  -0.014 0.988583    
-## year2013-01-01 -2.017e+01  1.385e+03  -0.015 0.988382    
-## year2014-01-01 -2.187e+01  1.385e+03  -0.016 0.987402    
-## year2015-01-01 -2.252e+01  1.385e+03  -0.016 0.987033    
-## year2016-01-01 -2.199e+01  1.385e+03  -0.016 0.987335    
-## year2017-01-01 -2.223e+01  1.385e+03  -0.016 0.987197    
-## year2018-01-01 -2.247e+01  1.385e+03  -0.016 0.987061    
-## year2019-01-01 -2.248e+01  1.385e+03  -0.016 0.987055    
-## weighted_probs -1.705e+00  5.005e-01  -3.407 0.000657 ***
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1972.6  on 26414  degrees of freedom
-## AIC: 2028.6
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)     4.75     0.520          9.13 6.68e-20
+## 2 year           -0.000595 0.0000347    -17.2  5.24e-66
+## 3 weighted_probs -1.77     0.501         -3.52 4.28e- 4
 ## 
 ## $European
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.6484  -0.0877  -0.0748  -0.0665   3.5052  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)
-## (Intercept)     1.652e+01  1.385e+03   0.012    0.990
-## year1994-01-01  3.895e-02  1.959e+03   0.000    1.000
-## year1995-01-01  4.088e-02  2.771e+03   0.000    1.000
-## year1996-01-01 -6.996e-03  1.959e+03   0.000    1.000
-## year1997-01-01  1.329e-02  2.190e+03   0.000    1.000
-## year1998-01-01  2.411e-02  2.771e+03   0.000    1.000
-## year1999-01-01 -1.664e-02  1.656e+03   0.000    1.000
-## year2000-01-01 -9.510e-03  1.579e+03   0.000    1.000
-## year2001-01-01 -6.537e-03  1.563e+03   0.000    1.000
-## year2002-01-01 -1.903e+01  1.385e+03  -0.014    0.989
-## year2003-01-01 -1.994e+01  1.385e+03  -0.014    0.989
-## year2004-01-01 -1.947e+01  1.385e+03  -0.014    0.989
-## year2005-01-01 -1.971e+01  1.385e+03  -0.014    0.989
-## year2006-01-01 -2.005e+01  1.385e+03  -0.014    0.988
-## year2007-01-01 -1.969e+01  1.385e+03  -0.014    0.989
-## year2008-01-01 -2.010e+01  1.385e+03  -0.015    0.988
-## year2009-01-01 -1.941e+01  1.385e+03  -0.014    0.989
-## year2010-01-01 -1.942e+01  1.385e+03  -0.014    0.989
-## year2011-01-01 -2.011e+01  1.385e+03  -0.015    0.988
-## year2012-01-01 -1.994e+01  1.385e+03  -0.014    0.989
-## year2013-01-01 -2.031e+01  1.385e+03  -0.015    0.988
-## year2014-01-01 -2.203e+01  1.385e+03  -0.016    0.987
-## year2015-01-01 -2.267e+01  1.385e+03  -0.016    0.987
-## year2016-01-01 -2.216e+01  1.385e+03  -0.016    0.987
-## year2017-01-01 -2.240e+01  1.385e+03  -0.016    0.987
-## year2018-01-01 -2.264e+01  1.385e+03  -0.016    0.987
-## year2019-01-01 -2.265e+01  1.385e+03  -0.016    0.987
-## weighted_probs  1.719e-01  1.118e-01   1.537    0.124
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1992.2  on 26414  degrees of freedom
-## AIC: 2048.2
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 3 x 5
+##   term            estimate std.error statistic  p.value
+##   <chr>              <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)     4.88     0.521          9.36 7.82e-21
+## 2 year           -0.000616 0.0000345    -17.8  3.48e-71
+## 3 weighted_probs  0.173    0.109          1.58 1.14e- 1
 ## 
 ## $OtherCategories
-## 
-## Call:
-## glm(formula = type ~ year + weighted_probs, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.4441  -0.0868  -0.0759  -0.0675   3.4965  
-## 
-## Coefficients:
-##                  Estimate Std. Error z value Pr(>|z|)
-## (Intercept)     1.655e+01  1.385e+03   0.012    0.990
-## year1994-01-01  6.888e-03  1.959e+03   0.000    1.000
-## year1995-01-01  1.202e-02  2.771e+03   0.000    1.000
-## year1996-01-01  1.050e-02  1.959e+03   0.000    1.000
-## year1997-01-01 -1.567e-02  2.190e+03   0.000    1.000
-## year1998-01-01  8.719e-03  2.771e+03   0.000    1.000
-## year1999-01-01  1.000e-02  1.656e+03   0.000    1.000
-## year2000-01-01  9.243e-04  1.580e+03   0.000    1.000
-## year2001-01-01  3.158e-03  1.563e+03   0.000    1.000
-## year2002-01-01 -1.902e+01  1.385e+03  -0.014    0.989
-## year2003-01-01 -1.992e+01  1.385e+03  -0.014    0.989
-## year2004-01-01 -1.946e+01  1.385e+03  -0.014    0.989
-## year2005-01-01 -1.970e+01  1.385e+03  -0.014    0.989
-## year2006-01-01 -2.004e+01  1.385e+03  -0.014    0.988
-## year2007-01-01 -1.968e+01  1.385e+03  -0.014    0.989
-## year2008-01-01 -2.010e+01  1.385e+03  -0.015    0.988
-## year2009-01-01 -1.940e+01  1.385e+03  -0.014    0.989
-## year2010-01-01 -1.941e+01  1.385e+03  -0.014    0.989
-## year2011-01-01 -2.010e+01  1.385e+03  -0.015    0.988
-## year2012-01-01 -1.993e+01  1.385e+03  -0.014    0.989
-## year2013-01-01 -2.031e+01  1.385e+03  -0.015    0.988
-## year2014-01-01 -2.203e+01  1.385e+03  -0.016    0.987
-## year2015-01-01 -2.267e+01  1.385e+03  -0.016    0.987
-## year2016-01-01 -2.216e+01  1.385e+03  -0.016    0.987
-## year2017-01-01 -2.240e+01  1.385e+03  -0.016    0.987
-## year2018-01-01 -2.264e+01  1.385e+03  -0.016    0.987
-## year2019-01-01 -2.265e+01  1.385e+03  -0.016    0.987
-## weighted_probs  4.746e-02  1.309e-01   0.363    0.717
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1994.1  on 26414  degrees of freedom
-## AIC: 2050.1
-## 
-## Number of Fisher Scoring iterations: 15
    -
    inte_list <- lapply(large_regions, inte_lm)
    -
    ## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    -
    lapply(inte_list, summary)
    +## # A tibble: 3 x 5 +## term estimate std.error statistic p.value +## <chr> <dbl> <dbl> <dbl> <dbl> +## 1 (Intercept) 4.94 0.519 9.52 1.79e-21 +## 2 year -0.000618 0.0000345 -17.9 1.17e-71 +## 3 weighted_probs 0.0518 0.136 0.381 7.03e- 1 +
    inte_list <- lapply(large_regions, inte_lm)
+lapply(inte_list, broom::tidy)
    ## [[1]]
-## 
-## Call:
-## glm(formula = type ~ weighted_probs * year, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.9159  -0.0866  -0.0745  -0.0664   3.5838  
-## 
-## Coefficients: (2 not defined because of singularities)
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  2.261e+03   0.007    0.994
-## weighted_probs                 3.033e-07  3.794e+03   0.000    1.000
-## year1994-01-01                 1.785e-07  1.105e+04   0.000    1.000
-## year1995-01-01                -1.181e-07  3.375e+03   0.000    1.000
-## year1996-01-01                 1.791e-07  3.361e+03   0.000    1.000
-## year1997-01-01                 1.793e-07  3.340e+03   0.000    1.000
-## year1998-01-01                -6.301e-08  3.035e+03   0.000    1.000
-## year1999-01-01                 1.792e-07  2.836e+03   0.000    1.000
