urlfile<-'https://raw.github.com/utjimmyx/resources/master/avocado_HAA.csv'
data <- read.csv("avocado.csv")
summary(data)
## date average_price total_volume type
## Length:12628 Min. :0.500 Min. : 253 Length:12628
## Class :character 1st Qu.:1.100 1st Qu.: 15733 Class :character
## Mode :character Median :1.320 Median : 94806 Mode :character
## Mean :1.359 Mean : 325259
## 3rd Qu.:1.570 3rd Qu.: 430222
## Max. :2.780 Max. :5660216
## year geography Mileage
## Min. :2017 Length:12628 Min. : 111
## 1st Qu.:2018 Class :character 1st Qu.:1097
## Median :2019 Mode :character Median :2193
## Mean :2019 Mean :1911
## 3rd Qu.:2020 3rd Qu.:2632
## Max. :2020 Max. :2998
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 = "magenta") +
scale_fill_manual(values = c("yellow","orange")) +
ggtitle("Frequency of Average Price - Organic vs. Conventional")
# Simple EFA with ggplot
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))
Farmers & Suppliers – Insights from this analysis can guide farmers in deciding whether to invest more in organic or conventional avocado production. By recognizing demand trends and pricing variations, they can make informed decisions about distribution and market positioning.
Marketers & Advertisers – Marketing teams can create more targeted campaigns based on regional preferences. Since Los Angeles has consistently high avocado sales, businesses can focus on local advertising, partnerships with restaurants, and promotions that align with consumer interests in health and food trends.