diff --git a/10.visualize-gender.Rmd b/10.visualize-gender.Rmd
index a005ea7..bef07f7 100644
--- a/10.visualize-gender.Rmd
+++ b/10.visualize-gender.Rmd
@@ -65,21 +65,13 @@ iscb_pubmed <- iscb_gender_df %>%
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
- )
+ group_by(type, year, gender)
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)),
+ mean_prob = mean(probabilities, na.rm = T),
+ se_prob = sd(probabilities, na.rm = T),
# n = mean(n),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
@@ -102,7 +94,7 @@ 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.png", fig_1, width = 5, height = 2.5, dpi = 600)
ggsave("figs/gender_breakdown.svg", fig_1, width = 5, height = 2.5)
```
@@ -131,8 +123,8 @@ fig_1d <- iscb_pubmed %>%
) %>%
group_by(type2, year, gender) %>%
summarise(
- mean_prob = mean(weighted_probs),
- se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+ mean_prob = mean(probabilities),
+ se_prob = sd(probabilities)/sqrt(n()),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
) %>%
@@ -173,7 +165,7 @@ iscb_pubmed_sum %>%
```{r echo = F}
get_p <- function(inte, colu) {
broom::tidy(inte) %>%
- filter(term == "weighted_probs") %>%
+ filter(term == "probabilities") %>%
pull(colu) %>%
sprintf("%0.5g", .)
}
@@ -181,7 +173,7 @@ get_p <- function(inte, colu) {
```{r}
iscb_lm <- iscb_pubmed %>%
- filter(gender == "probability_female", !is.na(weighted_probs)) %>%
+ filter(gender == "probability_female", !is.na(probabilities)) %>%
mutate(type = as.factor(type)) %>%
mutate(type = type %>% relevel(ref = "Pubmed authors"))
```
@@ -189,33 +181,33 @@ iscb_lm <- iscb_pubmed %>%
```{r}
scaled_iscb <- iscb_lm %>%
filter(year(year) >= 2002)
-# scaled_iscb$s_prob <- scale(scaled_iscb$weighted_probs, scale = F)
+# scaled_iscb$s_prob <- scale(scaled_iscb$probabilities, scale = F)
# scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)
-main_lm <- glm(type ~ year + weighted_probs,
+main_lm <- glm(type ~ year + probabilities,
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 ~ scale(year, scale = F) * scale(probabilities, scale = F),
# type ~ s_year * s_prob,
- type ~ year * weighted_probs,
+ type ~ year * probabilities,
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)
+# mean(scaled_iscb$probabilities)
```
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),
+# inte_lm <- glm(type ~ (year * probabilities),
# data = iscb_lm,
# family = 'binomial')
```
diff --git a/11.visualize-name-origins.Rmd b/11.visualize-name-origins.Rmd
index 2b9de29..b2de730 100644
--- a/11.visualize-name-origins.Rmd
+++ b/11.visualize-name-origins.Rmd
@@ -96,17 +96,12 @@ iscb_pubmed_oth <- iscb_nat_df %>%
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
- )
+ group_by(type, year, region)
iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
summarise(
- mean_prob = mean(weighted_probs),
- se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+ mean_prob = mean(probabilities),
+ se_prob = sd(probabilities)/sqrt(n()),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
)
@@ -127,7 +122,7 @@ for (conf in my_confs) {
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")
+ summarise(mean_prob = mean(probabilities), .groups = "drop")
}
```
@@ -169,7 +164,7 @@ fig_4b <- iscb_pubmed_sum_oth %>%
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.png", fig_4, width = 6.7, height = 5.5, dpi = 600)
ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)
```
@@ -185,7 +180,7 @@ iscb_lm <- iscb_pubmed_oth %>%
type = as.factor(type) %>% relevel(ref = "Pubmed authors")
)
main_lm <- function(regioni) {
- glm(type ~ year + weighted_probs,
+ glm(type ~ year + probabilities,
data = iscb_lm %>%
filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
@@ -193,9 +188,9 @@ main_lm <- function(regioni) {
}
inte_lm <- function(regioni) {
- glm(type ~ year * weighted_probs,
+ glm(type ~ year * probabilities,
data = iscb_lm %>%
- filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+ filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
)
}
@@ -215,7 +210,7 @@ Interaction terms do not predict `type` over and above the main effect of name o
```{r echo = F}
get_p <- function(i, colu) {
broom::tidy(main_list[[i]]) %>%
- filter(term == "weighted_probs") %>%
+ filter(term == "probabilities") %>%
pull(colu)
}
@@ -326,21 +321,16 @@ iscb_pubmed_oth_lag <- iscb_nat_df %>%
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
- )
+ group_by(type, year, region)
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,
+ glm(type ~ year + probabilities,
data = iscb_lm_lag %>%
- filter(region == regioni, !is.na(weighted_probs)),
+ filter(region == regioni, !is.na(probabilities)),
family = "binomial"
)
}
diff --git a/12.analyze-affiliation.Rmd b/12.analyze-affiliation.Rmd
index e700614..e85fc4f 100644
--- a/12.analyze-affiliation.Rmd
+++ b/12.analyze-affiliation.Rmd
@@ -293,7 +293,7 @@ enrichment_plot_right <- plot_obs_exp_right %>%
enrichment_plot <- cowplot::plot_grid(enrichment_plot_left, enrichment_plot_right,
rel_widths = c(1, 1.3))
enrichment_plot
-ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5)
+ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5, dpi = 600)
```
diff --git a/14.us-name-origin.Rmd b/14.us-name-origin.Rmd
index 06ca206..ef737ed 100644
--- a/14.us-name-origin.Rmd
+++ b/14.us-name-origin.Rmd
@@ -70,17 +70,12 @@ iscb_pubmed_oth <- iscb_nat_df %>%
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
- )
+ group_by(type, year, region)
iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
summarise(
- mean_prob = mean(weighted_probs),
- se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+ mean_prob = mean(probabilities),
+ se_prob = sd(probabilities)/sqrt(n()),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
)
@@ -119,7 +114,7 @@ fig_us_name_originb <- iscb_pubmed_sum_oth %>%
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.png", fig_us_name_origin, width = 6.5, height = 5.5, dpi = 600)
ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)
```
@@ -134,17 +129,17 @@ iscb_lm <- iscb_pubmed_oth %>%
type = relevel(as.factor(type), ref = "Pubmed authors")
)
main_lm <- function(regioni) {
- glm(type ~ year + weighted_probs,
+ glm(type ~ year + probabilities,
