Executive Summary

Dietary guidelines and nutrition labels guide public food choices, yet individual needs vary. This study aims to develop a personalized food recommendation algorithm based on nutrient profiles. I compared spectrum clustering and k-means clustering, finding k-means to be superior. This document outlines the methods, analyses, and practical applications of these findings.

Introduction

Nutrition’s Role in Health

Diet significantly impacts health outcomes, from cardiovascular disease (Shanta Retelny, Neuendorf, and Roth 2008) to cognitive function (Dani, Burrill, and Demmig-Adams 2005). However, many people struggle with unhealthy eating habits, often due to an overabundance of food choices. Improving dietary decisions through better food groupings could help mitigate this issue.

Issues with Current Food Groupings

Existing nutrition datasets categorize foods, but the methods are unclear, leading to inconsistencies in nutrient profiles within groups. This study reinterprets food groupings, aiming to create clusters that better align with individual dietary needs. Specifically, the results can be used to identify foods with the optimal combination of nutrients tailored to a person’s dietary requirements.

Study Goals

This analysis of public nutrient datasets aims to develop an algorithm that recommends foods based on nutrient profiles, identifying clusters with high intra-group and low inter-group similarity.

Methods

Exploratory Data Analysis

I used the Canadian Nutrient File (2015) dataset, containing nutrient values for over 5690 foods. After merging datasets by unique identifiers, I ran exploratory analyses. Despite some redundant and missing data, I opted for mean imputation to handle missing values, ensuring consistency in subsequent analyses.

Main Analyses

I compared k-means and spectrum clustering to group foods by nutrient profiles, aiming for high intra-group similarity and low inter-group similarity. The silhouette method was used to identify optimal cluster numbers, and data were standardized before analysis.

Results

K-means vs. Spectrum Clustering

K-means clustering

Using the silhouette method, 2 clusters were identified (see appendix). K-means clustering resulted in a high proportion of total sum of squares explained by between-cluster sum of squares, indicating good cluster separation.

Spectrum Clustering

A scree plot guided the selection of 10 principal components (PCs) for spectrum clustering. K-means clustering on these PCs produced 8 clusters (see appendix). The plot below shows the 8 clusters formed using spectrum clustering across the first two PCs, with labels for the centroid of each cluster.

Comparing Methods

K-means outperformed spectrum clustering, as evidenced by higher silhouette width and Dunn index scores, suggesting better-defined clusters.

Exploring “intelligent” clusters

To explore the ‘intelligent’ clusters created through k-means clustering, we plotted word clouds of the most commonly used words within each cluster. For instance, the first word cloud shows that the most frequently used word in cluster 1 was ‘raw,’ followed by ‘frozen,’ ‘boiled,’ and ‘canned.’ This suggests that the cluster is defined by how a food is stored or cooked. In terms of specific foods, ‘cereal’ is frequently mentioned, along with other sugary foods.

The word cloud for the second cluster shows that ‘raw’ is also frequently used in this cluster. Notably, a larger proportion of the foods in this cluster are related to meat and high-protein foods, such as ‘meat,’ ‘lean,’ ‘fat,’ and ‘fish.’

We also explored which group had the highest calorie content, finding that cluster 2 had a noticeably higher calorie content compared to cluster 1. Therefore, if someone is focused on reducing their overall calorie intake, it may be advisable to avoid foods found in cluster 2.

Case Study: Designing a Muscle-Gain Diet

After determining the best clustering approach based on food nutrient profiles, I provide a case study to illustrate how this tool can be used to make recommendations for a person with specific dietary needs.

Arnold is an aspiring professional bodybuilder interested in gaining muscle as quickly as possible. Understanding the importance of nutrition in muscle building, he plans to adjust his diet to achieve this goal, following the guidance he found here and here. Arnold, currently weighing 180 pounds, aims to consume 1.5 grams of protein per pound of bodyweight, which totals 270 grams of protein daily. Additionally, it is recommended that he consumes between 2-3 grams of carbohydrates per pound, equating to 360-540 grams of total carbohydrates per day. Finally, he should intake at least 3600 kilocalories daily to support muscle gain. Arnold also seeks to consume several other nutrients at levels above 2 standard deviations above the mean due to their muscle-building properties, including calcium, biotin, iron, vitamin C, selenium, Omega-3, vitamin D, vitamin B12, copper, magnesium, riboflavin, and zinc.

To simulate Arnold’s dietary needs, I created a dataset from my main analyses and compared Arnold’s daily nutrient targets to the daily nutrient recommendations for someone in the general population who matches him in all characteristics except activity level. I used the DRI Calculator for Healthcare Professionals from the National Argicultural Library to determine a point of comparison for Arnold’s nutrient intake. This tool calculates daily nutrient recommendations based on the Dietary Reference Intakes (DRIs) established by the Health and Medicine Division of the National Academies of Sciences, Engineering, and Medicine. I input gender (male), age (20), height (5 feet 10 inches), weight (180 pounds), and activity level (sedentary) for someone matching Arnold in all characteristics except activity level, as Arnold will have a higher activity level than the typical person. Therefore, the nutrient recommendations from this tool represent someone who matches Arnold in all characteristics but does not aspire to be a professional bodybuilder (and hence has a lower activity level). I then compared Arnold’s nutrient goals to these recommendations. For example, Arnold aims to consume 3600 kilocalories per day, compared to the recommended 2734 kilocalories for someone less active. Thus, Arnold is consuming approximately 1.32 times the recommended kilocalories. Similarly, Arnold will consume 1.2 times the recommended carbohydrates, 4.15 times the recommended total carbohydrates, 270 times the recommended saturated fats, and 10.8 times the recommended total fat. I created nutrient data for Arnold by multiplying the average value of each target nutrient across the dataset by the amount Arnold is consuming above the mean (e.g., 1.32 * average value of nutrient across all foods) for kilocalories, total carbohydrates, saturated fats, protein, and total fat. For the nutrients Arnold aims to consume at least 2 standard deviations above the mean (i.e., calcium, biotin, iron, vitamin C, selenium, Omega-3, vitamin D, vitamin B12, copper, magnesium, riboflavin, and zinc), I calculated and inserted the value representing 2 standard deviations above the mean into Arnold’s nutrient data.

