Week 3 Avocado Sales Analysis
David Galeana
2026-09-14
library(ggplot2)
library(dplyr)
avocado <- read.csv(
"avocado (1).csv",
stringsAsFactors = FALSE
)
names(avocado) <- trimws(names(avocado))Data Import and Preparation
head(avocado)## date average_price total_volume type year geography
## 1 2017/12/3 1.39 139970 conventional 2017 Albany
## 2 2017/12/3 1.44 3577 organic 2017 Albany
## 3 2017/12/3 1.07 504933 conventional 2017 Atlanta
## 4 2017/12/3 1.62 10609 organic 2017 Atlanta
## 5 2017/12/3 1.43 658939 conventional 2017 Baltimore/Washington
## 6 2017/12/3 1.58 38754 organic 2017 Baltimore/Washington
## Mileage
## 1 2832
## 2 2832
## 3 2199
## 4 2199
## 5 2679
## 6 2679str(avocado)## 'data.frame': 12628 obs. of 7 variables:
## $ date : chr "2017/12/3" "2017/12/3" "2017/12/3" "2017/12/3" ...
## $ average_price: num 1.39 1.44 1.07 1.62 1.43 1.58 1.14 1.77 1.4 1.88 ...
## $ total_volume : int 139970 3577 504933 10609 658939 38754 86646 1829 488588 21338 ...
## $ type : chr "conventional" "organic" "conventional" "organic" ...
## $ year : int 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 ...
## $ geography : chr "Albany" "Albany" "Atlanta" "Atlanta" ...
## $ Mileage : int 2832 2832 2199 2199 2679 2679 827 827 2998 2998 ...summary(avocado)## date average_price total_volume type
## Length :12628 Min. :0.500 Min. : 253 Length :12628
## N.unique : 154 1st Qu.:1.100 1st Qu.: 15733 N.unique : 2
## N.blank : 0 Median :1.320 Median : 94806 N.blank : 0
## Min.nchar: 8 Mean :1.359 Mean : 325259 Min.nchar: 7
## Max.nchar: 10 3rd Qu.:1.570 3rd Qu.: 430222 Max.nchar: 12
## Max. :2.780 Max. :5660216
## year geography Mileage
## Min. :2017 Length :12628 Min. : 111
## 1st Qu.:2018 N.unique : 41 1st Qu.:1097
## Median :2019 N.blank : 0 Median :2193
## Mean :2019 Min.nchar: 5 Mean :1911
## 3rd Qu.:2020 Max.nchar: 20 3rd Qu.:2632
## Max. :2020 Max. :2998Estimated Dollar Sales
Dollar sales are estimated by multiplying the average price by the total volume sold.
avocado <- avocado %>%
mutate(
dollar_sales = average_price * total_volume
)
head(avocado)## date average_price total_volume type year geography
## 1 2017/12/3 1.39 139970 conventional 2017 Albany
## 2 2017/12/3 1.44 3577 organic 2017 Albany
## 3 2017/12/3 1.07 504933 conventional 2017 Atlanta
## 4 2017/12/3 1.62 10609 organic 2017 Atlanta
## 5 2017/12/3 1.43 658939 conventional 2017 Baltimore/Washington
## 6 2017/12/3 1.58 38754 organic 2017 Baltimore/Washington
## Mileage dollar_sales
## 1 2832 194558.30
## 2 2832 5150.88
## 3 2199 540278.31
## 4 2199 17186.58
## 5 2679 942282.77
## 6 2679 61231.32Average Price Distribution
ggplot(avocado, aes(x = average_price, fill = type)) +
geom_histogram(
bins = 30,
color = "white"
) +
labs(
title = "Average Price of Organic and Conventional Avocados",
x = "Average Price",
y = "Number of Observations",
fill = "Avocado Type"
) +
theme_minimal()
Total Sales Volume by City
ggplot(
avocado,
aes(
x = reorder(geography, total_volume, FUN = sum),
y = total_volume,
fill = factor(year)
)
) +
geom_col() +
labs(
title = "Total Hass Avocado Volume by City",
x = "City",
y = "Total Volume",
fill = "Year"
) +
theme_minimal() +
theme(
axis.text.x = element_text(
angle = 90,
hjust = 1,
size = 7
)
)
Question 1: Price Range
price_range <- data.frame(
Lowest_Price = min(
avocado$average_price,
na.rm = TRUE
),
Highest_Price = max(
avocado$average_price,
na.rm = TRUE
)
)
