• dietaryindex

Getting Started

Title: Dietaryindex: A User-Friendly and Versatile R Package for Standardizing Dietary Pattern Analysis in Epidemiological and Clinical Studies

Authors: Jiada James Zhan et al.

The dietaryindex package currently contains the following key functions:

Generic functions
- DI_GM(), Dietary index - Gut Microbiome
  - Ref: https://pubmed.ncbi.nlm.nih.gov/38613077/
- HEI2020(), Healthy Eating Index 2020 & HEI-Toddlers-2020
  - Ref: https://pubmed.ncbi.nlm.nih.gov/37201748/
  - Ref: https://pubmed.ncbi.nlm.nih.gov/37209965/
- HEI2015(), Healthy Eating Index 2015
  - Ref: https://www.fns.usda.gov/how-hei-scored
- AHEI(), alternative healthy eating index
  - Ref: https://academic.oup.com/jn/article/142/6/1009/4688968?login=false
- AHEIP() , alternative healthy eating index - pregnancy
  - Ref: https://www.jandonline.org/article/S0002-8223(09)00288-0/fulltext
- DASH(), Dietary Approaches to Stop Hypertension
  - Ref:https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/414155
- DASHI(), DASH Index in nutrients from the DASH trial
  - Ref:
    - https://www.nejm.org/doi/10.1056/NEJM199704173361601?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200www.ncbi.nlm.nih.gov
    - https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/413957
- MED(), Alternate Mediterranean Diet Score (aMED)
  - Ref: https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.108.816736?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed
- MEDI(), Mediterranean diet in serving sizes from the PREDIMED trial
  - Ref: https://www.nejm.org/doi/full/10.1056/nejmoa1800389
- DII(), Dietary Inflammation Index
  - Ref: https://www.cambridge.org/core/journals/public-health-nutrition/article/designing-and-developing-a-literaturederived-populationbased-dietary-inflammatory-index/30BE2C2295CE93DC6B54F9F9AD50CC68
- ACS2020_V1(), American Cancer Society 2020 diet score
- ACS2020_V2(), Alternate calculation method of the American Cancer Society 2020 diet score, intended for use when percent calories from highly processed foods and refined grains is not available (uses daily servings per 1000 calories instead)
  - Ref: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2793171
- PHDI(), Planetary Health Diet Index from the EAT-Lancet Commission
  - Ref: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31788-4/fulltext
NHANES
- For NHANES data starting at the 2005-2006 cycle
  - DI_GM_NHANES_FPED(), Calculating DI_GM with 1 step using the NHANES_FPED data
  - HEI2020_NHANES_FPED(), Calculating HEI2020 & HEI-Toddlers-2020 with 1 step using the NHANES_FPED datasets
  - HEI2015_NHANES_FPED(), Calculating HEI2015 with 1 step using the NHANES_FPED data
  - AHEI_NHANES_FPED(), Calculating AHEI with 1 step using the NHANES_FPED data
  - DASH_NHANES_FPED(), Calculating DASH (quintile-based) with 1 step using the NHANES_FPED data
  - DASHI_NHANES_FPED(), Calculating DASHI (nutrient-based from the DASH trial) with 1 step using the NHANES_FPED data
  - MED_NHANES_FPED(), Calculating aMED (median-based) with 1 step using the NHANES_FPED data
  - MEDI_NHANES_FPED(), Calculating MEDI (serving size-based from the PREDIMED trial) with 1 step using the NHANES_FPED data
  - DII_NHANES_FPED(), Calculating DII with 1 step using the NHANES_FPED data
- For NHANES data between 1999-2004
  - DI_GM_NHANES_MPED(), Calculating DI_GM with 1 step using the NHANES_MPED data
  - HEI2020_NHANES_MPED(), Calculating HEI2020 with 1 step using the NHANES_MPED data
  - HEI2015_NHANES_MPED(), Calculating HEI2015 with 1 step using the NHANES_MPED data
  - AHEI_NHANES_MPED(), Calculating AHEI with 1 step using the NHANES_MPED data
  - DASH_NHANES_MPED(), Calculating DASH with 1 step using the NHANES_MPED data
  - DASHI_NHANES_MPED(), Calculating DASHI with 1 step using the NHANES_MPED data
  - MED_NHANES_MPED(), Calculating aMED with 1 step using the NHANES_MPED data
  - MEDI_NHANES_MPED(), Calculating MEDI with 1 step using the NHANES_MPED data
  - DII_NHANES_MPED(), Calculating DII with 1 step using the NHANES_MPED data
ASA24
- HEI2020_ASA24(), Calculating HEI2020 for non-toddlers (age > 2 years) with 1 step using the ASA24 data
- HEI2020_TODDLERS_ASA24(), Calculating HEI2020 for non-toddlers (age 1-2 years) with 1 step using the ASA24 data
- HEI2015_ASA24(), Calculating HEI2015 with 1 step using the ASA24 data
- AHEI_F_ASA24(), Calculate the AHEI (female only) within 1 step using the ASA24 data
- AHEI_M_ASA24(), Calculate the AHEI (male only) within 1 step using the ASA24 data
- DASH_ASA24(), Calculating DASH with 1 step using the ASA24 data
- MED_ASA24(), Calculating aMED with 1 step using the ASA24 data
- DII_ASA24(), Calculating DII with 1 step using the ASA24 data
DHQ3
- HEI2015_DHQ3(), Calculating HEI2015 with 1 step using the DHQ3 data
- AHEI_DHQ3(), Calculate the AHEI (female or male) within 1 step using the DHQ3 data
- DASH_DHQ3(), Calculating DASH with 1 step using the DHQ3 data. The data is Detailed analysis file, ending with detail.csv
- MED_DHQ3(), Calculating aMED with 1 step using the DHQ3 data
BLOCK
- HEI2015_BLOCK(), Calculating HEI2015 with 1 step using the BLOCK data
- MED_BLOCK(), Calculating aMED with 1 step using the BLOCK data
- DII_BLOCK(), Calculating DII with 1 step using the BLOCK data
- DASH_BLOCK(), Calculating DASH with 1 step using the BLOCK data
- AHEI_BLOCK(), Calculating AHEI with 1 step using the BLOCK data
- AHEIP_BLOCK(), Calculating AHEIP with 1 step using the BLOCK data

Note: all NHANES functions allow users to enter the first day data, or the second day data, or first day + second day data and return the results accordingly. See examples later.

dietaryindex has compiled NHANES data from 2005 - 2020 for your convenience. This includes NHANES 2005-2006, 2007-2008, 2009-2010, 2011-2012, 2013-2014, 2015-2016, 2017-2018, 2017-2020. To retrieve the data:

Download the NHANES_combined folder from the Google Drive (https://drive.google.com/drive/u/2/folders/1umjhuS22aHEW_bU5AjYa8vrae91gsb0D)
Download the NHANES_combined folder from the GitHub dietaryindex_NHANES page (https://github.com/jamesjiadazhan/dietaryindex_NHANES/tree/main/data/NHANES_combined)

Then, you may use the following codes to load the data:

# set up working dictionary
setwd("/Users/james/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Emory University - Ph.D./dietaryindex_package/NHANES_combined/dietaryindex_NHANES/data/NHANES_combined")

## NHANES 2005-2006, for example
load("NHANES_20052006.rda")

Examples for data format:

NHANES data format

# install.packages("devtools") #If you don't have "devtools" installed already
# devtools::install_github("jamesjiadazhan/dietaryindex") # Install the package from GitHub
# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
library(dietaryindex)

## load dplyr package for data processing
library(dplyr)
## load the survey package for NHANES data
library(survey)

## Warning: package 'survival' was built under R version 4.3.3

## load the gtsummary package for summarizing the results
library(gtsummary)

## Warning: package 'gtsummary' was built under R version 4.3.3

data("NHANES_20172018")

Food pattern data for the 2017-2018 first day data

knitr::kable(head(NHANES_20172018$FPED))

SEQN	RIAGENDR	RIDAGEYR	RIDRETH3	SDMVPSU	SDMVSTRA	INDFMIN2	INDFMPIR	WTDRD1	WTDR2D	DR1DRSTZ	DRABF	DRDINT	DR1TNUMF	DR1T_F_TOTAL	DR1T_F_CITMLB	DR1T_F_OTHER	DR1T_F_JUICE	DR1T_V_TOTAL	DR1T_V_DRKGR	DR1T_V_REDOR_TOTAL	DR1T_V_REDOR_TOMATO	DR1T_V_REDOR_OTHER	DR1T_V_STARCHY_TOTAL	DR1T_V_STARCHY_POTATO	DR1T_V_STARCHY_OTHER	DR1T_V_OTHER	DR1T_V_LEGUMES	DR1T_G_TOTAL	DR1T_G_WHOLE	DR1T_G_REFINED	DR1T_PF_TOTAL	DR1T_PF_MPS_TOTAL	DR1T_PF_MEAT	DR1T_PF_CUREDMEAT	DR1T_PF_ORGAN	DR1T_PF_POULT	DR1T_PF_SEAFD_HI	DR1T_PF_SEAFD_LOW	DR1T_PF_EGGS	DR1T_PF_SOY	DR1T_PF_NUTSDS	DR1T_PF_LEGUMES	DR1T_D_TOTAL	DR1T_D_MILK	DR1T_D_YOGURT	DR1T_D_CHEESE	DR1T_OILS	DR1T_SOLID_FATS	DR1T_ADD_SUGARS	DR1T_A_DRINKS
93703	2	2	6	2	145	15	5.00	0.000	NA	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93704	1	2	3	1	143	15	5.00	81714.005	82442.869	1	2	2	13	0.99	0	0.00	0.99	0.82	0.00	0.03	0.03	0.00	0.73	0.73	0.00	0.06	0.00	3.03	0.00	3.03	3.52	3.30	0.00	0.00	0	2.12	1.18	0	0.22	0.00	0.00	0.00	1.73	1.06	0	0.67	23.16	9.53	9.12	0
93705	2	66	4	2	145	3	0.82	7185.561	5640.391	1	2	2	17	0.00	0	0.00	0.00	1.50	0.31	0.59	0.16	0.42	0.14	0.11	0.03	0.46	0.15	2.10	0.00	2.10	0.80	0.00	0.00	0.00	0	0.00	0.00	0	0.01	0.79	0.00	0.58	0.22	0.03	0	0.19	21.68	31.14	19.42	0
93706	1	18	6	2	134	NA	NA	6463.883	0.000	1	2	1	8	0.00	0	0.00	0.00	0.72	0.00	0.50	0.30	0.20	0.00	0.00	0.00	0.23	0.00	4.51	0.00	4.51	8.79	8.79	0.00	0.00	0	8.79	0.00	0	0.00	0.00	0.00	0.00	2.03	0.00	0	2.03	58.16	39.73	1.64	0
93707	1	13	7	1	138	10	1.88	15333.777	22707.067	1	2	2	16	0.00	0	0.00	0.00	0.94	0.00	0.01	0.01	0.00	0.93	0.93	0.00	0.00	0.00	5.26	4.12	1.14	5.61	3.62	0.00	1.56	0	2.06	0.00	0	1.99	0.00	0.00	0.00	0.26	0.26	0	0.00	22.79	48.34	18.65	0
93708	2	66	6	2	138	6	1.63	10825.545	22481.854	1	2	2	14	0.49	0	0.49	0.00	2.33	0.00	0.04	0.00	0.04	0.03	0.00	0.03	2.26	0.00	3.53	0.34	3.19	4.73	2.16	2.16	0.00	0	0.00	0.00	0	0.00	0.00	2.57	0.00	0.06	0.06	0	0.00	19.13	31.15	6.00	0

Nutrient data for the 2017-2018 first day data

knitr::kable(head(NHANES_20172018$NUTRIENT))

SEQN	WTDRD1	WTDR2D	DR1DRSTZ	DR1EXMER	DRABF	DRDINT	DR1DBIH	DR1DAY	DR1LANG	DR1MRESP	DR1HELP	DBQ095Z	DBD100	DRQSPREP	DR1STY	DR1SKY	DRQSDIET	DRQSDT1	DRQSDT2	DRQSDT3	DRQSDT4	DRQSDT5	DRQSDT6	DRQSDT7	DRQSDT8	DRQSDT9	DRQSDT10	DRQSDT11	DRQSDT12	DRQSDT91	DR1TNUMF	DR1TKCAL	DR1TPROT	DR1TCARB	DR1TSUGR	DR1TFIBE	DR1TTFAT	DR1TSFAT	DR1TMFAT	DR1TPFAT	DR1TCHOL	DR1TATOC	DR1TATOA	DR1TRET	DR1TVARA	DR1TACAR	DR1TBCAR	DR1TCRYP	DR1TLYCO	DR1TLZ	DR1TVB1	DR1TVB2	DR1TNIAC	DR1TVB6	DR1TFOLA	DR1TFA	DR1TFF	DR1TFDFE	DR1TCHL	DR1TVB12	DR1TB12A	DR1TVC	DR1TVD	DR1TVK	DR1TCALC	DR1TPHOS	DR1TMAGN	DR1TIRON	DR1TZINC	DR1TCOPP	DR1TSODI	DR1TPOTA	DR1TSELE	DR1TCAFF	DR1TTHEO	DR1TALCO	DR1TMOIS	DR1TS040	DR1TS060	DR1TS080	DR1TS100	DR1TS120	DR1TS140	DR1TS160	DR1TS180	DR1TM161	DR1TM181	DR1TM201	DR1TM221	DR1TP182	DR1TP183	DR1TP184	DR1TP204	DR1TP205	DR1TP225	DR1TP226	DR1_300	DR1_320Z	DR1_330Z	DR1BWATZ	DR1TWSZ	DRD340	DRD350A	DRD350AQ	DRD350B	DRD350BQ	DRD350C	DRD350CQ	DRD350D	DRD350DQ	DRD350E	DRD350EQ	DRD350F	DRD350FQ	DRD350G	DRD350GQ	DRD350H	DRD350HQ	DRD350I	DRD350IQ	DRD350J	DRD350JQ	DRD350K	DRD360	DRD370A	DRD370AQ	DRD370B	DRD370BQ	DRD370C	DRD370CQ	DRD370D	DRD370DQ	DRD370E	DRD370EQ	DRD370F	DRD370FQ	DRD370G	DRD370GQ	DRD370H	DRD370HQ	DRD370I	DRD370IQ	DRD370J	DRD370JQ	DRD370K	DRD370KQ	DRD370L	DRD370LQ	DRD370M	DRD370MQ	DRD370N	DRD370NQ	DRD370O	DRD370OQ	DRD370P	DRD370PQ	DRD370Q	DRD370QQ	DRD370R	DRD370RQ	DRD370S	DRD370SQ	DRD370T	DRD370TQ	DRD370U	DRD370UQ	DRD370V
93703	0.000	NA	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93704	81714.005	82442.869	1	49	2	2	7	2	1	2	12	4	NA	2	2	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	13	1230	51.58	160.46	76.97	5.9	43.24	11.372	14.333	12.506	144	5.80	0	254	262	1	94	1	684	321	0.679	0.959	15.617	1.369	100	33	66	123	198.1	3.83	0	60.1	8.1	38.2	700	1170	151	3.55	4.18	0.497	2198	1970	52.9	8	54	0	875.00	0.219	0.166	0.119	0.273	0.291	1.050	6.577	2.098	0.337	13.682	0.392	0.029	11.087	1.054	0.031	0.079	0.080	0.027	0.150	2	240	240	0	91	1	2	NA	1	2	1	1	2	NA	2	NA	2	NA	2	NA	1	2	2	NA	2	NA	2	1	1	1	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	1	1	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2
93705	7185.561	5640.391	1	73	2	2	5	1	1	1	11	4	NA	3	2	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	17	1202	20.01	157.45	91.55	8.4	56.98	16.435	16.432	19.786	14	5.66	0	32	436	1551	4096	2	1573	1645	0.589	1.237	7.576	0.458	179	32	146	202	95.0	0.33	0	21.4	0.2	155.5	314	466	162	8.80	2.93	0.689	3574	1640	22.1	361	120	0	1773.88	0.156	0.077	0.058	0.122	0.145	0.447	8.951	5.980	0.118	16.047	0.101	0.014	17.805	1.943	0.000	0.014	0.001	0.001	0.001	2	315	315	0	1	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93706	6463.883	0.000	1	86	2	1	NA	6	1	1	12	1	1	3	2	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	8	1987	94.19	89.82	14.73	7.1	137.39	35.169	45.805	49.873	462	10.02	0	198	431	872	2363	2	4605	313	1.152	1.033	26.831	1.821	267	125	139	354	367.9	2.30	0	9.7	0.7	138.4	869	1025	187	8.52	8.05	0.614	3657	1247	118.5	0	0	0	3405.40	0.263	0.203	0.140	0.377	0.459	1.816	23.150	7.747	3.387	41.577	0.524	0.011	44.097	5.074	0.016	0.308	0.021	0.044	0.021	3	3042	0	3042	1	1	1	2	2	NA	2	NA	2	NA	1	2	1	1	2	NA	2	NA	2	NA	2	NA	2	1	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	1	1	2	NA	2	NA	2	NA	2	NA	1	3	2	NA	1	3	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2
93707	15333.777	22707.067	1	81	2	2	14	2	1	1	12	1	9	2	2	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	16	1775	59.48	188.15	84.22	10.9	89.18	33.252	33.712	12.424	585	8.92	0	443	449	0	70	9	516	579	0.755	1.296	13.920	0.951	124	15	110	135	369.4	2.08	0	12.9	2.7	23.5	535	945	215	8.42	8.30	0.955	2450	1769	91.9	21	133	0	2222.41	1.015	0.616	0.414	0.849	1.138	3.383	17.280	7.815	1.807	32.089	0.284	0.046	11.221	0.873	0.002	0.237	0.008	0.015	0.058	2	1785	1545	240	99	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	9	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93708	10825.545	22481.854	1	73	2	2	41	7	6	1	9	4	NA	4	2	NA	2	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	14	1251	50.96	123.71	49.84	16.6	65.49	17.446	29.000	14.823	71	6.20	0	35	236	323	2245	26	0	2148	1.143	0.840	15.368	1.096	260	74	185	311	175.7	1.09	0	146.4	0.8	137.0	412	635	248	11.49	6.45	1.049	2135	1631	54.3	33	69	0	2821.95	0.070	0.044	0.027	0.091	0.097	0.499	9.440	6.126	0.446	28.177	0.310	0.003	13.927	0.804	0.000	0.038	0.001	0.004	0.000	2	2160	720	1440	1	1	2	NA	2	NA	2	NA	1	2	2	NA	2	NA	2	NA	1	2	2	NA	2	NA	2	1	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	1	6	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2	NA	2

Demographic data for the 2017-2018 first and second day data

knitr::kable(head(NHANES_20172018$DEMO))

SEQN	SDDSRVYR	RIDSTATR	RIAGENDR	RIDAGEYR	RIDAGEMN	RIDRETH1	RIDRETH3	RIDEXMON	RIDEXAGM	DMQMILIZ	DMQADFC	DMDBORN4	DMDCITZN	DMDYRSUS	DMDEDUC3	DMDEDUC2	DMDMARTL	RIDEXPRG	SIALANG	SIAPROXY	SIAINTRP	FIALANG	FIAPROXY	FIAINTRP	MIALANG	MIAPROXY	MIAINTRP	AIALANGA	DMDHHSIZ	DMDFMSIZ	DMDHHSZA	DMDHHSZB	DMDHHSZE	DMDHRGND	DMDHRAGZ	DMDHREDZ	DMDHRMAZ	DMDHSEDZ	WTINT2YR	WTMEC2YR	SDMVPSU	SDMVSTRA	INDHHIN2	INDFMIN2	INDFMPIR
93703	10	2	2	2	NA	5	6	2	27	NA	NA	1	1	NA	NA	NA	NA	NA	1	1	2	1	2	2	NA	NA	NA	NA	5	5	3	0	0	1	2	3	1	3	9246.492	8539.731	2	145	15	15	5.00
93704	10	2	1	2	NA	3	3	1	33	NA	NA	1	1	NA	NA	NA	NA	NA	1	1	2	1	2	2	NA	NA	NA	NA	4	4	2	0	0	1	2	3	1	2	37338.768	42566.615	1	143	15	15	5.00
93705	10	2	2	66	NA	4	4	2	NA	2	NA	1	1	NA	NA	2	3	NA	1	2	2	1	2	2	1	2	2	1	1	1	0	0	1	2	4	1	2	NA	8614.571	8338.420	2	145	3	3	0.82
93706	10	2	1	18	NA	5	6	2	222	2	NA	1	1	NA	15	NA	NA	NA	1	2	2	NA	NA	NA	1	2	2	1	5	5	0	0	1	1	4	3	1	2	8548.633	8723.440	2	134	NA	NA	NA
93707	10	2	1	13	NA	5	7	2	158	NA	NA	1	1	NA	6	NA	NA	NA	1	1	2	1	2	2	1	2	2	1	7	7	0	3	0	1	3	2	1	3	6769.345	7064.610	1	138	10	10	1.88
93708	10	2	2	66	NA	5	6	2	NA	2	NA	2	1	7	NA	1	1	NA	1	2	1	1	2	2	1	2	1	3	2	2	0	0	2	1	4	1	1	1	13329.451	14372.489	2	138	6	6	1.63

Food pattern data for the 2017-2018 second day data

knitr::kable(head(NHANES_20172018$FPED2))

SEQN	RIAGENDR	RIDAGEYR	RIDRETH3	SDMVPSU	SDMVSTRA	INDFMIN2	INDFMPIR	WTDRD1	WTDR2D	DR2DRSTZ	DRABF	DRDINT	DR2TNUMF	DR2T_F_TOTAL	DR2T_F_CITMLB	DR2T_F_OTHER	DR2T_F_JUICE	DR2T_V_TOTAL	DR2T_V_DRKGR	DR2T_V_REDOR_TOTAL	DR2T_V_REDOR_TOMATO	DR2T_V_REDOR_OTHER	DR2T_V_STARCHY_TOTAL	DR2T_V_STARCHY_POTATO	DR2T_V_STARCHY_OTHER	DR2T_V_OTHER	DR2T_V_LEGUMES	DR2T_G_TOTAL	DR2T_G_WHOLE	DR2T_G_REFINED	DR2T_PF_TOTAL	DR2T_PF_MPS_TOTAL	DR2T_PF_MEAT	DR2T_PF_CUREDMEAT	DR2T_PF_ORGAN	DR2T_PF_POULT	DR2T_PF_SEAFD_HI	DR2T_PF_SEAFD_LOW	DR2T_PF_EGGS	DR2T_PF_SOY	DR2T_PF_NUTSDS	DR2T_PF_LEGUMES	DR2T_D_TOTAL	DR2T_D_MILK	DR2T_D_YOGURT	DR2T_D_CHEESE	DR2T_OILS	DR2T_SOLID_FATS	DR2T_ADD_SUGARS	DR2T_A_DRINKS
93703	2	2	6	2	145	15	5.00	0.000	NA	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93704	1	2	3	1	143	15	5.00	81714.005	82442.869	1	2	2	13	1.94	0.61	0.46	0.87	0.02	0	0.02	0.02	0.00	0.00	0	0.00	0.00	0.00	5.00	0.72	4.28	0.46	0.00	0.00	0.0	0	0.00	0.00	0.00	0.46	0.00	0.0	0.00	1.74	1.54	0	0.19	19.42	10.28	17.25	0
93705	2	66	4	2	145	3	0.82	7185.561	5640.391	1	2	2	14	0.03	0.00	0.00	0.03	0.29	0	0.28	0.25	0.03	0.00	0	0.00	0.01	0.23	5.39	0.00	5.39	2.75	0.30	0.00	0.3	0	0.00	0.00	0.00	2.05	0.00	0.4	0.91	0.52	0.00	0	0.51	25.40	20.01	8.67	0
93706	1	18	6	2	134	NA	NA	6463.883	0.000	5	2	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93707	1	13	7	1	138	10	1.88	15333.777	22707.067	1	2	2	14	0.00	0.00	0.00	0.00	0.70	0	0.06	0.06	0.00	0.64	0	0.64	0.00	0.00	2.94	0.90	2.04	10.29	7.98	6.69	0.0	0	1.29	0.00	0.00	2.25	0.07	0.0	0.00	0.04	0.04	0	0.00	30.07	38.10	18.15	0
93708	2	66	6	2	138	6	1.63	10825.545	22481.854	1	2	2	15	0.00	0.00	0.00	0.00	0.26	0	0.00	0.00	0.00	0.00	0	0.00	0.26	0.00	2.59	0.00	2.59	2.34	2.31	0.00	0.0	0	0.89	0.94	0.48	0.03	0.00	0.0	0.00	1.14	1.11	0	0.00	10.13	24.19	6.44	0

Nutrient data for the 2017-2018 second day data

knitr::kable(head(NHANES_20172018$NUTRIENT2))

SEQN	WTDRD1	WTDR2D	DR2DRSTZ	DR2EXMER	DRABF	DRDINT	DR2DBIH	DR2DAY	DR2LANG	DR2MRESP	DR2HELP	DR2TNUMF	DR2STY	DR2SKY	DR2TKCAL	DR2TPROT	DR2TCARB	DR2TSUGR	DR2TFIBE	DR2TTFAT	DR2TSFAT	DR2TMFAT	DR2TPFAT	DR2TCHOL	DR2TATOC	DR2TATOA	DR2TRET	DR2TVARA	DR2TACAR	DR2TBCAR	DR2TCRYP	DR2TLYCO	DR2TLZ	DR2TVB1	DR2TVB2	DR2TNIAC	DR2TVB6	DR2TFOLA	DR2TFA	DR2TFF	DR2TFDFE	DR2TCHL	DR2TVB12	DR2TB12A	DR2TVC	DR2TVD	DR2TVK	DR2TCALC	DR2TPHOS	DR2TMAGN	DR2TIRON	DR2TZINC	DR2TCOPP	DR2TSODI	DR2TPOTA	DR2TSELE	DR2TCAFF	DR2TTHEO	DR2TALCO	DR2TMOIS	DR2TS040	DR2TS060	DR2TS080	DR2TS100	DR2TS120	DR2TS140	DR2TS160	DR2TS180	DR2TM161	DR2TM181	DR2TM201	DR2TM221	DR2TP182	DR2TP183	DR2TP184	DR2TP204	DR2TP205	DR2TP225	DR2TP226	DR2_300	DR2_320Z	DR2_330Z	DR2BWATZ	DR2TWSZ
93703	0.000	NA	5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93704	81714.005	82442.869	1	87	2	2	18	6	1	2	12	13	2	NA	1356	30.49	229.69	134.37	8.2	37.02	10.368	10.443	13.249	70	3.75	0	562	589	0	299	67	3835	174	1.407	1.562	10.871	1.189	360	311	50	577	223.0	4.74	2.08	38.3	5.4	58.2	761	879	147	10.06	8.13	0.473	1345	1413	46.9	6	54	0	795.87	0.251	0.161	0.099	0.221	0.239	0.796	5.929	2.366	0.168	10.084	0.155	0.010	11.601	1.581	0.003	0.041	0.000	0.002	0.010	2	0	0	0	91
93705	7185.561	5640.391	1	91	2	2	15	4	1	1	12	14	2	NA	1235	38.52	147.49	43.04	13.1	55.19	14.079	19.882	17.072	402	6.15	0	217	267	156	510	15	7884	582	1.001	1.274	8.549	0.418	231	93	139	295	323.8	1.25	0.00	3.3	3.5	31.2	693	738	156	10.52	4.48	0.893	2447	1158	67.9	146	19	0	1973.77	0.104	0.082	0.063	0.170	0.217	0.613	8.728	3.519	0.441	19.072	0.176	0.003	15.339	1.365	0.001	0.201	0.002	0.011	0.059	2	960	0	960	1
93706	6463.883	0.000	5	NA	2	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
93707	15333.777	22707.067	1	78	2	2	22	3	1	1	4	14	1	1	1794	92.03	153.12	85.81	7.9	93.98	31.098	35.405	17.827	656	10.70	0	235	248	16	88	123	2136	1160	0.428	1.240	23.431	1.827	125	19	106	136	521.5	4.94	0.00	8.1	2.8	66.5	227	1136	180	13.69	14.23	0.834	2581	1805	122.2	10	322	0	1398.45	0.251	0.156	0.123	0.283	0.524	1.657	18.588	8.756	1.574	33.294	0.255	0.009	15.732	1.568	0.001	0.327	0.004	0.025	0.061	3	1020	780	240	99
93708	10825.545	22481.854	1	43	2	2	56	1	1	1	12	15	2	NA	842	32.85	83.92	42.37	2.8	41.52	14.962	13.227	8.662	126	2.98	0	277	323	2	536	3	0	236	0.483	0.640	7.768	0.525	103	57	46	143	139.6	3.36	0.00	3.4	10.9	49.1	535	599	99	2.77	2.98	0.332	1241	831	39.7	0	12	0	1934.55	0.226	0.169	0.115	0.264	0.361	1.265	9.269	2.986	0.630	11.879	0.779	0.025	7.398	0.984	0.025	0.043	0.082	0.035	0.132	2	1524	510	1014	1

