## Liking score
liking.df <- qualtrics %>%
select("ID", "FL-01_3":"FL-60_3") %>%
mutate(veg.liking = rowMeans(select(., "FL-01_3":"FL-06_3"), na.rm = TRUE),
veg.liking = (veg.liking+100)/2,
fruit.liking = rowMeans(select(., "FL-07_3":"FL-11_3"), na.rm = TRUE),
fruit.liking = (fruit.liking+100)/2,
saltyfat.liking = rowMeans(select(., "FL-12_3":"FL-15_3"), na.rm = TRUE),
saltyfat.liking = (saltyfat.liking+100)/2,
hfprotein.liking = rowMeans(select(., "FL-16_3":"FL-20_3"), na.rm = TRUE),
hfprotein.liking = (hfprotein.liking+100)/2,
alcohol.liking = rowMeans(select(., "FL-22_3":"FL-25_3"), na.rm = TRUE),
alcohol.liking = (alcohol.liking+100)/2,
wg.liking = rowMeans(select(., "FL-26_3":"FL-29_3"), na.rm = TRUE),
wg.liking = (wg.liking+100)/2,
carbs.liking = rowMeans(select(., "FL-30_3":"FL-34_3"), na.rm = TRUE),
carbs.liking = (carbs.liking+100)/2,
healthyfat.liking = rowMeans(select(., "FL-35_3":"FL-38_3"), na.rm = TRUE),
healthyfat.liking = (healthyfat.liking+100)/2,
sweets.liking = rowMeans(select(., "FL-39_3":"FL-43_3"), na.rm = TRUE),
sweets.liking = (sweets.liking+100)/2,
ssb.liking = rowMeans(select(., "FL-44_3":"FL-47_3"), na.rm = TRUE),
ssb.liking = (ssb.liking+100)/2,
sfbl.liking = rowMeans(select(., "FL-39_3":"FL-47_3"), na.rm = TRUE),
sfbl.liking = (sfbl.liking+100)/2,
sufat.liking = rowMeans(select(., c("FL-39_3","FL-40_3", "FL-43_3")), na.rm = TRUE),
sufat.liking = (sufat.liking+100)/2,
unhealthyfat.liking = rowMeans(select(., "FL-48_3":"FL-52_3"), na.rm = TRUE),
unhealthyfat.liking = (unhealthyfat.liking+100)/2,
protein.liking = rowMeans(select(., "FL-53_3":"FL-57_3"), na.rm = TRUE),
protein.liking = (protein.liking+100)/2,
spicy.liking = rowMeans(select(., "FL-58_3":"FL-60_3"), na.rm = TRUE),
spicy.liking = (spicy.liking+100)/2,) %>%
select(c("ID", ends_with(".liking")))
## Three factor questionnaire
TFQ.df <- qualtrics %>%
select("ID", "TFQ-01":"TFQ-21") %>%
mutate(uncontrolled = rowSums(select(., "TFQ-03", "TFQ-06", "TFQ-08", "TFQ-09", "TFQ-12", "TFQ-13", "TFQ-15", "TFQ-19", "TFQ-20"), na.rm = TRUE)) %>%
mutate(uncontrolled = ((uncontrolled-1)/36)*100, na.rm = TRUE) %>%
mutate(restraint = rowSums(select(., "TFQ-01", "TFQ-05", "TFQ-11"), na.rm = TRUE)) %>%
mutate(restraint = ((restraint-1)/12)*100, na.rm = TRUE) %>%
mutate(emotional = rowSums(select(., "TFQ-02", "TFQ-04", "TFQ-07", "TFQ-10", "TFQ-14", "TFQ-16"), na.rm = TRUE)) %>%
mutate(emotional = ((emotional-1)/24)*100, na.rm = TRUE) %>%
