Challenge 7 Will Munson

Visualizing Multiple Dimensions

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. Recreate at least two graphs from previous exercises, but introduce at least one additional dimension that you omitted before using ggplot functionality (color, shape, line, facet, etc) The goal is not to create unneeded chart ink (Tufte), but to concisely capture variation in additional dimensions that were collapsed in your earlier 2 or 3 dimensional graphs.

• Explain why you choose the specific graph type

5. If you haven’t tried in previous weeks, work this week to make your graphs “publication” ready with titles, captions, and pretty axis labels and other viewer-friendly features

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code. And anyone not familiar with Edward Tufte should check out his fantastic books and courses on data visualizaton.

(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.

• eggs ⭐
• abc_poll ⭐⭐
• australian_marriage ⭐⭐
• hotel_bookings ⭐⭐⭐
• air_bnb ⭐⭐⭐
• us_hh ⭐⭐⭐⭐
• faostat ⭐⭐⭐⭐⭐

eggs <- read_csv("_data/eggs_tidy.csv", show_col_types = FALSE)

Briefly describe the data

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.

eggs <- pivot_longer(eggs, col = c(large_half_dozen, large_dozen, extra_large_half_dozen, extra_large_dozen),
                              names_to = "Egg Package Type",
                              values_to = "Average Price")

eggs

# A tibble: 480 × 4
   month     year `Egg Package Type`     `Average Price`
   <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
# ℹ Use `print(n = ...)` to see more rows

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

I believe that everything is taken care of in terms of the data.

Visualization with Multiple Dimensions

eggs %>%
  mutate(month = match(month, month.name)) %>%
  mutate(MonthnYear = paste(year, month, "01", sep = "-")) %>%
  mutate(MonthnYear = as.Date(MonthnYear)) %>%
  ggplot(aes(x = MonthnYear, y = `Average Price`)) + 
  geom_line(aes(colour = `Egg Package Type`))

So, while observing the prices of eggs, we notice that prices immediately hiked up right around the time the Great Recession started. The only eggs that decreased in price since then were large dozen eggs.