R/sHEI.R
In wzhou7/Fruved: Score Fruved Instruments
Defines functions
sHEI_scores
Documented in
sHEI_scores
#' Comput sHEI scores
#'
#' Compute the estimated HEI scores using sHEI questionnaire responses
#' @param df The data frame with sHEI questionnaire responses
#' @param miss_val The value to use for missing data
#' @return A data frame with computed columns in addition
#' @examples
#' df_scored <- sHEI_scores(df);
#' df_scored <- sHEI_scores(df,miss_val=0);
#' @export
sHEI_scores <- function(df, miss_val=0){
# encode missing values
df$Q1[is.na(df$Q1)] <- 8 # Fruit
df$Q2[is.na(df$Q2)] <- 8 # Fruit Juice
df$Q3[is.na(df$Q3)] <- 8 # Vege
df$Q4[is.na(df$Q4)] <- 8 # Green vege
df$Q5[is.na(df$Q5)] <- 8 # Starchy
df$Q6[is.na(df$Q6)] <- 8 # Grains
df$Q7[is.na(df$Q7)] <- 6 # Grains2
df$Q8[is.na(df$Q8)] <- 8 # Wholegrains
df$Q9[is.na(df$Q9)] <- 6 # Wholegrains2
df$Q10[is.na(df$Q10)] <- 8 # Milk
df$Q11[is.na(df$Q11)] <- 6 # Milk2
df$Q12[is.na(df$Q12)] <- 8 # Lowfatmilk
df$Q13[is.na(df$Q13)] <- 6 # Lowfatmilk2
df$Q14[is.na(df$Q14)] <- 8 # Beans
df$Q15[is.na(df$Q15)] <- 8 # Nutseeds
df$Q16[is.na(df$Q16)] <- 8 # Seafood
df$Q17[is.na(df$Q17)] <- 6 # Seafood2
df$Q18[is.na(df$Q18)] <- 8 # Ssb
df$Q19[is.na(df$Q19)] <- 6 # Ssb2
df$Q20[is.na(df$Q20)] <- 4 # Addedsugars
df$Q21[is.na(df$Q21)] <- 4 # Satfat
df$Q22[is.na(df$Q22)] <- 4 # Water
#### encode combo items ###
# Q16_17 (seafood_combo)
df$Q16_17 <- 12 # missing value
df$Q16_17[df$Q16==1 & df$Q17==5] <- 1
df$Q16_17[df$Q16==1 & df$Q17==4] <- 2
df$Q16_17[df$Q16==1 & df$Q17==3] <- 3
df$Q16_17[df$Q16==1 & df$Q17==2] <- 4
df$Q16_17[df$Q16==1 & df$Q17==1] <- 5
df$Q16_17[df$Q16==2] <- 6
df$Q16_17[df$Q16==3] <- 7
df$Q16_17[df$Q16==4] <- 8
df$Q16_17[df$Q16==5] <- 9
df$Q16_17[df$Q16==6] <- 10
df$Q16_17[df$Q16==7] <- 11
# Q10_11 (milk_combo)
df$Q10_11 <- 12 # missing value
df$Q10_11[df$Q10==1 & df$Q11==5] <- 1
df$Q10_11[df$Q10==1 & df$Q11==4] <- 2
df$Q10_11[df$Q10==1 & df$Q11==3] <- 3
df$Q10_11[df$Q10==1 & df$Q11==2] <- 4
df$Q10_11[df$Q10==1 & df$Q11==1] <- 5
df$Q10_11[df$Q10==2] <- 6
df$Q10_11[df$Q10==3] <- 7
df$Q10_11[df$Q10==4] <- 8
df$Q10_11[df$Q10==5] <- 9
df$Q10_11[df$Q10==6] <- 10
df$Q10_11[df$Q10==7] <- 11
# Q12_13 (lowfatmilk_combo)
df$Q12_13 <- 12 # missing value
df$Q12_13[df$Q12==1 & df$Q13==5] <- 1
df$Q12_13[df$Q12==1 & df$Q13==4] <- 2
df$Q12_13[df$Q12==1 & df$Q13==3] <- 3
df$Q12_13[df$Q12==1 & df$Q13==2] <- 4
df$Q12_13[df$Q12==1 & df$Q13==1] <- 5
df$Q12_13[df$Q12==2] <- 6
df$Q12_13[df$Q12==3] <- 7
df$Q12_13[df$Q12==4] <- 8
df$Q12_13[df$Q12==5] <- 9
df$Q12_13[df$Q12==6] <- 10
df$Q12_13[df$Q12==7] <- 11
# Q18_19 (ssb_combo)
df$Q18_19 <- 12 # missing value
df$Q18_19[df$Q18==1 & df$Q19==5] <- 1
df$Q18_19[df$Q18==1 & df$Q19==4] <- 2
df$Q18_19[df$Q18==1 & df$Q19==3] <- 3
df$Q18_19[df$Q18==1 & df$Q19==2] <- 4
df$Q18_19[df$Q18==1 & df$Q19==1] <- 5
df$Q18_19[df$Q18==2] <- 6
df$Q18_19[df$Q18==3] <- 7
df$Q18_19[df$Q18==4] <- 8
df$Q18_19[df$Q18==5] <- 9
df$Q18_19[df$Q18==6] <- 10
df$Q18_19[df$Q18==7] <- 11
# Q8_9 (wholegrains_combo)
df$Q8_9 <- 12 # missing value
df$Q8_9[df$Q8==1 & df$Q9==5] <- 1
df$Q8_9[df$Q8==1 & df$Q9==4] <- 2
df$Q8_9[df$Q8==1 & df$Q9==3] <- 3
df$Q8_9[df$Q8==1 & df$Q9==2] <- 4
df$Q8_9[df$Q8==1 & df$Q9==1] <- 5
df$Q8_9[df$Q8==2] <- 6
df$Q8_9[df$Q8==3] <- 7
df$Q8_9[df$Q8==4] <- 8
df$Q8_9[df$Q8==5] <- 9
df$Q8_9[df$Q8==6] <- 10
df$Q8_9[df$Q8==7] <- 11
# Q6_7 (grains_combo)
df$Q6_7 <- 12 # missing value
df$Q6_7[df$Q6==1 & df$Q7==5] <- 1
df$Q6_7[df$Q6==1 & df$Q7==4] <- 2
df$Q6_7[df$Q6==1 & df$Q7==3] <- 3
df$Q6_7[df$Q6==1 & df$Q7==2] <- 4
df$Q6_7[df$Q6==1 & df$Q7==1] <- 5
df$Q6_7[df$Q6==2] <- 6
df$Q6_7[df$Q6==3] <- 7
df$Q6_7[df$Q6==4] <- 8
df$Q6_7[df$Q6==5] <- 9
df$Q6_7[df$Q6==6] <- 10
df$Q6_7[df$Q6==7] <- 11
#####################################################
#### Estimating sHEI Total Dietary Quality Score ####
#####################################################
# --------------------------------------------------#
# Total Fruits Component (total_fruits), 0-5
# IF Q1 (fruit) = 1 THEN total_fruits_Q1 = 0;
# IF Q1 (fruit) = 2 THEN total_fruits_Q1 = 2;
# IF Q1 (fruit) = 3 THEN total_fruits_Q1 = 3.5;
# IF Q1 (fruit) = 4,5,6,7 THEN total_fruits_Q1 = 5.
total_fruits_Q1 <- rep(miss_val,NROW(df))
total_fruits_Q1[df$Q1==1] <- 0
total_fruits_Q1[df$Q1==2] <- 2
total_fruits_Q1[df$Q1==3] <- 3.5
total_fruits_Q1[df$Q1 %in% c(4,5,6,7)] <- 5
# IF Q2 (fruitjuice) = 1 THEN total_fruits_Q2 = 0;
# IF Q2 (fruitjuice) = 2 THEN total_fruits_Q2 = 2;
# IF Q2 (fruitjuice) = 3 THEN total_fruits_Q2 = 3.5;
# IF Q2 (fruitjuice) = 4,5,6,7 THEN total_fruits_Q2 = 5.
total_fruits_Q2 <- rep(miss_val,NROW(df))