data = iscb_lm %>%
- filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+ filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
)
}
inte_lm <- function(regioni) {
- glm(type ~ weighted_probs * year,
+ glm(type ~ probabilities * year,
data = iscb_lm %>%
- filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+ filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
)
}
@@ -165,7 +160,7 @@ Interaction terms do not predict `type` over and above the main effect of name o
```{r echo = F}
get_exp <- function(i, colu) {
broom::tidy(main_list[[i]]) %>%
- filter(term == "weighted_probs") %>%
+ filter(term == "probabilities") %>%
pull(colu)
}
diff --git a/_output.yaml b/_output.yaml
index ea2b6fe..0681b68 100644
--- a/_output.yaml
+++ b/_output.yaml
@@ -3,3 +3,4 @@ html_document:
toc: true
toc_float: true
code_download: true
+ dpi: 600
diff --git a/docs/091.draw-roc.html b/docs/091.draw-roc.html
index 524ce17..387df08 100644
--- a/docs/091.draw-roc.html
+++ b/docs/091.draw-roc.html
@@ -1741,35 +1741,22 @@ Plotting ROC curves
Name origin prediction method performance
-
-
## ── 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() ──
-## x dplyr::filter() masks stats::filter()
-## x dplyr::lag() masks stats::lag()
-
# still need to install caret for the calibration function because tidymodels's
-# probably hasn't published this yet
-library(caret)
-
## Loading required package: lattice
+
library(tidyverse)
+# still need to install caret for the calibration function because tidymodels's
+# probably hasn't published this yet
+library(caret)
+
+source('utils/r-utils.R')
+theme_set(theme_bw())
+
roc_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/models/NamePrism_roc_curves.tsv') %>%
+ rename('region' = category) %>%
+ # recode_region_letter() %>%
+ recode_region() %>%
+ group_by(region) %>%
+ mutate(Sensitivity = tpr, Specificity = 1-fpr, dSens = c(abs(diff(1-tpr)), 0)) %>%
+ ungroup()
##
-## Attaching package: 'caret'
-
## The following object is masked from 'package:purrr':
-##
-## lift
-
source('utils/r-utils.R')
-theme_set(theme_bw())
-
roc_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/models/NamePrism_roc_curves.tsv') %>%
- rename('region' = category) %>%
- # recode_region_letter() %>%
- recode_region() %>%
- group_by(region) %>%
- 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(),
@@ -1780,38 +1767,38 @@ Name origin prediction method performance
## ℹ Unknown levels in `f`: OtherCategories
## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
auc_df <- roc_df %>%
- group_by(region) %>%
- # add_count() %>%
- summarise(auc = sum((1 - fpr) * dSens),
- n = n()) %>%
- arrange(desc(auc)) %>%
- mutate(auc_pct = 100 * auc,
- reg_auc = paste0(region, ', AUC = ', round(auc_pct, 1), '%'))
+
auc_df <- roc_df %>%
+ group_by(region) %>%
+ # add_count() %>%
+ summarise(auc = sum((1 - fpr) * dSens),
+ n = n()) %>%
+ arrange(desc(auc)) %>%
+ mutate(auc_pct = 100 * auc,
+ reg_auc = paste0(region, ', AUC = ', round(auc_pct, 1), '%'))
## `summarise()` ungrouping output (override with `.groups` argument)
-
# region_levels <- c('Celtic English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Muslim', 'Israeli', 'African')
-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('#b3de69', '#fdb462', '#bc80bd', '#8dd3c7', '#fccde5', '#ffffb3', '#ccebc5', '#bebada', '#80b1d3', '#fb8072')
-
-fig_3a <- roc_df %>%
- left_join(auc_df, by = 'region') %>%
- ggplot(aes(x = Sensitivity, y = Specificity, color = fct_relevel(reg_auc, as.character(auc_df$reg_auc)))) +
- scale_color_manual(values = region_cols) +
- geom_step(size = 1, alpha = 0.8) +
- coord_fixed() +
- scale_x_reverse(breaks = seq(1, 0, -0.2), labels = scales::percent) +
- scale_y_continuous(breaks = seq(0, 1, 0.2), labels = scales::percent, limits = c(NA, 1.05)) +
- theme(legend.position = c(0.62, 0.42),
- legend.title = element_blank(),
- legend.text.align = 1,
- legend.text = element_text(size = 7),
- legend.margin = margin(-0.2, 0.2, 0.2, 0, unit='cm'))
-
predictions_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/NamePrism_results_test.tsv') %>%
- mutate(y_true = as.factor(truth)) %>%
- select(-truth)
+
# region_levels <- c('Celtic English', 'European', 'East Asian', 'Hispanic', 'South Asian', 'Muslim', 'Israeli', 'African')
+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('#b3de69', '#fdb462', '#bc80bd', '#8dd3c7', '#fccde5', '#ffffb3', '#ccebc5', '#bebada', '#80b1d3', '#fb8072')
+
+fig_3a <- roc_df %>%
+ left_join(auc_df, by = 'region') %>%
+ ggplot(aes(x = Sensitivity, y = Specificity, color = fct_relevel(reg_auc, as.character(auc_df$reg_auc)))) +
+ scale_color_manual(values = region_cols) +
+ geom_step(size = 1, alpha = 0.8) +
+ coord_fixed() +
+ scale_x_reverse(breaks = seq(1, 0, -0.2), labels = scales::percent) +
+ scale_y_continuous(breaks = seq(0, 1, 0.2), labels = scales::percent, limits = c(NA, 1.05)) +
+ theme(legend.position = c(0.62, 0.42),
+ legend.title = element_blank(),
+ legend.text.align = 1,
+ legend.text = element_text(size = 7),
+ legend.margin = margin(-0.2, 0.2, 0.2, 0, unit='cm'))
+
predictions_df <- read_tsv('https://raw.githubusercontent.com/greenelab/wiki-nationality-estimate/7c22d0a5f661ce5aeb785215095deda40973ff17/data/NamePrism_results_test.tsv') %>%
+ mutate(y_true = as.factor(truth)) %>%
+ select(-truth)
##
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
## cols(
## African = col_double(),
## CelticEnglish = col_double(),
@@ -1825,83 +1812,79 @@ Name origin prediction method performance
## SouthAsian = col_double(),
## truth = col_character()
## )
-
regs <- predictions_df %>% select(African:SouthAsian) %>% colnames()
-cal_dfs <- list()
-for (reg in regs) {
- pred_reg <- predictions_df %>%
- mutate(y_true_bin = as.factor((y_true == reg))) %>%
- rename(prob = reg) %>%
- select(y_true_bin, prob)
-
- cal_dfs[[reg]] <- calibration(y_true_bin ~ prob,
- data = pred_reg,
- cuts = 11,
- class = 'TRUE')$data %>%
- mutate(region = reg)
-}
-
## Note: Using an external vector in selections is ambiguous.
-## ℹ Use `all_of(reg)` instead of `reg` to silence this message.
-## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
-## This message is displayed once per session.
