Noah_Chicken_Data

Included is a brief analysis of chicken data with a plot between year and the average price of each cut of chicken.

poultry <- read_csv("../../_data/eggs_tidy.csv")

poultry 

# A tibble: 120 × 6
   month      year large_half_dozen large_dozen extra_large_half_dozen
   <chr>     <dbl>            <dbl>       <dbl>                  <dbl>
 1 January    2004             126         230                    132 
 2 February   2004             128.        226.                   134.
 3 March      2004             131         225                    137 
 4 April      2004             131         225                    137 
 5 May        2004             131         225                    137 
 6 June       2004             134.        231.                   137 
 7 July       2004             134.        234.                   137 
 8 August     2004             134.        234.                   137 
 9 September  2004             130.        234.                   136.
10 October    2004             128.        234.                   136.
# … with 110 more rows, and 1 more variable: extra_large_dozen <dbl>

Note: This code isn’t running because the variables you are using aren’t in the original csv that I found - but this might be because you are using the poultry file not the eggs file. Sorry I couldn’t get it to work - Meredith

#poultry %>% group_by(year, Price_Dollar, Product) %>% ggplot() +
#  geom_line(mapping=aes(y=Price_Dollar, x=Year, color=Product), na.rm=TRUE)

Chicken Data Conclusion

The graph above suggests that the price of most chicken cuts remain relatively similar over time, however B/S Breast or boneless chicken breast appears to have increased in price over recent years. Thighs have also remained relatively similar

Noah Milstein