total_fruits_Q2[df$Q2==1] <- 0
total_fruits_Q2[df$Q2==2] <- 2
total_fruits_Q2[df$Q2==3] <- 3.5
total_fruits_Q2[df$Q2 %in% c(4,5,6,7)] <- 5
total_fruits = total_fruits_Q1 + total_fruits_Q2
# IF total_fruits > 5 THEN total_fruits = 5.
total_fruits[(!is.na(total_fruits)) & (total_fruits>5)] <- 5
# --------------------------------------------------#
# Whole Fruits Component (whole_fruits), 0-5
# IF Q1 (fruit) = 1 THEN whole_fruits = 0;
# IF Q1 (fruit) = 2 THEN whole_fruits = 2.5;
# IF Q1 (fruit) = 3,4,5,6,7 THEN whole_fruits = 5.
whole_fruits <- rep(miss_val,NROW(df))
whole_fruits[df$Q1==1] <- 0
whole_fruits[df$Q1==2] <- 2.5
whole_fruits[df$Q1 %in% c(3,4,5,6,7)] <- 5
# --------------------------------------------------#
# Total Vegetables Component (tot_veg), 0-5
# IF Q4 (greenvege) = 1 THEN tot_veg = 1.60;
# IF Q5 (startchy) = 2,3,4,5,6,7 AND Q4 (greenvege) = 2 THEN tot_veg = 2.46;
# IF Q5 (startchy) = 2,3,4,5,6,7 AND Q4 (greenvege) = 3,4,5,6,7 THEN tot_veg = 3.24;
# IF Q5 (startchy) = 1 AND Q4 (greenvege) = 2,3,4,5,6,7 THEN tot_veg = 3.56.
tot_veg <- rep(miss_val,NROW(df))
# Q4=1, Q5=1
# Q4=1, Q5=2,3,4,5,6,7
tot_veg[df$Q4==1] <- 1.60
# Q4=2, Q5=2,3,4,5,6,7
tot_veg[df$Q4==2 & df$Q5 %in% c(2,3,4,5,6,7)] <- 2.46
# Q4=3,4,5,6,7, Q5=2,3,4,5,6,7
tot_veg[df$Q4 %in% c(3,4,5,6,7) & df$Q5 %in% c(2,3,4,5,6,7)] <- 3.24
# Q4=2,3,4,5,6,7, Q5=1
tot_veg[df$Q4 %in% c(2,3,4,5,6,7) & df$Q5==1] <- 3.56
# --------------------------------------------------#
# Greens and Beans Component (greens_beans), 0-5
# IF Q4 (greenvege) = 1 THEN greens_beans_Q7 = 0;
# IF Q4 (greenvege) = 2,3,4,5,6,7 THEN greens_beans_Q7 = 5.
greens_beans_Q4 <- rep(miss_val,NROW(df))
greens_beans_Q4[df$Q4==1] <- 0
greens_beans_Q4[df$Q4 %in% c(2,3,4,5,6,7)] <- 5
# IF Q14 (beans) = 1 THEN greens_beans_Q14 = 0;
# IF Q14 (beans) = 2,3,4,5,6,7 THEN greens_beans_Q14 = 5.
greens_beans_Q14 <- rep(miss_val,NROW(df))
greens_beans_Q14[df$Q14==1] <- 0
greens_beans_Q14[df$Q14 %in% c(2,3,4,5,6,7)] <- 5
greens_beans = greens_beans_Q4 + greens_beans_Q14
# IF greens_beans > 5 THEN greens_beans = 5.
greens_beans[(!is.na(greens_beans)) & (greens_beans>5)] <- 5
# --------------------------------------------------#
# Whole Grains Component (whole_grains), 0-10
# IF Q8 (wholegrains) = 1 THEN whole_grains = 0.51;
# IF Gender = M AND Q8 (wholegrains) = 2,3,4,5,6,7 THEN whole_grains = 2.97;
# IF Gender = F AND Q8 (wholegrains) = 2,3 THEN whole_grains = 5.20;
# IF Gender = F AND Q8 (wholegrains) = 4,5,6,7 THEN whole_grains = 6.94.
whole_grains <- rep(miss_val,NROW(df))
whole_grains[df$Q8==1] <- 0.51
whole_grains[df$Q8 %in% c(2,3,4,5,6,7) & df$Gender=="M"] <- 2.97
whole_grains[df$Q8 %in% c(2,3) & df$Gender=="F"] <- 5.20
whole_grains[df$Q8 %in% c(4,5,6,7) & df$Gender=="F"] <- 6.94
# --------------------------------------------------#
# Dairy Component (Dairy), 0-10
# IF Gender = M AND Q10 (milk) = 1,2,3 THEN dairy = 3.22;
# IF Gender = F AND Q10 (milk) = 1,2,3 AND Q12 (lowfatmilk) = 1 THEN dairy = 3.32;
# IF Gender = F AND Q10 (milk) = 1,2,3 AND Q12 (lowfatmilk) = 2,3,4,5,6,7
# THEN dairy = 4.81;
# IF Q10 (milk) = 4,5,6,7 THEN dairy = 6.51.
dairy <- rep(miss_val,NROW(df))
dairy[df$Gender=="M" & df$Q10 %in% c(1,2,3)] <- 3.22
dairy[df$Gender=="F" & df$Q10 %in% c(1,2,3) & df$Q12==1] <- 3.32
dairy[df$Gender=="F" & df$Q10 %in% c(1,2,3) & df$Q12 %in% c(2,3,4,5,6,7)] <- 4.81
dairy[df$Q10 %in% c(4,5,6,7)] <- 6.51
# --------------------------------------------------#
# Total Protein Foods Component (tot_proteins), 0-5
# IF Gender = M AND Q16_17 (seafood_combo) = 1,2,3,4 THEN tot_proteins = 4.11;
# IF Gender = M AND Q16_17 (seafood_combo) = 5,6,7,8,9,10,11 THEN tot_proteins = 4.98;
# IF Gender = F THEN tot_proteins = 4.97.
tot_proteins <- rep(miss_val,NROW(df))
tot_proteins[df$Gender=="M" & df$Q16_17 %in% c(1,2,3,4)] <- 4.11
tot_proteins[df$Gender=="M" & df$Q16_17 %in% c(5,6,7,8,9,10,11)] <- 4.98
tot_proteins[df$Gender=="F"] <- 4.97
# --------------------------------------------------#
# Seafood and Plant Protein Component (Seafood_Plant), 0-5
# IF Gender = M AND Q15 (nutseeds) = 1,2 THEN seafood_plant = 0.49;
# IF Gender = F AND Q15 (nutseeds) = 1,2 THEN seafood_plant = 1.50;
# IF Q15 (nutseeds) = 3,4,5,6,7 THEN seafood_plant = 4.20.
seafood_plant <- rep(miss_val,NROW(df))
seafood_plant[df$Gender=="M" & df$Q15 %in% c(1,2)] <- 0.49
seafood_plant[df$Gender=="F" & df$Q15 %in% c(1,2)] <- 1.50
seafood_plant[df$Q15 %in% c(3,4,5,6,7)] <- 4.20
# --------------------------------------------------#
# Fatty Acid Ratio Component (Fatty_Acid), 0-10
# IF Q10 (milk) = 4,5,6,7 THEN fatty_acid = 2.56;
# IF Q21 (satfat) = 2,3 AND Q10_11 (milk_combo) = 1,2,3,4,5,6,7
# AND Q12-13 (lowfatmilk_combo) = 1,2 THEN fatty_acid = 2.63;
# IF Q21 (satfat) = 2,3 AND Q10_11 (milk_combo) = 1,2,3,4,5,6,7
# AND Q12_13 (lowfatmilk_combo) = 3,4,5,6,7,8,9,10,11
# THEN fatty_acid = 4.54;
# IF Q21 (satfat) = 1 AND Q10_11 (milk_combo) = 1,2,3,4,5,6,7
# THEN fatty_acid = 5.93.
fatty_acid <- rep(miss_val,NROW(df))
fatty_acid[df$Q10 %in% c(4,5,6,7)] <- 2.56
fatty_acid[df$Q21 %in% c(2,3) &
df$Q10_11 %in% c(1,2,3,4,5,6,7) &
df$Q12_13 %in% c(1,2)] <- 2.63
fatty_acid[df$Q21 %in% c(2,3) &
df$Q10_11 %in% c(1,2,3,4,5,6,7) &
df$Q12_13 %in% c(3,4,5,6,7,8,9,10,11)] <- 4.54
fatty_acid[df$Q21==1 &
df$Q10_11 %in% c(1,2,3,4,5,6,7)] <- 5.93
# --------------------------------------------------#
# Refined Grains Component (Refined_Grains), 0-10
refined_grains <- rep(miss_val,NROW(df))
# IF Q4 (greenvege) = 1 THEN refined_grains = 2.13;
refined_grains[df$Q4==1] <- 2.13
# IF Q6 (grains) = 3,4,5,6,7 AND Q16 (seafood) = 2,3,4,5,6,7
# AND Q4 (greenvege) = 2,3,4,5,6,7 THEN refined_grains = 2.27;
refined_grains[df$Q6 %in% c(3,4,5,6,7) &
df$Q16 %in% c(2,3,4,5,6,7) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 2.27
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 1,2
# AND Q16 (seafood) = 1 AND Q4 (greenvege) = 2,3,4,5,6,7
# THEN refined_grains = 4.73;
refined_grains[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(1,2) &
df$Q16==1 &
df$Q4 %in% c(2,3,4,5,6,7)] <- 4.73
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 3,4,5,6,7
# AND Q16 (seafood) = 1 AND Q4 (greenvege) = 2,3,4,5,6,7
# THEN refined_grains = 8.45;
refined_grains[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(3,4,5,6,7) &