-## year2000-01-01                 1.792e-07  2.596e+03   0.000    1.000
-## year2001-01-01                 1.791e-07  2.567e+03   0.000    1.000
-## year2002-01-01                -1.883e+01  2.261e+03  -0.008    0.993
-## year2003-01-01                -2.016e+01  2.261e+03  -0.009    0.993
-## year2004-01-01                -1.951e+01  2.261e+03  -0.009    0.993
-## year2005-01-01                -1.974e+01  2.261e+03  -0.009    0.993
-## year2006-01-01                -2.010e+01  2.261e+03  -0.009    0.993
-## year2007-01-01                -2.004e+01  2.261e+03  -0.009    0.993
-## year2008-01-01                -2.013e+01  2.261e+03  -0.009    0.993
-## year2009-01-01                -1.952e+01  2.261e+03  -0.009    0.993
-## year2010-01-01                -1.939e+01  2.261e+03  -0.009    0.993
-## year2011-01-01                -2.007e+01  2.261e+03  -0.009    0.993
-## year2012-01-01                -1.977e+01  2.261e+03  -0.009    0.993
-## year2013-01-01                -2.029e+01  2.261e+03  -0.009    0.993
-## year2014-01-01                -2.215e+01  2.261e+03  -0.010    0.992
-## year2015-01-01                -2.270e+01  2.261e+03  -0.010    0.992
-## year2016-01-01                -2.222e+01  2.261e+03  -0.010    0.992
-## year2017-01-01                -2.232e+01  2.261e+03  -0.010    0.992
-## year2018-01-01                -2.272e+01  2.261e+03  -0.010    0.992
-## year2019-01-01                -2.255e+01  2.261e+03  -0.010    0.992
-## weighted_probs:year1994-01-01 -3.027e-07  1.374e+04   0.000    1.000
-## weighted_probs:year1995-01-01         NA         NA      NA       NA
-## weighted_probs:year1996-01-01 -3.035e-07  4.926e+03   0.000    1.000
-## weighted_probs:year1997-01-01 -3.037e-07  9.415e+03   0.000    1.000
-## weighted_probs:year1998-01-01         NA         NA      NA       NA
-## weighted_probs:year1999-01-01 -3.035e-07  4.532e+03   0.000    1.000
-## weighted_probs:year2000-01-01 -3.035e-07  4.466e+03   0.000    1.000
-## weighted_probs:year2001-01-01 -3.035e-07  4.262e+03   0.000    1.000
-## weighted_probs:year2002-01-01 -6.572e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2003-01-01  5.211e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2004-01-01  1.002e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2005-01-01  1.119e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2006-01-01  1.729e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2007-01-01  7.828e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2008-01-01  9.151e-02  3.794e+03   0.000    1.000
-## weighted_probs:year2009-01-01  3.593e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2010-01-01 -5.646e-02  3.794e+03   0.000    1.000
-## weighted_probs:year2011-01-01 -9.107e-02  3.794e+03   0.000    1.000
-## weighted_probs:year2012-01-01 -6.291e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2013-01-01 -6.333e-02  3.794e+03   0.000    1.000
-## weighted_probs:year2014-01-01  3.413e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2015-01-01  9.374e-02  3.794e+03   0.000    1.000
-## weighted_probs:year2016-01-01  2.007e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2017-01-01 -3.198e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2018-01-01  2.793e-01  3.794e+03   0.000    1.000
-## weighted_probs:year2019-01-01 -4.811e-01  3.794e+03   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1985.6  on 26390  degrees of freedom
-## AIC: 2089.6
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)          5.01      0.563         8.89  5.91e-19
+## 2 weighted_probs      -0.228     0.591        -0.386 7.00e- 1
+## 3 year                -0.000623  0.0000377   -16.5   2.45e-61
+## 4 weighted_probs:year  0.0000201 0.0000425     0.473 6.36e- 1
 ## 
 ## [[2]]
-## 
-## Call:
-## glm(formula = type ~ weighted_probs * year, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.4447  -0.0949  -0.0758  -0.0713   3.7045  
-## 
-## Coefficients: (2 not defined because of singularities)
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  1.839e+03   0.009    0.993
-## weighted_probs                 9.566e-05  1.271e+05   0.000    1.000
-## year1994-01-01                 8.884e-07  2.678e+03   0.000    1.000
-## year1995-01-01                 8.009e-07  2.979e+03   0.000    1.000
-## year1996-01-01                 8.886e-07  4.288e+03   0.000    1.000
-## year1997-01-01                 8.884e-07  3.087e+03   0.000    1.000
-## year1998-01-01                -5.577e-07  2.861e+03   0.000    1.000
-## year1999-01-01                 8.883e-07  2.141e+03   0.000    1.000
-## year2000-01-01                 8.883e-07  2.228e+03   0.000    1.000
-## year2001-01-01                 8.884e-07  2.102e+03   0.000    1.000
-## year2002-01-01                -1.903e+01  1.839e+03  -0.010    0.992
-## year2003-01-01                -1.977e+01  1.839e+03  -0.011    0.991
-## year2004-01-01                -1.930e+01  1.839e+03  -0.010    0.992
-## year2005-01-01                -1.935e+01  1.839e+03  -0.011    0.992
-## year2006-01-01                -1.974e+01  1.839e+03  -0.011    0.991
-## year2007-01-01                -1.967e+01  1.839e+03  -0.011    0.991
-## year2008-01-01                -2.008e+01  1.839e+03  -0.011    0.991
-## year2009-01-01                -1.918e+01  1.839e+03  -0.010    0.992
-## year2010-01-01                -1.921e+01  1.839e+03  -0.010    0.992
-## year2011-01-01                -1.986e+01  1.839e+03  -0.011    0.991
-## year2012-01-01                -1.966e+01  1.839e+03  -0.011    0.991
-## year2013-01-01                -2.022e+01  1.839e+03  -0.011    0.991
-## year2014-01-01                -2.148e+01  1.839e+03  -0.012    0.991
-## year2015-01-01                -2.239e+01  1.839e+03  -0.012    0.990
-## year2016-01-01                -2.195e+01  1.839e+03  -0.012    0.990
-## year2017-01-01                -2.230e+01  1.839e+03  -0.012    0.990
-## year2018-01-01                -2.241e+01  1.839e+03  -0.012    0.990
-## year2019-01-01                -2.251e+01  1.839e+03  -0.012    0.990
-## weighted_probs:year1994-01-01 -9.567e-05  1.303e+05   0.000    1.000
-## weighted_probs:year1995-01-01         NA         NA      NA       NA
-## weighted_probs:year1996-01-01 -9.576e-05  1.950e+06   0.000    1.000
-## weighted_probs:year1997-01-01 -9.567e-05  1.430e+05   0.000    1.000
-## weighted_probs:year1998-01-01         NA         NA      NA       NA
-## weighted_probs:year1999-01-01 -9.567e-05  1.424e+05   0.000    1.000
