Social Media
May
2022-02-16
- Load packages and data set
- Cronbach’s alpha for food groups
- Scores decoding
- Correlations - sweet liking
- Correlations - sweet liking: females only
- Correlations - sugar intake
- Correlations - sugar intake: females only
- Correlations - fat sHEI score
- Correlations - fat sHEI score: females only
- Correlations - sweet + fat sHEI score
- Correlations - sweet + fat sHEI score: females only
- Correlations - sHEI
- Correlations - sHEI: females only
- Correlations - body appreciation
- Correlations - body appreciation: female only
- Partial correlation - controlling for age
- Moderator analysis - sugar intake
- Moderator analysis - SSB intake
- Moderator anaysis - fat X sugar intake
- Moderator anaysis - sHEI
- Liking hierarchial clustering
- sHEI hierarchial clustering
- Emotional overeating analysis
- TFEQ emotional analysis
- Uncontrolled eating analysis
- Restraint analysis
setwd(“/Users/maycheung/Documents/Research/After BC/Social Media/Results”)
Load packages and data set
Cronbach’s alpha for food groups
## Vegetable
full.df %>%
select(starts_with("veg"))%>%
select(!"vegetable.day") %>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 6
## Sample units: 97
## alpha: 0.773## Fruit
full.df %>%
select(starts_with("fruit"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 6
## Sample units: 99
## alpha: 0.638## Salty/fat
full.df %>%
select(starts_with("saltyfat"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 8
## Sample units: 85
## alpha: 0.683## Alcohol
full.df %>%
select(starts_with("alcohol"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 79
## alpha: 0.766## Whole grain
full.df %>%
select(starts_with("wg"))%>%
select(!c("wg.day", "wg.freq"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 4
## Sample units: 98
## alpha: 0.733## Carbs
full.df %>%
select(starts_with("carbs"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 99
## alpha: 0.452## Healthy fats
full.df %>%
select(starts_with("healthyfat"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 4
## Sample units: 98
## alpha: 0.628## Sweets
full.df %>%
select(starts_with("sweets"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 100
## alpha: 0.76## Sugar-sweetened beverages and sweets
full.df %>%
select(starts_with(c("ssb", "sweets")))%>%
select(!c("ssb.day", "ssb.freq")) %>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 9
## Sample units: 87
## alpha: 0.712## Unhealthy fat
full.df %>%
select(starts_with("unhealthyfat"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 99
## alpha: 0.506## Protein
full.df %>%
select(starts_with("protein"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 5
## Sample units: 76
## alpha: 0.581## Spicy foods
full.df %>%
select(starts_with("spicy"))%>%
na.omit() %>%
cronbach.alpha(.)##
## Cronbach's alpha for the '.' data-set
##
## Items: 3
## Sample units: 93
## alpha: 0.713Scores decoding
## Sweet liking score
liking.df <- full.df %>%
select(starts_with(c("ID", "ssb", "sweets", "veg", "fruit", "saltyfat", "fatprotein", "alcohol", "wg", "carbs", "healthyfat", "unhealthyfat", "protein", "spicy")))%>%
select(!c("ssb.day", "ssb.freq", "vegetable.day", "fruit.day", "wg.day", "wg.freq")) %>%
mutate(sweet.liking = rowMeans(select(., "ssb.soda":"sweets.chocolate"), na.rm = TRUE),
veg.liking = rowMeans(select(., "veg.greens":"veg.carrots"), na.rm = TRUE),
fruit.liking = rowMeans(select(., "fruit.citrus":"fruit.melon"), na.rm = TRUE),
saltyfat.liking = rowMeans(select(., "saltyfat.snacks":"saltyfat.delimeats"), na.rm = TRUE),
fatprotein.liking = fatprotein.friedmeats,
alcohol.liking = rowMeans(select(., "alcohol.beer":"alcohol.cocktails"), na.rm = TRUE),
wg.liking = rowMeans(select(., "wg.bread":"wg.brownrice"), na.rm = TRUE),
carbs.liking = rowMeans(select(., "carbs.rice":"carbs.cereal"), na.rm = TRUE),
healthyfat.liking = rowMeans(select(., "healthyfat.nuts":"healthyfat.avocado"), na.rm = TRUE),
unhealthyfat.liking = rowMeans(select(., "unhealthyfat.cheese":"unhealthyfat.oil"), na.rm = TRUE),
protein.liking = rowMeans(select(., "protein.chicken":"protein.fish"), na.rm = TRUE),
spicy.liking = rowMeans(select(., "spicy.hotsauce":"spicy.mustard"), na.rm = TRUE)) %>%
select(ends_with(".liking"))
## Body appreciation scale
BAS.df <- full.df %>%
select("ID", starts_with("BAS")) %>%
mutate(BAS = rowSums(select(., 2:11), na.rm = TRUE))
## Three factor questionnaire
TFQ.df <- full.df %>%
select("ID", starts_with("TFQ")) %>%
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(restraint = rowSums(select(., "TFQ.01", "TFQ.05", "TFQ.11", "TFQ.17", "TFQ.18", "TFQ.21"), na.rm = TRUE)) %>%
mutate(emotional = rowSums(select(., "TFQ.02", "TFQ.04", "TFQ.07", "TFQ.10", "TFQ.14", "TFQ.16"), na.rm = TRUE))
## Adult eating behavior questionnaire
AEBQ.df <- full.df %>%
select("ID", starts_with("AEBQ")) %>%
mutate(EF = rowSums(select(., "AEBQ.01", "AEBQ.03", "AEBQ.04"), na.rm = TRUE)) %>%
mutate(EOE = rowSums(select(., "AEBQ.05", "AEBQ.08", "AEBQ.10", "AEBQ.16"), na.rm = TRUE)) %>%
mutate(EUE = rowSums(select(., "AEBQ.15", "AEBQ.20", "AEBQ.27", "AEBQ.35"), na.rm = TRUE)) %>%
mutate(FF = rowSums(select(., "AEBQ.02", "AEBQ.07", "AEBQ.12", "AEBQ.19", "AEBQ.24"), na.rm = TRUE)) %>%
mutate(FR = rowSums(select(., "AEBQ.13", "AEBQ.22", "AEBQ.33"), na.rm = TRUE)) %>%
mutate(SE = rowSums(select(., "AEBQ.14", "AEBQ.25", "AEBQ.26", "AEBQ.29"), na.rm = TRUE)) %>%
mutate(H = rowSums(select(., "AEBQ.06", "AEBQ.09", "AEBQ.28", "AEBQ.32", "AEBQ.34"), na.rm = TRUE)) %>%
mutate(SR = rowSums(select(., "AEBQ.11", "AEBQ.23", "AEBQ.30", "AEBQ.31"), na.rm = TRUE))
## BMI
BMI.df <- full.df %>%
select("ID", "weight", "height") %>%
mutate(BMI = weight/(height^2)*703)
## sHEI score
HEI.df <- full.df %>%
select("ID", "sex", ends_with(c(".day", ".freq", ".amount"))) %>%
mutate(fruit.1 = case_when(
fruit.day == 1 ~ 0,
fruit.day == 2 ~ 2,
fruit.day == 3 ~ 3.5,
fruit.day >= 4 ~ 5)) %>%
mutate(fruit.2 = case_when (
juice.day == 1 ~ 0,
juice.day == 2 ~ 2,
juice.day == 3 ~ 3.5,
juice.day >= 4 ~ 5)) %>%
mutate(tfruitHEI = case_when(
fruit.1 + fruit.2 <5 ~ 0,
fruit.1 + fruit.2 >= 5 ~ 5)) %>%
mutate(wfruitHEI = case_when(
fruit.day == 1 ~ 0,
fruit.day == 2 ~ 2.5,
fruit.day >= 3 ~ 5)) %>%
mutate(vegHEI = case_when(
greens.day == 1 ~ 1.6,
greens.day == 2 & starchy.day >= 2 ~ 2.46,
greens.day >= 2 & starchy.day >= 2 ~ 3.24,
greens.day >= 2 & starchy.day == 1 ~ 3.56)) %>%
mutate(bean.1 = case_when(
greens.day == 1 ~ 0,
greens.day >= 2 ~5)) %>%
mutate(bean.2 = case_when(
beans.day == 1 ~ 0,
beans.day >= 2 ~ 5)) %>%
mutate(beanHEI = case_when(
bean.1 + bean.2 < 5 ~ 0,
bean.1 + bean.2 >= 5 ~ 5)) %>%
mutate(wgHEI = case_when(
wg.day == 1 ~ 0.51,
sex == 1 & wg.day >= 2 ~ 2.97,
sex == 2 & wg.day >= 2 & wg.day <= 3 ~ 5.20,
sex == 2 & wg.day >= 4 ~ 6.94)) %>%
mutate(dairyHEI = case_when(
sex == 1 & milk.day <= 3 ~ 3.22,
sex == 2 & milk.day <= 3 & lfmilk.day == 1 ~ 3.32,
sex == 2 & milk.day <= 3 & lfmilk.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.day <= 2 ~ 0.49,
sex == 2 & nuts.day <= 2 ~ 1.50,
nuts.day >= 3 ~ 4.20)) %>%
mutate(farHEI = case_when(
milk.day >= 4 ~ 2.56,
fat.amount >= 2 & fat.amount <= 3 & milk.freq >= 1 & lfmilk.freq <= 2 ~ 2.63,
fat.amount >= 2 & fat.amount <= 3 & is.na(milk.freq) | is.na(lfmilk.freq) ~ 2.63,
fat.amount >= 2 & fat.amount <= 3 & milk.freq >= 1 & lfmilk.freq >= 3 ~ 4.54,
fat.amount == 1 & milk.freq >= 1 ~ 5.93,
fat.amount == 1 & is.na(milk.freq) ~ 5.93)) %>%
mutate(grainHEI = case_when(
greens.day == 1 ~ 2.13,
grains.day >= 3 & seafood.day >= 2 & greens.day >= 2 ~ 2.27,
grains.day >= 3 & nuts.day >= 1 & nuts.day <= 2 & seafood.day == 1 & greens.day >= 2 ~ 4.73,
grains.day >= 3 & nuts.day >= 3 & seafood.day == 1 & greens.day >= 2 ~ 8.45,
grains.day >= 1 & grains.day <= 2 & greens.day >= 2 ~ 9.25)) %>%
mutate(sodiumHEI = case_when(
fruit.day >= 1 & fruit.day <= 2 & grains.day >= 3 & water.amount == 3 ~ 0.70,
fruit.day >= 3 & grains.day >= 3 & water.amount == 3 ~ 2.30,
grains.day >= 3 & water.amount >= 1 & water.amount <= 2 ~ 4.94,
grains.day >= 1 & grains.day <= 2 ~ 6.07)) %>%
mutate(ssb.day.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(
sugars.amount == 1 ~ 130,
sugars.amount == 2 ~ 260,
sugars.amount == 3 ~ 520)) %>%
mutate(sugar.intake = (ssb.day.cal + sugar.cal)) %>%
mutate(sugarHEI = case_when(
sugar.intake <= 130 ~ 0,
sugar.intake > 130 & sugar.intake < 520 ~ 5,
sugar.intake >= 520 ~ 0)) %>%
mutate(fatHEI = case_when(
ssb.day >= 3 ~ 1.82,
ssb.day <= 2 & grains.day <= 2 ~ 3.20,
ssb.day <= 2 & grains.day >= 3 & nuts.day <= 2 ~ 4.64,
ssb.day <= 2 & grains.day >= 3 & nuts.day >= 3 ~ 6.56)) %>%
select("ID", "sugar.intake", "sugars.amount",ends_with("HEI")) %>%
mutate(sHEI = rowSums(select(., "tfruitHEI":"fatHEI"), na.rm = TRUE)) %>%
mutate(SuFatHEI = rowSums(select(., c("sugarHEI", "fatHEI"))))
## Putting scores together
EB.df <- bind_cols(select(full.df, 1:17, "ssb.day", "sugars.amount"), select(BMI.df, "BMI"), select(liking.df, "sweet.liking"), select(HEI.df, c("sugar.intake", "sugarHEI", "fatHEI", "SuFatHEI", "sHEI")), select(BAS.df, "BAS"), select(TFQ.df, c("uncontrolled", "restraint", "emotional")), select(AEBQ.df, c("EF", "EOE", "EUE", "FF", "FR", "SE", "H", "SR"))) %>%
mutate(emo = rowSums(select(., c("emotional", "EOE"))))
## EB: females only
EB.fem.df <- EB.df %>% filter(sex == "2")
## EB: males only
EB.mal.df <- EB.df %>% filter(sex == "1")Correlations - sweet liking
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X BMI
corrfunc(EB.df, age, BMI, "Correlation - Age X BMI")## `geom_smooth()` using formula = 'y ~ x'
## Sex X sweet liking
corrfunc(EB.df, sex, sweet.liking, "Correlation - Sex X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Age X sweet liking
corrfunc(EB.df, age, sweet.liking, "Correlation - Age X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sweet liking
corrfunc(EB.df, BMI, sweet.liking, "Correlation - BMI X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X sweet liking
corrfunc(EB.df, sugar.intake, sweet.liking, "Correlation - Sugar Intake X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sweet liking
corrfunc(EB.df, ssb.day, sweet.liking, "Correlation - SSB X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sweet liking
corrfunc(EB.df, emotional, sweet.liking, "Correlation - Emotional Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sweet liking
corrfunc(EB.df, uncontrolled, sweet.liking, "Correlation - Uncontrolled Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sweet liking
corrfunc(EB.df, restraint, sweet.liking, "Correlation - Dietary Restraint X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sweet liking
corrfunc(EB.df, EF, sweet.liking, "Correlation - Enjoyment of Food X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sweet liking
corrfunc(EB.df, EOE, sweet.liking, "Correlation - Emotional Over-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sweet liking
corrfunc(EB.df, EUE, sweet.liking, "Correlation - Emotional Under-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sweet liking
corrfunc(EB.df, FF, sweet.liking, "Correlation - Food fussiness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sweet liking
corrfunc(EB.df, FR, sweet.liking, "Correlation - Food Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sweet liking
corrfunc(EB.df, SE, sweet.liking, "Correlation - Slowness in Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sweet liking
corrfunc(EB.df, H, sweet.liking, "Correlation - Hunger X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sweet liking
corrfunc(EB.df, SR, sweet.liking, "Correlation - Satiety Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sweet liking
corrfunc(EB.df, emo, sweet.liking, "Correlation - Emo X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet liking: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sweet liking
corrfunc(EB.fem.df, age, sweet.liking, "Correlation - Age X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sweet liking
corrfunc(EB.fem.df, BMI, sweet.liking, "Correlation - BMI X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X sweet liking
corrfunc(EB.fem.df, sugar.intake, sweet.liking, "Correlation - Sugar Intake X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sweet liking
corrfunc(EB.fem.df, ssb.day, sweet.liking, "Correlation - SSB X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sweet liking
corrfunc(EB.fem.df, emotional, sweet.liking, "Correlation - Emotional Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sweet liking
corrfunc(EB.fem.df, uncontrolled, sweet.liking, "Correlation - Uncontrolled Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sweet liking
corrfunc(EB.fem.df, restraint, sweet.liking, "Correlation - Dietary Restraint X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sweet liking
corrfunc(EB.fem.df, EF, sweet.liking, "Correlation - Enjoyment of Food X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sweet liking