-
-
## 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$region)))) +
- geom_abline(slope = 1, linetype = 2, alpha = 0.5) +
- scale_y_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(-0.005, 1.045)) +
- scale_x_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(0, 1)) +
- coord_fixed() +
- geom_point() +
- geom_line() +
- scale_color_manual(values = region_cols) +
- theme(legend.position = 'None') +
- labs(x = 'Predicted probability', y = 'Fraction of names')
+
regs <- predictions_df %>% select(African:SouthAsian) %>% colnames()
+cal_dfs <- list()
+for (reg in regs) {
+ pred_reg <- predictions_df %>%
+ mutate(y_true_bin = as.factor((y_true == reg))) %>%
+ rename(prob = reg) %>%
+ select(y_true_bin, prob)
+
+ cal_dfs[[reg]] <- calibration(y_true_bin ~ prob,
+ data = pred_reg,
+ cuts = 11,
+ class = 'TRUE')$data %>%
+ mutate(region = reg)
+}
+cal_dfs$EastAsian
+
## calibModelVar bin Percent Lower Upper
+## 1 prob [0,0.0909] 0.973038 0.9061138 1.043559
+## 2 prob (0.0909,0.182] 12.715105 10.7555376 14.887108
+## 3 prob (0.182,0.273] 20.620843 16.9791523 24.652952
+## 4 prob (0.273,0.364] 29.924242 24.4643714 35.841394
+## 5 prob (0.364,0.455] 35.897436 29.7515540 42.405681
+## 6 prob (0.455,0.545] 38.536585 31.8402892 45.569554
+## 7 prob (0.545,0.636] 45.637584 37.4635833 53.988516
+## 8 prob (0.636,0.727] 56.953642 48.6544756 64.974492
+## 9 prob (0.727,0.818] 61.421320 54.2394760 68.253900
+## 10 prob (0.818,0.909] 71.764706 66.6571343 76.488532
+## 11 prob (0.909,1] 97.209555 96.8524649 97.536348
+## Count midpoint region
+## 1 777 4.545455 EastAsian
+## 2 133 13.636364 EastAsian
+## 3 93 22.727273 EastAsian
+## 4 79 31.818182 EastAsian
+## 5 84 40.909091 EastAsian
+## 6 79 50.000000 EastAsian
+## 7 68 59.090909 EastAsian
+## 8 86 68.181818 EastAsian
+## 9 121 77.272727 EastAsian
+## 10 244 86.363636 EastAsian
+## 11 8953 95.454545 EastAsian
+
fig_3b <- bind_rows(cal_dfs) %>%
+ recode_region() %>%
+ ggplot(aes(x = midpoint/100, y = Percent/100, color = fct_relevel(region, as.character(auc_df$region)))) +
+ geom_abline(slope = 1, linetype = 2, alpha = 0.5) +
+ scale_y_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(-0.005, 1.045)) +
+ scale_x_continuous(labels = scales::percent_format(accuracy = 20L), breaks = seq(0, 1, 0.2), limits = c(0, 1)) +
+ coord_fixed() +
+ geom_point() +
+ geom_line() +
+ scale_color_manual(values = region_cols) +
+ theme(legend.position = 'None') +
+ labs(x = 'Predicted probability', y = 'Fraction of names')
## Warning: Problem with `mutate()` input `region`.
## ℹ Unknown levels in `f`: OtherCategories
## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
n_obs <- sum(auc_df$n)
-short_regs <- auc_df$region %>%
- as.character() %>%
- gsub(' names', '', .)
-
-heat_dat <- predictions_df %>%
- group_by(y_true) %>%
- summarise_if(is.numeric, mean, na.rm = T) %>%
- ungroup() %>%
- pivot_longer(- y_true, names_to = 'region', values_to = 'pred_prob') %>%
- recode_region() %>%
- rename('reg_hat' = region, 'region' = y_true) %>%
- recode_region() %>%
- rename('y_true' = region, 'region' = reg_hat) %>%
- left_join(auc_df, by = 'region') %>%
- mutate(scale_pred_prob = log2((pred_prob)/(n/n_obs)),
- region = region %>% gsub(' names', '', .) %>% fct_relevel(short_regs),
- y_true = y_true %>% gsub(' names', '', .) %>% fct_relevel(short_regs))
+
n_obs <- sum(auc_df$n)
+short_regs <- auc_df$region %>%
+ as.character() %>%
+ gsub(' names', '', .)
+
+heat_dat <- predictions_df %>%
+ group_by(y_true) %>%
+ summarise_if(is.numeric, mean, na.rm = T) %>%
+ ungroup() %>%
+ pivot_longer(- y_true, names_to = 'region', values_to = 'pred_prob') %>%
+ recode_region() %>%
+ rename('reg_hat' = region, 'region' = y_true) %>%
+ recode_region() %>%
+ rename('y_true' = region, 'region' = reg_hat) %>%
+ left_join(auc_df, by = 'region') %>%
+ mutate(scale_pred_prob = log2((pred_prob)/(n/n_obs)),
+ region = region %>% gsub(' names', '', .) %>% fct_relevel(short_regs),
+ y_true = y_true %>% gsub(' names', '', .) %>% fct_relevel(short_regs))
## Warning: Problem with `mutate()` input `region`.
## ℹ Unknown levels in `f`: OtherCategories
## ℹ Input `region` is `fct_recode(...)`.
@@ -1910,31 +1893,31 @@
Name origin prediction method performance
## ℹ Unknown levels in `f`: OtherCategories
## ℹ Input `region` is `fct_recode(...)`.
## Warning: Unknown levels in `f`: OtherCategories
-
fig_3c <- ggplot(heat_dat, aes(y_true, region,
- fill = scale_pred_prob)) +
- geom_tile() +
- scale_fill_gradientn(
- colours = c("#3CBC75FF","white","#440154FF"),
- values = scales::rescale(
- c(min(heat_dat$scale_pred_prob),
- 0,
- max(heat_dat$scale_pred_prob)))
- ) +
- coord_fixed() +
- labs(x = 'True region', y = 'Predicted region', fill = bquote(log[2]~'FC')) +
- theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
- legend.position = 'top',
- legend.key.height = unit(0.2, 'cm'),
- legend.title = element_text(vjust = 1),
- legend.margin = margin(0, 0,0, -1, unit='cm'),
- axis.title.x = element_text(margin = margin(t = 27, r = 0, b = 0, l = 0)),
- axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0)))
-
-fig_3 <- cowplot::plot_grid(fig_3a, fig_3b, fig_3c, labels = 'AUTO', nrow = 1,
- rel_widths = c(2,2,1.6))
-fig_3
+
fig_3c <- ggplot(heat_dat, aes(y_true, region,
+ fill = scale_pred_prob)) +
+ geom_tile() +
+ scale_fill_gradientn(
+ colours = c("#3CBC75FF","white","#440154FF"),
+ values = scales::rescale(
+ c(min(heat_dat$scale_pred_prob),
+ 0,
+ max(heat_dat$scale_pred_prob)))
+ ) +
+ coord_fixed() +
+ labs(x = 'True region', y = 'Predicted region', fill = bquote(log[2]~'FC')) +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
+ legend.position = 'top',
+ legend.key.height = unit(0.2, 'cm'),
+ legend.title = element_text(vjust = 1),
+ legend.margin = margin(0, 0,0, -1, unit='cm'),
+ axis.title.x = element_text(margin = margin(t = 27, r = 0, b = 0, l = 0)),
+ axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0)))
+
+fig_3 <- cowplot::plot_grid(fig_3a, fig_3b, fig_3c, labels = 'AUTO', nrow = 1,
+ rel_widths = c(2,2,1.6))
+fig_3
-
# ggsave('figs/fig_3.png', fig_3, height = 4, width = 10)
+
# ggsave('figs/fig_3.png', fig_3, height = 4, width = 10)
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
diff --git a/docs/092.save-raws-to-Rdata.html b/docs/092.save-raws-to-Rdata.html
index 6a6034e..c479ccb 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>,
+## # … 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) %>%
@@ -4385,7 +4386,7 @@ 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(),
@@ -4406,8 +4407,8 @@ 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 54e91c6..4db9784 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(),
@@ -4412,8 +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.