df$Q16==1 &
df$Q4 %in% c(2,3,4,5,6,7)] <- 8.45
# IF Q6 (grains) = 1,2 AND Q4 (greenvege) = 2,3,4,5,6,7
# THEN refined_grains = 9.25.
refined_grains[df$Q6 %in% c(1,2) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 9.25
# --------------------------------------------------#
# Sodium Component (Sodium), 0-10
sodium <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1,2 AND Q6 (grains) = 3,4,5,6,7 AND Q22 (water) = 3
# THEN sodium = 0.70;
sodium[df$Q1 %in% c(1,2) &
df$Q6 %in% c(3,4,5,6,7) &
df$Q22==3] <- 0.70
# IF Q1 (fruit) = 3,4,5,6,7 AND Q6 (grains) = 3,4,5,6,7 AND Q22 (water) = 3
# THEN sodium = 2.30;
sodium[df$Q1 %in% c(3,4,5,6,7) &
df$Q6 %in% c(3,4,5,6,7) &
df$Q22==3] <- 2.30
# IF Q6 (grains) = 3,4,5,6,7 AND Q22 (water) = 1,2 THEN sodium = 4.94;
sodium[df$Q6 %in% c(3,4,5,6,7) &
df$Q22 %in% c(1,2)] <- 4.94
# IF Q6 (grains) = 1,2 THEN sodium = 6.07.
sodium[df$Q6 %in% c(1,2)] <- 6.07
# --------------------------------------------------#
# Added Sugars Component (Added_Sugars), 0-10
# IF Q18 (ssb) = 1 THEN sugar_calories_Q18 = 0;
# IF Q18 (ssb) = 2 THEN sugar_calories_Q18 = 156;
# IF Q18 (ssb) = 3 THEN sugar_calories_Q18 = 312;
# IF Q18 (ssb) = 4 THEN sugar_calories_Q18 = 468;
# IF Q18 (ssb) = 5 THEN sugar_calories_Q18 = 624;
# IF Q18 (ssb) = 6 THEN sugar_calories_Q18 = 780;
# IF Q18 (ssb) = 7 THEN sugar_calories_Q18 = 936.
sugar_calories_Q18 <- rep(miss_val,NROW(df))
sugar_calories_Q18[df$Q18==1] <- 0
sugar_calories_Q18[df$Q18==2] <- 156
sugar_calories_Q18[df$Q18==3] <- 312
sugar_calories_Q18[df$Q18==4] <- 468
sugar_calories_Q18[df$Q18==5] <- 624
sugar_calories_Q18[df$Q18==6] <- 780
sugar_calories_Q18[df$Q18==7] <- 936
# IF Q20 (addedsugars) = 1 THEN sugar_calories_Q20 = 130;
# IF Q20 (addedsugars) = 2 THEN sugar_calories_Q20 = 260;
# IF Q20 (addedsugars) = 3 THEN sugar_calories_Q20 = 520.
sugar_calories_Q20 <- rep(miss_val,NROW(df))
sugar_calories_Q20[df$Q20==1] <- 130
sugar_calories_Q20[df$Q20==2] <- 260
sugar_calories_Q20[df$Q20==3] <- 520
sugar_calories = sugar_calories_Q18 + sugar_calories_Q20
# IF sugar_calories < =130 THEN added_sugars = 10;
# IF sugar_calories > 130 AND sugar_calories < 520 THEN added_sugars = 5;
# IF sugar_calories > =520 THEN added_sugars = 0.
added_sugars <- rep(miss_val,NROW(df))
added_sugars[(!is.na(sugar_calories)) & (sugar_calories<=130)] <- 10
added_sugars[(!is.na(sugar_calories)) & (sugar_calories>130) & (sugar_calories < 520)] <- 5
added_sugars[(!is.na(sugar_calories)) & (sugar_calories>=520)] <- 0
# --------------------------------------------------#
# Saturated Fats Component (sat_fat), 0-10
sat_fat <- rep(miss_val,NROW(df))
# IF Q18 (ssb) = 3,4,5,6,7 THEN sat_fat = 1.82;
sat_fat[df$Q18 %in% c(3,4,5,6,7)] <- 1.82
# IF Q6 (grains) = 1,2 AND Q18 (ssb) = 1,2 THEN sat_fat = 3.20;
sat_fat[df$Q6 %in% c(1,2) &
df$Q18 %in% c(1,2)] <- 3.20
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 1,2 AND Q18 (ssb) = 1,2
# THEN sat_fat = 4.64;
sat_fat[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(1,2) &
df$Q18 %in% c(1,2)] <- 4.64
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 3,4,5,6,7 AND
# Q18 (ssb) = 1,2 THEN sat_fat = 6.56.
sat_fat[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(3,4,5,6,7) &
df$Q18 %in% c(1,2)] <- 6.56
# --------------------------------------------------#
# Total DQ Score (0-100)
tot_score = total_fruits + whole_fruits + tot_veg +
greens_beans + whole_grains + dairy + tot_proteins +
seafood_plant + fatty_acid + refined_grains +
sodium + added_sugars + sat_fat
df_sHEI_scores <- data.frame(total_fruits, whole_fruits, tot_veg,
greens_beans, whole_grains, dairy, tot_proteins,
seafood_plant, fatty_acid, refined_grains,
sodium, added_sugars, sat_fat, tot_score)
names(df_sHEI_scores) <- paste0("sHEI_",names(df_sHEI_scores))
###########################################
#### Estimating Food Group Consumption ####
###########################################
# --------------------------------------------------#
# Total Fruit and Vegetable Servings in Cup Equivalents
# Including Legumes and French Fries (DSQfvl)
DSQfvl <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1 THEN DSQfvl = 1.81;
DSQfvl[df$Q1==1] <- 1.81
# IF Gender = F AND Q1 (fruit) = 2,3 THEN DSQfvl = 2.22;
DSQfvl[df$Gender=="F" & df$Q1 %in% c(2,3)] <- 2.22
# IF Gender = M AND Q1 (fruit) = 2,3 THEN DSQfvl = 2.53;
DSQfvl[df$Gender=="M" & df$Q1 %in% c(2,3)] <- 2.53
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfvl = 3.21.
DSQfvl[df$Q1 %in% c(4,5,6,7)] <- 3.21
# --------------------------------------------------#
# Total Fruit and Vegetable Servings in Cup Equivalents
# Including Legumes and Excluding French Fries (Dsqfvlnf)
DSQfvlnf <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1 THEN DSQfvlnf = 1.72;
DSQfvlnf[df$Q1==1] <- 1.72
# IF Gender = F AND Q1 (fruit) = 2,3 THEN DSQfvlnf = 2.15;
DSQfvlnf[df$Gender=="F" & df$Q1 %in% c(2,3)] <- 2.15
# IF Gender = M AND Q1 (fruit) = 2,3 THEN DSQfvlnf = 2.43;
DSQfvlnf[df$Gender=="M" & df$Q1 %in% c(2,3)] <- 2.43
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfvlnf = 3.10.
DSQfvlnf[df$Q1 %in% c(4,5,6,7)] <- 3.10
# --------------------------------------------------#
# Total Fruit Servings in Cup Equivalents (Dsqfrt)
DSQfrt <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1 THEN DSQfrt = 0.52;
DSQfrt[df$Q1==1] <- 0.52
# IF Q1 (fruit) = 2 THEN DSQfrt = 0.78;
DSQfrt[df$Q1==2] <- 0.78
# IF Q1 (fruit) = 3 THEN DSQfrt = 0.99;
DSQfrt[df$Q1==3] <- 0.99
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfrt = 1.52.