corrfunc(EB.fem.df, EOE, sweet.liking, "Correlation - Emotional Over-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sweet liking
corrfunc(EB.fem.df, EUE, sweet.liking, "Correlation - Emotional Under-Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sweet liking
corrfunc(EB.fem.df, FF, sweet.liking, "Correlation - Food fussiness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sweet liking
corrfunc(EB.fem.df, FR, sweet.liking, "Correlation - Food Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sweet liking
corrfunc(EB.fem.df, SE, sweet.liking, "Correlation - Slowness in Eating X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sweet liking
corrfunc(EB.fem.df, H, sweet.liking, "Correlation - Hunger X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sweet liking
corrfunc(EB.fem.df, SR, sweet.liking, "Correlation - Satiety Responsiveness X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sweet liking
corrfunc(EB.fem.df, emo, sweet.liking, "Correlation - Emo X Sweet Liking")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sugar intake
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sugar intake
corrfunc(EB.df, age, sugar.intake, "Correlation - Age X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sugar intake
corrfunc(EB.df, BMI, sugar.intake, "Correlation - BMI X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Sex X sugar intake
corrfunc(EB.df, sex, sugar.intake, "Correlation - Sex X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sugar intake
corrfunc(EB.df, sweet.liking, sugar.intake, "Correlation - Sweet Liking X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sugar intake
corrfunc(EB.df, ssb.day, sugar.intake, "Correlation - SSB X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sugar intake
corrfunc(EB.df, emotional, sugar.intake, "Correlation - Emotional Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sugar intake
corrfunc(EB.df, uncontrolled, sugar.intake, "Correlation - Uncontrolled Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sugar intake
corrfunc(EB.df, restraint, sugar.intake, "Correlation - Dietary Restraint X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sugar intake
corrfunc(EB.df, EF, sugar.intake, "Correlation - Enjoyment of Food X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sugar intake
corrfunc(EB.df, EOE, sugar.intake, "Correlation - Emotional Over-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sugar intake
corrfunc(EB.df, EUE, sugar.intake, "Correlation - Emotional Under-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sugar intake
corrfunc(EB.df, FF, sugar.intake, "Correlation - Food fussiness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sugar intake
corrfunc(EB.df, FR, sugar.intake, "Correlation - Food Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sugar intake
corrfunc(EB.df, SE, sugar.intake, "Correlation - Slowness in Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sugar intake
corrfunc(EB.df, H, sugar.intake, "Correlation - Hunger X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sugar intake
corrfunc(EB.df, SR, sugar.intake, "Correlation - Satiety Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sweet liking
corrfunc(EB.df, emo, sugar.intake, "Correlation - Emo X Sweet Intake")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sugar intake: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugar intake and traits
## Age X sugar intake
corrfunc(EB.fem.df, age, sugar.intake, "Correlation - Age X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sugar intake
corrfunc(EB.fem.df, BMI, sugar.intake, "Correlation - BMI X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sugar intake
corrfunc(EB.fem.df, sweet.liking, sugar.intake, "Correlation - Sweet Liking X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sugar intake
corrfunc(EB.fem.df, emotional, sugar.intake, "Correlation - Emotional Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sugar intake
corrfunc(EB.fem.df, uncontrolled, sugar.intake, "Correlation - Uncontrolled Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sugar intake
corrfunc(EB.fem.df, restraint, sugar.intake, "Correlation - Dietary Restraint X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sugar intake
corrfunc(EB.fem.df, EF, sugar.intake, "Correlation - Enjoyment of Food X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sugar intake
corrfunc(EB.fem.df, EOE, sugar.intake, "Correlation - Emotional Over-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sugar intake
corrfunc(EB.fem.df, EUE, sugar.intake, "Correlation - Emotional Under-Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sugar intake
corrfunc(EB.fem.df, FF, sugar.intake, "Correlation - Food fussiness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sugar intake
corrfunc(EB.fem.df, FR, sugar.intake, "Correlation - Food Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sugar intake
corrfunc(EB.fem.df, SE, sugar.intake, "Correlation - Slowness in Eating X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sugar intake
corrfunc(EB.fem.df, H, sugar.intake, "Correlation - Hunger X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sugar intake
corrfunc(EB.fem.df, SR, sugar.intake, "Correlation - Satiety Responsiveness X Sugar Intake")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sugar intake
corrfunc(EB.fem.df, emo, sugar.intake, "Correlation - Emo X Sweet Intake")## `geom_smooth()` using formula = 'y ~ x'
Correlations - fat sHEI score
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - fatHEI and traits
## Age X fatHEI
corrfunc(EB.df, age, fatHEI, "Correlation - Age X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X fatHEI
corrfunc(EB.df, BMI, fatHEI, "Correlation - BMI X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sex X fatHEI
corrfunc(EB.df, sex, fatHEI, "Correlation - Sex X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X fatHEI
corrfunc(EB.df, sweet.liking, fatHEI, "Correlation - Sweet Liking X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X fatHEI
corrfunc(EB.df, emotional, fatHEI, "Correlation - Emotional Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X fatHEI
corrfunc(EB.df, uncontrolled, fatHEI, "Correlation - Uncontrolled Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X fatHEI
corrfunc(EB.df, restraint, fatHEI, "Correlation - Dietary Restraint X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X fatHEI
corrfunc(EB.df, EF, fatHEI, "Correlation - Enjoyment of Food X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X fatHEI
corrfunc(EB.df, EOE, fatHEI, "Correlation - Emotional Over-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X fatHEI
corrfunc(EB.df, EUE, fatHEI, "Correlation - Emotional Under-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X fatHEI
corrfunc(EB.df, FF, fatHEI, "Correlation - Food fussiness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X fatHEI
corrfunc(EB.df, FR, fatHEI, "Correlation - Food Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X fatHEI
corrfunc(EB.df, SE, fatHEI, "Correlation - Slowness in Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X fatHEI
corrfunc(EB.df, H, fatHEI, "Correlation - Hunger X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X fatHEI
corrfunc(EB.df, SR, fatHEI, "Correlation - Satiety Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X fatHEI
corrfunc(EB.df, emo, fatHEI, "Correlation - Emo X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - fat sHEI score: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - fatHEI and traits
## Age X fatHEI
corrfunc(EB.fem.df, age, fatHEI, "Correlation - Age X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X fatHEI
corrfunc(EB.fem.df, BMI, fatHEI, "Correlation - BMI X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X fatHEI
corrfunc(EB.fem.df, sweet.liking, fatHEI, "Correlation - Sweet Liking X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X fatHEI
corrfunc(EB.fem.df, emotional, fatHEI, "Correlation - Emotional Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X fatHEI
corrfunc(EB.fem.df, uncontrolled, fatHEI, "Correlation - Uncontrolled Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X fatHEI
corrfunc(EB.fem.df, restraint, fatHEI, "Correlation - Dietary Restraint X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X fatHEI
corrfunc(EB.fem.df, EF, fatHEI, "Correlation - Enjoyment of Food X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X fatHEI
corrfunc(EB.fem.df, EOE, fatHEI, "Correlation - Emotional Over-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X fatHEI
corrfunc(EB.fem.df, EUE, fatHEI, "Correlation - Emotional Under-Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X fatHEI
corrfunc(EB.fem.df, FF, fatHEI, "Correlation - Food fussiness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X fatHEI
corrfunc(EB.fem.df, FR, fatHEI, "Correlation - Food Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X fatHEI
corrfunc(EB.fem.df, SE, fatHEI, "Correlation - Slowness in Eating X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X fatHEI
corrfunc(EB.fem.df, H, fatHEI, "Correlation - Hunger X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X fatHEI
corrfunc(EB.fem.df, SR, fatHEI, "Correlation - Satiety Responsiveness X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X fatHEI
corrfunc(EB.fem.df, emo, fatHEI, "Correlation - Emo X Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet + fat sHEI score
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugarHEI + fatHEI and traits
## Age X (sugarHEI + fatHEI)
corrfunc(EB.df, age, (sugarHEI + fatHEI), "Correlation - Age X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X (sugarHEI + fatHEI)
corrfunc(EB.df, BMI, (sugarHEI + fatHEI), "Correlation - BMI X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sex X (sugarHEI + fatHEI)
corrfunc(EB.df, sex, (sugarHEI + fatHEI), "Correlation - Sex X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X (sugarHEI + fatHEI)
corrfunc(EB.df, sweet.liking, (sugarHEI + fatHEI), "Correlation - Sweet Liking X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X (sugarHEI + fatHEI)
corrfunc(EB.df, emotional, (sugarHEI + fatHEI), "Correlation - Emotional Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X (sugarHEI + fatHEI)
corrfunc(EB.df, uncontrolled, (sugarHEI + fatHEI), "Correlation - Uncontrolled Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X (sugarHEI + fatHEI)
corrfunc(EB.df, restraint, (sugarHEI + fatHEI), "Correlation - Dietary Restraint X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X (sugarHEI + fatHEI)
corrfunc(EB.df, EF, (sugarHEI + fatHEI), "Correlation - Enjoyment of Food X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X (sugarHEI + fatHEI)
corrfunc(EB.df, EOE, (sugarHEI + fatHEI), "Correlation - Emotional Over-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X (sugarHEI + fatHEI)
corrfunc(EB.df, EUE, (sugarHEI + fatHEI), "Correlation - Emotional Under-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X (sugarHEI + fatHEI)
corrfunc(EB.df, FF, (sugarHEI + fatHEI), "Correlation - Food fussiness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.df, FR, (sugarHEI + fatHEI), "Correlation - Food Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X (sugarHEI + fatHEI)
corrfunc(EB.df, SE, (sugarHEI + fatHEI), "Correlation - Slowness in Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X (sugarHEI + fatHEI)
corrfunc(EB.df, H, (sugarHEI + fatHEI), "Correlation - Hunger X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.df, SR, (sugarHEI + fatHEI), "Correlation - Satiety Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X (sugarHEI + fatHEI)
corrfunc(EB.df, emo, (sugarHEI + fatHEI), "Correlation - Emo X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sweet + fat sHEI score: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sugarHEI + fatHEI and traits
## Age X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, age, (sugarHEI + fatHEI), "Correlation - Age X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## BMI X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, BMI, (sugarHEI + fatHEI), "Correlation - BMI X Sweet +Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, sweet.liking, (sugarHEI + fatHEI), "Correlation - Sweet Liking X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, emotional, (sugarHEI + fatHEI), "Correlation - Emotional Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, uncontrolled, (sugarHEI + fatHEI), "Correlation - Uncontrolled Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, restraint, (sugarHEI + fatHEI), "Correlation - Dietary Restraint X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, EF, (sugarHEI + fatHEI), "Correlation - Enjoyment of Food X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, EOE, (sugarHEI + fatHEI), "Correlation - Emotional Over-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, EUE, (sugarHEI + fatHEI), "Correlation - Emotional Under-Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, FF, (sugarHEI + fatHEI), "Correlation - Food fussiness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, FR, (sugarHEI + fatHEI), "Correlation - Food Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, SE, (sugarHEI + fatHEI), "Correlation - Slowness in Eating X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, H, (sugarHEI + fatHEI), "Correlation - Hunger X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, SR, (sugarHEI + fatHEI), "Correlation - Satiety Responsiveness X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X (sugarHEI + fatHEI)
corrfunc(EB.fem.df, emo, (sugarHEI + fatHEI), "Correlation - Emo X Sweet + Fat Score")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sHEI
