eps plots

.Rhistory

@@ -1,34 +1,3 @@
-geom_point(alpha = 0.5) +
-geom_smooth(method = "lm", se = FALSE) +
-theme_minimal() +
-labs(y = "Screentime Percentage of Skintone Groups Within Gender and Age Categories") +
-facet_wrap(~gender*age)
-ggsave("data/gender_x_age_x_skintone_trends.png", device = "png")
-ggplot(gender_x_age_x_skintone_data,
-aes(x = year, y = screentime, group = skintone, color = skintone)) +
-geom_point(alpha = 0.5) +
-geom_smooth(method = "lm", se = FALSE) +
-theme_minimal() +
-labs(y = "Screentime Percentage of Skintone Groups Within Gender and Age Categories") +
-facet_wrap(~gender*age, nrow = 4)
-ggsave("data/gender_x_age_x_skintone_trends.png", device = "png")
-age_trends
-skintone_trends
-gender_x_age_trends
-gender_x_age_trends %>% arrange(gender, age)
-gender_x_skintone_trends
-age_x_skintone_trends
-View(gender_x_age_skintone_trends)
-lang_data <- read.csv("lang_only_wide_form.csv")
-lang_data <- lang_data %>%
-View(lang_data)
-lang_data <- read.csv("lang_only_wide_form.csv")
-View(lang_data)
-lang_data <- lang_data %>%
-mutate(derogatory_per = 100 * derogatory / transcript)
-video_lang_data <- read.csv("video_lang_wide_form.csv")
-video_lang_data <- video_lang_data %>% mutate(derogatory_per = 100 * derogatory / transcript)
-boxplot(derogatory_per ~ lang, lang_data)
 boxplot(derogatory_per ~ lang:genre, video_lang_data)
 boxplot(derogatory_per ~ lang_ln:genre, video_lang_data)
 boxplot(derogatory_per ~ genre, video_lang_data)
@@ -510,3 +479,34 @@ gender_trends %>% as_tibble()
 gender_x_lang_trends
 age_trends
 age_x_lang_trends
+library(lme4)
+library(lmerTest)
+library(emmeans)
+library(tidyverse)
+library(Kendall)
+emm_options(pbkrtest.limit = 28776, lmertest.limit = 28776)
+ggplot(gender_x_age_data,
+aes(x = year, y = screentime, group = age, color = age)) +
+geom_smooth(method = "lm", se = FALSE) +
+theme_minimal() +
+labs(y = "Screentime Percentage of Age Groups Within Gender Categories") +
+facet_wrap(~gender, scales = "free")
+ggplot(age_x_skintone_data,
+aes(x = year, y = screentime, group = skintone, color = skintone)) +
+geom_smooth(method = "lm", se = FALSE) +
+theme_minimal() +
+labs(y = "Screentime Percentage of Skintone Groups Within Age Categories") +
+facet_wrap(~age, scales = "free")
+ggplot(age_x_skintone_data,
+aes(x = year, y = screentime, group = skintone, color = skintone)) +
+geom_smooth(method = "lm", se = FALSE) +
+theme_minimal() +
+labs(y = "Screentime Percentage of Skintone Groups Within Age Categories") +
+facet_wrap(~age)
+gender_x_age_skintone_trends <- emtrends(m_gender_x_age_x_skintone,
+~ skintone + age + gender,
+var="year") %>%
+as_tibble() %>% arrange(skintone, age, gender)
+gender_x_age_skintone_trends
+print(gender_x_age_skintone_trends)
+View(gender_x_age_skintone_trends)