DSQfrt[df$Q1 %in% c(4,5,6,7)] <- 1.52
# --------------------------------------------------#
# Total Vegetable Servings in Cup Equivalents Including
# Legumes and French Fries (DSQvlall)
DSQvlall <- rep(miss_val,NROW(df))
# IF Q3 (vege) = 1,2 THEN DSQvlall = 1.34;
DSQvlall[df$Q3 %in% c(1,2)] <- 1.34
# IF Gender = F AND Q3 (vege) = 3,4 THEN DSQvlall = 1.40;
DSQvlall[df$Gender=="F" & df$Q3 %in% c(3,4)] <- 1.40
# IF Gender = F AND Q3 (vege) = 5,6,7 THEN DSQvlall = 1.68;
DSQvlall[df$Gender=="F" & df$Q3 %in% c(5,6,7)] <- 1.68
# IF Gender = M AND Q3 (vege) = 3,4,5,6,7 THEN DSQvlall = 1.84.
DSQvlall[df$Gender=="M" & df$Q3 %in% c(3,4,5,6,7)] <- 1.84
# --------------------------------------------------#
# Total Vegetable Servings in Cup Equivalents Including
# Legumes and Excluding French Fries (Dsqvlnf)
DSQvlnf <- rep(miss_val,NROW(df))
# IF Q3 (vege) = 1,2 THEN DSQvlnf = 1.19;
DSQvlnf[df$Q3 %in% c(1,2)] <- 1.19
# IF Gender = F AND Q3 (vege) = 3,4 THEN DSQvlnf = 1.28;
DSQvlnf[df$Gender=="F" & df$Q3 %in% c(3,4)] <- 1.28
# IF Gender = F AND Q3 (vege) = 5,6,7 THEN DSQvlnf = 1.59;
DSQvlnf[df$Gender=="F" & df$Q3 %in% c(5,6,7)] <- 1.59
# IF Gender = M AND Q3 (vege) = 3,4,5,6,7 THEN DSQvlnf = 1.71.
DSQvlnf[df$Gender=="M" & df$Q3 %in% c(3,4,5,6,7)] <- 1.71
# --------------------------------------------------#
# Dairy Servings in Cup Equivalents (Dsqdairy)
DSQdairy <- rep(miss_val,NROW(df))
# IF Q10 (milk) = 1 THEN DSQdairy = 1.16;
DSQdairy[df$Q10==1] <- 1.16
# IF Gender = F AND Q10 (milk) = 2,3 THEN DSQdairy = 1.48;
DSQdairy[df$Gender=="F" & df$Q10 %in% c(2,3)] <- 1.48
# IF Gender = M AND Q10 (milk) = 2,3 THEN DSQdairy = 1.90;
DSQdairy[df$Gender=="M" & df$Q10 %in% c(2,3)] <- 1.90
# IF Q10 (milk) = 4,5,6,7 THEN DSQdairy = 2.36.
DSQdairy[df$Q10 %in% c(4,5,6,7)] <- 2.36
# --------------------------------------------------#
# Added Sugars in Teaspoon Equivalents (Dsqsug)
DSQsug <- rep(miss_val,NROW(df))
# IF Q18_19 (ssb_combo) = 1,2,3,4 THEN DSQsug = 13.26;
DSQsug[df$Q18_19 %in% c(1,2,3,4)] <- 13.26
# IF Q18_19 (ssb_combo) = 5,6 THEN DSQsug = 16.00;
DSQsug[df$Q18_19 %in% c(5,6)] <- 16.00
# IF Q18 (ssb) = 3,4,5,6,7 THEN DSQsug = 26.87.
DSQsug[df$Q18_19 %in% c(3,4,5,6,7)] <- 26.87
# --------------------------------------------------#
# Added Sugars from Sugar-Sweetened Beverages in Teaspoon Equivalents (DSQssb)
DSQssb <- rep(miss_val,NROW(df))
# IF Gender = F AND Q18_19 (ssb_combo) = 1,2,3,4 THEN DSQssb = 4.13;
DSQssb[df$Gender=="F" & df$Q18_19 %in% c(1,2,3,4)] <- 4.13
# IF Gender = M AND Q18_19 (ssb_combo) = 1,2,3,4 THEN DSQssb = 5.73;
DSQssb[df$Gender=="M" & df$Q18_19 %in% c(1,2,3,4)] <- 5.73
# IF Q18_19 (ssb_combo) = 5,6 THEN DSQssb = 6.79;
DSQssb[df$Q18_19 %in% c(5,6)] <- 6.79
# IF Q18 (ssb) = 3,4,5,6,7 THEN DSQssb = 15.78.
DSQssb[df$Q18 %in% c(3,4,5,6,7)] <- 15.78
# --------------------------------------------------#
# Whole Grains in Ounce Equivalents (DSQwhgr)
DSQwhgr <- rep(miss_val,NROW(df))
# IF Q8_9 (wholegrains_combo) = 1,2,3,4 THEN DSQwhgr= 0.50;
DSQwhgr[df$Q8_9 %in% c(1,2,3,4)] <- 0.50
# IF Q6_7 (grains_combo) = 1,2,3,4,5,6 AND Q8_9 (wholegrains_combo) = 5,6,7
# THEN DSQwhgr = 0.63;
DSQwhgr[df$Q6_7 %in% c(1,2,3,4,5,6) &
df$Q8_9 %in% c(5,6,7)] <- 0.63
# IF Q6 (grains) = 3,4,5,6,7 AND Q8_9 (wholegrains_combo) = 5,6,7
# THEN DSQwhgr = 0.77;
DSQwhgr[df$Q6 %in% c(3,4,5,6,7) &
df$Q8_9 %in% c(5,6,7)] <- 0.77
# IF Q8 (wholegrains) = 4,5,6,7 THEN DSQwhgr = 1.01.
DSQwhgr[df$Q8 %in% c(4,5,6,7)] <- 1.01
# --------------------------------------------------#
# Fiber in Grams (DSQfib)
DSQfib <- rep(miss_val,NROW(df))
# IF Gender = F AND Q1 (fruit) = 1,2,3 THEN DSQfib = 13.69;
DSQfib[df$Gender=="F" & df$Q1 %in% c(1,2,3)] <- 13.69
# IF Gender = M AND Q1 (fruit) = 1,2,3 THEN DSQfib = 16.74;
DSQfib[df$Gender=="M" & df$Q1 %in% c(1,2,3)] <- 16.74
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfib = 19.32.
DSQfib[df$Q1 %in% c(4,5,6,7)] <- 19.32
# --------------------------------------------------#
# Calcium in Milligrams (DSQcalc)
DSQcalc <- rep(miss_val,NROW(df))
# IF Gender = F AND Q10 (milk) = 1,2 THEN DSQcalc = 851.61;
DSQcalc[df$Gender=="F" & df$Q10 %in% c(1,2)] <- 851.61
# IF Gender = F AND Q10 (milk) = 3,4,5,6,7 THEN DSQcalc = 1010.78;
DSQcalc[df$Gender=="F" & df$Q10 %in% c(3,4,5,6,7)] <- 1010.78
# IF Gender = M AND Q10 (milk) = 1,2 THEN DSQcalc = 1062.55;
DSQcalc[df$Gender=="M" & df$Q10 %in% c(1,2)] <- 1062.55
# IF Gender = M AND Q10 (milk) = 3,4,5,6,7 THEN DSQcalc = 1319.71.
DSQcalc[df$Gender=="M" & df$Q10 %in% c(3,4,5,6,7)] <- 1319.71
# --------------------------------------------------#
# Green Vegetables in Cup Equivalents
greens <- rep(miss_val,NROW(df))
# IF Q4 (greenvege) = 1 THEN greens = 0.00
greens[df$Q4==1] <- 0.00
# IF Q3 (vege) = 1,2,3,4 AND Q7 (greenvege) = 2,3,4,5,6,7 THEN greens = 0.13
greens[df$Q3 %in% c(1,2,3,4) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 0.13
# IF Q3 (vege) = 5,6,7 AND Q7 (greenvege) = 2,3,4,5,6,7 THEN greens = 0.31
greens[df$Q3 %in% c(5,6,7) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 0.31
df_sHEI_consump <- data.frame(DSQfvl, DSQfvlnf, DSQfrt, DSQvlall, DSQvlnf,
DSQdairy, DSQsug, DSQssb, DSQwhgr, DSQfib,
DSQcalc, greens)
names(df_sHEI_consump) <- paste0("sHEI_",names(df_sHEI_consump))
df <- cbind(df,df_sHEI_scores)
df <- cbind(df,df_sHEI_consump)
return(df)
}
wzhou7/Fruved documentation built on June 15, 2025, 1:16 a.m.