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sHEI and traits
## Age X sHEI
corrfunc(EB.df, age, sHEI, "Correlation - Age X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sHEI
corrfunc(EB.df, BMI, sHEI, "Correlation - BMI X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Sex X sHEI
corrfunc(EB.df, sex, sHEI, "Correlation - Sex X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sHEI
corrfunc(EB.df, sweet.liking, sHEI, "Correlation - Sweet Liking X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sugar intake
corrfunc(EB.df, ssb.day, sHEI, "Correlation - SSB X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sHEI
corrfunc(EB.df, emotional, sHEI, "Correlation - Emotional Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sHEI
corrfunc(EB.df, uncontrolled, sHEI, "Correlation - Uncontrolled Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sHEI
corrfunc(EB.df, restraint, sHEI, "Correlation - Dietary Restraint X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sHEI
corrfunc(EB.df, EF, sHEI, "Correlation - Enjoyment of Food X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sHEI
corrfunc(EB.df, EOE, sHEI, "Correlation - Emotional Over-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.df, EUE, sHEI, "Correlation - Emotional Under-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sHEI
corrfunc(EB.df, FF, sHEI, "Correlation - Food fussiness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sHEI
corrfunc(EB.df, FR, sHEI, "Correlation - Food Responsiveness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X sHEI
corrfunc(EB.df, SE, sHEI, "Correlation - Slowness in Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X sHEI
corrfunc(EB.df, H, sHEI, "Correlation - Hunger X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X sHEI
corrfunc(EB.df, SR, sHEI, "Correlation - Satiety Responsiveness X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sHEI
corrfunc(EB.df, emo, sHEI, "Correlation - Emo X sHEI")## `geom_smooth()` using formula = 'y ~ x'
Correlations - sHEI: females only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - sHEI and traits
## Age X sHEI
corrfunc(EB.fem.df, age, sHEI, "Correlation - Age X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## BMI X sHEI
corrfunc(EB.fem.df, BMI, sHEI, "Correlation - BMI X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X sHEI
corrfunc(EB.fem.df, sweet.liking, sHEI, "Correlation - Sweet Liking X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## SSB X sHEI
corrfunc(EB.fem.df, ssb.day, sHEI, "Correlation - SSB X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X sHEI
corrfunc(EB.fem.df, emotional, sHEI, "Correlation - Emotional Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X sHEI
corrfunc(EB.fem.df, uncontrolled, sHEI, "Correlation - Uncontrolled Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X sHEI
corrfunc(EB.fem.df, restraint, sHEI, "Correlation - Dietary Restraint X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X sHEI
corrfunc(EB.fem.df, EF, sHEI, "Correlation - Enjoyment of Food X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X sHEI
corrfunc(EB.fem.df, EOE, sHEI, "Correlation - Emotional Over-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, EUE, sHEI, "Correlation - Emotional Under-Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X sHEI
corrfunc(EB.fem.df, FF, sHEI, "Correlation - Food fussiness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X sHEI
corrfunc(EB.fem.df, FR, sHEI, "Correlation - Food Responsiveness X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, SE, sHEI, "Correlation - Slowness in Eating X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, H, sHEI, "Correlation - Hunger X sHEI")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X sHEI
corrfunc(EB.fem.df, SR, sHEI, "Correlation - Satiety Responsiveness X sHEI") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X sHEI
corrfunc(EB.fem.df, emo, sHEI, "Correlation - Emo X sHEI")## `geom_smooth()` using formula = 'y ~ x'
Correlations - body appreciation
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - BAS and traits
## Age X BAS
corrfunc(EB.df, age, BAS, "Correlation - Age X BAS")## `geom_smooth()` using formula = 'y ~ x'
## BMI X BAS
corrfunc(EB.df, BMI, BAS, "Correlation - BMI X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sex X BAS
corrfunc(EB.df, sex, BAS, "Correlation - Sex X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X BAS
corrfunc(EB.df, BAS, sweet.liking, "Correlation - Sweet Liking X BAS")## `geom_smooth()` using formula = 'y ~ x'
## SSB X BAS
corrfunc(EB.df, ssb.day, BAS, "Correlation - SSB X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X BAS
corrfunc(EB.df, BAS, sugar.intake, "Correlation - Sugar Intake X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X BAS
corrfunc(EB.df, BAS, emotional, "Correlation - Emotional Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X BAS
corrfunc(EB.df, BAS, uncontrolled, "Correlation - Uncontrolled Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X BAS
corrfunc(EB.df, BAS, restraint, "Correlation - Dietary Restraint X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X BAS
corrfunc(EB.df, BAS, EF, "Correlation - Enjoyment of Food X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X BAS
corrfunc(EB.df, BAS, EOE, "Correlation - Emotional Over-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X BAS
corrfunc(EB.df, BAS, EUE, "Correlation - Emotional Under-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X BAS
corrfunc(EB.df, BAS, FF, "Correlation - Food fussiness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X BAS
corrfunc(EB.df, BAS, FR, "Correlation - Food Responsiveness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X BAS
corrfunc(EB.df, BAS, SE, "Correlation - Slowness in Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X BAS
corrfunc(EB.df, BAS, H, "Correlation - Hunger X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X BAS
corrfunc(EB.df, BAS, SR, "Correlation - Satiety Responsiveness X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X BAS
corrfunc(EB.df, BAS, emo, "Correlation - Emo X BAS")## `geom_smooth()` using formula = 'y ~ x'
Correlations - body appreciation: female only
## Correlation Function
corrfunc <- function(data, Cor1, Cor2, title) {
ggplot(data = data, aes(x = {{Cor1}}, y = {{Cor2}})) +
geom_point()+
geom_smooth(method = lm, se = FALSE)+
ggtitle(title) +
stat_cor(method = "pearson") +
theme_light()
}
## Correlations - BAS and traits
## Age X BMI
corrfunc(EB.fem.df, age, BMI, "Correlation - Age X BMI")## `geom_smooth()` using formula = 'y ~ x'
## Age X BAS
corrfunc(EB.fem.df, age, BAS, "Correlation - Age X BAS")## `geom_smooth()` using formula = 'y ~ x'
## BMI X BAS
corrfunc(EB.fem.df, BMI, BAS, "Correlation - BMI X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking X BAS
corrfunc(EB.fem.df, BAS, sweet.liking, "Correlation - Sweet Liking X BAS")## `geom_smooth()` using formula = 'y ~ x'
## SSB X BAS
corrfunc(EB.fem.df, ssb.day, BAS, "Correlation - SSB X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## Sugar intake X BAS
corrfunc(EB.fem.df, BAS, sugar.intake, "Correlation - Sugar Intake X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional eating X BAS
corrfunc(EB.fem.df, BAS, emotional, "Correlation - Emotional Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Uncontrolled eating X BAS
corrfunc(EB.fem.df, BAS, uncontrolled, "Correlation - Uncontrolled Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Restraint X BAS
corrfunc(EB.fem.df, BAS, restraint, "Correlation - Dietary Restraint X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Enjoyment of food X BAS
corrfunc(EB.fem.df, BAS, EF, "Correlation - Enjoyment of Food X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional over-eating X BAS
corrfunc(EB.fem.df, BAS, EOE, "Correlation - Emotional Over-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional under-eating X BAS
corrfunc(EB.fem.df, BAS, EUE, "Correlation - Emotional Under-Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Food fussiness X BAS
corrfunc(EB.fem.df, BAS, FF, "Correlation - Food fussiness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional food responsiveness X BAS
corrfunc(EB.fem.df, BAS, FR, "Correlation - Food Responsiveness X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Slowness in Eating X BAS
corrfunc(EB.fem.df, BAS, SE, "Correlation - Slowness in Eating X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Hunger X BAS
corrfunc(EB.fem.df, BAS, H, "Correlation - Hunger X BAS")## `geom_smooth()` using formula = 'y ~ x'
## Emotional Satiety Responsiveness X BAS
corrfunc(EB.fem.df, BAS, SR, "Correlation - Satiety Responsiveness X BAS") ## `geom_smooth()` using formula = 'y ~ x'
## (Emotional eating + emotional overeating) X BAS
corrfunc(EB.fem.df, BAS, emo, "Correlation - Emo X BAS")## `geom_smooth()` using formula = 'y ~ x'
Partial correlation - controlling for age
EB.df %>%
ggplot(aes(age, BMI)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## age and BMI are not correlationed - enter both as covariates## `geom_smooth()` using formula = 'y ~ x'
## Sweet liking and sugar intake
pcor.test(EB.df$sweet.liking, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.465998 1.507612e-06 5.133431 100 3 pearson## Emotional eating
pcor.test(EB.df$emotional, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.09480028 0.355667 -0.9281791 100 3 pearsonpcor.test(EB.df$emotional, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.09887438 0.335277 -0.9684537 100 3 pearsonpcor.test(EB.df$emotional, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.113663 0.2676304 1.115077 100 3 pearsonpcor.test(EB.df$emotional, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.06237228 0.5439016 -0.6091158 100 3 pearsonpcor.test(EB.df$emotional, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.03478036 0.7352061 -0.3392022 100 3 pearson## Uncontrolled eating
pcor.test(EB.df$uncontrolled, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.05640277 0.5831848 0.5506228 100 3 pearsonpcor.test(EB.df$uncontrolled, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.04571979 0.6565489 0.4460878 100 3 pearsonpcor.test(EB.df$uncontrolled, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.04609799 0.6538895 0.4497858 100 3 pearsonpcor.test(EB.df$uncontrolled, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1316364 0.1987006 -1.294296 100 3 pearsonpcor.test(EB.df$uncontrolled, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.04878689 0.6351085 -0.4760827 100 3 pearson## Dietary restrain
pcor.test(EB.df$restraint, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.2667175 0.008270309 -2.697353 100 3 pearsonpcor.test(EB.df$restraint, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.2857745 0.004546923 -2.9066 100 3 pearsonpcor.test(EB.df$restraint, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.07096717 0.4897187 0.6934509 100 3 pearsonpcor.test(EB.df$restraint, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.05265021 0.6085266 0.5138836 100 3 pearsonpcor.test(EB.df$restraint, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.2652879 0.008635819 2.681798 100 3 pearson## EOE
pcor.test(EB.df$EOE, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.0894113 0.3837926 -0.874978 100 3 pearsonpcor.test(EB.df$EOE, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.00718915 0.9442829 0.07007298 100 3 pearsonpcor.test(EB.df$EOE, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.1010441 0.3247268 0.9899222 100 3 pearsonpcor.test(EB.df$EOE, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1007911 0.3259457 -0.9874187 100 3 pearsonpcor.test(EB.df$EOE, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.03965949 0.6997271 -0.3868573 100 3 pearson## BAS
