R Notebook

#Installing libraries

library (readr)
library(texreg)
library(ggplot2)
library(ggthemes)
library(Zelig)
library(ggrepel)
library(HistData)
library(tidyverse)
library(gganimate)
library(dplyr)
library(viridis)
library (gifski)
library (devtools)
library (gapminder)
library (png)
library(plotly)
library(magrittr)

#filtering for USA
usa<-read_csv("C:/Users/Marcy/Documents/soc 712/master.csv")%>%
  filter (country=="United States")%>%
  rename(rate = "suicides/100k pop")
head (usa)

#filtering for Russian Federation. 

rus<-read_csv("C:/Users/Marcy/Documents/soc 712/master.csv")%>%
  filter (country=="Russian Federation")%>%
  rename(rate = "suicides/100k pop")
head (rus)

# Arranging all countries from highest to lowest rate of suicide
mw%>%
  filter(year==2015)%>%
  group_by(country)%>%
  summarise(rate=sum (rate))%>%
  arrange (-rate)

#Correlation of rate and sex model
model1 <- lm(rate ~ sex, data = mw)
summary(model1)

#Correlation of rate and sex plus age model
model2 <- lm (rate ~ sex + age, data = mw)
summary (model2)

#Correlation of rate and sex plus age plus generation
model3 <- lm (rate ~ sex + age + generation, data = mw)
summary (model3)

library(texreg)

htmlreg(list(model1, model2, model3))

Masrate <- usa %>%
  filter(sex %in% c("35-54 years"))

Masrate_plot <- ggplot(data = usa, aes(x = sex, y = rate)) +
  geom_line(aes(color = sex), size = 6)

Masrate_plot

# Gender Variation in Suicide Rates Worldwide 
dcoef <- mw %>% 
    group_by(`country`) %>% 
    do(mod = lm(rate ~ sex, data =.))
coef <- dcoef %>% do(data.frame(intc = coef(.$mod)[1]))
ggplot(coef, aes(x = intc)) + geom_density()+xlab("country")

#plotting of correlated variables below:



Masrate_plot <- ggplot(data = mw, aes(x = age, y = rate)) +
  geom_line(aes(color = age), size = 8)

Masrate_plot

Masrate_plot <- ggplot(data = usa, aes(x = age, y = rate)) +
  geom_line(aes(color = age), size = 8)

Masrate_plot

Masrate_plot <- ggplot(data = mw, aes(x = generation, y = rate)) +
  geom_line(aes(color = generation), size = 8)

Masrate_plot

Masrate_plot <- ggplot(data = rus, aes(x = generation, y = rate)) +
  geom_line(aes(color = generation), size = 8)

Masrate_plot

# span of 3 years
graph_data <- ggplot(mw, mapping =aes(x=year, y = rate))

graph1 <- graph_data + geom_smooth() 

graph1 + labs(title = 'Trend of Worldwide Suicide Rate Over the Span of 30 Years (World)') + theme_tufte() 

graph_data <- ggplot(usa, mapping =aes(x=year, y = rate))

graph1 <- graph_data + geom_smooth() 

graph1 + labs(title = 'Suicide Rate Over the Span of 30 Years (USA)') + theme_tufte() 

graph_data <- ggplot(rus, mapping =aes(x=year, y = rate))

graph1 <- graph_data + geom_smooth() 

graph1 + labs(title = 'Suicide Rate Over the Span of 30 Years (Russian Federation)') + theme_tufte() 

mw %>%
ggplot(aes(x=factor(generation),y=rate, fill = sex)) +
  geom_col(alpha = 0.8) +
  scale_size(range = c(4, 8)) + 
  guides(fill=guide_legend(title="Suicide Rate"))+
  labs(title = 'World Suicide Rate Change by Gender and Generation Over The Span of 30 Years',
       subtitle='Date: {frame_time}', 
       x = 'Year', 
       y = 'Ratio')+
  transition_time(year)+
  coord_flip()+
  theme_gray()

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ZV90aW1lfScsIA0KICAgICAgIHggPSAnWWVhcicsIA0KICAgICAgIHkgPSAnUmF0aW8nKSsNCiAgdHJhbnNpdGlvbl90aW1lKHllYXIpKw0KICBjb29yZF9mbGlwKCkrDQogIHRoZW1lX2dyYXkoKQ0KYGBgDQoNCmBgYA0KDQoNCg0KDQoNCg0KDQoNCg0KDQoNCg0KDQoNCg==