The data I will be working with for this assignment is drug use by age and is associated with the article how baby boomers get high. The data covers thirteen drugs across 17 age groups and its main objective is to create a story on how baby boomers get high. Some of th drugs that are covered include alcohol, marijuana, cocaine, heroine, crack, hallucinogen, inhalant, pain reliever, meth, sedative, Oxycontin, tranquilizer, and stimulant and the data is recorded over a period of 12 months by recording each drug and its frequency. ### Article Link The link to the article is: https://fivethirtyeight.com/features/how-baby-boomers-get-high/
# Reading data from the Github link
drug_use_by_age_url<-read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/drug-use-by-age/drug-use-by-age.csv", header= TRUE, sep=",")
drug_use_by_age_url## age n alcohol_use alcohol_frequency marijuana_use marijuana_frequency
## 1 12 2798 3.9 3 1.1 4
## 2 13 2757 8.5 6 3.4 15
## 3 14 2792 18.1 5 8.7 24
## 4 15 2956 29.2 6 14.5 25
## 5 16 3058 40.1 10 22.5 30
## 6 17 3038 49.3 13 28.0 36
## 7 18 2469 58.7 24 33.7 52
## 8 19 2223 64.6 36 33.4 60
## 9 20 2271 69.7 48 34.0 60
## 10 21 2354 83.2 52 33.0 52
## 11 22-23 4707 84.2 52 28.4 52
## 12 24-25 4591 83.1 52 24.9 60
## 13 26-29 2628 80.7 52 20.8 52
## 14 30-34 2864 77.5 52 16.4 72
## 15 35-49 7391 75.0 52 10.4 48
## 16 50-64 3923 67.2 52 7.3 52
## 17 65+ 2448 49.3 52 1.2 36
## cocaine_use cocaine_frequency crack_use crack_frequency heroin_use
## 1 0.1 5.0 0.0 - 0.1
## 2 0.1 1.0 0.0 3.0 0.0
## 3 0.1 5.5 0.0 - 0.1
## 4 0.5 4.0 0.1 9.5 0.2
## 5 1.0 7.0 0.0 1.0 0.1
## 6 2.0 5.0 0.1 21.0 0.1
## 7 3.2 5.0 0.4 10.0 0.4
## 8 4.1 5.5 0.5 2.0 0.5
## 9 4.9 8.0 0.6 5.0 0.9
## 10 4.8 5.0 0.5 17.0 0.6
## 11 4.5 5.0 0.5 5.0 1.1
## 12 4.0 6.0 0.5 6.0 0.7
## 13 3.2 5.0 0.4 6.0 0.6
## 14 2.1 8.0 0.5 15.0 0.4
## 15 1.5 15.0 0.5 48.0 0.1
## 16 0.9 36.0 0.4 62.0 0.1
## 17 0.0 - 0.0 - 0.0
## heroin_frequency hallucinogen_use hallucinogen_frequency inhalant_use
## 1 35.5 0.2 52 1.6
## 2 - 0.6 6 2.5
## 3 2.0 1.6 3 2.6
## 4 1.0 2.1 4 2.5
## 5 66.5 3.4 3 3.0
## 6 64.0 4.8 3 2.0
## 7 46.0 7.0 4 1.8
## 8 180.0 8.6 3 1.4
## 9 45.0 7.4 2 1.5
## 10 30.0 6.3 4 1.4
## 11 57.5 5.2 3 1.0
## 12 88.0 4.5 2 0.8
## 13 50.0 3.2 3 0.6
## 14 66.0 1.8 2 0.4
## 15 280.0 0.6 3 0.3
## 16 41.0 0.3 44 0.2
## 17 120.0 0.1 2 0.0
## inhalant_frequency pain_releiver_use pain_releiver_frequency oxycontin_use
## 1 19.0 2.0 36 0.1
## 2 12.0 2.4 14 0.1
## 3 5.0 3.9 12 0.4
## 4 5.5 5.5 10 0.8
## 5 3.0 6.2 7 1.1
## 6 4.0 8.5 9 1.4
## 7 4.0 9.2 12 1.7
## 8 3.0 9.4 12 1.5
## 9 4.0 10.0 10 1.7
## 10 2.0 9.0 15 1.3
## 11 4.0 10.0 15 1.7
## 12 2.0 9.0 15 1.3
## 13 4.0 8.3 13 1.2
## 14 3.5 5.9 22 0.9
## 15 10.0 4.2 12 0.3
## 16 13.5 2.5 12 0.4
## 17 - 0.6 24 0.0
## oxycontin_frequency tranquilizer_use tranquilizer_frequency stimulant_use
## 1 24.5 0.2 52.0 0.2
## 2 41.0 0.3 25.5 0.3
## 3 4.5 0.9 5.0 0.8
## 4 3.0 2.0 4.5 1.5
## 5 4.0 2.4 11.0 1.8
## 6 6.0 3.5 7.0 2.8
## 7 7.0 4.9 12.0 3.0
## 8 7.5 4.2 4.5 3.3
## 9 12.0 5.4 10.0 4.0
## 10 13.5 3.9 7.0 4.1
## 11 17.5 4.4 12.0 3.6
## 12 20.0 4.3 10.0 2.6
## 13 13.5 4.2 10.0 2.3
## 14 46.0 3.6 8.0 1.4
## 15 12.0 1.9 6.0 0.6
## 16 5.0 1.4 10.0 0.3
## 17 - 0.2 5.0 0.0
## stimulant_frequency meth_use meth_frequency sedative_use sedative_frequency
## 1 2.0 0.0 - 0.2 13.0
## 2 4.0 0.1 5.0 0.1 19.0
