Exploratory Data Analysis
Let’s begin by exploring the following topic:
- Average Sales Price for Chicago over Time
- Total Revenue for Locations within Great Lakes Region, excluding Chicago
- Total Revenue by Month by Year within Great Lakes Region, excluding Chicago
Average Sales Price - Chicago
hass %>%
filter(location == 'Chicago') %>%
ggplot(aes(x = week_ending)) +
geom_line(aes(y = avg_price_nonorg, color = 'Non Organic')) +
geom_line(aes(y = avg_price_org, color = 'Organic')) +
scale_color_manual(name = 'Type', values = c('Non Organic' = 'steelblue', "Organic" = 'darkgreen')) +
labs(x = 'Week Ending', y = 'Average Selling Price (US$)', title = 'Average Selling Price Over Time - Chicago', caption = "Source: Hass Avocado Board\nNot adjusted for inflation") +
theme(plot.background = element_rect(fill = "grey20"),
plot.title = element_text(color = "#FFFFFF"),
axis.title = element_text(color = "#FFFFFF"),
axis.text.x = element_text(color = 'grey50', angle = 45, hjust = 1),
axis.text.y = element_text(color = 'grey50'),
plot.caption = element_text(color = 'grey75'),
panel.background = element_blank(),
panel.grid.major = element_line(color = "grey50", size = 0.2),
panel.grid.minor = element_line(color = "grey50", size = 0.2),
legend.background = element_rect(fill = 'grey20'),
legend.key = element_rect(fill = 'grey20'),
legend.title = element_text(color = 'grey75'),
legend.text = element_text(color = 'grey75'),
legend.position = c(0.815, 0.85)
)
2017 does seem to have some peculiarities in terms of price. However, that can be attributed to a shortage in avocados. While this may seem a bit plain, let’s look at the total revenue for the other communities within the Great Lakes region and exclude Chicago - as it is a far larger city.
Total Revenue for Great Lakes Region without Chicago
hass %>%
filter(region == 'Great Lakes' & location != 'Chicago') %>%
mutate(
year = lubridate::year(week_ending)
) %>%
group_by(
year, location
) %>%
summarize(
total_revenue = sum((avg_price_nonorg * (plu4046 + plu4225 + plu4770 + small_nonorg_bag + large_nonorg_bag + xlarge_nonorg_bag)) + (avg_price_org * (plu94046 + plu94225 + plu94770 + small_org_bag + large_org_bag + xlarge_org_bag))) / 1000000,
.groups = 'drop'
) %>%
ggplot(aes(x = year, y = total_revenue, group = location)) +
geom_bar(aes(fill = location), color = 'white', stat = 'identity', position = 'dodge') +
labs(x = "Year", y = 'Total Revenue (US$)', fill = 'Location', title = 'Total Revenue within Great Lakes Region', caption = 'Souce: Hass Avocado Board\nNot adjusted for inflation') +
theme(plot.background = element_rect(fill = "grey20"),
plot.title = element_text(color = "#FFFFFF"),
axis.title = element_text(color = "#FFFFFF"),
axis.text.x = element_text(color = 'grey50', angle = 45, hjust = 1),
axis.text.y = element_text(color = 'grey50'),
plot.caption = element_text(color = 'grey75'),
panel.background = element_blank(),
panel.grid.major = element_line(color = "grey50", size = 0.2),
panel.grid.minor = element_line(color = "grey50", size = 0.2),
legend.background = element_rect(fill = 'grey20'),
legend.key = element_rect(fill = 'grey20'),
legend.title = element_text(color = 'grey75'),
legend.text = element_text(color = 'grey75')
)
Now this is interesting! Grand Rapids had more total revenue than Detroit in 2017, but in the years following, it seemed to take a big hit in revenue!
Revenue by Month - Great Lakes
Perhaps it may be interesting to dive down into the monthly level for each year to see the total revenue. Is there a particular month that stands out? Once again, we’ll exclude Chicago. Let’s find out.
hass %>%
filter(region == 'Great Lakes' & location != 'Chicago') %>%
mutate(
month = lubridate::month(week_ending, label = T, abbr = F),
year = lubridate::year(week_ending)
) %>%
group_by(year, month, location) %>%
summarize(
total_revenue = sum((avg_price_nonorg * (plu4046 + plu4225 + plu4770 + small_nonorg_bag + large_nonorg_bag + xlarge_nonorg_bag)) + (avg_price_org * (plu94046 + plu94225 + plu94770 + small_org_bag + large_org_bag + xlarge_org_bag))) / 1000000,
.groups = 'drop'
) %>%
ggplot(aes(x = month, y = total_revenue, group = location, color = location)) +
geom_line() +
facet_wrap(.~year, scales = 'free') +
labs(fill = 'Location', x = 'Month', y = 'Total Revenue (US$)', caption = 'Source: Hass Avocado Board\nNot adjusted for inflation', title = 'Total Revenue per Year by Month - Great Lakes Region') +
theme(plot.background = element_rect(fill = "grey20"),
plot.title = element_text(color = "#FFFFFF"),
axis.title = element_text(color = "#FFFFFF"),
axis.text.x = element_text(color = 'grey50', angle = 45, hjust = 1),
axis.text.y = element_text(color = 'grey50'),
plot.caption = element_text(color = 'grey75'),
panel.background = element_blank(),
panel.grid.major = element_line(color = "grey50", size = 0.2),
panel.grid.minor = element_line(color = "grey50", size = 0.2),
legend.background = element_rect(fill = 'grey20'),
legend.key = element_rect(fill = 'grey20'),
legend.title = element_text(color = 'grey75'),
legend.text = element_text(color = 'grey75'),
strip.background = element_rect(fill = 'grey50'),
strip.text = element_text(color = 'black')
)
It seems as if the revenue declines in the winter months. This could be due to reduced supply and demand. On the other hand, during the late spring and summer months, revenue is at its highest. Maybe guacamole is a summer dish? Not for me!