Challenge 5 - Darron Bunt

challenge_5

cereal

darron_bunt

Introduction to Visualization

Author

Darron Bunt

Published

November 13, 2022

library(tidyverse)
library(ggplot2)

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

Challenge Overview

Today’s challenge is to:

read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
create at least two univariate visualizations

try to make them “publication” ready
Explain why you choose the specific graph type

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")

Briefly describe the data

The cereal dataset shows the sodium (in mg) and sugar (in g) content of 20 different cereals. There is also a column classifying each cereal as either A (adult) or C (children’s).

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.

cereal_new <- cereal %>%
  rename(cereal_name = Cereal, sugar=Sugar)
cereal_new

# A tibble: 20 × 4
   cereal_name           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

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

If I am going to want to be able to compare the sugar and sodium contents of the cereals, I would need them to both be in grams. Given that sodium is currently in milligrams, I will have to mutate the data.

cereal_sodium <- cereal_new %>%
mutate(Sodium = Sodium/1000)
  cereal_sodium

# A tibble: 20 × 4
   cereal_name           Sodium sugar Type 
   <chr>                  <dbl> <dbl> <chr>
 1 Frosted Mini Wheats     0       11 A    
 2 Raisin Bran             0.34    18 A    
 3 All Bran                0.07     5 A    
 4 Apple Jacks             0.14    14 C    
 5 Captain Crunch          0.2     12 C    
 6 Cheerios                0.18     1 C    
 7 Cinnamon Toast Crunch   0.21    10 C    
 8 Crackling Oat Bran      0.15    16 A    
 9 Fiber One               0.1      0 A    
10 Frosted Flakes          0.13    12 C    
11 Froot Loops             0.14    14 C    
12 Honey Bunches of Oats   0.18     7 A    
13 Honey Nut Cheerios      0.19     9 C    
14 Life                    0.16     6 C    
15 Rice Krispies           0.29     3 C    
16 Honey Smacks            0.05    15 A    
17 Special K               0.22     4 A    
18 Wheaties                0.18     4 A    
19 Corn Flakes             0.2      3 A    
20 Honeycomb               0.21    11 C

If I wanted to be able to plot sodium and sugar onto the same bar, I am going to need that information to be in one column instead of two. Accordingly, I’m going to need to pivot the data.

cereal_grams <- cereal_sodium %>%
  pivot_longer(col =c("Sodium", "sugar"),
               names_to="Sodium_Sugar",
               values_to="Amount")
cereal_grams

# A tibble: 40 × 4
   cereal_name         Type  Sodium_Sugar Amount
   <chr>               <chr> <chr>         <dbl>
 1 Frosted Mini Wheats A     Sodium         0   
 2 Frosted Mini Wheats A     sugar         11   
 3 Raisin Bran         A     Sodium         0.34
 4 Raisin Bran         A     sugar         18   
 5 All Bran            A     Sodium         0.07
 6 All Bran            A     sugar          5   
 7 Apple Jacks         C     Sodium         0.14
 8 Apple Jacks         C     sugar         14   
 9 Captain Crunch      C     Sodium         0.2 
10 Captain Crunch      C     sugar         12   
# … with 30 more rows

Univariate Visualizations

Univariate visualizations only look at one variable at a time.

For my first univariate visualization I’m want to show the sodium content of each different cereal, starting with the highest amount.

cereal_sodium %>%
  arrange(Sodium) %>%
mutate(cereal_name=factor(cereal_name, levels=cereal_name)) %>%
    ggplot(aes(x=cereal_name, y=Sodium)) +
  geom_segment(aes(xend=cereal_name, yend=0), color="skyblue") +
  geom_point(colour="darkblue", size=2, alpha=0.5)+
  coord_flip()

Raisin Bran is the cereal highest in sodium, while Frosted Mini Wheats contain the least.

Next, I want to show the sugar content of each different cereal.

cereal_sodium %>%
  arrange(sugar) %>%
mutate(cereal_name=factor(cereal_name, levels=cereal_name)) %>%
    ggplot(aes(x=cereal_name, y=sugar)) +
  geom_bar(stat="identity") +
  coord_flip()

In addition to being the cereal highest in sodium, Raisin Bran is also the highest in sugar. Fiber one has the least sugar.

Bivariate Visualization(s)

A bivariate visualization is used to find out if there is a relationship between two sets of variables.

For example, we could try to answer the question as to whether cereals classified as “adult” contain less sugar than those that are classified as children’s. To visualize this, I have added a colour fill for each bar according to which cereals have been classified as adult or children’s.

cereal_sodium %>%
  arrange(sugar) %>%
mutate(cereal_name=factor(cereal_name, levels=cereal_name)) %>%
    ggplot(aes(x=cereal_name, y=sugar, fill=Type)) +
  geom_bar(stat="identity") +
  coord_flip()

Interestingly, the three cereals with the most sugar content (Raisin Bran, Crackling Oat Bran, and Honey Smacks) are labeled as adult cereals

Another bivariate visualization that we could complete would be to examine whether there is any relationship between the volume of sodium and sugar in when classifying cereals at adult or children’s.

ggplot(cereal_sodium, aes(x=Sodium, y=sugar, color=Type)) +
geom_point(size=3)

There does not appear to be a relationship between sugar content, sodium content, and designation as either an adult or children’s cereal.