Career

Previous language ES vs. new

CAREER_NEW_ES <- "es_career_separate.csv"
career_new <- read_csv(CAREER_NEW_ES) %>%
  select(-test, -bias_type) %>%
  rename(wiki_language_code = language_code,
         es_hand_translation_new = sYXab)

CAREER_OLD_ES <- "/Users/mollylewis/Documents/research/Projects/1_in_progress/IATLANG/writeup/cogsci2018/analysis/study2b/data/career_effect_sizes_hand_translations.csv"

career_old <-  read.csv(CAREER_OLD_ES, 
col.names = c("wiki_language_code", "test_id", "test_name", "es_hand_translation_old"), 
header = F,
                                                fill = TRUE)  %>%
  select(-test_id, -test_name)

no_gender_langs <- c("fa", "id", "ko", "zh", "tr")

all_career_lang_es <- full_join(career_new, career_old) %>%
  mutate(gender = ifelse(wiki_language_code %in% no_gender_langs,
         "no gender", "gender"))

ggplot(all_career_lang_es, aes(x = es_hand_translation_old, y = es_hand_translation_new, label = wiki_language_code)) +
  geom_abline(aes(slope=1, intercept=0), linetype = 2)  +
  geom_label(aes(fill = gender))  +
  geom_smooth(method = "lm") +
  xlim(0,1) +
  ylim(0,1) +
  ggtitle("language old vs. language new") +

  theme_classic()

Language ES vs. behavior

CAREER_BEHAVIORAL_ES <- "behavioral_by_language.csv"

career_behavioral <- read_csv(CAREER_BEHAVIORAL_ES) %>%
  select(wiki_language_code, es_behavioral_iat_weighted, es_behavioral_iat) %>%
  left_join(all_career_lang_es)

ggplot(career_behavioral, aes(x = es_behavioral_iat_weighted, 
                              y = es_hand_translation_new,
                              label = wiki_language_code)) +
  geom_label(aes(fill = gender))  +
  geom_smooth(method = "lm") +
  xlab("behavioral IAT") +
  ylab("language IAT (new)") +
  ggtitle("language vs. behavior") +
  theme_classic()

ggplot(career_behavioral, aes(x = es_behavioral_iat_weighted, 
                              y = es_hand_translation_old,
                              label = wiki_language_code)) +
  geom_label(aes(fill = gender))  +
  geom_smooth(method = "lm") +
  xlab("behavioral IAT") +
  ylab("language IAT (old)") +
  ggtitle("language vs. behavior") +
  theme_classic()

Genius

Previous language ES vs. new

GENIUS_NEW_ES <- "es_genius_separate.csv"


GENIUS_OLD_ES1 <- "/Users/mollylewis/Documents/research/Projects/1_in_progress/IATLANG/analyses/8_gender_genius/genius_effect_sizes_google_restricted.csv"

GENIUS_OLD_ES2 <- "/Users/mollylewis/Documents/research/Projects/1_in_progress/IATLANG/analyses/8_gender_genius/genius_effect_sizes_google_restricted2.csv"

genius_new <- read_csv(GENIUS_NEW_ES) %>%
    rename(wiki_language_code = language_code,
         es_hand_translation_new = sYXab)

## Restricted
IAT_lang_raw1 <- read_csv(GENIUS_OLD_ES1,
                                col_names = c("language_code", "test",
                                              "bias_type",
                                              "effect_size_restricted"))


IAT_lang_raw2 <- read_csv(GENIUS_OLD_ES2, 
                          col_names = c("language_code", "test",
                                        "bias_type", "effect_size_restricted")) 

IAT_lang_restricted_raw <- bind_rows(IAT_lang_raw1, IAT_lang_raw2)

genius_old <- IAT_lang_restricted_raw %>%
  add_row(language_code = "hiur", 
          test = "genius_gender", 
          bias_type = "genius_gender", 
          effect_size_restricted = mean(filter(IAT_lang_restricted_raw,
                                         language_code %in% c("hi", "ur")) %>% 
                                    pull(effect_size_restricted))) %>%
  filter(!(language_code %in% c("ur"))) %>%
  select(language_code, effect_size_restricted) %>%
  rename(wiki_language_code = language_code,
         es_hand_translation_old = effect_size_restricted)


all_gender_lang_es <- full_join(genius_old, genius_new) %>%
  mutate(gender = ifelse(wiki_language_code %in% no_gender_langs,
         "no gender", "gender"))

ggplot(all_gender_lang_es, aes(x = es_hand_translation_old, y = es_hand_translation_new, label = wiki_language_code)) +
  geom_abline(aes(slope=1, intercept=0), linetype = 2)  +
  geom_label(aes(fill = gender))  +
  geom_smooth(method = "lm") +
  xlim(0,1) +
  ylim(0,1) +
  ggtitle("language old vs. language new") +

  theme_classic()

We don’t really have enought languages here to look at this et.