This function creates a derived categorical variable that flags for binge drinking based on the number drinks consumed on a single day.
binge_drinker_fun( DHH_SEX, ALW_1, ALW_2A1, ALW_2A2, ALW_2A3, ALW_2A4, ALW_2A5, ALW_2A6, ALW_2A7 )
| DHH_SEX | sex of respondent (1 - male, 2 - female) |
|---|---|
| ALW_1 | Drinks in the last week (1 - yes, 2 - no) |
| ALW_2A1 | Number of drinks on Sunday |
| ALW_2A2 | Number of drinks on Monday |
| ALW_2A3 | Number of drinks on Tuesday |
| ALW_2A4 | Number of drinks on Wednesday |
| ALW_2A5 | Number of drinks on Thursday |
| ALW_2A6 | Number of drinks on Friday |
| ALW_2A7 | Number of drinks on Saturday |
Categorical variable (binge_drinker) with two categories:
1 - binge drinker
2 - non-binge drinker
In health research, binge drinking is defined as having an excess amount of alcohol in a single day. For males, this is defined as having five or more drinks; and for females it is four or more drinks. In the CCHS, respondents are asked to count the number of drinks they had during each day of the last week.
# Using binge_drinker_fun() to create binge_drinker values across CCHS cycles # binge_drinker_fun() is specified in variable_details.csv along with the # CCHS variables and cycles included. # To transform binge_drinker, use rec_with_table() for each CCHS cycle # and specify binge_drinker, along with the various alcohol and sex # variables. Then by using bind_rows() you can combine binge_drinker # across cycles. library(cchsflow) binge2001 <- rec_with_table( cchs2001_p, c( "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4", "ALW_2A5", "ALW_2A6", "ALW_2A7", "binge_drinker" ) )#> #>#>#> ALW_1 ALW_2A1 ALW_2A2 ALW_2A3 ALW_2A4 ALW_2A5 ALW_2A6 ALW_2A7 DHH_SEX #> 1 1 0 0 1 0 1 0 0 2 #> 2 NA(a) NA NA NA NA NA NA NA 2 #> 3 1 0 0 0 1 6 0 1 1 #> 4 2 NA NA NA NA NA NA NA 2 #> 5 1 0 0 0 0 0 8 0 2 #> 6 2 NA NA NA NA NA NA NA 1 #> binge_drinker #> 1 2 #> 2 NA(a) #> 3 1 #> 4 NA(a) #> 5 1 #> 6 NA(a)binge2009_2010 <- rec_with_table( cchs2009_2010_p, c( "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4", "ALW_2A5", "ALW_2A6", "ALW_2A7", "binge_drinker" ) )#> #>#>#> ALW_1 ALW_2A1 ALW_2A2 ALW_2A3 ALW_2A4 ALW_2A5 ALW_2A6 ALW_2A7 DHH_SEX #> 195 NA(a) NA NA NA NA NA NA NA 2 #> 196 2 NA NA NA NA NA NA NA 2 #> 197 NA(a) NA NA NA NA NA NA NA 2 #> 198 NA(a) NA NA NA NA NA NA NA 1 #> 199 NA(a) NA NA NA NA NA NA NA 2 #> 200 2 NA NA NA NA NA NA NA 2 #> binge_drinker #> 195 NA(a) #> 196 NA(a) #> 197 NA(a) #> 198 NA(a) #> 199 NA(a) #> 200 NA(a)#> ALW_1 ALW_2A1 ALW_2A2 ALW_2A3 ALW_2A4 ALW_2A5 ALW_2A6 ALW_2A7 DHH_SEX #> 1 1 0 0 1 0 1 0 0 2 #> 2 NA(a) NA NA NA NA NA NA NA 2 #> 3 1 0 0 0 1 6 0 1 1 #> 4 2 NA NA NA NA NA NA NA 2 #> 5 1 0 0 0 0 0 8 0 2 #> 6 2 NA NA NA NA NA NA NA 1 #> binge_drinker #> 1 2 #> 2 NA(a) #> 3 1 #> 4 NA(a) #> 5 1 #> 6 NA(a)#> ALW_1 ALW_2A1 ALW_2A2 ALW_2A3 ALW_2A4 ALW_2A5 ALW_2A6 ALW_2A7 DHH_SEX #> 395 NA(a) NA NA NA NA NA NA NA 2 #> 396 2 NA NA NA NA NA NA NA 2 #> 397 NA(a) NA NA NA NA NA NA NA 2 #> 398 NA(a) NA NA NA NA NA NA NA 1 #> 399 NA(a) NA NA NA NA NA NA NA 2 #> 400 2 NA NA NA NA NA NA NA 2 #> binge_drinker #> 395 NA(a) #> 396 NA(a) #> 397 NA(a) #> 398 NA(a) #> 399 NA(a) #> 400 NA(a)# Using binge_drinker_fun() to generate binge_drinker with user-inputted # values. # # Let's say you are a male, and you had drinks in the last week. Let's say # you had 3 drinks on Sunday, 1 drink on # Monday, 6 drinks on Tuesday, 0 drinks on Wednesday, 3 drinks on Thurday, # 8 drinks on Friday, and 2 drinks on Saturday. Using binge_drinker_fun(), # we can check if you would be classified as a drinker. binge <- binge_drinker_fun(DHH_SEX = 1, ALW_1 = 1, ALW_2A1 = 3, ALW_2A2 = 1, ALW_2A3 = 6, ALW_2A4 = 0, ALW_2A5 = 3, ALW_2A6 = 8, ALW_2A7 = 2) print(binge)#> [1] 1