Next, I identified the cluster that most closely matched Arnold’s nutrient needs by finding the cluster with the smallest Euclidean distance from Arnold’s goal nutrient profile. My analyses indicated that Arnold would best achieve his goals by eating foods in cluster 2. After identifying the cluster that best fit Arnold, I identified the foods with the smallest Euclidean distance from the center of cluster 2 in the original dataset to provide recommendations for the top 10 foods that meet Arnold’s target nutrient profile. The table below clearly shows that Arnold should focus on meats and fish to achieve his goal of becoming a professional bodybuilder.

Food	Distance
Duck, young duckling, domestic, White Pekin, breast, meat and skin, boneless, roasted	2.57
Fish, butterfish, baked or broiled	2.57
Veal, rib, lean and fat, roasted	2.59
Veal, shoulder, shank, lean and fat, roasted	2.60
Chicken, broiler, meat, skin, giblets and neck, batter dipped, fried	2.67
Chicken, roasting, meat, skin, giblets and neck, roasted	2.68
Chicken, broiler, drumstick, meat and skin, batter dipped, fried	2.69
Chicken, roasting, meat and skin, roasted	2.70
Veal, sirloin, lean and fat, roasted	2.71
Veal, ground, broiled	2.73

Conclusion

Contrary to our expectations, k-means clustering provided better results than spectrum clustering. Our case study illustrates how these clusters can inform dietary choices, such as optimizing nutrient intake for muscle gain.

Limitations

The dataset’s average nutrient values and focus on Canadian products limit generalizability. Additionally, nutrient values are standardized, requiring portion size adjustments for practical use. Future work could expand these analyses to other countries and explore food-specific goals.

