This project focuses on drug use in preteens and teens and then how the distribution changes later in life. Drugs can affect the way your brain develops and drug use, especially at a young age can majorly stunt the growth of the brain.

Drugs affect three areas of the brain: the brain stem, the limbic system and the cerebral cortex. The brain stem is in charge of all the things our body needs to stay alive. It helps us breathe, digest our food and move blood around. Our limbic system is the center of our emotional responses. The cerebral cortex is the control and information processing system.

library(tidyverse)
library(dbplyr)
library(ggplot2)
library(ggthemes)

This data set while very interesting caused many problems since it included the frequency and usage of each drug. I had to pull out the frequency because there was missing data, and it made the analysis possible. I did this when I imported the data and selected skip on the data that included frequency. I also had to change the “use” data to be numeric.

In reference to this data set A drug’s “use” is the percentage of those in an age group who used that drug in the past 12 months A drug’s “frequency” is the median number of times a user in an age group used that drug in the past 12 months.

CDC’s Website

drug_use_by_age <- read_csv("~/Desktop/MEA final project/drug-use-by-age.csv", 
                            col_types = cols(`alcohol-use` = col_number(), 
                                             `alcohol-frequency` = col_skip(), 
                                             `marijuana-use` = col_number(), `marijuana-frequency` = col_skip(), 
                                             `cocaine-use` = col_number(), `cocaine-frequency` = col_skip(), 
                                             `crack-use` = col_number(), `crack-frequency` = col_skip(), 
                                             `heroin-use` = col_number(), `heroin-frequency` = col_skip(), 
                                             `hallucinogen-use` = col_number(), 
                                             `hallucinogen-frequency` = col_skip(), 
                                             `inhalant-use` = col_number(), `inhalant-frequency` = col_skip(), 
                                             `pain-releiver-use` = col_number(), 
                                             `pain-releiver-frequency` = col_skip(), 
                                             `oxycontin-use` = col_number(), `oxycontin-frequency` = col_skip(), 
                                             `tranquilizer-use` = col_number(), 
                                             `tranquilizer-frequency` = col_skip(), 
                                             `stimulant-use` = col_number(), `stimulant-frequency` = col_skip(), 
                                             `meth-use` = col_number(), `meth-frequency` = col_skip(), 
                                             `sedative-use` = col_number(), `sedative-frequency` = col_skip()))

Naming Data

For the part of my project I wanted to look at the ages when the brain is developing which is ages 12- 25. The dataset is formatted in a way that when you get to age 22 it starts to group them together in groups of 2. I named this “tocollege_use” because these are the ages that are from middle school to college.

drug_use_by_age %>% 
  filter(age %in% c(12,13,14,15,16,17,18,19,20,21, "22-23", "24-25"))-> tocollege_use

Marjiana and Alcohol

Marjiana and Alcohol in Adolecence to Young Adult.

First I wanted to look at the teen use of Marijuana and see how it grows over time. Since these are the drugs that are the most accessible they have the highest percentage of use. You can see, Marijuana use reaches a peak at age 18 and but has a consistent growth before that. It is crazy to think that over 30% of 18 year olds are smoking weed when their brains don’t develop fully until age 25.

The graph on the right shows the increasing alcohol consumption till it is legal at age 21. At age 21 you can see that there is over 80% of 21 year olds that consume alcohol.

These graphs have different axis and the marijuana numbers are significantly smaller than the use of alcohol.

ggplot(tocollege_use, aes(x= age, y= `marijuana-use`, fill=age, color=age)) + 
  geom_col( )+
   ggtitle("Marijuana Use ages 12-25")+
  xlab("Age") +
  ylab("Marijuana Use")+
  theme_minimal()
ggplot(tocollege_use, aes(x= age, y= `alcohol-use`, fill=age, color=age)) + 
  geom_col()+
  ggtitle("Alchohol Use ages 12-25")+
  xlab("Age") +
  ylab("Alcohol Use")+
  theme_minimal()

Because these graphs could be deceiving, I put them into a chart together to show them on the same axis. This chart differs because this shows alcohol and marijuana use from ages 12 to 65+. Alcohol consumption is significantly higher because of its accessibility in the united states, but marijuana is a close second. You can see this is close to a normal curve with the highest percentages in the middle, centering around age 21.

Marijana and Alcohol Over Lifetime

Meth and Oxycotin

Meth and Oxycotin in Adolecence to Young Adult.

These charts compare Meth and Oxycotin in adolescence and young adults. Both are a relatively small percentages with 20 years old having the highest percentage of Meth users at .9%

ggplot(tocollege_use, aes(x= age, y= `meth-use`, fill=age, color=age)) + 
  geom_col( )+
   ggtitle("Meth Use ages 12-25")+
  xlab("Age") +
  ylab("Meth Use")+
  theme_minimal()
ggplot(tocollege_use, aes(x= age, y= `oxycontin-use`, fill=age, color=age)) + 
  geom_col()+
  ggtitle("Oxycontin Use ages 12-25")+
  xlab("Age") +
  ylab("Oxycontin Use")+
  theme_minimal()

Meth and Oxycotin Over Lifetime.

In the lifetime view Oxycontin has much higer percentages than Meth, even though both do not go above 2% of the tested population. Ages 18-23 are the peak times for Oxycontin and meth has its peak at age 20.

Meth has therapeutic value for conditions such as attention deficit disorder and narcolepsy and Oxycotin can be used for postsurgical pain or chronic pain , but both are used in an illegal recreational setting. [2]

Heroin and Crack

Heroin and Crack in Adolecence to Young Adult.

Heroin is a highly addictive and there are no medical uses for heroin.

These graphs are looking at Heroin and Crack in young adults. Both of these graphs for adolescence to young adults do not have percentages that go above 1%.

Crack has the lowest use of any other drug in ages 12-14.

ggplot(tocollege_use, aes(x= age, y= `crack-use`, fill=age, color=age)) + 
  geom_col( )+
   ggtitle("Crack Use ages 12-25")+
  xlab("Age") +
  ylab("Crack Use")+
  theme_minimal()
ggplot(tocollege_use, aes(x= age, y= `heroin-use`, fill=age, color=age)) + 
  geom_col()+
  ggtitle("Heroin Use ages 12-25")+
  xlab("Age") +
  ylab("Heroin Use")+
  theme_minimal()

Heroin and Crack Over Lifetime

This graph interested me because Heroin use peaks at age 23. This is not a large leap since at age 21 it is only 0.6% but since it is such a small y axis, it seems like a large leap to 1%+