Challenge 5

Pradhakshya Dhanakumar

Cereal

Author

Pradhakshya Dhanakumar

Published

April 18, 2023

Code

library(tidyverse)
library(ggplot2)
library(readxl)
library(ggrepel)
library(here)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read Data

Code

data <- read_csv("_data/cereal.csv")

Code

dim(data)

[1] 20  4

Code

colnames(data)

[1] "Cereal" "Sodium" "Sugar"  "Type"

Code

head(data)

# A tibble: 6 × 4
  Cereal              Sodium Sugar Type 
  <chr>                <dbl> <dbl> <chr>
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

Dataset Description:

The cereal dataset consists of 20 observations and 5 variables, including the name of the cereal, the amount of sodium and sugar per serving, a categorical variable indicating the type of cereal, and a numerical variable indicating the type of cereal’s health rating. The data is structured as a table with each row representing a different cereal and each column containing information about the cereal. The dataset provides information on various types of cereals, including popular brands such as Frosted Flakes, Raisin Bran, and Cheerios, and can be used to analyze the nutritional content of breakfast cereals and compare different types of cereals based on their sugar and sodium content.

Tidy Data

Code

#Check for missing values
sum(is.na(data))

[1] 0

Code

#renaming columns to lowercase and replace spaces with underscores:
names(data) <- tolower(names(data))
names(data) <- gsub(" ", "_", names(data))
head(data)

# A tibble: 6 × 4
  cereal              sodium sugar type 
  <chr>                <dbl> <dbl> <chr>
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

The data looks tidy for further analysis and visualizations.

Mutate Data

Code

# create a new column for the total amount of sugar and sodium per serving
data <- data %>%
  mutate(sugar_per_cup = sugar * 0.75,  # Assuming 1 cup is 3/4 of a serving
         sodium_per_cup = sodium * 0.75)  # Assuming 1 cup is 3/4 of a serving

Code

print(data)

# A tibble: 20 × 6
   cereal                sodium sugar type  sugar_per_cup sodium_per_cup
   <chr>                  <dbl> <dbl> <chr>         <dbl>          <dbl>
 1 Frosted Mini Wheats        0    11 A              8.25            0  
 2 Raisin Bran              340    18 A             13.5           255  
 3 All Bran                  70     5 A              3.75           52.5
 4 Apple Jacks              140    14 C             10.5           105  
 5 Captain Crunch           200    12 C              9             150  
 6 Cheerios                 180     1 C              0.75          135  
 7 Cinnamon Toast Crunch    210    10 C              7.5           158. 
 8 Crackling Oat Bran       150    16 A             12             112. 
 9 Fiber One                100     0 A              0              75  
10 Frosted Flakes           130    12 C              9              97.5
11 Froot Loops              140    14 C             10.5           105  
12 Honey Bunches of Oats    180     7 A              5.25          135  
13 Honey Nut Cheerios       190     9 C              6.75          142. 
14 Life                     160     6 C              4.5           120  
15 Rice Krispies            290     3 C              2.25          218. 
16 Honey Smacks              50    15 A             11.2            37.5
17 Special K                220     4 A              3             165  
18 Wheaties                 180     4 A              3             135  
19 Corn Flakes              200     3 A              2.25          150  
20 Honeycomb                210    11 C              8.25          158.

Univariate Visualization

Code

ggplot(data, aes(x = "", y = sodium)) +
  geom_boxplot() +
  labs(title = "Box Plot of Sodium Content in Breakfast Cereals", y = "Sodium (mg)")

A box plot for sodium in the cereal dataset can provide useful insights into the distribution of sodium content in the cereals. The vertical axis of the box plot represents the sodium values, while the horizontal axis represents the cereal variable. The box of the plot represents the interquartile range (IQR) of the sodium variable, where the upper and lower edges of the box indicate the 75th and 25th percentiles of the distribution, respectively. The line inside the box represents the median of the distribution. The whiskers above and below the box indicate the range of the data, while any outliers are represented by individual points outside of the whiskers. By examining the box plot, we can quickly identify the cereals with high and low sodium content and gain insights into the variability and distribution of sodium content across different cereal types.

Code

ggplot(data, aes(x = sugar)) +
  geom_histogram(binwidth = 2, fill = "dodgerblue") +
  labs(title = "Histogram of Sugar Content in Breakfast Cereals", x = "Sugar (g)", y = "Count")

A histogram of sugar content in breakfast cereals is a visualization tool that displays the frequency distribution of sugar content in the cereal dataset. The histogram provides a quick overview of the distribution of sugar content in the cereals, showing how many cereals fall into different sugar content ranges. We can use the histogram to identify the most common sugar content range and any outliers or unusual patterns in the data. The shape of the histogram can also provide insights into the skewness or symmetry of the sugar content distribution. Overall, a histogram of sugar content in breakfast cereals is a useful tool for exploring the distribution and frequency of sugar content in the dataset.

Bivariate Visualization

Code

ggplot(data, aes(x = sugar_per_cup, y = sodium_per_cup)) +
  geom_point() +
  labs(title = "Scatter Plot of Sugar and Sodium per Cup in Breakfast Cereals", x = "Sugar per Cup (g)", y = "Sodium per Cup (mg)")

A scatter plot of sugar and sodium per cup in breakfast cereals is a visualization tool that displays the relationship between two continuous variables, sugar and sodium, in the cereal dataset. The scatter plot allows us to examine the relationship between these two variables, revealing whether there is any correlation or pattern between sugar and sodium content in the cereals.The scatter plot can provide valuable insights into the cereal dataset, such as identifying clusters or patterns in the data and detecting any outliers or unusual values. It can also help us to explore the strength and direction of the relationship between sugar and sodium content in breakfast cereals. Overall, a scatter plot of sugar and sodium per cup in breakfast cereals is a useful tool for analyzing and visualizing the relationship between these two important nutrient components in the cereals.

Code

data$HighSugar <- ifelse(data$sugar >= 10, "Yes", "No")
ggplot(data, aes(x = type, fill = HighSugar)) +
  geom_bar() +
  labs(title = "Breakdown of Cereal Types by High Sugar Content", x = "Cereal Type", y = "Count") +
  scale_fill_manual(name = "High Sugar Content", values = c("Yes" = "#FF5733", "No" = "#C6E2FF")) +
  theme_classic()

After creating a new variable “HighSugar” based on sugar content, a stacked bar chart can be created to visualize the frequency distribution of cereal types based on high sugar content.The stacked bar chart allows us to compare the proportion of high and low sugar content cereals across different cereal types. The stacked bar chart also allows us to see the overall distribution of high and low sugar content cereals in the dataset, revealing that the majority of cereals have low sugar content. This information can be used to inform dietary choices and to identify potential high sugar content cereals to avoid.