select(c("ID", "uncontrolled", "restraint", "emotional"))
## Body appreciation scale
BAS.df <- qualtrics %>%
select("ID", starts_with("BAS")) %>%
mutate(BAS = rowSums(select(., 2:11), na.rm = TRUE))%>%
select(c("ID", "BAS"))
## Intuitive eating scale
IES.df <- qualtrics %>%
select("ID", starts_with("IES")) %>%
mutate(IES = (rowSums(select(., 2:24), na.rm = TRUE))/23) %>%
select(c("ID", "IES"))
## sHEI score
HEI.df <- qualtrics %>%
select("ID", "sex", "Fruit":"Water") %>%
mutate(fruit.1 = case_when(
Fruit == 1 ~ 0,
Fruit == 2 ~ 2,
Fruit == 3 ~ 3.5,
Fruit >= 4 ~ 5)) %>%
mutate(fruit.2 = case_when (
Juice == 1 ~ 0,
Juice == 2 ~ 2,
Juice == 3 ~ 3.5,
Juice >= 4 ~ 5)) %>%
mutate(tfruitHEI = case_when(
fruit.1 + fruit.2 <5 ~ 0,
fruit.1 + fruit.2 >= 5 ~ 5)) %>%
mutate(wfruitHEI = case_when(
Fruit == 1 ~ 0,
Fruit == 2 ~ 2.5,
Fruit >= 3 ~ 5)) %>%
mutate(vegHEI = case_when(
`Green veg` == 1 ~ 1.6,
`Green veg` == 2 & `Starchy veg` >= 2 ~ 2.46,
`Green veg` >= 2 & `Starchy veg` >= 2 ~ 3.24,
`Green veg` >= 2 & `Starchy veg` == 1 ~ 3.56)) %>%
mutate(bean.1 = case_when(
`Green veg` == 1 ~ 0,
`Green veg` >= 2 ~5)) %>%
mutate(bean.2 = case_when(
Beans == 1 ~ 0,
Beans >= 2 ~ 5)) %>%
mutate(beanHEI = case_when(
bean.1 + bean.2 < 5 ~ 0,
bean.1 + bean.2 >= 5 ~ 5)) %>%
mutate(wgHEI = case_when(
`Whole grains.day` == 1 ~ 0.51,
sex == 1 & `Whole grains.day` >= 2 ~ 2.97,
sex == 2 & `Whole grains.day` >= 2 & `Whole grains.day` <= 3 ~ 5.20,
sex == 2 & `Whole grains.day` >= 4 ~ 6.94)) %>%
mutate(dairyHEI = case_when(
sex == 1 & `Milk.day` <= 3 ~ 3.22,
sex == 2 & `Milk.day` <= 3 & `Low-fat milk.day` == 1 ~ 3.32,
sex == 2 & `Milk.day` <= 3 & `Low-fat milk.day` >= 2 ~ 4.81,
`Milk.day` >= 4 ~ 6.51)) %>%
mutate(tproteinHEI = case_when(
sex == 1 & `Seafood.freq` <= 4 ~ 4.11,
sex == 1 & `Seafood.freq` >= 5 ~ 4.98,
sex == 1 & is.na(`Seafood.freq`) ~ 4.11,
sex == 2 ~ 4.97)) %>%
mutate(sfpproteinHEI = case_when(
sex == 1 & Nuts <= 2 ~ 0.49,
sex == 2 & Nuts <= 2 ~ 1.50,
Nuts >= 3 ~ 4.20)) %>%
mutate(fatHEI = case_when(
`Milk.day` >= 4 ~ 2.56,
`Saturated fats` >= 2 & `Saturated fats` <= 3 & `Milk.freq` >= 1 & `Low-fat milk.freq` <= 2 ~ 2.63,
`Saturated fats` >= 2 & `Saturated fats` <= 3 & is.na(`Milk.freq`) | is.na(`Low-fat milk.freq`) ~ 2.63,
`Saturated fats` >= 2 & `Saturated fats` <= 3 & `Milk.freq` >= 1 & `Low-fat milk.freq` >= 3 ~ 4.54,
`Saturated fats` == 1 & `Milk.freq` >= 1 ~ 5.93,
`Saturated fats` == 1 & is.na(`Milk.freq`) ~ 5.93)) %>%
mutate(grainHEI = case_when(
`Green veg` == 1 ~ 2.13,