pcor.test(EB.df$BAS, EB.df$sweet.liking, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.04930358 0.6315254 0.481137 100 3 pearsonpcor.test(EB.df$BAS, EB.df$sugar.intake, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.08487779 0.4084608 -0.8302825 100 3 pearsonpcor.test(EB.df$BAS, EB.df$fatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.1966785 0.05350142 1.955174 100 3 pearsonpcor.test(EB.df$BAS, EB.df$SuFatHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.1692342 0.09749282 1.673631 100 3 pearsonpcor.test(EB.df$BAS, EB.df$sHEI, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.01043725 0.9191813 -0.1017352 100 3 pearson## BAS with eating behavior traits
pcor.test(EB.df$BAS, EB.df$emotional, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1084418 0.2903727 -1.06323 100 3 pearsonpcor.test(EB.df$BAS, EB.df$uncontrolled, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 0.07149773 0.4864703 0.6986617 100 3 pearsonpcor.test(EB.df$BAS, EB.df$restraint, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.1556176 0.1279901 -1.535479 100 3 pearsonpcor.test(EB.df$BAS, EB.df$EOE, EB.df[,c("age", "sex", "BMI")])## estimate p.value statistic n gp Method
## 1 -0.124501 0.2243545 -1.223001 100 3 pearsonModerator analysis - sugar intake
## Sweet liking and sugar intake
SL.M1 <- lm(sugar.intake ~ sweet.liking, EB.df)
summary(SL.M1)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -346.79 -154.26 -24.81 88.69 700.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.0548 34.3154 7.433 4.03e-11 ***
## sweet.liking 3.9993 0.8066 4.958 2.98e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 218.9 on 98 degrees of freedom
## Multiple R-squared: 0.2006, Adjusted R-squared: 0.1924
## F-statistic: 24.58 on 1 and 98 DF, p-value: 2.985e-06SL.M2 <- lm(sugar.intake ~ sweet.liking + sweet.liking*emotional + emotional, EB.df)
summary(SL.M2)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * emotional +
## emotional, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -345.55 -144.61 -34.11 71.36 701.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 350.54916 115.84527 3.026 0.00318 **
## sweet.liking 3.12360 2.58097 1.210 0.22916
## emotional -8.08693 9.45549 -0.855 0.39453
## sweet.liking:emotional 0.07921 0.20880 0.379 0.70525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 219.7 on 96 degrees of freedom
## Multiple R-squared: 0.211, Adjusted R-squared: 0.1863
## F-statistic: 8.557 on 3 and 96 DF, p-value: 4.307e-05SL.M3 <- lm(sugar.intake ~ sweet.liking + sweet.liking*EOE + EOE, EB.df)
summary(SL.M3)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * EOE +
## EOE, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -353.33 -156.36 -23.85 85.50 695.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 291.6343 102.6919 2.840 0.00551 **
## sweet.liking 2.9448 2.4007 1.227 0.22297
## EOE -3.7616 9.9286 -0.379 0.70563
## sweet.liking:EOE 0.1073 0.2299 0.467 0.64186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 220.9 on 96 degrees of freedom
## Multiple R-squared: 0.2024, Adjusted R-squared: 0.1774
## F-statistic: 8.119 on 3 and 96 DF, p-value: 7.121e-05SL.M4 <- lm(sugar.intake ~ sweet.liking + sweet.liking*BMI + BMI, EB.df)
summary(SL.M4)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * BMI +
## BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -347.13 -144.79 -30.77 83.20 695.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 384.34072 188.49157 2.039 0.0442 *
## sweet.liking 3.14938 4.08253 0.771 0.4423
## BMI -5.57759 8.10800 -0.688 0.4932
## sweet.liking:BMI 0.04517 0.17229 0.262 0.7937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 219.7 on 96 degrees of freedom
## Multiple R-squared: 0.2113, Adjusted R-squared: 0.1866
## F-statistic: 8.571 on 3 and 96 DF, p-value: 4.239e-05SL.M5 <- lm(sugar.intake ~ sweet.liking + sweet.liking*emo + emo, EB.df)
summary(SL.M5)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * emo +
## emo, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -344.55 -146.82 -30.71 79.85 701.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 332.82421 120.03963 2.773 0.00668 **
## sweet.liking 2.85944 2.70356 1.058 0.29287
## emo -3.61869 5.36379 -0.675 0.50152
## sweet.liking:emo 0.05404 0.11967 0.452 0.65261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 220.5 on 96 degrees of freedom
## Multiple R-squared: 0.205, Adjusted R-squared: 0.1801
## F-statistic: 8.25 on 3 and 96 DF, p-value: 6.121e-05SL.M6 <- lm(sugar.intake ~ sweet.liking + sweet.liking*emo + sweet.liking*BMI + emo + BMI, EB.df)
summary(SL.M6)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * emo +
## sweet.liking * BMI + emo + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -349.74 -149.48 -25.56 85.47 692.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 439.01800 215.45963 2.038 0.0444 *
## sweet.liking 1.94654 4.75838 0.409 0.6834
## emo -2.89567 5.43769 -0.533 0.5956
## BMI -5.25931 8.28122 -0.635 0.5269
## sweet.liking:emo 0.06092 0.12071 0.505 0.6150
## sweet.liking:BMI 0.04053 0.17407 0.233 0.8164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 221.6 on 94 degrees of freedom
## Multiple R-squared: 0.2137, Adjusted R-squared: 0.1719
## F-statistic: 5.11 on 5 and 94 DF, p-value: 0.0003448SL.M7 <- lm(sugar.intake ~ sweet.liking + sweet.liking*EOE + sweet.liking*BMI + EOE + BMI, EB.df)
summary(SL.M7)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * EOE +
## sweet.liking * BMI + EOE + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -360.77 -158.13 -19.94 78.49 683.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 401.24627 198.36061 2.023 0.0459 *
## sweet.liking 2.37891 4.36199 0.545 0.5868
## EOE -1.88191 10.22675 -0.184 0.8544
## BMI -5.50164 8.40482 -0.655 0.5143
## sweet.liking:EOE 0.11999 0.23643 0.508 0.6130
## sweet.liking:BMI 0.02852 0.17776 0.160 0.8729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 221.5 on 94 degrees of freedom
## Multiple R-squared: 0.215, Adjusted R-squared: 0.1732
## F-statistic: 5.149 on 5 and 94 DF, p-value: 0.0003221SL.M8 <- lm(sugar.intake ~ BAS, EB.df)
summary(SL.M8)##
## Call:
## lm(formula = sugar.intake ~ BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -291.39 -134.60 -105.37 72.36 823.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 474.967 109.226 4.348 3.36e-05 ***
## BAS -2.435 2.918 -0.835 0.406
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 243.9 on 98 degrees of freedom
## Multiple R-squared: 0.007059, Adjusted R-squared: -0.003073
## F-statistic: 0.6967 on 1 and 98 DF, p-value: 0.4059SL.M9 <- lm(sugar.intake ~ sweet.liking + sweet.liking*BAS + BAS, EB.df)
summary(SL.M9)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * BAS +
## BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -347.92 -147.42 -30.26 97.34 694.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 521.7567 176.7195 2.952 0.00396 **
## sweet.liking -1.8943 4.2428 -0.446 0.65627
## BAS -6.9940 4.5598 -1.534 0.12836
## sweet.liking:BAS 0.1535 0.1090 1.408 0.16240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 218.4 on 96 degrees of freedom
## Multiple R-squared: 0.2202, Adjusted R-squared: 0.1959
## F-statistic: 9.038 on 3 and 96 DF, p-value: 2.496e-05SL.M10 <- lm(sugar.intake ~ sweet.liking + sweet.liking*restraint + restraint, EB.df)
summary(SL.M10)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * restraint +
## restraint, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -333.71 -125.46 -32.36 80.11 673.83
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.15978 144.81686 1.748 0.08364 .
## sweet.liking 8.89468 3.24805 2.738 0.00736 **
## restraint 0.08851 9.57372 0.009 0.99264
## sweet.liking:restraint -0.36495 0.22097 -1.652 0.10189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 212.7 on 96 degrees of freedom
## Multiple R-squared: 0.2604, Adjusted R-squared: 0.2373
## F-statistic: 11.27 on 3 and 96 DF, p-value: 2.13e-06SL.M11 <- lm(sugar.intake ~ sweet.liking + sweet.liking*restraint + restraint + BAS + sweet.liking*BAS, EB.df)
summary(SL.M11)##
## Call:
## lm(formula = sugar.intake ~ sweet.liking + sweet.liking * restraint +
## restraint + BAS + sweet.liking * BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -349.51 -127.23 -44.10 88.05 674.68
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 551.0497 248.4624 2.218 0.029 *
## sweet.liking 4.0936 6.0008 0.682 0.497
## restraint -2.6892 9.7740 -0.275 0.784
## BAS -6.7953 4.5301 -1.500 0.137
## sweet.liking:restraint -0.3202 0.2288 -1.399 0.165
## sweet.liking:BAS 0.1068 0.1098 0.973 0.333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 212.2 on 94 degrees of freedom
## Multiple R-squared: 0.2792, Adjusted R-squared: 0.2408
## F-statistic: 7.282 on 5 and 94 DF, p-value: 8.368e-06Moderator analysis - SSB intake
## Sweet liking and sugar intake
SSB.M1 <- lm(ssb.day ~ sweet.liking , EB.df)
summary(SSB.M1)##
## Call:
## lm(formula = ssb.day ~ sweet.liking, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3153 -0.7606 -0.2281 0.3476 4.7836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.188824 0.174325 6.82 7.56e-10 ***
## sweet.liking 0.017126 0.004097 4.18 6.35e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.112 on 98 degrees of freedom
## Multiple R-squared: 0.1513, Adjusted R-squared: 0.1426
## F-statistic: 17.47 on 1 and 98 DF, p-value: 6.35e-05SSB.M2 <- lm(ssb.day ~ sweet.liking + sweet.liking*emotional + emotional, EB.df)
summary(SSB.M2)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * emotional +
## emotional, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4098 -0.7424 -0.2106 0.3682 4.8012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.499617 0.586568 2.557 0.0121 *
## sweet.liking 0.019474 0.013068 1.490 0.1395
## emotional -0.025893 0.047877 -0.541 0.5899
## sweet.liking:emotional -0.000170 0.001057 -0.161 0.8726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.112 on 96 degrees of freedom
## Multiple R-squared: 0.1679, Adjusted R-squared: 0.1419
## F-statistic: 6.456 on 3 and 96 DF, p-value: 0.0004995SSB.M3 <- lm(ssb.day ~ sweet.liking + sweet.liking*EOE + EOE, EB.df)
summary(SSB.M3)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * EOE + EOE,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3093 -0.7866 -0.2271 0.3524 4.7814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3472200 0.5219862 2.581 0.0114 *
## sweet.liking 0.0143688 0.0122029 1.177 0.2419
## EOE -0.0162623 0.0504676 -0.322 0.7480
## sweet.liking:EOE 0.0002839 0.0011687 0.243 0.8086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.123 on 96 degrees of freedom
## Multiple R-squared: 0.1522, Adjusted R-squared: 0.1257
## F-statistic: 5.746 on 3 and 96 DF, p-value: 0.001169SSB.M4 <- lm(ssb.day ~ sweet.liking + sweet.liking*BMI + BMI, EB.df)
summary(SSB.M4)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * BMI + BMI,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3362 -0.7569 -0.2118 0.3572 4.7694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6413009 0.9626302 1.705 0.0914 .
## sweet.liking 0.0105108 0.0208496 0.504 0.6153
## BMI -0.0196951 0.0414077 -0.476 0.6354
## sweet.liking:BMI 0.0003010 0.0008799 0.342 0.7331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.122 on 96 degrees of freedom
## Multiple R-squared: 0.1538, Adjusted R-squared: 0.1273
## F-statistic: 5.814 on 3 and 96 DF, p-value: 0.001077SSB.M5 <- lm(ssb.day ~ sweet.liking + sweet.liking*emo + emo, EB.df)
summary(SSB.M5)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * emo + emo,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3787 -0.7679 -0.1844 0.3105 4.8091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.460e+00 6.091e-01 2.398 0.0184 *
## sweet.liking 1.678e-02 1.372e-02 1.223 0.2243
## emo -1.253e-02 2.722e-02 -0.461 0.6462
## sweet.liking:emo 2.474e-05 6.072e-04 0.041 0.9676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.119 on 96 degrees of freedom
## Multiple R-squared: 0.1579, Adjusted R-squared: 0.1316
## F-statistic: 6.002 on 3 and 96 DF, p-value: 0.0008591SSB.M6 <- lm(ssb.day ~ sweet.liking + sweet.liking*emo + sweet.liking*BMI + emo + BMI, EB.df)
summary(SSB.M6)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * emo + sweet.liking *
## BMI + emo + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4326 -0.7574 -0.2032 0.3325 4.8011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.778e+00 1.099e+00 1.619 0.109
## sweet.liking 1.012e-02 2.426e-02 0.417 0.677
## emo -1.175e-02 2.773e-02 -0.424 0.673
## BMI -1.467e-02 4.223e-02 -0.348 0.729
## sweet.liking:emo 1.876e-05 6.155e-04 0.030 0.976
## sweet.liking:BMI 2.965e-04 8.876e-04 0.334 0.739
##
## Residual standard error: 1.13 on 94 degrees of freedom
## Multiple R-squared: 0.159, Adjusted R-squared: 0.1143
## F-statistic: 3.555 on 5 and 94 DF, p-value: 0.005468SSB.M7 <- lm(ssb.day ~ sweet.liking + sweet.liking*EOE + sweet.liking*BMI + EOE + BMI, EB.df)
summary(SSB.M7)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * EOE + sweet.liking *
## BMI + EOE + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3137 -0.7720 -0.2138 0.3702 4.7627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7038197 1.0151811 1.678 0.0966 .