ASA24 data format:

ASA24 dietary data for each individual

data("ASA24_exp")
knitr::kable(head(ASA24_exp))

RecallRecId	UserName	UserID	RecallNo	RecallStatus	IntakeStartDateTime	IntakeEndDateTime	ReportingDate	Lang	NumFoods	NumCodes	AmtUsual	KCAL	PROT	TFAT	CARB	MOIS	CAFF	THEO	SUGR	FIBE	CALC	IRON	MAGN	PHOS	POTA	SODI	ZINC	COPP	SELE	VC	VB1	VB2	NIAC	VB6	FOLA	FA	FF	FDFE	VB12	VARA	RET	BCAR	ACAR	CRYP	LYCO	LZ	ATOC	VK	CHOLE	SFAT	S040	S060	S080	S100	S120	S140	S160	S180	MFAT	M161	M181	M201	M221	PFAT	P182	P183	P184	P204	P205	P225	P226	VITD	CHOLN	VITE_ADD	B12_ADD	F_TOTAL	F_CITMLB	F_OTHER	F_JUICE	V_TOTAL	V_DRKGR	V_REDOR_TOTAL	V_REDOR_TOMATO	V_REDOR_OTHER	V_STARCHY_TOTAL	V_STARCHY_POTATO	V_STARCHY_OTHER	V_OTHER	V_LEGUMES	G_TOTAL	G_WHOLE	G_REFINED	PF_TOTAL	PF_MPS_TOTAL	PF_CUREDMEAT	PF_POULT	PF_EGGS	PF_SOY	PF_NUTSDS	PF_LEGUMES	D_TOTAL	D_MILK	OILS	SOLID_FATS	ADD_SUGARS	DataComp
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	1	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	1/18/22 0:00	1/18/22 23:59	1/19/22	1	23	23	1	2019.1261	64.00808	97.71591	235.86038	2386.6157	36.88	41.2	74.68865	23.631891	1104.3000	15.024851	483.81516	1150.5633	2965.253	3415.849	8.317065	1.6922004	103.06994	53.30264	1.6393786	1.5903095	22.102181	1.5142930	393.9672	149.24784	244.7193	496.3751	2.959247	960.4294	320.5494	7330.914	765.8908	22.90329	813.3750	16872.0702	20.156954	567.0490	217.0211	20.891319	0.2926330	0.2030142	0.1435912	0.2999671	0.3031705	1.0094686	12.634695	4.442925	46.080027	0.6288449	44.737310	0.5006182	0.00030	25.233201	23.46477	1.5142505	0.00030	0.1190309	0.000528	0.0043849	0.0265132	5.545659	320.0657	6.8564	1.5372	0.40730	0.0000	0.39530	0.01200	2.006828	0.971500	0.3217283	0.1807500	0.1409784	0.36150	0.180750	0.180750	0.3521000	0.0930	6.86142	1.784250	5.077170	5.913528	0.00000	0.00000	0.00000	0.9115442	0.2126250	4.789359	0.3690	0.6840	0.6840	61.89215	12.830923	10.928636	1
960a78fa-fdf8-417d-a5d2-e29d7984ac8c	1	e6541891-8a22-49e5-b1c0-fbb853234a73	2	2	1/18/22 0:00	1/18/22 23:59	1/19/22	1	23	23	1	1009.5630	32.00404	48.85796	117.93019	1193.3079	18.44	20.6	37.34432	11.815945	552.1500	7.512426	241.90758	575.2816	1482.626	1707.924	4.158532	0.8461002	51.53497	26.65132	0.8196893	0.7951547	11.051090	0.7571465	196.9836	74.62392	122.3597	248.1876	1.479623	480.2147	160.2747	3665.457	382.9454	11.45165	406.6875	8436.0351	10.078477	283.5245	108.5106	10.445659	0.1463165	0.1015071	0.0717956	0.1499836	0.1515853	0.5047343	6.317347	2.221462	23.040013	0.3144225	22.368655	0.2503091	0.00015	12.616600	11.73238	0.7571252	0.00015	0.0595154	0.000264	0.0021924	0.0132566	2.772829	160.0329	3.4282	0.7686	0.20365	0.0000	0.19765	0.00600	1.003414	0.485750	0.1608642	0.0903750	0.0704892	0.18075	0.090375	0.090375	0.1760500	0.0465	3.43071	0.892125	2.538585	2.956764	0.00000	0.00000	0.00000	0.4557721	0.1063125	2.394679	0.1845	0.3420	0.3420	30.94608	6.415462	5.464318	1
cee8c74d-51a2-4629-b92f-6bbdb5793fd6	2	dbb278b0-b589-442a-88fd-9a68fd986a91	1	2	1/18/22 0:00	1/18/22 23:59	1/19/22	1	10	10	2	1246.7375	48.24150	55.36362	145.31270	2452.3924	96.00	0.0	71.76457	10.042250	831.9350	5.861650	186.70250	758.3275	2361.782	3808.492	4.948700	0.7704950	41.95375	155.40625	0.5238725	1.0762800	10.140018	1.4799800	306.3900	41.98000	264.4100	332.7700	0.421650	592.5575	275.4900	3394.180	750.6900	103.62000	7350.6575	1641.6150	7.406650	214.1842	135.8750	16.216413	0.4035600	0.1527600	0.1392600	0.2849400	0.2373300	1.5209750	8.760355	3.949800	17.413210	0.5344450	16.516858	0.0928200	0.00090	15.803205	14.12976	1.5046100	0.00090	0.0810300	0.000000	0.0065100	0.0089550	2.802000	256.0770	0.0000	0.0000	1.48800	0.0000	0.00000	1.48800	2.140250	0.618450	0.3484250	0.2520250	0.0964000	0.42190	0.096400	0.325500	0.7514750	0.1446	2.99510	0.079800	2.915300	2.903700	2.53890	0.00000	2.53890	0.3648000	0.0000000	0.000000	0.4820	0.0456	0.0456	19.24660	23.699800	5.198400	1
cee8c74d-51a2-4629-b92f-6bbdb5793fd7	2	dbb278b0-b589-442a-88fd-9a68fd986a91	2	2	1/18/22 0:00	1/18/22 23:59	1/19/22	1	10	10	2	623.3687	24.12075	27.68181	72.65635	1226.1962	48.00	0.0	35.88229	5.021125	415.9675	2.930825	93.35125	379.1637	1180.891	1904.246	2.474350	0.3852475	20.97687	77.70312	0.2619362	0.5381400	5.070009	0.7399900	153.1950	20.99000	132.2050	166.3850	0.210825	296.2788	137.7450	1697.090	375.3450	51.81000	3675.3288	820.8075	3.703325	107.0921	67.9375	8.108206	0.2017800	0.0763800	0.0696300	0.1424700	0.1186650	0.7604875	4.380178	1.974900	8.706605	0.2672225	8.258429	0.0464100	0.00045	7.901603	7.06488	0.7523050	0.00045	0.0405150	0.000000	0.0032550	0.0044775	1.401000	128.0385	0.0000	0.0000	0.74400	0.0000	0.00000	0.74400	1.070125	0.309225	0.1742125	0.1260125	0.0482000	0.21095	0.048200	0.162750	0.3757375	0.0723	1.49755	0.039900	1.457650	1.451850	1.26945	0.00000	1.26945	0.1824000	0.0000000	0.000000	0.2410	0.0228	0.0228	9.62330	11.849900	2.599200	1
1f9daf51-8540-4fef-9f49-fe56c4b1ac54	3	f0115426-e6f8-4c6f-aeaa-065c615c85bd	1	2	1/18/22 0:00	1/18/22 23:59	1/19/22	1	14	14	2	2206.4860	65.89972	100.11426	267.77062	1770.4026	0.00	0.0	123.37104	16.349200	1136.0940	18.384940	221.62600	1205.8780	2440.166	4415.830	11.695100	0.7360300	68.76180	208.30220	1.8280460	2.2464920	29.188010	2.7669520	437.1660	160.00000	277.1660	553.6660	6.074620	821.8680	751.6020	637.836	125.8860	303.95200	100.6600	1051.0980	11.616760	117.3554	219.8120	26.972348	0.0040000	0.0024000	0.0108500	0.0122500	0.0100900	1.4957080	16.320104	7.901016	41.234728	1.8123080	38.692992	0.3343900	0.00184	22.686492	20.30833	1.9270220	0.00380	0.1777100	0.012290	0.0056700	0.0158500	4.339800	279.7190	3.4282	3.2986	1.88170	0.9936	0.00000	0.88810	2.800000	0.000000	0.3960000	0.0000000	0.3960000	0.75400	0.490000	0.264000	1.6500000	0.0000	5.48100	0.272000	5.209000	6.792320	6.36552	2.75562	3.60990	0.2560000	0.0000000	0.170800	0.0000	0.0560	0.0560	29.05440	49.324960	17.793340	1
1f9daf51-8540-4fef-9f49-fe56c4b1ac55	3	f0115426-e6f8-4c6f-aeaa-065c615c85bd	2	2	1/18/22 0:00	1/18/22 23:59	1/19/22	1	14	14	2	1103.2430	32.94986	50.05713	133.88531	885.2013	0.00	0.0	61.68552	8.174600	568.0470	9.192470	110.81300	602.9390	1220.083	2207.915	5.847550	0.3680150	34.38090	104.15110	0.9140230	1.1232460	14.594005	1.3834760	218.5830	80.00000	138.5830	276.8330	3.037310	410.9340	375.8010	318.918	62.9430	151.97600	50.3300	525.5490	5.808380	58.6777	109.9060	13.486174	0.0020000	0.0012000	0.0054250	0.0061250	0.0050450	0.7478540	8.160052	3.950508	20.617364	0.9061540	19.346496	0.1671950	0.00092	11.343246	10.15416	0.9635110	0.00190	0.0888550	0.006145	0.0028350	0.0079250	2.169900	139.8595	1.7141	1.6493	0.94085	0.4968	0.00000	0.44405	1.400000	0.000000	0.1980000	0.0000000	0.1980000	0.37700	0.245000	0.132000	0.8250000	0.0000	2.74050	0.136000	2.604500	3.396160	3.18276	1.37781	1.80495	0.1280000	0.0000000	0.085400	0.0000	0.0280	0.0280	14.52720	24.662480	8.896670	1

ASA24 detailed food data for all foods consumed by each individual

data("ASA24_exp_detailed")
knitr::kable(head(ASA24_exp_detailed))

RecallRecId	UserName	UserID	RecallNo	RecallStatus	IntakeStartDateTime	IntakeEndDateTime	ReportingDate	Lang	Occ_No	Occ_Time	Occ_Name	EatWith	WatchTVuseComputer	Location	FoodNum	FoodType	FoodSrce	CodeNum	FoodCode	HowMany	PortionCode	FoodAmt	KCAL	PROT	TFAT	CARB	MOIS	CAFF	THEO	SUGR	FIBE	CALC	IRON	MAGN	PHOS	POTA	SODI	ZINC	COPP	SELE	VC	VB1	VB2	NIAC	VB6	FOLA	FA	FF	FDFE	VB12	VARA	RET	BCAR	CRYP	LZ	ATOC	VK	CHOLE	SFAT	S040	S060	S080	S100	S120	S140	S160	S180	MFAT	M161	M181	M201	PFAT	P182	P183	P204	P225	P226	VITD	CHOLN	VITE_ADD	B12_ADD	F_TOTAL	F_JUICE	V_LEGUMES	G_TOTAL	G_REFINED	PF_TOTAL	PF_EGGS	PF_NUTSDS	PF_LEGUMES	D_TOTAL	D_MILK	OILS	SOLID_FATS	ADD_SUGARS	FoodComp	Food_Description
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	THR01	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	01/18/2022 0:00	01/18/2022 23:59	01/19/2022	1	1	01/18/2022 7:00	1	NA	4	1	1	1	Supermarket or grocery store	1	51180010	0.5	62015	34.5	91.08	3.6432	0.4554	18.0711	11.66445	0.0	0.0	2.90835	0.552	36.225	1.23165	10.005	34.155	36.915	145.59	0.28635	0.04416	7.4175	0.00	0.19596	0.11868	1.557675	0.02415	36.57	28.29	8.28	56.235	0.0000	0.00	0.00	0.0	0	0.0	0.0345	0.069	0.0	0.1242	0.0000	0.0000	0.0000	0.0000	0.000	0.00138	0.078315	0.039675	0.13455	0.002415	0.06417	0.001725	0.18147	0.173535	0.00828	0.0000	0.0000	0.0000	0.00	5.037	0.0000	0.0000	0.000	0.000	0.000	1.45935	1.45935	0.0000	0.00	0.0000	0.000	0.000	0.000	0.0000	0.000	0.39675	1	Bagel
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	THR01	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	01/18/2022 0:00	01/18/2022 23:59	01/19/2022	1	1	01/18/2022 7:00	1	NA	4	1	2	1	Supermarket or grocery store	2	31105030	1.0	61238	40.0	86.00	5.3080	6.8960	0.3040	26.72800	0.0	0.0	0.15600	0.000	23.600	0.74400	5.200	83.600	58.400	182.80	0.54400	0.03040	12.9600	0.00	0.01440	0.18360	0.030000	0.06840	14.80	0.00	14.80	14.800	0.3200	67.60	67.60	0.0	4	212.4	0.7800	3.520	157.2	1.7148	0.0016	0.0000	0.0016	0.0024	0.000	0.01400	1.223600	0.434800	2.75160	0.095200	2.62440	0.023200	1.99360	1.697200	0.16640	0.0796	0.0028	0.0244	0.84	105.520	0.0000	0.0000	0.000	0.000	0.000	0.00000	0.00000	0.7600	0.76	0.0000	0.000	0.000	0.000	2.8800	1.796	0.00000	1	Egg, whole, fried with oil
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	THR01	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	01/18/2022 0:00	01/18/2022 23:59	01/19/2022	1	1	01/18/2022 7:00	1	NA	4	1	3	1	Supermarket or grocery store	3	41205070	2.0	21000	30.0	78.00	2.4540	5.3340	5.9580	15.44700	0.0	0.0	0.87300	1.770	33.900	1.00800	13.500	70.800	75.300	144.30	0.52200	0.15300	2.6700	1.35	0.09150	0.03870	0.408900	0.03660	33.90	0.00	33.90	33.900	0.0000	0.30	0.00	5.1	0	0.6	0.3270	1.710	0.0	0.7287	0.0000	0.0000	0.0000	0.0006	0.000	0.00840	0.515400	0.172800	2.56620	0.032400	2.52060	0.009900	1.75350	1.707600	0.04440	0.0000	0.0000	0.0000	0.00	8.640	0.0000	0.0000	0.012	0.012	0.093	0.00000	0.00000	0.3660	0.00	0.3660	0.369	0.000	0.000	3.9450	0.087	0.00000	1	Hummus, plain
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	THR01	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	01/18/2022 0:00	01/18/2022 23:59	01/19/2022	1	1	01/18/2022 7:00	1	NA	4	1	4	1	Not applicable	4	94000100	32.0	30000	960.0	0.00	0.0000	0.0000	0.0000	959.04000	0.0	0.0	0.00000	0.000	28.800	0.00000	9.600	0.000	0.000	38.40	0.09600	0.09600	0.0000	0.00	0.00000	0.00000	0.000000	0.00000	0.00	0.00	0.00	0.000	0.0000	0.00	0.00	0.0	0	0.0	0.0000	0.000	0.0	0.0000	0.0000	0.0000	0.0000	0.0000	0.000	0.00000	0.000000	0.000000	0.00000	0.000000	0.00000	0.000000	0.00000	0.000000	0.00000	0.0000	0.0000	0.0000	0.00	0.000	0.0000	0.0000	0.000	0.000	0.000	0.00000	0.00000	0.0000	0.00	0.0000	0.000	0.000	0.000	0.0000	0.000	0.00000	1	Water, tap
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	THR01	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	01/18/2022 0:00	01/18/2022 23:59	01/19/2022	1	1	01/18/2022 7:00	1	NA	4	1	5	1	Supermarket or grocery store	5	92306800	6.0	30000	180.0	90.00	2.7900	1.7640	16.1640	158.67000	16.2	1.8	15.87600	0.180	102.600	0.07200	12.600	77.400	151.200	39.60	0.37800	0.02340	2.7000	0.00	0.03420	0.16560	0.081000	0.03240	9.00	0.00	9.00	9.000	0.4140	45.00	45.00	3.6	0	0.0	0.0360	0.180	7.2	1.0728	0.0504	0.0378	0.0306	0.0414	0.045	0.18000	0.468000	0.203400	0.46260	0.012600	0.44100	0.000000	0.09000	0.061200	0.02880	0.0000	0.0000	0.0000	1.08	13.680	0.0000	0.0000	0.000	0.000	0.000	0.00000	0.00000	0.0000	0.00	0.0000	0.000	0.342	0.342	0.0000	1.206	2.75400	1	Tea, hot, chai, with milk
960a78fa-fdf8-417d-a5d2-e29d7984ac8d	THR01	e6541891-8a22-49e5-b1c0-fbb853234a73	1	2	01/18/2022 0:00	01/18/2022 23:59	01/19/2022	1	1	01/18/2022 7:00	1	NA	4	1	6	2	Supermarket or grocery store	6	11350020	0.5	10205	122.0	18.30	0.7198	1.3420	0.7076	118.40100	0.0	0.0	0.00000	0.000	240.340	0.42700	8.540	12.200	81.740	86.62	0.08540	0.03538	0.2440	0.00	0.07320	0.05734	0.091500	0.01830	1.22	0.00	1.22	1.220	0.7686	52.46	52.46	0.0	0	0.0	3.4282	0.000	0.0	0.0000	0.0000	0.0000	0.0000	0.0000	0.000	0.00000	0.000000	0.000000	0.86376	0.007320	0.85644	0.000000	0.28792	0.287920	0.00000	0.0000	0.0000	0.0000	1.22	0.000	3.4282	0.7686	0.000	0.000	0.000	0.00000	0.00000	0.1708	0.00	0.1708	0.000	0.000	0.000	0.7686	0.000	0.00000	1	Almond milk, unsweetened

DHQ3 data format:

DHQ3 dietary data for each individual

#Use the example data
data("DHQ3_exp")
knitr::kable(head(DHQ3_exp))