R Package Documentation
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Defines functions sHEI_scores
Documented in sHEI_scores
#' Comput sHEI scores
#'
#' Compute the estimated HEI scores using sHEI questionnaire responses
#' @param df The data frame with sHEI questionnaire responses
#' @param miss_val The value to use for missing data
#' @return A data frame with computed columns in addition
#' @examples
#' df_scored <- sHEI_scores(df);
#' df_scored <- sHEI_scores(df,miss_val=0);
#' @export
sHEI_scores <- function(df, miss_val=0){
# encode missing values
df$Q1[is.na(df$Q1)] <- 8 # Fruit
df$Q2[is.na(df$Q2)] <- 8 # Fruit Juice
df$Q3[is.na(df$Q3)] <- 8 # Vege
df$Q4[is.na(df$Q4)] <- 8 # Green vege
df$Q5[is.na(df$Q5)] <- 8 # Starchy
df$Q6[is.na(df$Q6)] <- 8 # Grains
df$Q7[is.na(df$Q7)] <- 6 # Grains2
df$Q8[is.na(df$Q8)] <- 8 # Wholegrains
df$Q9[is.na(df$Q9)] <- 6 # Wholegrains2
df$Q10[is.na(df$Q10)] <- 8 # Milk
df$Q11[is.na(df$Q11)] <- 6 # Milk2
df$Q12[is.na(df$Q12)] <- 8 # Lowfatmilk
df$Q13[is.na(df$Q13)] <- 6 # Lowfatmilk2
df$Q14[is.na(df$Q14)] <- 8 # Beans
df$Q15[is.na(df$Q15)] <- 8 # Nutseeds
df$Q16[is.na(df$Q16)] <- 8 # Seafood
df$Q17[is.na(df$Q17)] <- 6 # Seafood2
df$Q18[is.na(df$Q18)] <- 8 # Ssb
df$Q19[is.na(df$Q19)] <- 6 # Ssb2
df$Q20[is.na(df$Q20)] <- 4 # Addedsugars
df$Q21[is.na(df$Q21)] <- 4 # Satfat
df$Q22[is.na(df$Q22)] <- 4 # Water
#### encode combo items ###
# Q16_17 (seafood_combo)
df$Q16_17 <- 12 # missing value
df$Q16_17[df$Q16==1 & df$Q17==5] <- 1
df$Q16_17[df$Q16==1 & df$Q17==4] <- 2
df$Q16_17[df$Q16==1 & df$Q17==3] <- 3
df$Q16_17[df$Q16==1 & df$Q17==2] <- 4
df$Q16_17[df$Q16==1 & df$Q17==1] <- 5
df$Q16_17[df$Q16==2] <- 6
df$Q16_17[df$Q16==3] <- 7
df$Q16_17[df$Q16==4] <- 8
df$Q16_17[df$Q16==5] <- 9
df$Q16_17[df$Q16==6] <- 10
df$Q16_17[df$Q16==7] <- 11
# Q10_11 (milk_combo)
df$Q10_11 <- 12 # missing value
df$Q10_11[df$Q10==1 & df$Q11==5] <- 1
df$Q10_11[df$Q10==1 & df$Q11==4] <- 2
df$Q10_11[df$Q10==1 & df$Q11==3] <- 3
df$Q10_11[df$Q10==1 & df$Q11==2] <- 4
df$Q10_11[df$Q10==1 & df$Q11==1] <- 5
df$Q10_11[df$Q10==2] <- 6
df$Q10_11[df$Q10==3] <- 7
df$Q10_11[df$Q10==4] <- 8
df$Q10_11[df$Q10==5] <- 9
df$Q10_11[df$Q10==6] <- 10
df$Q10_11[df$Q10==7] <- 11
# Q12_13 (lowfatmilk_combo)
df$Q12_13 <- 12 # missing value
df$Q12_13[df$Q12==1 & df$Q13==5] <- 1
df$Q12_13[df$Q12==1 & df$Q13==4] <- 2
df$Q12_13[df$Q12==1 & df$Q13==3] <- 3
df$Q12_13[df$Q12==1 & df$Q13==2] <- 4
df$Q12_13[df$Q12==1 & df$Q13==1] <- 5
df$Q12_13[df$Q12==2] <- 6
df$Q12_13[df$Q12==3] <- 7
df$Q12_13[df$Q12==4] <- 8
df$Q12_13[df$Q12==5] <- 9
df$Q12_13[df$Q12==6] <- 10
df$Q12_13[df$Q12==7] <- 11
# Q18_19 (ssb_combo)
df$Q18_19 <- 12 # missing value
df$Q18_19[df$Q18==1 & df$Q19==5] <- 1
df$Q18_19[df$Q18==1 & df$Q19==4] <- 2
df$Q18_19[df$Q18==1 & df$Q19==3] <- 3
df$Q18_19[df$Q18==1 & df$Q19==2] <- 4
df$Q18_19[df$Q18==1 & df$Q19==1] <- 5
df$Q18_19[df$Q18==2] <- 6
df$Q18_19[df$Q18==3] <- 7
df$Q18_19[df$Q18==4] <- 8
df$Q18_19[df$Q18==5] <- 9
df$Q18_19[df$Q18==6] <- 10
df$Q18_19[df$Q18==7] <- 11
# Q8_9 (wholegrains_combo)
df$Q8_9 <- 12 # missing value
df$Q8_9[df$Q8==1 & df$Q9==5] <- 1
df$Q8_9[df$Q8==1 & df$Q9==4] <- 2
df$Q8_9[df$Q8==1 & df$Q9==3] <- 3
df$Q8_9[df$Q8==1 & df$Q9==2] <- 4
df$Q8_9[df$Q8==1 & df$Q9==1] <- 5
df$Q8_9[df$Q8==2] <- 6
df$Q8_9[df$Q8==3] <- 7
df$Q8_9[df$Q8==4] <- 8
df$Q8_9[df$Q8==5] <- 9
df$Q8_9[df$Q8==6] <- 10
df$Q8_9[df$Q8==7] <- 11
# Q6_7 (grains_combo)
df$Q6_7 <- 12 # missing value
df$Q6_7[df$Q6==1 & df$Q7==5] <- 1
df$Q6_7[df$Q6==1 & df$Q7==4] <- 2
df$Q6_7[df$Q6==1 & df$Q7==3] <- 3
df$Q6_7[df$Q6==1 & df$Q7==2] <- 4
df$Q6_7[df$Q6==1 & df$Q7==1] <- 5
df$Q6_7[df$Q6==2] <- 6
df$Q6_7[df$Q6==3] <- 7
df$Q6_7[df$Q6==4] <- 8
df$Q6_7[df$Q6==5] <- 9
df$Q6_7[df$Q6==6] <- 10
df$Q6_7[df$Q6==7] <- 11
#####################################################
#### Estimating sHEI Total Dietary Quality Score ####
#####################################################
# --------------------------------------------------#
# Total Fruits Component (total_fruits), 0-5
# IF Q1 (fruit) = 1 THEN total_fruits_Q1 = 0;
# IF Q1 (fruit) = 2 THEN total_fruits_Q1 = 2;
# IF Q1 (fruit) = 3 THEN total_fruits_Q1 = 3.5;
# IF Q1 (fruit) = 4,5,6,7 THEN total_fruits_Q1 = 5.
total_fruits_Q1 <- rep(miss_val,NROW(df))
total_fruits_Q1[df$Q1==1] <- 0
total_fruits_Q1[df$Q1==2] <- 2
total_fruits_Q1[df$Q1==3] <- 3.5
total_fruits_Q1[df$Q1 %in% c(4,5,6,7)] <- 5
# IF Q2 (fruitjuice) = 1 THEN total_fruits_Q2 = 0;
# IF Q2 (fruitjuice) = 2 THEN total_fruits_Q2 = 2;
# IF Q2 (fruitjuice) = 3 THEN total_fruits_Q2 = 3.5;
# IF Q2 (fruitjuice) = 4,5,6,7 THEN total_fruits_Q2 = 5.
total_fruits_Q2 <- rep(miss_val,NROW(df))