price_range## Lowest_Price Highest_Price
## 1 0.5 2.78The lowest average avocado price recorded was $0.50 and the highest was $2.78.
The Hass Avocado Board’s marketing research can benefit several stakeholders. Growers can use the information to better understand demand and pricing. Retailers can use sales trends to make inventory and promotion decisions. Marketers can use consumer and market information to determine where and how to advertise Hass avocados.
Question 2: City Dollar Sales
city_sales <- avocado %>%
filter(year %in% c(2017, 2018)) %>%
group_by(year, type, geography) %>%
summarise(
dollar_sales = sum(
dollar_sales,
na.rm = TRUE
),
total_volume = sum(
total_volume,
na.rm = TRUE
),
average_price = mean(
average_price,
na.rm = TRUE
),
.groups = "drop"
)
highest_city <- city_sales %>%
group_by(year, type) %>%
slice_max(
order_by = dollar_sales,
n = 1,
with_ties = FALSE
) %>%
ungroup()
highest_city## # A tibble: 4 × 6
## year type geography dollar_sales total_volume average_price
## <int> <chr> <chr> <dbl> <int> <dbl>
## 1 2017 conventional Los Angeles 13456647. 13753082 0.99
## 2 2017 organic New York 720451. 395248 1.82
## 3 2018 conventional Los Angeles 153526216. 145880803 1.07
## 4 2018 organic New York 9391204. 5312552 1.82The results show that Los Angeles had the highest conventional Hass avocado dollar sales in both 2017 and 2018, while New York had the highest organic Hass avocado dollar sales in both years.
These cities may have strong avocado sales because they have large populations, many grocery stores and restaurants, and high consumer demand for fresh foods. Los Angeles also has a strong avocado food culture, while New York has a large market for organic and premium food products.
Customized Figure 1: Dollar Sales by Year and Type
annual_sales <- avocado %>%
filter(year %in% c(2017, 2018)) %>%
group_by(year, type) %>%
summarise(
dollar_sales = sum(
dollar_sales,
na.rm = TRUE
),
.groups = "drop"
)
ggplot(
annual_sales,
aes(
x = factor(year),
y = dollar_sales,
fill = type
)
) +
geom_col(position = "dodge") +
scale_y_continuous(
labels = scales::dollar
) +
labs(
title = "Estimated Hass Avocado Dollar Sales",
subtitle = "2017 and 2018",
x = "Year",
y = "Estimated Dollar Sales",
fill = "Avocado Type"
) +
theme_minimal()
Business Insight
The figure shows that conventional avocados generate much more dollar sales than organic avocados. Businesses can use this information when deciding how much inventory, shelf space, and advertising to dedicate to each avocado type.
Customized Figure 2: Price and Sales Volume
regional_2018 <- avocado %>%
filter(year == 2018) %>%
group_by(type, geography) %>%
summarise(
total_volume = sum(
total_volume,
na.rm = TRUE
),
average_price = mean(
average_price,
na.rm = TRUE
),
.groups = "drop"
)
ggplot(
regional_2018,
aes(
x = total_volume,
y = average_price,
color = type
)
) +
geom_point(
size = 2,
alpha = 0.75
) +
geom_smooth(
method = "lm",
se = TRUE
) +
scale_x_log10() +
labs(
title = "Regional Price and Sales Volume in 2018",
subtitle = "Organic vs. Conventional Hass Avocados",
x = "Total Regional Volume (Log Scale)",
y = "Average Price",
color = "Avocado Type"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
Business Insight
The second figure compares price and sales volume across different markets. Organic avocados tend to have higher prices, while conventional avocados generally sell in greater volumes. Businesses can use this information to balance premium pricing with higher-volume sales strategies.