Challenge 7: Visualizing Multiple Dimensions

Code

library(tidyverse)
library("readxl")
library(ggplot2)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE)

Data Description

For this challenge, I have decided to work with eggs dataset. This dataset has monthly price amount of different sizes(large and extra large) of half dozen and dozen eggs.

Code

eggs_data <-read_csv("_data/eggs_tidy.csv")

Rows: 120 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): month
dbl (5): year, large_half_dozen, large_dozen, extra_large_half_dozen, extra_...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Code

eggs_data

# A tibble: 120 × 6
   month      year large_half_dozen large_dozen extra_large_half_dozen extra_l…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 
 7 July       2004             134.        234.                   137       241 
 8 August     2004             134.        234.                   137       241 
 9 September  2004             130.        234.                   136.      241 
10 October    2004             128.        234.                   136.      241 
# … with 110 more rows, and abbreviated variable name ¹​extra_large_dozen

Tidying Data

In order to plot a graph of varying prices for different sizes and amount of eggs, I need to pivot the data longer and separate the data based on size and amount. This is done as follows

Code

eggs_tidy <- eggs_data %>%
  pivot_longer(cols=3:6, names_to="type", values_to = "price") %>%
  mutate(type=str_replace(type,"extra_large","extra large"),
         type=str_replace(type,"half_dozen","half dozen")) %>%
  separate(type,into=c("size","amount"),sep="_") %>%
  mutate(date = str_c(year, month, sep=" ")) %>%
  mutate(date = ym(date))
eggs_tidy

# A tibble: 480 × 6
   month     year size        amount     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
 7 February  2004 extra large half dozen  134. 2004-02-01
 8 February  2004 extra large dozen       230  2004-02-01
 9 March     2004 large       half dozen  131  2004-03-01
10 March     2004 large       dozen       225  2004-03-01
# … with 470 more rows

Code

ggplot(eggs_tidy,aes(date,price)) +
  geom_line(color = "steelblue", size = 1)+
  scale_x_date()+
  scale_y_continuous(labels=scales::dollar_format(), limits=c(0,300)) +
  facet_grid(size ~ amount) +
  labs(x="Date",y="Average Price Per Carton")+
  theme(axis.text.x=element_text(angle=90))

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Since we are just trying to visualize variation of price with time for different sizes and amount, I decided to plot a simple line graph in a 4x4 grid, plotting all the four different kinds