Exploratory Data Analysis
Let’s begin by exploring the following two topics:
- Top Region by Revenue in 2019 & all time
- Proportion of Revenue by Region of all time
Top Region by Sales Revenue in 2019
hass_region %>%
mutate(
year = lubridate::year(week_ending)
) %>%
filter(year == 2019) %>%
group_by(year, region) %>%
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)),
.groups = 'drop'
) %>%
slice(which.max(total_revenue))
#> # A tibble: 1 x 3
#> year region total_revenue
#> <dbl> <chr> <dbl>
#> 1 2019 California 434629996.Surprising to see that the state of California has more revenue from Avocado sales than all the other regions combined. Let’s take a look at this for all regions in 2019.
hass_region %>%
mutate(
year = lubridate::year(week_ending)
) %>%
filter(year == 2019) %>%
group_by(week_ending, region) %>%
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)),
.groups = 'drop'
) %>%
ggplot(aes(x = week_ending)) +
geom_line(aes(y = total_revenue, color = region)) +
labs(x = 'Month/Year', y = 'Revenue (US$)', title = 'Total Revenue for 2019 Across Regions', caption = 'Source: Hass Avocado Board\nNot adjusted for inflation') +
scale_color_manual(name = 'Region', values = c('California' = 'orange', 'Great Lakes' = 'blue', 'Midsouth' = 'yellow', 'Northeast' = 'steelblue', 'Plains' = 'darkgreen', 'South Central' = 'red', 'Southeast' = 'magenta', 'West' = 'darkgray')) +
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')
)
Interestingly, California and the West regions are fairly close. Oddly, the Plains region seems to have the lowest revenue. It’s so low, that it almost appears to be anomalous - but I digress.
Proportion of Revenue (All Time)
If we consider the entire dataset, what percentage of contribution to total revenue does each region make? Let’s find out.
hass_region %>%
group_by(region) %>%
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)),
.groups = 'drop'
) %>%
arrange(desc(region)) %>%
mutate(
prop = round(total_revenue / sum(total_revenue) * 100),
ypos = cumsum(prop) - (0.5*prop),
txt = paste0(region, ': ', prop,'%')
) %>%
ggplot(aes(x = "", y = prop, fill = region)) +
geom_bar(stat = 'identity', width = 1, color = 'white') +
coord_polar(theta = 'y') +
theme_void() +
ggrepel::geom_label_repel(aes(y = ypos, label = txt), show.legend = FALSE, color = 'black', size = 3, nudge_x = 1) +
labs(title = 'Revenue Proportion by Region', caption = 'Source: Hass Avocado Board') +
theme(
# plot.background = element_rect(fill = "grey20"),
plot.title = element_text(color = "#000000"),
plot.caption = element_text(color = 'grey75'),
panel.background = element_blank(),
legend.position = 'none'
)