`Grains.day` >= 3 & `Seafood.day` >= 2 & `Green veg` >= 2 ~ 2.27,
`Grains.day` >= 3 & Nuts >= 1 & Nuts <= 2 & `Seafood.day` == 1 & `Green veg` >= 2 ~ 4.73,
`Grains.day` >= 3 & Nuts >= 3 & `Seafood.day` == 1 & `Green veg` >= 2 ~ 8.45,
`Grains.day` >= 1 & `Grains.day` <= 2 & `Green veg` >= 2 ~ 9.25)) %>%
mutate(sodiumHEI = case_when(
Fruit >= 1 & Fruit <= 2 & `Grains.day` >= 3 & Water == 3 ~ 0.70,
Fruit >= 3 & `Grains.day` >= 3 & Water == 3 ~ 2.30,
`Grains.day` >= 3 & Water >= 1 & Water <= 2 ~ 4.94,
`Grains.day` >= 1 & `Grains.day` <= 2 ~ 6.07)) %>%
mutate(ssb.cal = case_when(
`SSB.day` == 1 ~ 0,
`SSB.day` == 2 ~ 156,
`SSB.day` == 3 ~ 312,
`SSB.day` == 4 ~ 468,
`SSB.day` == 5 ~ 624,
`SSB.day` == 6 ~ 780,
`SSB.day` == 7 ~ 936),
sugar.cal = case_when(
`Added sugars` == 1 ~ 130,
`Added sugars` == 2 ~ 260,
`Added sugars` == 3 ~ 520)) %>%
mutate(sugar.intake = (ssb.cal + sugar.cal)) %>%
mutate(sugarHEI = case_when(
sugar.intake <= 130 ~ 10,
sugar.intake > 130 & sugar.cal < 520 ~ 5,
sugar.intake >= 520 ~ 0)) %>%
mutate(sugar.tsp = case_when(
`SSB.freq` <= 4 ~ 13.26,
`SSB.freq` >= 5 & `SSB.freq` <=6 ~ 16.00,
`SSB.day` ==2 ~ 16.00,
`SSB.day` >=3 ~ 26.87)) %>%
mutate(satfatHEI = case_when(
`SSB.day` >= 3 ~ 1.82,
`SSB.day` <= 2 & `Grains.day` <= 2 ~ 3.20,
`SSB.day` <= 2 & `Grains.day` >= 3 & Nuts <= 2 ~ 4.64,
`SSB.day` <= 2 & `Grains.day` >= 3 & Nuts >= 3 ~ 6.56)) %>%
select("ID", "sugar.intake", "sugar.tsp", ends_with("HEI")) %>%
mutate(sHEI = rowSums(select(., "tfruitHEI":"satfatHEI"), na.rm = TRUE)) %>%
mutate(SuFatHEI = rowSums(select(., c("sugarHEI", "satfatHEI"))))
## Putting scores together
qualtrics$ID <- as.character(qualtrics$ID)
anthro$ID <- as.character(anthro$ID)
oatmilk.liking.wide$ID <- as.character(oatmilk.liking.wide$ID)
oatmilk.sweet.wide$ID <- as.character(oatmilk.sweet.wide$ID)
oatmilk.creamy.wide$ID <- as.character(oatmilk.creamy.wide$ID)
oatmilk.bitter.wide$ID <- as.character(oatmilk.bitter.wide$ID)
liking.df$ID <- as.character(liking.df$ID)
HEI.df$ID <- as.character(HEI.df$ID)
TFQ.df$ID <- as.character(TFQ.df$ID)
BAS.df$ID <- as.character(BAS.df$ID)
IES.df$ID <- as.character(IES.df$ID)
### Missing stress survey data
full.df <- qualtrics %>%
select(c("ID", "age", "race", "ethn", "usb", "education", "hinc", "exec", "plantmilk", "plantmilk_freq")) %>%
left_join(anthro, by = "ID") %>%
left_join(oatmilk.liking.wide, by = "ID") %>%
left_join(oatmilk.sweet.wide, by = "ID") %>%
left_join(oatmilk.creamy.wide, by = "ID") %>%
