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library(readr)
avocado <- 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(avocado)
##      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

Part 2

library(ggplot2)
head(avocado)
## # A tibble: 6 × 7
##   date      average_price total_volume type          year geography      Mileage
##   <chr>             <dbl>        <dbl> <chr>        <dbl> <chr>            <dbl>
## 1 2017/12/3          1.39       139970 conventional  2017 Albany            2832
## 2 2017/12/3          1.44         3577 organic       2017 Albany            2832
## 3 2017/12/3          1.07       504933 conventional  2017 Atlanta           2199
## 4 2017/12/3          1.62        10609 organic       2017 Atlanta           2199
## 5 2017/12/3          1.43       658939 conventional  2017 Baltimore/Was…    2679
## 6 2017/12/3          1.58        38754 organic       2017 Baltimore/Was…    2679
ggplot(data = avocado, aes(x = average_price)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.