Appendix

Missing Values in Original Dataset

Data Frame Summary

data_mean_imp

Dimensions: 5690 x 151
Duplicates: 0

No	Variable	Stats / Values	Freqs (% of Valid)
1	PROTEIN [numeric]	Mean (sd) : 11.1 (10.8) min < med < max: 0 < 7.6 < 85.6 IQR (CV) : 16.6 (1)	2261 distinct values
2	FAT (TOTAL LIPIDS) [numeric]	Mean (sd) : 10 (16.7) min < med < max: 0 < 3.8 < 100 IQR (CV) : 11.6 (1.7)	1913 distinct values
3	CARBOHYDRATE, TOTAL (BY DIFFERENCE) [numeric]	Mean (sd) : 22 (26.5) min < med < max: 0 < 10.3 < 100 IQR (CV) : 31.6 (1.2)	2756 distinct values
4	ASH, TOTAL [numeric]	Mean (sd) : 1.9 (3.5) min < med < max: 0 < 1.2 < 99.8 IQR (CV) : 1.2 (1.8)	647 distinct values
5	ENERGY (KILOCALORIES) [numeric]	Mean (sd) : 219 (174) min < med < max: 0 < 174 < 902 IQR (CV) : 240 (0.8)	665 distinct values
6	ALCOHOL [numeric]	Mean (sd) : 0.1 (1.8) min < med < max: 0 < 0 < 42.5 IQR (CV) : 0 (14.3)	38 distinct values
7	MOISTURE [numeric]	Mean (sd) : 55 (31) min < med < max: 0 < 64.7 < 100 IQR (CV) : 50.3 (0.6)	3417 distinct values
8	CAFFEINE [numeric]	Mean (sd) : 3.9 (97.8) min < med < max: 0 < 0 < 5714 IQR (CV) : 0 (24.9)	62 distinct values
9	THEOBROMINE [numeric]	Mean (sd) : 7 (74.8) min < med < max: 0 < 0 < 2634 IQR (CV) : 0 (10.7)	131 distinct values
10	ENERGY (KILOJOULES) [numeric]	Mean (sd) : 915 (727) min < med < max: 0 < 727 < 3774 IQR (CV) : 1006 (0.8)	1659 distinct values
11	SUGARS, TOTAL [numeric]	Mean (sd) : 7.7 (13.6) min < med < max: 0 < 3 < 99.8 IQR (CV) : 7.7 (1.8)	1506 distinct values
12	FIBRE, TOTAL DIETARY [numeric]	Mean (sd) : 2.4 (4.7) min < med < max: 0 < 1 < 79 IQR (CV) : 2.7 (1.9)	238 distinct values
13	CALCIUM [numeric]	Mean (sd) : 76.9 (219) min < med < max: 0 < 25 < 7364 IQR (CV) : 63 (2.8)	469 distinct values
14	IRON [numeric]	Mean (sd) : 2.6 (5.6) min < med < max: 0 < 1.1 < 124 IQR (CV) : 2.1 (2.2)	883 distinct values
15	MAGNESIUM [numeric]	Mean (sd) : 39.7 (63.6) min < med < max: 0 < 22 < 781 IQR (CV) : 26.7 (1.6)	303 distinct values
16	PHOSPHORUS [numeric]	Mean (sd) : 168 (233) min < med < max: 0 < 136 < 9918 IQR (CV) : 172 (1.4)	621 distinct values
17	POTASSIUM [numeric]	Mean (sd) : 308 (441) min < med < max: 0 < 240 < 16500 IQR (CV) : 208 (1.4)	896 distinct values
18	SODIUM [numeric]	Mean (sd) : 333 (1214) min < med < max: 0 < 83 < 38758 IQR (CV) : 335 (3.6)	1100 distinct values
19	ZINC [numeric]	Mean (sd) : 1.6 (3) min < med < max: 0 < 0.9 < 91 IQR (CV) : 1.7 (1.8)	696 distinct values
20	COPPER [numeric]	Mean (sd) : 0.2 (0.6) min < med < max: 0 < 0.1 < 15.1 IQR (CV) : 0.2 (2.8)	788 distinct values
21	MANGANESE [numeric]	Mean (sd) : 0.6 (3.5) min < med < max: 0 < 0.2 < 133 IQR (CV) : 0.6 (5.8)	1227 distinct values
22	SELENIUM [numeric]	Mean (sd) : 14.6 (34.3) min < med < max: 0 < 10 < 1917 IQR (CV) : 16.7 (2.4)	615 distinct values
23	RETINOL [numeric]	Mean (sd) : 88.8 (802) min < med < max: 0 < 0 < 30000 IQR (CV) : 27 (9)	327 distinct values
24	BETA CAROTENE [numeric]	Mean (sd) : 292 (1610) min < med < max: 0 < 2 < 42891 IQR (CV) : 120 (5.5)	613 distinct values
25	ALPHA-TOCOPHEROL [numeric]	Mean (sd) : 1.2 (3.5) min < med < max: 0 < 0.6 < 149 IQR (CV) : 1 (3)	448 distinct values
26	VITAMIN D (INTERNATIONAL UNITS) [numeric]	Mean (sd) : 23.9 (226) min < med < max: 0 < 0 < 12716 IQR (CV) : 18 (9.5)	215 distinct values
27	VITAMIN D (D2 + D3) [numeric]	Mean (sd) : 0.6 (5.9) min < med < max: 0 < 0 < 318 IQR (CV) : 0.4 (9.4)	130 distinct values
28	VITAMIN C [numeric]	Mean (sd) : 8.2 (52.3) min < med < max: 0 < 0.2 < 1900 IQR (CV) : 4.6 (6.4)	459 distinct values
29	THIAMIN [numeric]	Mean (sd) : 0.2 (0.5) min < med < max: 0 < 0.1 < 23.4 IQR (CV) : 0.2 (2.5)	813 distinct values
30	RIBOFLAVIN [numeric]	Mean (sd) : 0.2 (0.4) min < med < max: 0 < 0.1 < 17.5 IQR (CV) : 0.2 (2)	710 distinct values
31	NIACIN (NICOTINIC ACID) PREFORMED [numeric]	Mean (sd) : 3.1 (4.3) min < med < max: 0 < 1.8 < 128 IQR (CV) : 4.3 (1.4)	2829 distinct values
32	TOTAL NIACIN EQUIVALENT [numeric]	Mean (sd) : 5.2 (5.5) min < med < max: 0 < 3.9 < 132 IQR (CV) : 6.9 (1.1)	3909 distinct values
33	PANTOTHENIC ACID [numeric]	Mean (sd) : 0.6 (0.9) min < med < max: 0 < 0.6 < 21.9 IQR (CV) : 0.5 (1.3)	1317 distinct values
34	VITAMIN B-6 [numeric]	Mean (sd) : 0.2 (1) min < med < max: 0 < 0.1 < 68.8 IQR (CV) : 0.2 (4.3)	757 distinct values
35	TOTAL FOLACIN [numeric]	Mean (sd) : 37.7 (89.9) min < med < max: 0 < 14 < 3786 IQR (CV) : 32.7 (2.4)	291 distinct values
36	VITAMIN B-12 [numeric]	Mean (sd) : 1.1 (6.5) min < med < max: 0 < 0.1 < 380 IQR (CV) : 1 (5.9)	900 distinct values
37	VITAMIN K [numeric]	Mean (sd) : 20.8 (74.6) min < med < max: 0 < 20.8 < 1714 IQR (CV) : 19.4 (3.6)	435 distinct values
38	FOLIC ACID [numeric]	Mean (sd) : 8.4 (48.4) min < med < max: 0 < 0 < 2993 IQR (CV) : 0 (5.7)	161 distinct values
39	TRYPTOPHAN [numeric]	Mean (sd) : 0.1 (0.1) min < med < max: 0 < 0.1 < 1.6 IQR (CV) : 0.1 (0.8)	459 distinct values
40	THREONINE [numeric]	Mean (sd) : 0.5 (0.4) min < med < max: 0 < 0.5 < 3.7 IQR (CV) : 0.5 (0.8)	1301 distinct values
41	ISOLEUCINE [numeric]	Mean (sd) : 0.6 (0.4) min < med < max: 0 < 0.6 < 5 IQR (CV) : 0.5 (0.8)	1370 distinct values