@@ -4492,8 +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 1f524c3..19daa4c 100644
--- a/docs/10.visualize-gender.html
+++ b/docs/10.visualize-gender.html
@@ -1682,7 +1682,7 @@ Load data
alpha_threshold <- qnorm(0.975)
gender_df <- read_tsv("data/gender/genderize.tsv")
##
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
## cols(
## fore_name_simple = col_character(),
## n_authors = col_double(),
@@ -1730,21 +1730,13 @@ Prepare data frames for later analyses
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
- )
+ group_by(type, year, gender)
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)),
+ mean_prob = mean(probabilities, na.rm = T),
+ se_prob = sd(probabilities, na.rm = T),
# n = mean(n),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
@@ -1760,15 +1752,15 @@ Figure 2: ISCB Fellows and keynote speakers appear more evenly split between
# 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.png", fig_1, width = 5, height = 2.5, dpi = 600)
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
## <chr> <dbl>
## 1 Keynote speakers/Fellows 0.303
-## 2 Pubmed authors 0.268
+## 2 Pubmed authors 0.277
Supplementary Figure S2
@@ -1784,8 +1776,8 @@
Supplementary Figure S2
) %>%
group_by(type2, year, gender) %>%
summarise(
- mean_prob = mean(weighted_probs),
- se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+ mean_prob = mean(probabilities),
+ se_prob = sd(probabilities)/sqrt(n()),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
) %>%
@@ -1794,8 +1786,8 @@
Supplementary Figure S2
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.
+
## Scale for 'x' is already present. Adding another scale for
+## 'x', which will replace the existing scale.
@@ -1809,58 +1801,59 @@ Mean and standard deviation of predicted probabilities
) +
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).
-
+## Warning: Removed 13171 rows containing non-finite values
+## (stat_smooth).
+
Hypothesis testing
iscb_lm <- iscb_pubmed %>%
- filter(gender == "probability_female", !is.na(weighted_probs)) %>%
+ filter(gender == "probability_female", !is.na(probabilities)) %>%
mutate(type = as.factor(type)) %>%
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$s_prob <- scale(scaled_iscb$probabilities, scale = F)
# scaled_iscb$s_year <- scale(scaled_iscb$year, scale = F)
-main_lm <- glm(type ~ year + weighted_probs,
+main_lm <- glm(type ~ year + probabilities,
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
+## 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 2.46e-17
+## 3 probabilities 0.193 0.146 1.33 1.85e- 1
inte_lm <- glm(
- # type ~ scale(year, scale = F) * scale(weighted_probs, scale = F),
+ # type ~ scale(year, scale = F) * scale(probabilities, scale = F),
# type ~ s_year * s_prob,
- type ~ year * weighted_probs,
+ type ~ year * probabilities,
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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -1.77 0.568 -3.11 1.85e- 3
+## 2 year -0.000291 0.0000383 -7.59 3.22e-14
+## 3 probabilities -0.992 1.29 -0.771 4.41e- 1
+## 4 year:probabilities 0.0000787 0.0000846 0.930 3.52e- 1
anova(main_lm, inte_lm, test = "Chisq")
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 153942 4582.3
-## 2 153941 4581.8 1 0.47034 0.4928
+## 1 153942 4582.8
+## 2 153941 4582.0 1 0.86975 0.351
# 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.
+# mean(scaled_iscb$probabilities)
+
The two groups of scientists did not have a significant association with the gender predicted from fore names (P = 0.18469). 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)
@@ -1878,55 +1871,73 @@ Hypothesis testing
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
-## [1] stats graphics grDevices utils datasets methods base
+## [1] stats graphics grDevices utils datasets methods
+## [7] base
##
## other attached packages:
-## [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
+## [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
-## [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
+## [1] colorspace_2.0-0 ellipsis_0.3.1
+## [3] class_7.3-17 rprojroot_1.3-2
+## [5] fs_1.5.0 rstudioapi_0.12
+## [7] farver_2.0.3 remotes_2.2.0
+## [9] prodlim_2019.11.13 fansi_0.4.1
+## [11] xml2_1.3.2 codetools_0.2-16
+## [13] splines_4.0.3 knitr_1.30
+## [15] pkgload_1.1.0 jsonlite_1.7.1
+## [17] pROC_1.16.2 dbplyr_2.0.0
+## [19] rgeos_0.5-5 compiler_4.0.3
+## [21] httr_1.4.2 backports_1.2.0
+## [23] assertthat_0.2.1 Matrix_1.2-18
+## [25] cli_2.1.0 htmltools_0.5.0
+## [27] prettyunits_1.1.1 tools_4.0.3
+## [29] gtable_0.3.0 glue_1.4.2
+## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4
+## [33] Rcpp_1.0.5 cellranger_1.1.0
+## [35] vctrs_0.3.4 svglite_1.2.3.2
+## [37] nlme_3.1-149 iterators_1.0.13
+## [39] crosstalk_1.1.0.1 timeDate_3043.102
+## [41] gower_0.2.2 xfun_0.19
+## [43] ps_1.4.0 testthat_3.0.0
+## [45] rvest_0.3.6 lifecycle_0.2.0
+## [47] devtools_2.3.2 MASS_7.3-53
+## [49] scales_1.1.1 ipred_0.9-9
+## [51] hms_0.5.3 RColorBrewer_1.1-2
+## [53] yaml_2.2.1 curl_4.3
+## [55] memoise_1.1.0 rpart_4.1-15
+## [57] stringi_1.5.3 desc_1.2.0
+## [59] foreach_1.5.1 e1071_1.7-4
+## [61] pkgbuild_1.1.0 lava_1.6.8.1
+## [63] systemfonts_0.3.2 rlang_0.4.8
+## [65] pkgconfig_2.0.3 evaluate_0.14
+## [67] sf_0.9-6 recipes_0.1.15
+## [69] htmlwidgets_1.5.2 labeling_0.4.2
+## [71] cowplot_1.1.0 tidyselect_1.1.0
+## [73] processx_3.4.4 plyr_1.8.6
+## [75] magrittr_1.5 R6_2.5.0
+## [77] generics_0.1.0 DBI_1.1.0
+## [79] mgcv_1.8-33 pillar_1.4.6
+## [81] haven_2.3.1 withr_2.3.0
+## [83] units_0.6-7 survival_3.2-7
+## [85] sp_1.4-4 nnet_7.3-14
+## [87] modelr_0.1.8 crayon_1.3.4
+## [89] KernSmooth_2.23-17 utf8_1.1.4
+## [91] rmarkdown_2.5 usethis_1.6.3
+## [93] grid_4.0.3 readxl_1.3.1
