Cereal Visualization

Code

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE)

Read in the Data

Code

cereal <- read.csv("_data/cereal.csv")
cereal

                  Cereal Sodium Sugar Type
1    Frosted Mini Wheats      0    11    A
2            Raisin Bran    340    18    A
3               All Bran     70     5    A
4            Apple Jacks    140    14    C
5         Captain Crunch    200    12    C
6               Cheerios    180     1    C
7  Cinnamon Toast Crunch    210    10    C
8     Crackling Oat Bran    150    16    A
9              Fiber One    100     0    A
10        Frosted Flakes    130    12    C
11           Froot Loops    140    14    C
12 Honey Bunches of Oats    180     7    A
13    Honey Nut Cheerios    190     9    C
14                  Life    160     6    C
15         Rice Krispies    290     3    C
16          Honey Smacks     50    15    A
17             Special K    220     4    A
18              Wheaties    180     4    A
19           Corn Flakes    200     3    A
20             Honeycomb    210    11    C

The data set shows the sugar (g) and sodium (mg) content per serving for 20 different cereals. It also categorizes the cereals as children cereals or adult cereals (in the type column).

Tidy the Data

In order to make the data tidy, I pivoted it longer to a 40x4 tibble so that each row can be a separate observation, either the sugar content or the sodium content.

Code

cereal_tidy <- pivot_longer(cereal, c(Sodium, Sugar), names_to = "Chemical", values_to = "Amount")
cereal_tidy

# A tibble: 40 × 4
   Cereal              Type  Chemical Amount
   <chr>               <chr> <chr>     <int>
 1 Frosted Mini Wheats A     Sodium        0
 2 Frosted Mini Wheats A     Sugar        11
 3 Raisin Bran         A     Sodium      340
 4 Raisin Bran         A     Sugar        18
 5 All Bran            A     Sodium       70
 6 All Bran            A     Sugar         5
 7 Apple Jacks         C     Sodium      140
 8 Apple Jacks         C     Sugar        14
 9 Captain Crunch      C     Sodium      200
10 Captain Crunch      C     Sugar        12
# … with 30 more rows

Descriptive Statistics

Code

cereal_tidy %>%
  select(Type)%>%
  table()

Type
 A  C 
20 20 

Code

cereal_tidy %>%
  filter("Sodium" == Chemical) %>%
  group_by(Type)%>%
  summarize(Mean = mean(Amount), Median = median(Amount), Minimum =min(Amount), Maximum = max(Amount), Std = sd(Amount), IQR= IQR(Amount))

# A tibble: 2 × 7
  Type   Mean Median Minimum Maximum   Std   IQR
  <chr> <dbl>  <dbl>   <int>   <int> <dbl> <dbl>
1 A       149    165       0     340  98.1 118. 
2 C       185    185     130     290  47.4  62.5

Code

cereal_tidy %>%
  filter("Sugar" == Chemical) %>%
  group_by(Type)%>%
  summarize(Mean = mean(Amount), Median = median(Amount), Minimum =min(Amount), Maximum = max(Amount), Std = sd(Amount), IQR= IQR(Amount))

# A tibble: 2 × 7
  Type   Mean Median Minimum Maximum   Std   IQR
  <chr> <dbl>  <dbl>   <int>   <int> <dbl> <dbl>
1 A       8.3    6         0      18  6.25 10   
2 C       9.2   10.5       1      14  4.49  5.25

We can see that there are 10 adult cereals, and 10 children cereals, with the children cereals having slightly higher sodium and sugar content than the adult ones.

Univariate Visualizations

Code

cereal_tidy %>%
  filter(Chemical == "Sodium") %>%
  ggplot(aes(Amount)) +
  geom_density(fill= "blue")+
  theme_minimal()+
  labs(title = "Sodium Content in Cereal", x= "Sodium Content (mg)", y= "Density")

Code

cereal_tidy %>%
  filter(Chemical == "Sugar") %>%
  ggplot(aes(Amount)) +
  geom_density(fill= "red")+
  theme_minimal()+
  labs(title = "Sugar Content in Cereal", x= "Sugar Content (g)", y= "Density")

For Univariate Visualizations, I am choosing to make density plots for the sugar and sodium content across different cereals. I am choosing this because the sodium content is a continuous variable, so while a chart like a bar plot will tell us the frequency of each exact amount of sodium or sugar, a density graph better shows the overall shape of the pattern in the data, and can let us make more general observations.

Bivariate Visualizations

Code

cereal %>%
  ggplot(aes(x= Sodium, y= Sugar)) +
  geom_point() + 
  geom_smooth() +
  theme_minimal() +
  labs(title= "Sugar and Sodium Correlation in Cereals", x= "Sodium Content (mg)", y= "Sugar Content(g)")

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

At this point I see that while the tidy data is better for somethings, it is easier to use the original data set to perform correlations between the different chemicals in the same cereal. Here I chose a point plot with a smooth function to see if there was any link between the sugar and sodium content, but there appears to not be a strong correlation.

We have the variable distinguishing Adult and Children cereal, so the last two plots, I wanted to compare the sugar and sodium content. I chose to make two boxplots to be able to compare them side by side because we are using a categorical and a continuous variable.

Code

cereal %>%
  mutate(Type= case_match(Type, "C" ~ "Child", "A"~ "Adult")) %>%
  ggplot(aes(x= Type, y= Sodium))+
  geom_boxplot() +
  theme_minimal() +
  labs(title = "Sodium Content Comparison in Adult and Children's cereal", y= "Sodium content(mg)", x= "Cereal Type")

Code

cereal %>%
  mutate(Type= case_match(Type, "C" ~ "Child", "A"~ "Adult")) %>%
  ggplot(aes(x= Type, y= Sugar))+
  geom_boxplot() +
  theme_minimal() +
  labs(title = "Sugar Content Comparison in Adult and Children's cereal", y= "Sugar content(g)", x= "Cereal Type")

These graphs provide us meaningful information about the differences between adult and children cereals. While cereals for children have a more consistent sodium content, it has only a slightly higher average amount of sodium. It also shows that kids cereals tend to have more sugar, while adult cereals cover a wider range, and have a larger varience.