## 3 12.0 0.1 24.0 0.2 16.5
## 4 6.0 0.3 10.5 0.4 30.0
## 5 9.5 0.3 36.0 0.2 3.0
## 6 9.0 0.6 48.0 0.5 6.5
## 7 8.0 0.5 12.0 0.4 10.0
## 8 6.0 0.4 105.0 0.3 6.0
## 9 12.0 0.9 12.0 0.5 4.0
## 10 10.0 0.6 2.0 0.3 9.0
## 11 10.0 0.6 46.0 0.2 52.0
## 12 10.0 0.7 21.0 0.2 17.5
## 13 7.0 0.6 30.0 0.4 4.0
## 14 12.0 0.4 54.0 0.4 10.0
## 15 24.0 0.2 104.0 0.3 10.0
## 16 24.0 0.2 30.0 0.2 104.0
## 17 364.0 0.0 - 0.0 15.0# subset into percentages and put into dataframe
drug_use <- data.frame(drug_use_by_age_url$age, drug_use_by_age_url$n, drug_use_by_age_url$alcohol_use, drug_use_by_age_url$marijuana_use, drug_use_by_age_url$cocaine_use, drug_use_by_age_url$crack_use, drug_use_by_age_url$heroin_use, drug_use_by_age_url$hallucinogen_use, drug_use_by_age_url$inhalant_use, drug_use_by_age_url$pain_releiver_use, drug_use_by_age_url$oxycontin_frequency, drug_use_by_age_url$tranquilizer_use, drug_use_by_age_url$stimulant_use, drug_use_by_age_url$meth_use, drug_use_by_age_url$sedative_use)
colnames(drug_use) <- c("Age", "Number of People", "Alcohol use", "Marijuana Use", "Cocaine Use", "Crack Use", "Heroine Use", "Hallucinogen Use", "Inhalant Use", "Pain Reliever Use", "Oxycontin use", "Tranquilizer Use", "Stimulant Use", "Meth Use", "Sedative Use")
drug_use## Age Number of People Alcohol use Marijuana Use Cocaine Use Crack Use
## 1 12 2798 3.9 1.1 0.1 0.0
## 2 13 2757 8.5 3.4 0.1 0.0
## 3 14 2792 18.1 8.7 0.1 0.0
## 4 15 2956 29.2 14.5 0.5 0.1
## 5 16 3058 40.1 22.5 1.0 0.0
## 6 17 3038 49.3 28.0 2.0 0.1
## 7 18 2469 58.7 33.7 3.2 0.4
## 8 19 2223 64.6 33.4 4.1 0.5
## 9 20 2271 69.7 34.0 4.9 0.6
## 10 21 2354 83.2 33.0 4.8 0.5
## 11 22-23 4707 84.2 28.4 4.5 0.5
## 12 24-25 4591 83.1 24.9 4.0 0.5
## 13 26-29 2628 80.7 20.8 3.2 0.4
## 14 30-34 2864 77.5 16.4 2.1 0.5
## 15 35-49 7391 75.0 10.4 1.5 0.5
## 16 50-64 3923 67.2 7.3 0.9 0.4
## 17 65+ 2448 49.3 1.2 0.0 0.0
## Heroine Use Hallucinogen Use Inhalant Use Pain Reliever Use Oxycontin use
## 1 0.1 0.2 1.6 2.0 24.5
## 2 0.0 0.6 2.5 2.4 41.0
## 3 0.1 1.6 2.6 3.9 4.5
## 4 0.2 2.1 2.5 5.5 3.0
## 5 0.1 3.4 3.0 6.2 4.0
## 6 0.1 4.8 2.0 8.5 6.0
## 7 0.4 7.0 1.8 9.2 7.0
## 8 0.5 8.6 1.4 9.4 7.5
## 9 0.9 7.4 1.5 10.0 12.0
## 10 0.6 6.3 1.4 9.0 13.5
## 11 1.1 5.2 1.0 10.0 17.5
## 12 0.7 4.5 0.8 9.0 20.0
## 13 0.6 3.2 0.6 8.3 13.5
## 14 0.4 1.8 0.4 5.9 46.0
## 15 0.1 0.6 0.3 4.2 12.0
## 16 0.1 0.3 0.2 2.5 5.0
## 17 0.0 0.1 0.0 0.6 -
## Tranquilizer Use Stimulant Use Meth Use Sedative Use
## 1 0.2 0.2 0.0 0.2
## 2 0.3 0.3 0.1 0.1
## 3 0.9 0.8 0.1 0.2
## 4 2.0 1.5 0.3 0.4
## 5 2.4 1.8 0.3 0.2
## 6 3.5 2.8 0.6 0.5
## 7 4.9 3.0 0.5 0.4
## 8 4.2 3.3 0.4 0.3
## 9 5.4 4.0 0.9 0.5
## 10 3.9 4.1 0.6 0.3
## 11 4.4 3.6 0.6 0.2
## 12 4.3 2.6 0.7 0.2
## 13 4.2 2.3 0.6 0.4
## 14 3.6 1.4 0.4 0.4
## 15 1.9 0.6 0.2 0.3
## 16 1.4 0.3 0.2 0.2
## 17 0.2 0.0 0.0 0.0The data above shows the drug use across people of various ages. Over the past decade, baby boomers have exhibited higher rates of illicit drug use and drug-related issues than previous generations. Data shows that while many consume alcohol (67%) and marijuana, fewer use other drugs. For instance, boomers who use cocaine reported 36 days of use per year, significantly higher than younger groups. Although younger boomers (50-54) show increasing drug use compared to older ones (60-64), overall rates remain lower than younger age groups.