Challenge 5

Introduction to Visualization

library(tidyverse)
library(ggplot2)
library(readxl)

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

5. 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%>%
  arrange(desc(cereal$Sugar))

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

round(max(cereal$Sodium) - min(cereal$Sodium))/30

[1] 11.33333

Briefly describe the data

The data set identifies several types of commercial cereals by their sodium level, sugar level, and “type” (a letter, the meaning of which is unclear).

Tidy Data (as needed)

The data appears to be tidied already. I will create univariate graphs of Sugar content, then Sodium content, and a Bivariate graph (scatter plot) with both Sugar and Sodium factored in.

The range of Sugar content varies from 0 to 18, so I’ll create 9 bins. This will place 2 observations in each bin. For Sodium, I’ll use 10 bins.

Univariate Visualizations

I chose a basic histogram for the univariate plots. I thought it demonstrated the relationship between count and variable in an orderly and simple way.

ggplot(cereal, aes(Sugar)) + geom_histogram(bins = 6) + labs(x = "Sugar (in grams)")

ggplot(cereal, aes(Sodium)) + geom_histogram(bins = 10) + labs(x = "Sodium (in grams)")

Bivariate Visualization(s)

The following is a scatter plot, which I chose because it takes the focus from the cereal names and places it solely on the sodium/sugar relationship.

ggplot(cereal, aes(Sugar, Sodium)) + geom_point() + labs(title = "Sugar versus Sodium Content in Cereals", x = "Sugar in Grams", y = "Sodium in Grams")