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library(readr)
data <- read_csv('avocado.csv')
## Rows: 12628 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): date, type, geography
## dbl (4): average_price, total_volume, year, Mileage
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(data)
##         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.   :2998
## DataViz
hist(data$average_price,
     main = "Histogram of average_price",
     xlab = "Price in USD (US Dollar)")

library(ggplot2)
ggplot(data, aes(x = average_price, fill = type)) + 
  geom_histogram(bins = 30, col = "black") + 
  scale_fill_manual(values = c("yellow", "blue")) +
  ggtitle("Frequency of Average Price - Organic vs. Conventional")

ggplot() + 
  geom_col(data, mapping = aes(x = reorder(geography, total_volume), 
                               y = total_volume, fill = year)) +
  xlab("geography") +
  ylab("total_volume") +
  theme(axis.text.x = element_text(angle = 90, size = 7))

Discussion Questions

Question 1: Price range

summary(data$average_price)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.500   1.100   1.320   1.359   1.570   2.780
data[which.min(data$average_price), ]
## # A tibble: 1 × 7
##   date      average_price total_volume type          year geography Mileage
##   <chr>             <dbl>        <dbl> <chr>        <dbl> <chr>       <dbl>
## 1 2018/4/22           0.5      2335867 conventional  2018 Houston      1656
data[which.max(data$average_price), ]
## # A tibble: 1 × 7
##   date      average_price total_volume type     year geography Mileage
##   <chr>             <dbl>        <dbl> <chr>   <dbl> <chr>       <dbl>
## 1 2019/7/14          2.78        15994 organic  2019 San Diego     253

Prices ranged from $0.50 (conventional, Houston, week of April 22, 2018) to $2.78 (organic, San Diego, week of July 14, 2019). Stakeholders could use HAB’s research to plan supply (producers), set pricing and promotions (retailers), and run marketing campaigns (marketers/health professionals).

Question 2: Top city by dollar sales, 2017 vs. 2018

data$dollar_sales <- data$average_price * data$total_volume

sales <- aggregate(dollar_sales ~ year + type + geography,
                    data = subset(data, year %in% c(2017, 2018)),
                    FUN = sum)

top_city <- sales[order(sales$year, sales$type, -sales$dollar_sales), ]
top_city <- do.call(rbind, by(top_city, list(top_city$year, top_city$type), head, n = 1))
top_city$dollar_sales <- format(round(top_city$dollar_sales), big.mark = ",")
top_city[order(top_city$year, top_city$type), ]
##     year         type   geography dollar_sales
## 81  2017 conventional Los Angeles   13,456,647
## 103 2017      organic    New York      720,451
## 82  2018 conventional Los Angeles  153,526,216
## 104 2018      organic    New York    9,391,204

Los Angeles led conventional sales and New York led organic sales in both 2017 and 2018. This is likely due to larger population, higher grocery spending, and a strong local demand for organic/healthy foods.

Question 3: Custom figures using ggplot2

weekly <- aggregate(average_price ~ date + type, data = data, FUN = mean)

ggplot(weekly, aes(x = date, y = average_price, color = type)) +
  geom_line() +
  labs(title = "Weekly Average Avocado Price Over Time",
       x = NULL, y = "Average Price (USD)", color = "Type")
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

city <- aggregate(cbind(average_price, Mileage) ~ geography + type, data = data, FUN = mean)

ggplot(city, aes(x = Mileage, y = average_price)) +
  geom_point(color = "darkgreen") +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  facet_wrap(~ type) +
  labs(title = "Distance from Growing Region vs. Average Price",
       x = "Mileage", y = "Average Price (USD)")
## `geom_smooth()` using formula = 'y ~ x'

Figure 1 shows organic avocados cost more than conventional every week, with prices rising in summer and dropping in winter. Figure 2 checks whether distance from the growing region raises prices — a rising trend line would suggest shipping distance adds cost.