Challenge 5 Solution

challenge_5
cereal
Introduction to Visualization
Author

Shreya Varma

Published

May 30, 2023

library(tidyverse)
library(ggplot2)

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

Challenge Overview

Today’s challenge is to:

  1. read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
  2. tidy data (as needed, including sanity checks)
  3. mutate variables as needed (including sanity checks)
  4. create at least two univariate visualizations
  • try to make them “publication” ready
  • Explain why you choose the specific graph type
  1. Create at least one bivariate visualization
  • try to make them “publication” ready
  • Explain why you choose the specific graph type

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.

(be sure to only include the category tags for the data you use!)

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

  • cereal.csv ⭐
  • Total_cost_for_top_15_pathogens_2018.xlsx ⭐
  • Australian Marriage ⭐⭐
  • AB_NYC_2019.csv ⭐⭐⭐
  • StateCounty2012.xls ⭐⭐⭐
  • Public School Characteristics ⭐⭐⭐⭐
  • USA Households ⭐⭐⭐⭐⭐
cereal <- read_csv("_data/cereal.csv")
cereal
# A tibble: 20 × 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    
 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    
summary(cereal)
    Cereal              Sodium          Sugar           Type          
 Length:20          Min.   :  0.0   Min.   : 0.00   Length:20         
 Class :character   1st Qu.:137.5   1st Qu.: 4.00   Class :character  
 Mode  :character   Median :180.0   Median : 9.50   Mode  :character  
                    Mean   :167.0   Mean   : 8.75                     
                    3rd Qu.:202.5   3rd Qu.:12.50                     
                    Max.   :340.0   Max.   :18.00

Briefly describe the data

This dataset contains the data of sodium, sugar and type of different cereals in US. It is a small dataset with 20 rows and 4 columns.

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

The Sodium and Sugar values are present in numbers but we don’t know the units for it. The type column contains values A and C but we are not sure what they mean to mutate it thus we can leave it as it is. Thus there is not much we can do to tidy it,

unique(cereal$Type)
[1] "A" "C"

Univariate Visualizations

We can check the sugar and sodium levels across all cereals using a box plot. Thus will give us an overall of max, min, median quantity of sugar and sodium for cereals. I have also created a graph of top 5 cereals with highest sugar and sodium. Out of the 20 cereals, Raisin bran seems to have the highest sodium as well as highest sugar and thus should probably be avoided.

ggplot(cereal)+
  geom_boxplot(aes(x="Sugar", y=Sugar)) +
  labs(title = "Sugar level in Cereal", y="Quantity")

ggplot(cereal)+
  geom_boxplot(aes(x="Sodium", y=Sodium)) +
  labs(title = "Sodium level in Cereal", y="Quantity")

cereal_sugar <- head(arrange(cereal, desc(Sugar)),5)

ggplot(data = cereal_sugar, aes(x = Cereal, y = Sugar)) +
  geom_bar(stat = "identity") +
  labs(title = "Top 5 High sugar cereals", x = "Cereal", y = "Sugar quantity")

cereal_sodium <- head(arrange(cereal, desc(Sodium)),5)

ggplot(data = cereal_sodium, aes(x = Cereal, y = Sodium)) +
  geom_bar(stat = "identity") +
  labs(title = "Top 5 High Sodium cereals", x = "Cereal", y = "Sodium quantity")

Bivariate Visualization(s)

I created a scatter plot of Sugar and Sodium for cereal which are distinguished by Type A or C based on the color. This graph gives us a picture of sodium and sugar level in cereal together.

ggplot(cereal)+
  geom_point(aes(x = Sugar, y = Sodium, color = Type))+
  labs(title="Scatter plot of Sodium vs Sugar in Cereal boxes", x = "Sugar", y = "Sodium")