Respondent ID	Record Number	Sex (1=male; 2=female)	Age	Questionnaire Date (YYYYMMDD)	Gram weight (g)	Energy (kcal)	*Gluten (g)	Alcohol (g)	Protein (g)	*Nitrogen (g)	*Total protein (g)	*Animal protein (g)	*Vegetable protein (g)	Total fat (g)	*Solid fat (g)	Total saturated fatty acids (g)	Total monounsaturated fatty acids (g)	Total polyunsaturated fatty acids (g)	*Total saturated fatty acids (g)	*Total monounsaturated fatty acids (g)	*Total polyunsaturated fatty acids (g)	*Polyunsaturated to saturated fatty acid ratio	Cholesterol (mg)	*Cholesterol to saturated fatty acid index	Carbohydrate (g)	Total sugars (g)	*Total sugars (g)	*Added sugars (g)	*Added sugars by total sugars (g)	*Available carbohydrate (g)	*Glycemic load (glucose reference)	*Glycemic load (bread reference)	*Fructose (g)	*Galactose (g)	*Glucose (g)	*Lactose (g)	*Maltose (g)	*Sucrose (g)	*Starch (g)	Dietary fiber (g)	*Total dietary fiber (g)	*Soluble dietary fiber (g)	*Insoluble dietary fiber (g)	*Pectins (g)	Retinol (mcg)	*Total vitamin A activity (International Units) (IU)	Vitamin A, retinol activity (mcg)	*Total vitamin A activity (RE) (mcg)	Beta-carotene (mcg)	*Beta-carotene (mcg)	Alpha-carotene (mcg)	Beta-cryptoxanthin (mcg)	Lutein + zeaxanthin (mcg)	Lycopene (mcg)	Vitamin E as alpha-tocopherol (mg)	*Vitamin E (Alpha Tocopherol) (mg)	Added alpha-tocopherol (mg)	*Total alpha-tocopherol (mg)	*Beta-tocopherol (mg)	*Gamma-tocopherol (mg)	*Delta-tocopherol (mg)	*Vitamin E (International Units) (IU)	*Natural alpha-tocopherol (RRR-alpha-tocopherol or d-alpha-tocopherol) (mg)	*Synthetic alpha-tocopherol (all rac-alpha-tocopherol or dl-alpha-tocopherol) (mg)	Vitamin K (mcg)	Vitamin C (mg)	Thiamin (Vitamin B1) (mg)	Riboflavin (Vitamin B2) (mg)	Niacin (mg)	*Niacin (mg)	Vitamin B6 (mg)	Total folate (mcg)	Folate, dietary folate (mcg)	Food folate (mcg)	Folic acid (mcg)	Vitamin B12 (mcg)	Added vitamin B12 (mcg)	*Pantothenic acid (mg)	Vitamin D (D2 + D3) (mcg)	*Vitamin D (calciferol) (mcg)	*Vitamin D (ergocalciferol) (mcg)	*Vitamin D (cholecalciferol (mcg)	Calcium (mg)	Phosphorus (mg)	Magnesium (mg)	Iron (mg)	Zinc (mg)	Copper (mg)	Selenium (mcg)	Sodium (mg)	Potassium (mg)	*Manganese (mg)	SFA 4:0 (Butanoic) (g)	SFA 6:0 (Hexanoic) (g)	SFA 8:0 (Octanoic) (g)	SFA 10:0 (Decanoic) (g)	SFA 12:0 (Dodecanoic) (g)	SFA 14:0 (Tetradecanoic) (g)	SFA 16:0 (Hexadecanoic) (g)	*SFA 17:0 (margaric acid) (g)	SFA 18:0 (Octadecanoic) (g)	*SFA 20:0 (arachidic acid) (g)	*SFA 22:0 (behenic acid) (g)	MFA 16:1 (Hexadecenoic) (g)	MFA 18:1 (Octadecenoic) (g)	MFA 20:1 (Eicosenoic) (g)	MFA 22:1 (Docosenoic) (g)	*MFA 14:1 (Myristoleic) (g)	PFA 18:2 (Octadecadienoic) (g)	PFA 18:3 (Octadecatrienoic) (g)	*PFA 18:3 N3 (Alpha linolenic) (g)	PFA 18:4 (Octadecatetraenoic) (g)	PFA 20:4 (Eicosatetraenoic) (g)	PFA 20:5 (Eicosapentaenoic) (g)	PFA 22:5 (Docosapentaenoic) (g)	PFA 22:6 (Docosahexaenoic) (g)	*Trans 18:1 (Trans-octadecenoic acid [elaidic acid]) (g)	*Trans 18:2 (Trans-octadecadienoic acid [linolelaidic acid]; incl. c-t, t-c, t-t) (g)	*Trans 16:1 (Trans-hexadecenoic acid) (g)	*Total trans fatty acitds (g)	*Omega-3 fatty acids (g)	*CLA 18:2 (Linoleic) (g)	*CLA cis9 trans11 (g)	*CLA trans10 cis12 (g)	*Tryptophan (g)	*Threonine (g)	*Isoleucine (g)	*Leucine (g)	*Lysine (g)	*Methionine (g)	*Cystine (g)	*Phenylalanine (g)	*Tyrosine (g)	*Valine (g)	*Arginine (g)	*Histidine (g)	*Alanine (g)	*Aspartic acid (g)	*Glutamin acid (g)	*Glycine (g)	*Proline (g)	*Serine (g)	*Daidzein (mg)	*Genistein (mg)	*Glycitein (mg)	*Coumestrol (mg)	*Biochanin A (mg)	*Formononetin (mg)	*Erythritol (g)	*Inositol (g)	*Mannitol (g)	*Pinitol (g)	*Sorbitol (g)	*Xylitol (g)	Caffeine (mg)	Theobromine (mg)	Moisture (g)	*Water (g)	Total Choline (mg)	*Aspartame (mg)	*Saccharin (mg)	*Phytic acid (mg)	*Oxalic acid (mg)	*3-Methylhistidine (mg)	*Sucrose polyester (g)	*Ash (g)	*Acesulfame potassium (mg)	*Sucralose (mg)	*Tagatose (g)	*Betaine (mg)	Citrus, melon, berry fruit (cups)	Other fruit (cups)	Fruits (cups)	Juice fruit (cups)	Total fruit (cups)	Dark-green vegetable (cups)	Red/orange tomato vegetable (cups)	Red/orange other vegetable (cups)	Total red/orange vegetable (cups)	White potato starchy vegetable (cups)	Other starchy vegetable (cups)	Total starchy vegetable (cups)	Other vegetable (cups)	Total vegetable (cups)	Legumes vegetable (cups)	Whole grain (oz)	Refined grain (oz)	Total number of grain (oz)	Meat from beef, pork, veal, lamb, and game protein foods (oz)	Cured meat protein foods (oz)	Meat from organ meat protein foods (oz)	Poultry protein foods (oz)	Seafood high in omega-3 protein foods (oz)	Seafood low in omega-3 protein foods (oz)	Seafood (oz)	Total meat, poultry, seafood protein foods (oz)	Eggs protein foods (oz)	Meat, poultry, and eggs (oz)	Soy products protein foods (oz)	Nuts and seeds protein foods (oz)	Legumes protein foods (oz)	Nuts, seeds, soy, and legumes (oz)	Total protein foods (oz)	Milk (cups)	Yogurt (cups)	Cheese (cups)	Total dairy (cups)	Oil (g)	Solid fat (g)	Added sugars (tsp)	Alcohol (drink(s))	Energy from fat (% kcal)	Energy from carbohydrates (% kcal)	Energy from protein (% kcal)	Energy from alcohol (% kcal)	Energy from saturated fatty acids (% kcal)	Energy from monounsaturated fatty acids (% kcal)	Energy from polyunsaturated fatty acids (% kcal)	SUPP_ENERGY_KCAL_DSID	SUPP_TOTAL_FAT_G_DSID	SUPP_TOTAL_POLYUNSATURATED_FATTY_ACIDS_G_DSID	SUPP_CHOLESTEROL_MG_DSID	SUPP_CARBOHYDRATE_G_DSID	SUPP_TOTAL_SUGARS_G_DSID	SUPP_TOTAL_VITAMIN_A_ACTIVITY_IU_DSID	SUPP_VITAMIN_A_RAE_MCG_DSID	SUPP_BETA_CAROTENE_PERCENT_DSID	SUPP_LUTEIN_ZEAXANTHIN_MCG_DSID	SUPP_BIOTIN_MCG_DSID	SUPP_VITAMIN_E_AS_ALPHA_TOCOPHEROL_MG_DSID	SUPP_VITAMIN_E_IU_DSID	SUPP_VITAMIN_K_MCG_DSID	SUPP_VITAMIN_C_MG_DSID	SUPP_THIAMIN_VITAMIN_B1_MG_DSID	SUPP_RIBOFLAVIN_VITAMIN_B2_MG_DSID	SUPP_NIACIN_MG_DSID	SUPP_VITAMIN_B6_MG_DSID	SUPP_FOLATE_DFE_MCG_DSID	SUPP_FOLIC_ACID_MCG_DSID	SUPP_VITAMIN_B12_MCG_DSID	SUPP_PANTOTHENIC_ACID_MG_DSID	SUPP_VITAMIN_D_D2+D3_MCG_DSID	SUPP_BORON_MCG_DSID	SUPP_CALCIUM_MG_DSID	SUPP_CHLORIDE_MG_DSID	SUPP_CHROMIUM_MCG_DSID	SUPP_COPPER_MG_DSID	SUPP_IODINE_MCG_DSID	SUPP_IRON_MG_DSID	SUPP_MAGNESIUM_MG_DSID	SUPP_MANGANESE_MG_DSID	SUPP_MOLYBDENUM_MCG_DSID	SUPP_NICKEL_MCG_DSID	SUPP_PHOSPHORUS_MG_DSID	SUPP_POTASSIUM_MG_DSID	SUPP_SELENIUM_MCG_DSID	SUPP_SILICON_MG_DSID	SUPP_TIN_MCG_DSID	SUPP_VANADIUM_MCG_DSID	SUPP_ZINC_MG_DSID	SUPP_PFA_20_5_EICOSAPENTAENOIC_ACID_G_DSID	SUPP_PFA_22_6_DOCOSAHEXAENOIC_ACID_G_DSID	SUPP_OMEGA_3_FATTY_ACIDS_G_DSID	SUPP_CHOLINE_MG_DSID	Total HEI-2015 Score	HEI-2015 - Total Vegetables - Component Score	HEI-2015 - Greens and Beans - Component Score	HEI-2015 - Total Fruits - Component Score	HEI-2015 - Whole Fruits - Component Score	HEI-2015 - Whole Grains - Component Score	HEI-2015 - Dairy - Component Score	HEI-2015 - Total Protein Foods - Component Score	HEI-2015 - Seafood and Plant Proteins - Component Score	HEI-2015 - Fatty Acids - Component Score	HEI-2015 - Sodium - Component Score	HEI-2015 - Refined Grains - Component Score	HEI-2015 - Saturated Fats - Component Score	HEI-2015 - Added Sugars - Component Score	HEI-2015 - Density of Total Vegetables per 1000 Kcal	HEI-2015 - Density of Greens and Beans per 1000 Kcal	HEI-2015 - Density of Total Fruits per 1000 Kcal	HEI-2015 - Density of Whole Fruits per 1000 Kcal	HEI-2015 - Density of Whole Grains per 1000 Kcal	HEI-2015 - Density of Dairy per 1000 Kcal	HEI-2015 - Density of Total Protein Foods per 1000 Kcal	HEI-2015 - Density of Seafood and Plant Proteins per 1000 Kcal	HEI-2015 - Fatty Acid Ratio	HEI-2015 - Density of Sodium per 1000 Kcal	HEI-2015 - Density of Refined Grains per 1000 Kcal	HEI-2015 - Percent of Calories from Saturated Fats	HEI-2015 - Percent of Calories from Added Sugars
1	1	1	24	20220108	3898.66	1849.01	5.09	0.00	87.88	14.25	88.38	54.11	34.27	75.11	27.64	22.48	27.15	18.85	23.57	24.38	19.52	19.98	238.41	35.57	209.48	51.82	51.61	16.87	13.67	192.49	108.61	155.30	10.56	0.19	11.59	12.31	1.66	14.98	125.49	22.13	23.35	5.53	17.81	3.07	251.24	13821.42	833.93	1566.50	6627.53	7739.48	650.51	75.45	7224.13	3568.97	7.76	7.61	0.00	9.69	0.44	18.51	5.05	11.33	7.61	0.00	398.50	77.83	2.15	1.80	25.98	42.49	2.05	436.72	528.47	306.57	130.27	4.40	0.26	4.85	4.97	5.26	0.01	5.26	966.28	1321.90	437.24	14.61	13.36	1.47	123.76	3360.72	2787.80	4.59	0.33	0.20	0.31	0.43	1.14	1.90	12.10	0.08	5.38	0.13	0.10	0.99	25.39	0.33	0.05	0.16	15.32	3.16	3.09	0.01	0.11	0.04	0.02	0.07	2.05	0.36	0.04	2.47	3.27	0.11	0.08	0.03	1.00	3.51	3.94	6.93	6.20	1.97	1.11	3.96	2.99	4.62	5.18	2.62	4.44	8.20	15.80	3.85	4.93	4.10	1.58	2.08	0.33	0.06	0.47	0.00	0.00	0.41	0.18	0.01	0.15	0.02	181.66	11.75	3507.29	3531.87	354.78	25.12	0.00	849.95	326.50	21.94	0.00	16.67	4.21	0.54	0.39	140.98	0.31	0.62	0.93	0.00	0.93	0.53	0.17	0.17	0.34	0.09	0.10	0.17	0.67	1.72	0.54	2.29	3.94	6.23	4.42	0.30	0.01	0.26	0.27	0.18	0.45	5.47	0.33	5.32	0.38	0.09	2.18	2.65	6.26	0.96	0.04	0.15	1.14	31.12	23.51	3.29	0.00	36.56	45.32	19.01	0.00	10.94	13.22	9.18	0.00	0.00	0.00	114.29	0.00	0.00	1000.00	300.00	8.29	0.00	8.57	5.74	8.57	7.14	17.14	0.43	0.49	5.71	0.57	194.29	114.29	1.71	2.86	2.86	21.43	57.14	20.57	10.00	0.14	42.86	5.14	14.29	0.66	12.86	1.43	5.71	22.86	15.71	0.57	2.86	2.86	3.14	0.00	0.00	0.00	0.00	74.69	5.00	5.00	3.16	5.00	8.25	4.76	5.00	5.00	6.51	2.03	8.67	6.32	10.00	1.23	0.58	0.50	0.50	1.24	0.62	4.57	1.67	2.05	1.82	2.13	10.94	2.85
2	2	2	24	20220126	2371.25	1109.42	6.13	0.00	38.77	6.63	39.97	9.76	30.21	44.95	11.37	10.65	17.96	13.01	10.57	15.58	13.88	13.52	138.93	17.47	146.33	39.47	38.56	12.39	10.83	121.57	67.11	95.94	9.92	0.12	10.03	1.68	2.27	14.14	74.37	20.13	21.16	5.24	15.93	2.65	220.55	11657.31	734.01	1301.83	5879.28	6585.29	496.10	82.43	2800.94	2671.64	7.66	8.79	1.49	10.60	0.76	10.59	2.44	13.36	8.45	0.77	138.07	68.13	1.33	1.03	12.53	20.05	1.43	373.02	468.66	236.34	136.78	1.64	0.99	2.82	1.75	1.80	0.27	1.53	563.58	718.57	233.49	11.96	6.85	1.11	51.72	1841.31	1724.09	3.64	0.12	0.07	0.10	0.14	0.40	0.63	6.17	0.04	2.39	0.19	0.29	0.36	17.20	0.21	0.00	0.03	11.53	1.31	1.30	0.00	0.07	0.00	0.01	0.02	0.81	0.16	0.02	0.99	1.32	0.02	0.02	0.00	0.48	1.40	1.67	2.97	2.02	0.71	0.63	1.98	1.27	1.96	2.57	1.02	1.73	3.86	8.32	1.68	2.63	2.03	1.67	2.35	0.36	0.00	0.41	0.01	0.00	0.29	0.14	0.03	0.09	0.01	0.40	5.62	2129.17	2139.36	183.66	1.37	0.17	806.87	311.97	1.32	0.01	11.70	0.04	0.04	0.27	99.39	0.23	0.63	0.87	0.01	0.88	0.30	0.17	0.27	0.44	0.10	0.04	0.14	0.34	1.22	0.42	1.27	3.44	4.72	0.16	0.03	0.00	0.17	0.00	0.01	0.01	0.37	0.59	0.96	0.41	1.44	1.67	3.51	2.81	0.11	0.01	0.23	0.36	22.09	11.49	3.03	0.00	36.46	52.76	13.98	0.00	8.64	14.57	10.55	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.99	0.00	0.00	0.00	9.95	6.64	1.33	1.66	0.13	45.13	26.55	1.00	0.37	1.66	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	77.84	5.00	5.00	4.97	5.00	7.65	2.46	5.00	5.00	10.00	3.78	4.78	9.20	10.00	1.48	0.64	0.80	0.78	1.15	0.32	4.03	3.17	2.91	1.66	3.10	8.64	4.37
3	3	2	23	20220126	5364.16	2133.67	5.35	5.88	74.24	12.01	74.22	39.80	34.42	75.58	34.91	22.30	29.17	17.31	22.31	30.90	15.75	60.57	201.31	32.25	291.08	137.34	134.10	70.97	64.43	259.50	145.27	207.70	34.64	0.23	31.54	7.76	1.90	59.33	93.86	28.81	32.69	10.95	21.75	5.54	262.22	20258.83	1192.88	2184.25	9996.00	11679.51	2152.91	190.05	8073.37	16992.22	13.38	12.48	1.86	14.96	0.39	15.61	2.42	18.98	11.87	1.34	438.97	283.18	1.58	2.43	28.44	40.13	2.95	556.30	620.57	464.83	91.52	3.96	0.77	8.71	3.65	2.99	0.00	2.99	986.56	1320.70	411.69	14.16	11.09	1.74	86.98	3985.08	4446.37	6.33	0.50	0.29	0.20	0.44	0.48	1.91	12.47	0.12	5.09	0.17	0.15	1.01	26.96	0.27	0.01	0.15	15.18	1.74	1.62	0.01	0.10	0.03	0.02	0.07	3.32	0.45	0.06	3.84	1.80	0.12	0.10	0.02	0.83	2.85	3.10	5.40	4.69	1.47	0.91	3.12	2.31	3.56	4.20	1.90	3.55	7.19	13.93	3.12	4.37	3.19	0.65	0.75	0.09	0.30	0.69	0.01	0.13	0.79	0.57	0.02	0.55	0.03	555.54	50.94	4891.41	4927.34	360.63	3.81	0.01	796.90	365.39	12.98	0.03	22.95	5.07	6.65	0.52	134.98	0.74	0.60	1.34	1.19	2.53	0.74	0.83	0.40	1.22	0.66	0.43	1.09	1.17	4.22	0.34	0.73	3.15	3.87	1.55	0.82	0.00	0.79	0.33	0.34	0.67	3.85	0.25	3.41	0.07	0.61	1.35	2.03	4.78	0.33	0.16	0.43	0.93	29.19	29.73	15.61	0.42	31.88	54.57	13.92	1.93	9.40	12.31	7.30	15.00	0.00	0.00	800.00	3.00	3.00	2500.00	750.00	0.00	275.00	1015.00	10.10	15.00	0.00	530.00	0.00	0.00	10.00	4.00	680.00	400.00	1012.00	10.00	45.00	150.00	0.00	0.00	120.00	0.00	0.00	0.00	114.29	0.00	37.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	50.00	0.00	0.00	0.00	0.04	69.46	5.00	5.00	5.00	5.00	2.27	3.35	5.00	5.00	6.81	1.47	10.00	8.24	7.33	2.14	0.51	1.19	0.63	0.34	0.43	2.88	1.27	2.08	1.87	1.47	9.40	11.71
4	4	2	23	20220126	1937.19	1170.43	3.14	1.51	29.28	4.58	28.27	14.37	13.90	31.92	14.29	8.38	12.02	8.55	8.79	11.83	8.77	29.33	67.69	12.29	197.39	111.50	109.45	56.12	49.51	179.54	107.52	153.70	30.57	0.18	27.86	4.36	1.75	43.89	52.80	13.14	12.99	4.15	8.82	2.33	258.65	4888.50	435.15	668.06	1917.67	2394.84	272.32	146.77	1370.05	1989.07	9.57	10.03	5.24	13.52	0.35	8.00	1.25	16.42	7.95	4.62	72.78	183.72	1.09	1.24	17.12	19.88	2.06	322.73	437.01	159.52	163.20	3.50	2.25	4.50	3.67	2.61	0.50	2.11	824.13	654.44	183.48	11.41	6.96	0.74	35.28	1412.48	2052.53	2.51	0.16	0.09	0.07	0.14	0.22	0.63	4.69	0.03	2.02	0.06	0.04	0.32	11.38	0.11	0.01	0.03	7.78	0.59	0.59	0.01	0.03	0.02	0.01	0.04	1.80	0.22	0.01	2.04	0.68	0.04	0.03	0.01	0.31	1.03	1.15	2.05	1.71	0.58	0.36	1.21	0.90	1.38	1.62	0.71	1.41	2.80	5.35	1.20	1.85	1.24	0.13	0.17	0.03	0.08	0.05	0.01	0.10	0.53	0.14	0.00	0.62	0.02	6.67	7.19	1667.00	1679.93	125.70	5.98	0.45	401.48	115.24	4.35	0.06	8.57	4.60	7.74	0.92	75.51	0.54	0.39	0.93	1.40	2.33	0.16	0.08	0.08	0.15	0.53	0.05	0.58	0.21	1.11	0.03	0.80	1.73	2.53	0.42	0.26	0.00	0.31	0.17	0.24	0.41	1.41	0.11	1.09	0.02	0.26	0.13	0.42	1.80	0.14	0.11	0.17	0.44	14.94	9.93	11.87	0.11	24.54	67.46	10.01	0.90	6.44	9.24	6.58	15.00	0.00	0.00	800.00	3.00	3.00	2500.00	750.00	0.00	275.00	15.00	10.10	15.00	0.00	30.00	0.00	0.00	10.00	4.00	680.00	400.00	12.00	10.00	20.00	150.00	0.00	0.00	120.00	0.00	0.00	0.00	0.00	0.00	37.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	77.27	4.43	4.19	5.00	5.00	4.57	2.86	3.30	4.43	9.66	8.81	10.00	10.00	5.01	0.97	0.17	1.99	0.79	0.68	0.37	1.65	0.71	2.46	1.21	1.48	6.44	16.23
5	5	2	30	20220126	3578.55	1237.92	5.87	8.73	49.24	7.79	48.30	32.48	15.82	49.18	27.57	17.39	17.41	10.05	17.49	18.41	8.35	9.65	197.83	27.70	139.93	53.26	54.70	26.87	24.23	126.27	73.49	105.06	11.50	0.15	11.24	5.77	1.77	24.04	58.74	12.51	11.73	3.73	7.97	1.91	256.32	5398.06	454.31	718.86	2242.58	2703.33	202.94	53.34	1909.52	2940.11	5.22	5.91	0.31	7.31	0.19	7.87	1.89	8.97	5.49	0.93	118.51	56.61	0.91	1.20	12.60	22.08	1.16	256.09	323.94	159.13	97.04	2.25	0.17	2.91	2.08	2.57	0.01	2.56	743.55	843.68	200.67	7.83	5.99	0.92	69.94	2049.54	1540.33	1.62	0.48	0.26	0.20	0.42	0.53	1.72	9.15	0.10	3.99	0.10	0.09	0.68	16.12	0.15	0.00	0.10	8.81	0.96	0.81	0.01	0.08	0.01	0.01	0.04	2.41	0.32	0.04	2.78	0.90	0.08	0.07	0.01	0.60	1.88	2.21	3.83	3.15	1.08	0.64	2.16	1.68	2.49	2.40	1.29	2.18	4.18	9.66	1.82	3.41	2.26	0.29	0.45	0.06	0.02	0.08	0.00	0.00	0.21	0.32	0.00	0.27	0.02	15.74	24.79	3319.15	3334.35	201.59	0.89	0.10	338.68	104.73	6.80	0.02	12.60	0.15	0.02	0.75	82.83	0.15	0.95	1.10	0.12	1.22	0.37	0.14	0.06	0.20	0.07	0.08	0.14	0.28	0.98	0.06	0.29	3.85	4.14	0.46	0.17	0.00	0.87	0.07	0.31	0.38	1.94	0.57	2.06	0.07	0.27	0.24	0.58	2.86	0.19	0.17	0.85	1.24	14.61	24.71	5.58	0.63	35.75	45.21	15.91	4.94	12.64	12.66	7.31	4.29	0.00	0.00	228.57	0.86	0.86	714.29	214.29	0.00	78.57	4.29	2.89	4.29	0.00	8.57	0.00	0.00	2.86	1.14	194.29	114.29	3.43	2.86	5.71	42.86	0.00	0.00	34.29	0.00	0.00	0.00	0.00	0.00	10.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	63.23	3.84	5.00	5.00	5.00	1.55	7.69	4.98	4.83	2.92	3.83	4.76	4.20	9.63	0.84	0.35	0.98	0.89	0.23	1.00	2.49	0.77	1.58	1.66	3.11	12.64	7.21
6	6	2	30	20220126	3412.63	758.65	0.67	1.88	20.20	3.41	20.92	9.99	10.93	31.40	6.18	6.80	14.63	8.28	6.93	13.41	8.62	20.26	129.62	13.58	95.41	10.13	10.07	3.96	3.50	93.36	58.50	83.65	2.00	0.03	2.64	0.89	0.16	4.34	69.59	4.73	5.53	1.82	3.70	0.63	83.94	4063.41	261.56	467.80	1956.21	2253.71	341.29	18.72	2132.81	1167.57	4.22	3.88	0.11	4.87	0.34	7.58	2.03	5.82	3.82	0.14	113.93	22.91	0.67	0.55	7.69	12.24	0.66	202.32	283.54	87.75	114.94	0.77	0.10	2.44	1.13	1.10	0.00	1.10	309.88	369.15	146.88	5.39	3.43	0.65	40.47	1340.90	829.81	2.95	0.05	0.02	0.07	0.09	0.37	0.33	4.15	0.02	1.43	0.09	0.06	0.31	14.14	0.12	0.00	0.01	6.81	1.33	1.33	0.00	0.06	0.00	0.00	0.03	0.59	0.10	0.01	0.71	1.37	0.01	0.01	0.00	0.24	0.83	0.93	1.64	1.21	0.49	0.34	0.98	0.72	1.13	1.39	0.55	1.12	1.95	3.70	0.89	1.04	1.05	0.10	0.10	0.01	0.04	0.00	0.00	0.00	0.13	0.09	0.00	0.03	0.01	152.97	11.60	3255.11	3266.47	125.21	0.43	0.00	313.46	110.63	2.68	0.00	6.36	0.30	0.52	0.07	38.48	0.01	0.08	0.09	0.07	0.16	0.17	0.09	0.07	0.15	0.01	0.01	0.02	0.19	0.51	0.00	0.86	2.70	3.57	0.41	0.09	0.00	0.01	0.05	0.05	0.09	0.61	0.56	1.07	0.01	0.04	0.00	0.06	1.23	0.05	0.01	0.06	0.12	21.02	5.80	0.79	0.13	37.26	50.31	10.65	1.73	8.06	17.35	9.83	2.86	0.29	0.14	288.57	0.00	0.00	2500.00	750.00	20.71	1714.29	30.00	52.64	78.57	17.86	150.29	29.64	6.93	21.43	2.00	680.00	400.00	8.57	8.71	14.29	53.57	142.86	51.43	25.00	0.59	107.14	12.86	35.71	1.64	32.14	3.57	14.29	57.14	39.29	1.43	7.14	7.14	17.80	0.09	0.05	0.14	0.00	59.61	3.07	5.00	1.33	1.46	7.60	1.18	3.25	1.25	10.00	2.58	2.96	9.92	10.00	0.68	0.22	0.21	0.12	1.14	0.15	1.63	0.20	3.37	1.77	3.56	8.06	1.67

DHQ3 detailed food data for all foods consumed by each individual

#Use the example data
data("DHQ3_exp_detailed")
knitr::kable(head(DHQ3_exp_detailed))

Respondent ID	Sex (1=male; 2=female)	Question ID	Food ID	Size (0=sm; 1=med; 2=lg)	Daily Freq	Gram weight (g)	Energy (kcal)	Protein (g)	*Total protein (g)	*Vegetable protein (g)	Carbohydrate (g)	*Available carbohydrate (g)	Calcium (mg)	Phosphorus (mg)	Magnesium (mg)	Iron (mg)	Zinc (mg)	Copper (mg)	Selenium (mcg)	Sodium (mg)	Potassium (mg)	*Manganese (mg)	Caffeine (mg)	Moisture (g)	*Water (g)	*Aspartame (mg)	*Oxalic acid (mg)	*Ash (g)	*Betaine (mg)
1	1	11	1140.4	1	0.01	4.62	0.03	0.01	0.00	0.00	0.00	0.00	0.16	0.27	0.02	0.00	0.00	0.00	0.00	0.22	0.23	0.00	0.00	4.61	4.62	2.38	0.01	0.00	0.00
1	1	11	1140.3	1	0.01	4.69	0.08	0.01	0.01	0.01	0.01	0.01	0.15	0.37	0.05	0.00	0.00	0.00	0.00	0.36	0.34	0.00	0.58	4.67	4.67	2.41	0.01	0.00	0.00
1	1	12	1140.4	1	0.02	9.26	0.06	0.01	0.01	0.01	0.01	0.00	0.32	0.54	0.04	0.00	0.00	0.00	0.01	0.44	0.46	0.00	0.00	9.24	9.25	4.77	0.02	0.01	0.01
1	1	12	1140.3	1	0.02	9.40	0.16	0.01	0.01	0.01	0.02	0.02	0.30	0.74	0.10	0.01	0.00	0.00	0.00	0.72	0.68	0.01	1.17	9.36	9.36	4.82	0.01	0.00	0.01
1	1	16	1154.2	1	2.33	1033.78	0.00	0.00	0.00	0.00	0.00	0.00	101.91	0.00	20.35	0.00	0.02	0.07	0.00	26.18	0.63	0.00	0.00	1033.54	1033.24	0.00	0.00	0.53	0.00
1	1	17	1154.2	1	2.88	1277.89	0.00	0.00	0.00	0.00	0.00	0.00	125.98	0.00	25.15	0.00	0.03	0.09	0.00	32.36	0.78	0.00	0.00	1277.60	1277.23	0.00	0.00	0.65	0.00

Block FFQ data format:

#Use the example data
data("BLOCK_exp")
knitr::kable(head(BLOCK_exp))