42	LEUCINE [numeric]	Mean (sd) : 1 (0.7) min < med < max: 0 < 1 < 7.2 IQR (CV) : 0.9 (0.8)	1835 distinct values
43	LYSINE [numeric]	Mean (sd) : 0.9 (0.8) min < med < max: 0 < 0.9 < 5.8 IQR (CV) : 0.9 (0.9)	1699 distinct values
44	METHIONINE [numeric]	Mean (sd) : 0.3 (0.2) min < med < max: 0 < 0.3 < 3.2 IQR (CV) : 0.3 (0.8)	860 distinct values
45	CYSTINE [numeric]	Mean (sd) : 0.2 (0.1) min < med < max: 0 < 0.2 < 2.1 IQR (CV) : 0.1 (0.8)	496 distinct values
46	PHENYLALANINE [numeric]	Mean (sd) : 0.5 (0.4) min < med < max: 0 < 0.5 < 5.2 IQR (CV) : 0.4 (0.7)	1275 distinct values
47	TYROSINE [numeric]	Mean (sd) : 0.4 (0.3) min < med < max: 0 < 0.4 < 3.3 IQR (CV) : 0.4 (0.8)	1132 distinct values
48	VALINE [numeric]	Mean (sd) : 0.6 (0.5) min < med < max: 0 < 0.6 < 6.2 IQR (CV) : 0.5 (0.8)	1429 distinct values
49	ARGININE [numeric]	Mean (sd) : 0.8 (0.7) min < med < max: 0 < 0.8 < 7.4 IQR (CV) : 0.8 (0.9)	1627 distinct values
50	HISTIDINE [numeric]	Mean (sd) : 0.4 (0.3) min < med < max: 0 < 0.4 < 2.3 IQR (CV) : 0.3 (0.8)	1076 distinct values
51	ALANINE [numeric]	Mean (sd) : 0.7 (0.5) min < med < max: 0 < 0.7 < 8 IQR (CV) : 0.6 (0.8)	1490 distinct values
52	ASPARTIC ACID [numeric]	Mean (sd) : 1.2 (0.9) min < med < max: 0 < 1.2 < 10.2 IQR (CV) : 1 (0.8)	1937 distinct values
53	GLUTAMIC ACID [numeric]	Mean (sd) : 2.4 (10.1) min < med < max: 0 < 2.4 < 757 IQR (CV) : 1.7 (4.3)	2433 distinct values
54	GLYCINE [numeric]	Mean (sd) : 0.6 (0.6) min < med < max: 0 < 0.6 < 19 IQR (CV) : 0.6 (0.9)	1439 distinct values
55	PROLINE [numeric]	Mean (sd) : 0.7 (0.5) min < med < max: 0 < 0.7 < 12.3 IQR (CV) : 0.5 (0.8)	1420 distinct values
56	SERINE [numeric]	Mean (sd) : 0.5 (0.4) min < med < max: 0 < 0.5 < 6.1 IQR (CV) : 0.4 (0.7)	1289 distinct values
57	CHOLESTEROL [numeric]	Mean (sd) : 41.5 (136) min < med < max: 0 < 2 < 3100 IQR (CV) : 59 (3.3)	292 distinct values
58	FATTY ACIDS, TRANS, TOTAL [numeric]	Mean (sd) : 0.3 (1.1) min < med < max: 0 < 0.3 < 37.6 IQR (CV) : 0.2 (3.6)	499 distinct values
59	FATTY ACIDS, SATURATED, TOTAL [numeric]	Mean (sd) : 3.1 (5.7) min < med < max: 0 < 1.3 < 95.6 IQR (CV) : 3.3 (1.8)	2813 distinct values
60	FATTY ACIDS, SATURATED, 8:0, OCTANOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 7.5 IQR (CV) : 0 (6)	267 distinct values
61	FATTY ACIDS, SATURATED, 10:0, DECANOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 6 IQR (CV) : 0 (4.4)	351 distinct values
62	FATTY ACIDS, SATURATED, 12:0, DODECANOIC [numeric]	Mean (sd) : 0.2 (1.5) min < med < max: 0 < 0 < 47 IQR (CV) : 0.2 (7.7)	449 distinct values
63	FATTY ACIDS, SATURATED, 14:0, TETRADECANOIC [numeric]	Mean (sd) : 0.2 (0.8) min < med < max: 0 < 0.1 < 22.8 IQR (CV) : 0.2 (3.3)	788 distinct values
64	FATTY ACIDS, SATURATED, 16:0, HEXADECANOIC [numeric]	Mean (sd) : 1.7 (2.6) min < med < max: 0 < 0.9 < 43.5 IQR (CV) : 1.8 (1.6)	2323 distinct values
65	FATTY ACIDS, SATURATED, 18:0, OCTADECANOIC [numeric]	Mean (sd) : 0.8 (1.6) min < med < max: 0 < 0.4 < 33.2 IQR (CV) : 0.8 (1.9)	1676 distinct values
66	FATTY ACIDS, MONOUNSATURATED, 18:1undifferentiated, OCTADECENOIC [numeric]	Mean (sd) : 3.5 (6.8) min < med < max: 0 < 1.4 < 82.6 IQR (CV) : 3.4 (1.9)	2709 distinct values
67	FATTY ACIDS, POLYUNSATURATED, 18:2undifferentiated, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 1.8 (4.4) min < med < max: 0 < 0.5 < 74.6 IQR (CV) : 1.7 (2.4)	2080 distinct values
68	FATTY ACIDS, POLYUNSATURATED, 18:3undifferentiated, LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0.2 (1.2) min < med < max: 0 < 0.1 < 53.4 IQR (CV) : 0.2 (5.6)	690 distinct values
69	FATTY ACIDS, POLYUNSATURATED, 20:4, EICOSATETRAENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 1.8 IQR (CV) : 0 (2.3)	262 distinct values
70	FATTY ACIDS, POLYUNSATURATED, 22:6 n-3, DOCOSAHEXAENOIC (DHA) [numeric]	Mean (sd) : 0 (0.5) min < med < max: 0 < 0 < 18.2 IQR (CV) : 0 (9.5)	297 distinct values
71	FATTY ACIDS, MONOUNSATURATED, 16:1undifferentiated, HEXADECENOIC [numeric]	Mean (sd) : 0.2 (0.9) min < med < max: 0 < 0.1 < 18.9 IQR (CV) : 0.2 (3.7)	771 distinct values
72	FATTY ACIDS, POLYUNSATURATED, 18:4, OCTADECATETRAENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 3 IQR (CV) : 0 (8.4)	127 distinct values
73	FATTY ACIDS, POLYUNSATURATED, 20:5 n-3, EICOSAPENTAENOIC (EPA) [numeric]	Mean (sd) : 0 (0.4) min < med < max: 0 < 0 < 13.2 IQR (CV) : 0 (8.5)	260 distinct values
74	FATTY ACIDS, MONOUNSATURATED, 22:1undifferentiated, DOCOSENOIC [numeric]	Mean (sd) : 0 (0.7) min < med < max: 0 < 0 < 41.2 IQR (CV) : 0 (14.8)	200 distinct values
75	FATTY ACIDS, POLYUNSATURATED, 22:5 n-3, DOCOSAPENTAENOIC (DPA) [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 5.6 IQR (CV) : 0 (10.9)	163 distinct values
76	FATTY ACIDS, MONOUNSATURATED, TOTAL [numeric]	Mean (sd) : 3.9 (7.6) min < med < max: 0 < 1.4 < 83.7 IQR (CV) : 4.2 (1.9)	2881 distinct values
77	FATTY ACIDS, POLYUNSATURATED, TOTAL [numeric]	Mean (sd) : 2.2 (5.1) min < med < max: 0 < 0.7 < 74.6 IQR (CV) : 2 (2.3)	2382 distinct values