+## [95] data.table_1.13.2 callr_3.5.1
+## [97] ModelMetrics_1.2.2.2 reprex_0.3.0
+## [99] digest_0.6.27 classInt_0.4-3
+## [101] stats4_4.0.3 munsell_0.5.0
+## [103] viridisLite_0.3.0 sessioninfo_1.1.1
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
diff --git a/docs/11.visualize-name-origins.html b/docs/11.visualize-name-origins.html
index 34d453b..f8c841c 100644
--- a/docs/11.visualize-name-origins.html
+++ b/docs/11.visualize-name-origins.html
@@ -1694,7 +1694,7 @@ Representation analysis of name origins
rename("region" = Region) %>%
recode_region()
##
-## ── Column specification ────────────────────────────────────────────────────────
+## ── Column specification ─────────────────────────────────────────
## cols(
## Country = col_character(),
## Region = col_character()
@@ -1747,17 +1747,12 @@ Descriptive statistics
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
- )
+ group_by(type, year, region)
iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
summarise(
- mean_prob = mean(weighted_probs),
- se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+ mean_prob = mean(probabilities),
+ se_prob = sd(probabilities)/sqrt(n()),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
)
@@ -1777,7 +1772,7 @@ By conference keynotes/fellows
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")
+ summarise(mean_prob = mean(probabilities), .groups = "drop")
}
save(my_world, iscb_pubmed_oth, iscb_nat, file = "Rdata/iscb-pubmed_nat.Rdata")
@@ -1815,8 +1810,8 @@ Figure 4
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.png", fig_4, width = 6.7, height = 5.5, dpi = 600)
ggsave("figs/region_breakdown.svg", fig_4, width = 6.7, height = 5.5)
@@ -1830,7 +1825,7 @@ Hypothesis testing
type = as.factor(type) %>% relevel(ref = "Pubmed authors")
)
main_lm <- function(regioni) {
- glm(type ~ year + weighted_probs,
+ glm(type ~ year + probabilities,
data = iscb_lm %>%
filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
@@ -1838,122 +1833,120 @@ Hypothesis testing
}
inte_lm <- function(regioni) {
- glm(type ~ year * weighted_probs,
+ glm(type ~ year * probabilities,
data = iscb_lm %>%
- filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+ filter(region == regioni, !is.na(probabilities), 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
+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) -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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -2.62 0.489 -5.36 8.47e- 8
+## 2 year -0.000250 0.0000321 -7.78 7.40e-15
+## 3 probabilities 0.869 0.139 6.26 3.97e-10
##
## $EastAsian
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -2.29 0.479 -4.77 1.81e- 6
+## 2 year -0.000241 0.0000320 -7.51 6.02e-14
+## 3 probabilities -1.75 0.250 -7.00 2.51e-12
##
## $European
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -2.15 0.484 -4.45 8.72e- 6
+## 2 year -0.000271 0.0000320 -8.46 2.62e-17
+## 3 probabilities 0.222 0.137 1.62 1.05e- 1
##
## $OtherCategories
## # 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, broom::tidy)
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -2.07 0.479 -4.33 1.52e- 5
+## 2 year -0.000274 0.0000319 -8.58 9.22e-18
+## 3 probabilities 0.159 0.151 1.05 2.95e- 1
+inte_list <- lapply(large_regions, inte_lm)
+lapply(inte_list, broom::tidy)
## [[1]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -2.48 0.627 -3.96 7.57e- 5
+## 2 year -0.000259 0.0000414 -6.26 3.96e-10
+## 3 probabilities 0.451 1.19 0.380 7.04e- 1
+## 4 year:probabilities 0.0000283 0.0000796 0.355 7.23e- 1
##
## [[2]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -2.45 0.500 -4.90 9.36e- 7
+## 2 year -0.000229 0.0000334 -6.87 6.37e-12
+## 3 probabilities 0.853 2.19 0.389 6.97e- 1
+## 4 year:probabilities -0.000172 0.000146 -1.18 2.38e- 1
##
## [[3]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -1.66 0.614 -2.71 6.75e- 3
+## 2 year -0.000303 0.0000410 -7.39 1.44e-13
+## 3 probabilities -1.28 1.20 -1.07 2.85e- 1
+## 4 year:probabilities 0.000101 0.0000796 1.27 2.05e- 1
##
## [[4]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) -1.55 0.598 -2.60 9.42e- 3
+## 2 year -0.000309 0.0000401 -7.70 1.31e-14
+## 3 probabilities -1.76 1.35 -1.30 1.92e- 1
+## 4 year:probabilities 0.000127 0.0000882 1.44 1.50e- 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
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 163886 4627.2
-## 2 163885 4627.1 1 0.14187 0.7064
+## 1 163886 4599.0
+## 2 163885 4598.9 1 0.12594 0.7227
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 163886 4576.6
-## 2 163885 4574.6 1 2.0829 0.149
+## 1 163886 4554.1
+## 2 163885 4552.7 1 1.3867 0.239
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 163886 4634.1
-## 2 163885 4633.4 1 0.74565 0.3879
+## 1 163886 4632.2
+## 2 163885 4630.6 1 1.607 0.2049
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ year * weighted_probs
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ year * probabilities
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 163886 4634.3
-## 2 163885 4633.1 1 1.2103 0.2713
+## 1 163886 4633.7
+## 2 163885 4631.6 1 2.0781 0.1494
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 1.2141832 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).
+
A Celtic/English name has 2.3850497 the odds of being selected as an honoree, significantly higher compared to other names (\(\beta_\textrm{Celtic/English} =\) 0.86922, P = 3.9713e-10). An East Asian name has 0.1731846 the odds of being selected as an honoree, significantly lower than to other names (\(\beta_\textrm{East Asian} =\) -1.7534, P = 2.5132e-12). The two groups of scientists did not have a significant association with names predicted to be European (P = 0.10527) or in Other categories (P = 0.29469).
