Compiled date: 2024-10-29
Last edited: 2022-01-12
License: GPL-3
Run the following code to install the Bioconductor version of the package.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("fobitools")
library(fobitools)
We will also need some additional CRAN packages that will be very useful in this vignette.
library(tidyverse)
library(kableExtra)
In nutritional studies, dietary data are usually collected by using different questionnaires such as FFQs (food frequency questionnaires) or 24h-DRs (24 hours dietary recall). Commonly, the text collected in these questionnaires require a manual preprocessing step before being analyzed.
This is an example of how an FFQ could look like in a common nutritional study.
load("data/sample_ffq.rda")
sample_ffq %>%
dplyr::slice(1L:10L) %>%
kbl(row.names = FALSE, booktabs = TRUE) %>%
kable_styling(latex_options = c("striped"))
ID | Name |
---|---|
ID_001 | Beef: roast, steak, mince, stew casserole, curry or bolognese |
ID_002 | Beefburgers |
ID_003 | Pork: roast, chops, stew, slice or curry |
ID_004 | Lamb: roast, chops, stew or curry |
ID_005 | Chicken, turkey or other poultry: including fried, casseroles or curry |
ID_006 | Bacon |
ID_007 | Ham |
ID_008 | Corned beef, Spam, luncheon meats |
ID_009 | Sausages |
ID_0010 | Savoury pies, e.g. meat pie, pork pie, pasties, steak & kidney pie, sausage rolls, scotch egg |
The fobitools::annotate_foods()
function allows the automatic annotation of free nutritional text using the FOBI ontology (Castellano-Escuder et al. 2020). This function provides users with a table of food IDs, food names, FOBI IDs and FOBI names of the FOBI terms that match the input text. The input should be structured as a two column data frame, indicating the food IDs (first column) and food names (second column). Note that food names can be provided both as words and complex strings.
This function includes a text mining algorithm composed of 5 sequential layers. In this process, singulars and plurals are analyzed, irrelevant words are removed, each string of the text input is tokenized and each word is analyzed independently, and the semantic similarity between input text and FOBI items is computed. Finally, this function also shows the percentage of the annotated input text.
annotated_text <- fobitools::annotate_foods(sample_ffq)
#> 89.57% annotated
#> 3.182 sec elapsed
annotated_text$annotated %>%
dplyr::slice(1L:10L) %>%
kbl(row.names = FALSE, booktabs = TRUE) %>%
kable_styling(latex_options = c("striped"))
FOOD_ID | FOOD_NAME | FOBI_ID | FOBI_NAME |
---|---|---|---|
ID_00100 | Oranges, satsumas, mandarins, tangerines, clementines | FOODON:03309832 | orange (whole, raw) |
ID_00101 | Grapefruit | FOODON:03301702 | grapefruit (whole, raw) |
ID_00102 | Bananas | FOODON:03311513 | banana (whole, ripe) |
ID_00103 | Grapes | FOODON:03301123 | grape (whole, raw) |
ID_00104 | Melon | FOODON:03301593 | melon (raw) |
ID_00105 | *Peaches, plums, apricots, nectarines | FOODON:03301107 | nectarine (whole, raw) |
ID_00106 | *Strawberries, raspberries, kiwi fruit | FOODON:03305656 | fruit (dried) |
ID_00106 | *Strawberries, raspberries, kiwi fruit | FOODON:03414363 | kiwi |
ID_00106 | *Strawberries, raspberries, kiwi fruit | FOODON:00001057 | plant fruit food product |
ID_00107 | Tinned fruit | FOODON:03305656 | fruit (dried) |
Additionally, the similarity argument indicates the semantic similarity cutoff used at the last layer of the text mining pipeline. It is a numeric value between 1 (exact match) and 0 (very poor match). Users can modify this value to obtain more or less accurated annotations. Authors do not recommend values below 0.85 (default).
annotated_text_95 <- fobitools::annotate_foods(sample_ffq, similarity = 0.95)
#> 86.5% annotated
#> 3.042 sec elapsed
annotated_text_95$annotated %>%
dplyr::slice(1L:10L) %>%
kbl(row.names = FALSE, booktabs = TRUE) %>%
kable_styling(latex_options = c("striped"))
FOOD_ID | FOOD_NAME | FOBI_ID | FOBI_NAME |
---|---|---|---|
ID_00100 | Oranges, satsumas, mandarins, tangerines, clementines | FOODON:03309832 | orange (whole, raw) |
ID_00101 | Grapefruit | FOODON:03301702 | grapefruit (whole, raw) |
ID_00102 | Bananas | FOODON:03311513 | banana (whole, ripe) |
ID_00103 | Grapes | FOODON:03301123 | grape (whole, raw) |
ID_00104 | Melon | FOODON:03301593 | melon (raw) |
ID_00105 | *Peaches, plums, apricots, nectarines | FOODON:03301107 | nectarine (whole, raw) |
ID_00106 | *Strawberries, raspberries, kiwi fruit | FOODON:03305656 | fruit (dried) |
ID_00106 | *Strawberries, raspberries, kiwi fruit | FOODON:03414363 | kiwi |
ID_00106 | *Strawberries, raspberries, kiwi fruit | FOODON:00001057 | plant fruit food product |
ID_00107 | Tinned fruit | FOODON:03305656 | fruit (dried) |
See that by increasing the similarity value from 0.85 (default value) to 0.95 (a more accurate annotation), the percentage of annotated terms decreases from 89.57% to 86.5%. Let’s check those food items annotated with similarity = 0.85
but not with similarity = 0.95
.
annotated_text$annotated %>%
filter(!FOOD_ID %in% annotated_text_95$annotated$FOOD_ID) %>%
kbl(row.names = FALSE, booktabs = TRUE) %>%
kable_styling(latex_options = c("striped"))
FOOD_ID | FOOD_NAME | FOBI_ID | FOBI_NAME |
---|---|---|---|
ID_00124 | Beansprouts…130 | FOODON:00002753 | bean (whole) |
ID_00127 | Watercress | FOODON:00002340 | water food product |
ID_00140 | Beansprouts…171 | FOODON:00002753 | bean (whole) |
ID_00143 | Brocoli | FOODON:03301713 | broccoli floret (whole, raw) |
ID_002 | Beefburgers | FOODON:00002737 | beef hamburger (dish) |
Then, with the fobitools::fobi_graph()
function we can visualize the annotated food terms with their corresponding FOBI relationships.
terms <- annotated_text$annotated %>%
pull(FOBI_ID)
fobitools::fobi_graph(terms = terms,
get = NULL,
layout = "lgl",
labels = TRUE,
legend = TRUE,
labelsize = 6,
legendSize = 20)