78	NATURALLY OCCURRING FOLATE [numeric]	Mean (sd) : 29.2 (71.9) min < med < max: 0 < 11 < 2340 IQR (CV) : 24.2 (2.5)	262 distinct values
79	RETINOL ACTIVITY EQUIVALENTS [numeric]	Mean (sd) : 115 (817) min < med < max: 0 < 5 < 30000 IQR (CV) : 43 (7.1)	464 distinct values
80	DIETARY FOLATE EQUIVALENTS [numeric]	Mean (sd) : 44.4 (114) min < med < max: 0 < 15 < 5881 IQR (CV) : 39.4 (2.6)	333 distinct values
81	FATTY ACIDS, POLYUNSATURATED, 18:2 c,c n-6, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 2.3 (3.9) min < med < max: 0 < 2.3 < 74.6 IQR (CV) : 1.5 (1.7)	1286 distinct values
82	FATTY ACIDS, POLYUNSATURATED, 20:3, EICOSATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.4 IQR (CV) : 0 (5.5)	90 distinct values
83	FATTY ACIDS, POLYUNSATURATED, 18:3 c,c,c n-3 LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0.2 (1.1) min < med < max: 0 < 0.1 < 53.4 IQR (CV) : 0.2 (5.6)	620 distinct values
84	FATTY ACIDS, POLYUNSATURATED, 18:3 c,c,c n-6, g-LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1 IQR (CV) : 0 (17.6)	50 distinct values
85	BETA CRYPTOXANTHIN [numeric]	Mean (sd) : 15.2 (152) min < med < max: 0 < 1 < 6252 IQR (CV) : 15.2 (10)	129 distinct values
86	LYCOPENE [numeric]	Mean (sd) : 220 (1390) min < med < max: 0 < 0 < 46260 IQR (CV) : 220 (6.3)	191 distinct values
87	LUTEIN AND ZEAXANTHIN [numeric]	Mean (sd) : 260 (1063) min < med < max: 0 < 104 < 19697 IQR (CV) : 260 (4.1)	420 distinct values
88	FATTY ACIDS, POLYUNSATURATED, 20:3 n-6, EICOSATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.4 IQR (CV) : 0 (13.9)	72 distinct values
89	FATTY ACIDS, POLYUNSATURATED, 20:4 n-6, ARACHIDONIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 1.8 IQR (CV) : 0 (2)	228 distinct values
90	FATTY ACIDS, POLYUNSATURATED, 20:3 n-3 EICOSATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1 IQR (CV) : 0 (15.9)	53 distinct values
91	VITAMIN B12, ADDED [numeric]	Mean (sd) : 1 (5) min < med < max: 0 < 1 < 380 IQR (CV) : 0 (5.2)	29 distinct values
92	ALPHA-TOCOPHEROL, ADDED [numeric]	Mean (sd) : 0.1 (0.3) min < med < max: 0 < 0.1 < 16.9 IQR (CV) : 0 (3.4)	12 distinct values
93	VITAMIN D2, ERGOCALCIFEROL [numeric]	Mean (sd) : 0.3 (0.5) min < med < max: 0 < 0.3 < 28.1 IQR (CV) : 0 (1.5)	23 distinct values
94	FATTY ACIDS, SATURATED, 4:0, BUTANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 3.2 IQR (CV) : 0 (4.1)	275 distinct values
95	FATTY ACIDS, SATURATED, 6:0, HEXANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 2 IQR (CV) : 0 (4)	225 distinct values
96	ALPHA CAROTENE [numeric]	Mean (sd) : 40.8 (297) min < med < max: 0 < 1 < 14251 IQR (CV) : 40.8 (7.3)	165 distinct values
97	FATTY ACIDS, MONOUNSATURATED, 22:1c, DOCOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.1 IQR (CV) : 0 (4)	101 distinct values
98	FATTY ACIDS, POLYUNSATURATED, 18:3i, LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.3 IQR (CV) : 0 (2.2)	55 distinct values
99	FATTY ACIDS, MONOUNSATURATED, 22:1t, DOCOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.1 IQR (CV) : 0 (12.8)	17 distinct values
100	SUCROSE [numeric]	Mean (sd) : 2 (5) min < med < max: 0 < 2 < 99.8 IQR (CV) : 2 (2.5)	488 distinct values
101	GLUCOSE [numeric]	Mean (sd) : 0.8 (1.7) min < med < max: 0 < 0.8 < 35.8 IQR (CV) : 0.8 (2.2)	400 distinct values
102	FRUCTOSE [numeric]	Mean (sd) : 0.7 (1.7) min < med < max: 0 < 0.7 < 55.6 IQR (CV) : 0.7 (2.4)	388 distinct values
103	LACTOSE [numeric]	Mean (sd) : 0.3 (0.8) min < med < max: 0 < 0.3 < 13.2 IQR (CV) : 0.3 (2.7)	226 distinct values
104	MALTOSE [numeric]	Mean (sd) : 0.2 (0.5) min < med < max: 0 < 0.2 < 16.4 IQR (CV) : 0.2 (2.6)	218 distinct values
105	GALACTOSE [numeric]	Mean (sd) : 0 (0.3) min < med < max: 0 < 0 < 19.9 IQR (CV) : 0 (9.5)	54 distinct values
106	FATTY ACIDS, SATURATED, 20:0, EICOSANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 4.6 IQR (CV) : 0 (2.7)	184 distinct values
107	FATTY ACIDS, SATURATED, 22:0, DOCOSANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 3.7 IQR (CV) : 0 (3.4)	134 distinct values
108	FATTY ACIDS, MONOUNSATURATED, 14:1, TETRADECENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 1.8 IQR (CV) : 0 (2.4)	157 distinct values
109	FATTY ACIDS, MONOUNSATURATED, 20:1, EICOSENOIC [numeric]	Mean (sd) : 0.1 (0.5) min < med < max: 0 < 0 < 15 IQR (CV) : 0.1 (5.3)	366 distinct values
110	FATTY ACIDS, SATURATED, 15:0, PENTADECANOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.9 IQR (CV) : 0 (1.7)	122 distinct values
111	FATTY ACIDS, SATURATED, 17:0, HEPTADECANOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.8 IQR (CV) : 0 (1.2)	190 distinct values
112	FATTY ACIDS, SATURATED, 24:0, TETRACOSANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 4.7 IQR (CV) : 0 (5.2)	92 distinct values
113	STARCH [numeric]	Mean (sd) : 4 (6.7) min < med < max: 0 < 4 < 73.3 IQR (CV) : 4 (1.7)	361 distinct values
114	BETA-TOCOPHEROL [numeric]	Mean (sd) : 0.1 (0.2) min < med < max: 0 < 0.1 < 10.5 IQR (CV) : 0 (1.9)	66 distinct values
115	GAMMA-TOCOPHEROL [numeric]	Mean (sd) : 2.3 (2.1) min < med < max: 0 < 2.3 < 65.2 IQR (CV) : 0 (0.9)	275 distinct values