-## weighted_probs:year2000-01-01 -9.565e-05  1.597e+05   0.000    1.000
-## weighted_probs:year2001-01-01 -9.566e-05  1.536e+05   0.000    1.000
-## weighted_probs:year2002-01-01  1.380e-01  1.271e+05   0.000    1.000
-## weighted_probs:year2003-01-01 -9.030e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2004-01-01 -8.390e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2005-01-01 -2.229e+01  1.271e+05   0.000    1.000
-## weighted_probs:year2006-01-01 -1.632e+01  1.271e+05   0.000    1.000
-## weighted_probs:year2007-01-01 -9.985e-02  1.271e+05   0.000    1.000
-## weighted_probs:year2008-01-01 -1.389e-01  1.271e+05   0.000    1.000
-## weighted_probs:year2009-01-01 -9.254e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2010-01-01 -5.983e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2011-01-01 -1.351e+01  1.271e+05   0.000    1.000
-## weighted_probs:year2012-01-01 -9.074e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2013-01-01 -6.626e-01  1.271e+05   0.000    1.000
-## weighted_probs:year2014-01-01 -4.322e+01  1.271e+05   0.000    1.000
-## weighted_probs:year2015-01-01 -5.494e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2016-01-01 -2.295e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2017-01-01 -6.581e-01  1.271e+05   0.000    1.000
-## weighted_probs:year2018-01-01 -2.467e+00  1.271e+05   0.000    1.000
-## weighted_probs:year2019-01-01 -1.020e+00  1.271e+05   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1953.1  on 26390  degrees of freedom
-## AIC: 2057.1
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)          4.70      0.540         8.72  2.89e-18
+## 2 weighted_probs      -0.524     4.08         -0.128 8.98e- 1
+## 3 year                -0.000592  0.0000359   -16.5   6.46e-61
+## 4 weighted_probs:year -0.0000794 0.000261     -0.304 7.61e- 1
 ## 
 ## [[3]]
-## 
-## Call:
-## glm(formula = type ~ weighted_probs * year, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -0.7432  -0.0882  -0.0752  -0.0649   3.5346  
-## 
-## Coefficients: (2 not defined because of singularities)
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  1.727e+03   0.010    0.992
-## weighted_probs                -2.147e-07  4.121e+03   0.000    1.000
-## year1994-01-01                -6.953e-08  2.308e+04   0.000    1.000
-## year1995-01-01                -7.042e-08  2.949e+03   0.000    1.000
-## year1996-01-01                -7.179e-08  2.437e+03   0.000    1.000
-## year1997-01-01                -7.194e-08  4.360e+03   0.000    1.000
-## year1998-01-01                -4.958e-08  2.837e+03   0.000    1.000
-## year1999-01-01                -7.177e-08  2.161e+03   0.000    1.000
-## year2000-01-01                -7.156e-08  2.008e+03   0.000    1.000
-## year2001-01-01                -7.128e-08  2.002e+03   0.000    1.000
-## year2002-01-01                -1.925e+01  1.727e+03  -0.011    0.991
-## year2003-01-01                -1.998e+01  1.727e+03  -0.012    0.991
-## year2004-01-01                -1.946e+01  1.727e+03  -0.011    0.991
-## year2005-01-01                -1.973e+01  1.727e+03  -0.011    0.991
-## year2006-01-01                -2.016e+01  1.727e+03  -0.012    0.991
-## year2007-01-01                -1.947e+01  1.727e+03  -0.011    0.991
-## year2008-01-01                -2.003e+01  1.727e+03  -0.012    0.991
-## year2009-01-01                -1.937e+01  1.727e+03  -0.011    0.991
-## year2010-01-01                -1.949e+01  1.727e+03  -0.011    0.991
-## year2011-01-01                -2.032e+01  1.727e+03  -0.012    0.991
-## year2012-01-01                -2.020e+01  1.727e+03  -0.012    0.991
-## year2013-01-01                -2.033e+01  1.727e+03  -0.012    0.991
-## year2014-01-01                -2.200e+01  1.727e+03  -0.013    0.990
-## year2015-01-01                -2.273e+01  1.727e+03  -0.013    0.990
-## year2016-01-01                -2.224e+01  1.727e+03  -0.013    0.990
-## year2017-01-01                -2.244e+01  1.727e+03  -0.013    0.990
-## year2018-01-01                -2.276e+01  1.727e+03  -0.013    0.989
-## year2019-01-01                -2.281e+01  1.727e+03  -0.013    0.989
-## weighted_probs:year1994-01-01  8.770e-08  1.402e+06   0.000    1.000
-## weighted_probs:year1995-01-01         NA         NA      NA       NA
-## weighted_probs:year1996-01-01  2.151e-07  5.382e+03   0.000    1.000
-## weighted_probs:year1997-01-01  2.173e-07  2.223e+04   0.000    1.000
-## weighted_probs:year1998-01-01         NA         NA      NA       NA
-## weighted_probs:year1999-01-01  2.147e-07  4.917e+03   0.000    1.000
-## weighted_probs:year2000-01-01  2.148e-07  4.695e+03   0.000    1.000
-## weighted_probs:year2001-01-01  2.143e-07  4.808e+03   0.000    1.000
-## weighted_probs:year2002-01-01  7.054e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2003-01-01  1.685e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2004-01-01 -2.523e-02  4.121e+03   0.000    1.000
-## weighted_probs:year2005-01-01  8.222e-02  4.121e+03   0.000    1.000
-## weighted_probs:year2006-01-01  3.867e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2007-01-01 -1.298e+00  4.121e+03   0.000    1.000
-## weighted_probs:year2008-01-01 -3.182e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2009-01-01 -8.382e-02  4.121e+03   0.000    1.000
-## weighted_probs:year2010-01-01  2.568e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2011-01-01  6.134e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2012-01-01  7.233e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2013-01-01  9.890e-02  4.121e+03   0.000    1.000
-## weighted_probs:year2014-01-01 -1.283e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2015-01-01  2.281e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2016-01-01  3.110e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2017-01-01  1.407e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2018-01-01  4.559e-01  4.121e+03   0.000    1.000
-## weighted_probs:year2019-01-01  5.439e-01  4.121e+03   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1985.0  on 26390  degrees of freedom
-## AIC: 2089
-## 
-## Number of Fisher Scoring iterations: 15
-## 
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)          5.07      0.573         8.85  9.11e-19
+## 2 weighted_probs      -0.462     0.821        -0.563 5.73e- 1
+## 3 year                -0.000629  0.0000382   -16.5   7.11e-61
+## 4 weighted_probs:year  0.0000437 0.0000549     0.796 4.26e- 1