RESPONDENTID	BOOKNUM	TODAYSDATE	SEX	PREGNANT	AGE	WEIGHT	HEIGHTFEET	HEIGHTINCHES	BMI	DT_KCAL	DT_PROT	DT_TFAT	DT_CARB	DT_CALC	DT_PHOS	DT_IRON	DT_SODI	DT_POTA	DT_GSH_T	DT_GSH_R	DT_THIA	DT_RIBO	DT_NIAC	DT_VITC	DT_SFAT	DT_MFAT	DT_PFAT	DT_CHOL	DT_FIBE	DT_SOLFIBR	DT_FOLFD	DT_ATOC	DT_ZINC	DT_AN_ZN	DT_VITB6	DT_MAGN	DT_VARAE	DT_RET	DT_ACARO	DT_BCARO	DT_CRYPT	DT_LUTZE	DT_LYCO	DT_FOLAC	DT_VB12	DT_VITD	DT_VITK	DT_COPP	DT_SEL	DT_SUG_T	DT_TRFAT	DT_ISOFLV	DT_QUERC	DT_CYSTEN	DT_METHI	DT_CYSTI	FOL_DFE	GI	GL	DT_ARGININE	DT_FA182	DT_FA183	DT_FA184	DT_FA204	DT_FA205	DT_FA225	DT_FA226	DT_TOTN6	DT_TOTN3	DT_FRUCT	DT_LACT	DT_MALT	DT_GALAC	DT_SUCR	DT_GLUC	TOTAL_CHOLINE	FREE_CHOLINE	PHOSPHOCHOLINE	GLYCEROPHOSPHOCHOLINE_GPC	PHOSPHATIDYLCHOLINE_PTD	BETAINE	SPHINGOMYELIN_SM	DT_CAFFN	DT_THEOB	DT_ALCO	GROUP_SOLID_COUNT	GROUP_SOLID_TOTAL_FREQUENCY	GROUP_SOLID_TOTAL_GRAMS	PCTFAT	PCTPROT	PCTCARB	PCTSWEET	PCTALCH	BA_PFAT	BA_PPROT	BA_PCARB	GROUP_BEANFIBER_TOTAL_FIBE	GROUP_VEGETABLESFRUITFIBER_TOTAL_FIBE	GROUP_GRAINFIBER_TOTAL_FIBE	GROUP_SUGARYBEVG_TOTAL_GRAMS	GROUP_SUGARYBEVG_TOTAL_KCAL	VEGSRV	FRUITSRV	GRAINSRV	MEATSRV	DAIRYSRV	FATSRV	WGRAINS	SUP_VITA	SUP_VITC	SUP_VITD	SUP_VITE	SUP_IRON	SUP_CA	SUP_ZINC	SUP_B1	SUP_B6	SUP_B12	SUP_FOL	SUP_CU	SUP_SE	SUP_B2	SUP_NIAC	ADD_SUG	A_BEV	DFAT_OIL	DFAT_SOL	D_CHEESE	D_MILK	D_TOTAL	D_YOGURT	F_CITMLB	F_OTHER	F_TOTAL	F_SOLID	F_JUICE	JUICE100	G_NWHL	G_TOTAL	G_WHL	LEGUMES	M_EGG	M_FISH_HI	M_FISH_LO	M_FRANK	M_MEAT	M_MPF	M_NUTSD	M_ORGAN	M_POULT	M_SOY	V_DPYEL	V_DRKGR	V_OTHER	V_POTATO	V_STARCY	V_TOMATO	V_TOTAL	PSFRUIT	PSVEGNBP	PSVEGDKG	PSVEGORN	PSLEGSOY	PSVEGPOT	PSVEGOTH	PSGTOT	PSGWHL	PSMFP	PSNUTSD	PSEGGS	PSDAIRY	PSOILS	BREAKSANDFREQ	BREAKSANDQUAN	OTHEREGGSFREQ	OTHEREGGSQUAN	SAUSAGEFREQ	SAUSAGEQUAN	BACONFREQ	BACONQUAN	PANCAKESFREQ	PANCAKESQUAN	COOKEDCEREALFREQ	COOKEDCEREALQUAN	COLDCEREALFREQ	COLDCEREALQUAN	MILKONCEREALFREQ	BUTTERMILKFREQ	BUTTERMILKQUAN	YOGURTONLYFREQ	YOGURTONLYQUAN	CHEESEFREQ	CHEESEQUAN	BANANAFREQ	BANANAQUAN	APPLESPEARSFREQ	APPLESPEARSQUAN	ORANGESFREQ	ORANGESQUAN	GRAPEFRUITFREQ	GRAPEFRUITQUAN	PEACHESFREQ	PEACHESQUAN	CANTALOUPEFREQ	CANTALOUPEQUAN	STRAWBERRIESFREQ	STRAWBERRIESQUAN	WATERMELONFREQ	WATERMELONQUAN	OTHERFRUITFREQ	OTHERFRUITQUAN	CANNEDFRUITFREQ	CANNEDFRUITQUAN	BROCCOLIFREQ	BROCCOLIQUAN	CARROTSFREQ	CARROTSQUAN	CORNFREQ	CORNQUAN	BEANSPEASFREQ	BEANSPEASQUAN	SPINACHCOOKEDFREQ	SPINACHCOOKEDQUAN	GREENSFREQ	GREENSQUAN	SWEETPOTATOESFREQ	SWEETPOTATOESQUAN	FRIESFREQ	FRIESQUAN	POTATOESFREQ	POTATOESQUAN	COLESLAWCABBAGEFREQ	COLESLAWCABBAGEQUAN	GREENSALADFREQ	GREENSALADQUAN	TOMATOESFREQ	TOMATOESQUAN	SALADDRESSINGFREQ	SALADDRESSINGQUAN	OTHERVEGGIESFREQ	OTHERVEGGIESQUAN	REFRIEDBEANSFREQ	REFRIEDBEANSQUAN	PINTOBEANSFREQ	PINTOBEANSQUAN	VEGGIESTEWFREQ	VEGGIESTEWQUAN	VEGSOUPFREQ	VEGSOUPQUAN	PEASOUPFREQ	PEASOUPQUAN	OTHERSOUPFREQ	OTHERSOUPQUAN	PIZZAFREQ	PIZZAQUAN	SPAGHETTIFREQ	SPAGHETTIQUAN	MACARONIFREQ	MACARONIQUAN	OTHERNOODLESFREQ	OTHERNOODLESQUAN	TOFUFREQ	TOFUQUAN	MEATSUBSTITUTEFREQ	MEATSUBSTITUTEQUAN	EATMEAT	BURGERFREQ	BURGERQUAN	HOTDOGFREQ	HOTDOGQUAN	BOLOGNAFREQ	BOLOGNAQUAN	MEATLOAFFREQ	MEATLOAFQUAN	STEAKFREQ	STEAKQUAN	TACOSFREQ	TACOSQUAN	RIBSFREQ	RIBSQUAN	PORKFREQ	PORKQUAN	VEALFREQ	VEALQUAN	LIVERFREQ	LIVERQUAN	FEETFREQ	FEETQUAN	MENUDOFREQ	MENUDOQUAN	MIXEDBEEFPORKFREQ	MIXEDBEEFPORKQUAN	FRIEDCHICKENFREQ	FRIEDCHICKENQUAN	NOTFRIEDCHICKENFREQ	NOTFRIEDCHICKENQUAN	MIXEDCHICKFREQ	MIXEDCHICKQUAN	OYSTERSFREQ	OYSTERSQUAN	SHELLFISHFREQ	SHELLFISHQUAN	TUNAFREQ	TUNAQUAN	FRIEDFISHFREQ	FRIEDFISHQUAN	NOTFRIEDFISHFREQ	NOTFRIEDFISHQUAN	BISCUITSFREQ	BISCUITSQUAN	BUNSFREQ	BUNSQUAN	BAGELFREQ	BAGELQUAN	TORTILLASFREQ	TORTILLASQUAN	CORNBREADFREQ	CORNBREADQUAN	OTHERBREADFREQ	OTHERBREADQUAN	RICEFREQ	RICEQUAN	MARGARINEFREQ	MARGARINEQUAN	BUTTERFREQ	BUTTERQUAN	PEANUTBUTTERFREQ	PEANUTBUTTERQUAN	JELLYFREQ	JELLYQUAN	MAYOFREQ	MAYOQUAN	SALSAFREQ	SALSAQUAN	MUSTARDFREQ	MUSTARDQUAN	SALTYSNACKSFREQ	SALTYSNACKSQUAN	CRACKERFREQ	CRACKERQUAN	NUTSFREQ	NUTSQUAN	POWERBARSFREQ	POWERBARSQUAN	BREAKFASTBARSFREQ	BREAKFASTBARSQUAN	DONUTFREQ	DONUTQUAN	CAKEFREQ	CAKEQUAN	COOKIESFREQ	COOKIESQUAN	ICECREAMFROYOFREQ	ICECREAMFROYOQUAN	CHOCOLATESYRUPFREQ	CHOCOLATESYRUPQUAN	PUMPKINPIEFREQ	PUMPKINPIEQUAN	OTHERPIEFREQ	OTHERPIEQUAN	CHOCOLATECANDYFREQ	CHOCOLATECANDYQUAN	CANDYFREQ	CANDYQUAN	MILKFREQ	MILKQUAN	DIETSHAKESFREQ	DIETSHAKESQUAN	TOMATOJUICEFREQ	TOMATOJUICEQUAN	ORANGEJUICEFREQ	ORANGEJUICEQUAN	REALJUICEFREQ	REALJUICEQUAN	HICFREQ	HICQUAN	SOMEJUICEFREQ	SOMEJUICEQUAN	ICEDTEAFREQ	ICEDTEAQUAN	KOOLAIDFREQ	KOOLAIDQUAN	SOFTDRINKSFREQ	SOFTDRINKSQUAN	BEERFREQ	BEERQUAN	WINEFREQ	WINEQUAN	LIQUORFREQ	LIQUORQUAN	WATERFREQ	WATERQUAN	COFFEEFREQ	COFFEEQUAN	HOTTEAFREQ	HOTTEAQUAN	CREAMINCOFFEE	CREAMINTEA	SUGARINCOFFEE	COFFEESUGARTEASPOONS	SUGARINTEA	TEASUGARTEASPOONS	VEGGIESFREQ	FRUITSFREQ	FATOILFREQ	MILKTYPE	DIETSHAKESTYPE	ORANGEJUICETYPE	SOFTDRINKSTYPE	ICEDTEATYPE	BEERTYPE	BURGERTYPE	HOTDOGTYPE	BOLOGNATYPE	SPAGHETTITYPE	CHEESETYPE	SALADDRESSTYPE	POWERBARSTYPE	BREAKFASTBARSTYPE	BREADTYPE	TORTILLATYPE	CHOCCANDYTYPE	COOKIESTYPE	CAKETYPE	ICECREAMFROYOTYPE	JELLYTYPE	FATONMEATTYPE	CHICKENSKINTYPE	COOKINGFATHALF	COOKINGFATSTICKMARG	COOKINGFATDIET	COOKINGFATVEGGIE	COOKINGFATCRISCO	LCCEREALTYPE	CHEERIOSTYPE	TOTALTYPE	FIBERONETYPE	PRODUCT19TYPE	ALLBRANTYPE	OTHERFIBERTYPE	SWEETENEDTYPE	CORNFLAKESTYPE	PRENATALAMOUNT	PRENATALYEARS	ONEADAYAMOUNT	ONEADAYYEARS	STRESSTABSAMOUNT	STRESSTABSYEARS	VITAMINAAMOUNT	VITAMINAYEARS	BETACAROTENEAMOUNT	BETACAROTENEYEARS	VITAMINCAMOUNT	VITAMINCYEARS	VITAMINEAMOUNT	VITAMINEYEARS	FOLATEAMOUNT	FOLATEYEARS	CALCIUMAMOUNT	CALCIUMYEARS	VITAMINDAMOUNT	VITAMINDYEARS	ZINCAMOUNT	ZINCYEARS	IRONAMOUNT	IRONYEARS	SELENIUMAMOUNT	SELENIUMYEARS	OMEGA3AMOUNT	OMEGA3YEARS	PROBIOTICSAMOUNT	PROBIOTICSYEARS	MINERALSYESORNO	MGVITAMINCPERDAY	MGVITAMINEPERDAY	OTHEREGGSTYPE	ATEANYFISH	FRIEDFISH2FREQ	FRIEDFISH2QUAN	TUNA2FREQ	TUNA2QUAN	SALMONFREQ	SALMONQUAN	HALIBUTFREQ	HALIBUTQUAN	TROUTFREQ	TROUTQUAN	MACKERELFREQ	MACKERELQUAN	HERRINGFREQ	HERRINGQUAN	SARDINESFREQ	SARDINESQUAN	OTHERWHITEFISHFREQ	OTHERWHITEFISHQUAN
1	NA	NA	1	NA	NA	NA	NA	NA	NA	1236.28	38.06	43.17	183.70	497.60	771.04	10.74	1754.18	1728.82	29.94	17.51	1.05	1.34	11.83	105.74	13.09	16.43	9.96	236.49	13.53	4.29	153.72	5.43	6.53	2.50	1.51	203.68	468.00	326.82	58.74	1539.07	247.27	1762.05	2712.87	125.77	2.66	94.51	90.16	1.01	56.13	97.01	1.070	0.418	2.32	0.506	0.787	0.541	367.53	54.44	92.65	1983.86	8.88	0.737	0.00248	0.0876	0.01600	0.00305	0.0371	8.97	0.796	28.75	6.06	2.45	0.2620	29.51	24.96	225.79	36.98	6.46	23.51	147.31	57.75	11.17	0.953	9.07	1.500e-04	65	7.07	777.05	31.43	12.31	59.44	7.57	0.00	31.43	12.31	59.44	1.440	5.29	6.98	73.96	27.41	0.720	2.870	4.80	1.18	0.761	1.66	0.3080	1515	85	200	5	27	40	11	1.4	1.9	2.6	600	1	60	1.4	18	9.12	0.000	13.15	20.73	0.333	0.316	0.878	0.2170	0.758	1.600	2.340	1.470	0.868	0.851	2.36	3.94	1.590	0.025800	0.767	0.0145	0.0943	0.244	0.605	1.03	0.66500	0.003750	0.0679	0.01140	0.00768	0.154000	0.1870	0.1990	0.027700	0.0957	0.672	2.010	0.440	0.148000	0.00707	0.026600	0.1840	0.283	4.06	1.740	1.05	0.46000	0.892	0.761	1.210	2	1	7	2	4	1	4	4	4	2	7	3	7	4	7	4	4	4	3	7	1	4	2	2	2	1	2	2	2	2	2	2	3	2	2	4	4	7	3	3	2	1	2	2	1	2	2	4	3	1	2	3	3	1	2	3	2	4	2	3	1	4	4	4	1	4	3	1	2	1	2	4	2	1	3	1	3	1	3	4	4	4	2	4	3	2	2	2	2	1	2	1	1	1	3	1	3	2	3	2	3	2	3	3	2	3	2	1	2	3	1	2	1	2	1	2	1	3	1	2	2	1	1	2	1	2	1	2	1	2	2	2	2	2	1	2	6	1	3	2	1	2	1	1	4	2	4	2	3	2	7	1	1	1	4	2	4	2	4	1	3	2	3	2	6	4	5	1	7	1	1	1	4	1	2	2	4	2	3	1	2	4	1	2	1	2	1	2	4	2	4	2	1	1	1	2	1	2	7	2	7	2	2	2	2	3	1	1	4	3	2	1	1	1	1	2	1	1	9	4	1	1	1	1	4	4	1	1	1	1	2	7	1	2	2	2	2	4	1	1	2	2	2	2	2	3	3	1	2	3	3	3	3	2	1	2	0	0	0	1	0	M	1	M	M	M	M	M	1	M	4	1	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	M	3	2	2	2	3	2	2	2	1	2	1	2	1	2	1	2	1	2	1	2
2	NA	NA	1	NA	NA	NA	NA	NA	NA	2396.83	68.37	111.94	286.85	625.03	1125.41	11.22	2746.08	1796.85	42.98	27.32	1.08	1.34	17.45	39.80	36.42	46.59	20.74	266.90	11.05	3.90	99.19	6.34	10.57	6.97	1.23	220.81	316.83	278.79	37.98	409.13	47.20	289.38	1412.01	130.08	3.72	40.34	37.46	1.20	86.85	174.16	7.480	2.150	3.77	1.070	1.440	0.654	320.32	50.04	138.01	3339.03	18.14	1.410	0.00264	0.1280	0.00920	0.00556	0.0238	18.27	1.450	47.92	9.35	2.15	0.0789	61.37	40.92	236.80	44.32	6.00	42.89	130.19	123.02	13.58	68.870	143.72	7.420e-05	55	5.80	665.56	42.03	11.41	47.87	44.76	0.00	42.03	11.41	47.87	0.335	2.65	5.64	868.60	327.64	0.916	0.319	5.02	2.79	0.522	4.85	0.0353	1515	185	200	5	92	40	61	1.4	1.9	2.6	1000	1	60	1.4	18	35.95	0.000	20.90	74.63	0.646	0.298	0.988	0.0218	0.123	0.258	0.378	0.208	0.169	0.108	5.15	5.40	0.252	0.030300	0.306	0.0195	0.1120	0.531	2.620	4.33	0.77300	0.013400	1.0300	0.03540	0.01180	0.000813	0.0911	0.4800	0.006450	0.0640	0.667	0.319	0.283	0.000813	0.00612	0.029500	0.4520	0.210	5.28	0.562	4.36	0.56400	0.338	0.956	0.330	4	1	4	3	4	2	4	3	4	3	2	3	4	4	4	2	2	3	2	4	2	2	2	2	2	2	2	1	2	2	2	2	1	2	1	2	2	2	2	4	3	1	2	1	2	2	1	1	2	1	2	1	2	2	1	7	3	2	3	1	2	1	2	1	2	1	2	1	2	1	2	2	2	1	3	1	3	1	3	3	3	4	3	1	2	6	3	1	2	1	2	1	1	1	6	3	4	2	4	2	1	2	4	3	6	3	2	2	4	3	1	2	1	2	2	2	1	3	1	2	7	2	1	2	1	2	1	2	3	2	1	2	2	2	1	2	4	2	4	2	1	2	1	1	2	2	2	2	4	3	1	1	1	1	2	2	2	2	2	2	1	2	2	2	7	3	4	2	4	2	1	1	1	1	7	2	7	3	5	3	3	3	1	2	2	2	3	2	8	4	1	1	1	1	1	2	1	2	3	2	4	2	4	2	4	3	6	2	4	3	9	2	1	1	1	2	1	1	9	4	1	1	1	1	4	4	1	1	1	1	1	1	5	2	2	2	2	2	1	2	2	2	2	2	3	3	3	3	2	3	3	3	3	2	1	2	0	0	0	0	1	M	M	M	M	M	M	M	1	1	4	1	1	M	1	M	1	M	1	M	4	1	1	M	4	1	1	M	1	M	4	1	4	1	1	M	1	M	1	M	1	M	M	4	1	M	2	M	2	M	2	M	2	M	2	M	2	M	2	M	2	M	2
3	NA	NA	1	NA	NA	NA	NA	NA	NA	3195.49	121.19	153.43	334.52	1398.52	2165.03	23.13	5679.46	3652.43	63.13	37.69	2.41	3.43	32.72	104.63	49.50	60.98	31.59	491.79	20.75	7.17	244.03	15.15	18.24	10.91	2.90	383.39	1086.41	907.71	224.09	1968.79	147.50	2027.03	9433.25	317.49	10.04	403.63	129.25	1.58	160.82	136.47	4.750	1.740	3.27	1.510	2.620	1.550	783.77	54.88	172.20	6017.81	27.25	2.950	0.00323	0.2010	0.02600	0.00995	0.0670	27.45	3.050	25.32	32.54	4.53	0.3090	37.58	27.85	490.77	102.77	20.01	80.42	257.12	212.84	28.52	4.550	19.20	1.177e+01	69	10.77	1171.41	43.21	15.17	41.87	8.46	1.16	43.72	15.35	42.37	1.340	5.66	13.25	398.42	143.61	1.550	1.150	12.53	5.03	2.670	4.44	0.8930	0	0	0	0	0	0	0	0.0	0.0	0.0	0	0	0	0.0	0	15.83	0.808	34.31	92.91	0.575	2.510	3.100	0.0000	0.403	0.744	1.150	0.910	0.239	0.151	8.74	10.61	1.870	0.061700	0.885	0.2480	0.9130	3.250	2.370	7.77	0.44100	0.014500	0.9480	0.02690	0.06390	0.194000	0.2460	0.5530	0.125000	0.4520	1.640	0.916	0.932	0.192000	0.04030	0.056200	0.5150	0.688	10.60	2.530	7.95	0.33600	0.967	3.030	4.180	6	1	6	2	7	2	4	3	6	3	7	3	7	4	8	1	3	1	3	4	2	8	2	1	2	2	2	1	2	4	3	2	2	6	2	5	3	4	3	4	3	6	2	4	2	7	2	1	2	2	1	4	3	2	2	7	2	4	2	1	2	4	3	1	2	5	3	4	2	2	2	4	2	2	3	2	3	1	3	4	3	5	2	7	3	4	3	2	3	1	2	1	1	1	4	1	8	2	5	2	4	3	6	2	6	3	2	2	1	2	1	2	1	2	2	2	1	3	4	3	6	2	1	2	4	3	1	2	4	3	4	2	4	3	4	2	7	3	5	2	4	2	1	1	4	3	6	2	4	3	4	2	4	2	1	2	4	2	6	2	6	2	6	2	9	3	6	2	5	2	1	1	4	2	4	2	2	2	2	1	2	3	1	2	2	2	4	2	5	2	5	2	9	2	1	2	4	2	4	2	4	2	4	2	4	2	1	1	7	3	4	2	1	1	1	2	4	3	9	2	1	1	1	1	4	4	1	1	1	1	3	5	6	2	2	2	2	4	1	1	2	1	2	2	2	3	2	1	2	3	2	2	2	1	2	2	1	0	0	1	0	M	M	M	M	M	M	M	1	1	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	1	M	M	1	1	M	2	M	2	M	2	M	2	M	2	M	2	M	2	M	2	M	2
4	NA	NA	1	NA	NA	NA	NA	NA	NA	2374.05	61.73	73.84	385.21	952.67	1210.01	14.11	2539.16	2609.20	32.88	18.66	1.66	2.10	18.16	171.26	26.85	27.51	14.37	209.16	24.83	7.72	214.55	8.27	8.65	2.12	2.00	310.63	899.72	562.35	83.56	3875.43	354.89	627.65	4632.37	153.28	3.09	170.80	43.22	1.49	81.72	207.60	3.210	2.070	21.26	0.776	1.170	0.711	475.13	56.80	204.69	2134.28	12.53	0.992	0.00141	0.0840	0.00582	0.00164	0.0119	12.61	1.010	39.68	15.81	8.86	0.1340	139.92	35.68	241.97	71.14	10.47	45.70	104.36	227.86	8.87	116.860	103.99	1.270e-03	31	9.08	780.32	27.99	10.40	64.90	39.90	0.00	27.99	10.40	64.90	0.000	8.02	16.28	1330.00	331.08	0.758	2.700	6.79	1.14	1.140	6.02	0.0000	0	0	0	0	0	0	0	0.0	0.0	0.0	0	0	0	0.0	0	33.83	0.000	11.61	44.92	0.281	1.090	1.390	0.0000	1.050	1.900	2.930	2.030	0.899	0.837	7.83	9.46	1.640	0.000645	0.452	0.0265	0.0258	0.300	0.464	1.15	0.31200	0.000000	0.3300	0.06580	0.26100	0.000374	0.0260	0.1510	0.000884	0.1600	0.581	2.090	0.357	0.000374	0.21300	0.000781	0.1460	0.144	9.72	2.150	1.24	0.28600	0.544	1.310	0.199	7	1	3	1	5	1	1	1	7	2	1	3	7	4	6	1	3	1	3	1	1	6	2	7	3	7	3	1	2	7	2	1	2	7	1	3	4	4	2	1	2	1	2	1	2	1	2	1	2	1	2	1	2	7	2	1	2	1	2	1	2	1	2	1	2	1	2	1	2	1	2	1	2	1	3	1	3	1	3	1	3	6	1	1	2	3	2	1	2	1	2	1	1	1	1	1	1	1	1	2	1	2	1	2	1	2	1	2	5	1	1	2	1	2	1	2	1	3	1	2	5	2	1	2	2	3	1	2	1	2	1	2	1	2	2	2	6	2	1	1	7	2	1	1	1	2	7	2	2	2	6	2	7	2	1	2	1	2	1	2	1	2	3	2	9	2	1	2	1	1	1	1	7	2	3	2	2	2	9	4	3	3	1	2	1	2	1	2	7	2	6	3	7	2	1	2	5	2	6	2	6	3	1	2	1	2	9	3	9	2	1	1	1	1	1	2	1	1	9	4	1	1	1	1	4	4	1	1	1	1	1	7	1	2	2	2	2	2	1	2	2	2	2	2	3	3	1	3	2	2	3	3	3	2	2	2	0	1	0	0	0	M	1	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	M	1	M	M	2	1	M	2	M	2	M	2	M	2	M	2	M	2	M	2	M	2	M	2
5	NA	NA	1	NA	NA	NA	NA	NA	NA	1965.05	47.03	34.53	385.79	749.59	1019.08	15.62	2044.02	3977.15	69.15	49.24	1.50	1.69	12.26	457.86	11.56	12.23	6.61	222.03	21.12	6.71	568.66	8.53	6.67	2.01	2.04	387.32	1412.56	280.63	123.72	13149.44	755.76	19834.62	4700.64	50.68	2.26	79.12	887.52	1.51	59.65	300.38	0.439	0.244	11.10	0.718	0.978	0.716	654.82	48.36	176.37	2750.03	5.11	0.946	0.00130	0.0840	0.00936	0.00257	0.0219	5.19	0.981	131.35	6.66	1.26	0.1450	38.92	97.96	304.64	69.69	15.70	40.95	170.32	919.27	8.50	72.300	3.79	0.000e+00	50	8.33	1107.19	15.81	9.57	78.53	29.42	0.00	15.81	9.57	78.53	0.937	16.94	3.77	1341.37	569.92	4.910	5.000	2.65	1.52	0.498	1.79	0.0486	1515	85	200	5	27	40	11	1.4	1.9	2.6	600	1	60	1.4	18	33.44	0.000	2.37	20.99	0.183	0.537	0.787	0.0494	3.700	3.070	6.670	2.670	4.000	3.800	1.84	3.09	1.250	0.009700	0.639	0.3850	0.3800	0.205	0.585	1.73	0.00544	0.000297	0.1680	0.00148	0.00718	1.670000	0.3720	0.0545	0.225000	0.0768	2.400	5.100	2.340	1.660000	0.01080	0.005570	0.0359	0.666	3.00	1.190	1.80	0.00263	0.803	0.705	0.358	7	1	6	1	2	1	3	2	4	2	8	3	5	2	8	5	1	3	2	1	1	9	2	9	2	5	2	1	2	5	2	9	2	9	3	9	3	9	2	1	2	8	3	1	2	8	2	1	2	9	3	1	2	1	2	2	1	2	1	1	2	8	3	1	2	3	2	1	2	2	1	1	2	1	3	5	3	1	3	2	2	2	1	3	3	3	2	2	2	1	2	1	1	1	3	1	2	1	1	2	4	2	2	1	2	1	1	2	3	3	1	2	1	2	1	2	1	3	1	2	3	1	3	2	1	2	1	2	2	1	1	2	1	2	7	1	1	1	2	1	1	2	1	1	2	1	3	1	3	2	1	1	1	1	1	2	1	2	2	1	1	2	1	2	3	2	1	2	1	1	1	1	1	1	1	1	1	1	1	2	3	2	1	2	1	2	1	2	2	1	1	1	2	3	1	2	1	2	9	3	9	3	7	3	1	2	1	1	2	1	9	4	1	1	1	2	1	1	8	4	1	1	5	1	4	4	1	1	2	1	6	8	1	1	2	2	2	4	1	1	1	2	1	2	3	3	3	1	2	3	3	3	3	2	1	2	0	0	1	0	0	M	1	M	M	M	M	M	M	M	4	1	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	1	M	M	3	2	2	1	1	2	2	1	1	2	1	2	1	2	1	2	1	2	2	1

Your own dietary assessment tool data format:

#Use the example data
data("HEI2020_VALIDATION")
knitr::kable(head(HEI2020_VALIDATION))

id	gender	age	kcal	total_fruit	whole_fruit	total_vegetable	green_and_bean	total_protein	seafood_plant_protein	whole_grain	dairy	fatty_acid	refined_grain	sodium	added_sugar	saturated_fat	EXP_HEI2020_ALL	EXP_HEI2020_TOTALFRT	EXP_HEI2020_FRT	EXP_HEI2020_VEG	EXP_HEI2020_GREENNBEAN	EXP_HEI2020_TOTALPRO	EXP_HEI2020_SEAPLANTPRO	EXP_HEI2020_WHOLEGRAIN	EXP_HEI2020_DAIRY	EXP_HEI2020_FATTYACID	EXP_HEI2020_REFINEDGRAIN	EXP_HEI2020_SODIUM	EXP_HEI2020_ADDEDSUGAR	EXP_HEI2020_SATFAT
1	2	18	1000	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.20	1.80	1.10	6.50	8.0	40	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0	10	10	10	10
2	2	18	1000	0.08	0.04	0.11	0.02	0.25	0.08	0.15	0.13	1.33	2.05	1.19	8.45	8.8	42	0.5	0.5	0.5	0.5	0.5	0.5	1	1	1	9	9	9	9
3	2	18	1000	0.16	0.08	0.22	0.04	0.50	0.16	0.30	0.26	1.46	2.30	1.28	10.40	9.6	44	1.0	1.0	1.0	1.0	1.0	1.0	2	2	2	8	8	8	8
4	2	18	1000	0.24	0.12	0.33	0.06	0.75	0.24	0.45	0.39	1.59	2.55	1.37	12.35	10.4	46	1.5	1.5	1.5	1.5	1.5	1.5	3	3	3	7	7	7	7
5	2	18	1000	0.32	0.16	0.44	0.08	1.00	0.32	0.60	0.52	1.72	2.80	1.46	14.30	11.2	48	2.0	2.0	2.0	2.0	2.0	2.0	4	4	4	6	6	6	6
6	2	18	1000	0.40	0.20	0.55	0.10	1.25	0.40	0.75	0.65	1.85	3.05	1.55	16.25	12.0	50	2.5	2.5	2.5	2.5	2.5	2.5	5	5	5	5	5	5	5

Examples for using dietaryindex’s functions:

Remember, when using dietaryindex’s functions for NHANES, the complex survey design has to be taken into account! See the examples below. ___

NHANES functions

Calculating HEI2020 for NHANES_FPED (after 2005)

# Using the first day nutrition data
FPED_PATH_1 = "/Users/james/Desktop/fped_dr1tot_1718.sas7bdat"
NUTRIENT_PATH_1 = "/Users/james/Desktop/DR1TOT_J.XPT"
DEMO_PATH_1 = "/Users/james/Desktop/DEMO_J.XPT"

HEI2020_NHANES_FPED(FPED_PATH=FPED_PATH_1, NUTRIENT_PATH=NUTRIENT_PATH_1, DEMO_PATH=DEMO_PATH_1)

# only the first day nutrition data
# Use the NHANES example data in 2017-2018 using only the first day nutrition data
HEI2020_NHANES_FPED_unweighted_d1 = HEI2020_NHANES_FPED(FPED_PATH=NHANES_20172018$FPED, NUTRIENT_PATH=NHANES_20172018$NUTRIENT, DEMO_PATH=NHANES_20172018$DEMO)

# set up survey design for NHANES data 2017-2018 day 1
NHANES_20172018_design_d1 = NHANES_20172018$FPED %>%
    filter(!is.na(WTDRD1)) %>%
    ## select the variables needed for the survey design
    select(SEQN, SDMVPSU, SDMVSTRA, WTDRD1)

# Merge the NHANES data with weighting variables with the HEI2020 results
NHANES_20172018_design_d1_HEI2020 = inner_join(HEI2020_NHANES_FPED_unweighted_d1, NHANES_20172018_design_d1, by = "SEQN")
print(NHANES_20172018_design_d1_HEI2020)

## # A tibble: 7,284 × 19
##     SEQN RIDAGEYR HEI2020_ALL HEI2020_TOTALFRT HEI2020_FRT HEI2020_VEG
##    <dbl>    <dbl>       <dbl>            <dbl>       <dbl>       <dbl>
##  1 93704        2        63.5            5           0          3.03  
##  2 93705       66        41.1            0           0          5     
##  3 93706       18        44.5            0           0          1.65  
##  4 93707       13        41.6            0           0          2.41  
##  5 93708       66        58.5            2.45        4.90       5     
##  6 93711       56        67.1            2.13        3.12       5     
##  7 93712       18        69.5            5           5          3.49  
##  8 93713       67        52.9            4.69        5          4.57  
##  9 93714       54        38.6            0.376       0.702      0.638 
## 10 93715       71        40.0            1.55        3.11       0.0706
## # ℹ 7,274 more rows
## # ℹ 13 more variables: HEI2020_GREENNBEAN <dbl>, HEI2020_TOTALPRO <dbl>,
## #   HEI2020_SEAPLANTPRO <dbl>, HEI2020_WHOLEGRAIN <dbl>, HEI2020_DAIRY <dbl>,
## #   HEI2020_FATTYACID <dbl>, HEI2020_REFINEDGRAIN <dbl>, HEI2020_SODIUM <dbl>,
## #   HEI2020_ADDEDSUGAR <dbl>, HEI2020_SATFAT <dbl>, SDMVPSU <dbl>,
## #   SDMVSTRA <dbl>, WTDRD1 <dbl>

# set up survey design for NHANES data 2017-2018 day 1
NHANES_design_1718_d1 <- svydesign(
    ## Masked variance pseudo-PSU
    id = ~SDMVPSU, 
    ## Masked variance pseudo-stratum
    strata = ~SDMVSTRA, 
    ## Dietary day one sample weight
    weight = ~WTDRD1, 
    data = NHANES_20172018_design_d1_HEI2020, 
    nest = TRUE) 