left_join(oatmilk.bitter.wide, by = "ID") %>%
left_join(liking.df, by = "ID") %>%
left_join(HEI.df, by = "ID") %>%
left_join(TFQ.df, by = "ID") %>%
left_join(BAS.df, by = "ID") %>%
left_join(IES.df, by = "ID") %>%
left_join(bia, by = "ID") %>%
mutate(BMIcat = case_when(
BMI < 18.5 ~ "0",
BMI >= 18.5 & BMI < 25 & race != "Asian" ~ "1",
BMI >= 25 & BMI < 30 & race != "Asian" ~ "2",
BMI > 30 & race != "Asian" ~ "3",
BMI >= 18.5 & BMI < 23 & race == "Asian" ~ "1",
BMI >= 23 & BMI < 25 & race == "Asian" ~ "2",
BMI > 25 & race == "Asian" ~ "3")) %>%
mutate(sugarcat = ntile(sugar.intake, 3)) %>%
mutate(DQ = ntile(sHEI, 3)) %>%
mutate(sweetPref = case_when(
sweetpref.atF2 == 1 ~ "Low",
sweetpref.atF2 == 2 ~ "High")) %>%
mutate(fatPref = case_when(
fatpref.atS1 == 1 ~ "Low",
fatpref.atS1 == 2 ~ "Medium",
fatpref.atS1 == 3 ~ "High"))
## Joining sweetPref with oat milk dfs
sweetPref <- full.df %>% select(ID, sweetPref)
oatmilk.liking.long <- oatmilk.liking.long %>%
left_join(sweetPref, by = "ID")
oatmilk.sweet.long <- oatmilk.sweet.long %>%
left_join(sweetPref, by = "ID")
oatmilk.creamy.long <- oatmilk.creamy.long %>%
left_join(sweetPref, by = "ID")
oatmilk.bitter.long <- oatmilk.bitter.long %>%
left_join(sweetPref, by = "ID")
oatmilk.liking.wide <- oatmilk.liking.wide %>%
left_join(sweetPref, by = "ID")
oatmilk.sweet.wide <- oatmilk.sweet.wide %>%
left_join(sweetPref, by = "ID")
oatmilk.creamy.wide <- oatmilk.creamy.wide %>%
left_join(sweetPref, by = "ID")
oatmilk.bitter.wide <- oatmilk.bitter.wide %>%
left_join(sweetPref, by = "ID")
## Joining fatPref with oat milk dfs
fatPref <- full.df %>% select(ID, fatPref)
oatmilk.liking.long <- oatmilk.liking.long %>%
left_join(fatPref, by = "ID")
oatmilk.sweet.long <- oatmilk.sweet.long %>%
left_join(fatPref, by = "ID")
oatmilk.creamy.long <- oatmilk.creamy.long %>%
left_join(fatPref, by = "ID")
oatmilk.bitter.long <- oatmilk.bitter.long %>%
left_join(fatPref, by = "ID")
oatmilk.liking.wide <- oatmilk.liking.wide %>%
left_join(fatPref, by = "ID")
oatmilk.sweet.wide <- oatmilk.sweet.wide %>%
left_join(fatPref, by = "ID")
oatmilk.creamy.wide <- oatmilk.creamy.wide %>%
left_join(fatPref, by = "ID")
oatmilk.bitter.wide <- oatmilk.bitter.wide %>%
left_join(fatPref, by = "ID")
full.df$sweetPref <- factor(full.df$sweetPref , levels = c("Low", "High"))
full.df$fatPref <- factor(full.df$fatPref , levels = c("Low", "Medium", "High"))
full.df$prefcal.cat <- factor(full.df$prefcal.cat , levels = c("1", "2", "3"))
