| Type: | Package |
| Title: | Food Quotient and Nutrient Analysis for HSFFQ |
| Version: | 0.1.1 |
| Description: | Aids in analysing data from a food frequency questionnaire known as the Harvard Service Food Frequency Questionnaire (HSFFQ). Functions from this package use answers from the HSFFQ to generate estimates of daily consumed micronutrients, calories, macronutrients on an individual level. The package also calculates food quotients on individual and group levels. Foodquotient calculation is an often tedious step in the calculation of total human energy expenditure (TEE) using the doubly labeled water method, which is the gold standard for measuring TEE. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Depends: | R (≥ 2.10) |
| URL: | <https://naldc.nal.usda.gov/catalog/32818> |
| NeedsCompilation: | no |
| Packaged: | 2023-08-22 22:39:06 UTC; katepogue |
| Author: | Kate Pogue [aut, cre] |
| Maintainer: | Kate Pogue <katepogue222@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-08-22 23:50:02 UTC |
Frequency Factors for American Children with Age of Participant
Description
A small set of food frequency questionnaire data including 32 children living in the United States. f1:f85 represents the frequency with which participants consumed 85 respective foods. Numbers 1-9 correspond to the following: 1: never 2: 1-3 times per month 3: once per week 4: 2-4 times per week 5: 5-6 times per week 6: 1 per day 7: 2-3 times per day 8: 4-5 times per day 9: 6 times per day
Usage
age_freq
Format
## 'age_freq' A data frame with 32 rows and 86 columns:
- a
age of participant
- f1
milk frequency factor
- f2
hot chocolate frequency factor
- f3
cheese frequency factor
- f4
yogurt frequency frequency factor
- f5
ice cream frequency frequency factor
- f6
pudding frequency factor
- f7
orange juice frequency factor
- f8
other juice frequency factor
- f9
fruit drink frequency factor
- f10
banana frequency factor
- f11
peaches frequency factor
- f12
mixed fruit frequency factor
- f13
orange frequency factor
- f14
apple and pear frequency factor
- f15
applesauce frequency factor
- f16
grapes frequency factor
- f17
strawberries frequency factor
- f18
melon frequency factor
- f19
pineapple frequency factor
- f20
raisins frequency factor
- f21
corn frequency factor
- f22
peas frequency factor
- f23
tomato frequency factor
- f24
peppers frequency factor
- f25
carrot frequency factor
- f26
broccoli frequency factor
- f27
green beans frequency factor
- f28
spinach frequency factor
- f29
greens frequency factor
- f30
mixed vegetable frequency factor
- f31
squash frequency factor
- f32
zucchini frequency factor
- f33
fried potatoes frequency factor
- f34
other potatoes frequency factor
- f35
sweet potatoes frequency factor
- f36
cabbage frequency factor
- f37
lettuce frequency factor
- f38
mayonnaise frequency factor
- f39
chips frequency factor
- f40
popcorn frequency factor
- f41
crackers frequency factor
- f42
nuts frequency factor
- f43
cookies frequency factor
- f44
cake frequency factor
- f45
pie frequency factor
- f46
jello frequency factor
- f47
chocolate frequency factor
- f48
candy frequency factor
- f49
coffee frequency factor
- f50
soda frequency factor
- f51
sugarfree soda frequency factor
- f52
beans frequency factor
- f53
rice frequency factor
- f54
pasta frequency factor
- f55
pizza frequency factor
- f56
tacos frequency factor
- f57
mac and cheese frequency factor
- f58
hot dogs frequency factor
- f59
sausage frequency factor
- f60
hamburger frequency factor
- f61
tuna frequency factor
- f62
fried fish frequency factor
- f63
other fish frequency factor
- f64
cold cuts frequency factor
- f65
chicken nuggets frequency factor
- f66
other chicken frequency factor
- f67
pork frequency factor
- f68
beef frequency factor
- f69
organ meats frequency factor
- f70
peanut butter frequency factor
- f71
bread frequency factor
- f72
butter frequency factor
- f73
margarine frequency factor
- f74
vegetabele soup frequency factor
- f75
soup frequency factor
- f76
tortilla frequency factor
- f77
eggs frequency factor
- f78
bacon frequency factor
- f79
hot cereal frequency factor
- f80
cold cereal frequency factor
- f81
donuts frequency factor
- f82
muffins frequency factor
- f83
pancake frequency factor
- f84
bagel frequency factor
- f85
biscuit frequency factor
Source
<Baylor Human Evolutionary Biology and Health Lab>
Frequency Factor
Description
The Frequency Factor function converts values 1-9, representing different frequency factor responses from the hsffq, to average daily servings consumed for that individual.