# summarize the weighted results
tbl_svysummary(
    data=NHANES_design_1718_d1,
    statistic = list(all_categorical() ~ "{n_unweighted} ({p}%)", all_continuous() ~ "{mean} ({mean.std.error})")
    )

Characteristic	N = 316,222,039¹
Respondent sequence number	98,306 (46)
Age in years at screening	39 (1)
HEI2020_ALL	49 (1)
HEI2020_TOTALFRT	2.01 (0.07)
HEI2020_FRT	2.08 (0.09)
HEI2020_VEG	2.77 (0.05)
HEI2020_GREENNBEAN	1.41 (0.06)
HEI2020_TOTALPRO	4.07 (0.04)
HEI2020_SEAPLANTPRO	2.20 (0.05)
HEI2020_WHOLEGRAIN	2.4 (0.1)
HEI2020_DAIRY	5.1 (0.1)
HEI2020_FATTYACID	4.7 (0.1)
HEI2020_REFINEDGRAIN	5.8 (0.1)
HEI2020_SODIUM	4.7 (0.1)
HEI2020_ADDEDSUGAR	6.7 (0.1)
HEI2020_SATFAT	5.3 (0.1)
Masked variance pseudo-PSU
1	3,537 (49%)
2	3,747 (51%)
Masked variance pseudo-stratum	140.9 (0.2)
Dietary day one sample weight	112,339 (6,641)
¹ Mean (SE); n (unweighted) (%)

##################################################
# first day + second day nutrition data
# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
HEI2020_NHANES_FPED_unweighted_d1d2 = HEI2020_NHANES_FPED(FPED_PATH=NHANES_20172018$FPED, NUTRIENT_PATH=NHANES_20172018$NUTRIENT, DEMO_PATH=NHANES_20172018$DEMO, FPED_PATH2=NHANES_20172018$FPED2, NUTRIENT_PATH2=NHANES_20172018$NUTRIENT2)

# set up survey design for NHANES data 2017-2018 day 1 and day 2
## filter out the missing values for the weight variable WTDR2D
NHANES_20172018_design_d1d2 = NHANES_20172018$FPED %>%
    filter(!is.na(WTDR2D)) %>%
    ## select the variables needed for the survey design
    select(SEQN, SDMVPSU, SDMVSTRA, WTDR2D)

# Merge the NHANES data with weighting variables with the HEI2020 results
NHANES_20172018_design_d1d2_HEI2020 = inner_join(HEI2020_NHANES_FPED_unweighted_d1d2, NHANES_20172018_design_d1d2, by = "SEQN")
print(NHANES_20172018_design_d1d2_HEI2020)

## # A tibble: 6,315 × 19
##     SEQN RIDAGEYR HEI2020_ALL HEI2020_TOTALFRT HEI2020_FRT HEI2020_VEG
##    <dbl>    <dbl>       <dbl>            <dbl>       <dbl>       <dbl>
##  1 93704        2        61.7           5            2.5         1.55 
##  2 93705       66        43.0           0.0759       0           3.46 
##  3 93707       13        38.9           0            0           2.09 
##  4 93708       66        49.9           1.22         2.45        3.20 
##  5 93711       56        69.0           3.57         2.85        3.75 
##  6 93712       18        46.4           2.5          2.5         2.42 
##  7 93713       67        55.4           4.84         5           3.47 
##  8 93714       54        36.1           0.321        0.617       1.85 
##  9 93715       71        30.7           0.777        1.55        0.614
## 10 93716       61        65.0           5            5           4.03 
## # ℹ 6,305 more rows
## # ℹ 13 more variables: HEI2020_GREENNBEAN <dbl>, HEI2020_TOTALPRO <dbl>,
## #   HEI2020_SEAPLANTPRO <dbl>, HEI2020_WHOLEGRAIN <dbl>, HEI2020_DAIRY <dbl>,
## #   HEI2020_FATTYACID <dbl>, HEI2020_REFINEDGRAIN <dbl>, HEI2020_SODIUM <dbl>,
## #   HEI2020_ADDEDSUGAR <dbl>, HEI2020_SATFAT <dbl>, SDMVPSU <dbl>,
## #   SDMVSTRA <dbl>, WTDR2D <dbl>

# calculate the weighted results using the survey design
NHANES_design_1718_d1d2 <- svydesign(
    ## Masked variance pseudo-PSU
    id = ~SDMVPSU, 
    ## Masked variance pseudo-stratum
    strata = ~SDMVSTRA, 
    ## Dietary two-day sample weight
    weight = ~WTDR2D, 
    data = NHANES_20172018_design_d1d2_HEI2020,  
    nest = TRUE)

# summarize the weighted results
tbl_svysummary(
    data=NHANES_design_1718_d1d2,
    statistic = list(all_categorical() ~ "{n_unweighted} ({p}%)", all_continuous() ~ "{mean} ({mean.std.error})")
    )

Characteristic	N = 316,082,073¹
Respondent sequence number	98,308 (64)
Age in years at screening	39 (1)
HEI2020_ALL	50 (1)
HEI2020_TOTALFRT	2.10 (0.08)
HEI2020_FRT	2.12 (0.09)
HEI2020_VEG	2.78 (0.05)
HEI2020_GREENNBEAN	1.42 (0.06)
HEI2020_TOTALPRO	4.09 (0.03)
HEI2020_SEAPLANTPRO	2.19 (0.06)
HEI2020_WHOLEGRAIN	2.60 (0.11)
HEI2020_DAIRY	5.15 (0.08)
HEI2020_FATTYACID	4.72 (0.08)
HEI2020_REFINEDGRAIN	5.89 (0.06)
HEI2020_SODIUM	4.61 (0.07)
HEI2020_ADDEDSUGAR	6.92 (0.10)
HEI2020_SATFAT	5.31 (0.08)
Masked variance pseudo-PSU
1	3,124 (50%)
2	3,191 (50%)
Masked variance pseudo-stratum	141.0 (0.2)
Dietary two-day sample weight	141,577 (9,356)
¹ Mean (SE); n (unweighted) (%)

# the sequence of DEMO, FPED, NUTRIENT data entry do not matter if you use "DEMO_PATH", "FPED_PATH2", "NUTRIENT_PATH2" to specify the data input

Calculating DI-GM for NHANES (after 2005)

Calculating dietary index is just the first step. Please include complex survey design when analzying NHANES data as shown above.

# Use the NHANES example data in 2017-2018 using the first day 24-hour recall data
library(dietaryindex)
data("NHANES_20172018")
DI_GM_NHANES_FPED(FPED_IND_PATH = NHANES_20172018$FPED_IND, NUTRIENT_IND_PATH = NHANES_20172018$NUTRIENT_IND)

## The default food codes for avocado from 17-18 FNDDS file is used.

## The default food codes for broccoli from 17-18 FNDDS file is used.

## The default food codes for chickpea from 17-18 FNDDS file is used.

## The default food codes for coffee from 17-18 FNDDS file is used.

## The default food codes for cranberry from 17-18 FNDDS file is used.

## The default food codes for fermented dairy from 17-18 FNDDS file is used.

## The default food codes for green tea from 17-18 FNDDS file is used.

## The default food codes for soybean from 17-18 FNDDS file is used.

## Calculating DI_GM index using day 1 data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 7,483 × 17
##     SEQN RIAGENDR DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##    <dbl>    <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1 93704        1           4             0              0              0
##  2 93705        2           5             0              0              0
##  3 93706        1           3             0              0              0
##  4 93707        1           3             0              0              0
##  5 93708        2           5             0              0              0
##  6 93710        2           5             0              1              0
##  7 93711        1           6             0              0              0
##  8 93712        1           4             0              0              0
##  9 93713        1           4             0              0              0
## 10 93714        2           5             0              0              0
## # ℹ 7,473 more rows
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

# Use the NHANES example data in 2017-2018 using the second day 24-hour recall data
library(dietaryindex)
data("NHANES_20172018")
DI_GM_NHANES_FPED(FPED_IND_PATH2 = NHANES_20172018$FPED_IND2, NUTRIENT_IND_PATH2 = NHANES_20172018$NUTRIENT_IND2)

## The default food codes for avocado from 17-18 FNDDS file is used.

## The default food codes for broccoli from 17-18 FNDDS file is used.

## The default food codes for chickpea from 17-18 FNDDS file is used.

## The default food codes for coffee from 17-18 FNDDS file is used.

## The default food codes for cranberry from 17-18 FNDDS file is used.

## The default food codes for fermented dairy from 17-18 FNDDS file is used.

## The default food codes for green tea from 17-18 FNDDS file is used.

## The default food codes for soybean from 17-18 FNDDS file is used.

## Calculating DI_GM index using day 2 data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 6,500 × 17
##     SEQN RIAGENDR DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##    <dbl>    <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1 93704        1           4             0              0              0
##  2 93705        2           4             0              0              0
##  3 93707        1           3             0              0              0
##  4 93708        2           3             0              0              0
##  5 93710        2           2             0              0              0
##  6 93711        1           4             0              0              0
##  7 93712        1           3             0              0              0
##  8 93713        1           6             0              0              0
##  9 93714        2           3             0              0              0
## 10 93715        1           3             0              0              0
## # ℹ 6,490 more rows
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
library(dietaryindex)
data("NHANES_20172018")
DI_GM_NHANES_FPED(FPED_IND_PATH = NHANES_20172018$FPED_IND, NUTRIENT_IND_PATH = NHANES_20172018$NUTRIENT_IND, FPED_IND_PATH2 = NHANES_20172018$FPED_IND2, NUTRIENT_IND_PATH2 = NHANES_20172018$NUTRIENT_IND2)

## The default food codes for avocado from 17-18 FNDDS file is used.

## The default food codes for broccoli from 17-18 FNDDS file is used.

## The default food codes for chickpea from 17-18 FNDDS file is used.

## The default food codes for coffee from 17-18 FNDDS file is used.

## The default food codes for cranberry from 17-18 FNDDS file is used.

## The default food codes for fermented dairy from 17-18 FNDDS file is used.

## The default food codes for green tea from 17-18 FNDDS file is used.

## The default food codes for soybean from 17-18 FNDDS file is used.

## Calculating DI_GM index using day 1 data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## Calculating DI_GM index using day 2 data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 6,488 × 17
##     SEQN RIAGENDR DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##    <dbl>    <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1 93704        1         4               0            0                0
##  2 93705        2         4.5             0            0                0
##  3 93707        1         3               0            0                0
##  4 93708        2         4               0            0                0
##  5 93710        2         3.5             0            0.5              0
##  6 93711        1         5               0            0                0
##  7 93712        1         3.5             0            0                0
##  8 93713        1         5               0            0                0
##  9 93714        2         4               0            0                0
## 10 93715        1         3               0            0                0
## # ℹ 6,478 more rows
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

# Use the NHANES example data in 2003-2004 using the first day 24-hour recall data
library(dietaryindex)
data("NHANES_20032004")
DI_GM_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO)

## The default food codes for avocado from 17-18 FNDDS file is used.

## The default food codes for broccoli from 17-18 FNDDS file is used.

## The default food codes for chickpea from 17-18 FNDDS file is used.

## The default food codes for coffee from 17-18 FNDDS file is used.

## The default food codes for cranberry from 17-18 FNDDS file is used.

## The default food codes for fermented dairy from 17-18 FNDDS file is used.

## The default food codes for green tea from 17-18 FNDDS file is used.

## The default food codes for soybean from 17-18 FNDDS file is used.

## Calculating the DI_GM total and component scores for the first day data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 8,272 × 17
##     SEQN RIAGENDR DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##    <dbl>    <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1 21005        1           4             0              0              0
##  2 21006        2           3             0              0              0
##  3 21007        2           2             0              0              0
##  4 21008        1           1             0              0              0
##  5 21009        1           5             0              0              0
##  6 21010        2           4             0              0              0
##  7 21012        1           3             0              0              0
##  8 21013        2           2             0              0              0
##  9 21014        1           5             0              0              0
## 10 21015        1           7             0              0              0
## # ℹ 8,262 more rows
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

# Use the NHANES example data in 2003-2004 using the second day 24-hour recall data
library(dietaryindex)
data("NHANES_20032004")
DI_GM_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## The default food codes for avocado from 17-18 FNDDS file is used.

## The default food codes for broccoli from 17-18 FNDDS file is used.

## The default food codes for chickpea from 17-18 FNDDS file is used.

## The default food codes for coffee from 17-18 FNDDS file is used.

## The default food codes for cranberry from 17-18 FNDDS file is used.

## The default food codes for fermented dairy from 17-18 FNDDS file is used.

## The default food codes for green tea from 17-18 FNDDS file is used.

## The default food codes for soybean from 17-18 FNDDS file is used.

## Calculating the DI_GM total and component scores for the second day data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 7,648 × 17
##     SEQN RIAGENDR DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##    <dbl>    <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1 21005        1           2             0              0              0
##  2 21006        2           3             0              0              0
##  3 21007        2           2             0              0              0
##  4 21008        1           3             0              0              0
##  5 21009        1           4             0              0              0
##  6 21010        2           5             0              0              0
##  7 21012        1           3             0              0              0
##  8 21013        2           3             0              0              0
##  9 21014        1           4             0              0              0
## 10 21015        1           6             0              0              0
## # ℹ 7,638 more rows
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

# Use the NHANES example data in 2003-2004 using the first day + second day nutrition data
library(dietaryindex)
data("NHANES_20032004")
DI_GM_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## The default food codes for avocado from 17-18 FNDDS file is used.

## The default food codes for broccoli from 17-18 FNDDS file is used.

## The default food codes for chickpea from 17-18 FNDDS file is used.

## The default food codes for coffee from 17-18 FNDDS file is used.

## The default food codes for cranberry from 17-18 FNDDS file is used.

## The default food codes for fermented dairy from 17-18 FNDDS file is used.

## The default food codes for green tea from 17-18 FNDDS file is used.

## The default food codes for soybean from 17-18 FNDDS file is used.

## Calculating the DI_GM total and component scores for the first day data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## Calculating the DI_GM total and component scores for the second day data...

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 7,647 × 17
##     SEQN RIAGENDR DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##    <dbl>    <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1 21005        1         3               0              0              0
##  2 21006        2         3               0              0              0
##  3 21007        2         2               0              0              0
##  4 21008        1         2               0              0              0
##  5 21009        1         4.5             0              0              0
##  6 21010        2         4.5             0              0              0
##  7 21012        1         3               0              0              0
##  8 21013        2         2.5             0              0              0
##  9 21014        1         4.5             0              0              0
## 10 21015        1         6.5             0              0              0
## # ℹ 7,637 more rows
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

Calculating DII for NHANES_FPED (after 2005)

# This function was improved by Zhe Xu (zxu@cmu.edu.cn) to include the other DII ingredients, including Garlic, Ginger, Onion, Pepper, Thyme/oregano, and other Flavonoids, in the calculation.
FPED_PATH = "/Users/james/Desktop/fped_dr1tot_1718.sas7bdat"
NUTRIENT_PATH = "/Users/james/Desktop/DR1TOT_J.XPT"
DEMO_PATH = "/Users/james/Desktop/DEMO_J.XPT"

DII_NHANES_FPED(FPED_PATH, NUTRIENT_PATH, DEMO_PATH)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
library(dietaryindex)
data("NHANES_20172018")
data("DII_OTHER_INGREDIENTS_day1")
data("DII_OTHER_INGREDIENTS_day2")
DII_NHANES_FPED(FPED_PATH = NHANES_20172018$FPED, NUTRIENT_PATH = NHANES_20172018$NUTRIENT, DEMO_PATH = NHANES_20172018$DEMO, FPED_PATH2 = NHANES_20172018$FPED2, NUTRIENT_PATH2 = NHANES_20172018$NUTRIENT2, OTHER_INGREDIENTS1 = DII_OTHER_INGREDIENTS_day1, OTHER_INGREDIENTS2 = DII_OTHER_INGREDIENTS_day2)

## VITD is included in the calculation in the first day of NHANES data.

## VITD is included in the calculation in the second day of NHANES data.

## Reminder: This function does not use all the original DII variables. Eugenol, trans fat, turmeric, Green/black tea, Rosemary are not included.

## Day 1 and Day 2 data are used for the calculation.

## Note: Flavonoid data is only available for NHANES 2007-2010 and 2017-2018.

## # A tibble: 6,174 × 42
##     SEQN DII_ALL DII_NOETOH    ALCOHOL  VITB12    VITB6 BCAROTENE CAFFEINE
##    <dbl>   <dbl>      <dbl>      <dbl>   <dbl>    <dbl>     <dbl>    <dbl>
##  1 93704   5.81       5.53   0.278     -0.0263  0.0738      0.560   0.0849
##  2 93705   5.35       5.07   0.278     -0.0942  0.305       0.223   0.0833
##  3 93707   6.31       6.03   0.278     -0.0427  0.0267      0.564   0.0849
##  4 93708   5.81       5.53   0.278     -0.0721  0.216       0.451   0.0849
##  5 93711  -0.139     -0.139 -0.0000238 -0.0578 -0.185       0.223   0.0828
##  6 93712   2.90       2.62   0.278      0.0207 -0.329       0.534   0.0845
##  7 93713   2.13       1.85   0.278     -0.0477 -0.140       0.274   0.0826
##  8 93714   5.95       5.67   0.278     -0.0601  0.00904     0.561   0.0846
##  9 93715   7.07       6.79   0.278     -0.0745  0.309       0.562   0.0824
## 10 93716   0.662      0.384  0.278      0.0245 -0.357      -0.237   0.0844
## # ℹ 6,164 more rows
## # ℹ 34 more variables: CARB <dbl>, CHOLES <dbl>, KCAL <dbl>, TOTALFAT <dbl>,
## #   FIBER <dbl>, FOLICACID <dbl>, IRON <dbl>, MG <dbl>, MUFA <dbl>,
## #   NIACIN <dbl>, N3FAT <dbl>, N6FAT <dbl>, PROTEIN <dbl>, PUFA <dbl>,
## #   RIBOFLAVIN <dbl>, SATFAT <dbl>, SE <dbl>, THIAMIN <dbl>, VITA <dbl>,
## #   VITC <dbl>, VITE <dbl>, ZN <dbl>, VITD <dbl>, GARLIC <dbl>, GINGER <dbl>,
## #   ONION <dbl>, FLA3OL <dbl>, FLAVONES <dbl>, FLAVONOLS <dbl>, …

Calculating DII for NHANES_MPED (1999-2004)

# Use the NHANES example data in 2003-2004 using the first day + second day nutrition data
library(dietaryindex)
data("NHANES_20032004")
data("DII_OTHER_INGREDIENTS_day1")
data("DII_OTHER_INGREDIENTS_day2")
DII_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2, OTHER_INGREDIENTS1 = DII_OTHER_INGREDIENTS_day1, OTHER_INGREDIENTS2 = DII_OTHER_INGREDIENTS_day2)

## VITD is not included in the calculation in the first day of NHANES data.

## It is normal to see warnings if you do not provide all arguments using DII. The algorithm will only count the arguments you enter to calculate the DII. All warnings are about the first column you don't use. For example, if you only entered alcohol, vitamin b12, and vitamin b6, all warnings would remind you that bcarotene is not found.

## VITD is not included in the calculation in the first day of NHANES data.

## It is normal to see warnings if you do not provide all arguments using DII. The algorithm will only count the arguments you enter to calculate the DII. All warnings are about the first column you don't use. For example, if you only entered alcohol, vitamin b12, and vitamin b6, all warnings would remind you that bcarotene is not found.

## Reminder: This function does not use all the original DII variables. Eugenol, trans fat, turmeric, Green/black tea, Rosemary are not included.

## Day 1 and Day 2 data are used for the calculation.

## Note: Flavonoid data is only available for NHANES 2007-2010 and 2017-2018.

## # A tibble: 7,647 × 48
##     SEQN DII_ALL DII_NOETOH ALCOHOL_DII VITB12_DII VITB6_DII BCAROTENE_DII
##    <dbl>   <dbl>      <dbl>       <dbl>      <dbl>     <dbl>         <dbl>
##  1 21005    2.68      2.40        0.278    -0.0305   -0.107          0.555
##  2 21006    4.76      4.48        0.278    -0.0868    0.224          0.563
##  3 21007    4.82      4.54        0.278    -0.0904    0.277          0.540
##  4 21008    4.28      4.00        0.278    -0.0383    0.150          0.537
##  5 21009    1.21      0.937       0.278     0.0337   -0.293          0.528
##  6 21010    3.34      3.61       -0.274    -0.0499    0.0303         0.563
##  7 21012    3.23      3.51       -0.278     0.0360   -0.194          0.549
##  8 21013    4.57      4.29        0.278    -0.0565    0.150          0.559
##  9 21014    1.93      1.65        0.278     0.0706   -0.204          0.524
## 10 21015    2.39      2.11        0.278    -0.0551   -0.0627         0.134
## # ℹ 7,637 more rows
## # ℹ 41 more variables: CAFFEINE_DII <dbl>, CARB_DII <dbl>, CHOLES_DII <dbl>,
## #   KCAL_DII <dbl>, EUGENOL_DII <dbl>, TOTALFAT_DII <dbl>, FIBER_DII <dbl>,
## #   FOLICACID_DII <dbl>, GARLIC_DII <dbl>, GINGER_DII <dbl>, IRON_DII <dbl>,
## #   MG_DII <dbl>, MUFA_DII <dbl>, NIACIN_DII <dbl>, N3FAT_DII <dbl>,
## #   N6FAT_DII <dbl>, ONION_DII <dbl>, PROTEIN_DII <dbl>, PUFA_DII <dbl>,
## #   RIBOFLAVIN_DII <dbl>, SAFFRON_DII <dbl>, SATFAT_DII <dbl>, SE_DII <dbl>, …

Calculating HEI2020 for NHANES_MPED (before 2005)

# Use the NHANES example data in 2003-2004 using the first day + second day nutrition data
data("NHANES_20032004")
HEI2020_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## # A tibble: 7,915 × 17
##     SEQN TOTALKCAL_HEI2020 RIDAGEYR HEI2020_ALL HEI2020_TOTALFRT HEI2020_FRT
##    <dbl>             <dbl>    <dbl>       <dbl>            <dbl>       <dbl>
##  1 21005             2598.       19        39.0            0.489       0    
##  2 21006             1198        16        37.8            0.738       0    
##  3 21007              943        14        43.0            3.97        1.03 
##  4 21008             2236.       17        28.4            0           0    
##  5 21009             4540.       55        43.4            0.182       0.311
##  6 21010             1834.       52        53.7            0.406       0.812
##  7 21012             2786        63        37.5            0           0    
##  8 21013             1420.       13        44.6            2.44        2.5  
##  9 21014             2079         3        49.0            2.5         0.880
## 10 21015             1638.       83        66.8            3.37        4.87 
## # ℹ 7,905 more rows
## # ℹ 11 more variables: HEI2020_VEG <dbl>, HEI2020_GREENNBEAN <dbl>,
## #   HEI2020_TOTALPRO <dbl>, HEI2020_SEAPLANTPRO <dbl>,
## #   HEI2020_WHOLEGRAIN <dbl>, HEI2020_DAIRY <dbl>, HEI2020_FATTYACID <dbl>,
## #   HEI2020_REFINEDGRAIN <dbl>, HEI2020_SODIUM <dbl>, HEI2020_ADDEDSUGAR <dbl>,
## #   HEI2020_SATFAT <dbl>

Calculating HEI2015 for NHANES_FPED (after 2005)

# Using the first day nutrition data
FPED_PATH_1 = "/Users/james/Desktop/fped_dr1tot_1718.sas7bdat"
NUTRIENT_PATH_1 = "/Users/james/Desktop/DR1TOT_J.XPT"
DEMO_PATH_1 = "/Users/james/Desktop/DEMO_J.XPT"

HEI2015_NHANES_FPED(FPED_PATH=FPED_PATH_1, NUTRIENT_PATH=NUTRIENT_PATH_1, DEMO_PATH=DEMO_PATH_1)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
data("NHANES_20172018")
HEI2015_NHANES_FPED(FPED_PATH=NHANES_20172018$FPED, NUTRIENT_PATH=NHANES_20172018$NUTRIENT, DEMO_PATH=NHANES_20172018$DEMO, FPED_PATH2=NHANES_20172018$FPED2, NUTRIENT_PATH2=NHANES_20172018$NUTRIENT2)

## # A tibble: 6,177 × 15
##     SEQN HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT HEI2015_VEG HEI2015_GREENNBEAN
##    <dbl>       <dbl>            <dbl>       <dbl>       <dbl>              <dbl>
##  1 93704        61.7           5            2.5         1.55                0   
##  2 93705        43.0           0.0759       0           3.46                4.83
##  3 93707        38.9           0            0           2.09                0   
##  4 93708        49.9           1.22         2.45        3.20                0   
##  5 93711        69.0           3.57         2.85        3.75                4.27
##  6 93712        46.4           2.5          2.5         2.42                2.5 
##  7 93713        55.4           4.84         5           3.47                1.13
##  8 93714        36.1           0.321        0.617       1.85                0   
##  9 93715        30.7           0.777        1.55        0.614               0   
## 10 93716        65.0           5            5           4.03                4.73
## # ℹ 6,167 more rows
## # ℹ 9 more variables: HEI2015_TOTALPRO <dbl>, HEI2015_SEAPLANTPRO <dbl>,
## #   HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>, HEI2015_FATTYACID <dbl>,
## #   HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>, HEI2015_ADDEDSUGAR <dbl>,
## #   HEI2015_SATFAT <dbl>

Calculating HEI2015 for NHANES_MPED (betweeen 1999-2004)

data("NHANES_20032004")
HEI2015_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## # A tibble: 7,650 × 16
##     SEQN TOTALKCAL_HEI2015 HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT HEI2015_VEG
##    <dbl>             <dbl>       <dbl>            <dbl>       <dbl>       <dbl>
##  1 21005             2598.        53.7            0.489       0            2.28
##  2 21006             1198         46.3            0.738       0            3.95
##  3 21007              943         42.5            3.97        1.03         3.15
##  4 21008             2236.        45.8            0           0            2.37
##  5 21009             4540.        59.3            0.182       0.311        3.01
##  6 21010             1834.        59.4            0.406       0.812        4.10
##  7 21012             2786         54.0            0           0            2.89
##  8 21013             1420.        50.6            2.44        2.5          2.52
##  9 21014             2079         63.4            2.5         0.880        4.25
## 10 21015             1638.        73.7            3.37        4.87         3.42
## # ℹ 7,640 more rows
## # ℹ 10 more variables: HEI2015_GREENNBEAN <dbl>, HEI2015_TOTALPRO <dbl>,
## #   HEI2015_SEAPLANTPRO <dbl>, HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>,
## #   HEI2015_FATTYACID <dbl>, HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>,
## #   HEI2015_ADDEDSUGAR <dbl>, HEI2015_SATFAT <dbl>

Calculating AHEI for NHANES_FPED (after 2005)

FPED_IND_PATH = "/Users/james/Desktop/data/fped_dr1iff.sas7bdat"
NUTRIENT_IND_PATH = "/Users/james/Desktop/data/DR1IFF_J"

AHEI_NHANES_FPED(FPED_IND_PATH, NUTRIENT_IND_PATH)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
data("NHANES_20172018")
AHEI_NHANES_FPED(NHANES_20172018$FPED_IND, NHANES_20172018$NUTRIENT_IND, NHANES_20172018$FPED_IND2, NHANES_20172018$NUTRIENT_IND2)

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## Trans fat is not avaiable for NHANES, so it is not included in the AHEI score.