116	DELTA-TOCOPHEROL [numeric]	Mean (sd) : 0.4 (0.5) min < med < max: 0 < 0.4 < 15.4 IQR (CV) : 0 (1.2)	149 distinct values
117	FATTY ACIDS, MONOUNSATURATED, 16:1t, HEXADECENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 6.1 IQR (CV) : 0 (10.8)	74 distinct values
118	FATTY ACIDS, MONOUNSATURATED, 18:1t, OCTADECENOIC [numeric]	Mean (sd) : 0.1 (0.4) min < med < max: 0 < 0.1 < 20.2 IQR (CV) : 0 (2.9)	296 distinct values
119	FATTY ACIDS, POLYUNSATURATED, 18:2i, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 2.3 IQR (CV) : 0 (1.8)	141 distinct values
120	FATTY ACIDS, MONOUNSATURATED, 24:1c, TETRACOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.6 IQR (CV) : 0 (4.1)	46 distinct values
121	FATTY ACIDS, MONOUNSATURATED, 16:1c, HEXADECENOIC [numeric]	Mean (sd) : 0.1 (0.2) min < med < max: 0 < 0.1 < 6.9 IQR (CV) : 0 (1.3)	397 distinct values
122	FATTY ACIDS, POLYUNSATURATED, 20:2 c,c EICOSADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.7 IQR (CV) : 0 (1.7)	129 distinct values
123	FATTY ACIDS, MONOUNSATURATED, 18:1c, OCTADECENOIC [numeric]	Mean (sd) : 4.7 (37.8) min < med < max: 0 < 4.7 < 2845 IQR (CV) : 0 (8.1)	1067 distinct values
124	FATTY ACIDS, MONOUNSATURATED, 17:1, HEPTADECENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.1 IQR (CV) : 0 (1.5)	136 distinct values
125	FATTY ACIDS, TOTAL TRANS-MONOENOIC [numeric]	Mean (sd) : 0.1 (0.4) min < med < max: 0 < 0.1 < 20.2 IQR (CV) : 0 (3.1)	286 distinct values
126	FATTY ACIDS, MONOUNSATURATED, 15:1, PENTADECENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 6 IQR (CV) : 0 (15)	28 distinct values
127	FATTY ACIDS, POLYUNSATURATED, CONJUGATED, 18:2 cla, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.1 IQR (CV) : 0 (2.2)	91 distinct values
128	FATTY ACIDS, POLYUNSATURATED, 22:4 n-6, DOCOSATETRAENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.3 IQR (CV) : 0 (1.6)	67 distinct values
129	FATTY ACIDS, TOTAL TRANS-POLYENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 2.5 IQR (CV) : 0 (1.8)	155 distinct values
130	CHOLINE, TOTAL [numeric]	Mean (sd) : 39.1 (50.2) min < med < max: 0 < 39.1 < 2403 IQR (CV) : 20.4 (1.3)	911 distinct values
131	BETAINE [numeric]	Mean (sd) : 10.6 (13.8) min < med < max: 0 < 10.6 < 630 IQR (CV) : 0 (1.3)	258 distinct values
132	FATTY ACIDS, POLYUNSATURATED, TOTAL OMEGA N-3 [numeric]	Mean (sd) : 0.5 (1.4) min < med < max: 0 < 0.5 < 53.4 IQR (CV) : 0.3 (2.9)	549 distinct values
133	FATTY ACIDS, POLYUNSATURATED, TOTAL OMEGA N-6 [numeric]	Mean (sd) : 3.1 (13.8) min < med < max: 0 < 3.1 < 953 IQR (CV) : 1.7 (4.5)	1056 distinct values
134	ASPARTAME [numeric]	Mean (sd) : 51.1 (49.6) min < med < max: 0 < 51.1 < 3722 IQR (CV) : 0 (1)	0.00 : 82 ( 1.4%) 37.00 : 1 ( 0.0%) 42.00 : 1 ( 0.0%) 51.15!: 5603 (98.5%) 52.00 : 1 ( 0.0%) 597.00 : 1 ( 0.0%) 3722.00 : 1 ( 0.0%) ! rounded
135	TOTAL PLANT STEROL [numeric]	Mean (sd) : 26.4 (28.2) min < med < max: 0 < 26.4 < 1190 IQR (CV) : 0 (1.1)	118 distinct values
136	MANNITOL [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.2 IQR (CV) : 0 (8.6)	4 distinct values
137	SORBITOL [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 2.3 IQR (CV) : 0 (6.9)	0.00 : 1375 (24.2%) 0.01!: 4304 (75.6%) 0.10 : 1 ( 0.0%) 0.20 : 1 ( 0.0%) 0.30 : 1 ( 0.0%) 0.60 : 2 ( 0.0%) 0.80 : 1 ( 0.0%) 1.00 : 3 ( 0.1%) 2.10 : 1 ( 0.0%) 2.30 : 1 ( 0.0%) ! rounded
138	STIGMASTEROL [numeric]	Mean (sd) : 1.3 (1.8) min < med < max: 0 < 1.3 < 59 IQR (CV) : 0 (1.4)	27 distinct values
139	TOTAL MONOSACCARIDES [numeric]	Mean (sd) : 0.8 (1.5) min < med < max: 0 < 0.8 < 30.6 IQR (CV) : 0.7 (1.9)	268 distinct values
140	TOTAL DISACCHARIDES [numeric]	Mean (sd) : 1.5 (2.8) min < med < max: 0 < 1.5 < 47.2 IQR (CV) : 1.3 (1.9)	296 distinct values
141	BETA-SITOSTEROL [numeric]	Mean (sd) : 14 (14) min < med < max: 0 < 14 < 621 IQR (CV) : 0 (1)	55 distinct values
142	HYDROXYPROLINE [numeric]	Mean (sd) : 0.1 (0) min < med < max: 0 < 0.1 < 0.7 IQR (CV) : 0 (0.4)	198 distinct values
143	FATTY ACIDS, SATURATED, 13:0 TRIDECANOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.1 IQR (CV) : 0 (3)	11 distinct values
144	FATTY ACIDS, POLYUNSATURATED, 21:5 [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.3 IQR (CV) : 0 (4.7)	13 distinct values
145	FATTY ACIDS, MONOUNSATURATED, 24:1undifferentiated, TETRACOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 3 IQR (CV) : 0 (8.9)	33 distinct values
146	FATTY ACIDS, POLYUNSATURATED, 22:3, [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.1 IQR (CV) : 0 (5.5)	11 distinct values
147	FATTY ACIDS, POLYUNSATURATED, 22:2, DOCOSADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0 IQR (CV) : 0 (6.5)	5 distinct values
148	FATTY ACIDS, POLYUNSATURATED, 18:2t,t , OCTADECADIENENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.5 IQR (CV) : 0 (2.5)	60 distinct values
149	CAMPESTEROL [numeric]	Mean (sd) : 3.8 (3.6) min < med < max: 0 < 3.8 < 189 IQR (CV) : 0 (0.9)	28 distinct values
150	BIOTIN [numeric]	Mean (sd) : 6.1 (0.9) min < med < max: 0 < 6.1 < 31.6 IQR (CV) : 0 (0.1)	72 distinct values
151	OXALIC ACID [numeric]	Mean (sd) : 0.3 (0) min < med < max: 0 < 0.3 < 1.7 IQR (CV) : 0 (0.1)	28 distinct values

