NutrienTrackeR
Andrea Rodriguez Martinez, Rafael Ayala, Yacine Debbabi, Lara Selles Vidal
Jun 16, 2018
Introduction
Motivation: Diet and nutrition play a key role in the development, prevention and treatment of noncommunicable diseases (NCD), such as type 2 diabetes, obesity or cardiovascular diseases (Lachat et al. 2013). For example, inadequate intake of fruits and vegetables contributes to 2.7 million NCD-related deaths per year(Hall et al. 2009). Food and nutrient databases provide the basic infrastructure for the assessment of diet quality and for the development of food-based dietary guidelines (Elmadfa & Meyer 2010; Ahuja et al. 2012).
NutrienTrackeR is a tool set for food information and dietary
assessment. It uses food composition data from several reference
databases, including: USDA (US), CIQUAL (France), BEDCA (Spain), CIF
(Canada) and STFCJ (Japan). NutrienTrackeR calculates the intake
levels for both macronutrients and micronutrients, and compares them
with the recommended dietary allowances. It also includes a number of
visualization tools, such as time series plots of nutrient intake, or
pie-charts showing the main foods contributing to the intake levels of a
given nutrient.
Installation instructions
Before installing NutrienTrackeR, please make sure you have the
latest version of R is installed. To install NutrienTrackeR,
start R and enter:
Once installed, the package can be loaded as shown below:
Food composition datasets
NutrienTrackeR includes three different food composition tables,
which provide information on the average nutritional value of foods
consumed in the United States (USDA standard reference database), France
(CIQUAL database) and Spain (BEDCA database). All nutritional values are
provided per 100 grams of food.
# USDA dataset
USDA_dataset <- food_composition_data$USDA
# CIQUAL dataset
CIQUAL_dataset <- food_composition_data$CIQUAL
# BEDCA dataset
BEDCA_dataset <- food_composition_data$BEDCA
NutrienTrackeR includes a series of functionalities to facilitate
the manipulation of these datasets. For example, the function
getNutrientNames() gets the names of all the nutrients included
in a given food composition table.
# Get nutrients included in the USDA dataset
nutrients_USDA <- getNutrientNames(food_database = "USDA")
print(head(nutrients_USDA), 4)## [1] "Water (g)" "Energy (kcal)"
## [3] "Protein (g)" "Total lipid (fat) (g)"
## [5] "Ash (g)" "Carbohydrate, by difference (g)"
The function subsetFoodRichIn() selects the foods with the
highest content of a nutrient of interest.
# Top 2 high-protein CIQUAL foods
subsetFoodRichIn(nutrient_name = "Protein (g)", food_database = "CIQUAL", n = 2)[, "food_name"]## 11007 22004
## "Gelatine, dried" "Egg white, powder"# Top 3 high-protein BEDCA foods within "Fruits and fruit products"
subsetFoodRichIn(nutrient_name = "Protein (g)", food_database = "BEDCA", food_group = "Fruits and fruit products", n = 3)[, "food_name"]## 908 946 909
## "Coconut, dried" "Peach, dried" "Coconut, raw"
The function findFoodName() searches for food names based on
query keywords.
# Find the USDA food name "Tomatoes, green, raw"
findFoodName(keywords = c("Tomato", "raw"), food_database = "USDA")## 11527
## "Tomatoes, green, raw"
## 11529
## "Tomatoes, red, ripe, raw, year round average"
## 11695
## "Tomatoes, orange, raw"
## 11696
## "Tomatoes, yellow, raw"
Dietary assessment tools
Preparing the input
NutrienTrackeR allows assessing the dietary intake of an
individual, based on the food composition database of choice (i.e. USDA,
CIQUAL or BEDCA). For this, the user needs to provide a matrix or a list
of matrices, where each matrix reports all the foods eaten in a given
day. The matrix must have two columns: 1) “food”, reporting food names;
and 2) “units”, reporting the number of units eaten (1 unit = 100 grams
of food). The dataset sample_diet_USDA is an example of a
one-week diet, using foods from the USDA database.
## food units
## [1,] "Cereals, QUAKER, Quick Oats, Dry" "1"
## [2,] "Oranges, raw, California, valencias" "0.75"
## [3,] "Kiwifruit, green, raw" "0.5"
## [4,] "Yogurt, plain, low fat" "1"
## [5,] "Tomatoes, green, raw" "0.75"
## [6,] "Lettuce, iceberg (includes crisphead types), raw" "0.5"
Nutrient calculator
The function dietBalance() calculates the daily nutrient intake
of an individual and compares it with the NIH recommendations
(recommended dietary allowances (RDA) and tolerable upper intake levels
(TUIL)). The nutrient requirements are dependent on age and gender, and
therefore these parameters need to be specified when using the function
dietBalance(). In this example, we will calculate the nutrient
intake from the dataset sample_diet_USDA, assuming that this data
was provided by a 27-year old women.
# Calculate nutrient intake
daily_intake <- dietBalance(my_daily_food = sample_diet_USDA, food_database = "USDA",
age = 27, gender = "female")## The results correspond to an average of 7 days## Total energy intake (kcal): 1795## The intake level of the following nutrients is below the RDA:## nutrient proportion(%RDA) group
## 1 Water (g) 30.97156 macronutrient
## 2 Calcium, Ca (mg) 58.25710 mineral
## 3 Sodium, Na (mg) 59.84527 mineral
## 4 Potassium, K (mg) 67.10106 mineral
## 5 Iron, Fe (mg) 99.56111 mineral
## 6 Vitamin D (D2 + D3) (ug) 11.28667 vitamin
## 7 Vitamin E (alpha-tocopherol) (mg) 31.04000 vitamin
## 8 Vitamin A, RAE (ug) 45.67343 vitamin
## 9 Choline, total (mg) 85.32682 vitamin
## 10 Folate, DFE (ug) 96.62500 vitamin## The intake level of the following nutrients is above the TUIL:## nutrient proportion(%RDA) group
## 1 Magnesium, Mg (mg) 223.7326 mineral
## 2 Manganese, Mn (mg) 643.8333 mineral
Visualization tools
The output of dietBalance() can be visualized with several
functions. For instance, nutrientIntakePlot() generates a barplot
of nutrient intake levels.
The function nutrientPiePlot() generates a pie-chart showing the
relative contribution of each food to the total intake of a given
nutrient.
#Load ggplott2
library(ggplot2)
## Generate plot
q <- nutrientPiePlot(daily_intake, nutrient_name = "Magnesium, Mg (mg)")
## Adjust font size
q + theme(axis.title = element_text(size = 29), axis.text = element_text(size = 29), legend.title = element_text(size = 22),legend.text = element_text(size = 20))
The function nutrientsTimeTrend() allows visualizing time trends
of nutrient intake levels.
# Generate plot
p <- nutrientsTimeTrend(my_daily_food = sample_diet_USDA, food_database = "USDA",
nutrients = c("Calcium, Ca (mg)", "Iron, Fe (mg)"))
# Adjust font size
p + theme(axis.title = element_text(size = 18), axis.text = element_text(size = 16), legend.title = element_text(size = 18),legend.text = element_text(size = 18))
Shiny app
A shiny app is available, which can be run locally by executing
NutrienTrackeRapp(). Alternatively, the app can be accessed at https://rafaelayala.shinyapps.io/NutrienTrackeR/