drugs {C443}R Documentation

Drug consumption data set

Description

A dataset collected by Fehrman et al. (2017), freely available on the UCI Machine Learning Repository (Lichman, 2013) containing records of 1885 respondents regarding their use of 18 types of drugs, and their measurements on 12 predictors. #' All predictors were originally categorical and were quantified by Fehrman et al. (2017). The meaning of the values can be found on https://archive.ics.uci.edu/ml/datasets/Drug+consumption+%28quantified%29. The original response categories for each drug were: never used the drug, used it over a decade ago, or in the last decade, year, month, week, or day. We transformed these into binary response categories, where 0 (non-user) consists of the categories never used the drug and used it over a decade ago and 1 (user) consists of all other categories.

Usage

drugs

Format

A data frame with 1185 rows and 32 variables:

ID

Respondent ID

Age

Age of respondent

Gender

Gender of respondent, where 0.48 denotes female and -0.48 denotes male

Edu

Level of education of participant

Country

Country of current residence of participant

Ethn

Ethnicity of participant

Neuro

NEO-FFI-R Neuroticism score

Extr

NEO-FFI-R Extraversion score

Open

NEO-FFI-R Openness to experience score

Agree

NEO-FFI-R Agreeableness score

Consc

NEO-FFI-R Conscientiousness score

Impul

Impulsiveness score measured by BIS-11

Sensat

Sensation seeking score measured by ImpSS

Alc

Alcohol user (1) or non-user (0)

Amphet

Amphetamine user (1) or non-user (0)

Amyl

Amyl nitrite user (1) or non-user (0)

Benzos

Benzodiazepine user (1) or non-user (0)

Caff

Caffeine user (1) or non-user (0)

Can

Cannabis user (1) or non-user (0)

Choco

Chocolate user (1) or non-user (0)

Coke

Coke user (1) or non-user (0)

Crack

Crack user (1) or non-user (0)

Ecst

Ecstacy user (1) or non-user (0)

Her

Heroin user (1) or non-user (0)

Ket

Ketamine user (1) or non-user (0)

Leghighs

Legal Highs user (1) or non-user (0)

LSD

LSD user (1) or non-user (0)

Meth

Methadone user (1) or non-user (0)

Mush

Magical Mushroom user (1) or non-user (0)

Nico

Nicotine user (1) or non-user (0)

Semeron

Semeron user (1) or non-user (0), fictitious drug to identify over-claimers

VSA

volatile substance abuse user(1) or non-user (0)

Source

https://archive.ics.uci.edu/ml/machine-learning-databases/00373/

References

Fehrman, E., Muhammad, A. K., Mirkes, E. M., Egan, V., & Gorban, A. N. (2017). The Five Factor Model of personality and evaluation of drug consumption risk. In Data Science (pp. 231-242). Springer, Cham. Lichman, M. (2013). UCI machine learning repository.


[Package C443 version 3.2.2 Index]