Optimal Number of Clusters

Optimal Number of Clusters for K-means

Scree Plot from PCA in Spectrum Clustering

Optimal Number of Clusters for Spectrum Clustering on 10 PCs

R Packages

List of R Packages Used

Matrix (version 1.5.1; Bates D et al., 2022)
SnowballC (version 0.7.1; Bouchet-Valat M, 2023)
clValid (version 0.7; Brock G et al., 2008)
summarytools (version 1.0.1; Comtois D, 2022)
fields (version 15.2; Douglas Nychka et al., 2021)
data.table (version 1.14.6; Dowle M, Srinivasan A, 2022)
tm (version 0.7.11; Feinerer I, Hornik K, 2023)
wordcloud (version 2.6; Fellows I, 2018)
car (version 3.1.1; Fox J, Weisberg S, 2019)
carData (version 3.0.5; Fox J et al., 2022)
glmnet (version 4.1.8; Friedman J et al., 2010)
spam (version 2.10.0; Furrer R et al., 2023)
viridisLite (version 0.4.2; Garnier et al., 2023)
fpc (version 2.2.12; Hennig C, 2024)
stargazer (version 5.2.3; Hlavac M, 2022)
NLP (version 0.2.1; Hornik K, 2020)
factoextra (version 1.0.7; Kassambara A, Mundt F, 2020)
sjPlot (version 2.8.12; Lüdecke D, 2022)
cluster (version 2.1.4; Maechler M et al., 2022)
varhandle (version 2.0.6; Mahmoudian M, 2023)
report (version 0.5.9; Makowski D et al., 2023)
imputeTS (version 3.3; Moritz S, Bartz-Beielstein T, 2017)
here (version 1.0.1; Müller K, 2020)
tibble (version 3.2.1; Müller K, Wickham H, 2023)
RColorBrewer (version 1.1.3; Neuwirth E, 2022)
R (version 4.2.2; R Core Team, 2022)
pacman (version 0.5.1; Rinker TW, Kurkiewicz D, 2018)
pROC (version 1.18.4; Robin X et al., 2011)
GGally (version 2.2.1; Schloerke B et al., 2024)
gtsummary (version 2.0.1; Sjoberg D et al., 2021)
corrplot (version 0.92; Wei T, Simko V, 2021)
ggplot2 (version 3.4.4; Wickham H, 2016)
forcats (version 0.5.2; Wickham H, 2022)
stringr (version 1.5.1; Wickham H, 2023)
tidyverse (version 1.3.2; Wickham H et al., 2019)
dplyr (version 1.1.4; Wickham H et al., 2023)
purrr (version 1.0.2; Wickham H, Henry L, 2023)
readr (version 2.1.5; Wickham H et al., 2024)
tidyr (version 1.3.1; Wickham H et al., 2024)

