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library(ggplot2)
library(scales)
library(dplyr)
library(readr)
library(tidyr)
library(janitor)

rm(list=ls())
df <- read_csv("./prevalence-of-drug-use-disorders-by-age.csv") %>%
filter(Code =="USA") %>% select(-Entity,-Code)

colnames(df) <- c("Year","age_14","age_19","age_24",
                  "age_29","age_34","all_age_percent","age5_14","age15_49",
                                    "age_70","standard_percent")
df <- df %>% select(Year,age_14,age_19,age_24,age_29,age_34,age5_14,
                    age15_49,age_70,all_age_percent,standard_percent)


* Two Quick Plots:
ggplot(df) + geom_line(aes(x=Year,y=all_age_percent)) +
  labs(title="All Age Precent")

ggplot(df) + geom_line(aes(x=Year,y=standard_percent)) +
  labs(title="Age-standardized Percent") +


* User dplyr pivot_longer to re-arrange longer
age_longer <- pivot_longer(df,cols=age_14:standard_percent)
age_longer$Percent <- age_longer$value /100
head(age_longer)

* Age 5-14 and Age 15-49

age_longer %>% filter(name=="age5_14") %>%
  ggplot() + geom_line(aes(x=Year,y=Percent,col=name)) +
  labs(title="Drug by Year for Ages 5 - 14",
       y="Percent of Drug Use") +
  scale_y_continuous(labels=percent)


age_longer %>% filter(name=="age15_49") %>%
  ggplot() + geom_line(aes(x=Year,y=Percent,col=name)) +
  labs(title="Drug by Year for Ages 15 - 49",y="Percent of Drug Use") +
  scale_y_continuous(labels=percent)

* Percent by Age Groups: 10 - 70

short_list <-age_longer %>%
  filter(! (name %in% c("age15_49","all_age_percent","standard_percent","age5_14")))

 ggplot(short_list) + geom_line(aes(x=Year,y=Percent,col=name)) +
  scale_y_continuous(labels=percent) + guides(color = guide_legend(override.aes = list(size = 2))) +
  labs(title="USA Prevalence of Drug Use Disorders by Age")

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