## # A tibble: 6,488 × 13
##     SEQN AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN AHEI_NUTSLEG
##    <dbl>    <dbl>       <dbl>    <dbl>    <dbl>       <dbl>        <dbl>
##  1 93704     43.8        42.6     0.22    1.34        1.13          0   
##  2 93705     39.9        38.6     3.05    0           0            10   
##  3 93707     33.6        32.4     1.42    0           6.42          0.35
##  4 93708     49.1        47.9     5.18    0.612       0.643         5   
##  5 93710     44.2        42.9     0.43    2.11        2.83          1.25
##  6 93711     53.3        48.5     6.75    1.62        3.97         10   
##  7 93712     26.4        25.2     3.5     2.28        2.69          5   
##  8 93713     38.8        37.6     6.08    4.61        0.961         1.85
##  9 93714     34.8        33.6     0.04    0.25        4.99          0   
## 10 93715     20.1        18.9     0.44    0.4         0             0   
## # ℹ 6,478 more rows
## # ℹ 6 more variables: AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>, AHEI_SSB_FRTJ <dbl>,
## #   AHEI_REDPROC_MEAT <dbl>, AHEI_SODIUM <dbl>, AHEI_ALCOHOL <dbl>

Calculating AHEI for NHANES_MPED (1999-2004)

# Use the NHANES example data in 2003-2004 using the first day + second day nutrition data
data("NHANES_20032004")
AHEI_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## [1] "Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used."

## # A tibble: 7,647 × 13
##     SEQN AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN AHEI_NUTSLEG
##    <dbl>    <dbl>       <dbl>    <dbl>    <dbl>       <dbl>        <dbl>
##  1 21005     25.9        23.4    1.79     0          5            2.07  
##  2 21006     24.4        21.9    0.761    0          1.21         1.48  
##  3 21007     26.0        23.5    3.09     0.154      0            0     
##  4 21008     21.3        18.8    4.71     0          2.31         0.0363
##  5 21009     37.1        34.6    3.29     0.277      0.0156       0.458 
##  6 21010     30.8        28.3    1.27     0.353      0            5.59  
##  7 21012     23.5        20.1    2.60     0          1.44         5.06  
##  8 21013     20.9        18.4    1.30     0.653      0            0.0617
##  9 21014     41.5        39.0    4.83     0.445      1.06         5.03  
## 10 21015     46.3        43.8    3.04     2.11       3.38         5.03  
## # ℹ 7,637 more rows
## # ℹ 6 more variables: AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>, AHEI_SSB_FRTJ <dbl>,
## #   AHEI_REDPROC_MEAT <dbl>, AHEI_SODIUM <dbl>, AHEI_ALCOHOL <dbl>

Calculating aMED for NHANES_FPED (after 2005)

FPED_PATH = "/Users/james/Desktop/fped_dr1tot_1718.sas7bdat"
NUTRIENT_PATH = "/Users/james/Desktop/DR1TOT_J.XPT"
DEMO_PATH = "/Users/james/Desktop/DEMO_J.XPT"

MED_NHANES_FPED(FPED_PATH, NUTRIENT_PATH, DEMO_PATH)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
data("NHANES_20172018")
MED_NHANES_FPED(FPED_PATH=NHANES_20172018$FPED, NUTRIENT_PATH=NHANES_20172018$NUTRIENT, DEMO_PATH=NHANES_20172018$DEMO, FPED_PATH2=NHANES_20172018$FPED2, NUTRIENT_PATH2=NHANES_20172018$NUTRIENT2)

## Reminder: this MED index uses medians to rank participants' food/drink serving sizes and then calculate MED component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 6,174 × 12
##     SEQN MED_ALL MED_NOETOH MED_FRT MED_VEG MED_WGRAIN MED_LEGUMES MED_NUTS
##    <dbl>   <dbl>      <dbl>   <dbl>   <dbl>      <dbl>       <dbl>    <dbl>
##  1 93704     3.5        3.5     1       0          0.5         0        0  
##  2 93705     3.5        3.5     0       0.5        0           1        0.5
##  3 93707     2.5        2.5     0       0.5        1           0.5      0  
##  4 93708     3.5        3.5     0.5     0.5        0.5         0        0.5
##  5 93711     5.5        5.5     1       1          0.5         1        1  
##  6 93712     3          3       0.5     1          0.5         0.5      0  
##  7 93713     3.5        3.5     1       1          0.5         0.5      0  
##  8 93714     1.5        1.5     0       0          0.5         0        0  
##  9 93715     0.5        0.5     0       0          0           0        0  
## 10 93716     6          6       1       1          0.5         1        1  
## # ℹ 6,164 more rows
## # ℹ 4 more variables: MED_FISH <dbl>, MED_REDPROC_MEAT <dbl>,
## #   MED_MONSATFAT <dbl>, MED_ALCOHOL <dbl>

Calculating aMED for NHANES_MPED (1999-2004)

# Use the NHANES example data in 2003-2004 using the first day + second day nutrition data
data("NHANES_20032004")
MED_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## Reminder: this MED index uses medians to rank participants' food/drink serving sizes and then calculate MED component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 7,647 × 12
##     SEQN MED_ALL MED_NOETOH MED_FRT MED_VEG MED_WGRAIN MED_LEGUMES MED_NUTS
##    <dbl>   <dbl>      <dbl>   <dbl>   <dbl>      <dbl>       <dbl>    <dbl>
##  1 21005     2          2       0       0.5        0.5         0        0.5
##  2 21006     3.5        3.5     0       0          0.5         0.5      0.5
##  3 21007     2.5        2.5     0       0.5        0           0        0  
##  4 21008     3.5        3.5     0       1          0.5         1        0  
##  5 21009     4          4       0       0.5        0.5         0.5      0  
##  6 21010     2          1.5     0       0          0           1        0  
##  7 21012     3          3       0       0.5        0.5         1        0  
##  8 21013     2.5        2.5     0       0          0           0.5      0.5
##  9 21014     5          5       0.5     1          0.5         1        0.5
## 10 21015     4.5        4.5     0.5     0.5        1           1        0.5
## # ℹ 7,637 more rows
## # ℹ 4 more variables: MED_FISH <dbl>, MED_REDPROC_MEAT <dbl>,
## #   MED_MONSATFAT <dbl>, MED_ALCOHOL <dbl>

Calculating MEDI for NHANES_FPED (after 2005)

FPED_IND_PATH = "/Users/james/Desktop/data/fped_dr1iff.sas7bdat"
NUTRIENT_IND_PATH = "/Users/james/Desktop/data/DR1IFF_J"

MEDI_NHANES_FPED(FPED_IND_PATH, NUTRIENT_IND_PATH)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
data("NHANES_20172018")
MEDI_NHANES_FPED(FPED_IND_PATH=NHANES_20172018$FPED_IND, NUTRIENT_IND_PATH=NHANES_20172018$NUTRIENT_IND, FPED_IND_PATH2=NHANES_20172018$FPED_IND2, NUTRIENT_IND_PATH2=NHANES_20172018$NUTRIENT_IND2)

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## Since no SWEETS code is provided, the default SSB code from 17-18 FNDDS file is used

## Since no FAT_OIL code is provided, the default FAT_OIL code from 17-18 FNDDS file is used

## Since no OLIVE_OIL code is provided, the default OLIVE_OIL code from 17-18 FNDDS file is used

## # A tibble: 6,488 × 14
##     SEQN MEDI_ALL MEDI_NOETOH MEDI_OLIVE_OIL MEDI_FRT MEDI_VEG MEDI_LEGUMES
##    <dbl>    <dbl>       <dbl>          <dbl>    <dbl>    <dbl>        <dbl>
##  1 93704      3           3                0        0      0            0  
##  2 93705      3.5         3.5              0        0      0            1  
##  3 93707      2           2                0        0      0            0  
##  4 93708      3.5         3.5              0        0      0.5          0  
##  5 93710      3           3                0        0      0            0  
##  6 93711      5           4.5              0        0      0.5          1  
##  7 93712      3           3                0        0      0            0.5
##  8 93713      2.5         2.5              0        0      0.5          0  
##  9 93714      3           3                0        0      0            0  
## 10 93715      3           3                0        0      0            0  
## # ℹ 6,478 more rows
## # ℹ 7 more variables: MEDI_NUTS <dbl>, MEDI_FISH <dbl>, MEDI_ALCOHOL <dbl>,
## #   MEDI_SSB <dbl>, MEDI_SWEETS <dbl>, MEDI_DISCRET_FAT <dbl>,
## #   MEDI_REDPROC_MEAT <dbl>

Calculating MEDI for NHANES_MPED (1999-2004)

# Use the NHANES example data in 2003-2004 using the first day + second day nutrition data
data("NHANES_20032004")
MEDI_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## Since no SWEETS code is provided, the default SSB code from 17-18 FNDDS file is used

## Since no FAT_OIL code is provided, the default FAT_OIL code from 17-18 FNDDS file is used

## Since no OLIVE_OIL code is provided, the default OLIVE_OIL code from 17-18 FNDDS file is used

## # A tibble: 7,647 × 14
##     SEQN MEDI_ALL MEDI_NOETOH MEDI_OLIVE_OIL MEDI_FRT MEDI_VEG MEDI_LEGUMES
##    <dbl>    <dbl>       <dbl>          <dbl>    <dbl>    <dbl>        <dbl>
##  1 21005      1.5         1.5              0        0        0          0  
##  2 21006      3.5         3.5              0        0        0          0  
##  3 21007      2           2                0        0        0          0  
##  4 21008      2.5         2.5              0        0        0          0  
##  5 21009      2.5         2.5              0        0        0          0  
##  6 21010      4           3                0        0        0          0.5
##  7 21012      2.5         1.5              0        0        0          0.5
##  8 21013      1.5         1.5              0        0        0          0  
##  9 21014      4           4                0        0        0          0.5
## 10 21015      3           3                0        0        0          0  
## # ℹ 7,637 more rows
## # ℹ 7 more variables: MEDI_NUTS <dbl>, MEDI_FISH <dbl>, MEDI_ALCOHOL <dbl>,
## #   MEDI_SSB <dbl>, MEDI_SWEETS <dbl>, MEDI_DISCRET_FAT <dbl>,
## #   MEDI_REDPROC_MEAT <dbl>

Calculating DASH for NHANES_FPED (after 2005)

FPED_IND_PATH = "/Users/james/Desktop/data/fped_dr1iff.sas7bdat"
NUTRIENT_IND_PATH = "/Users/james/Desktop/data/DR1IFF_J"

DASH_NHANES_FPED(FPED_IND_PATH, NUTRIENT_IND_PATH)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
data("NHANES_20172018")
DASH_NHANES_FPED(NHANES_20172018$FPED_IND, NHANES_20172018$NUTRIENT_IND, NHANES_20172018$FPED_IND2, NHANES_20172018$NUTRIENT_IND2)

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## Since no skim milk code is provided, the default skim milk code from 17-18 FNDDS file is used.

## Since no low-fat cheese code is provided, the default low-fat cheese code from 17-18 FNDDS file is used.

## Reminder: this DASH index uses quintiles to rank participants' food/drink serving sizes and then calculate DASH component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 6,488 × 10
##     SEQN DASH_ALL DASH_FRT DASH_VEG DASH_NUTSLEG DASH_WGRAIN DASH_LOWF_DAIRY
##    <dbl>    <dbl>    <dbl>    <dbl>        <dbl>       <dbl>           <dbl>
##  1 93704     23.5      4.5      1            1           2.5               1
##  2 93705     18.5      1.5      3            4           1                 1
##  3 93707     20        1        2            2           4.5               1
##  4 93708     23        2        3.5          3           2                 1
##  5 93710     25.5      3.5      1.5          2.5         3.5               1
##  6 93711     31        4.5      4.5          5           3                 5
##  7 93712     18        3        3.5          3           3                 1
##  8 93713     24.5      4.5      4.5          2.5         2.5               1
##  9 93714     17.5      2.5      1            1           3                 3
## 10 93715     14.5      2        1.5          1           1                 1
## # ℹ 6,478 more rows
## # ℹ 3 more variables: DASH_SODIUM <dbl>, DASH_REDPROC_MEAT <dbl>,
## #   DASH_SSB_FRTJ <dbl>

Calculating DASH for NHANES_MPED (1999-2004)

# Use the NHANES example data in 1999-2004 using the first day + second day nutrition data
data("NHANES_20032004")
DASH_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## Since no skim milk code is provided, the default skim milk code from 17-18 FNDDS file is used.

## Since no low-fat cheese code is provided, the default low-fat cheese code from 17-18 FNDDS file is used.

## Reminder: this DASH index uses quintiles to rank participants' food/drink serving sizes and then calculate DASH component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 7,647 × 10
##     SEQN DASH_ALL DASH_FRT DASH_VEG DASH_NUTSLEG DASH_WGRAIN DASH_LOWF_DAIRY
##    <dbl>    <dbl>    <dbl>    <dbl>        <dbl>       <dbl>           <dbl>
##  1 21005     15.5      2        2            2.5         3                 1
##  2 21006     18        2        2            2.5         2.5               1
##  3 21007     18        3        3            1           1                 1
##  4 21008     17.5      1        3.5          2.5         3                 1
##  5 21009     19.5      2        3            2           2                 1
##  6 21010     18        1.5      2            4           1                 1
##  7 21012     17.5      1        3            4           2.5               1
##  8 21013     16        2.5      2            2           1                 1
##  9 21014     25.5      3        4            4           2.5               1
## 10 21015     27.5      3        3            4           4.5               1
## # ℹ 7,637 more rows
## # ℹ 3 more variables: DASH_SODIUM <dbl>, DASH_REDPROC_MEAT <dbl>,
## #   DASH_SSB_FRTJ <dbl>

Calculating DASHI for NHANES_FPED (after 2005)

FPED_IND_PATH = "/Users/james/Desktop/data/fped_dr1iff.sas7bdat"
NUTRIENT_IND_PATH = "/Users/james/Desktop/data/DR1IFF_J"

DASHI_NHANES_FPED(FPED_IND_PATH, NUTRIENT_IND_PATH)

# Use the NHANES example data in 2017-2018 using the first day + second day nutrition data
data("NHANES_20172018")
DASHI_NHANES_FPED(NUTRIENT_PATH = NHANES_20172018$NUTRIENT, NUTRIENT_PATH2 = NHANES_20172018$NUTRIENT2)

## # A tibble: 6,488 × 11
##     SEQN DASHI_ALL DASHI_TOTAL_FAT DASHI_SAT_FAT DASHI_CHOLESTEROL DASHI_SODIUM
##    <dbl>     <dbl>           <dbl>         <dbl>             <dbl>        <dbl>
##  1 93704      4.72          0.768         0.840              0.680       0.5   
##  2 93705      2.49          0             0.472              0.5         0     
##  3 93707      1.15          0             0.0199             0           0.0844
##  4 93708      2.80          0             0.173              0.5         0     
##  5 93710      4.21          0.653         0.490              0.5         0.5   
##  6 93711      3.62          0.0920        0.490              0.312       0.5   
##  7 93712      3.89          0.844         0.640              0.629       0     
##  8 93713      3.79          0.5           0.428              0.889       0.772 
##  9 93714      1.68          0             0                  0           0     
## 10 93715      1.82          0             0.239              0.162       0.5   
## # ℹ 6,478 more rows
## # ℹ 5 more variables: DASHI_PROTEIN <dbl>, DASHI_FIBER <dbl>,
## #   DASHI_POTASSIUM <dbl>, DASHI_MAGNESIUM <dbl>, DASHI_CALCIUM <dbl>

Calculating DASHI for NHANES_FPED (1999-2004)

# Use the NHANES example data in 1999-2004 using the first day + second day nutrition data
data("NHANES_20032004")
DASHI_NHANES_MPED(MPED_PER_100_GRAM_PATH = NHANES_20032004$MPED_PER_100_GRAM, WJFRT = NHANES_20032004$WJFRT, NUTRIENT_PATH = NHANES_20032004$NUTRIENT, NUTRIENT_IND_PATH = NHANES_20032004$NUTRIENT_IND, DEMO_PATH = NHANES_20032004$DEMO, NUTRIENT_PATH2 = NHANES_20032004$NUTRIENT2, NUTRIENT_IND_PATH2 = NHANES_20032004$NUTRIENT_IND2)

## # A tibble: 7,647 × 11
##     SEQN DASHI_ALL DASHI_TOTAL_FAT DASHI_SAT_FAT DASHI_CHOLESTEROL DASHI_SODIUM
##    <dbl>     <dbl>           <dbl>         <dbl>             <dbl>        <dbl>
##  1 21005      2.28           0.321         0.585             0.550        0    
##  2 21006      2.53           0.591         0.577             0.5          0    
##  3 21007      2.97           0.5           0.546             0.5          0.300
##  4 21008      1.54           0.185         0.313             0.381        0    
##  5 21009      1.73           0             0.454             0.294        0.5  
##  6 21010      4.92           0.812         0.435             0.781        1    
##  7 21012      1.30           0.113         0.388             0            0    
##  8 21013      2.94           0.410         0.614             0.968        0.672
##  9 21014      5.17           0.725         0.373             0.742        0    
## 10 21015      4.84           0.373         0.394             0.661        0.677
## # ℹ 7,637 more rows
## # ℹ 5 more variables: DASHI_PROTEIN <dbl>, DASHI_FIBER <dbl>,
## #   DASHI_POTASSIUM <dbl>, DASHI_MAGNESIUM <dbl>, DASHI_CALCIUM <dbl>

ASA24 functions

Calculating DII for ASA24

DATA_PATH = "/Users/james/Desktop/data/Totals.csv"
DII_ASA24(DATA_PATH)

#Use the example data
data("ASA24_exp")
DII_ASA24(ASA24_exp)

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## # A tibble: 21 × 32
## # Groups:   UserName [21]
##    UserName UserID DII_ALL DII_NOETOH ALCOHOL   VITB12  VITB6 BCAROTENE CAFFEINE
##       <dbl> <chr>    <dbl>      <dbl>   <dbl>    <dbl>  <dbl>     <dbl>    <dbl>
##  1        1 e6541… -0.0932     -0.371   0.278 -0.0766   0.127    -0.408   0.0848
##  2        2 dbb27…  2.93        2.65    0.278 -0.0982   0.136     0.295   0.0845
##  3        3 f0115…  1.39        1.11    0.278 -0.0185  -0.214     0.549   0.0850
##  4        4 337d8…  1.28        1.00    0.278  0.00997 -0.185     0.538   0.0850
##  5        5 998f0…  3.84        3.56    0.274 -0.0934   0.239     0.565   0.0844
##  6        6 aae33…  2.29        2.01    0.278 -0.0846  -0.359     0.554   0.0846
##  7        7 76e0f…  1.05        0.768   0.278 -0.0781  -0.206    -0.125   0.0839
##  8        8 24de9…  0.178       0.409  -0.231 -0.0608  -0.252     0.459   0.0850
##  9        9 8f61a…  0.805       0.527   0.278 -0.0908   0.134     0.564   0.0847
## 10       10 b47ab…  1.98        1.70    0.278 -0.0949   0.184     0.537   0.0845
## # ℹ 11 more rows
## # ℹ 23 more variables: CARB <dbl>, CHOLES <dbl>, KCAL <dbl>, TOTALFAT <dbl>,
## #   FIBER <dbl>, FOLICACID <dbl>, IRON <dbl>, MG <dbl>, MUFA <dbl>,
## #   NIACIN <dbl>, N3FAT <dbl>, N6FAT <dbl>, PROTEIN <dbl>, PUFA <dbl>,
## #   RIBOFLAVIN <dbl>, SATFAT <dbl>, SE <dbl>, THIAMIN <dbl>, VITA <dbl>,
## #   VITC <dbl>, VITD <dbl>, VITE <dbl>, ZN <dbl>

Calculating HEI2020 for ASA24

DATA_PATH = "/Users/james/Desktop/data/Totals.csv"
# if you want to summarize all recalls into a result via average
HEI2020_ASA24(DATA_PATH, RECALL_SUMMARIZE = TRUE)
# if you want to use all individual recalls
HEI2020_ASA24(DATA_PATH, RECALL_SUMMARIZE = FALSE)

#Use the example data
data("ASA24_exp")
HEI2015_ASA24(ASA24_exp, RECALL_SUMMARIZE = TRUE)

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## # A tibble: 21 × 17
## # Groups:   UserName, UserID [21]
##    UserName UserID            TOTALKCAL HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT
##       <dbl> <chr>                 <dbl>       <dbl>            <dbl>       <dbl>
##  1        1 e6541891-8a22-49…     1514.        69.8             1.26        2.45
##  2        2 dbb278b0-b589-44…      935.        52.8             5           0   
##  3        3 f0115426-e6f8-4c…     1655.        51.2             5           5   
##  4        4 337d84a7-3bff-48…     1571.        47.5             0           0   
##  5        5 998f098a-8584-49…      907.        45.2             0           0   
##  6        6 aae330db-8f22-4d…     1271.        52.9             3.78        5   
##  7        7 76e0f3e1-8e17-49…     2662.        48.4             1.51        2.23
##  8        8 24de994f-e06a-41…     2039.        69.0             2.76        5   
##  9        9 8f61ae64-6a82-4f…     1469.        70.9             5           5   
## 10       10 b47ab1b8-e62b-4b…     1636.        61.2             2.64        0   
## # ℹ 11 more rows
## # ℹ 11 more variables: HEI2015_VEG <dbl>, HEI2015_GREENNBEAN <dbl>,
## #   HEI2015_TOTALPRO <dbl>, HEI2015_SEAPLANTPRO <dbl>,
## #   HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>, HEI2015_FATTYACID <dbl>,
## #   HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>, HEI2015_ADDEDSUGAR <dbl>,
## #   HEI2015_SATFAT <dbl>

HEI2015_ASA24(ASA24_exp, RECALL_SUMMARIZE = FALSE)

## RECALL_SUMMARIZE is FALSE, skipping summarization step...

## # A tibble: 42 × 17
##    UserName UserID            TOTALKCAL HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT
##       <dbl> <chr>                 <dbl>       <dbl>            <dbl>       <dbl>
##  1        1 e6541891-8a22-49…     2019.        69.8             1.26        2.45
##  2        1 e6541891-8a22-49…     1010.        69.8             1.26        2.45
##  3        2 dbb278b0-b589-44…     1247.        52.8             5           0   
##  4        2 dbb278b0-b589-44…      623.        52.8             5           0   
##  5        3 f0115426-e6f8-4c…     2206.        51.2             5           5   
##  6        3 f0115426-e6f8-4c…     1103.        51.2             5           5   
##  7        4 337d84a7-3bff-48…     2095.        47.5             0           0   
##  8        4 337d84a7-3bff-48…     1047.        47.5             0           0   
##  9        5 998f098a-8584-49…     1209.        45.2             0           0   
## 10        5 998f098a-8584-49…      605.        45.2             0           0   
## # ℹ 32 more rows
## # ℹ 11 more variables: HEI2015_VEG <dbl>, HEI2015_GREENNBEAN <dbl>,
## #   HEI2015_TOTALPRO <dbl>, HEI2015_SEAPLANTPRO <dbl>,
## #   HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>, HEI2015_FATTYACID <dbl>,
## #   HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>, HEI2015_ADDEDSUGAR <dbl>,
## #   HEI2015_SATFAT <dbl>

Calculating HEI2020_toddlers for ASA24

DATA_PATH = "/Users/james/Desktop/data/Totals.csv"
# if you want to summarize all recalls into a result via average
HEI2020_TODDLERS_ASA24(DATA_PATH, RECALL_SUMMARIZE = TRUE)
# if you want to use all individual recalls
HEI2020_TODDLERS_ASA24(DATA_PATH, RECALL_SUMMARIZE = FALSE)

#Use the example data
data("ASA24_exp")
HEI2020_TODDLERS_ASA24(ASA24_exp, RECALL_SUMMARIZE = TRUE)

## [1] "RECALL_SUMMARIZE = TRUE, summarizing HEI2020_TODDLERS for ASA24 data by averaging over all possible recalls per person per day..."
## [1] "The results should be only used for HEI-Toddlers 2020 (age 1-2 years), not for HEI-2020 (non-toddlers, age > 2 years)."

## # A tibble: 21 × 17
## # Groups:   UserName, UserID [21]
##    UserName UserID         TOTALKCAL HEI2020_TODDLERS_ALL HEI2020_TODDLERS_TOT…¹
##       <dbl> <chr>              <dbl>                <dbl>                  <dbl>
##  1        1 e6541891-8a22…     1514.                 60.8                   1.44
##  2        2 dbb278b0-b589…      935.                 55.2                   5   
##  3        3 f0115426-e6f8…     1655.                 47.8                   5   
##  4        4 337d84a7-3bff…     1571.                 50.7                   0   
##  5        5 998f098a-8584…      907.                 39.3                   0   
##  6        6 aae330db-8f22…     1271.                 46.9                   4.32
##  7        7 76e0f3e1-8e17…     2662.                 45.4                   1.73
##  8        8 24de994f-e06a…     2039.                 70.3                   3.16
##  9        9 8f61ae64-6a82…     1469.                 69.1                   5   
## 10       10 b47ab1b8-e62b…     1636.                 59.2                   3.01
## # ℹ 11 more rows
## # ℹ abbreviated name: ¹HEI2020_TODDLERS_TOTALFRT
## # ℹ 12 more variables: HEI2020_TODDLERS_FRT <dbl>, HEI2020_TODDLERS_VEG <dbl>,
## #   HEI2020_TODDLERS_GREENNBEAN <dbl>, HEI2020_TODDLERS_TOTALPRO <dbl>,
## #   HEI2020_TODDLERS_SEAPLANTPRO <dbl>, HEI2020_TODDLERS_WHOLEGRAIN <dbl>,
## #   HEI2020_TODDLERS_DAIRY <dbl>, HEI2020_TODDLERS_FATTYACID <dbl>,
## #   HEI2020_TODDLERS_REFINEDGRAIN <dbl>, HEI2020_TODDLERS_SODIUM <dbl>, …

HEI2020_TODDLERS_ASA24(ASA24_exp, RECALL_SUMMARIZE = FALSE)

## [1] "RECALL_SUMMARIZE is FALSE, skipping summarization step..."
## [1] "The results should be only used for HEI-Toddlers 2020 (age 1-2 years), not for HEI-2020 (non-toddlers, age > 2 years)."

