Challenge 7 Submission

Visualizing Multiple Dimensions

library(tidyverse)
library(ggplot2)
library(readr)
library(summarytools)
library(lubridate)

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

Eggs <- read_csv(("B:/Needels/Documents/DACCS 601/DACSS_601_New/posts/_data/eggs_tidy.csv"))
Eggs

# 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

Briefly describe the data

This data is about the number of eggs coming from various dozen sizes. The dates vary from January 2004 until December 2013. This data looks solid however the extra large dozen column is all by itself and it isn’t very tidy. In order to make it tidy I decided to make it long by having each category of dozen be a different data point so that extra large dozen can fit in. I also lubridated the date in case I wanted to use the month in a data analysis.

Tidy Data (as needed)

#pivot longer to make each carton size into one category
EggsTidy <- Eggs%>%
  pivot_longer(col = c(large_half_dozen, large_dozen, extra_large_half_dozen, extra_large_dozen),
                         names_to= "carton_size",
                         values_to= "price_per_pound")
EggsTidy

# A tibble: 480 × 4
   month     year carton_size            price_per_pound
   <chr>    <dbl> <chr>                            <dbl>
 1 January   2004 large_half_dozen                  126 
 2 January   2004 large_dozen                       230 
 3 January   2004 extra_large_half_dozen            132 
 4 January   2004 extra_large_dozen                 230 
 5 February  2004 large_half_dozen                  128.
 6 February  2004 large_dozen                       226.
 7 February  2004 extra_large_half_dozen            134.
 8 February  2004 extra_large_dozen                 230 
 9 March     2004 large_half_dozen                  131 
10 March     2004 large_dozen                       225 
# … with 470 more rows

#lubridate in order to make month and year combined, unfortunately it doesn't have the day so it assumes every month is on the first.
EggsLubridate <- EggsTidy%>%
  mutate( date = str_c(month, year , sep = "-"),
          date = my(date))
EggsLubridate

# A tibble: 480 × 5
   month     year carton_size            price_per_pound date      
   <chr>    <dbl> <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

Visualization with Multiple Dimensions

I decided to go with a histogram with the first graph because I wanted to show the amount of cartons that sold at a specific price. I took it a step further this time by adding a title and newly named x and y axis’. Plus the color shows the distribution of prices quite nicely.

The second graph is a stacked bar chart and it works excellently. Many of the dates overlap with each other and I was trying to figure out a way to combine the specific data points. This combines all of the prices per pound and generates them into a year by year profit. It also does a good job separating the amounts because of the colors so you can easily see which carton_size had the greatest amount sold.

#I went with a point graph except this time I wanted to add a color fo each specific carton size. It looks nice but there are multiple data points on the same day so its weird seeing it stacked on each other
EggsLubridate%>%
  ggplot(aes(x= price_per_pound,color = carton_size)) + geom_histogram() + labs(title = "The number of prices per pound for carton sizes", x = "Price Per Pound", y = "Amount")

#stacked bar graph with a title listed below. I filled the colors with each carton size as well thanks to the pivot_long.
EggsLubridate%>%
  ggplot(aes(x = year, y = price_per_pound, fill = carton_size)) + geom_bar(position = "stack", stat = "identity") + labs(title = "Total Price per Pound for Given Years", x = "Year", y = "price per pound")