foods: Foods consumption of individuals

In phytomosaic/ecole: ecole: School of Ecology Package

Description

Foods consumption of individual class members, and nutritional characteristics of those foods.

Usage

foods

Format

A list of 2 data.frames:
- spe Foods abundance matrix: 132 observations (individual people) of 32 foods. Values are how often each person consumed the foods, on 0-10 scale.
- env Nutritional matrix: 32 observations (foods) of 12 nutritional characteristics variables. Descriptions below.

Details

Coding for variables in the second matrix:

calPerServ = calories per serving, based primarily on (Margen, S. 1992. The Wellness Encyclopedia of Food and Nutrition, Health Letter Associates) and (Carper, J. and Patricia A. Krause, 1974. The All-in-one Calorie Counter, Bantam). A few items were obtained by checking nutritional information on packages.
fatGrams = grams of fat per serving, based primarily on Margen (op. cit.).
Animal = binary variable, 0 = not from animal products, 1 = from animals.
RedMeat = binary variable, 0 = not red meat, 1= red meat.
Plant = binary variable, 0 = not from plant products, 1 = primarily plant product.
GreenPlt = consumed as a green plant (cooked or not, but containing chlorophyll).
PctCalFat = percent of calories from fat, based on 100 x (fat,g/serving) x (9 cal/g) / (cal/serving).
PctEaten = percent of people in data with nonzero values for this food.
AveScore = average score on food questionnaire (n=83).
FastFood = binary variable indicating whether the food is commonly served at fast food restaurants (1) or not (0).
HealthFd = binary variable indicating whether the food is typically considered a “health food” item, especially as a substitute for another common food (e.g. tahini instead of peanut butter).
Gourmet = binary variable indicating whether the food is typically considered a “gourmet food” item, seldom eaten on normal occasions.

Source

The unpublished data are from members of McCune's Community Analysis classes, past and present. Nutritional information is cited above.

Examples

# split into two data.frames
data(foods)
spe <- foods$spe
env <- foods$env

phytomosaic/ecole documentation built on Jan. 2, 2022, 11:24 p.m.