Usage
fq(f)
Arguments
f |
1-9, representing different frequency factor responses from the hsffq. These can be in a dataframe, vector, or just single values |
Value
a dataframe, vector, or single value of the same dimension as the input, with each position holding the average daily servings consumed for each food (columns) for each individual(rows).
Examples
test <- c(1, 5, 7, 3, 9, 2, 4, 3, 6, 8)
fq(test)
rquestionnaire <- function(n, n_food_questions = 85) {
mat <- matrix(
sample(1:9, n_food_questions*n, replace = TRUE),
nrow = n, ncol = n_food_questions
)
df <- data.frame( age = round(runif(n, 2, 11), digits = 1) )
cbind(df, as.data.frame(mat))
}
df <- rquestionnaire(6)
fq(df)
Frequency Factors for American Children
Description
A small set of data including 32 children living in the United States. f1:f85 represents the frequency with which participants consumed 85 respective foods. Numbers 1-9 correspond to the following: 1: never 2: 1-3 times per month 3: once per week 4: 2-4 times per week 5: 5-6 times per week 6: 1 per day 7: 2-3 times per day 8: 4-5 times per day 9: 6 times per day
Usage
freq
Format
## 'freq' A data frame with 32 rows and 85 columns:
- f1
milk frequency factor
- f2
hot chocolate frequency factor
- f3
cheese frequency factor
- f4
yogurt frequency frequency factor
- f5
ice cream frequency frequency factor
- f6
pudding frequency factor
- f7
orange juice frequency factor
- f8
other juice frequency factor
- f9
fruit drink frequency factor
- f10
banana frequency factor
- f11
peaches frequency factor
- f12
mixed fruit frequency factor
- f13
orange frequency factor
- f14
apple and pear frequency factor
- f15
applesauce frequency factor
- f16
grapes frequency factor
- f17
strawberries frequency factor
- f18
melon frequency factor
- f19
pineapple frequency factor
- f20
raisins frequency factor
- f21
corn frequency factor
- f22
peas frequency factor
- f23
tomato frequency factor
- f24
peppers frequency factor
- f25
carrot frequency factor
- f26
broccoli frequency factor
- f27
green beans frequency factor
- f28
spinach frequency factor
- f29
greens frequency factor
- f30
mixed vegetable frequency factor
- f31
squash frequency factor
- f32
zucchini frequency factor
- f33
fried potatoes frequency factor
- f34
other potatoes frequency factor
- f35
sweet potatoes frequency factor
- f36
cabbage frequency factor
- f37
lettuce frequency factor
- f38
mayonnaise frequency factor
- f39
chips frequency factor
- f40
popcorn frequency factor
- f41
crackers frequency factor
- f42
nuts frequency factor
- f43
cookies frequency factor
- f44
cake frequency factor
- f45
pie frequency factor
- f46
jello frequency factor
- f47
chocolate frequency factor
- f48
candy frequency factor
- f49
coffee frequency factor
- f50
soda frequency factor
- f51
sugarfree soda frequency factor
- f52
beans frequency factor
- f53
rice frequency factor
- f54
pasta frequency factor
- f55
pizza frequency factor
- f56
tacos frequency factor
- f57
mac and cheese frequency factor
- f58
hot dogs frequency factor
- f59
sausage frequency factor
- f60
hamburger frequency factor
- f61
tuna frequency factor
- f62
fried fish frequency factor
- f63
other fish frequency factor
- f64
cold cuts frequency factor
- f65
chicken nuggets frequency factor
- f66
other chicken frequency factor
- f67
pork frequency factor
- f68
beef frequency factor
- f69
organ meats frequency factor
- f70
peanut butter frequency factor
- f71
bread frequency factor
- f72
butter frequency factor
- f73
margarine frequency factor
- f74
vegetabele soup frequency factor
- f75
soup frequency factor
- f76
tortilla frequency factor
- f77
eggs frequency factor
- f78
bacon frequency factor
- f79
hot cereal frequency factor
- f80
cold cereal frequency factor
- f81
donuts frequency factor
- f82
muffins frequency factor
- f83
pancake frequency factor
- f84
bagel frequency factor
- f85
biscuit frequency factor
Source
<Baylor Human Evolutionary Biology Lab>
Grams
Description
The grams function takes the age of a participant and their responses on the hsffq to generate an estimate of the participant's total daily grams consumed for each food.
