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<br>
<p style="text-align:center;font-size:200%;color:Red"> NutrienTrackeR </p>
<p style="text-align:center;font-size:100%;color:Gray"> Andrea Rodriguez Martinez,
Rafael Ayala, Yacine Debbabi, Lara Selles Vidal </p>
<p style="text-align:center;font-size:100%;color:Gray"> Jun 16, 2018 </p>
<br>
# Introduction
<p style="font-size:110%"> <b>Motivation:</b> 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 [@lachat2013]. For example, inadequate intake
of fruits and vegetables contributes to 2.7 million NCD-related deaths per year[@hall2009].
Food and nutrient databases provide the basic infrastructure for the assessment of
diet quality and for the development of food-based dietary guidelines [@ahuja2012;
@elmadfa2010]. </p>
<p style="font-size:110%"> <b>NutrienTrackeR</b> 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).
<b>NutrienTrackeR</b> 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. <br><br> </p>
# Installation instructions
<p style="font-size:110%"> Before installing <b>NutrienTrackeR</b>, please make sure you
have the latest version of R is installed. To install <b>NutrienTrackeR</b>,
start R and enter: <br><p>
```{r tidy = TRUE, eval = FALSE}
install.packages("NutrienTrackeR")
```
<p style="font-size:110%"> Once installed, the package can be loaded as shown below: <br><p>
```{r tidy = TRUE, eval = TRUE}
library(NutrienTrackeR)
```
<br>
# Food composition datasets
<p style="font-size:110%"> <b>NutrienTrackeR</b> 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. <br><p>
```{r tidy = TRUE}
# USDA dataset
USDA_dataset <- food_composition_data$USDA
# CIQUAL dataset
CIQUAL_dataset <- food_composition_data$CIQUAL
# BEDCA dataset
BEDCA_dataset <- food_composition_data$BEDCA
```
<p style="font-size:110%"> <b>NutrienTrackeR</b> includes a series of functionalities
to facilitate the manipulation of these datasets. For example, the function
<b>getNutrientNames()</b> gets the names of all the nutrients included in a given
food composition table. <br><p>
```{r tidy = TRUE}
# Get nutrients included in the USDA dataset
nutrients_USDA <- getNutrientNames(food_database = "USDA")
print(head(nutrients_USDA), 4)
```
<p style="font-size:110%"> The function <b>subsetFoodRichIn()</b> selects the foods with
the highest content of a nutrient of interest. <br><p>
```{r tidy = TRUE, tidy.opts=list(indent = 2, width.cutoff = 150)}
# Top 2 high-protein CIQUAL foods
subsetFoodRichIn(nutrient_name = "Protein (g)", food_database = "CIQUAL", n = 2)[, "food_name"]
# 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"]
```
<p style="font-size:110%"> The function <b>findFoodName()</b> searches for food names
based on query keywords. <br><p>
```{r tidy = TRUE, tidy.opts=list(indent = 2, width.cutoff = 150)}
# Find the USDA food name "Tomatoes, green, raw"
findFoodName(keywords = c("Tomato", "raw"), food_database = "USDA")
```
<br><p>
# Dietary assessment tools
## Preparing the input
<p style="font-size:110%"> <b>NutrienTrackeR</b> 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 <b>sample_diet_USDA</b>
is an example of a one-week diet, using foods from the USDA database.<br><p>
```{r tidy = TRUE, tidy.opts=list(indent = 2, width.cutoff = 150)}
# Foods eaten in day 1
head(sample_diet_USDA[[1]])
```
<br><p>
## Nutrient calculator
<p style="font-size:110%"> The function <b>dietBalance()</b> 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 <b>dietBalance()</b>. In this example,
we will calculate the nutrient intake from the dataset <b>sample_diet_USDA</b>, assuming
that this data was provided by a 27-year old women. <br><p>
```{r tidy = TRUE, tidy.opts=list(indent = 2, width.cutoff = 50), message = TRUE}
# Calculate nutrient intake
daily_intake <- dietBalance(my_daily_food = sample_diet_USDA, food_database = "USDA",
age = 27, gender = "female")
```
<br><p>
## Visualization tools
<p style="font-size:110%"> The output of <b>dietBalance()</b> can be visualized
with several functions. For instance, <b>nutrientIntakePlot()</b> generates a
barplot of nutrient intake levels.<br><p>
```{r tidy = TRUE, results='asis', fig.pos = "center"}
nutrientIntakePlot(daily_intake)
```
<br><p>
<p style="font-size:110%"> The function <b>nutrientPiePlot()</b> generates a pie-chart
showing the relative contribution of each food to the total intake of a given nutrient.<br><p>
```{r tidy = TRUE, tidy.opts=list(indent = 2, width.cutoff = 180), results='asis', fig.pos = "center", fig.width = 20, fig.height = 10}
#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))
```
<p style="font-size:110%"> The function <b>nutrientsTimeTrend()</b> allows visualizing
time trends of nutrient intake levels.<br><p>
```{r tidy = TRUE, tidy.opts=list(indent = 2, width.cutoff = 180), results='asis', fig.pos = "center", fig.width = 12, fig.height = 6}
# 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))
```
<br><p>
## Shiny app
<p style="font-size:110%"> A shiny app is available, which can be run locally
by executing <b>NutrienTrackeRapp()</b>. Alternatively, the app can be accessed
at https://rafaelayala.shinyapps.io/NutrienTrackeR/ <br><p>
# References