Challenge 5

challenge_5
Tenzin Latoe
cereal
Introduction to Visualization
Author

Tenzin Latoe

Published

July 13, 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 ⭐
cereal_data <- read.csv("_data/cereal.csv")
cereal_data
Cereal Sodium Sugar Type
Frosted Mini Wheats 0 11 A
Raisin Bran 340 18 A
All Bran 70 5 A
Apple Jacks 140 14 C
Captain Crunch 200 12 C
Cheerios 180 1 C
Cinnamon Toast Crunch 210 10 C
Crackling Oat Bran 150 16 A
Fiber One 100 0 A
Frosted Flakes 130 12 C
Froot Loops 140 14 C
Honey Bunches of Oats 180 7 A
Honey Nut Cheerios 190 9 C
Life 160 6 C
Rice Krispies 290 3 C
Honey Smacks 50 15 A
Special K 220 4 A
Wheaties 180 4 A
Corn Flakes 200 3 A
Honeycomb 210 11 C

Briefly describe the data

dim((cereal_data))
[1] 20  4
head(cereal_data)
Cereal Sodium Sugar Type
Frosted Mini Wheats 0 11 A
Raisin Bran 340 18 A
All Bran 70 5 A
Apple Jacks 140 14 C
Captain Crunch 200 12 C
Cheerios 180 1 C

This dataset shows 20 rows and 4 columns. The four column names are: Cereal, Sodium, Sugar, and Type. There are 20 enteries of different types of cereals.

#Column names
colnames(cereal_data)
[1] "Cereal" "Sodium" "Sugar"  "Type"  
#Types of names
unique(cereal_data$Cereal)
 [1] "Frosted Mini Wheats"   "Raisin Bran"           "All Bran"             
 [4] "Apple Jacks"           "Captain Crunch"        "Cheerios"             
 [7] "Cinnamon Toast Crunch" "Crackling Oat Bran"    "Fiber One"            
[10] "Frosted Flakes"        "Froot Loops"           "Honey Bunches of Oats"
[13] "Honey Nut Cheerios"    "Life"                  "Rice Krispies"        
[16] "Honey Smacks"          "Special K"             "Wheaties"             
[19] "Corn Flakes"           "Honeycomb"

Tidy Data (as needed)

#checking for missing values
is.na(cereal_data)
      Cereal Sodium Sugar  Type
 [1,]  FALSE  FALSE FALSE FALSE
 [2,]  FALSE  FALSE FALSE FALSE
 [3,]  FALSE  FALSE FALSE FALSE
 [4,]  FALSE  FALSE FALSE FALSE
 [5,]  FALSE  FALSE FALSE FALSE
 [6,]  FALSE  FALSE FALSE FALSE
 [7,]  FALSE  FALSE FALSE FALSE
 [8,]  FALSE  FALSE FALSE FALSE
 [9,]  FALSE  FALSE FALSE FALSE
[10,]  FALSE  FALSE FALSE FALSE
[11,]  FALSE  FALSE FALSE FALSE
[12,]  FALSE  FALSE FALSE FALSE
[13,]  FALSE  FALSE FALSE FALSE
[14,]  FALSE  FALSE FALSE FALSE
[15,]  FALSE  FALSE FALSE FALSE
[16,]  FALSE  FALSE FALSE FALSE
[17,]  FALSE  FALSE FALSE FALSE
[18,]  FALSE  FALSE FALSE FALSE
[19,]  FALSE  FALSE FALSE FALSE
[20,]  FALSE  FALSE FALSE FALSE

Running the function above shows that there are no missing values in this data set. This data is already tidy.

Univariate Visualizations

#Geo_Col
ggplot(cereal_data, aes(x= Sugar , y=Cereal,)) + 
    geom_col(fill= "mediumturquoise",
             color= "black")

Using the ggplot() fuction, I did a simple bar graph to illustrate the sugars per serving for each type of cereal. This illustrated that Raisan Bran has the highest, while Fiber One was the lowest.

#Geom_boxplot
ggplot(cereal_data, aes(fill= Type, y=Sodium, x=Type)) + 
    geom_boxplot()+guides(fill='none')

Next, the box plot illustrates how the distribution of the quantities of sodium in the data set varies based on the two different types of cereals:A or C.

Bivariate Visualization(s)

#Geom_point 

ggplot(cereal_data, aes(x= Sugar , y=Sodium,
                        col=Type)) + 
    geom_point()+geom_smooth(se=FALSE)

Scatter plot used to display sodium and sugar per cereal type to show the relationship between them, and smoothed to provide an estimate of the trend between the two variables.