Alternative approach
@@ -2059,21 +2052,16 @@
Time lag
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
- )
+ group_by(type, year, region)
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,
+ glm(type ~ year + probabilities,
data = iscb_lm_lag %>%
- filter(region == regioni, !is.na(weighted_probs)),
+ filter(region == regioni, !is.na(probabilities)),
family = "binomial"
)
}
@@ -2083,35 +2071,35 @@
Time lag
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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 29.0 1.19 24.5 4.51e-132
+## 2 year -0.00209 0.0000750 -27.9 5.63e-171
+## 3 probabilities 0.130 0.170 0.767 4.43e- 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 28.9 1.18 24.6 3.99e-133
+## 2 year -0.00208 0.0000749 -27.7 2.99e-169
+## 3 probabilities -1.01 0.280 -3.60 3.20e- 4
##
## $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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 29.1 1.18 24.7 1.06e-134
+## 2 year -0.00210 0.0000748 -28.0 8.91e-173
+## 3 probabilities 0.0595 0.166 0.358 7.20e- 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 29.1 1.18 24.8 2.82e-135
+## 2 year -0.00210 0.0000748 -28.1 7.58e-174
+## 3 probabilities 0.432 0.183 2.36 1.80e- 2
Supplementary Figure S5
@@ -2148,55 +2136,73 @@ Supplementary Figure S5
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
-## [1] stats graphics grDevices utils datasets methods base
+## [1] stats graphics grDevices utils datasets methods
+## [7] base
##
## other attached packages:
-## [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
+## [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
-## [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
+## [1] colorspace_2.0-0 ellipsis_0.3.1
+## [3] class_7.3-17 rprojroot_1.3-2
+## [5] fs_1.5.0 rstudioapi_0.12
+## [7] farver_2.0.3 remotes_2.2.0
+## [9] prodlim_2019.11.13 fansi_0.4.1
+## [11] xml2_1.3.2 codetools_0.2-16
+## [13] splines_4.0.3 knitr_1.30
+## [15] pkgload_1.1.0 jsonlite_1.7.1
+## [17] pROC_1.16.2 dbplyr_2.0.0
+## [19] rgeos_0.5-5 compiler_4.0.3
+## [21] httr_1.4.2 backports_1.2.0
+## [23] assertthat_0.2.1 Matrix_1.2-18
+## [25] cli_2.1.0 htmltools_0.5.0
+## [27] prettyunits_1.1.1 tools_4.0.3
+## [29] gtable_0.3.0 glue_1.4.2
+## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4
+## [33] Rcpp_1.0.5 cellranger_1.1.0
+## [35] vctrs_0.3.4 svglite_1.2.3.2
+## [37] nlme_3.1-149 iterators_1.0.13
+## [39] crosstalk_1.1.0.1 timeDate_3043.102
+## [41] gower_0.2.2 xfun_0.19
+## [43] ps_1.4.0 testthat_3.0.0
+## [45] rvest_0.3.6 lifecycle_0.2.0
+## [47] devtools_2.3.2 MASS_7.3-53
+## [49] scales_1.1.1 ipred_0.9-9
+## [51] hms_0.5.3 RColorBrewer_1.1-2
+## [53] yaml_2.2.1 curl_4.3
+## [55] memoise_1.1.0 rpart_4.1-15
+## [57] stringi_1.5.3 desc_1.2.0
+## [59] foreach_1.5.1 e1071_1.7-4
+## [61] pkgbuild_1.1.0 lava_1.6.8.1
+## [63] systemfonts_0.3.2 rlang_0.4.8
+## [65] pkgconfig_2.0.3 evaluate_0.14
+## [67] sf_0.9-6 recipes_0.1.15
+## [69] htmlwidgets_1.5.2 labeling_0.4.2
+## [71] cowplot_1.1.0 tidyselect_1.1.0
+## [73] processx_3.4.4 plyr_1.8.6
+## [75] magrittr_1.5 R6_2.5.0
+## [77] generics_0.1.0 DBI_1.1.0
+## [79] mgcv_1.8-33 pillar_1.4.6
+## [81] haven_2.3.1 withr_2.3.0
+## [83] units_0.6-7 survival_3.2-7
+## [85] sp_1.4-4 nnet_7.3-14
+## [87] modelr_0.1.8 crayon_1.3.4
+## [89] KernSmooth_2.23-17 utf8_1.1.4
+## [91] rmarkdown_2.5 usethis_1.6.3
+## [93] grid_4.0.3 readxl_1.3.1
+## [95] data.table_1.13.2 callr_3.5.1
+## [97] ModelMetrics_1.2.2.2 reprex_0.3.0
+## [99] digest_0.6.27 classInt_0.4-3
+## [101] stats4_4.0.3 munsell_0.5.0
+## [103] viridisLite_0.3.0 sessioninfo_1.1.1
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
diff --git a/docs/12.analyze-affiliation.html b/docs/12.analyze-affiliation.html
index 9f5c206..1a725a0 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().
@@ -4434,7 +4434,7 @@
Log enrichment figure
rel_widths = c(1, 1.3))
enrichment_plot
-
ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5)
+
ggsave('figs/enrichment-plot.png', enrichment_plot, width = 5.5, height = 3.5, dpi = 600)
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
@@ -4452,56 +4452,74 @@ Log enrichment figure
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
-## [1] stats graphics grDevices utils datasets methods base
+## [1] stats graphics grDevices utils datasets methods
+## [7] base
##
## other attached packages:
-## [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
+## [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
-## [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
+## [1] colorspace_2.0-0 ellipsis_0.3.1
+## [3] class_7.3-17 rprojroot_1.3-2
+## [5] fs_1.5.0 rstudioapi_0.12
+## [7] farver_2.0.3 remotes_2.2.0
+## [9] prodlim_2019.11.13 fansi_0.4.1
+## [11] xml2_1.3.2 codetools_0.2-16
+## [13] splines_4.0.3 knitr_1.30
+## [15] pkgload_1.1.0 jsonlite_1.7.1
+## [17] pROC_1.16.2 dbplyr_2.0.0
+## [19] rgeos_0.5-5 compiler_4.0.3
+## [21] httr_1.4.2 backports_1.2.0
+## [23] assertthat_0.2.1 Matrix_1.2-18
+## [25] cli_2.1.0 htmltools_0.5.0
+## [27] prettyunits_1.1.1 tools_4.0.3
+## [29] gtable_0.3.0 glue_1.4.2
+## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4
+## [33] Rcpp_1.0.5 cellranger_1.1.0
+## [35] vctrs_0.3.4 svglite_1.2.3.2
+## [37] nlme_3.1-149 iterators_1.0.13
+## [39] crosstalk_1.1.0.1 timeDate_3043.102
+## [41] gower_0.2.2 xfun_0.19
+## [43] ps_1.4.0 testthat_3.0.0
+## [45] rvest_0.3.6 lifecycle_0.2.0
+## [47] devtools_2.3.2 MASS_7.3-53
+## [49] scales_1.1.1 ipred_0.9-9
+## [51] hms_0.5.3 RColorBrewer_1.1-2
+## [53] yaml_2.2.1 curl_4.3
+## [55] memoise_1.1.0 rpart_4.1-15
+## [57] stringi_1.5.3 desc_1.2.0
+## [59] foreach_1.5.1 e1071_1.7-4
+## [61] pkgbuild_1.1.0 lava_1.6.8.1
+## [63] systemfonts_0.3.2 rlang_0.4.8
+## [65] pkgconfig_2.0.3 evaluate_0.14
+## [67] sf_0.9-6 recipes_0.1.15
+## [69] htmlwidgets_1.5.2 labeling_0.4.2
+## [71] cowplot_1.1.0 tidyselect_1.1.0
+## [73] processx_3.4.4 plyr_1.8.6
+## [75] magrittr_1.5 R6_2.5.0
+## [77] generics_0.1.0 DBI_1.1.0
+## [79] mgcv_1.8-33 pillar_1.4.6
+## [81] haven_2.3.1 withr_2.3.0
+## [83] units_0.6-7 survival_3.2-7
+## [85] sp_1.4-4 nnet_7.3-14
+## [87] modelr_0.1.8 crayon_1.3.4
+## [89] KernSmooth_2.23-17 utf8_1.1.4
+## [91] rmarkdown_2.5 usethis_1.6.3
+## [93] grid_4.0.3 readxl_1.3.1
+## [95] data.table_1.13.2 callr_3.5.1
+## [97] ModelMetrics_1.2.2.2 reprex_0.3.0
+## [99] digest_0.6.27 classInt_0.4-3
+## [101] stats4_4.0.3 munsell_0.5.0
+## [103] viridisLite_0.3.0 sessioninfo_1.1.1
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
diff --git a/docs/13.us-race-analysis.html b/docs/13.us-race-analysis.html