Usage
grams(row)
Arguments
row |
A numeric vector with components 'age', representing the age of the participant, and 'f1' to 'f85', representing different frequency factor responses from the hsffq. |
Value
A numeric vector of length 85, representing the estimated total daily grams of each food consumed for the participant.
Examples
random_integers <- sample(1:9, 85, replace=TRUE)
vec <- c(6.2, random_integers)
grams(vec)
rquestionnaire <- function(n, n_food_questions = 85) {
mat <- matrix(
sample(1:9, n_food_questions*n, replace = TRUE),
nrow = n, ncol = n_food_questions
)
df <- data.frame( age = round(runif(n, 2, 11), digits = 1) )
cbind(df, as.data.frame(mat))
}
df <- rquestionnaire(2)
df_results <- data.frame()
for (i in 1:nrow(df)) {
result <- grams(df[i,])
df_results <- rbind(df_results, result)
}
Harvard Foood Frequency Questionnaire Nutrition Information
Description
This dataframe is used internally by the functions of foodquotient and includes portion size information by age for each of the 85 foods included in the HSFFQ from the HSFFQ user's manual. Additionally, nutrient information is included for each of the 85 foods, pulled from the USDA's public search tool.
Usage
hsffq()
Value
A portion size and nutrient information reference data frame.
Food Quotient Based on Macronutrients
Description
The macquotient function calculates a food quotient for a participant based on average daily protein, carbs, and fat consumed for an individual or a group. In contrast to the quotient function, macquotient is able to generate reliable average food quotients for a group of people rather than only individual level. Group level estimates are recomended in some studies to control for response bias.
Usage
macquotient(row)
Arguments
row |
contains three components. p average daily grams of protein consumed f average daily grams of fat consumed c/ average daily grams of carbohydrates consumed |
Value
one value per participant will be returned, representing the food quotient for the individual
Examples
vec <- c(34.5,43, 212.4)
macquotient(vec)
vec1 <- c(34.5,43, 212.4)
vec2 <- c(40.1,52, 240)
df <- rbind(vec1, vec2)
df_results <- data.frame()
for (i in 1:nrow(df)) {
result <- macquotient(df[i,])
df_results <- rbind(df_results, result)
}
Macronutrients
Description
The Macronutrients function takes the age of a participant and their responses on the hsffq to generate estimates of the participant's total daily protein, carbohydrate, and fat consumed for each food.