## # A tibble: 42 × 17
##    UserName UserID         TOTALKCAL HEI2020_TODDLERS_ALL HEI2020_TODDLERS_TOT…¹
##       <dbl> <chr>              <dbl>                <dbl>                  <dbl>
##  1        1 e6541891-8a22…     2019.                 60.8                   1.44
##  2        1 e6541891-8a22…     1010.                 60.8                   1.44
##  3        2 dbb278b0-b589…     1247.                 55.2                   5   
##  4        2 dbb278b0-b589…      623.                 55.2                   5   
##  5        3 f0115426-e6f8…     2206.                 47.8                   5   
##  6        3 f0115426-e6f8…     1103.                 47.8                   5   
##  7        4 337d84a7-3bff…     2095.                 50.7                   0   
##  8        4 337d84a7-3bff…     1047.                 50.7                   0   
##  9        5 998f098a-8584…     1209.                 39.3                   0   
## 10        5 998f098a-8584…      605.                 39.3                   0   
## # ℹ 32 more rows
## # ℹ abbreviated name: ¹HEI2020_TODDLERS_TOTALFRT
## # ℹ 12 more variables: HEI2020_TODDLERS_FRT <dbl>, HEI2020_TODDLERS_VEG <dbl>,
## #   HEI2020_TODDLERS_GREENNBEAN <dbl>, HEI2020_TODDLERS_TOTALPRO <dbl>,
## #   HEI2020_TODDLERS_SEAPLANTPRO <dbl>, HEI2020_TODDLERS_WHOLEGRAIN <dbl>,
## #   HEI2020_TODDLERS_DAIRY <dbl>, HEI2020_TODDLERS_FATTYACID <dbl>,
## #   HEI2020_TODDLERS_REFINEDGRAIN <dbl>, HEI2020_TODDLERS_SODIUM <dbl>, …

Calculating HEI2015 for ASA24

DATA_PATH = "/Users/james/Desktop/data/Totals.csv"
HEI2015_ASA24(DATA_PATH)

#Use the example data
data("ASA24_exp")
HEI2015_ASA24(ASA24_exp)

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## # A tibble: 21 × 17
## # Groups:   UserName, UserID [21]
##    UserName UserID            TOTALKCAL HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT
##       <dbl> <chr>                 <dbl>       <dbl>            <dbl>       <dbl>
##  1        1 e6541891-8a22-49…     1514.        69.8             1.26        2.45
##  2        2 dbb278b0-b589-44…      935.        52.8             5           0   
##  3        3 f0115426-e6f8-4c…     1655.        51.2             5           5   
##  4        4 337d84a7-3bff-48…     1571.        47.5             0           0   
##  5        5 998f098a-8584-49…      907.        45.2             0           0   
##  6        6 aae330db-8f22-4d…     1271.        52.9             3.78        5   
##  7        7 76e0f3e1-8e17-49…     2662.        48.4             1.51        2.23
##  8        8 24de994f-e06a-41…     2039.        69.0             2.76        5   
##  9        9 8f61ae64-6a82-4f…     1469.        70.9             5           5   
## 10       10 b47ab1b8-e62b-4b…     1636.        61.2             2.64        0   
## # ℹ 11 more rows
## # ℹ 11 more variables: HEI2015_VEG <dbl>, HEI2015_GREENNBEAN <dbl>,
## #   HEI2015_TOTALPRO <dbl>, HEI2015_SEAPLANTPRO <dbl>,
## #   HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>, HEI2015_FATTYACID <dbl>,
## #   HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>, HEI2015_ADDEDSUGAR <dbl>,
## #   HEI2015_SATFAT <dbl>

Calculating aMED for ASA24

DATA_PATH = "/Users/james/Desktop/data/Totals.csv"
MED_ASA24(DATA_PATH)

#Use the example data
data("ASA24_exp")
MED_ASA24(ASA24_exp)

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## Reminder: this MED index uses medians to rank participants' food/drink serving sizes and then calculate MED component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 21 × 13
## # Groups:   UserName, UserID [21]
##    UserName UserID     MED_ALL MED_NOETOH MED_FRT MED_VEG MED_WGRAIN MED_LEGUMES
##       <dbl> <chr>        <dbl>      <dbl>   <dbl>   <dbl>      <dbl>       <dbl>
##  1        1 e6541891-…       7          7       1       1          1           1
##  2        2 dbb278b0-…       6          6       1       1          1           1
##  3        3 f0115426-…       5          5       1       1          1           0
##  4        4 337d84a7-…       6          6       0       1          1           1
##  5        5 998f098a-…       2          2       0       0          1           0
##  6        6 aae330db-…       4          4       1       1          0           0
##  7        7 76e0f3e1-…       5          5       1       1          1           0
##  8        8 24de994f-…       8          7       1       1          1           1
##  9        9 8f61ae64-…       6          6       1       1          1           1
## 10       10 b47ab1b8-…       6          6       1       1          1           1
## # ℹ 11 more rows
## # ℹ 5 more variables: MED_NUTS <dbl>, MED_FISH <dbl>, MED_REDPROC_MEAT <dbl>,
## #   MED_MONSATFAT <dbl>, MED_ALCOHOL <dbl>

Calculating DASH for ASA24

DATA_PATH = "/Users/james/Desktop/data/items.csv"
DASH_ASA24(DATA_PATH)

#Use the example data
data("ASA24_exp_detailed")
DASH_ASA24(ASA24_exp_detailed)

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## Since no skim milk code is provided, the default skim milk code from 17-18 FNDDS file is used.

## Since no low-fat cheese code is provided, the default low-fat cheese code from 17-18 FNDDS file is used.

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## Reminder: this DASH index uses quintiles to rank participants' food/drink serving sizes and then calculate DASH component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 21 × 11
## # Groups:   UserName, UserID [21]
##    UserName UserID           DASH_ALL DASH_FRT DASH_VEG DASH_NUTSLEG DASH_WGRAIN
##    <chr>    <chr>               <dbl>    <dbl>    <dbl>        <dbl>       <dbl>
##  1 THR01    e6541891-8a22-4…       28        5        5            5           5
##  2 THR02    dbb278b0-b589-4…       32        5        5            5           5
##  3 THR03    f0115426-e6f8-4…       28        5        5            5           5
##  4 THR04    337d84a7-3bff-4…       28        1        5            5           5
##  5 THR05    998f098a-8584-4…       16        1        1            1           5
##  6 THR06    aae330db-8f22-4…       24        5        5            1           1
##  7 THR07    76e0f3e1-8e17-4…       28        5        5            1           5
##  8 THR08    24de994f-e06a-4…       36        5        5            5           5
##  9 THR09    8f61ae64-6a82-4…       28        5        5            5           5
## 10 THR10    b47ab1b8-e62b-4…       36        5        5            5           5
## # ℹ 11 more rows
## # ℹ 4 more variables: DASH_LOWF_DAIRY <dbl>, DASH_SODIUM <dbl>,
## #   DASH_REDPROC_MEAT <dbl>, DASH_SSB_FRTJ <dbl>

Calculating AHEI for ASA24

DATA_PATH = "/Users/james/Desktop/data/items.csv"
AHEI_F_ASA24(DATA_PATH)
AHEI_M_ASA24(DATA_PATH)

#Use the example data
data("ASA24_exp_detailed")
AHEI_F_ASA24(ASA24_exp_detailed) # for participants who are all female

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## Reminder: this AHEI index is for female only. Please stratify your data first and provide female only data.

## # A tibble: 21 × 15
## # Groups:   UserName, UserID [21]
##    UserName UserID          AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN_F
##    <chr>    <chr>              <dbl>       <dbl>    <dbl>    <dbl>         <dbl>
##  1 THR01    e6541891-8a22-…     52.2        49.7    5.36     0.988         6.74 
##  2 THR02    dbb278b0-b589-…     44.9        42.4    6.94     0             0.302
##  3 THR03    f0115426-e6f8-…     39.1        36.6    9.24     2.48          1.03 
##  4 THR04    337d84a7-3bff-…     52.7        50.2    3.84     0             3.80 
##  5 THR05    998f098a-8584-…     46.1        36.7    0        0             2.06 
##  6 THR06    aae330db-8f22-…     45.1        42.6    3.16     2.56          0    
##  7 THR07    76e0f3e1-8e17-…     51.8        49.3    7.66     1.58          3.24 
##  8 THR08    24de994f-e06a-…     72.1        65.3    2.62     2.97         10    
##  9 THR09    8f61ae64-6a82-…     48.6        46.1    0.202    3.16          2.10 
## 10 THR10    b47ab1b8-e62b-…     58.6        56.1    5.27     0            10    
## # ℹ 11 more rows
## # ℹ 8 more variables: AHEI_NUTSLEG <dbl>, AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>,
## #   AHEI_SSB_FRTJ <dbl>, AHEI_REDPROC_MEAT <dbl>, AHEI_SODIUM <dbl>,
## #   AHEI_ALCOHOL_F <dbl>, SSB_FRTJ_SERV <dbl>

AHEI_M_ASA24(ASA24_exp_detailed) # for participants who are all male

## Since no SSB code is provided, the default SSB code from 17-18 FNDDS file is used.

## RECALL_SUMMARIZE = TRUE, summarizing HEI2015 for ASA24 data by averaging over all possible recalls per person per day...

## Reminder: this AHEI index is for male only. Please stratify your data first and provide male only data.

## # A tibble: 21 × 14
## # Groups:   UserName, UserID [21]
##    UserName UserID          AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN_M
##    <chr>    <chr>              <dbl>       <dbl>    <dbl>    <dbl>         <dbl>
##  1 THR01    e6541891-8a22-…     51.1        48.6    5.36     0.988         5.62 
##  2 THR02    dbb278b0-b589-…     44.9        42.4    6.94     0             0.251
##  3 THR03    f0115426-e6f8-…     39.0        36.5    9.24     2.48          0.857
##  4 THR04    337d84a7-3bff-…     52.1        49.6    3.84     0             3.17 
##  5 THR05    998f098a-8584-…     45.7        36.3    0        0             1.72 
##  6 THR06    aae330db-8f22-…     45.1        42.6    3.16     2.56          0    
##  7 THR07    76e0f3e1-8e17-…     51.2        48.7    7.66     1.58          2.70 
##  8 THR08    24de994f-e06a-…     75.3        65.3    2.62     2.97         10    
##  9 THR09    8f61ae64-6a82-…     48.2        45.7    0.202    3.16          1.75 
## 10 THR10    b47ab1b8-e62b-…     58.6        56.1    5.27     0            10    
## # ℹ 11 more rows
## # ℹ 7 more variables: AHEI_NUTSLEG <dbl>, AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>,
## #   AHEI_SSB_FRTJ <dbl>, AHEI_REDPROC_MEAT <dbl>, AHEI_SODIUM <dbl>,
## #   AHEI_ALCOHOL_M <dbl>

DHQ3 functions

Calculating HEI2015 for DHQ3

DATA_PATH = "/Users/james/Desktop/data/results.csv"
HEI2015_DHQ3(DATA_PATH)

#Use the example data
data("DHQ3_exp")
HEI2015_DHQ3(DHQ3_exp)

## # A tibble: 23 × 16
##    `Respondent ID` TOTALKCAL HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT
##              <dbl>     <dbl>       <dbl>            <dbl>       <dbl>
##  1               1     1849.        74.7            3.14        5    
##  2               2     1109.        77.9            4.96        5    
##  3               3     2134.        69.5            5           5    
##  4               4     1170.        77.1            5           5    
##  5               5     1238.        63.3            5           5    
##  6               6      759.        59.5            1.32        1.48 
##  7               7      727.        67.9            5           5    
##  8               8     1462.        66.7            3.59        5    
##  9               9     1080.        72.4            5           5    
## 10              10     1552.        55.5            0.684       0.644
## # ℹ 13 more rows
## # ℹ 11 more variables: HEI2015_VEG <dbl>, HEI2015_GREENNBEAN <dbl>,
## #   HEI2015_TOTALPRO <dbl>, HEI2015_SEAPLANTPRO <dbl>,
## #   HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>, HEI2015_FATTYACID <dbl>,
## #   HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>, HEI2015_ADDEDSUGAR <dbl>,
## #   HEI2015_SATFAT <dbl>

Calculating aMED for DHQ3

DATA_PATH = "/Users/james/Desktop/data/results.csv"
MED_DHQ3(DATA_PATH)

#Use the example data
data("DHQ3_exp")
MED_DHQ3(DHQ3_exp)

## # A tibble: 23 × 12
##    RESPONDENTID MED_ALL MED_NOETOH MED_FRT MED_VEG MED_WGRAIN MED_LEGUMES
##           <dbl>   <dbl>      <dbl>   <dbl>   <dbl>      <dbl>       <dbl>
##  1            1       5          5       1       1          1           1
##  2            2       7          7       1       1          1           1
##  3            3       6          6       1       1          1           1
##  4            4       4          4       1       0          1           0
##  5            5       2          2       1       0          0           0
##  6            6       3          3       0       0          1           0
##  7            7       4          4       1       1          0           1
##  8            8       4          4       0       1          0           1
##  9            9       2          1       1       0          0           0
## 10           10       5          5       0       0          1           1
## # ℹ 13 more rows
## # ℹ 5 more variables: MED_NUTS <dbl>, MED_FISH <dbl>, MED_REDPROC_MEAT <dbl>,
## #   MED_MONSATFAT <dbl>, MED_ALCOHOL <dbl>

Calculating AHEI for DHQ3

DATA_PATH = "/Users/james/Desktop/data/detail.csv"
AHEI_DHQ3(DATA_PATH)

#Use the example data
data("DHQ3_exp_detailed")
AHEI_DHQ3(DHQ3_exp_detailed)

## # A tibble: 23 × 15
##    RESPONDENTID GENDER AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN
##           <dbl>  <dbl>    <dbl>       <dbl>    <dbl>    <dbl>       <dbl>
##  1            1      1     61.7        59.2     5.36    2.3         7.18 
##  2            2      2     68.5        66.0     3.62    2.12        4.84 
##  3            3      2     58.3        49.9    10       3.33        2.65 
##  4            4      2     46.1        43.6     1.72    2.33        2.99 
##  5            5      2     60.4        50.4     2.68    2.75        1.06 
##  6            6      2     51.7        48.9     1.58    0.175       3.21 
##  7            7      1     54.8        52.3     4.12    3.18        1.48 
##  8            8      2     52.8        45.8     4.54    1.8         1.97 
##  9            9      2     61.7        51.7     1.58    2.55        0.983
## 10           10      2     54.2        50.6     2.1     0.175       5.44 
## # ℹ 13 more rows
## # ℹ 8 more variables: AHEI_NUTSLEG <dbl>, AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>,
## #   AHEI_SSB_FRTJ <dbl>, AHEI_REDPROC_MEAT <dbl>, AHEI_TRANS <dbl>,
## #   AHEI_SODIUM <dbl>, AHEI_ALCOHOL <dbl>

Calculating DASH for DHQ3

DATA_PATH = "/Users/james/Desktop/data/detail.csv"
DASH_DHQ3(DATA_PATH)

#Use the example data. Attention: the example data here is DHQ3_exp_detailed, which is different from DHQ3_exp. DHQ3_exp_detailed is only used for DASH_DHQ3
data("DHQ3_exp_detailed")
DASH_DHQ3(DHQ3_exp_detailed)

## Reminder: this DASH index uses quintiles to rank participants' food/drink serving sizes and then calculate DASH component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 23 × 10
##    `Respondent ID` DASH_ALL DASH_FRT DASH_VEG DASH_NUTSLEG DASH_WGRAIN
##              <dbl>    <dbl>    <dbl>    <dbl>        <dbl>       <dbl>
##  1               1       25        3        4            5           5
##  2               2       29        3        3            5           5
##  3               3       24        5        5            4           3
##  4               4       23        5        1            2           3
##  5               5       25        4        2            2           2
##  6               6       19        1        1            1           3
##  7               7       26        4        4            3           3
##  8               8       20        3        4            4           3
##  9               9       20        4        1            2           1
## 10              10       20        1        2            3           5
## # ℹ 13 more rows
## # ℹ 4 more variables: DASH_LOWF_DAIRY <dbl>, DASH_SODIUM <dbl>,
## #   DASH_REDPROC_MEAT <dbl>, DASH_SSB_FRTJ <dbl>

BLOCK FFQ functions

Calculating AHEI for BLOCK

DATA_PATH = "/Users/james/Desktop/block_exp.csv"
AHEI_BLOCK(DATA_PATH)

#Use the example data
data("BLOCK_exp")
AHEI_BLOCK(BLOCK_exp)

## # A tibble: 5 × 15
##   RESPONDENTID GENDER AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN
##          <dbl>  <dbl>    <dbl>       <dbl>    <dbl>    <dbl>       <dbl>
## 1            1      1     48.1        45.6    1.58     3.69        5.01 
## 2            2      1     33.5        31.0    0.695    0.527       0.794
## 3            3      1     49.1        39.1    3.94     2.63        5.89 
## 4            4      1     47.1        44.6    1.79     5.39        5.17 
## 5            5      1     52.5        50.0    6.06     9.18        3.94 
## # ℹ 8 more variables: AHEI_NUTSLEG <dbl>, AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>,
## #   AHEI_SSB_FRTJ <dbl>, AHEI_REDPROC_MEAT <dbl>, AHEI_TRANS <dbl>,
## #   AHEI_SODIUM <dbl>, AHEI_ALCOHOL <dbl>

Calculating AHEIP for BLOCK

#Use the example data
data("BLOCK_exp")
AHEIP_BLOCK(BLOCK_exp)

## # A tibble: 5 × 11
##   RESPONDENTID AHEIP_ALL AHEIP_VEG AHEIP_FRT AHEIP_WHITEREAD AHEIP_FIBER
##          <dbl>     <dbl>     <dbl>     <dbl>           <dbl>       <dbl>
## 1            1      38.3     1.58      3.69            0.351        5.41
## 2            2      26.0     0.695     0.527           0.717        4.42
## 3            3      51.6     3.94      2.63            0.477        8.3 
## 4            4      47.4     1.79      5.39            0.756        9.93
## 5            5      62.5     6.06      9.18            2.06         8.45
## # ℹ 5 more variables: AHEIP_TRANS <dbl>, AHEIP_POLYSAT <dbl>,
## #   AHEIP_CALCIUM <dbl>, AHEIP_FOLATE <dbl>, AHEIP_IRON <dbl>

Calculating DASH for BLOCK

#Use the example data
data("BLOCK_exp")
DASH_BLOCK(BLOCK_exp)

## Reminder: this DASH index uses quintiles to rank participants' food/drink serving sizes and then calculate DASH component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 5 × 25
##   RESPONDENTID DASH_ALL DASH_FRT DASH_VEG DASH_NUTSLEG DASH_WGRAIN
##          <dbl>    <dbl>    <dbl>    <dbl>        <dbl>       <dbl>
## 1            1       28        3        2            4           3
## 2            2       19        1        1            5           1
## 3            3       23        2        4            3           5
## 4            4       23        4        3            2           4
## 5            5       28        5        5            1           2
## # ℹ 19 more variables: DASH_LOWF_DAIRY <dbl>, DASH_SODIUM <dbl>,
## #   DASH_REDPROC_MEAT <dbl>, DASH_SSB_FRTJ <dbl>, DT_KCAL <dbl>,
## #   F_BERRIES <dbl>, F_WHOLE <dbl>, FRT_FRTJ_SERV <dbl>, VEG_SERV <dbl>,
## #   NUTSLEG_SERV <dbl>, WGRAIN_SERV <dbl>, LOWF_MILK_SERV <dbl>,
## #   YOGURT_SERV <dbl>, LOWF_ICECREAMFROYO_SERV <dbl>, LOWF_CHEESE_SERV <dbl>,
## #   LOWF_DAIRY_SERV <dbl>, SODIUM_SERV <dbl>, REDPROC_MEAT_SERV <dbl>,
## #   SSB_FRTJ_SERV <dbl>

Calculating DII for BLOCK

#Use the example data
data("BLOCK_exp")
DII_BLOCK(BLOCK_exp)

## # A tibble: 5 × 34
##   RESPONDENTID DII_ALL DII_NOETOH ALCOHOL  VITB12   VITB6 BCAROTENE CAFFEINE
##          <dbl>   <dbl>      <dbl>   <dbl>   <dbl>   <dbl>     <dbl>    <dbl>
## 1            1   3.74        3.46   0.278 -0.0682 -0.0157    0.464    0.0850
## 2            2   2.65        2.37   0.278 -0.0428  0.0928    0.552    0.0845
## 3            3  -1.59       -1.72   0.124  0.0986 -0.346     0.403    0.0849
## 4            4  -1.13       -1.41   0.278 -0.0588 -0.192    -0.0426   0.0842
## 5            5  -0.724      -1.00   0.278 -0.0758 -0.204    -0.584    0.0845
## # ℹ 26 more variables: CARB <dbl>, CHOLES <dbl>, KCAL <dbl>, TOTALFAT <dbl>,
## #   FIBER <dbl>, FOLICACID <dbl>, IRON <dbl>, MG <dbl>, MUFA <dbl>,
## #   NIACIN <dbl>, N3FAT <dbl>, N6FAT <dbl>, PROTEIN <dbl>, PUFA <dbl>,
## #   RIBOFLAVIN <dbl>, SATFAT <dbl>, SE <dbl>, THIAMIN <dbl>, TRANSFAT <dbl>,
## #   VITA <dbl>, VITC <dbl>, VITD <dbl>, VITE <dbl>, ZN <dbl>, TEA <dbl>,
## #   ISOFLAVONES <dbl>

Calculating HEI2015 for BLOCK

#Use the example data
data("BLOCK_exp")
HEI2015_BLOCK(BLOCK_exp)

## # A tibble: 5 × 15
##   RESPONDENTID HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT HEI2015_VEG
##          <dbl>       <dbl>            <dbl>       <dbl>       <dbl>
## 1            1        76.6            5            5           2.57
## 2            2        42.0            0.986        1.08        1.32
## 3            3        56.0            2.25         3.56        2.42
## 4            4        48.7            5            5           1.11
## 5            5        65.7            5            5           5   
## # ℹ 10 more variables: HEI2015_GREENNBEAN <dbl>, HEI2015_TOTALPRO <dbl>,
## #   HEI2015_SEAPLANTPRO <dbl>, HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>,
## #   HEI2015_FATTYACID <dbl>, HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>,
## #   HEI2015_ADDEDSUGAR <dbl>, HEI2015_SATFAT <dbl>

Calculating aMED for BLOCK

#Use the example data
data("BLOCK_exp")
MED_BLOCK(BLOCK_exp)

## Reminder: this MED index uses medians to rank participants' food/drink serving sizes and then calculate MED component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 5 × 12
##   RESPONDENTID MED_ALL MED_NOETOH MED_FRT MED_VEG MED_WGRAIN MED_LEGUMES
##          <dbl>   <dbl>      <dbl>   <dbl>   <dbl>      <dbl>       <dbl>
## 1            1       6          6       1       0          1           1
## 2            2       4          4       0       0          0           1
## 3            3       7          6       0       1          1           1
## 4            4       3          3       1       1          1           0
## 5            5       4          4       1       1          0           0
## # ℹ 5 more variables: MED_NUTS <dbl>, MED_FISH <dbl>, MED_REDPROC_MEAT <dbl>,
## #   MED_MONSATFAT <dbl>, MED_ALCOHOL <dbl>

Generic functions (works for any dietary assessment tool)

Calculating DI_GM for your own dietary assessment tool

data("DI_GM_VALIDATION")
DI_GM(SERV_DATA = DI_GM_VALIDATION, RESPONDENTID = DI_GM_VALIDATION$id, GENDER = DI_GM_VALIDATION$gender, AVOCADO = DI_GM_VALIDATION$avocado, BROCCOLI = DI_GM_VALIDATION$broccoli, CHICKPEA = DI_GM_VALIDATION$chickpea, COFFEE = DI_GM_VALIDATION$coffee, CRANBERRY = DI_GM_VALIDATION$cranberry, FERMENTED_DAIRY = DI_GM_VALIDATION$fermented_dairy, FIBER = DI_GM_VALIDATION$fiber, GREEN_TEA = DI_GM_VALIDATION$green_tea, SOYBEAN = DI_GM_VALIDATION$soybean, WHOLE_GRAIN = DI_GM_VALIDATION$whole_grain, TOTAL_FAT_PERCENTAGE = DI_GM_VALIDATION$total_fat_percentage, PROCESSED_MEAT = DI_GM_VALIDATION$processed_meat, RED_MEAT = DI_GM_VALIDATION$red_meat, REFINED_GRAIN = DI_GM_VALIDATION$refined_grain)

## Reminder: this DI_GM index uses sex-specific medians to rank participants' food/drink serving sizes and then calculate DI_GM component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 10 × 17
##    RESPONDENTID GENDER DI_GM_TOTAL DI_GM_AVOCADO DI_GM_BROCCOLI DI_GM_CHICKPEA
##           <dbl>  <dbl>       <dbl>         <dbl>          <dbl>          <dbl>
##  1            1      2           0             0              0              0
##  2            2      2           0             0              0              0
##  3            3      2          11             1              1              1
##  4            4      2          14             1              1              1
##  5            5      2          14             1              1              1
##  6            6      1          14             1              1              1
##  7            7      1          14             1              1              1
##  8            8      1          11             1              1              1
##  9            9      1           0             0              0              0
## 10           10      1           0             0              0              0
## # ℹ 11 more variables: DI_GM_COFFEE <dbl>, DI_GM_CRANBERRY <dbl>,
## #   DI_GM_FERMENTED_DAIRY <dbl>, DI_GM_FIBER <dbl>, DI_GM_GREEN_TEA <dbl>,
## #   DI_GM_SOYBEAN <dbl>, DI_GM_WHOLE_GRAIN <dbl>,
## #   DI_GM_TOTAL_FAT_PERCENTAGE <dbl>, DI_GM_PROCESSED_MEAT <dbl>,
## #   DI_GM_RED_MEAT <dbl>, DI_GM_REFINED_GRAIN <dbl>

Calculating HEI2020 for your own dietary assessment tool

#Use the example data
data("HEI2020_VALIDATION")
HEI2020(SERV_DATA = HEI2020_VALIDATION,RESPONDENTID = HEI2020_VALIDATION$id,AGE = HEI2020_VALIDATION$age,TOTALKCAL_HEI2020 = HEI2020_VALIDATION$kcal,TOTALFRT_SERV_HEI2020 = HEI2020_VALIDATION$total_fruit,FRT_SERV_HEI2020 = HEI2020_VALIDATION$whole_fruit,VEG_SERV_HEI2020 = HEI2020_VALIDATION$total_vegetable,GREENNBEAN_SERV_HEI2020 = HEI2020_VALIDATION$green_and_bean,TOTALPRO_SERV_HEI2020 = HEI2020_VALIDATION$total_protein,SEAPLANTPRO_SERV_HEI2020 = HEI2020_VALIDATION$seafood_plant_protein,WHOLEGRAIN_SERV_HEI2020 = HEI2020_VALIDATION$whole_grain,DAIRY_SERV_HEI2020 = HEI2020_VALIDATION$dairy,FATTYACID_SERV_HEI2020 = HEI2020_VALIDATION$fatty_acid,REFINEDGRAIN_SERV_HEI2020 = HEI2020_VALIDATION$refined_grain,SODIUM_SERV_HEI2020 = HEI2020_VALIDATION$sodium,ADDEDSUGAR_SERV_HEI2020 = HEI2020_VALIDATION$added_sugar,SATFAT_SERV_HEI2020 = HEI2020_VALIDATION$saturated_fat)

## # A tibble: 22 × 17
##    RESPONDENTID   AGE TOTALKCAL_HEI2020 HEI2020_ALL HEI2020_TOTALFRT HEI2020_FRT
##           <dbl> <dbl>             <dbl>       <dbl>            <dbl>       <dbl>
##  1            1    18              1000          40              0           0  
##  2            2    18              1000          42              0.5         0.5
##  3            3    18              1000          44              1           1  
##  4            4    18              1000          46              1.5         1.5
##  5            5    18              1000          48              2           2  
##  6            6    18              1000          50              2.5         2.5
##  7            7    18              1000          52              3           3  
##  8            8    18              1000          54              3.5         3.5
##  9            9    18              1000          56              4           4  
## 10           10    18              1000          58              4.5         4.5
## # ℹ 12 more rows
## # ℹ 11 more variables: HEI2020_VEG <dbl>, HEI2020_GREENNBEAN <dbl>,
## #   HEI2020_TOTALPRO <dbl>, HEI2020_SEAPLANTPRO <dbl>,
## #   HEI2020_WHOLEGRAIN <dbl>, HEI2020_DAIRY <dbl>, HEI2020_FATTYACID <dbl>,
## #   HEI2020_REFINEDGRAIN <dbl>, HEI2020_SODIUM <dbl>, HEI2020_ADDEDSUGAR <dbl>,
## #   HEI2020_SATFAT <dbl>

Calculating HEI2015 for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

HEI2015(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$TOTALKCAL, SERV_DATA$VEG_SERV, SERV_DATA$FRT_SERV, SERV_DATA$WGRAIN_SERV, SERV_DATA$NUTSLEG_SERV, SERV_DATA$N3FAT_SERV, SERV_DATA$PUFA_SERV, SERV_DATA$SSB_FRTJ_SERV, SERV_DATA$REDPROC_MEAT_SERV, SERV_DATA$TRANS_SERV, SERV_DATA$SODIUM_SERV, SERV_DATA$ALCOHOL_SERV)

#Use the example data
data("SERV_DATA_exp")
HEI2015(SERV_DATA_exp, SERV_DATA_exp$UserName, SERV_DATA_exp$TOTALKCAL, SERV_DATA_exp$TOTALFRT_SERV_HEI2015_exp, SERV_DATA_exp$FRT_SERV_HEI2015_exp, SERV_DATA_exp$VEG_SERV_HEI2015_exp, SERV_DATA_exp$GREENNBEAN_SERV_HEI2015_exp, SERV_DATA_exp$TOTALPRO_SERV_HEI2015_exp,  SERV_DATA_exp$SEAPLANTPRO_SERV_HEI2015_exp, SERV_DATA_exp$WHOLEGRAIN_SERV_HEI2015_exp, SERV_DATA_exp$DAIRY_SERV_HEI2015_exp, SERV_DATA_exp$FATTYACID_SERV_HEI2015_exp, SERV_DATA_exp$REFINEDGRAIN_SERV_HEI2015_exp,  SERV_DATA_exp$SODIUM_SERV_HEI2015_exp, SERV_DATA_exp$ADDEDSUGAR_SERV_HEI2015_exp, SERV_DATA_exp$SATFAT_SERV_HEI2015_exp)