index 50c5b40..111e87d 100644
--- a/docs/13.us-race-analysis.html
+++ b/docs/13.us-race-analysis.html
@@ -1674,8 +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') %>%
@@ -1685,8 +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)
@@ -1795,10 +1795,14 @@ Hypothesis testing
## -0.8113 -0.5414 -0.0942 0.2651 16.6148
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 0.612841 0.004475 136.937 <2e-16 ***
-## year -0.041522 0.004554 -9.118 <2e-16 ***
-## typeKeynote speakers/Fellows 0.083082 0.038991 2.131 0.0331 *
+## Estimate Std. Error t value
+## (Intercept) 0.612841 0.004475 136.937
+## year -0.041522 0.004554 -9.118
+## typeKeynote speakers/Fellows 0.083082 0.038991 2.131
+## Pr(>|t|)
+## (Intercept) <2e-16 ***
+## year <2e-16 ***
+## typeKeynote speakers/Fellows 0.0331 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -1820,10 +1824,14 @@ Hypothesis testing
## -0.2870 -0.2596 -0.2303 0.1112 6.2454
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 0.247463 0.003178 77.861 < 2e-16 ***
-## year 0.043328 0.003234 13.397 < 2e-16 ***
-## typeKeynote speakers/Fellows -0.099391 0.027691 -3.589 0.000332 ***
+## Estimate Std. Error t value
+## (Intercept) 0.247463 0.003178 77.861
+## year 0.043328 0.003234 13.397
+## typeKeynote speakers/Fellows -0.099391 0.027691 -3.589
+## Pr(>|t|)
+## (Intercept) < 2e-16 ***
+## year < 2e-16 ***
+## typeKeynote speakers/Fellows 0.000332 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -1845,10 +1853,14 @@ Hypothesis testing
## -0.1573 -0.1143 -0.0837 0.0159 9.6671
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 0.139696 0.001617 86.395 <2e-16 ***
-## year -0.001807 0.001645 -1.098 0.272
-## typeKeynote speakers/Fellows 0.016309 0.014088 1.158 0.247
+## Estimate Std. Error t value
+## (Intercept) 0.139696 0.001617 86.395
+## year -0.001807 0.001645 -1.098
+## typeKeynote speakers/Fellows 0.016309 0.014088 1.158
+## Pr(>|t|)
+## (Intercept) <2e-16 ***
+## year 0.272
+## typeKeynote speakers/Fellows 0.247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
diff --git a/docs/14.us-name-origin.html b/docs/14.us-name-origin.html
index c71358d..c5d0a57 100644
--- a/docs/14.us-name-origin.html
+++ b/docs/14.us-name-origin.html
@@ -1720,17 +1720,12 @@ Organize data
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
- )
+ group_by(type, year, region)
iscb_pubmed_sum_oth <- iscb_pubmed_oth %>%
summarise(
- mean_prob = mean(weighted_probs),
- se_prob = sqrt(var(probabilities) * sum(weight^2) / (sum(weight)^2)),
+ mean_prob = mean(probabilities),
+ se_prob = sd(probabilities)/sqrt(n()),
me_prob = alpha_threshold * se_prob,
.groups = "drop"
)
@@ -1768,8 +1763,8 @@ Figures for paper
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.png", fig_us_name_origin, width = 6.5, height = 5.5, dpi = 600)
ggsave("figs/us_name_origin.svg", fig_us_name_origin, width = 6.5, height = 5.5)
@@ -1782,17 +1777,17 @@
Hypothesis testing
type = relevel(as.factor(type), ref = "Pubmed authors")
)
main_lm <- function(regioni) {
- glm(type ~ year + weighted_probs,
+ glm(type ~ year + probabilities,
data = iscb_lm %>%
- filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+ filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
)
}
inte_lm <- function(regioni) {
- glm(type ~ weighted_probs * year,
+ glm(type ~ probabilities * year,
data = iscb_lm %>%
- filter(region == regioni, !is.na(weighted_probs), year(year) >= 2002),
+ filter(region == regioni, !is.na(probabilities), year(year) >= 2002),
family = "binomial"
)
}
@@ -1803,108 +1798,108 @@
Hypothesis testing
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) 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 4.76 0.535 8.89 5.86e-19
+## 2 year -0.000611 0.0000348 -17.6 3.87e-69
+## 3 probabilities 0.269 0.185 1.46 1.46e- 1
##
## $EastAsian
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 4.73 0.519 9.12 7.71e-20
+## 2 year -0.000592 0.0000346 -17.1 9.97e-66
+## 3 probabilities -1.89 0.455 -4.14 3.43e- 5
##
## $European
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 4.78 0.524 9.12 7.75e-20
+## 2 year -0.000614 0.0000345 -17.8 6.78e-71
+## 3 probabilities 0.446 0.194 2.30 2.16e- 2
##
## $OtherCategories
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 4.93 0.520 9.48 2.58e-21
+## 2 year -0.000618 0.0000345 -17.9 1.18e-71
+## 3 probabilities 0.0957 0.212 0.451 6.52e- 1
inte_list <- lapply(large_regions, inte_lm)
lapply(inte_list, broom::tidy)
## [[1]]
## # 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
+## 1 (Intercept) 4.81 0.730 6.60 4.16e-11
+## 2 probabilities 0.128 1.32 0.0969 9.23e- 1
+## 3 year -0.000615 0.0000479 -12.8 9.81e-38
+## 4 probabilities:year 0.00000953 0.0000883 0.108 9.14e- 1
##
## [[2]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 4.71 0.536 8.78 1.57e-18
+## 2 probabilities -1.29 3.81 -0.339 7.34e- 1
+## 3 year -0.000591 0.0000357 -16.5 1.68e-61
+## 4 probabilities:year -0.0000380 0.000243 -0.157 8.76e- 1
##
## [[3]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 5.17 0.674 7.67 1.77e-14
+## 2 probabilities -0.809 1.40 -0.577 5.64e- 1
+## 3 year -0.000640 0.0000449 -14.3 3.42e-46
+## 4 probabilities:year 0.0000840 0.0000926 0.906 3.65e- 1
##
## [[4]]
## # 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
+## term estimate std.error statistic p.value
+## <chr> <dbl> <dbl> <dbl> <dbl>
+## 1 (Intercept) 5.22 0.670 7.79 6.76e-15
+## 2 probabilities -0.964 1.59 -0.605 5.45e- 1
+## 3 year -0.000637 0.0000447 -14.2 4.57e-46
+## 4 probabilities:year 0.0000701 0.000104 0.673 5.01e- 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
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 26398 2066.7
-## 2 26397 2066.5 1 0.2267 0.634
+## 1 26398 2064.9
+## 2 26397 2064.9 1 0.011635 0.9141
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 26398 2043.8
-## 2 26397 2043.7 1 0.089951 0.7642
+## 1 26398 2036.7
+## 2 26397 2036.7 1 0.024177 0.8764
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
-## 1 26398 2064.9
-## 2 26397 2064.2 1 0.63868 0.4242
+## 1 26398 2061.9
+## 2 26397 2061.1 1 0.82361 0.3641
## Analysis of Deviance Table
##
-## Model 1: type ~ year + weighted_probs
-## Model 2: type ~ weighted_probs * year
+## Model 1: type ~ year + probabilities
+## Model 2: type ~ probabilities * year
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 26398 2066.8
-## 2 26397 2066.7 1 0.13195 0.7164
+## 2 26397 2066.3 1 0.45537 0.4998
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 0.1709525 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).
