Breakfast Cereal Dataset

A multivariate dataset describing seventy-seven commonly available breakfast cereals, based on the information now available on the FDA food label. The variable rating is a likely response variable in statistical models.

Usage

data("cereal")

Format

A data frame with 77 observations on the following 16 variables.

name: cereal name, a character vector
mfr: manufacturer (A G K N P Q R), a character vector
type: type (cold/hot), a character vector
calories: calories (number), a numeric vector
protein: protein(g), a numeric vector
fat: fat(g), a numeric vector
sodium: sodium(mg), a numeric vector
fiber: dietary fiber(g), a numeric vector
carbo: complex carbohydrates(g), a numeric vector
sugars: sugars(g), a numeric vector
potass: potassium(mg), a numeric vector
vitamins: vitamins & minerals (0, 25, or 100, respectively indicating "none added"; "enriched"; "FDA recommended"), a numeric vector
shelf: display shelf (1, 2, or 3, counting from the floor), a numeric vector
weight: weight (in ounces) of one serving (serving size), a numeric vector
cups: cups per serving, a numeric vector
rating: health rating of the cereal (unknown calculation method), a numeric vector

Source

From the American Statistical Association 1993 Statistical Graphics Exposition, 'Serial Correlation or Cereal Correlation ??', https://community.amstat.org/jointscsg-section/dataexpo/dataexpo1993.

Details

This dataset was used in the poster competition for the American Statistical association 1993 Statistical Graphics Exposition, titled Serial Correlation or Cereal Correlation ??.

The call for participation reads: "A multivariate dataset describing seventy-seven commonly available breakfast cereals, based on the information now available on the newly-mandated F&DA food label. What are you getting when you eat a bowl of cereal? Can you get a lot of fiber without a lot of calories? Can you describe what cereals are displayed on high, low, and middle shelves? The good news is that none of the cereals for which we collected data had any cholesterol, and manufacturers rarely use artificial sweeteners and colors, nowadays. However, there is still a lot of data for the consumer to understand while choosing a good breakfast cereal."

Further details on the variables and suggested analyses are available at https://community.amstat.org/jointscsg-section/dataexpo/dataexpo1993

The abbreviations for manufacturer, mfr, stand for:

A: American Home Food Products
G: General Mills
K: Kellog
N: Nabisco
P: Post
Q: Quaker Oats
R: Ralston Purina

References

Jean Dos Santos, Breakfast Cereals: Data Analysis and Clustering, (Kaggle link doesn't work) Does a bunch of data cleaning and exploratory data analysis in R.

Examples

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
data(cereal)
str(cereal)
#> tibble [77 × 16] (S3: tbl_df/tbl/data.frame)
#>  $ name    : chr [1:77] "100% Bran" "100% Natural Bran" "All-Bran" "All-Bran with Extra Fiber" ...
#>  $ mfr     : chr [1:77] "N" "Q" "K" "K" ...
#>  $ type    : chr [1:77] "C" "C" "C" "C" ...
#>  $ calories: num [1:77] 70 120 70 50 110 110 110 130 90 90 ...
#>  $ protein : num [1:77] 4 3 4 4 2 2 2 3 2 3 ...
#>  $ fat     : num [1:77] 1 5 1 0 2 2 0 2 1 0 ...
#>  $ sodium  : num [1:77] 130 15 260 140 200 180 125 210 200 210 ...
#>  $ fiber   : num [1:77] 10 2 9 14 1 1.5 1 2 4 5 ...
#>  $ carbo   : num [1:77] 5 8 7 8 14 10.5 11 18 15 13 ...
#>  $ sugars  : num [1:77] 6 8 5 0 8 10 14 8 6 5 ...
#>  $ potass  : num [1:77] 280 135 320 330 NA 70 30 100 125 190 ...
#>  $ vitamins: num [1:77] 25 0 25 25 25 25 25 25 25 25 ...
#>  $ shelf   : num [1:77] 3 3 3 3 3 1 2 3 1 3 ...
#>  $ weight  : num [1:77] 1 1 1 1 1 1 1 1.33 1 1 ...
#>  $ cups    : num [1:77] 0.33 1 0.33 0.5 0.75 0.75 1 0.75 0.67 0.67 ...
#>  $ rating  : num [1:77] 68.4 34 59.4 93.7 34.4 ...

# Add explicit name of manufacturer
# names for manufacturers
mfr_names <- c(
  "A" = "American Home Foods",
  "G" = "General Mills",
  "K" = "Kellog",
  "N" = "Nabisco",
  "P" = "Post",
  "Q" = "Quaker Oats",
  "R" = "Ralston Purina"
)

# recode `mfr` as `mfr_name`
cereal <- cereal |>
  mutate(mfr_name = recode(mfr, !!!mfr_names))

# density plot of ratings
library(ggplot2)
ggplot(data = cereal,
       aes(x = rating, fill = mfr_name, color = mfr_name)) +
  geom_density(alpha = 0.1) +
  theme_classic(base_size = 14) + 
  theme(legend.position = "bottom")
#> Warning: Groups with fewer than two data points have been dropped.
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_density()`).