## sweet.liking 0.0090658 0.0223241 0.406 0.6856
## EOE -0.0109859 0.0523390 -0.210 0.8342
## BMI -0.0177485 0.0430146 -0.413 0.6808
## sweet.liking:EOE 0.0002428 0.0012100 0.201 0.8414
## sweet.liking:BMI 0.0002605 0.0009097 0.286 0.7753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.133 on 94 degrees of freedom
## Multiple R-squared: 0.1542, Adjusted R-squared: 0.1092
## F-statistic: 3.427 on 5 and 94 DF, p-value: 0.006885SSB.M8 <- lm(ssb.day ~ BAS, EB.df)
summary(SSB.M8)##
## Call:
## lm(formula = ssb.day ~ BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9024 -0.7573 -0.6663 0.2944 5.3042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.108949 0.539162 3.912 0.000169 ***
## BAS -0.009837 0.014402 -0.683 0.496217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.204 on 98 degrees of freedom
## Multiple R-squared: 0.004738, Adjusted R-squared: -0.005418
## F-statistic: 0.4665 on 1 and 98 DF, p-value: 0.4962SSB.M9 <- lm(ssb.day ~ sweet.liking + sweet.liking*BAS + BAS, EB.df)
summary(SSB.M9)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * BAS + BAS,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2884 -0.7722 -0.1648 0.2447 4.7500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3566703 0.9005025 2.617 0.0103 *
## sweet.liking -0.0094853 0.0216199 -0.439 0.6618
## BAS -0.0305967 0.0232353 -1.317 0.1910
## sweet.liking:BAS 0.0006935 0.0005555 1.248 0.2150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.113 on 96 degrees of freedom
## Multiple R-squared: 0.1671, Adjusted R-squared: 0.1411
## F-statistic: 6.42 on 3 and 96 DF, p-value: 0.0005213SSB.M10 <- lm(ssb.day ~ sweet.liking + sweet.liking*restraint + restraint, EB.df)
summary(SSB.M10)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * restraint +
## restraint, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6136 -0.6630 -0.2688 0.3599 4.5599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.781144 0.744876 1.049 0.29695
## sweet.liking 0.044587 0.016707 2.669 0.00894 **
## restraint 0.027531 0.049243 0.559 0.57740
## sweet.liking:restraint -0.001999 0.001137 -1.758 0.08186 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 96 degrees of freedom
## Multiple R-squared: 0.1951, Adjusted R-squared: 0.1699
## F-statistic: 7.755 on 3 and 96 DF, p-value: 0.0001085SSB.M11 <- lm(ssb.day ~ sweet.liking + sweet.liking*restraint + restraint + BAS + sweet.liking*BAS, EB.df)
summary(SSB.M11)##
## Call:
## lm(formula = ssb.day ~ sweet.liking + sweet.liking * restraint +
## restraint + BAS + sweet.liking * BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5931 -0.6582 -0.2660 0.3257 4.5857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9768017 1.2847550 1.539 0.127
## sweet.liking 0.0245855 0.0310291 0.792 0.430
## restraint 0.0162719 0.0505396 0.322 0.748
## BAS -0.0272095 0.0234245 -1.162 0.248
## sweet.liking:restraint -0.0018079 0.0011831 -1.528 0.130
## sweet.liking:BAS 0.0004442 0.0005679 0.782 0.436
##
## Residual standard error: 1.097 on 94 degrees of freedom
## Multiple R-squared: 0.2072, Adjusted R-squared: 0.165
## F-statistic: 4.913 on 5 and 94 DF, p-value: 0.0004874Moderator anaysis - fat X sugar intake
## Sweet liking and sugar intake
SF.M1 <- lm(SuFatHEI ~ sweet.liking, EB.df)
summary(SF.M1)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.014 -3.346 1.058 2.744 5.198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.99995 0.50934 15.707 <2e-16 ***
## sweet.liking -0.03027 0.01197 -2.528 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.249 on 98 degrees of freedom
## Multiple R-squared: 0.06123, Adjusted R-squared: 0.05165
## F-statistic: 6.391 on 1 and 98 DF, p-value: 0.01307SF.M2 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*emotional + emotional, EB.df)
summary(SF.M2)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * emotional +
## emotional, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.053 -3.270 1.054 2.654 5.250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.9704848 1.7301175 4.607 1.26e-05 ***
## sweet.liking -0.0349877 0.0385460 -0.908 0.366
## emotional 0.0020853 0.1412151 0.015 0.988
## sweet.liking:emotional 0.0003864 0.0031184 0.124 0.902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.281 on 96 degrees of freedom
## Multiple R-squared: 0.06197, Adjusted R-squared: 0.03266
## F-statistic: 2.114 on 3 and 96 DF, p-value: 0.1035SF.M3 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*EOE + EOE, EB.df)
summary(SF.M3)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * EOE + EOE,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.389 -3.200 1.060 2.752 5.307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.281563 1.521836 4.785 6.19e-06 ***
## sweet.liking -0.006697 0.035577 -0.188 0.851
## EOE 0.073915 0.147137 0.502 0.617
## sweet.liking:EOE -0.002392 0.003407 -0.702 0.484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.273 on 96 degrees of freedom
## Multiple R-squared: 0.06631, Adjusted R-squared: 0.03713
## F-statistic: 2.272 on 3 and 96 DF, p-value: 0.08504SF.M4 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*BMI + BMI, EB.df)
summary(SF.M4)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * BMI + BMI,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4763 -3.1628 0.9551 2.5533 5.2626
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.605305 2.806524 2.354 0.0206 *
## sweet.liking -0.021334 0.060786 -0.351 0.7264
## BMI 0.060156 0.120723 0.498 0.6194
## sweet.liking:BMI -0.000478 0.002565 -0.186 0.8526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.27 on 96 degrees of freedom
## Multiple R-squared: 0.06798, Adjusted R-squared: 0.03885
## F-statistic: 2.334 on 3 and 96 DF, p-value: 0.07879SF.M5 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*emo + emo, EB.df)
summary(SF.M5)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * emo + emo,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.996 -3.437 1.076 2.709 5.221
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.5345834 1.7859024 4.219 5.57e-05 ***
## sweet.liking -0.0194452 0.0402225 -0.483 0.630
## emo 0.0217645 0.0798003 0.273 0.786
## sweet.liking:emo -0.0005038 0.0017803 -0.283 0.778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.281 on 96 degrees of freedom
## Multiple R-squared: 0.06203, Adjusted R-squared: 0.03272
## F-statistic: 2.116 on 3 and 96 DF, p-value: 0.1032SF.M6 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*emo + sweet.liking*BMI + emo + BMI, EB.df)
summary(SF.M6)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * emo + sweet.liking *
## BMI + emo + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4776 -3.2364 0.9417 2.5837 5.3446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2452697 3.2101256 1.945 0.0547 .
## sweet.liking -0.0095953 0.0708950 -0.135 0.8926
## emo 0.0125532 0.0810160 0.155 0.8772
## BMI 0.0641925 0.1233816 0.520 0.6041
## sweet.liking:emo -0.0005957 0.0017985 -0.331 0.7412
## sweet.liking:BMI -0.0004370 0.0025934 -0.168 0.8666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.302 on 94 degrees of freedom
## Multiple R-squared: 0.06968, Adjusted R-squared: 0.02019
## F-statistic: 1.408 on 5 and 94 DF, p-value: 0.2285SF.M7 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*EOE + sweet.liking*BMI + EOE + BMI, EB.df)
summary(SF.M7)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * EOE + sweet.liking *
## BMI + EOE + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7406 -3.1472 0.9022 2.5350 5.4532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1853257 2.9482886 2.098 0.0386 *
## sweet.liking -0.0047935 0.0648335 -0.074 0.9412
## EOE 0.0542061 0.1520029 0.357 0.7222
## BMI 0.0551850 0.1249231 0.442 0.6597
## sweet.liking:EOE -0.0026090 0.0035141 -0.742 0.4597
## sweet.liking:BMI -0.0001033 0.0026420 -0.039 0.9689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.291 on 94 degrees of freedom
## Multiple R-squared: 0.07563, Adjusted R-squared: 0.02646
## F-statistic: 1.538 on 5 and 94 DF, p-value: 0.1855SF.M8 <- lm(SuFatHEI ~ BAS, EB.df)
summary(SF.M8)##
## Call:
## lm(formula = SuFatHEI ~ BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.680 -3.033 1.285 2.479 5.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.30113 1.49093 3.556 0.000583 ***
## BAS 0.04678 0.03983 1.175 0.242988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.33 on 98 degrees of freedom
## Multiple R-squared: 0.01388, Adjusted R-squared: 0.003821
## F-statistic: 1.38 on 1 and 98 DF, p-value: 0.243SF.M9 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*BAS + BAS, EB.df)
summary(SF.M9)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * BAS + BAS,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.091 -3.168 1.010 2.737 5.009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.1041313 2.6391563 2.692 0.00838 **
## sweet.liking -0.0482314 0.0633629 -0.761 0.44841
## BAS 0.0248448 0.0680970 0.365 0.71603
## sweet.liking:BAS 0.0004883 0.0016281 0.300 0.76486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.262 on 96 degrees of freedom
## Multiple R-squared: 0.07303, Adjusted R-squared: 0.04406
## F-statistic: 2.521 on 3 and 96 DF, p-value: 0.06245SF.M10 <- lm(SuFatHEI ~ sweet.liking + sweet.liking*restraint + restraint, EB.df)
summary(SF.M10)##
## Call:
## lm(formula = SuFatHEI ~ sweet.liking + sweet.liking * restraint +
## restraint, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9122 -2.7325 0.9217 2.5376 5.1285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.531328 2.176831 5.297 7.46e-07 ***
## sweet.liking -0.136649 0.048823 -2.799 0.0062 **
## restraint -0.239568 0.143908 -1.665 0.0992 .
## sweet.liking:restraint 0.007504 0.003322 2.259 0.0261 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.197 on 96 degrees of freedom
## Multiple R-squared: 0.1093, Adjusted R-squared: 0.08142
## F-statistic: 3.925 on 3 and 96 DF, p-value: 0.01087Moderator anaysis - sHEI
## Sweet liking and sHEI
sHEI.M1 <- lm(sHEI ~ sweet.liking, EB.df)
summary(sHEI.M1)##
## Call:
## lm(formula = sHEI ~ sweet.liking, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.066 -5.066 0.339 5.907 17.701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.87649 1.41960 34.430 <2e-16 ***
## sweet.liking -0.08034 0.03337 -2.408 0.0179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.054 on 98 degrees of freedom
## Multiple R-squared: 0.05585, Adjusted R-squared: 0.04622
## F-statistic: 5.797 on 1 and 98 DF, p-value: 0.01792sHEI.M2 <- lm(sHEI ~ sweet.liking + sweet.liking*emotional + emotional, EB.df)
summary(sHEI.M2)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * emotional +
## emotional, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.7584 -5.6091 0.6168 6.4193 17.3723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.855443 4.791118 9.780 4.42e-16 ***
## sweet.liking -0.098450 0.106743 -0.922 0.359
## emotional 0.168145 0.391059 0.430 0.668
## sweet.liking:emotional 0.001339 0.008636 0.155 0.877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.086 on 96 degrees of freedom
## Multiple R-squared: 0.06869, Adjusted R-squared: 0.03959
## F-statistic: 2.36 on 3 and 96 DF, p-value: 0.07626sHEI.M3 <- lm(sHEI ~ sweet.liking + sweet.liking*EOE + EOE, EB.df)
summary(sHEI.M3)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * EOE + EOE,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.9480 -5.2532 0.6558 5.2715 16.7541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.635868 4.177445 9.967 <2e-16 ***
## sweet.liking 0.044076 0.097660 0.451 0.6528
## EOE 0.743361 0.403891 1.840 0.0688 .
## sweet.liking:EOE -0.012817 0.009353 -1.370 0.1738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.985 on 96 degrees of freedom
## Multiple R-squared: 0.08915, Adjusted R-squared: 0.06069
## F-statistic: 3.132 on 3 and 96 DF, p-value: 0.02915sHEI.M4 <- lm(sHEI ~ sweet.liking + sweet.liking*age + BMI, EB.df)
summary(sHEI.M4)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * age + BMI,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.0786 -5.5465 0.1908 5.4337 17.7569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.039476 4.531989 9.938 2.24e-16 ***
## sweet.liking -0.030965 0.088167 -0.351 0.7262
## age -0.059202 0.099526 -0.595 0.5534
## BMI 0.233618 0.136657 1.710 0.0906 .
## sweet.liking:age -0.002450 0.002624 -0.934 0.3529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.998 on 95 degrees of freedom
## Multiple R-squared: 0.09606, Adjusted R-squared: 0.058
## F-statistic: 2.524 on 4 and 95 DF, p-value: 0.04592sHEI.M5 <- lm(sHEI ~ sweet.liking + sweet.liking*emo + emo, EB.df)
summary(sHEI.M5)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * emo + emo,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.056 -5.753 0.630 5.832 16.330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.101787 4.926740 8.749 7.24e-14 ***
## sweet.liking -0.009758 0.110961 -0.088 0.930
## emo 0.268314 0.220144 1.219 0.226
## sweet.liking:emo -0.003378 0.004911 -0.688 0.493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.051 on 96 degrees of freedom
## Multiple R-squared: 0.07584, Adjusted R-squared: 0.04696
## F-statistic: 2.626 on 3 and 96 DF, p-value: 0.05478sHEI.M6 <- lm(sHEI ~ sweet.liking + sweet.liking*emo + sweet.liking*age + emo + BMI, EB.df)
summary(sHEI.M6)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * emo + sweet.liking *
## age + emo + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.0143 -5.3928 0.7018 5.3331 16.6890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.560175 6.898743 5.734 1.21e-07 ***
## sweet.liking 0.091491 0.156024 0.586 0.559
## emo 0.237360 0.226692 1.047 0.298
## age -0.039068 0.102695 -0.380 0.704
## BMI 0.220215 0.150890 1.459 0.148
## sweet.liking:emo -0.004918 0.005060 -0.972 0.334
## sweet.liking:age -0.002932 0.002733 -1.073 0.286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.04 on 93 degrees of freedom
## Multiple R-squared: 0.1068, Adjusted R-squared: 0.0492
## F-statistic: 1.854 on 6 and 93 DF, p-value: 0.09712sHEI.M7 <- lm(sHEI ~ sweet.liking + sweet.liking*EOE + sweet.liking*age + EOE + BMI, EB.df)
summary(sHEI.M7)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * EOE + sweet.liking *
## age + EOE + BMI, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.908 -5.484 1.010 5.396 17.244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.931767 6.050623 6.269 1.13e-08 ***