+
An East Asian name has 0.1516018 the odds of being selected as an honoree, significantly lower compared to other names (\(\beta_\textrm{East Asian} =\) -1.8865, P = 3.4282e-05). The two groups of scientists did not have a significant association with names predicted to be Celtic/English (P = 0.14566), European (P = 0.021596), or in Other categories (P = 0.65199).
Supplement
@@ -1948,7 +1943,8 @@ Supplementary Figure S7
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
-## [1] stats graphics grDevices utils datasets methods base
+## [1] stats graphics grDevices utils datasets methods
+## [7] base
##
## other attached packages:
## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1
@@ -1959,45 +1955,62 @@ Supplementary Figure S7
## [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
-## [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
+## [1] colorspace_2.0-0 ellipsis_0.3.1
+## [3] class_7.3-17 rprojroot_1.3-2
+## [5] fs_1.5.0 rstudioapi_0.12
+## [7] farver_2.0.3 remotes_2.2.0
+## [9] prodlim_2019.11.13 fansi_0.4.1
+## [11] xml2_1.3.2 codetools_0.2-16
+## [13] splines_4.0.3 knitr_1.30
+## [15] pkgload_1.1.0 jsonlite_1.7.1
+## [17] pROC_1.16.2 dbplyr_2.0.0
+## [19] rgeos_0.5-5 compiler_4.0.3
+## [21] httr_1.4.2 backports_1.2.0
+## [23] assertthat_0.2.1 Matrix_1.2-18
+## [25] cli_2.1.0 htmltools_0.5.0
+## [27] prettyunits_1.1.1 tools_4.0.3
+## [29] gtable_0.3.0 glue_1.4.2
+## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4
+## [33] Rcpp_1.0.5 cellranger_1.1.0
+## [35] vctrs_0.3.4 svglite_1.2.3.2
+## [37] nlme_3.1-149 iterators_1.0.13
+## [39] crosstalk_1.1.0.1 timeDate_3043.102
+## [41] gower_0.2.2 xfun_0.19
+## [43] ps_1.4.0 testthat_3.0.0
+## [45] rvest_0.3.6 lifecycle_0.2.0
+## [47] devtools_2.3.2 MASS_7.3-53
+## [49] scales_1.1.1 ipred_0.9-9
+## [51] hms_0.5.3 RColorBrewer_1.1-2
+## [53] yaml_2.2.1 curl_4.3
+## [55] memoise_1.1.0 rpart_4.1-15
+## [57] stringi_1.5.3 desc_1.2.0
+## [59] foreach_1.5.1 e1071_1.7-4
+## [61] pkgbuild_1.1.0 lava_1.6.8.1
+## [63] systemfonts_0.3.2 rlang_0.4.8
+## [65] pkgconfig_2.0.3 evaluate_0.14
+## [67] sf_0.9-6 recipes_0.1.15
+## [69] htmlwidgets_1.5.2 labeling_0.4.2
+## [71] cowplot_1.1.0 tidyselect_1.1.0
+## [73] processx_3.4.4 plyr_1.8.6
+## [75] magrittr_1.5 R6_2.5.0
+## [77] generics_0.1.0 DBI_1.1.0
+## [79] mgcv_1.8-33 pillar_1.4.6
+## [81] haven_2.3.1 withr_2.3.0
+## [83] units_0.6-7 survival_3.2-7
+## [85] sp_1.4-4 nnet_7.3-14
+## [87] modelr_0.1.8 crayon_1.3.4
+## [89] KernSmooth_2.23-17 utf8_1.1.4
+## [91] rmarkdown_2.5 usethis_1.6.3
+## [93] grid_4.0.3 readxl_1.3.1
+## [95] data.table_1.13.2 callr_3.5.1
+## [97] ModelMetrics_1.2.2.2 reprex_0.3.0
+## [99] digest_0.6.27 classInt_0.4-3
+## [101] stats4_4.0.3 munsell_0.5.0
## [103] viridisLite_0.3.0 sessioninfo_1.1.1
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
diff --git a/docs/15.analyze-2020.html b/docs/15.analyze-2020.html
index 237c854..2211437 100644
--- a/docs/15.analyze-2020.html
+++ b/docs/15.analyze-2020.html
@@ -1693,8 +1693,10 @@ Gender
n_confs <- length(my_confs)
table(iscb_gender_df$afflcountries)
##
-## China Italy Japan United Kingdom United States
-## 1 1 1 1 13
+## China Italy Japan United Kingdom
+## 1 1 1 1
+## United States
+## 13
mean(iscb_gender_df$probability_male, na.rm = T)
## [1] 0.584375
Proportion of US affiliation: 76.47%. Mean probability of being male: 58.44%.
@@ -1803,7 +1805,8 @@ Name origins
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
-## [1] stats graphics grDevices utils datasets methods base
+## [1] stats graphics grDevices utils datasets methods
+## [7] base
##
## other attached packages:
## [1] broom_0.7.2 DT_0.16 epitools_0.5-10.1
@@ -1814,40 +1817,57 @@ Name origins
## [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
-## [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
+## [1] colorspace_2.0-0 ellipsis_0.3.1
+## [3] class_7.3-17 rprojroot_1.3-2
+## [5] fs_1.5.0 rstudioapi_0.12
+## [7] farver_2.0.3 remotes_2.2.0
+## [9] prodlim_2019.11.13 fansi_0.4.1
+## [11] xml2_1.3.2 codetools_0.2-16
+## [13] splines_4.0.3 knitr_1.30
+## [15] pkgload_1.1.0 jsonlite_1.7.1
+## [17] pROC_1.16.2 dbplyr_2.0.0
+## [19] rgeos_0.5-5 compiler_4.0.3
+## [21] httr_1.4.2 backports_1.2.0
+## [23] assertthat_0.2.1 Matrix_1.2-18
+## [25] cli_2.1.0 htmltools_0.5.0
+## [27] prettyunits_1.1.1 tools_4.0.3
+## [29] gtable_0.3.0 glue_1.4.2
+## [31] rnaturalearthdata_0.1.0 reshape2_1.4.4
+## [33] Rcpp_1.0.5 cellranger_1.1.0
+## [35] vctrs_0.3.4 svglite_1.2.3.2
+## [37] nlme_3.1-149 iterators_1.0.13
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diff --git a/figs/enrichment-plot.png b/figs/enrichment-plot.png
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diff --git a/figs/us_name_origin.png b/figs/us_name_origin.png
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diff --git a/figs/versions.pdf b/figs/versions.pdf
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