 ## 
 ## [[4]]
-## 
-## Call:
-## glm(formula = type ~ weighted_probs * year, family = "binomial", 
-##     data = iscb_lm %>% filter(region == regioni, !is.na(weighted_probs)))
-## 
-## Deviance Residuals: 
-##     Min       1Q   Median       3Q      Max  
-## -1.1945  -0.0876  -0.0720  -0.0671   3.5058  
-## 
-## Coefficients: (2 not defined because of singularities)
-##                                 Estimate Std. Error z value Pr(>|z|)
-## (Intercept)                    1.657e+01  2.111e+03   0.008    0.994
-## weighted_probs                -1.127e-07  5.917e+03   0.000    1.000
-## year1994-01-01                -6.732e-08  3.745e+03   0.000    1.000
-## year1995-01-01                -6.591e-08  3.153e+03   0.000    1.000
-## year1996-01-01                -6.759e-08  2.965e+03   0.000    1.000
-## year1997-01-01                -6.714e-08  4.337e+03   0.000    1.000
-## year1998-01-01                -5.799e-08  2.978e+03   0.000    1.000
-## year1999-01-01                -6.742e-08  2.720e+03   0.000    1.000
-## year2000-01-01                -6.735e-08  2.449e+03   0.000    1.000
-## year2001-01-01                -6.752e-08  2.341e+03   0.000    1.000
-## year2002-01-01                -1.925e+01  2.111e+03  -0.009    0.993
-## year2003-01-01                -1.956e+01  2.111e+03  -0.009    0.993
-## year2004-01-01                -1.936e+01  2.111e+03  -0.009    0.993
-## year2005-01-01                -1.976e+01  2.111e+03  -0.009    0.993
-## year2006-01-01                -1.996e+01  2.111e+03  -0.009    0.992
-## year2007-01-01                -1.939e+01  2.111e+03  -0.009    0.993
-## year2008-01-01                -2.014e+01  2.111e+03  -0.010    0.992
-## year2009-01-01                -1.941e+01  2.111e+03  -0.009    0.993
-## year2010-01-01                -1.963e+01  2.111e+03  -0.009    0.993
-## year2011-01-01                -1.998e+01  2.111e+03  -0.009    0.992
-## year2012-01-01                -1.991e+01  2.111e+03  -0.009    0.992
-## year2013-01-01                -2.041e+01  2.111e+03  -0.010    0.992
-## year2014-01-01                -2.210e+01  2.111e+03  -0.010    0.992
-## year2015-01-01                -2.271e+01  2.111e+03  -0.011    0.991
-## year2016-01-01                -2.213e+01  2.111e+03  -0.010    0.992
-## year2017-01-01                -2.252e+01  2.111e+03  -0.011    0.991
-## year2018-01-01                -2.261e+01  2.111e+03  -0.011    0.991
-## year2019-01-01                -2.270e+01  2.111e+03  -0.011    0.991
-## weighted_probs:year1994-01-01  1.113e-07  2.323e+04   0.000    1.000
-## weighted_probs:year1995-01-01         NA         NA      NA       NA
-## weighted_probs:year1996-01-01  1.154e-07  3.364e+04   0.000    1.000
-## weighted_probs:year1997-01-01  1.126e-07  8.179e+03   0.000    1.000
-## weighted_probs:year1998-01-01         NA         NA      NA       NA
-## weighted_probs:year1999-01-01  1.121e-07  2.601e+04   0.000    1.000
-## weighted_probs:year2000-01-01  1.127e-07  7.110e+03   0.000    1.000
-## weighted_probs:year2001-01-01  1.125e-07  6.870e+03   0.000    1.000
-## weighted_probs:year2002-01-01  9.966e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2003-01-01 -2.696e+00  5.917e+03   0.000    1.000
-## weighted_probs:year2004-01-01 -4.243e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2005-01-01  1.989e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2006-01-01 -3.199e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2007-01-01 -1.480e+00  5.917e+03   0.000    1.000
-## weighted_probs:year2008-01-01  1.422e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2009-01-01  5.110e-02  5.917e+03   0.000    1.000
-## weighted_probs:year2010-01-01  8.824e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2011-01-01 -5.120e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2012-01-01 -7.207e-02  5.917e+03   0.000    1.000
-## weighted_probs:year2013-01-01  3.531e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2014-01-01  2.293e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2015-01-01  1.584e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2016-01-01 -1.152e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2017-01-01  3.618e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2018-01-01 -1.096e-01  5.917e+03   0.000    1.000
-## weighted_probs:year2019-01-01  1.475e-01  5.917e+03   0.000    1.000
-## 
-## (Dispersion parameter for binomial family taken to be 1)
-## 
-##     Null deviance: 2725.4  on 26441  degrees of freedom
-## Residual deviance: 1984.7  on 26390  degrees of freedom
-## AIC: 2088.7
-## 
-## Number of Fisher Scoring iterations: 15
    -
    for (i in 1:4){
-  print(anova(main_list[[i]], inte_list[[i]], test = 'Chisq'))
+## # A tibble: 4 x 5
+##   term                  estimate std.error statistic  p.value
+##   <chr>                    <dbl>     <dbl>     <dbl>    <dbl>
+## 1 (Intercept)          5.04      0.587         8.58  9.33e-18
+## 2 weighted_probs      -0.320     1.04         -0.308 7.58e- 1
+## 3 year                -0.000625  0.0000393   -15.9   6.63e-57
+## 4 weighted_probs:year  0.0000251 0.0000690     0.364 7.16e- 1
    +
    for (i in 1:4) {
+  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
 }
    ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ weighted_probs * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26414     1994.0                     
-## 2     26390     1985.6 24   8.3858   0.9986
+## 1     26398     2066.7                     
+## 2     26397     2066.5  1   0.2267    0.634
 ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ weighted_probs * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26414     1972.6                     
-## 2     26390     1953.1 24   19.504   0.7246
+## 1     26398     2043.8                     
+## 2     26397     2043.7  1 0.089951   0.7642
 ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ weighted_probs * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26414     1992.2                     
-## 2     26390     1985.0 24   7.1976   0.9996
+## 1     26398     2064.9                     
+## 2     26397     2064.2  1  0.63868   0.4242
 ## Analysis of Deviance Table
 ## 
 ## Model 1: type ~ year + weighted_probs
 ## Model 2: type ~ weighted_probs * year
 ##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1     26414     1994.1                     
-## 2     26390     1984.7 24   9.3931   0.9966
    +## 1 26398 2066.8 +## 2 26397 2066.7 1 0.13195 0.7164

