Nutritional information and manufacturer data for 70+ popular US breakfast cereals
Format
A matrix containing 77 observations and 16 attributes.
namename of cereal
manufmanufacturer of cereal, coded into seven categories: "A" for American Home Food Products, "G" for General Mills, "K" for Kelloggs, "N" for Nabisco, "P" for Post, "Q" for Quaker Oats, and "R" for Ralston Purina
typecold or hot
caloriescalories per serving
proteingrams of protein
fatgrams of fat
sodiummilligrams of sodium
fibergrams of dietary fiber
carbograms of complex carbohydrates
sugarsgrams of sugars
potassmilligrams of potassium
vitaminsvitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended
shelfdisplay shelf (1, 2, or 3, counting from the floor)
weightweight in ounces of one serving
cupsnumber of cups in one serving
ratinga rating of the cereals
References
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.
Examples
data(cereal)
str(cereal)
#> 'data.frame': 77 obs. of 16 variables:
#> $ name : Factor w/ 77 levels "100% Bran","100% Natural Bran",..: 1 2 3 4 5 6 7 8 9 10 ...
#> $ manuf : Factor w/ 7 levels "A","G","K","N",..: 4 6 3 3 7 2 3 2 7 5 ...
#> $ type : Factor w/ 2 levels "cold","hot": 1 1 1 1 1 1 1 1 1 1 ...
#> $ calories: int 70 120 70 50 110 110 110 130 90 90 ...
#> $ protein : int 4 3 4 4 2 2 2 3 2 3 ...
#> $ fat : int 1 5 1 0 2 2 0 2 1 0 ...
#> $ sodium : int 130 15 260 140 200 180 125 210 200 210 ...
#> $ fiber : num 10 2 9 14 1 1.5 1 2 4 5 ...
#> $ carbo : num 5 8 7 8 14 10.5 11 18 15 13 ...
#> $ sugars : int 6 8 5 0 8 10 14 8 6 5 ...
#> $ potass : int 280 135 320 330 -1 70 30 100 125 190 ...
#> $ vitamins: int 25 0 25 25 25 25 25 25 25 25 ...
#> $ shelf : int 3 3 3 3 3 1 2 3 1 3 ...
#> $ weight : num 1 1 1 1 1 1 1 1.33 1 1 ...
#> $ cups : num 0.33 1 0.33 0.5 0.75 0.75 1 0.75 0.67 0.67 ...
#> $ rating : num 68.4 34 59.4 93.7 34.4 ...