total_fruits_Q2[df$Q2==1] <- 0
total_fruits_Q2[df$Q2==2] <- 2
total_fruits_Q2[df$Q2==3] <- 3.5
total_fruits_Q2[df$Q2 %in% c(4,5,6,7)] <- 5
total_fruits = total_fruits_Q1 + total_fruits_Q2
# IF total_fruits > 5 THEN total_fruits = 5.
total_fruits[(!is.na(total_fruits)) & (total_fruits>5)] <- 5
# --------------------------------------------------#
# Whole Fruits Component (whole_fruits), 0-5
# IF Q1 (fruit) = 1 THEN whole_fruits = 0;
# IF Q1 (fruit) = 2 THEN whole_fruits = 2.5;
# IF Q1 (fruit) = 3,4,5,6,7 THEN whole_fruits = 5.
whole_fruits <- rep(miss_val,NROW(df))
whole_fruits[df$Q1==1] <- 0
whole_fruits[df$Q1==2] <- 2.5
whole_fruits[df$Q1 %in% c(3,4,5,6,7)] <- 5
# --------------------------------------------------#
# Total Vegetables Component (tot_veg), 0-5
# IF Q4 (greenvege) = 1 THEN tot_veg = 1.60;
# IF Q5 (startchy) = 2,3,4,5,6,7 AND Q4 (greenvege) = 2 THEN tot_veg = 2.46;
# IF Q5 (startchy) = 2,3,4,5,6,7 AND Q4 (greenvege) = 3,4,5,6,7 THEN tot_veg = 3.24;
# IF Q5 (startchy) = 1 AND Q4 (greenvege) = 2,3,4,5,6,7 THEN tot_veg = 3.56.
tot_veg <- rep(miss_val,NROW(df))
# Q4=1, Q5=1
# Q4=1, Q5=2,3,4,5,6,7
tot_veg[df$Q4==1] <- 1.60
# Q4=2, Q5=2,3,4,5,6,7
tot_veg[df$Q4==2 & df$Q5 %in% c(2,3,4,5,6,7)] <- 2.46
# Q4=3,4,5,6,7, Q5=2,3,4,5,6,7
tot_veg[df$Q4 %in% c(3,4,5,6,7) & df$Q5 %in% c(2,3,4,5,6,7)] <- 3.24
# Q4=2,3,4,5,6,7, Q5=1
tot_veg[df$Q4 %in% c(2,3,4,5,6,7) & df$Q5==1] <- 3.56
# --------------------------------------------------#
# Greens and Beans Component (greens_beans), 0-5
# IF Q4 (greenvege) = 1 THEN greens_beans_Q7 = 0;
# IF Q4 (greenvege) = 2,3,4,5,6,7 THEN greens_beans_Q7 = 5.
greens_beans_Q4 <- rep(miss_val,NROW(df))
greens_beans_Q4[df$Q4==1] <- 0
greens_beans_Q4[df$Q4 %in% c(2,3,4,5,6,7)] <- 5
# IF Q14 (beans) = 1 THEN greens_beans_Q14 = 0;
# IF Q14 (beans) = 2,3,4,5,6,7 THEN greens_beans_Q14 = 5.
greens_beans_Q14 <- rep(miss_val,NROW(df))
greens_beans_Q14[df$Q14==1] <- 0
greens_beans_Q14[df$Q14 %in% c(2,3,4,5,6,7)] <- 5
greens_beans = greens_beans_Q4 + greens_beans_Q14
# IF greens_beans > 5 THEN greens_beans = 5.
greens_beans[(!is.na(greens_beans)) & (greens_beans>5)] <- 5
# --------------------------------------------------#
# Whole Grains Component (whole_grains), 0-10
# IF Q8 (wholegrains) = 1 THEN whole_grains = 0.51;
# IF Gender = M AND Q8 (wholegrains) = 2,3,4,5,6,7 THEN whole_grains = 2.97;
# IF Gender = F AND Q8 (wholegrains) = 2,3 THEN whole_grains = 5.20;
# IF Gender = F AND Q8 (wholegrains) = 4,5,6,7 THEN whole_grains = 6.94.
whole_grains <- rep(miss_val,NROW(df))
whole_grains[df$Q8==1] <- 0.51
whole_grains[df$Q8 %in% c(2,3,4,5,6,7) & df$Gender=="M"] <- 2.97
whole_grains[df$Q8 %in% c(2,3) & df$Gender=="F"] <- 5.20
whole_grains[df$Q8 %in% c(4,5,6,7) & df$Gender=="F"] <- 6.94
# --------------------------------------------------#
# Dairy Component (Dairy), 0-10
# IF Gender = M AND Q10 (milk) = 1,2,3 THEN dairy = 3.22;
# IF Gender = F AND Q10 (milk) = 1,2,3 AND Q12 (lowfatmilk) = 1 THEN dairy = 3.32;
# IF Gender = F AND Q10 (milk) = 1,2,3 AND Q12 (lowfatmilk) = 2,3,4,5,6,7
# THEN dairy = 4.81;
# IF Q10 (milk) = 4,5,6,7 THEN dairy = 6.51.
dairy <- rep(miss_val,NROW(df))
dairy[df$Gender=="M" & df$Q10 %in% c(1,2,3)] <- 3.22
dairy[df$Gender=="F" & df$Q10 %in% c(1,2,3) & df$Q12==1] <- 3.32
dairy[df$Gender=="F" & df$Q10 %in% c(1,2,3) & df$Q12 %in% c(2,3,4,5,6,7)] <- 4.81
dairy[df$Q10 %in% c(4,5,6,7)] <- 6.51
# --------------------------------------------------#
# Total Protein Foods Component (tot_proteins), 0-5
# IF Gender = M AND Q16_17 (seafood_combo) = 1,2,3,4 THEN tot_proteins = 4.11;
# IF Gender = M AND Q16_17 (seafood_combo) = 5,6,7,8,9,10,11 THEN tot_proteins = 4.98;
# IF Gender = F THEN tot_proteins = 4.97.
tot_proteins <- rep(miss_val,NROW(df))
tot_proteins[df$Gender=="M" & df$Q16_17 %in% c(1,2,3,4)] <- 4.11
tot_proteins[df$Gender=="M" & df$Q16_17 %in% c(5,6,7,8,9,10,11)] <- 4.98
tot_proteins[df$Gender=="F"] <- 4.97
# --------------------------------------------------#
# Seafood and Plant Protein Component (Seafood_Plant), 0-5
# IF Gender = M AND Q15 (nutseeds) = 1,2 THEN seafood_plant = 0.49;
# IF Gender = F AND Q15 (nutseeds) = 1,2 THEN seafood_plant = 1.50;
# IF Q15 (nutseeds) = 3,4,5,6,7 THEN seafood_plant = 4.20.
seafood_plant <- rep(miss_val,NROW(df))
seafood_plant[df$Gender=="M" & df$Q15 %in% c(1,2)] <- 0.49
seafood_plant[df$Gender=="F" & df$Q15 %in% c(1,2)] <- 1.50
seafood_plant[df$Q15 %in% c(3,4,5,6,7)] <- 4.20
# --------------------------------------------------#
# Fatty Acid Ratio Component (Fatty_Acid), 0-10
# IF Q10 (milk) = 4,5,6,7 THEN fatty_acid = 2.56;
# IF Q21 (satfat) = 2,3 AND Q10_11 (milk_combo) = 1,2,3,4,5,6,7
# AND Q12-13 (lowfatmilk_combo) = 1,2 THEN fatty_acid = 2.63;
# IF Q21 (satfat) = 2,3 AND Q10_11 (milk_combo) = 1,2,3,4,5,6,7
# AND Q12_13 (lowfatmilk_combo) = 3,4,5,6,7,8,9,10,11
# THEN fatty_acid = 4.54;
# IF Q21 (satfat) = 1 AND Q10_11 (milk_combo) = 1,2,3,4,5,6,7
# THEN fatty_acid = 5.93.
fatty_acid <- rep(miss_val,NROW(df))
fatty_acid[df$Q10 %in% c(4,5,6,7)] <- 2.56
fatty_acid[df$Q21 %in% c(2,3) &
df$Q10_11 %in% c(1,2,3,4,5,6,7) &
df$Q12_13 %in% c(1,2)] <- 2.63
fatty_acid[df$Q21 %in% c(2,3) &
df$Q10_11 %in% c(1,2,3,4,5,6,7) &
df$Q12_13 %in% c(3,4,5,6,7,8,9,10,11)] <- 4.54
fatty_acid[df$Q21==1 &
df$Q10_11 %in% c(1,2,3,4,5,6,7)] <- 5.93
# --------------------------------------------------#
# Refined Grains Component (Refined_Grains), 0-10
refined_grains <- rep(miss_val,NROW(df))
# IF Q4 (greenvege) = 1 THEN refined_grains = 2.13;
refined_grains[df$Q4==1] <- 2.13
# IF Q6 (grains) = 3,4,5,6,7 AND Q16 (seafood) = 2,3,4,5,6,7
# AND Q4 (greenvege) = 2,3,4,5,6,7 THEN refined_grains = 2.27;
refined_grains[df$Q6 %in% c(3,4,5,6,7) &
df$Q16 %in% c(2,3,4,5,6,7) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 2.27
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 1,2
# AND Q16 (seafood) = 1 AND Q4 (greenvege) = 2,3,4,5,6,7
# THEN refined_grains = 4.73;
refined_grains[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(1,2) &
df$Q16==1 &
df$Q4 %in% c(2,3,4,5,6,7)] <- 4.73
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 3,4,5,6,7
# AND Q16 (seafood) = 1 AND Q4 (greenvege) = 2,3,4,5,6,7
# THEN refined_grains = 8.45;
refined_grains[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(3,4,5,6,7) &