    Interaction terms do not predict type over and above the main effect of name origin probability and year (p > 0.01).

    Conclusion

    -

    An East Asian name has 0.1817074 the odds of being selected as an honoree, significantly lower compared to other names (\(\beta_\textrm{East Asian} =\) -1.7054, P = 0.00065666). The two groups of scientists did not have a significant association with names predicted to be Celtic/English (P = 0.61364), European (P = 0.12421), or in Other categories (P = 0.71685).

    +

    An East Asian name has 0.1709525 the odds of being selected as an honoree, significantly lower compared to other names (\(\beta_\textrm{East Asian} =\) -1.7664, P = 0.00042758). The two groups of scientists did not have a significant association with names predicted to be Celtic/English (P = 0.60355), European (P = 0.11373), or in Other categories (P = 0.70348).

    Supplement

    Supplementary Figure S7

    It’s difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction. There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field. However, just because a nationality isn’t underrepresented against the field doesn’t mean scientists from that nationality are appropriately represented.

    -
    df2 <- iscb_pubmed_oth %>% 
-  filter(region != 'OtherCategories') %>% 
+
    df2 <- iscb_pubmed_oth %>%
+  filter(region != "OtherCategories") %>%
   recode_region()
 
 fig_s7 <- iscb_pubmed_sum %>%
   recode_region() %>%
-  gam_and_ci(df2 = df2,
-             start_y = start_year, end_y = end_year) +
+  gam_and_ci(
+    df2 = df2,
+    start_y = start_year, end_y = end_year
+  ) +
   theme(legend.position = c(0.8, 0.1)) +
   facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
-  
+
 fig_s7
    
 
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    
 
    
-
    ggsave('figs/fig_s7.png', fig_s7, width = 6, height = 6)
    
+
    ggsave("figs/fig_s7.png", fig_s7, width = 6, height = 6)
    
 
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    
-
    ggsave('figs/fig_s7.svg', fig_s7, width = 6, height = 6)
    
+
    ggsave("figs/fig_s7.svg", fig_s7, width = 6, height = 6)
    
 
    ## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'
    
 
    sessionInfo()
    
 
    ## R version 4.0.3 (2020-10-10)
@@ -2365,52 +1940,64 @@ 
    Supplementary Figure S7
    
 ## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
 ## 
 ## locale:
-##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
-##  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
-##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
-## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
+##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
+##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
+##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
+## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
 ## attached base packages:
 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-##  [1] broom_0.7.2         DT_0.16             epitools_0.5-10.1   gdtools_0.2.2      
-##  [5] wru_0.1-10          rnaturalearth_0.1.0 lubridate_1.7.9.2   caret_6.0-86       
-##  [9] lattice_0.20-41     forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
-## [13] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2         tibble_3.0.4       
-## [17] ggplot2_3.3.2       tidyverse_1.3.0    
+##  [1] broom_0.7.2         DT_0.16             epitools_0.5-10.1  
+##  [4] gdtools_0.2.2       wru_0.1-10          rnaturalearth_0.1.0
+##  [7] lubridate_1.7.9.2   caret_6.0-86        lattice_0.20-41    
+## [10] forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
+## [13] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
+## [16] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0    
 ## 
 ## loaded via a namespace (and not attached):
-##   [1] colorspace_2.0-0        ellipsis_0.3.1          class_7.3-17            rprojroot_1.3-2        
-##   [5] fs_1.5.0                rstudioapi_0.12         farver_2.0.3            remotes_2.2.0          
-##   [9] prodlim_2019.11.13      fansi_0.4.1             xml2_1.3.2              codetools_0.2-16       
-##  [13] splines_4.0.3           knitr_1.30              pkgload_1.1.0           jsonlite_1.7.1         
-##  [17] pROC_1.16.2             dbplyr_2.0.0            rgeos_0.5-5             compiler_4.0.3         
-##  [21] httr_1.4.2              backports_1.2.0         assertthat_0.2.1        Matrix_1.2-18          
-##  [25] cli_2.1.0               htmltools_0.5.0         prettyunits_1.1.1       tools_4.0.3            
-##  [29] gtable_0.3.0            glue_1.4.2              rnaturalearthdata_0.1.0 reshape2_1.4.4         
-##  [33] Rcpp_1.0.5              cellranger_1.1.0        vctrs_0.3.4             svglite_1.2.3.2        
-##  [37] nlme_3.1-149            iterators_1.0.13        crosstalk_1.1.0.1       timeDate_3043.102      
-##  [41] gower_0.2.2             xfun_0.19               ps_1.4.0                testthat_3.0.0         
-##  [45] rvest_0.3.6             lifecycle_0.2.0         devtools_2.3.2          MASS_7.3-53            
-##  [49] scales_1.1.1            ipred_0.9-9             hms_0.5.3               RColorBrewer_1.1-2     
-##  [53] yaml_2.2.1              curl_4.3                memoise_1.1.0           rpart_4.1-15           
-##  [57] stringi_1.5.3           desc_1.2.0              foreach_1.5.1           e1071_1.7-4            
-##  [61] pkgbuild_1.1.0          lava_1.6.8.1            systemfonts_0.3.2       rlang_0.4.8            
-##  [65] pkgconfig_2.0.3         evaluate_0.14           sf_0.9-6                recipes_0.1.15         
-##  [69] htmlwidgets_1.5.2       labeling_0.4.2          cowplot_1.1.0           tidyselect_1.1.0       
-##  [73] processx_3.4.4          plyr_1.8.6              magrittr_1.5            R6_2.5.0               
-##  [77] generics_0.1.0          DBI_1.1.0               mgcv_1.8-33             pillar_1.4.6           
-##  [81] haven_2.3.1             withr_2.3.0             units_0.6-7             survival_3.2-7         
-##  [85] sp_1.4-4                nnet_7.3-14             modelr_0.1.8            crayon_1.3.4           
-##  [89] KernSmooth_2.23-17      utf8_1.1.4              rmarkdown_2.5           usethis_1.6.3          
-##  [93] grid_4.0.3              readxl_1.3.1            data.table_1.13.2       callr_3.5.1            
-##  [97] ModelMetrics_1.2.2.2    reprex_0.3.0            digest_0.6.27           classInt_0.4-3         
-## [101] stats4_4.0.3            munsell_0.5.0           viridisLite_0.3.0       sessioninfo_1.1.1
    
+##   [1] colorspace_2.0-0        ellipsis_0.3.1          class_7.3-17           
+##   [4] rprojroot_1.3-2         fs_1.5.0                rstudioapi_0.12        
+##   [7] farver_2.0.3            remotes_2.2.0           prodlim_2019.11.13     
+##  [10] fansi_0.4.1             xml2_1.3.2              codetools_0.2-16       
+##  [13] splines_4.0.3           knitr_1.30              pkgload_1.1.0          
+##  [16] jsonlite_1.7.1          pROC_1.16.2             dbplyr_2.0.0           
+##  [19] rgeos_0.5-5             compiler_4.0.3          httr_1.4.2             
+##  [22] backports_1.2.0         assertthat_0.2.1        Matrix_1.2-18          
+##  [25] cli_2.1.0               htmltools_0.5.0         prettyunits_1.1.1      
+##  [28] tools_4.0.3             gtable_0.3.0            glue_1.4.2             
+##  [31] rnaturalearthdata_0.1.0 reshape2_1.4.4          Rcpp_1.0.5             
+##  [34] cellranger_1.1.0        vctrs_0.3.4             svglite_1.2.3.2        
+##  [37] nlme_3.1-149            iterators_1.0.13        crosstalk_1.1.0.1      
+##  [40] timeDate_3043.102       gower_0.2.2             xfun_0.19              
+##  [43] ps_1.4.0                testthat_3.0.0          rvest_0.3.6            
+##  [46] lifecycle_0.2.0         devtools_2.3.2          MASS_7.3-53            
+##  [49] scales_1.1.1            ipred_0.9-9             hms_0.5.3              
+##  [52] RColorBrewer_1.1-2      yaml_2.2.1              curl_4.3               
+##  [55] memoise_1.1.0           rpart_4.1-15            stringi_1.5.3          
+##  [58] desc_1.2.0              foreach_1.5.1           e1071_1.7-4            
+##  [61] pkgbuild_1.1.0          lava_1.6.8.1            systemfonts_0.3.2      
+##  [64] rlang_0.4.8             pkgconfig_2.0.3         evaluate_0.14          
+##  [67] sf_0.9-6                recipes_0.1.15          htmlwidgets_1.5.2      
+##  [70] labeling_0.4.2          cowplot_1.1.0           tidyselect_1.1.0       
+##  [73] processx_3.4.4          plyr_1.8.6              magrittr_1.5           
+##  [76] R6_2.5.0                generics_0.1.0          DBI_1.1.0              
+##  [79] mgcv_1.8-33             pillar_1.4.6            haven_2.3.1            
+##  [82] withr_2.3.0             units_0.6-7             survival_3.2-7         
+##  [85] sp_1.4-4                nnet_7.3-14             modelr_0.1.8           
+##  [88] crayon_1.3.4            KernSmooth_2.23-17      utf8_1.1.4             
+##  [91] rmarkdown_2.5           usethis_1.6.3           grid_4.0.3             
+##  [94] readxl_1.3.1            data.table_1.13.2       callr_3.5.1            
+##  [97] ModelMetrics_1.2.2.2    reprex_0.3.0            digest_0.6.27          
+## [100] classInt_0.4-3          stats4_4.0.3            munsell_0.5.0          
+## [103] viridisLite_0.3.0       sessioninfo_1.1.1
    -
    ---
title: "Representation analysis of name origin in the US"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
source('utils/r-utils.R')
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load('Rdata/raws.Rdata')