## # A tibble: 21 × 16
##    RESPONDENTID TOTALKCAL_HEI2015 HEI2015_ALL HEI2015_TOTALFRT HEI2015_FRT
##           <dbl>             <dbl>       <dbl>            <dbl>       <dbl>
##  1            1             2019.        69.8             1.26        2.45
##  2            2             1247.        52.8             5           0   
##  3            3             2206.        51.2             5           5   
##  4            4             2095.        47.5             0           0   
##  5            5             1209.        45.2             0           0   
##  6            6             1694.        52.9             3.78        5   
##  7            7             3549.        48.4             1.51        2.23
##  8            8             2718.        69.0             2.76        5   
##  9            9             1959.        70.9             5           5   
## 10           10             2181.        61.2             2.64        0   
## # ℹ 11 more rows
## # ℹ 11 more variables: HEI2015_VEG <dbl>, HEI2015_GREENNBEAN <dbl>,
## #   HEI2015_TOTALPRO <dbl>, HEI2015_SEAPLANTPRO <dbl>,
## #   HEI2015_WHOLEGRAIN <dbl>, HEI2015_DAIRY <dbl>, HEI2015_FATTYACID <dbl>,
## #   HEI2015_REFINEDGRAIN <dbl>, HEI2015_SODIUM <dbl>, HEI2015_ADDEDSUGAR <dbl>,
## #   HEI2015_SATFAT <dbl>

Calculating AHEI for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

AHEI(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$GENDER, SERV_DATA$VEG_SERV, SERV_DATA$FRT_SERV, SERV_DATA$WGRAIN_SERV, SERV_DATA$NUTSLEG_SERV, SERV_DATA$N3FAT_SERV, SERV_DATA$PUFA_SERV, SERV_DATA$SSB_FRTJ_SERV, SERV_DATA$REDPROC_MEAT_SERV, SERV_DATA$TRANS_SERV,SODIUM_SERV, SERV_DATA$ALCOHOL_SERV)

#Use the example data
data("SERV_DATA_exp")
AHEI(SERV_DATA_exp, SERV_DATA_exp$UserName, SERV_DATA_exp$SEX, SERV_DATA_exp$TOTALKCAL, SERV_DATA_exp$VEG_SERV_AHEI_exp, SERV_DATA_exp$FRT_SERV_AHEI_exp, SERV_DATA_exp$WGRAIN_SERV_AHEI_exp, SERV_DATA_exp$NUTSLEG_SERV_AHEI_exp, SERV_DATA_exp$N3FAT_SERV_AHEI_exp, SERV_DATA_exp$PUFA_SERV_AHEI_exp, SERV_DATA_exp$SSB_FRTJ_SERV_AHEI_exp, SERV_DATA_exp$REDPROC_MEAT_SERV_AHEI_exp, SERV_DATA_exp$TRANS_SERV_AHEI_exp, SERV_DATA_exp$SODIUM_SERV_AHEI_exp, SERV_DATA_exp$ALCOHOL_SERV_AHEI_exp)

## # A tibble: 21 × 15
##    RESPONDENTID GENDER AHEI_ALL AHEI_NOETOH AHEI_VEG AHEI_FRT AHEI_WGRAIN
##           <dbl>  <dbl>    <dbl>       <dbl>    <dbl>    <dbl>       <dbl>
##  1            1      1     56.9        54.4    2.68     0.988       5.62 
##  2            2      1     43.4        40.9    3.47     0           0.251
##  3            3      1     34.3        31.8    4.62     2.48        0.857
##  4            4      1     50.1        47.6    1.92     0           3.17 
##  5            5      1     47.3        37.9    0        0           1.72 
##  6            6      1     52.2        49.7    1.58     2.56        0    
##  7            7      1     47.4        44.9    3.83     1.58        2.70 
##  8            8      1     80.2        70.2    1.31     2.97       10    
##  9            9      1     57.0        54.5    0.101    3.16        1.75 
## 10           10      1     56.0        53.5    2.63     0          10    
## # ℹ 11 more rows
## # ℹ 8 more variables: AHEI_NUTSLEG <dbl>, AHEI_N3FAT <dbl>, AHEI_PUFA <dbl>,
## #   AHEI_SSB_FRTJ <dbl>, AHEI_REDPROC_MEAT <dbl>, AHEI_TRANS <dbl>,
## #   AHEI_SODIUM <dbl>, AHEI_ALCOHOL <dbl>

Calculating DASH for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

DASH(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$TOTALKCAL_DASH, SERV_DATA$FRT_FRTJ_SERV_DASH, SERV_DATA$VEG_SERV_DASH, SERV_DATA$NUTSLEG_SERV_DASH, SERV_DATA$WGRAIN_SERV_DASH, SERV_DATA$LOWF_DAIRY_SERV_DASH, SERV_DATA$SODIUM_SERV_DASH, SERV_DATA$REDPROC_MEAT_SERV_DASH, SERV_DATA$SSB_FRTJ_SERV_DASH)

#Use the example data
data("SERV_DATA_exp")
DASH(SERV_DATA_exp, SERV_DATA_exp$UserName, SERV_DATA_exp$TOTALKCAL, SERV_DATA_exp$FRT_FRTJ_SERV_DASH_exp, SERV_DATA_exp$VEG_SERV_DASH_exp, SERV_DATA_exp$NUTSLEG_SERV_DASH_exp, SERV_DATA_exp$WGRAIN_SERV_DASH_exp, SERV_DATA_exp$LOWF_DAIRY_SERV_DASH_exp, SERV_DATA_exp$SODIUM_SERV_DASH_exp, SERV_DATA_exp$REDPROC_MEAT_SERV_DASH_exp, SERV_DATA_exp$SSB_FRTJ_SERV_DASH_exp)

## Reminder: this DASH index uses quintiles to rank participants' food/drink serving sizes and then calculate DASH component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 21 × 10
##    RESPONDENTID DASH_ALL DASH_FRT DASH_VEG DASH_NUTSLEG DASH_WGRAIN
##           <dbl>    <dbl>    <dbl>    <dbl>        <dbl>       <dbl>
##  1            1       29        2        3            5           5
##  2            2       22        5        3            2           2
##  3            3       18        5        5            2           2
##  4            4       25        1        2            3           5
##  5            5       16        1        1            1           3
##  6            6       23        4        2            1           1
##  7            7       26        3        4            1           4
##  8            8       29        4        1            3           5
##  9            9       27        5        1            5           3
## 10           10       29        3        3            4           5
## # ℹ 11 more rows
## # ℹ 4 more variables: DASH_LOWF_DAIRY <dbl>, DASH_SODIUM <dbl>,
## #   DASH_REDPROC_MEAT <dbl>, DASH_SSB_FRTJ <dbl>

Calculating DASHI for your own dietary assessment tool

#Use the example data
data("DASH_trial")
DASHI(SERV_DATA = DASH_trial, RESPONDENTID = DASH_trial$Diet_Type,TOTALKCAL_DASHI = DASH_trial$Kcal, TOTAL_FAT_DASHI = DASH_trial$Totalfat_Percent, SAT_FAT_DASHI = DASH_trial$Satfat_Percent, PROTEIN_DASHI = DASH_trial$Protein_Percent, CHOLESTEROL_DASHI = DASH_trial$Cholesterol, FIBER_DASHI = DASH_trial$Fiber, POTASSIUM_DASHI = DASH_trial$Potassium, MAGNESIUM_DASHI = DASH_trial$Magnesium, CALCIUM_DASHI = DASH_trial$Calcium, SODIUM_DASHI = DASH_trial$Sodium)

## # A tibble: 5 × 12
##   RESPONDENTID    DASHI_ALL TOTALKCAL_DASHI DASHI_TOTAL_FAT DASHI_SAT_FAT
##   <chr>               <dbl>           <dbl>           <dbl>         <dbl>
## 1 DASH                7.79             2100           1              0.9 
## 2 DASH_lowSodium      8.79             2100           1              0.9 
## 3 DASH_MedSodium      8.76             2100           1              0.9 
## 4 DASH_HighSodium     7.79             2100           1              0.9 
## 5 Control             0.833            2100           0.130          0.19
## # ℹ 7 more variables: DASHI_PROTEIN <dbl>, DASHI_CHOLESTEROL <dbl>,
## #   DASHI_FIBER <dbl>, DASHI_POTASSIUM <dbl>, DASHI_MAGNESIUM <dbl>,
## #   DASHI_CALCIUM <dbl>, DASHI_SODIUM <dbl>

Calculating aMED for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

MED(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$FRT_FRTJ_SERV, SERV_DATA$VEG_SERV, SERV_DATA$WGRAIN_SERV, SERV_DATA$LEGUMES_SERV, SERV_DATA$NUTS_SERV,FISH_SERV, SERV_DATA$REDPROC_MEAT_SERV, SERV_DATA$MONSATFAT_SERV, SERV_DATA$ALCOHOL_SERV)

#Use the example data
data("SERV_DATA_exp")
MED(SERV_DATA_exp, SERV_DATA_exp$UserName, SERV_DATA_exp$FRT_FRTJ_SERV_MED_exp, SERV_DATA_exp$VEG_SERV_MED_exp, SERV_DATA_exp$WGRAIN_SERV_MED_exp, SERV_DATA_exp$LEGUMES_SERV_MED_exp, SERV_DATA_exp$NUTS_SERV_MED_exp, SERV_DATA_exp$FISH_SERV_MED_exp, SERV_DATA_exp$REDPROC_MEAT_SERV_MED_exp, SERV_DATA_exp$MONSATFAT_SERV_MED_exp, SERV_DATA_exp$ALCOHOL_SERV_MED_exp)

## Reminder: this MED index uses medians to rank participants' food/drink serving sizes and then calculate MED component scores, which may generate results that are specific to your study population but not comparable to other populations.

## # A tibble: 21 × 12
##    RESPONDENTID MED_ALL MED_NOETOH MED_FRT MED_VEG MED_WGRAIN MED_LEGUMES
##           <dbl>   <dbl>      <dbl>   <dbl>   <dbl>      <dbl>       <dbl>
##  1            1       6          6       0       1          1           1
##  2            2       4          4       1       1          0           1
##  3            3       4          4       1       1          0           0
##  4            4       4          4       0       0          1           1
##  5            5       0          0       0       0          0           0
##  6            6       3          3       1       0          0           0
##  7            7       4          4       1       1          1           0
##  8            8       5          5       1       0          1           0
##  9            9       5          5       1       0          1           1
## 10           10       4          4       1       0          1           1
## # ℹ 11 more rows
## # ℹ 5 more variables: MED_NUTS <dbl>, MED_FISH <dbl>, MED_REDPROC_MEAT <dbl>,
## #   MED_MONSATFAT <dbl>, MED_ALCOHOL <dbl>

Calculating MEDI for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

MEDI(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$OLIVE_OIL_SERV_MEDI, SERV_DATA$FRT_SERV_MEDI, SERV_DATA$VEG_SERV_MEDI, SERV_DATA$LEGUMES_SERV_MEDI, SERV_DATA$NUTS_SERV_MEDI, SERV_DATA$FISH_SEAFOOD_SERV_MEDI, SERV_DATA$ALCOHOL_SERV_MEDI, SERV_DATA$SSB_SERV_MEDI, SERV_DATA$SWEETS_SERV_MEDI, SERV_DATA$DISCRET_FAT_SERV_MEDI, SERV_DATA$REDPROC_MEAT_SERV_MEDI)

#Use the example data
data("PREDIMED_trial")

# MEDI is 0/1 point scoring criteria.
MEDI(
  SERV_DATA = PREDIMED_trial,
  RESPONDENTID = PREDIMED_trial$Diet_Type,
  OLIVE_OIL_SERV_MEDI = PREDIMED_trial$Virgin_Oliveoil,
  FRT_SERV_MEDI = PREDIMED_trial$Fruits, 
  VEG_SERV_MEDI = PREDIMED_trial$Vegetables,
  LEGUMES_SERV_MEDI = PREDIMED_trial$Legumes,
  NUTS_SERV_MEDI = PREDIMED_trial$Total_nuts,
  FISH_SEAFOOD_SERV_MEDI = PREDIMED_trial$Fish_Seafood,
  ALCOHOL_SERV_MEDI = PREDIMED_trial$Alcohol,
  SSB_SERV_MEDI = PREDIMED_trial$Soda_Drinks,
  SWEETS_SERV_MEDI = PREDIMED_trial$Sweets,
  DISCRET_FAT_SERV_MEDI = PREDIMED_trial$Refined_Oliveoil,
  REDPROC_MEAT_SERV_MEDI = PREDIMED_trial$Meat)

## # A tibble: 3 × 14
##   RESPONDENTID MEDI_ALL MEDI_NOETOH MEDI_OLIVE_OIL MEDI_FRT MEDI_VEG
##   <chr>           <dbl>       <dbl>          <dbl>    <dbl>    <dbl>
## 1 Med_Oliveoil        8           7              1        1        1
## 2 Med_Nuts            7           7              0        1        1
## 3 Control             5           5              0        0        1
## # ℹ 8 more variables: MEDI_LEGUMES <dbl>, MEDI_NUTS <dbl>, MEDI_FISH <dbl>,
## #   MEDI_ALCOHOL <dbl>, MEDI_SSB <dbl>, MEDI_SWEETS <dbl>,
## #   MEDI_DISCRET_FAT <dbl>, MEDI_REDPROC_MEAT <dbl>

# MEDI_V2 is 5 point scoring criteria
MEDI_V2(
    SERV_DATA = PREDIMED_trial,
    RESPONDENTID = PREDIMED_trial$Diet_Type,
    OLIVE_OIL_SERV_MEDI = PREDIMED_trial$Virgin_Oliveoil,
    FRT_SERV_MEDI = PREDIMED_trial$Fruits, 
    VEG_SERV_MEDI = PREDIMED_trial$Vegetables,
    LEGUMES_SERV_MEDI = PREDIMED_trial$Legumes,
    NUTS_SERV_MEDI = PREDIMED_trial$Total_nuts,
    FISH_SEAFOOD_SERV_MEDI = PREDIMED_trial$Fish_Seafood,
    ALCOHOL_SERV_MEDI = PREDIMED_trial$Alcohol,
    SSB_SERV_MEDI = PREDIMED_trial$Soda_Drinks,
    SWEETS_SERV_MEDI = PREDIMED_trial$Sweets,
    DISCRET_FAT_SERV_MEDI = PREDIMED_trial$Refined_Oliveoil,
    REDPROC_MEAT_SERV_MEDI = PREDIMED_trial$Meat
)

## # A tibble: 3 × 14
##   RESPONDENTID MEDI_V2_ALL MEDI_V2_NOETOH MEDI_OLIVE_OIL MEDI_FRT MEDI_VEG
##   <chr>              <dbl>          <dbl>          <dbl>    <dbl>    <dbl>
## 1 Med_Oliveoil        42.5           35.0           5        5           5
## 2 Med_Nuts            36.7           30.8           3.19     5           5
## 3 Control             33.7           29.1           2.27     4.92        5
## # ℹ 8 more variables: MEDI_LEGUMES <dbl>, MEDI_NUTS <dbl>, MEDI_FISH <dbl>,
## #   MEDI_ALCOHOL <dbl>, MEDI_SSB <dbl>, MEDI_SWEETS <dbl>,
## #   MEDI_DISCRET_FAT <dbl>, MEDI_REDPROC_MEAT <dbl>

Calculating DII for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

DII(SERV_DATA, SERV_DATA$RESPONDENTID, REPEATNUM=1, SERV_DATA$ALCOHOL_DII, SERV_DATA$VITB12_DII, SERV_DATA$VITB6_DII, SERV_DATA$BCAROTENE_DII, SERV_DATA$CAFFEINE_DII, SERV_DATA$CARB_DII, SERV_DATA$CHOLES_DII, SERV_DATA$KCAL_DII, SERV_DATA$EUGENOL_DII, SERV_DATA$TOTALFAT_DII, SERV_DATA$FIBER_DII, SERV_DATA$FOLICACID_DII, SERV_DATA$GARLIC_DII, SERV_DATA$GINGER_DII, SERV_DATA$IRON_DII, SERV_DATA$MG_DII, SERV_DATA$MUFA_DII, SERV_DATA$NIACIN_DII, SERV_DATA$N3FAT_DII, SERV_DATA$N6FAT_DII, SERV_DATA$ONION_DII, SERV_DATA$PROTEIN_DII, SERV_DATA$PUFA_DII, SERV_DATA$RIBOFLAVIN_DII, SERV_DATA$SAFFRON_DII, SERV_DATA$SATFAT_DII, SERV_DATA$SE_DII, SERV_DATA$THIAMIN_DII, SERV_DATA$TRANSFAT_DII, SERV_DATA$TURMERIC_DII, SERV_DATA$VITA_DII, SERV_DATA$VITC_DII, SERV_DATA$VITD_DII, SERV_DATA$VITE_DII, SERV_DATA$ZN_DII, SERV_DATA$TEA_DII, SERV_DATA$FLA3OL_DII, SERV_DATA$FLAVONES_DII, SERV_DATA$FLAVONOLS_DII, SERV_DATA$FLAVONONES_DII, SERV_DATA$ANTHOC_DII, SERV_DATA$ISOFLAVONES_DII, SERV_DATA$PEPPER_DII, SERV_DATA$THYME_DII, SERV_DATA$ROSEMARY_DII)

#Use the example data
data("DHQ3_exp")
DII(DHQ3_exp, DHQ3_exp$`Respondent ID`, 1, DHQ3_exp$`Alcohol (g)`, DHQ3_exp$`Vitamin B12 (mcg)`, DHQ3_exp$`Vitamin B6 (mg)`)

## It is normal to see warnings if you do not provide all arguments using DII. The algorithm will only count the arguments you enter to calculate the DII. All warnings are about the first column you don't use. For example, if you only entered alcohol, vitamin b12, and vitamin b6, all warnings would remind you that bcarotene is not found.

## Warning: Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.
## Unknown or uninitialised column: `BCAROTENE_DII`.

## # A tibble: 23 × 49
## # Groups:   RESPONDENTID [23]
##    RESPONDENTID DII_ALL DII_NOETOH REPEATNUM ALCOHOL_DII VITB12_DII VITB6_DII
##           <dbl>   <dbl>      <dbl>     <dbl>       <dbl>      <dbl>     <dbl>
##  1            1  0.0479    -0.230          1       0.278    -0.0232   -0.207 
##  2            2  0.208     -0.0697         1       0.278    -0.0855    0.0157
##  3            3 -0.115     -0.384          1       0.270    -0.0361   -0.348 
##  4            4  0.0194    -0.258          1       0.278    -0.0486   -0.210 
##  5            5  0.277      0.0425         1       0.234    -0.0760    0.119 
##  6            6  0.448      0.170          1       0.278    -0.0949    0.265 
##  7            7  0.348      0.0699         1       0.278    -0.0869    0.157 
##  8            8 -0.0124    -0.286          1       0.274    -0.0574   -0.229 
##  9            9  0.101     -0.0525         1       0.153    -0.0682    0.0157
## 10           10  0.170     -0.108          1       0.277    -0.0297   -0.0778
## # ℹ 13 more rows
## # ℹ 42 more variables: BCAROTENE_DII <dbl>, CAFFEINE_DII <int>, CARB_DII <int>,
## #   CHOLES_DII <int>, KCAL_DII <int>, EUGENOL_DII <int>, TOTALFAT_DII <int>,
## #   FIBER_DII <int>, FOLICACID_DII <int>, GARLIC_DII <int>, GINGER_DII <int>,
## #   IRON_DII <int>, MG_DII <int>, MUFA_DII <int>, NIACIN_DII <int>,
## #   N3FAT_DII <int>, N6FAT_DII <int>, ONION_DII <int>, PROTEIN_DII <int>,
## #   PUFA_DII <int>, RIBOFLAVIN_DII <int>, SAFFRON_DII <int>, …

Calculating ACS2020_V1 or ACS2020_V2 for your own dietary assessment tool

DATA_PATH <- "/Users/james/Desktop/data.csv"
SERV_DATA <- read_csv(DATA_PATH)

ACS2020_V1(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$GENDER, SERV_DATA$VEG_SERV_ACS2020, SERV_DATA$VEG_ITEMS_SERV_ACS2020, SERV_DATA$FRT_SERV_ACS2020, SERV_DATA$FRT_ITEMS_SERV_ACS2020, SERV_DATA$WGRAIN_SERV_ACS2020, SERV_DATA$SSB_FRTJ_SERV_ACS2020, SERV_DATA$REDPROC_MEAT_SERV_ACS2020, SERV_DATA$HPFRG_RATIO_SERV_ACS2020)

ACS2020_V2(SERV_DATA, SERV_DATA$RESPONDENTID, SERV_DATA$GENDER, SERV_DATA$TOTALKCAL_ACS2020, SERV_DATA$VEG_SERV_ACS2020, SERV_DATA$VEG_ITEMS_SERV_ACS2020, SERV_DATA$FRT_SERV_ACS2020, SERV_DATA$FRT_ITEMS_SERV_ACS2020, SERV_DATA$WGRAIN_SERV_ACS2020, SERV_DATA$SSB_FRTJ_SERV_ACS2020, SERV_DATA$REDPROC_MEAT_SERV_ACS2020, SERV_DATA$HPFRG_SERV_ACS2020)

Calculating PHDI for your own dietary assessment tool

#Use the example data
data("PHDI_VALIDATION")

PHDI(SERV_DATA=PHDI_VALIDATION, PHDI_VALIDATION$id, PHDI_VALIDATION$gender, PHDI_VALIDATION$TOTALKCAL_PHDI, PHDI_VALIDATION$WGRAIN_SERV_PHDI, PHDI_VALIDATION$STARCHY_VEG_SERV_PHDI, PHDI_VALIDATION$VEG_SERV_PHDI, PHDI_VALIDATION$FRT_SERV_PHDI, PHDI_VALIDATION$DAIRY_SERV_PHDI, PHDI_VALIDATION$REDPROC_MEAT_SERV_PHDI, PHDI_VALIDATION$POULTRY_SERV_PHDI, PHDI_VALIDATION$EGG_SERV_PHDI, PHDI_VALIDATION$FISH_SERV_PHDI, PHDI_VALIDATION$NUTS_SERV_PHDI, PHDI_VALIDATION$LEGUMES_SERV_PHDI, PHDI_VALIDATION$SOY_SERV_PHDI, PHDI_VALIDATION$ADDED_FAT_UNSAT_SERV_PHDI, PHDI_VALIDATION$ADDED_FAT_SAT_TRANS_SERV_PHDI, PHDI_VALIDATION$ADDED_SUGAR_SERV_PHDI)

## # A tibble: 26 × 19
##    RESPONDENTID GENDER PHDI_ALL TOTALKCAL_PHDI PHDI_WGRAIN PHDI_STARCHY_VEG
##           <dbl>  <dbl>    <dbl>          <dbl>       <dbl>            <dbl>
##  1            1      2        0           2000           0                0
##  2            2      2        0           2000           0                0
##  3            3      2       14           2000           1                1
##  4            4      2       28           2000           2                2
##  5            5      2       42           2000           3                3
##  6            6      2       56           2000           4                4
##  7            7      2       70           2000           5                5
##  8            8      2       84           2000           6                6
##  9            9      2       98           2000           7                7
## 10           10      2      112           2000           8                8
## # ℹ 16 more rows
## # ℹ 13 more variables: PHDI_VEG <dbl>, PHDI_FRT <dbl>, PHDI_DAIRY <dbl>,
## #   PHDI_REDPROC_MEAT <dbl>, PHDI_POULTRY <dbl>, PHDI_EGG <dbl>,
## #   PHDI_FISH <dbl>, PHDI_NUTS <dbl>, PHDI_LEGUMES <dbl>, PHDI_SOY <dbl>,
## #   PHDI_ADDED_FAT_UNSAT <dbl>, PHDI_ADDED_FAT_SAT <dbl>,
## #   PHDI_ADDED_SUGAR <dbl>

Processing dietaryindex outputs

Merge 2 dietary index results together

data(NHANES_20152016)
data(NHANES_20172018)

# HEI_NHANES_FPED
## 2017-2018 day 1 and day 2
HEI2020_NHANES_FPED_1718 = HEI2020_NHANES_FPED(
    FPED_PATH=NHANES_20172018$FPED, 
    NUTRIENT_PATH=NHANES_20172018$NUTRIENT, 
    DEMO_PATH=NHANES_20172018$DEMO, 
    FPED_PATH2=NHANES_20172018$FPED2, 
    NUTRIENT_PATH2=NHANES_20172018$NUTRIENT2)

## 2015-2016 day 1 and day 2
HEI2020_NHANES_FPED_1516 = HEI2020_NHANES_FPED(
    FPED_PATH=NHANES_20152016$FPED, 
    NUTRIENT_PATH=NHANES_20152016$NUTRIENT, 
    DEMO_PATH=NHANES_20152016$DEMO, 
    FPED_PATH2=NHANES_20152016$FPED2, 
    NUTRIENT_PATH2=NHANES_20152016$NUTRIENT2)

# Now, merge the two datasets
HEI2020_NHANES_FPED_17181516 = rbind(HEI2020_NHANES_FPED_1718, HEI2020_NHANES_FPED_1516)

# Save the result on your computer
# HEI2020_NHANES_FPED_17181516_df = as.data.frame(HEI2020_NHANES_FPED_17181516)
# readr::write_csv(HEI2020_NHANES_FPED_17181516_df, "/your_output_file_location/HEI2020_NHANES_FPED_17181516_df.csv")

Merge dietary index results with other NHANES data

data(NHANES_20172018)

# HEI_NHANES_FPED
## 2017-2018 day 1 and day 2
HEI2020_NHANES_FPED_1718 = HEI2020_NHANES_FPED(
    FPED_PATH=NHANES_20172018$FPED, 
    NUTRIENT_PATH=NHANES_20172018$NUTRIENT, 
    DEMO_PATH=NHANES_20172018$DEMO, 
    FPED_PATH2=NHANES_20172018$FPED2, 
    NUTRIENT_PATH2=NHANES_20172018$NUTRIENT2)

# Blood pressure data
## library(haven)
BP_1718 = read_xpt("BPX_J.XPT")

## library(dplyr)
BP_HEI2020_1718 = inner_join(HEI2020_NHANES_FPED_1718, BP_1718, by="SEQN")

Add dietary index output to your own data and save the result

# Store the output of HEI2015 in "HEI2015_output"
data("SERV_DATA_exp")
HEI2015_output = HEI2015(SERV_DATA_exp, SERV_DATA_exp$UserName, SERV_DATA_exp$TOTALKCAL, SERV_DATA_exp$TOTALFRT_SERV_HEI2015_exp, SERV_DATA_exp$FRT_SERV_HEI2015_exp, SERV_DATA_exp$VEG_SERV_HEI2015_exp, SERV_DATA_exp$GREENNBEAN_SERV_HEI2015_exp, SERV_DATA_exp$TOTALPRO_SERV_HEI2015_exp,  SERV_DATA_exp$SEAPLANTPRO_SERV_HEI2015_exp, SERV_DATA_exp$WHOLEGRAIN_SERV_HEI2015_exp, SERV_DATA_exp$DAIRY_SERV_HEI2015_exp, SERV_DATA_exp$FATTYACID_SERV_HEI2015_exp, SERV_DATA_exp$REFINEDGRAIN_SERV_HEI2015_exp,  SERV_DATA_exp$SODIUM_SERV_HEI2015_exp, SERV_DATA_exp$ADDEDSUGAR_SERV_HEI2015_exp, SERV_DATA_exp$SATFAT_SERV_HEI2015_exp)


# Merge the HEI2015_output with your own data by the participant ID
# Here, the HEI2015_output'S participant ID is "RESPONDENTID", while the participant ID in your selected data may vary (SERV_DATA_exp's participant ID UserName) and it should be the column name of SERV_DATA$RESPONDENTID in the HEI2015 function
Merged_HEI2015_output = left_join(SERV_DATA_exp, HEI2015_output, by=c("UserName" = "RESPONDENTID"))

# Save the result on your computer
readr::write_csv(Merged_HEI2015_output, "/your_output_file_location/Merged_HEI2015_output.csv")