drugs | R Documentation |
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/dataset/373/drug+consumption+quantified. 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.
drugs
A data frame with 1185 rows and 32 variables:
Respondent ID
Age of respondent
Gender of respondent, where 0.48 denotes female and -0.48 denotes male
Level of education of participant
Country of current residence of participant
Ethnicity of participant
NEO-FFI-R Neuroticism score
NEO-FFI-R Extraversion score
NEO-FFI-R Openness to experience score
NEO-FFI-R Agreeableness score
NEO-FFI-R Conscientiousness score
Impulsiveness score measured by BIS-11
Sensation seeking score measured by ImpSS
Alcohol user (1) or non-user (0)
Amphetamine user (1) or non-user (0)
Amyl nitrite user (1) or non-user (0)
Benzodiazepine user (1) or non-user (0)
Caffeine user (1) or non-user (0)
Cannabis user (1) or non-user (0)
Chocolate user (1) or non-user (0)
Coke user (1) or non-user (0)
Crack user (1) or non-user (0)
Ecstacy user (1) or non-user (0)
Heroin user (1) or non-user (0)
Ketamine user (1) or non-user (0)
Legal Highs user (1) or non-user (0)
LSD user (1) or non-user (0)
Methadone user (1) or non-user (0)
Magical Mushroom user (1) or non-user (0)
Nicotine user (1) or non-user (0)
Semeron user (1) or non-user (0), fictitious drug to identify over-claimers
volatile substance abuse user(1) or non-user (0)
https://archive.ics.uci.edu/dataset/373/drug+consumption+quantified
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.
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