Usage
macros(row)
Arguments
row |
vector with 86 entries consisting of 2 components f1:f85 1-9, representing different frequency factor responses from the hsffq. These will be stored in columns 2-86 in the row you plug in A value representing participant's age. This will be stored in column 1 of the input row |
Value
the row or dataframe returned will have 3 entries, representing total daily amounts of protein, carbohydrates, and fat for each participant
Examples
random_integers <- sample(1:9, 85, replace=TRUE)
vec <- c(6.2, random_integers)
grams(vec)
rquestionnaire <- function(n, n_food_questions = 85) {
mat <- matrix(
sample(1:9, n_food_questions*n, replace = TRUE),
nrow = n, ncol = n_food_questions
)
df <- data.frame( age = round(runif(n, 2, 11), digits = 1) )
cbind(df, as.data.frame(mat))
}
df <- rquestionnaire(3)
df_results <- data.frame()
for (i in 1:nrow(df)) {
result <- macros(df[i,])
df_results <- rbind(df_results, result)
}
Micronutrients
Description
The Micronutrients function takes the age of a participant and their responses on the hsffq to generate an estimate of the participant's total daily micronutrients consumed for each food.
Usage
micros(row)
Arguments
row |
contains two components. f1:f85 1-9, representing different frequency factor responses from the hsffq. These will be stored in columns 2-86 in the row you plug in . A value representing participant's age. This will be stored in column 1 of the input row |
Value
the row or dataframe returned will have 7 entries, representing total daily amounts of 7 micronutrients for each participant
Examples
random_integers <- sample(1:8, 85, replace=TRUE)
vec <- c(6.2, random_integers)
micros(vec)
rquestionnaire <- function(n, n_food_questions = 85) {
mat <- matrix(
sample(1:9, n_food_questions*n, replace = TRUE),
nrow = n, ncol = n_food_questions
)
df <- data.frame( age = round(runif(n, 2, 11), digits = 1) )
cbind(df, as.data.frame(mat))
}
df <- rquestionnaire(4)
df_results <- data.frame()
for (i in 1:nrow(df)) {
result <- micros(df[i,])
df_results <- rbind(df_results, result)
}
Nutrients
Description
The Nutrients function takes the age of a participant and their responses on the hsffq to generate an estimate of the participant's total daily micronutrients, macronutrients, and calories consumed for each food
Usage
nutrients(row)
Arguments
row |
/contains two components. f1:f85 1-9, representing different frequency factor responses from the hsffq. These will be stored in columns 2-86 in the row you plug in A value representing participant's age. This will be stored in column 1 of the input row |
Value
the row or dataframe returned will have 11 entries, representing total daily amounts of 7 micronutrients, 3 macronutrients, and calories for each participant. These columns will be labeled
Examples
random_integers <- sample(1:8, 85, replace=TRUE)
vec <- c(6.2, random_integers)
nutrients(vec)
rquestionnaire <- function(n, n_food_questions = 85) {
mat <- matrix(
sample(1:9, n_food_questions*n, replace = TRUE),
nrow = n, ncol = n_food_questions
)
df <- data.frame( age = round(runif(n, 2, 11), digits = 1) )
cbind(df, as.data.frame(mat))
}
df <- rquestionnaire(5)
df_results <- data.frame()
for (i in 1:nrow(df)) {
result <- nutrients(df[i,])
df_results <- rbind(df_results, result)
}
Food quotient based on hsffq results
Description
The quotient function calculates individual level food quotients based on the individual's answers to the hsffq. This function is only recommended to calculate at the individual level.
Usage
quotient(row)
Arguments
row |
contains two components. f1:f85 1-9, representing different frequency factor responses from the hsffq. These will be stored in columns 2-86 in the row you plug in A value representing participant's age. This will be stored in column 1 of the input row/ |
Value
one value per participant will be returned, representing the food quotient for the individual
Examples
random_integers <- sample(1:8, 85, replace=TRUE)
vec <- c(6.2, random_integers)
quotient(vec)
rquestionnaire <- function(n, n_food_questions = 85) {
mat <- matrix(
sample(1:9, n_food_questions*n, replace = TRUE),
nrow = n, ncol = n_food_questions
)
df <- data.frame( age = round(runif(n, 2, 11), digits = 1) )
cbind(df, as.data.frame(mat))
}
df <- rquestionnaire(6)
df_results <- data.frame()
for (i in 1:nrow(df)) {
result <- quotient(df[i,])
df_results <- rbind(df_results, result)
}