nutrient: Nutrient data table.
In hadley/usdanutrients: USDA nutrient data (release SR26)

Description Usage Format Examples

Nutrient values and information about the values, including expanded statistical information.

nutrient

A data frame with 632,894 observations and 18 variables:

food_id: Food identifier. Joins with food
nutr_id: Nutrient identifier. Joins with nutrient_def
value: Amount of nutrient in 100g.
num_points: Number of analyses used to compute value. If the number of data points is 0, the value was calculated or imputed.
se: Standard error of the mean. NA if cannot be calculated. The standard error is also not given if the number of data points is less than three
source_type_id: Code indicating type of data. Joins with source_type
deriv_id: Data derivation code. Joins with deriv
impute_id: Food identifier for the item used to calculate a missing value. Populated only for items added or updated starting with SR14.
fortified: Indicates a vitamin or mineral added for fortification or enrichment. This field is populated for ready-to-eat breakfast cereals and many brand-name hot cereals in food group 8.
num_studies: Number of studies.
min,max: Minimum & maximum value from studies
df: degrees of freedom
lwr,upr: Lower and upper 95% CI around mean
comments: Statistical comments
modified: Month value added or modified
cc: Confidence Code indicating data quality, based on evaluation of sample plan, sample handling, analytical method, analytical quality control, and number of samples analyzed. Not included in this release, but is planned for future releases.

nutrient

# To find a name nutrient information for a specified food, you need to
# join food, nutrient and nutrient_def
if (require("dplyr") && require("tidyr")) {
  nutr_info <- food %>%
    select(food_id, food) %>%
    inner_join(nutrient %>% select(food_id, nutr_id, value)) %>%
    inner_join(nutrient_def %>% select(nutr_id, nutr_abbr))

 # Often easiest to understand if you spread the nutrient values out
 # in to columns. We'll do this for the 10 most prevalent nutrients
 top_nutr <- nutrient %>%
   group_by(nutr_id) %>%
   tally(sort = TRUE) %>%
   head(10)

 nutr_info %>%
   semi_join(top_nutr) %>%
   select(food_id, food, nutr_abbr, value) %>%
   spread(nutr_abbr, value)
}