Visualizing cereal dataset

challenge_5

cereal

readr

Introduction to Visualization

Author

Susmita Madineni

Published

March 29, 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 ⭐

library(readr)
cereal_data <- read_csv("_data/cereal.csv")
View(cereal_data)

# Preview the first few rows of the dataset
head(cereal_data)

# A tibble: 6 × 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

# Understanding the dimensions of the dataset 
dim(cereal_data)

[1] 20  4

# Identifying the column names of the dataset 
colnames(cereal_data)

[1] "Cereal" "Sodium" "Sugar"  "Type"

# Identifying the data types of the columns
sapply(cereal_data, class)

     Cereal      Sodium       Sugar        Type 
"character"   "numeric"   "numeric" "character"

table(sapply(cereal_data, function(x) typeof(x)))


character    double 
        2         2

sapply(cereal_data, function(x) n_distinct(x))

Cereal Sodium  Sugar   Type 
    20     15     15      2

Briefly describe the data

Based on the above, we can say that the cereal dataset has 20 different cereals describing the amount of sodium, sugar present in it and a type(category) assigned to each cereal. There are total 20 rows and 4 columns, out of which two columns(cereal, type) are of class character and two coulmns(sodium, sugar) are of class numeric. We can also observe that each cereal is categorized into two types(A, C). ## 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 data looks tidy enough to use for various analyses and data visualizations. We can convert the sodium into grams and compare it with the sugar, we can pivot longer the dataset to use it for visualizations.

cereal_data <- cereal_data %>%
  arrange(Cereal) %>%
  mutate(Sodium = Sodium/1000)
View(cereal_data)

cereal_pivot_data <- pivot_longer(cereal_data, cols = contains("S"), 
                                names_to= c("additive"),
                                values_to = "quantity")
View(cereal_pivot_data)

The two columns- additive and quantity have been added at the end. Additive column has two values- Sodium, Sugar and the respective value of these two additives are present in quantity column.

Univariate Visualizations

Based on the above cereal data, we can analyze how much sodium is present in a particular type of cereal.

# Visualization showing sodium levels in type C cereal
cereal_data %>%
  filter(Type == 'C') %>%
  arrange(Sodium) %>%
  ggplot(aes(x=Cereal, y=Sodium)) + geom_segment(aes(xend=Cereal, yend=0)) + geom_point(color='blue', fill='black',shape=20, size = 3) + theme_minimal() + coord_flip(expand = TRUE) + labs(title = "Sodium levels in Type C cereals", y = "Sodium in mgs", x = "Cereal Name")

# Visualization showing sodium levels in type A cereal
cereal_data %>%
  filter(Type == 'A') %>%
  arrange(Sodium) %>%
  ggplot(aes(x=Cereal, y=Sodium)) + geom_segment(aes(xend=Cereal, yend=0)) + geom_point(color='blue', fill='black',shape=20, size = 3) + theme_minimal() + coord_flip(expand = TRUE) + labs(title = "Sodium levels in Type A cereals", y = "Sodium in mgs", x = "Cereal Name")

Bivariate Visualization(s)

Now I want to construct a stacked bar chart to compare the sodium and sugar levels in a particular type of cereal.

cereal_pivot_data %>%
  filter(Type == 'A') %>%
  mutate(Cereal = fct_reorder(Cereal, desc(quantity))) %>%
  ggplot(aes(x=Cereal, y=quantity, fill=additive)) + geom_bar(stat = "identity", position="stack", width = 0.75, color = "red") + theme_minimal() + coord_flip(expand = FALSE) + labs(title = "Additive levels in Type A cereals", y = "Additives", x = "Cereal Name")

Based on the above visualizations, the users can understand which cereals are better to consume for a healthier lifestyle and can also use this as a reference for various other analyses.

--- title: "Visualizing cereal dataset" author: "Susmita Madineni" description: "Introduction to Visualization" date: "03/29/2022" format: html: toc: true code-copy: true code-tools: true categories: - challenge_5 - cereal - readr --- ```{r} #| label: setup #| warning: false #| message: false 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 5) Create at least one bivariate visualization - try to make them "publication" ready - Explain why you choose the specific graph type [R Graph Gallery](https://r-graph-gallery.com/) 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 ⭐ ```{r} library(readr) cereal_data <- read_csv("_data/cereal.csv") View(cereal_data) ``` ```{r} # Preview the first few rows of the dataset head(cereal_data) # Understanding the dimensions of the dataset dim(cereal_data) ``` ```{r} # Identifying the column names of the dataset colnames(cereal_data) # Identifying the data types of the columns sapply(cereal_data, class) table(sapply(cereal_data, function(x) typeof(x))) sapply(cereal_data, function(x) n_distinct(x)) ``` ### Briefly describe the data Based on the above, we can say that the cereal dataset has 20 different cereals describing the amount of sodium, sugar present in it and a type(category) assigned to each cereal. There are total 20 rows and 4 columns, out of which two columns(cereal, type) are of class character and two coulmns(sodium, sugar) are of class numeric. We can also observe that each cereal is categorized into two types(A, C). ## 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 data looks tidy enough to use for various analyses and data visualizations. We can convert the sodium into grams and compare it with the sugar, we can pivot longer the dataset to use it for visualizations. ```{r} cereal_data <- cereal_data %>% arrange(Cereal) %>% mutate(Sodium = Sodium/1000) View(cereal_data) ``` ```{r} cereal_pivot_data <- pivot_longer(cereal_data, cols = contains("S"), names_to= c("additive"), values_to = "quantity") View(cereal_pivot_data) ``` The two columns- additive and quantity have been added at the end. Additive column has two values- Sodium, Sugar and the respective value of these two additives are present in quantity column. ## Univariate Visualizations Based on the above cereal data, we can analyze how much sodium is present in a particular type of cereal. ```{r} # Visualization showing sodium levels in type C cereal cereal_data %>% filter(Type == 'C') %>% arrange(Sodium) %>% ggplot(aes(x=Cereal, y=Sodium)) + geom_segment(aes(xend=Cereal, yend=0)) + geom_point(color='blue', fill='black',shape=20, size = 3) + theme_minimal() + coord_flip(expand = TRUE) + labs(title = "Sodium levels in Type C cereals", y = "Sodium in mgs", x = "Cereal Name") ``` ```{r} # Visualization showing sodium levels in type A cereal cereal_data %>% filter(Type == 'A') %>% arrange(Sodium) %>% ggplot(aes(x=Cereal, y=Sodium)) + geom_segment(aes(xend=Cereal, yend=0)) + geom_point(color='blue', fill='black',shape=20, size = 3) + theme_minimal() + coord_flip(expand = TRUE) + labs(title = "Sodium levels in Type A cereals", y = "Sodium in mgs", x = "Cereal Name") ``` ## Bivariate Visualization(s) Now I want to construct a stacked bar chart to compare the sodium and sugar levels in a particular type of cereal. ```{r} cereal_pivot_data %>% filter(Type == 'A') %>% mutate(Cereal = fct_reorder(Cereal, desc(quantity))) %>% ggplot(aes(x=Cereal, y=quantity, fill=additive)) + geom_bar(stat = "identity", position="stack", width = 0.75, color = "red") + theme_minimal() + coord_flip(expand = FALSE) + labs(title = "Additive levels in Type A cereals", y = "Additives", x = "Cereal Name") ``` Based on the above visualizations, the users can understand which cereals are better to consume for a healthier lifestyle and can also use this as a reference for various other analyses.