## sweet.liking 0.148660 0.139052 1.069 0.2878
## EOE 0.682084 0.408483 1.670 0.0983 .
## age -0.045517 0.100082 -0.455 0.6503
## BMI 0.231248 0.147668 1.566 0.1207
## sweet.liking:EOE -0.016126 0.009517 -1.694 0.0935 .
## sweet.liking:age -0.003141 0.002680 -1.172 0.2442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.946 on 93 degrees of freedom
## Multiple R-squared: 0.1254, Adjusted R-squared: 0.06893
## F-statistic: 2.222 on 6 and 93 DF, p-value: 0.04772sHEI.M8 <- lm(sHEI ~ BAS, EB.df)
summary(sHEI.M8)##
## Call:
## lm(formula = sHEI ~ BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.9555 -5.2382 0.9438 5.9027 21.3052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 50.2013 4.1525 12.089 <2e-16 ***
## BAS -0.1085 0.1109 -0.978 0.331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.273 on 98 degrees of freedom
## Multiple R-squared: 0.00966, Adjusted R-squared: -0.0004454
## F-statistic: 0.9559 on 1 and 98 DF, p-value: 0.3306sHEI.M9 <- lm(sHEI ~ sweet.liking + sweet.liking*BAS + BAS, EB.df)
summary(sHEI.M9)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * BAS + BAS,
## data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.2450 -5.1422 0.3868 6.0415 18.1293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.8264133 7.3533281 7.184 1.45e-10 ***
## sweet.liking -0.0648864 0.1765443 -0.368 0.714
## BAS -0.1072637 0.1897347 -0.565 0.573
## sweet.liking:BAS -0.0004583 0.0045362 -0.101 0.920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.088 on 96 degrees of freedom
## Multiple R-squared: 0.06833, Adjusted R-squared: 0.03922
## F-statistic: 2.347 on 3 and 96 DF, p-value: 0.07752sHEI.M10 <- lm(sHEI ~ sweet.liking + sweet.liking*restraint + restraint, EB.df)
summary(sHEI.M10)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * restraint +
## restraint, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.1032 -4.7145 0.3389 5.1838 17.6077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.9926022 6.0804477 6.742 1.17e-09 ***
## sweet.liking -0.0594226 0.1363762 -0.436 0.664
## restraint 0.5366331 0.4019732 1.335 0.185
## sweet.liking:restraint -0.0005958 0.0092779 -0.064 0.949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.931 on 96 degrees of freedom
## Multiple R-squared: 0.1002, Adjusted R-squared: 0.07211
## F-statistic: 3.565 on 3 and 96 DF, p-value: 0.01701sHEI.M11 <- lm(sHEI ~ sweet.liking + sweet.liking*restraint + restraint + BAS + sweet.liking*BAS, EB.df)
summary(sHEI.M11)##
## Call:
## lm(formula = sHEI ~ sweet.liking + sweet.liking * restraint +
## restraint + BAS + sweet.liking * BAS, data = EB.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.1437 -4.9450 0.7785 4.8715 17.8813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.7355731 10.5363354 4.151 7.27e-05 ***
## sweet.liking -0.0387800 0.2544713 -0.152 0.879
## restraint 0.5208251 0.4144780 1.257 0.212
## BAS -0.0683364 0.1921054 -0.356 0.723
## sweet.liking:restraint -0.0011611 0.0097027 -0.120 0.905
## sweet.liking:BAS -0.0003875 0.0046573 -0.083 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.999 on 94 degrees of freedom
## Multiple R-squared: 0.1055, Adjusted R-squared: 0.05793
## F-statistic: 2.218 on 5 and 94 DF, p-value: 0.05887Liking hierarchial clustering
liking.df[is.na(liking.df)] <- 0 ## replace "NA"s with 0s
res.dist <- dist(x = liking.df, method = "euclidean") ## calculate distance
Likingclust <- hclust(res.dist, method = "ward.D") ## create clusters
plot(Likingclust) ## create dendrogram to visualize clusters
fviz_nbclust(liking.df, FUNcluster = hcut, method = "wss") ## identify number of clusters
Likinggroup <- cutree(Likingclust, k = 4) ## break clusters into desirable number of groups
km.res <- kmeans(liking.df, 4, nstart = 25)
fviz_cluster(km.res, data = liking.df, ggtheme = theme_light())
## calculate mean score for each cluster
tapply(liking.df$sweet.liking, Likinggroup, mean)## 1 2 3 4
## 44.236442 21.605820 8.787594 44.339506tapply(liking.df$unhealthyfat.liking, Likinggroup, mean)## 1 2 3 4
## 39.300000 23.552381 1.415789 38.100000tapply(liking.df$alcohol.liking, Likinggroup, mean)## 1 2 3 4
## 22.333730 40.357143 -3.123684 -52.559259tapply(liking.df$saltyfat.liking, Likinggroup, mean)## 1 2 3 4
## 57.461139 41.476757 8.763158 37.785648## identify individuals within each cluster
Likeclust1 <- subset(liking.df, Likinggroup == 1)
Likeclust2 <- subset(liking.df, Likinggroup == 2)
Likeclust3 <- subset(liking.df, Likinggroup == 3)
Likeclust4 <- subset(liking.df, Likinggroup == 4)sHEI hierarchial clustering
HEI.clust.df <- select(HEI.df, -c("ID", "sugar.intake"))
HEI.clust.df[is.na(HEI.clust.df)] <- 0 ## replace "NA"s with 0s
res.dist <- dist(x = HEI.clust.df[1:13], method = "euclidean") ## calculate distance
HEIclust <- hclust(res.dist, method = "ward.D") ## create clusters
plot(HEIclust) ## create dendrogram to visualize clusters
fviz_nbclust(HEI.clust.df, FUNcluster = hcut, method = "wss") ## identify number of clusters
HEIgroup <- cutree(HEIclust, k = 4) ## break clusters into desirable number of groups
km.res <- kmeans(HEI.clust.df[1:13], 4, nstart = 25)
fviz_cluster(km.res, data = HEI.clust.df[1:13], ggtheme = theme_light())
## calculate mean score for each cluster
tapply(HEI.clust.df$sugarHEI, HEIgroup, mean)## 1 2 3 4
## 2.954545 4.687500 2.592593 1.923077tapply(HEI.clust.df$fatHEI, HEIgroup, mean)## 1 2 3 4
## 4.378182 3.200000 4.191111 3.370769tapply(HEI.clust.df$SuFatHEI, HEIgroup, mean)## 1 2 3 4
## 7.332727 7.887500 6.783704 5.293846tapply(HEI.clust.df$sHEI, HEIgroup, mean)## 1 2 3 4
## 43.37705 52.02938 53.81778 33.09615## identify individuals within each cluster
HEIclust1 <- subset(HEI.clust.df, HEIgroup == 1)
HEIclust2 <- subset(HEI.clust.df, HEIgroup == 2)
HEIclust3 <- subset(HEI.clust.df, HEIgroup == 3)
HEIclust4 <- subset(HEI.clust.df, HEIgroup == 4)Emotional overeating analysis
## Emotional overeating
EB.df %>%
summary()## ID age height weight
## Length:100 Min. :18.00 Min. :53.00 Min. : 71.0
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126.8
## Mode :character Median :24.00 Median :65.00 Median :143.0
## Mean :28.04 Mean :65.32 Mean :152.7
## 3rd Qu.:30.00 3rd Qu.:68.00 3rd Qu.:175.0
## Max. :68.00 Max. :76.00 Max. :320.0
##
## sex sex.other race race.other ethnicity
## Min. :1.00 Min. : NA Min. :1.000 Min. : NA Min. :1.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.00
## Median :2.00 Median : NA Median :4.000 Median : NA Median :2.00
## Mean :1.72 Mean :NaN Mean :4.232 Mean :NaN Mean :1.81
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.00
## Max. :2.00 Max. : NA Max. :7.000 Max. : NA Max. :2.00
## NA's :100 NA's :1 NA's :100
## us.born birthplace agetous timeinus education
## Min. :1.00 Min. : NA Min. :1.00 Min. :1.00 Min. : 5.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 6.00
## Median :1.00 Median : NA Median :1.00 Median :1.00 Median : 9.00
## Mean :1.35 Mean :NaN Mean :2.46 Mean :2.33 Mean : 8.15
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:4.25 3rd Qu.:3.25 3rd Qu.: 9.00
## Max. :2.00 Max. : NA Max. :7.00 Max. :8.00 Max. :12.00
## NA's :100
## income.personal income.household exercise ssb.day sugars.amount
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:4.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.00
## Median :3.00 Median :5.50 Median :3.00 Median :1.00 Median :2.00
## Mean :3.63 Mean :5.36 Mean :3.41 Mean :1.75 Mean :1.94
## 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :8.00 Max. :8.00 Max. :8.00 Max. :7.00 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI fatHEI
## Min. :11.81 Min. :-62.11 Min. : 130.0 Min. :0 Min. :1.820
## 1st Qu.:21.08 1st Qu.: 21.12 1st Qu.: 260.0 1st Qu.:0 1st Qu.:3.200
## Median :24.44 Median : 37.50 Median : 260.0 Median :5 Median :4.640
## Mean :25.20 Mean : 32.77 Mean : 386.1 Mean :3 Mean :4.008
## 3rd Qu.:27.18 3rd Qu.: 51.61 3rd Qu.: 442.0 3rd Qu.:5 3rd Qu.:4.640
## Max. :55.78 Max. : 85.00 Max. :1196.0 Max. :5 Max. :6.560
##
## SuFatHEI sHEI BAS uncontrolled
## Min. : 1.820 Min. :21.45 Min. :10.00 Min. : 9.00
## 1st Qu.: 4.280 1st Qu.:41.36 1st Qu.:31.00 1st Qu.:16.00
## Median : 8.200 Median :46.97 Median :37.00 Median :18.00
## Mean : 7.008 Mean :46.24 Mean :36.49 Mean :18.65
## 3rd Qu.: 9.640 3rd Qu.:51.99 3rd Qu.:43.00 3rd Qu.:22.00
## Max. :11.560 Max. :67.06 Max. :50.00 Max. :34.00
##
## restraint emotional EF EOE
## Min. : 6.00 Min. : 6.00 Min. : 3.00 Min. : 4.00
## 1st Qu.:11.00 1st Qu.: 8.75 1st Qu.:12.00 1st Qu.: 6.00
## Median :14.00 Median :12.00 Median :13.00 Median : 9.00
## Mean :13.90 Mean :12.28 Mean :12.64 Mean : 9.94
## 3rd Qu.:16.25 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:13.00
## Max. :24.00 Max. :24.00 Max. :15.00 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. : 4.00
## 1st Qu.: 9.00 1st Qu.: 6.75 1st Qu.: 9.00 1st Qu.: 8.00
## Median :12.00 Median :10.00 Median :10.00 Median :11.00
## Mean :12.22 Mean : 9.95 Mean : 9.98 Mean :11.39
## 3rd Qu.:15.25 3rd Qu.:12.25 3rd Qu.:11.00 3rd Qu.:15.00
## Max. :20.00 Max. :21.00 Max. :15.00 Max. :20.00
##
## H SR emo
## Min. : 8.00 Min. : 4.00 Min. :10.00
## 1st Qu.:13.00 1st Qu.: 8.75 1st Qu.:15.75
## Median :16.00 Median :11.00 Median :21.50
## Mean :15.53 Mean :10.93 Mean :22.22
## 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:29.00
## Max. :24.00 Max. :19.00 Max. :42.00
## EB.df <- EB.df %>%
mutate(EOE.level = case_when(
EOE < 12 ~ "Low",
EOE >= 12 ~ "High"))
EB.df %>%
group_by(EOE.level) %>%
count()## # A tibble: 2 × 2
## # Groups: EOE.level [2]
## EOE.level n
## <chr> <int>
## 1 High 40
## 2 Low 60EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = EOE.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
## Emotional overeating: females only
EB.df %>%
summary()## ID age height weight
## Length:100 Min. :18.00 Min. :53.00 Min. : 71.0
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126.8
