This function creates a categorical derived diet variable (diet_score_cat3) that categorizes derived diet score (diet_score).
diet_score_fun_cat(diet_score)derived variable that calculates diet score.
See diet_score_fun for documentation on how variable
was derived.
value for diet score categories using diet_score_cat3 variable.
The diet score is based on consumption of fruit, salad, potatoes, carrots, other vegetables and juice. 2 baseline points plus summation of total points for diet attributes. Negative overall scores are recoded to 0, resulting in a range from 0 to 10.The categories were based on the Mortality Population Risk Tool (Douglas Manuel et al. 2016).
diet_score_cat3 uses the derived variable diet_score. diet_score uses sex, and fruit and vegetable variables that have been transformed by cchsflow (see documentation on diet_score). In order to categorize diet across CCHS cycles, sex, and fruit and vegetable variables must be transformed and harmonized.
# Using the diet_score_fun_cat function to categorize the derived diet
# variable across CCHS cycles.
# diet_score_fun_cat() is specified in the variable_details.csv.
# To create a harmonized diet_score_cat3 variable across CCHS cycles, use
# rec_with_table() for each CCHS cycle.
# Since diet_score is also a derived variable, you will have to specify
# the variables that are derived from it.
# Using merge_rec_data(), you can combine diet_score_cat3 across cycles.
library(cchsflow)
diet_score_cat2009_2010 <- rec_with_table(
cchs2009_2010_p, c(
"FVCDFRU", "FVCDSAL", "FVCDPOT", "FVCDCAR", "FVCDVEG", "FVCDJUI",
"DHH_SEX", "diet_score", "diet_score_cat3"
)
)
#> No variable_details detected.
#> Loading cchsflow variable_details
#> Using the passed data variable name as database_name
#> NOTE for FVCDCAR: 2015 onward changed question to look at all orange-coloured vegetables
#> NOTE for FVCDCAR: Don't know (999.7) and refusal (999.8) not included in 2001, 2015-2016, 2017-2018 CCHS
#> NOTE for FVCDFRU: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
#> NOTE for FVCDJUI: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
#> NOTE for FVCDPOT: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
#> NOTE for FVCDSAL: 2015 onwards changed question to look at dark green vegetables
#> NOTE for FVCDSAL: Don't know (999.7) and refusal (999.8) not included in 2001, 2015-2016, 2017-2018 CCHS
#> NOTE for FVCDVEG: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
head(diet_score_cat2009_2010)
#> DHH_SEX FVCDCAR FVCDFRU FVCDJUI FVCDPOT FVCDSAL FVCDVEG diet_score
#> 1 2 0.1 4.0 4.0 0.1 0.3 4 4.0
#> 2 2 0.1 1.0 0.1 0.1 0.4 2 5.6
#> 3 2 0.1 0.1 1.0 0.0 0.3 1 3.5
#> 4 1 NA NA NA NA NA NA NA
#> 5 1 0.4 0.6 1.0 0.4 0.1 6 9.5
#> 6 2 0.3 1.0 0.0 0.3 0.3 1 4.9
#> diet_score_cat3
#> 1 2
#> 2 2
#> 3 2
#> 4 NA(b)
#> 5 3
#> 6 2
diet_score_cat2011_2012 <- rec_with_table(
cchs2011_2012_p,c(
"FVCDFRU", "FVCDSAL", "FVCDPOT", "FVCDCAR", "FVCDVEG", "FVCDJUI",
"DHH_SEX", "diet_score", "diet_score_cat3"
)
)
#> No variable_details detected.
#> Loading cchsflow variable_details
#> Using the passed data variable name as database_name
#> NOTE for FVCDCAR: 2015 onward changed question to look at all orange-coloured vegetables
#> NOTE for FVCDCAR: Don't know (999.7) and refusal (999.8) not included in 2001, 2015-2016, 2017-2018 CCHS
#> NOTE for FVCDFRU: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
#> NOTE for FVCDJUI: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
#> NOTE for FVCDPOT: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
#> NOTE for FVCDSAL: 2015 onwards changed question to look at dark green vegetables
#> NOTE for FVCDSAL: Don't know (999.7) and refusal (999.8) not included in 2001, 2015-2016, 2017-2018 CCHS
#> NOTE for FVCDVEG: Don't know (999.7) and refusal (999.8) not included in 2001 CCHS
tail(diet_score_cat2011_2012)
#> DHH_SEX FVCDCAR FVCDFRU FVCDJUI FVCDPOT FVCDSAL FVCDVEG diet_score
#> 195 1 0.1 1.0 0.1 0.1 1.0 1.0 5.2
#> 196 1 0.0 0.4 0.7 0.3 0.0 0.4 1.1
#> 197 1 0.1 0.1 1.0 0.6 0.3 0.7 3.8
#> 198 2 0.1 1.0 0.3 0.1 0.4 0.4 4.0
#> 199 1 1.0 1.0 0.1 1.0 0.3 1.0 4.3
#> 200 2 0.3 2.0 0.0 0.6 0.4 4.0 9.3
#> diet_score_cat3
#> 195 2
#> 196 1
#> 197 2
#> 198 2
#> 199 2
#> 200 3
combined_diet_score_cat <- suppressWarnings(merge_rec_data(
diet_score_cat2009_2010, diet_score_cat2011_2012))
head(combined_diet_score_cat)
#> DHH_SEX FVCDCAR FVCDFRU FVCDJUI FVCDPOT FVCDSAL FVCDVEG diet_score
#> 1 2 0.1 4.0 4.0 0.1 0.3 4 4.0
#> 2 2 0.1 1.0 0.1 0.1 0.4 2 5.6
#> 3 2 0.1 0.1 1.0 0.0 0.3 1 3.5
#> 4 1 NA NA NA NA NA NA NA
#> 5 1 0.4 0.6 1.0 0.4 0.1 6 9.5
#> 6 2 0.3 1.0 0.0 0.3 0.3 1 4.9
#> diet_score_cat3
#> 1 2
#> 2 2
#> 3 2
#> 4 NA(b)
#> 5 3
#> 6 2
tail(combined_diet_score_cat)
#> DHH_SEX FVCDCAR FVCDFRU FVCDJUI FVCDPOT FVCDSAL FVCDVEG diet_score
#> 395 1 0.1 1.0 0.1 0.1 1.0 1.0 5.2
#> 396 1 0.0 0.4 0.7 0.3 0.0 0.4 1.1
#> 397 1 0.1 0.1 1.0 0.6 0.3 0.7 3.8
#> 398 2 0.1 1.0 0.3 0.1 0.4 0.4 4.0
#> 399 1 1.0 1.0 0.1 1.0 0.3 1.0 4.3
#> 400 2 0.3 2.0 0.0 0.6 0.4 4.0 9.3
#> diet_score_cat3
#> 395 2
#> 396 1
#> 397 2
#> 398 2
#> 399 2
#> 400 3