DI_GM Calculation
DI_GM.RdCalculate the Dietary Index for Gut Microbiota (DI-GM), using given the serving sizes of foods and nutrients consumed per 1 day
Usage
DI_GM(
SERV_DATA,
RESPONDENTID,
GENDER,
AVOCADO,
BROCCOLI,
CHICKPEA,
COFFEE,
CRANBERRY,
FERMENTED_DAIRY,
FIBER,
GREEN_TEA,
SOYBEAN,
WHOLE_GRAIN,
TOTAL_FAT_PERCENTAGE,
PROCESSED_MEAT,
RED_MEAT,
REFINED_GRAIN
)Arguments
- SERV_DATA
The raw data file that includes all the serving sizes of foods and nutrients
- RESPONDENTID
The unique participant ID for each participant
- GENDER
The gender of the participant
- AVOCADO
The serving size of Avocado. Unit does not matter here since the scoring is based on the median intake of the food in the population. However, it is important to keep the same unit for all indivudal food consumption data.
- BROCCOLI
The serving size of Broccoli. Unit does not matter here as indicated previously.
- CHICKPEA
The serving size of Chickpea. Unit does not matter here as indicated previously.
- COFFEE
The serving size of Coffee. Unit does not matter here as indicated previously.
- CRANBERRY
The serving size of Cranberry. Unit does not matter here as indicated previously.
- FERMENTED_DAIRY
The serving size of Fermented Dairy. Unit does not matter here as indicated previously.
- FIBER
The serving size of Fiber. Unit does not matter here as indicated previously.
- GREEN_TEA
The serving size of Green Tea. Unit does not matter here as indicated previously.
- SOYBEAN
The serving size of Soybean. Unit does not matter here as indicated previously.
- WHOLE_GRAIN
The serving size of Whole Grain. Unit does not matter here as indicated previously.
- TOTAL_FAT_PERCENTAGE
The total fat calorie percentage of the total calories consumed. Unit is percent calories.
- PROCESSED_MEAT
The serving size of Processed Meat. Unit does not matter here as indicated previously.
- RED_MEAT
The serving size of Red Meat. Unit does not matter here as indicated previously.
- REFINED_GRAIN
The serving size of Refined Grain. Unit does not matter here as indicated previously.
Examples
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>