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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      
##  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
urlfile<-'https://raw.github.com/utjimmyx/resources/master/avocado_HAA.csv'
data<-read.csv(urlfile, fileEncoding="UTF-8-BOM")
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        
##  Min.   :2017   Length:12628      
##  1st Qu.:2018   Class :character  
##  Median :2019   Mode  :character  
##  Mean   :2019                     
##  3rd Qu.:2020                     
##  Max.   :2020
library(plyr)
str(data)
## 'data.frame':    12628 obs. of  6 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" ...
# Let's build a simple histogram
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 = "red") + 
  scale_fill_manual(values = c("purple", "pink")) +
  ggtitle("Frequency of Average Price - Oragnic 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)) 

# Sample response for year 2017 - The plot shows that Los Angels has the highest amount of sales in 2017.

Question 1

With organic and conventional avocados, the price difference shows that organic avocados are higher in price compared to conventional. Conventional avocados are more in quantity, while also being affordable compared to organic.

Question 2

The city that has the largest sales of organic avocado in 2017 and 2018, is Los Angeles. I think this is because LA is a very populated city, with health and wellness oriented people.

Question 3

Stakeholders could benefit from my marketing research because I have provided three data sets that include, price comparison, geography, and frequency in buying avocados. These data sets shows where avocados are popular, while also comparing price between organic and conventional.