Bivariate analysis Week 3

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library(readr)
data <- read_csv ("conventional.csv")

## Rows: 6314 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.

plot(total_volume ~ average_price, data = data)

For Organic Avocados

library(readr)
data <- read_csv("conventional.csv")

## Rows: 6314 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.

plot(total_volume ~ average_price, data = data)

Answers

library(ggplot2)
head(data)

## # A tibble: 6 × 7
##   date      average_price total_volume type          year geography      Mileage
##   <chr>             <dbl>        <dbl> <chr>        <dbl> <chr>            <dbl>
## 1 12/3/2017          1.39       139970 conventional  2017 Albany            2832
## 2 12/3/2017          1.07       504933 conventional  2017 Atlanta           2199
## 3 12/3/2017          1.43       658939 conventional  2017 Baltimore/Was…    2679
## 4 12/3/2017          1.14        86646 conventional  2017 Boise              827
## 5 12/3/2017          1.4        488588 conventional  2017 Boston            2998
## 6 12/3/2017          1.13       153282 conventional  2017 Buffalo/Roche…    2552

ggplot(data, aes(x= average_price)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.