## Mode :character Median :24.00 Median :65.00 Median :143.0
## Mean :28.04 Mean :65.32 Mean :152.7
## 3rd Qu.:30.00 3rd Qu.:68.00 3rd Qu.:175.0
## Max. :68.00 Max. :76.00 Max. :320.0
##
## sex sex.other race race.other ethnicity
## Min. :1.00 Min. : NA Min. :1.000 Min. : NA Min. :1.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.00
## Median :2.00 Median : NA Median :4.000 Median : NA Median :2.00
## Mean :1.72 Mean :NaN Mean :4.232 Mean :NaN Mean :1.81
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.00
## Max. :2.00 Max. : NA Max. :7.000 Max. : NA Max. :2.00
## NA's :100 NA's :1 NA's :100
## us.born birthplace agetous timeinus education
## Min. :1.00 Min. : NA Min. :1.00 Min. :1.00 Min. : 5.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 6.00
## Median :1.00 Median : NA Median :1.00 Median :1.00 Median : 9.00
## Mean :1.35 Mean :NaN Mean :2.46 Mean :2.33 Mean : 8.15
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:4.25 3rd Qu.:3.25 3rd Qu.: 9.00
## Max. :2.00 Max. : NA Max. :7.00 Max. :8.00 Max. :12.00
## NA's :100
## income.personal income.household exercise ssb.day sugars.amount
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:4.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.00
## Median :3.00 Median :5.50 Median :3.00 Median :1.00 Median :2.00
## Mean :3.63 Mean :5.36 Mean :3.41 Mean :1.75 Mean :1.94
## 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :8.00 Max. :8.00 Max. :8.00 Max. :7.00 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI fatHEI
## Min. :11.81 Min. :-62.11 Min. : 130.0 Min. :0 Min. :1.820
## 1st Qu.:21.08 1st Qu.: 21.12 1st Qu.: 260.0 1st Qu.:0 1st Qu.:3.200
## Median :24.44 Median : 37.50 Median : 260.0 Median :5 Median :4.640
## Mean :25.20 Mean : 32.77 Mean : 386.1 Mean :3 Mean :4.008
## 3rd Qu.:27.18 3rd Qu.: 51.61 3rd Qu.: 442.0 3rd Qu.:5 3rd Qu.:4.640
## Max. :55.78 Max. : 85.00 Max. :1196.0 Max. :5 Max. :6.560
##
## SuFatHEI sHEI BAS uncontrolled
## Min. : 1.820 Min. :21.45 Min. :10.00 Min. : 9.00
## 1st Qu.: 4.280 1st Qu.:41.36 1st Qu.:31.00 1st Qu.:16.00
## Median : 8.200 Median :46.97 Median :37.00 Median :18.00
## Mean : 7.008 Mean :46.24 Mean :36.49 Mean :18.65
## 3rd Qu.: 9.640 3rd Qu.:51.99 3rd Qu.:43.00 3rd Qu.:22.00
## Max. :11.560 Max. :67.06 Max. :50.00 Max. :34.00
##
## restraint emotional EF EOE
## Min. : 6.00 Min. : 6.00 Min. : 3.00 Min. : 4.00
## 1st Qu.:11.00 1st Qu.: 8.75 1st Qu.:12.00 1st Qu.: 6.00
## Median :14.00 Median :12.00 Median :13.00 Median : 9.00
## Mean :13.90 Mean :12.28 Mean :12.64 Mean : 9.94
## 3rd Qu.:16.25 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:13.00
## Max. :24.00 Max. :24.00 Max. :15.00 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. : 4.00
## 1st Qu.: 9.00 1st Qu.: 6.75 1st Qu.: 9.00 1st Qu.: 8.00
## Median :12.00 Median :10.00 Median :10.00 Median :11.00
## Mean :12.22 Mean : 9.95 Mean : 9.98 Mean :11.39
## 3rd Qu.:15.25 3rd Qu.:12.25 3rd Qu.:11.00 3rd Qu.:15.00
## Max. :20.00 Max. :21.00 Max. :15.00 Max. :20.00
##
## H SR emo EOE.level
## Min. : 8.00 Min. : 4.00 Min. :10.00 Length:100
## 1st Qu.:13.00 1st Qu.: 8.75 1st Qu.:15.75 Class :character
## Median :16.00 Median :11.00 Median :21.50 Mode :character
## Mean :15.53 Mean :10.93 Mean :22.22
## 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:29.00
## Max. :24.00 Max. :19.00 Max. :42.00
## EB.fem.df <- EB.fem.df %>%
mutate(EOE.level = case_when(
EOE <= 9 ~ "Low",
EOE > 9 ~ "High"))
EB.fem.df %>%
group_by(EOE.level) %>%
count()## # A tibble: 2 × 2
## # Groups: EOE.level [2]
## EOE.level n
## <chr> <int>
## 1 High 42
## 2 Low 30EB.fem.df %>% ggplot(aes(sweet.liking, sugar.intake, color = EOE.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
TFEQ emotional analysis
## TFEQ Emotional
EB.df %>%
summary()## ID age height weight
## Length:100 Min. :18.00 Min. :53.00 Min. : 71.0
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126.8
## Mode :character Median :24.00 Median :65.00 Median :143.0
## Mean :28.04 Mean :65.32 Mean :152.7
## 3rd Qu.:30.00 3rd Qu.:68.00 3rd Qu.:175.0
## Max. :68.00 Max. :76.00 Max. :320.0
##
## sex sex.other race race.other ethnicity
## Min. :1.00 Min. : NA Min. :1.000 Min. : NA Min. :1.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.00
## Median :2.00 Median : NA Median :4.000 Median : NA Median :2.00
## Mean :1.72 Mean :NaN Mean :4.232 Mean :NaN Mean :1.81
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.00
## Max. :2.00 Max. : NA Max. :7.000 Max. : NA Max. :2.00
## NA's :100 NA's :1 NA's :100
## us.born birthplace agetous timeinus education
## Min. :1.00 Min. : NA Min. :1.00 Min. :1.00 Min. : 5.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 6.00
## Median :1.00 Median : NA Median :1.00 Median :1.00 Median : 9.00
## Mean :1.35 Mean :NaN Mean :2.46 Mean :2.33 Mean : 8.15
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:4.25 3rd Qu.:3.25 3rd Qu.: 9.00
## Max. :2.00 Max. : NA Max. :7.00 Max. :8.00 Max. :12.00
## NA's :100
## income.personal income.household exercise ssb.day sugars.amount
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:4.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.00
## Median :3.00 Median :5.50 Median :3.00 Median :1.00 Median :2.00
## Mean :3.63 Mean :5.36 Mean :3.41 Mean :1.75 Mean :1.94
## 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :8.00 Max. :8.00 Max. :8.00 Max. :7.00 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI fatHEI
## Min. :11.81 Min. :-62.11 Min. : 130.0 Min. :0 Min. :1.820
## 1st Qu.:21.08 1st Qu.: 21.12 1st Qu.: 260.0 1st Qu.:0 1st Qu.:3.200
## Median :24.44 Median : 37.50 Median : 260.0 Median :5 Median :4.640
## Mean :25.20 Mean : 32.77 Mean : 386.1 Mean :3 Mean :4.008
## 3rd Qu.:27.18 3rd Qu.: 51.61 3rd Qu.: 442.0 3rd Qu.:5 3rd Qu.:4.640
## Max. :55.78 Max. : 85.00 Max. :1196.0 Max. :5 Max. :6.560
##
## SuFatHEI sHEI BAS uncontrolled
## Min. : 1.820 Min. :21.45 Min. :10.00 Min. : 9.00
## 1st Qu.: 4.280 1st Qu.:41.36 1st Qu.:31.00 1st Qu.:16.00
## Median : 8.200 Median :46.97 Median :37.00 Median :18.00
## Mean : 7.008 Mean :46.24 Mean :36.49 Mean :18.65
## 3rd Qu.: 9.640 3rd Qu.:51.99 3rd Qu.:43.00 3rd Qu.:22.00
## Max. :11.560 Max. :67.06 Max. :50.00 Max. :34.00
##
## restraint emotional EF EOE
## Min. : 6.00 Min. : 6.00 Min. : 3.00 Min. : 4.00
## 1st Qu.:11.00 1st Qu.: 8.75 1st Qu.:12.00 1st Qu.: 6.00
## Median :14.00 Median :12.00 Median :13.00 Median : 9.00
## Mean :13.90 Mean :12.28 Mean :12.64 Mean : 9.94
## 3rd Qu.:16.25 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:13.00
## Max. :24.00 Max. :24.00 Max. :15.00 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. : 4.00
## 1st Qu.: 9.00 1st Qu.: 6.75 1st Qu.: 9.00 1st Qu.: 8.00
## Median :12.00 Median :10.00 Median :10.00 Median :11.00
## Mean :12.22 Mean : 9.95 Mean : 9.98 Mean :11.39
## 3rd Qu.:15.25 3rd Qu.:12.25 3rd Qu.:11.00 3rd Qu.:15.00
## Max. :20.00 Max. :21.00 Max. :15.00 Max. :20.00
##
## H SR emo EOE.level
## Min. : 8.00 Min. : 4.00 Min. :10.00 Length:100
## 1st Qu.:13.00 1st Qu.: 8.75 1st Qu.:15.75 Class :character
## Median :16.00 Median :11.00 Median :21.50 Mode :character
## Mean :15.53 Mean :10.93 Mean :22.22
## 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:29.00
## Max. :24.00 Max. :19.00 Max. :42.00
## EB.df <- EB.df %>%
mutate(emo.level = case_when(
emotional <= 12 ~ "Low",
emotional > 12 ~ "High"))
EB.df %>%
group_by(emo.level) %>%
count()## # A tibble: 2 × 2
## # Groups: emo.level [2]
## emo.level n
## <chr> <int>
## 1 High 47
## 2 Low 53EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = emo.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
## TFEQ Emotional: females only
EB.df %>%
summary()## ID age height weight
## Length:100 Min. :18.00 Min. :53.00 Min. : 71.0
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126.8
## Mode :character Median :24.00 Median :65.00 Median :143.0
## Mean :28.04 Mean :65.32 Mean :152.7
## 3rd Qu.:30.00 3rd Qu.:68.00 3rd Qu.:175.0
## Max. :68.00 Max. :76.00 Max. :320.0
##
## sex sex.other race race.other ethnicity
## Min. :1.00 Min. : NA Min. :1.000 Min. : NA Min. :1.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.00
## Median :2.00 Median : NA Median :4.000 Median : NA Median :2.00
## Mean :1.72 Mean :NaN Mean :4.232 Mean :NaN Mean :1.81
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.00
## Max. :2.00 Max. : NA Max. :7.000 Max. : NA Max. :2.00
## NA's :100 NA's :1 NA's :100
## us.born birthplace agetous timeinus education
## Min. :1.00 Min. : NA Min. :1.00 Min. :1.00 Min. : 5.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 6.00
## Median :1.00 Median : NA Median :1.00 Median :1.00 Median : 9.00
## Mean :1.35 Mean :NaN Mean :2.46 Mean :2.33 Mean : 8.15
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:4.25 3rd Qu.:3.25 3rd Qu.: 9.00
## Max. :2.00 Max. : NA Max. :7.00 Max. :8.00 Max. :12.00
## NA's :100
## income.personal income.household exercise ssb.day sugars.amount
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:4.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.00