df$Q16==1 &
df$Q4 %in% c(2,3,4,5,6,7)] <- 8.45
# IF Q6 (grains) = 1,2 AND Q4 (greenvege) = 2,3,4,5,6,7
# THEN refined_grains = 9.25.
refined_grains[df$Q6 %in% c(1,2) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 9.25
# --------------------------------------------------#
# Sodium Component (Sodium), 0-10
sodium <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1,2 AND Q6 (grains) = 3,4,5,6,7 AND Q22 (water) = 3
# THEN sodium = 0.70;
sodium[df$Q1 %in% c(1,2) &
df$Q6 %in% c(3,4,5,6,7) &
df$Q22==3] <- 0.70
# IF Q1 (fruit) = 3,4,5,6,7 AND Q6 (grains) = 3,4,5,6,7 AND Q22 (water) = 3
# THEN sodium = 2.30;
sodium[df$Q1 %in% c(3,4,5,6,7) &
df$Q6 %in% c(3,4,5,6,7) &
df$Q22==3] <- 2.30
# IF Q6 (grains) = 3,4,5,6,7 AND Q22 (water) = 1,2 THEN sodium = 4.94;
sodium[df$Q6 %in% c(3,4,5,6,7) &
df$Q22 %in% c(1,2)] <- 4.94
# IF Q6 (grains) = 1,2 THEN sodium = 6.07.
sodium[df$Q6 %in% c(1,2)] <- 6.07
# --------------------------------------------------#
# Added Sugars Component (Added_Sugars), 0-10
# IF Q18 (ssb) = 1 THEN sugar_calories_Q18 = 0;
# IF Q18 (ssb) = 2 THEN sugar_calories_Q18 = 156;
# IF Q18 (ssb) = 3 THEN sugar_calories_Q18 = 312;
# IF Q18 (ssb) = 4 THEN sugar_calories_Q18 = 468;
# IF Q18 (ssb) = 5 THEN sugar_calories_Q18 = 624;
# IF Q18 (ssb) = 6 THEN sugar_calories_Q18 = 780;
# IF Q18 (ssb) = 7 THEN sugar_calories_Q18 = 936.
sugar_calories_Q18 <- rep(miss_val,NROW(df))
sugar_calories_Q18[df$Q18==1] <- 0
sugar_calories_Q18[df$Q18==2] <- 156
sugar_calories_Q18[df$Q18==3] <- 312
sugar_calories_Q18[df$Q18==4] <- 468
sugar_calories_Q18[df$Q18==5] <- 624
sugar_calories_Q18[df$Q18==6] <- 780
sugar_calories_Q18[df$Q18==7] <- 936
# IF Q20 (addedsugars) = 1 THEN sugar_calories_Q20 = 130;
# IF Q20 (addedsugars) = 2 THEN sugar_calories_Q20 = 260;
# IF Q20 (addedsugars) = 3 THEN sugar_calories_Q20 = 520.
sugar_calories_Q20 <- rep(miss_val,NROW(df))
sugar_calories_Q20[df$Q20==1] <- 130
sugar_calories_Q20[df$Q20==2] <- 260
sugar_calories_Q20[df$Q20==3] <- 520
sugar_calories = sugar_calories_Q18 + sugar_calories_Q20
# IF sugar_calories < =130 THEN added_sugars = 10;
# IF sugar_calories > 130 AND sugar_calories < 520 THEN added_sugars = 5;
# IF sugar_calories > =520 THEN added_sugars = 0.
added_sugars <- rep(miss_val,NROW(df))
added_sugars[(!is.na(sugar_calories)) & (sugar_calories<=130)] <- 10
added_sugars[(!is.na(sugar_calories)) & (sugar_calories>130) & (sugar_calories < 520)] <- 5
added_sugars[(!is.na(sugar_calories)) & (sugar_calories>=520)] <- 0
# --------------------------------------------------#
# Saturated Fats Component (sat_fat), 0-10
sat_fat <- rep(miss_val,NROW(df))
# IF Q18 (ssb) = 3,4,5,6,7 THEN sat_fat = 1.82;
sat_fat[df$Q18 %in% c(3,4,5,6,7)] <- 1.82
# IF Q6 (grains) = 1,2 AND Q18 (ssb) = 1,2 THEN sat_fat = 3.20;
sat_fat[df$Q6 %in% c(1,2) &
df$Q18 %in% c(1,2)] <- 3.20
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 1,2 AND Q18 (ssb) = 1,2
# THEN sat_fat = 4.64;
sat_fat[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(1,2) &
df$Q18 %in% c(1,2)] <- 4.64
# IF Q6 (grains) = 3,4,5,6,7 AND Q15 (nutseeds) = 3,4,5,6,7 AND
# Q18 (ssb) = 1,2 THEN sat_fat = 6.56.
sat_fat[df$Q6 %in% c(3,4,5,6,7) &
df$Q15 %in% c(3,4,5,6,7) &
df$Q18 %in% c(1,2)] <- 6.56
# --------------------------------------------------#
# Total DQ Score (0-100)
tot_score = total_fruits + whole_fruits + tot_veg +
greens_beans + whole_grains + dairy + tot_proteins +
seafood_plant + fatty_acid + refined_grains +
sodium + added_sugars + sat_fat
df_sHEI_scores <- data.frame(total_fruits, whole_fruits, tot_veg,
greens_beans, whole_grains, dairy, tot_proteins,
seafood_plant, fatty_acid, refined_grains,
sodium, added_sugars, sat_fat, tot_score)
names(df_sHEI_scores) <- paste0("sHEI_",names(df_sHEI_scores))
###########################################
#### Estimating Food Group Consumption ####
###########################################
# --------------------------------------------------#
# Total Fruit and Vegetable Servings in Cup Equivalents
# Including Legumes and French Fries (DSQfvl)
DSQfvl <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1 THEN DSQfvl = 1.81;
DSQfvl[df$Q1==1] <- 1.81
# IF Gender = F AND Q1 (fruit) = 2,3 THEN DSQfvl = 2.22;
DSQfvl[df$Gender=="F" & df$Q1 %in% c(2,3)] <- 2.22
# IF Gender = M AND Q1 (fruit) = 2,3 THEN DSQfvl = 2.53;
DSQfvl[df$Gender=="M" & df$Q1 %in% c(2,3)] <- 2.53
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfvl = 3.21.
DSQfvl[df$Q1 %in% c(4,5,6,7)] <- 3.21
# --------------------------------------------------#
# Total Fruit and Vegetable Servings in Cup Equivalents
# Including Legumes and Excluding French Fries (Dsqfvlnf)
DSQfvlnf <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1 THEN DSQfvlnf = 1.72;
DSQfvlnf[df$Q1==1] <- 1.72
# IF Gender = F AND Q1 (fruit) = 2,3 THEN DSQfvlnf = 2.15;
DSQfvlnf[df$Gender=="F" & df$Q1 %in% c(2,3)] <- 2.15
# IF Gender = M AND Q1 (fruit) = 2,3 THEN DSQfvlnf = 2.43;
DSQfvlnf[df$Gender=="M" & df$Q1 %in% c(2,3)] <- 2.43
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfvlnf = 3.10.
DSQfvlnf[df$Q1 %in% c(4,5,6,7)] <- 3.10
# --------------------------------------------------#
# Total Fruit Servings in Cup Equivalents (Dsqfrt)
DSQfrt <- rep(miss_val,NROW(df))
# IF Q1 (fruit) = 1 THEN DSQfrt = 0.52;
DSQfrt[df$Q1==1] <- 0.52
# IF Q1 (fruit) = 2 THEN DSQfrt = 0.78;
DSQfrt[df$Q1==2] <- 0.78
# IF Q1 (fruit) = 3 THEN DSQfrt = 0.99;
DSQfrt[df$Q1==3] <- 0.99
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfrt = 1.52.