alpha_threshold <- qnorm(0.975)

pubmed_nat_df <-  corr_authors %>%
  filter(year(year) >= 2002) %>% 
  separate_rows(countries, sep = ',') %>% 
  filter(countries == 'US') %>% 
  left_join(nationalize_df, by = c('fore_name', 'last_name')) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  separate_rows(afflcountries, sep = '\\|') %>% 
  filter(afflcountries == 'United States') %>% 
  left_join(nationalize_df, by = c('fore_name', 'last_name'))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c('Celtic/English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Arabic', 'Hebrew', 'African', 'Nordic', 'Greek'), 'names')

region_cols <- c('#ffffb3', '#fccde5', '#b3de69', '#fdb462', '#80b1d3', '#8dd3c7', '#bebada', '#fb8072', '#bc80bd', '#ccebc5')
```

## Organize data

Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename('journal' = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(type = 'Keynote speakers/Fellows',
         adjusted_citations = 1) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = 'Pubmed authors')
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
               names_to = 'region',
               values_to = 'probabilities') %>%
  filter(!is.na(probabilities)) %>% 
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations/pmc_citations_year,
    weighted_probs = probabilities*weight
  ) 

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>% 
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2)/(sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = 'drop'
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != 'OtherCategories')
```

## Figures for paper

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_us_name_origina <- iscb_pubmed_sum %>%
  filter(year < '2020-01-01') %>%
  region_breakdown('main', region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2))

large_regions <- c('CelticEnglish', 'EastAsian', 'European', 'OtherCategories')

## Mean and standard deviation of predicted probabilities:
fig_us_name_originb <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(df2 = iscb_pubmed_oth %>%
               filter(region %in% large_regions) %>%
               recode_region(),
             start_y = start_year, end_y = end_year) + 
  theme(legend.position = c(0.88, 0.83),
        panel.grid.minor = element_blank(),
        legend.margin = margin(-0.5, 0, 0, 0, unit='cm'),
        legend.text = element_text(size = 6)) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = 'AUTO', ncol = 1, rel_heights = c(1.3,1))
fig_us_name_origin
ggsave('figs/us_name_origin.png', fig_us_name_origin, width = 6.5, height = 5.5)
ggsave('figs/us_name_origin.svg', fig_us_name_origin, width = 6.5, height = 5.5)
```

## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>% 
  ungroup() %>% 
  mutate(
    # year = c(scale(year)),
    year = as.factor(year),
    type = relevel(as.factor(type), ref = 'Pubmed authors'))
main_lm <- function(regioni){
  glm(type ~ year + weighted_probs, 
      data = iscb_lm %>% 
        filter(region == regioni, !is.na(weighted_probs)),
      family = 'binomial')
}

inte_lm <- function(regioni){
  glm(type ~ weighted_probs*year, 
      data = iscb_lm %>% 
  filter(region == regioni, !is.na(weighted_probs)),
      family = 'binomial')
}

main_list <- lapply(large_regions, main_lm)

names(main_list) <- large_regions
lapply(main_list, summary)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, summary)
for (i in 1:4){
  print(anova(main_list[[i]], inte_list[[i]], test = 'Chisq'))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_exp <- function(i, colu){
  broom::tidy(main_list[[i]]) %>% 
    filter(term == 'weighted_probs') %>% 
    pull(colu) 
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion

An East Asian name has `r exp(get_exp(2, 'estimate'))` the odds of being selected as an honoree, significantly lower compared to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_exp(2, 'estimate'))`, _P_ = `r print_p(get_exp(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be Celtic/English (_P_ = `r print_p(get_exp(1, 'p.value'))`), European (_P_ = `r print_p(get_exp(3, 'p.value'))`), or in Other categories (_P_ = `r print_p(get_exp(4, 'p.value'))`).

## Supplement

### Supplementary Figure S7 {#sup_fig_s7}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
df2 <- iscb_pubmed_oth %>% 
  filter(region != 'OtherCategories') %>% 
  recode_region()

fig_s7 <- iscb_pubmed_sum %>%
  recode_region() %>%
  gam_and_ci(df2 = df2,
             start_y = start_year, end_y = end_year) +
  theme(legend.position = c(0.8, 0.1)) +
  facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)
  
fig_s7
ggsave('figs/fig_s7.png', fig_s7, width = 6, height = 6)
ggsave('figs/fig_s7.svg', fig_s7, width = 6, height = 6)
```


```{r}
sessionInfo()
```

    +
    ---
title: "Representation analysis of name origin in the US"
---

```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
source("utils/r-utils.R")
theme_set(theme_bw() + theme(legend.title = element_blank()))
```

Only keep articles from 2002 because few authors had nationality predictions before 2002 (mostly due to missing metadata).
See [093.summary-stats](docs/093.summary-stats.html) for more details.

```{r}
load("Rdata/raws.Rdata")

alpha_threshold <- qnorm(0.975)

pubmed_nat_df <- corr_authors %>%
  filter(year(year) >= 2002) %>%
  separate_rows(countries, sep = ",") %>%
  filter(countries == "US") %>%
  left_join(nationalize_df, by = c("fore_name", "last_name")) %>%
  group_by(pmid, journal, publication_date, year, adjusted_citations) %>%
  summarise_at(vars(African:SouthAsian), mean, na.rm = T) %>%
  ungroup()

iscb_nat_df <- keynotes %>%
  separate_rows(afflcountries, sep = "\\|") %>%
  filter(afflcountries == "United States") %>%
  left_join(nationalize_df, by = c("fore_name", "last_name"))

start_year <- 1992
end_year <- 2019
n_years <- end_year - start_year
my_jours <- unique(pubmed_nat_df$journal)
my_confs <- unique(iscb_nat_df$conference)
n_jours <- length(my_jours)
n_confs <- length(my_confs)
region_levels <- paste(c("Celtic/English", "European", "East Asian", "Hispanic", "South Asian", "Arabic", "Hebrew", "African", "Nordic", "Greek"), "names")

region_cols <- c("#ffffb3", "#fccde5", "#b3de69", "#fdb462", "#80b1d3", "#8dd3c7", "#bebada", "#fb8072", "#bc80bd", "#ccebc5")
```

