Challenge 7

Visualizing Multiple Dimensions

library(tidyverse)
library(ggplot2)
library(lubridate)


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

Read in data

eggs_data <- read_csv('_data/eggs_tidy.csv')
head(eggs_data)

# A tibble: 6 × 6
  month     year large_half_dozen large_dozen extra_large_half_dozen extra_lar…¹
  <chr>    <dbl>            <dbl>       <dbl>                  <dbl>       <dbl>
1 January   2004             126         230                    132         230 
2 February  2004             128.        226.                   134.        230 
3 March     2004             131         225                    137         230 
4 April     2004             131         225                    137         234.
5 May       2004             131         225                    137         236 
6 June      2004             134.        231.                   137         241 
# … with abbreviated variable name ¹​extra_large_dozen

Briefly describe the data

Given the month and year, this dataset describes the average price for a carton of eggs. Carton sizes are as follows: big half dozen, large dozen, extra large half dozen, and extra large dozen.

Tidy Data (as needed)

I’ll rearrange the dataset utilizing pivot functionality to aid in visualization. Following that, divide the egg carton type into two columns: one for the size and one for the number (half or full dozen). This allows us to plot each attribute independently. The “between extra_large and half_dozen will be removed to maintain only one” in the name, separating size and quantity. These will be divided into two columns. Furthermore, lubridate will be used to transform the date into the right format.

eggs <- eggs_data %>%
    pivot_longer(cols = c(`large_half_dozen`, `large_dozen`, `extra_large_half_dozen`, `extra_large_dozen`), values_to = "Price ($)") %>%
    mutate(name = str_replace(name, "extra_large", "Extra Large"), name = str_replace(name, "half_dozen", "Half Dozen"), name = str_replace(name, "dozen", "Dozen"), name = str_replace(name, "large", "Large")) %>%
    separate(name, into = c("Size", "Quantity"), sep = "_") %>%
    mutate(Date = ym(paste(`year`, `month`, sep = "-")))
head(eggs)

# A tibble: 6 × 6
  month     year Size        Quantity   `Price ($)` Date      
  <chr>    <dbl> <chr>       <chr>            <dbl> <date>    
1 January   2004 Large       Half Dozen        126  2004-01-01
2 January   2004 Large       Dozen             230  2004-01-01
3 January   2004 Extra Large Half Dozen        132  2004-01-01
4 January   2004 Extra Large Dozen             230  2004-01-01
5 February  2004 Large       Half Dozen        128. 2004-02-01
6 February  2004 Large       Dozen             226. 2004-02-01

Visualization with Multiple Dimensions

ggplot(eggs, aes(Date, `Price ($)`, col = Size)) +
    ggtitle('Cost Evolution for Different Types of Egg Cartons') +
    geom_line() +
    facet_wrap(~Quantity, scales = 'free_y')

The above graph type was chosen to illustrate the price variation for extra large and large eggs over the course of time. I divided the graph based on the carton size since the prices vary accordingly. The graph reveals a decrease in prices for large eggs sold by the dozen and an increase for those sold by the half dozen.

ggplot(eggs, aes(Date, `Price ($)`, fill = Quantity)) +
    ggtitle('Price Variations Based on Egg Size') +
    geom_bar(position = "stack", stat = "identity") +
    facet_wrap(~Size)

In the second graph, I replaced the grouping variable with the egg size and then stacked the prices of both quantities. This visual representation allows us to observe the cumulative price changes depending on the size of the eggs. It’s evident from this visualization that large eggs are consistently cheaper than extra large ones and that both egg sizes follow a similar trend over time.