DSQfrt[df$Q1 %in% c(4,5,6,7)] <- 1.52
# --------------------------------------------------#
# Total Vegetable Servings in Cup Equivalents Including
# Legumes and French Fries (DSQvlall)
DSQvlall <- rep(miss_val,NROW(df))
# IF Q3 (vege) = 1,2 THEN DSQvlall = 1.34;
DSQvlall[df$Q3 %in% c(1,2)] <- 1.34
# IF Gender = F AND Q3 (vege) = 3,4 THEN DSQvlall = 1.40;
DSQvlall[df$Gender=="F" & df$Q3 %in% c(3,4)] <- 1.40
# IF Gender = F AND Q3 (vege) = 5,6,7 THEN DSQvlall = 1.68;
DSQvlall[df$Gender=="F" & df$Q3 %in% c(5,6,7)] <- 1.68
# IF Gender = M AND Q3 (vege) = 3,4,5,6,7 THEN DSQvlall = 1.84.
DSQvlall[df$Gender=="M" & df$Q3 %in% c(3,4,5,6,7)] <- 1.84
# --------------------------------------------------#
# Total Vegetable Servings in Cup Equivalents Including
# Legumes and Excluding French Fries (Dsqvlnf)
DSQvlnf <- rep(miss_val,NROW(df))
# IF Q3 (vege) = 1,2 THEN DSQvlnf = 1.19;
DSQvlnf[df$Q3 %in% c(1,2)] <- 1.19
# IF Gender = F AND Q3 (vege) = 3,4 THEN DSQvlnf = 1.28;
DSQvlnf[df$Gender=="F" & df$Q3 %in% c(3,4)] <- 1.28
# IF Gender = F AND Q3 (vege) = 5,6,7 THEN DSQvlnf = 1.59;
DSQvlnf[df$Gender=="F" & df$Q3 %in% c(5,6,7)] <- 1.59
# IF Gender = M AND Q3 (vege) = 3,4,5,6,7 THEN DSQvlnf = 1.71.
DSQvlnf[df$Gender=="M" & df$Q3 %in% c(3,4,5,6,7)] <- 1.71
# --------------------------------------------------#
# Dairy Servings in Cup Equivalents (Dsqdairy)
DSQdairy <- rep(miss_val,NROW(df))
# IF Q10 (milk) = 1 THEN DSQdairy = 1.16;
DSQdairy[df$Q10==1] <- 1.16
# IF Gender = F AND Q10 (milk) = 2,3 THEN DSQdairy = 1.48;
DSQdairy[df$Gender=="F" & df$Q10 %in% c(2,3)] <- 1.48
# IF Gender = M AND Q10 (milk) = 2,3 THEN DSQdairy = 1.90;
DSQdairy[df$Gender=="M" & df$Q10 %in% c(2,3)] <- 1.90
# IF Q10 (milk) = 4,5,6,7 THEN DSQdairy = 2.36.
DSQdairy[df$Q10 %in% c(4,5,6,7)] <- 2.36
# --------------------------------------------------#
# Added Sugars in Teaspoon Equivalents (Dsqsug)
DSQsug <- rep(miss_val,NROW(df))
# IF Q18_19 (ssb_combo) = 1,2,3,4 THEN DSQsug = 13.26;
DSQsug[df$Q18_19 %in% c(1,2,3,4)] <- 13.26
# IF Q18_19 (ssb_combo) = 5,6 THEN DSQsug = 16.00;
DSQsug[df$Q18_19 %in% c(5,6)] <- 16.00
# IF Q18 (ssb) = 3,4,5,6,7 THEN DSQsug = 26.87.
DSQsug[df$Q18_19 %in% c(3,4,5,6,7)] <- 26.87
# --------------------------------------------------#
# Added Sugars from Sugar-Sweetened Beverages in Teaspoon Equivalents (DSQssb)
DSQssb <- rep(miss_val,NROW(df))
# IF Gender = F AND Q18_19 (ssb_combo) = 1,2,3,4 THEN DSQssb = 4.13;
DSQssb[df$Gender=="F" & df$Q18_19 %in% c(1,2,3,4)] <- 4.13
# IF Gender = M AND Q18_19 (ssb_combo) = 1,2,3,4 THEN DSQssb = 5.73;
DSQssb[df$Gender=="M" & df$Q18_19 %in% c(1,2,3,4)] <- 5.73
# IF Q18_19 (ssb_combo) = 5,6 THEN DSQssb = 6.79;
DSQssb[df$Q18_19 %in% c(5,6)] <- 6.79
# IF Q18 (ssb) = 3,4,5,6,7 THEN DSQssb = 15.78.
DSQssb[df$Q18 %in% c(3,4,5,6,7)] <- 15.78
# --------------------------------------------------#
# Whole Grains in Ounce Equivalents (DSQwhgr)
DSQwhgr <- rep(miss_val,NROW(df))
# IF Q8_9 (wholegrains_combo) = 1,2,3,4 THEN DSQwhgr= 0.50;
DSQwhgr[df$Q8_9 %in% c(1,2,3,4)] <- 0.50
# IF Q6_7 (grains_combo) = 1,2,3,4,5,6 AND Q8_9 (wholegrains_combo) = 5,6,7
# THEN DSQwhgr = 0.63;
DSQwhgr[df$Q6_7 %in% c(1,2,3,4,5,6) &
df$Q8_9 %in% c(5,6,7)] <- 0.63
# IF Q6 (grains) = 3,4,5,6,7 AND Q8_9 (wholegrains_combo) = 5,6,7
# THEN DSQwhgr = 0.77;
DSQwhgr[df$Q6 %in% c(3,4,5,6,7) &
df$Q8_9 %in% c(5,6,7)] <- 0.77
# IF Q8 (wholegrains) = 4,5,6,7 THEN DSQwhgr = 1.01.
DSQwhgr[df$Q8 %in% c(4,5,6,7)] <- 1.01
# --------------------------------------------------#
# Fiber in Grams (DSQfib)
DSQfib <- rep(miss_val,NROW(df))
# IF Gender = F AND Q1 (fruit) = 1,2,3 THEN DSQfib = 13.69;
DSQfib[df$Gender=="F" & df$Q1 %in% c(1,2,3)] <- 13.69
# IF Gender = M AND Q1 (fruit) = 1,2,3 THEN DSQfib = 16.74;
DSQfib[df$Gender=="M" & df$Q1 %in% c(1,2,3)] <- 16.74
# IF Q1 (fruit) = 4,5,6,7 THEN DSQfib = 19.32.
DSQfib[df$Q1 %in% c(4,5,6,7)] <- 19.32
# --------------------------------------------------#
# Calcium in Milligrams (DSQcalc)
DSQcalc <- rep(miss_val,NROW(df))
# IF Gender = F AND Q10 (milk) = 1,2 THEN DSQcalc = 851.61;
DSQcalc[df$Gender=="F" & df$Q10 %in% c(1,2)] <- 851.61
# IF Gender = F AND Q10 (milk) = 3,4,5,6,7 THEN DSQcalc = 1010.78;
DSQcalc[df$Gender=="F" & df$Q10 %in% c(3,4,5,6,7)] <- 1010.78
# IF Gender = M AND Q10 (milk) = 1,2 THEN DSQcalc = 1062.55;
DSQcalc[df$Gender=="M" & df$Q10 %in% c(1,2)] <- 1062.55
# IF Gender = M AND Q10 (milk) = 3,4,5,6,7 THEN DSQcalc = 1319.71.
DSQcalc[df$Gender=="M" & df$Q10 %in% c(3,4,5,6,7)] <- 1319.71
# --------------------------------------------------#
# Green Vegetables in Cup Equivalents
greens <- rep(miss_val,NROW(df))
# IF Q4 (greenvege) = 1 THEN greens = 0.00
greens[df$Q4==1] <- 0.00
# IF Q3 (vege) = 1,2,3,4 AND Q7 (greenvege) = 2,3,4,5,6,7 THEN greens = 0.13
greens[df$Q3 %in% c(1,2,3,4) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 0.13
# IF Q3 (vege) = 5,6,7 AND Q7 (greenvege) = 2,3,4,5,6,7 THEN greens = 0.31
greens[df$Q3 %in% c(5,6,7) &
df$Q4 %in% c(2,3,4,5,6,7)] <- 0.31
df_sHEI_consump <- data.frame(DSQfvl, DSQfvlnf, DSQfrt, DSQvlall, DSQvlnf,
DSQdairy, DSQsug, DSQssb, DSQwhgr, DSQfib,
DSQcalc, greens)
names(df_sHEI_consump) <- paste0("sHEI_",names(df_sHEI_consump))
df <- cbind(df,df_sHEI_scores)
df <- cbind(df,df_sHEI_consump)
return(df)
}
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