## Median :3.00 Median :5.50 Median :3.00 Median :1.00 Median :2.00
## Mean :3.63 Mean :5.36 Mean :3.41 Mean :1.75 Mean :1.94
## 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :8.00 Max. :8.00 Max. :8.00 Max. :7.00 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI fatHEI
## Min. :11.81 Min. :-62.11 Min. : 130.0 Min. :0 Min. :1.820
## 1st Qu.:21.08 1st Qu.: 21.12 1st Qu.: 260.0 1st Qu.:0 1st Qu.:3.200
## Median :24.44 Median : 37.50 Median : 260.0 Median :5 Median :4.640
## Mean :25.20 Mean : 32.77 Mean : 386.1 Mean :3 Mean :4.008
## 3rd Qu.:27.18 3rd Qu.: 51.61 3rd Qu.: 442.0 3rd Qu.:5 3rd Qu.:4.640
## Max. :55.78 Max. : 85.00 Max. :1196.0 Max. :5 Max. :6.560
##
## SuFatHEI sHEI BAS uncontrolled
## Min. : 1.820 Min. :21.45 Min. :10.00 Min. : 9.00
## 1st Qu.: 4.280 1st Qu.:41.36 1st Qu.:31.00 1st Qu.:16.00
## Median : 8.200 Median :46.97 Median :37.00 Median :18.00
## Mean : 7.008 Mean :46.24 Mean :36.49 Mean :18.65
## 3rd Qu.: 9.640 3rd Qu.:51.99 3rd Qu.:43.00 3rd Qu.:22.00
## Max. :11.560 Max. :67.06 Max. :50.00 Max. :34.00
##
## restraint emotional EF EOE
## Min. : 6.00 Min. : 6.00 Min. : 3.00 Min. : 4.00
## 1st Qu.:11.00 1st Qu.: 8.75 1st Qu.:12.00 1st Qu.: 6.00
## Median :14.00 Median :12.00 Median :13.00 Median : 9.00
## Mean :13.90 Mean :12.28 Mean :12.64 Mean : 9.94
## 3rd Qu.:16.25 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:13.00
## Max. :24.00 Max. :24.00 Max. :15.00 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. : 4.00
## 1st Qu.: 9.00 1st Qu.: 6.75 1st Qu.: 9.00 1st Qu.: 8.00
## Median :12.00 Median :10.00 Median :10.00 Median :11.00
## Mean :12.22 Mean : 9.95 Mean : 9.98 Mean :11.39
## 3rd Qu.:15.25 3rd Qu.:12.25 3rd Qu.:11.00 3rd Qu.:15.00
## Max. :20.00 Max. :21.00 Max. :15.00 Max. :20.00
##
## H SR emo EOE.level
## Min. : 8.00 Min. : 4.00 Min. :10.00 Length:100
## 1st Qu.:13.00 1st Qu.: 8.75 1st Qu.:15.75 Class :character
## Median :16.00 Median :11.00 Median :21.50 Mode :character
## Mean :15.53 Mean :10.93 Mean :22.22
## 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:29.00
## Max. :24.00 Max. :19.00 Max. :42.00
##
## emo.level
## Length:100
## Class :character
## Mode :character
##
##
##
## EB.fem.df <- EB.fem.df %>%
mutate(emo.level = case_when(
emotional <= 12 ~ "Low",
emotional > 12 ~ "High"))
EB.fem.df %>%
group_by(emo.level) %>%
count()## # A tibble: 2 × 2
## # Groups: emo.level [2]
## emo.level n
## <chr> <int>
## 1 High 40
## 2 Low 32EB.fem.df %>% ggplot(aes(sweet.liking, sugar.intake, color = emo.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
Uncontrolled eating analysis
## Emotional overeating
EB.df %>%
summary()## ID age height weight
## Length:100 Min. :18.00 Min. :53.00 Min. : 71.0
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126.8
## Mode :character Median :24.00 Median :65.00 Median :143.0
## Mean :28.04 Mean :65.32 Mean :152.7
## 3rd Qu.:30.00 3rd Qu.:68.00 3rd Qu.:175.0
## Max. :68.00 Max. :76.00 Max. :320.0
##
## sex sex.other race race.other ethnicity
## Min. :1.00 Min. : NA Min. :1.000 Min. : NA Min. :1.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.00
## Median :2.00 Median : NA Median :4.000 Median : NA Median :2.00
## Mean :1.72 Mean :NaN Mean :4.232 Mean :NaN Mean :1.81
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.00
## Max. :2.00 Max. : NA Max. :7.000 Max. : NA Max. :2.00
## NA's :100 NA's :1 NA's :100
## us.born birthplace agetous timeinus education
## Min. :1.00 Min. : NA Min. :1.00 Min. :1.00 Min. : 5.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 6.00
## Median :1.00 Median : NA Median :1.00 Median :1.00 Median : 9.00
## Mean :1.35 Mean :NaN Mean :2.46 Mean :2.33 Mean : 8.15
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:4.25 3rd Qu.:3.25 3rd Qu.: 9.00
## Max. :2.00 Max. : NA Max. :7.00 Max. :8.00 Max. :12.00
## NA's :100
## income.personal income.household exercise ssb.day sugars.amount
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:4.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.00
## Median :3.00 Median :5.50 Median :3.00 Median :1.00 Median :2.00
## Mean :3.63 Mean :5.36 Mean :3.41 Mean :1.75 Mean :1.94
## 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :8.00 Max. :8.00 Max. :8.00 Max. :7.00 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI fatHEI
## Min. :11.81 Min. :-62.11 Min. : 130.0 Min. :0 Min. :1.820
## 1st Qu.:21.08 1st Qu.: 21.12 1st Qu.: 260.0 1st Qu.:0 1st Qu.:3.200
## Median :24.44 Median : 37.50 Median : 260.0 Median :5 Median :4.640
## Mean :25.20 Mean : 32.77 Mean : 386.1 Mean :3 Mean :4.008
## 3rd Qu.:27.18 3rd Qu.: 51.61 3rd Qu.: 442.0 3rd Qu.:5 3rd Qu.:4.640
## Max. :55.78 Max. : 85.00 Max. :1196.0 Max. :5 Max. :6.560
##
## SuFatHEI sHEI BAS uncontrolled
## Min. : 1.820 Min. :21.45 Min. :10.00 Min. : 9.00
## 1st Qu.: 4.280 1st Qu.:41.36 1st Qu.:31.00 1st Qu.:16.00
## Median : 8.200 Median :46.97 Median :37.00 Median :18.00
## Mean : 7.008 Mean :46.24 Mean :36.49 Mean :18.65
## 3rd Qu.: 9.640 3rd Qu.:51.99 3rd Qu.:43.00 3rd Qu.:22.00
## Max. :11.560 Max. :67.06 Max. :50.00 Max. :34.00
##
## restraint emotional EF EOE
## Min. : 6.00 Min. : 6.00 Min. : 3.00 Min. : 4.00
## 1st Qu.:11.00 1st Qu.: 8.75 1st Qu.:12.00 1st Qu.: 6.00
## Median :14.00 Median :12.00 Median :13.00 Median : 9.00
## Mean :13.90 Mean :12.28 Mean :12.64 Mean : 9.94
## 3rd Qu.:16.25 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:13.00
## Max. :24.00 Max. :24.00 Max. :15.00 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. : 4.00
## 1st Qu.: 9.00 1st Qu.: 6.75 1st Qu.: 9.00 1st Qu.: 8.00
## Median :12.00 Median :10.00 Median :10.00 Median :11.00
## Mean :12.22 Mean : 9.95 Mean : 9.98 Mean :11.39
## 3rd Qu.:15.25 3rd Qu.:12.25 3rd Qu.:11.00 3rd Qu.:15.00
## Max. :20.00 Max. :21.00 Max. :15.00 Max. :20.00
##
## H SR emo EOE.level
## Min. : 8.00 Min. : 4.00 Min. :10.00 Length:100
## 1st Qu.:13.00 1st Qu.: 8.75 1st Qu.:15.75 Class :character
## Median :16.00 Median :11.00 Median :21.50 Mode :character
## Mean :15.53 Mean :10.93 Mean :22.22
## 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:29.00
## Max. :24.00 Max. :19.00 Max. :42.00
##
## emo.level
## Length:100
## Class :character
## Mode :character
##
##
##
## EB.df <- EB.df %>%
mutate(uncontrolled.level = case_when(
uncontrolled < 18 ~ "Low",
uncontrolled >= 18 ~ "High"))
EB.df %>%
group_by(uncontrolled.level) %>%
count()## # A tibble: 2 × 2
## # Groups: uncontrolled.level [2]
## uncontrolled.level n
## <chr> <int>
## 1 High 58
## 2 Low 42EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = uncontrolled.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'
Restraint analysis
## Emotional overeating
EB.df %>%
summary()## ID age height weight
## Length:100 Min. :18.00 Min. :53.00 Min. : 71.0
## Class :character 1st Qu.:21.00 1st Qu.:63.00 1st Qu.:126.8
## Mode :character Median :24.00 Median :65.00 Median :143.0
## Mean :28.04 Mean :65.32 Mean :152.7
## 3rd Qu.:30.00 3rd Qu.:68.00 3rd Qu.:175.0
## Max. :68.00 Max. :76.00 Max. :320.0
##
## sex sex.other race race.other ethnicity
## Min. :1.00 Min. : NA Min. :1.000 Min. : NA Min. :1.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:3.000 1st Qu.: NA 1st Qu.:2.00
## Median :2.00 Median : NA Median :4.000 Median : NA Median :2.00
## Mean :1.72 Mean :NaN Mean :4.232 Mean :NaN Mean :1.81
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:5.000 3rd Qu.: NA 3rd Qu.:2.00
## Max. :2.00 Max. : NA Max. :7.000 Max. : NA Max. :2.00
## NA's :100 NA's :1 NA's :100
## us.born birthplace agetous timeinus education
## Min. :1.00 Min. : NA Min. :1.00 Min. :1.00 Min. : 5.00
## 1st Qu.:1.00 1st Qu.: NA 1st Qu.:1.00 1st Qu.:1.00 1st Qu.: 6.00
## Median :1.00 Median : NA Median :1.00 Median :1.00 Median : 9.00
## Mean :1.35 Mean :NaN Mean :2.46 Mean :2.33 Mean : 8.15
## 3rd Qu.:2.00 3rd Qu.: NA 3rd Qu.:4.25 3rd Qu.:3.25 3rd Qu.: 9.00
## Max. :2.00 Max. : NA Max. :7.00 Max. :8.00 Max. :12.00
## NA's :100
## income.personal income.household exercise ssb.day sugars.amount
## Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00 Min. :1.00
## 1st Qu.:2.00 1st Qu.:4.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:2.00
## Median :3.00 Median :5.50 Median :3.00 Median :1.00 Median :2.00
## Mean :3.63 Mean :5.36 Mean :3.41 Mean :1.75 Mean :1.94
## 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:5.00 3rd Qu.:2.00 3rd Qu.:2.00
## Max. :8.00 Max. :8.00 Max. :8.00 Max. :7.00 Max. :3.00
##
## BMI sweet.liking sugar.intake sugarHEI fatHEI
## Min. :11.81 Min. :-62.11 Min. : 130.0 Min. :0 Min. :1.820
## 1st Qu.:21.08 1st Qu.: 21.12 1st Qu.: 260.0 1st Qu.:0 1st Qu.:3.200
## Median :24.44 Median : 37.50 Median : 260.0 Median :5 Median :4.640
## Mean :25.20 Mean : 32.77 Mean : 386.1 Mean :3 Mean :4.008
## 3rd Qu.:27.18 3rd Qu.: 51.61 3rd Qu.: 442.0 3rd Qu.:5 3rd Qu.:4.640
## Max. :55.78 Max. : 85.00 Max. :1196.0 Max. :5 Max. :6.560
##
## SuFatHEI sHEI BAS uncontrolled
## Min. : 1.820 Min. :21.45 Min. :10.00 Min. : 9.00
## 1st Qu.: 4.280 1st Qu.:41.36 1st Qu.:31.00 1st Qu.:16.00
## Median : 8.200 Median :46.97 Median :37.00 Median :18.00
## Mean : 7.008 Mean :46.24 Mean :36.49 Mean :18.65
## 3rd Qu.: 9.640 3rd Qu.:51.99 3rd Qu.:43.00 3rd Qu.:22.00
## Max. :11.560 Max. :67.06 Max. :50.00 Max. :34.00
##
## restraint emotional EF EOE
## Min. : 6.00 Min. : 6.00 Min. : 3.00 Min. : 4.00
## 1st Qu.:11.00 1st Qu.: 8.75 1st Qu.:12.00 1st Qu.: 6.00
## Median :14.00 Median :12.00 Median :13.00 Median : 9.00
## Mean :13.90 Mean :12.28 Mean :12.64 Mean : 9.94
## 3rd Qu.:16.25 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:13.00
## Max. :24.00 Max. :24.00 Max. :15.00 Max. :19.00
##
## EUE FF FR SE
## Min. : 4.00 Min. : 5.00 Min. : 4.00 Min. : 4.00
## 1st Qu.: 9.00 1st Qu.: 6.75 1st Qu.: 9.00 1st Qu.: 8.00
## Median :12.00 Median :10.00 Median :10.00 Median :11.00
## Mean :12.22 Mean : 9.95 Mean : 9.98 Mean :11.39
## 3rd Qu.:15.25 3rd Qu.:12.25 3rd Qu.:11.00 3rd Qu.:15.00
## Max. :20.00 Max. :21.00 Max. :15.00 Max. :20.00
##
## H SR emo EOE.level
## Min. : 8.00 Min. : 4.00 Min. :10.00 Length:100
## 1st Qu.:13.00 1st Qu.: 8.75 1st Qu.:15.75 Class :character
## Median :16.00 Median :11.00 Median :21.50 Mode :character
## Mean :15.53 Mean :10.93 Mean :22.22
## 3rd Qu.:18.00 3rd Qu.:13.00 3rd Qu.:29.00
## Max. :24.00 Max. :19.00 Max. :42.00
##
## emo.level uncontrolled.level
## Length:100 Length:100
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## EB.df <- EB.df %>%
mutate(restraint.level = case_when(
restraint < 14 ~ "Low",
restraint >= 14 ~ "High"))
EB.df %>%
group_by(restraint.level) %>%
count()## # A tibble: 2 × 2
## # Groups: restraint.level [2]
## restraint.level n
## <chr> <int>
## 1 High 55
## 2 Low 45EB.df %>% ggplot(aes(sweet.liking, sugar.intake, color = restraint.level)) +
geom_point() +
geom_smooth(method = lm, se = FALSE)+
stat_cor(method = "pearson") +
theme_light() ## `geom_smooth()` using formula = 'y ~ x'