## Organize data

Prepare data frames for later analyses:

- rbind results of race predictions in iscb and Pubmed
- pivot long
- compute mean, sd, marginal error

```{r}
iscb_pubmed_oth <- iscb_nat_df %>%
  rename("journal" = conference) %>%
  select(year, journal, African:SouthAsian, publication_date) %>%
  mutate(
    type = "Keynote speakers/Fellows",
    adjusted_citations = 1
  ) %>%
  bind_rows(
    pubmed_nat_df %>%
      select(year, journal, African:SouthAsian, publication_date, adjusted_citations) %>%
      mutate(type = "Pubmed authors")
  ) %>%
  mutate(OtherCategories = SouthAsian + Hispanic + Jewish + Muslim + Nordic + Greek + African) %>%
  pivot_longer(c(African:SouthAsian, OtherCategories),
    names_to = "region",
    values_to = "probabilities"
  ) %>%
  filter(!is.na(probabilities)) %>%
  group_by(type, year, region) %>%
  mutate(
    pmc_citations_year = mean(adjusted_citations),
    weight = adjusted_citations / pmc_citations_year,
    weighted_probs = probabilities * weight
  )

iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
  summarise(
    mean_prob = mean(weighted_probs),
    se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
    me_prob = alpha_threshold * se_prob,
    .groups = "drop"
  )

iscb_pubmed_sum <- iscb_pubmed_sum_oth %>%
  filter(region != "OtherCategories")
```

## Figures for paper

```{r fig.height=7, fig.width=9, warning=FALSE}
fig_us_name_origina <- iscb_pubmed_sum %>%
  filter(year < "2020-01-01") %>%
  region_breakdown("main", region_levels, fct_rev(type)) +
  guides(fill = guide_legend(nrow = 2))

large_regions <- c("CelticEnglish", "EastAsian", "European", "OtherCategories")

## Mean and standard deviation of predicted probabilities:
fig_us_name_originb <- iscb_pubmed_sum_oth %>%
  filter(region %in% large_regions) %>%
  recode_region() %>%
  gam_and_ci(
    df2 = iscb_pubmed_oth %>%
      filter(region %in% large_regions) %>%
      recode_region(),
    start_y = start_year, end_y = end_year
  ) +
  theme(
    legend.position = c(0.88, 0.83),
    panel.grid.minor = element_blank(),
    legend.margin = margin(-0.5, 0, 0, 0, unit = "cm"),
    legend.text = element_text(size = 6)
  ) +
  facet_wrap(vars(fct_relevel(region, large_regions)), nrow = 1)

fig_us_name_origin <- cowplot::plot_grid(fig_us_name_origina, fig_us_name_originb, labels = "AUTO", ncol = 1, rel_heights = c(1.3, 1))
fig_us_name_origin
ggsave("figs/us_name_origin.png", fig_us_name_origin, width = 6.5, height = 5.5)
ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)
```

## Hypothesis testing

```{r}
iscb_lm <- iscb_pubmed_oth %>%
  ungroup() %>%
  mutate(
    # year = c(scale(year)),
    # year = as.factor(year),
    type = relevel(as.factor(type), ref = "Pubmed authors")
  )
main_lm <- function(regioni) {
  glm(type ~ year + weighted_probs,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
    family = "binomial"
  )
}

inte_lm <- function(regioni) {
  glm(type ~ weighted_probs * year,
    data = iscb_lm %>%
      filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
    family = "binomial"
  )
}

main_list <- lapply(large_regions, main_lm)

names(main_list) <- large_regions
lapply(main_list, broom::tidy)

inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
for (i in 1:4) {
  print(anova(main_list[[i]], inte_list[[i]], test = "Chisq"))
}
```
Interaction terms do not predict `type` over and above the main effect of name origin probability and year (_p_ > 0.01).

```{r echo = F}
get_exp <- function(i, colu) {
  broom::tidy(main_list[[i]]) %>%
    filter(term == "weighted_probs") %>%
    pull(colu)
}

print_p <- function(x) sprintf("%0.5g", x)
```

## Conclusion

An East Asian name has `r exp(get_exp(2, 'estimate'))` the odds of being selected as an honoree, significantly lower compared to other names ($\beta_\textrm{East Asian} =$ `r print_p(get_exp(2, 'estimate'))`, _P_ = `r print_p(get_exp(2, 'p.value'))`).
The two groups of scientists did not have a significant association with names predicted to be Celtic/English (_P_ = `r print_p(get_exp(1, 'p.value'))`), European (_P_ = `r print_p(get_exp(3, 'p.value'))`), or in Other categories (_P_ = `r print_p(get_exp(4, 'p.value'))`).

## Supplement

### Supplementary Figure S7 {#sup_fig_s7}
It's difficult to come to a conclusion for other regions with so few data points and the imperfect accuracy of our prediction.
There seems to be little difference between the proportion of keynote speakers of African, Arabic, South Asian and Hispanic origin than those in the field.
However, just because a nationality isn't underrepresented against the field doesn't mean scientists from that nationality are appropriately represented.

```{r fig.height=6, warning=FALSE}
df2 <- iscb_pubmed_oth %>%
  filter(region != "OtherCategories") %>%
  recode_region()

fig_s7 <- iscb_pubmed_sum %>%
  recode_region() %>%
  gam_and_ci(
    df2 = df2,
    start_y = start_year, end_y = end_year
  ) +
  theme(legend.position = c(0.8, 0.1)) +
  facet_wrap(vars(fct_relevel(region, region_levels)), ncol = 3)

fig_s7
ggsave("figs/fig_s7.png", fig_s7, width = 6, height = 6)
ggsave("figs/fig_s7.svg", fig_s7, width = 6, height = 6)
```


```{r}
sessionInfo()
```

    diff --git a/docs/15.analyze-2020.html b/docs/15.analyze-2020.html index 92a096f..237c854 100644 --- a/docs/15.analyze-2020.html +++ b/docs/15.analyze-2020.html @@ -1795,48 +1795,60 @@