Citations for R Packages Used

Bates D, Maechler M, Jagan M (2022). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.5-1, https://CRAN.R-project.org/package=Matrix.
Bouchet-Valat M (2023). SnowballC: Snowball Stemmers Based on the C ‘libstemmer’ UTF-8 Library. R package version 0.7.1, https://CRAN.R-project.org/package=SnowballC.
Brock G, Pihur V, Datta S, Datta S (2008). “clValid: An R Package for Cluster Validation.” Journal of Statistical Software, 25(4), 1-22. https://www.jstatsoft.org/v25/i04/.
Comtois D (2022). summarytools: Tools to Quickly and Neatly Summarize Data. R package version 1.0.1, https://CRAN.R-project.org/package=summarytools.
Douglas Nychka, Reinhard Furrer, John Paige, Stephan Sain (2021). “fields: Tools for spatial data.” R package version 15.2, https://github.com/dnychka/fieldsRPackage.
Dowle M, Srinivasan A (2022). data.table: Extension of data.frame. R package version 1.14.6, https://CRAN.R-project.org/package=data.table.
Feinerer I, Hornik K (2023). tm: Text Mining Package. R package version 0.7-11, https://CRAN.R-project.org/package=tm. Feinerer I, Hornik K, Meyer D (2008). “Text Mining Infrastructure in R.” Journal of Statistical Software, 25(5), 1-54. doi:10.18637/jss.v025.i05 https://doi.org/10.18637/jss.v025.i05.
Fellows I (2018). wordcloud: Word Clouds. R package version 2.6, https://CRAN.R-project.org/package=wordcloud.
Fox J, Weisberg S (2019). An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.
Fox J, Weisberg S, Price B (2022). carData: Companion to Applied Regression Data Sets. R package version 3.0-5, https://CRAN.R-project.org/package=carData.
Friedman J, Tibshirani R, Hastie T (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1-22. doi:10.18637/jss.v033.i01 https://doi.org/10.18637/jss.v033.i01. Simon N, Friedman J, Tibshirani R, Hastie T (2011). “Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent.” Journal of Statistical Software, 39(5), 1-13. doi:10.18637/jss.v039.i05 https://doi.org/10.18637/jss.v039.i05. Tay JK, Narasimhan B, Hastie T (2023). “Elastic Net Regularization Paths for All Generalized Linear Models.” Journal of Statistical Software, 106(1), 1-31. doi:10.18637/jss.v106.i01 https://doi.org/10.18637/jss.v106.i01.
Furrer R, Flury R, Gerber F (2023). spam: SPArse Matrix. R package version 2.10-0, https://CRAN.R-project.org/package=spam. Furrer R, Sain SR (2010). “spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields.” Journal of Statistical Software, 36(10), 1-25. doi:10.18637/jss.v036.i10 https://doi.org/10.18637/jss.v036.i10. Gerber F, Furrer R (2015). “Pitfalls in the Implementation of Bayesian Hierarchical Modeling of Areal Count Data: An Illustration Using BYM and Leroux Models.” Journal of Statistical Software, Code Snippets, 63(1), 1-32. doi:10.18637/jss.v063.c01 https://doi.org/10.18637/jss.v063.c01. Gerber F, Moesinger K, Furrer R (2017). “Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data.” Computer & Geoscience, 104, 109-119. ISSN 0098-3004, doi:10.1016/j.cageo.2016.11.015 https://doi.org/10.1016/j.cageo.2016.11.015.
Garnier, Simon, Ross, Noam, Rudis, Robert, Camargo, Pedro A, Sciaini, Marco, Scherer, Cédric (2023). viridis(Lite) - Colorblind-Friendly Color Maps for R. doi:10.5281/zenodo.4678327 https://doi.org/10.5281/zenodo.4678327, viridisLite package version 0.4.2, https://sjmgarnier.github.io/viridis/.
Hennig C (2024). fpc: Flexible Procedures for Clustering. R package version 2.2-12, https://CRAN.R-project.org/package=fpc.
Hlavac M (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables. Social Policy Institute, Bratislava, Slovakia. R package version 5.2.3, https://CRAN.R-project.org/package=stargazer.
Hornik K (2020). NLP: Natural Language Processing Infrastructure. R package version 0.2-1, https://CRAN.R-project.org/package=NLP.
Kassambara A, Mundt F (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7, https://CRAN.R-project.org/package=factoextra.
Lüdecke D (2022). sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.12, https://CRAN.R-project.org/package=sjPlot.
Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2022). cluster: Cluster Analysis Basics and Extensions. R package version 2.1.4 - For new features, see the ‘Changelog’ file (in the package source), https://CRAN.R-project.org/package=cluster.
Mahmoudian M (2023). varhandle: Functions for Robust Variable Handling. R package version 2.0.6, https://CRAN.R-project.org/package=varhandle.
Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B (2023). “Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.” CRAN. https://easystats.github.io/report/.
Moritz S, Bartz-Beielstein T (2017). “imputeTS: Time Series Missing Value Imputation in R.” The R Journal, 9(1), 207-218. doi:10.32614/RJ-2017-009 https://doi.org/10.32614/RJ-2017-009.
Müller K (2020). here: A Simpler Way to Find Your Files. R package version 1.0.1, https://CRAN.R-project.org/package=here.
Müller K, Wickham H (2023). tibble: Simple Data Frames. R package version 3.2.1, https://CRAN.R-project.org/package=tibble.
Neuwirth E (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3, https://CRAN.R-project.org/package=RColorBrewer.
R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Rinker TW, Kurkiewicz D (2018). pacman: Package Management for R. version 0.5.0, http://github.com/trinker/pacman.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J, Müller M (2011). “pROC: an open-source package for R and S+ to analyze and compare ROC curves.” BMC Bioinformatics, 12, 77.
Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2024). GGally: Extension to ‘ggplot2’. R package version 2.2.1, https://CRAN.R-project.org/package=GGally.
Sjoberg D, Whiting K, Curry M, Lavery J, Larmarange J (2021). “Reproducible Summary Tables with the gtsummary Package.” The R Journal, 13, 570-580. doi:10.32614/RJ-2021-053 https://doi.org/10.32614/RJ-2021-053, https://doi.org/10.32614/RJ-2021-053.
Wei T, Simko V (2021). R package ‘corrplot’: Visualization of a Correlation Matrix. (Version 0.92), https://github.com/taiyun/corrplot.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
Wickham H (2022). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.2, https://CRAN.R-project.org/package=forcats.
Wickham H (2023). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.5.1, https://CRAN.R-project.org/package=stringr.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://CRAN.R-project.org/package=dplyr.
Wickham H, Henry L (2023). purrr: Functional Programming Tools. R package version 1.0.2, https://CRAN.R-project.org/package=purrr.
Wickham H, Hester J, Bryan J (2024). readr: Read Rectangular Text Data. R package version 2.1.5, https://CRAN.R-project.org/package=readr.
Wickham H, Vaughan D, Girlich M (2024). tidyr: Tidy Messy Data. R package version 1.3.1, https://CRAN.R-project.org/package=tidyr.

References

Dani, Jennifer, Courtney Burrill, and Barbara Demmig-Adams. 2005. “The remarkable role of nutrition in learning and behaviour.” Nutrition and Food Science 35 (4): 258–63. https://doi.org/10.1108/00346650510605658.

Shanta Retelny, Victoria, Annie Neuendorf, and Julie L. Roth. 2008. “Nutrition protocols for the prevention of cardiovascular Disease.” Nutrition in Clinical Practice 23 (5): 468–76. https://doi.org/10.1177/0884533608323425.

Recommendations for an Intelligent Diet

Jeesung Ahn

04/15/2022