Challenge 5 Instructions

Introduction to Visualization

library(tidyverse)
library(ggplot2)
library(plotly)
library(gapminder)

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

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    

Briefly describe the data

This data set contains the nutritional value and type of 20 different cereal brands. The data focuses on sodium and sugar in these specific brands along with the type of cereal it is. I believe that C = cold cereal and I am assuming A = hot cereal.

Tidy Data (as needed)

The data is already is tidy so in this case there is no reason to do a sanity check. I know that the data does is tidy because when I first read in the data is was easy to read and organized. Additionally, none of the data has to be pivoted_longer() nor is there a need for the n*2(k-2) formula.

Univariate Visualizations

# Creating a ggplot 

cereal_1 <- ggplot(data= cereal) +
  geom_point( mapping = aes(x = Cereal, y = Sodium, color = Cereal))

 cereal_2 <- cereal_1 +
  theme(axis.text.x = element_blank())
 cereal_2

ggplotly(cereal_2)

Bivariate Visualization(s)

new_cereal <- ggplot(data = cereal) +
  geom_point(mapping = aes(x = Type, y = Sugar, color = Type))
new_cereal

ggplotly(new_cereal)

In the bivariate graph I used the function ggplot() to compare the type with the amount of sugar that is in one of the 20 cereals. I then used the function ggplotly() to make the graph interactive so when you hover over a point it tell what type it is, how much sugar, and what the cereal brand is.