    Name origins

    ## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so ## ## locale: -## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 -## [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C -## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C -## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C +## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C +## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 +## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C +## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C +## [9] LC_ADDRESS=C LC_TELEPHONE=C +## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: -## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1 gdtools_0.2.2 -## [5] wru_0.1-10 rnaturalearth_0.1.0 lubridate_1.7.9.2 caret_6.0-86 -## [9] lattice_0.20-41 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 -## [13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 -## [17] ggplot2_3.3.2 tidyverse_1.3.0 +## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1 +## [4] gdtools_0.2.2 wru_0.1-10 rnaturalearth_0.1.0 +## [7] lubridate_1.7.9.2 caret_6.0-86 lattice_0.20-41 +## [10] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 +## [13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 +## [16] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 ## ## loaded via a namespace (and not attached): -## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 rprojroot_1.3-2 -## [5] fs_1.5.0 rstudioapi_0.12 farver_2.0.3 remotes_2.2.0 -## [9] prodlim_2019.11.13 fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 -## [13] splines_4.0.3 knitr_1.30 pkgload_1.1.0 jsonlite_1.7.1 -## [17] pROC_1.16.2 dbplyr_2.0.0 rgeos_0.5-5 compiler_4.0.3 -## [21] httr_1.4.2 backports_1.2.0 assertthat_0.2.1 Matrix_1.2-18 -## [25] cli_2.1.0 htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.3 -## [29] gtable_0.3.0 glue_1.4.2 rnaturalearthdata_0.1.0 reshape2_1.4.4 -## [33] Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.4 svglite_1.2.3.2 -## [37] nlme_3.1-149 iterators_1.0.13 crosstalk_1.1.0.1 timeDate_3043.102 -## [41] gower_0.2.2 xfun_0.19 ps_1.4.0 testthat_3.0.0 -## [45] rvest_0.3.6 lifecycle_0.2.0 devtools_2.3.2 MASS_7.3-53 -## [49] scales_1.1.1 ipred_0.9-9 hms_0.5.3 RColorBrewer_1.1-2 -## [53] yaml_2.2.1 curl_4.3 memoise_1.1.0 rpart_4.1-15 -## [57] stringi_1.5.3 desc_1.2.0 foreach_1.5.1 e1071_1.7-4 -## [61] pkgbuild_1.1.0 lava_1.6.8.1 systemfonts_0.3.2 rlang_0.4.8 -## [65] pkgconfig_2.0.3 evaluate_0.14 sf_0.9-6 recipes_0.1.15 -## [69] htmlwidgets_1.5.2 labeling_0.4.2 cowplot_1.1.0 tidyselect_1.1.0 -## [73] processx_3.4.4 plyr_1.8.6 magrittr_1.5 R6_2.5.0 -## [77] generics_0.1.0 DBI_1.1.0 mgcv_1.8-33 pillar_1.4.6 -## [81] haven_2.3.1 withr_2.3.0 units_0.6-7 survival_3.2-7 -## [85] sp_1.4-4 nnet_7.3-14 modelr_0.1.8 crayon_1.3.4 -## [89] KernSmooth_2.23-17 utf8_1.1.4 rmarkdown_2.5 usethis_1.6.3 -## [93] grid_4.0.3 readxl_1.3.1 data.table_1.13.2 callr_3.5.1 -## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 digest_0.6.27 classInt_0.4-3 -## [101] stats4_4.0.3 munsell_0.5.0 viridisLite_0.3.0 sessioninfo_1.1.1 +## [1] colorspace_2.0-0 ellipsis_0.3.1 class_7.3-17 +## [4] rprojroot_1.3-2 fs_1.5.0 rstudioapi_0.12 +## [7] farver_2.0.3 remotes_2.2.0 prodlim_2019.11.13 +## [10] fansi_0.4.1 xml2_1.3.2 codetools_0.2-16 +## [13] splines_4.0.3 knitr_1.30 pkgload_1.1.0 +## [16] jsonlite_1.7.1 pROC_1.16.2 dbplyr_2.0.0 +## [19] rgeos_0.5-5 compiler_4.0.3 httr_1.4.2 +## [22] backports_1.2.0 assertthat_0.2.1 Matrix_1.2-18 +## [25] cli_2.1.0 htmltools_0.5.0 prettyunits_1.1.1 +## [28] tools_4.0.3 gtable_0.3.0 glue_1.4.2 +## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4 Rcpp_1.0.5 +## [34] cellranger_1.1.0 vctrs_0.3.4 svglite_1.2.3.2 +## [37] nlme_3.1-149 iterators_1.0.13 crosstalk_1.1.0.1 +## [40] timeDate_3043.102 gower_0.2.2 xfun_0.19 +## [43] ps_1.4.0 testthat_3.0.0 rvest_0.3.6 +## [46] lifecycle_0.2.0 devtools_2.3.2 MASS_7.3-53 +## [49] scales_1.1.1 ipred_0.9-9 hms_0.5.3 +## [52] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3 +## [55] memoise_1.1.0 rpart_4.1-15 stringi_1.5.3 +## [58] desc_1.2.0 foreach_1.5.1 e1071_1.7-4 +## [61] pkgbuild_1.1.0 lava_1.6.8.1 systemfonts_0.3.2 +## [64] rlang_0.4.8 pkgconfig_2.0.3 evaluate_0.14 +## [67] sf_0.9-6 recipes_0.1.15 htmlwidgets_1.5.2 +## [70] labeling_0.4.2 cowplot_1.1.0 tidyselect_1.1.0 +## [73] processx_3.4.4 plyr_1.8.6 magrittr_1.5 +## [76] R6_2.5.0 generics_0.1.0 DBI_1.1.0 +## [79] mgcv_1.8-33 pillar_1.4.6 haven_2.3.1 +## [82] withr_2.3.0 units_0.6-7 survival_3.2-7 +## [85] sp_1.4-4 nnet_7.3-14 modelr_0.1.8 +## [88] crayon_1.3.4 KernSmooth_2.23-17 utf8_1.1.4 +## [91] rmarkdown_2.5 usethis_1.6.3 grid_4.0.3 +## [94] readxl_1.3.1 data.table_1.13.2 callr_3.5.1 +## [97] ModelMetrics_1.2.2.2 reprex_0.3.0 digest_0.6.27 +## [100] classInt_0.4-3 stats4_4.0.3 munsell_0.5.0 +## [103] viridisLite_0.3.0 sessioninfo_1.1.1
    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-598,254 +598,254 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + @@ -854,20 +854,20 @@ - - + + - -Arab/Turk/Pers names + +Arab/Turk/Pers names - - + + @@ -876,8 +876,8 @@ - - + + @@ -886,206 +886,206 @@ - - + + - -African names + +African names - - + + - -Nordic names + +Nordic names - - + + - -Greek names + +Greek names - - + + - -Hispanic names + +Hispanic names - - + + - -South Asian names + +South Asian names - - + + - -Hebrew names + +Hebrew names - - + + - -Celtic/English names + +Celtic/English names - - + + - -European names + +European names - - + + - -East Asian names + +East Asian names - - - - - - -'95 -'00 -'05 -'10 -'15 -'20 - - - - - - -'95 -'00 -'05 -'10 -'15 -'20 - - - - - - -'95 -'00 -'05 -'10 -'15 -'20 -0% -20% -40% -60% -80% -100% - - - - - - -0% -20% -40% -60% -80% -100% - - - - - - -0% -20% -40% -60% -80% -100% - - - - - - -0% -20% -40% -60% -80% -100% - - - - - - -Estimated composition - - - - -Pubmed authors - - - - -Keynote speakers/Fellows + + + + + + +'95 +'00 +'05 +'10 +'15 +'20 + + + + + + +'95 +'00 +'05 +'10 +'15 +'20 + + + + + + +'95 +'00 +'05 +'10 +'15 +'20 +0% +20% +40% +60% +80% +100% + + + + + + +0% +20% +40% +60% +80% +100% + + + + + + +0% +20% +40% +60% +80% +100% + + + + + + +0% +20% +40% +60% +80% +100% + + + + + + +Estimated composition + + + + +Pubmed authors + + + + +Keynote speakers/Fellows