NoahHw3

A short description of the post.

Chicken Data In the code below I am loading all of the packages I will be using throughout this assignment

library(tidyverse) 
library(dplyr) 
library(knitr) 
library(readxl) 
library(ggplot2) 
library(broom) 

The code below is my file path for importing the data regarding chicken meat prices in excel form

poultry <- read_excel("../../_data/poultry_tidy.xlsx") 

The Poultry The Poultry data is broken into 4 columns, Product which is the cut of chicken, Year and Month corresponding to each observation. Price_Dollar represents the price of each cut at a month and year.

poultry 

# A tibble: 600 × 4
   Product  Year Month     Price_Dollar
   <chr>   <dbl> <chr>            <dbl>
 1 Whole    2013 January           2.38
 2 Whole    2013 February          2.38
 3 Whole    2013 March             2.38
 4 Whole    2013 April             2.38
 5 Whole    2013 May               2.38
 6 Whole    2013 June              2.38
 7 Whole    2013 July              2.38
 8 Whole    2013 August            2.38
 9 Whole    2013 September         2.38
10 Whole    2013 October           2.38
# … with 590 more rows

Below is my initial data table manipulation which I did to get a specific product, in this case boneless breast with its price and the year in which that price was observed

poultry %>% group_by(`Price_Dollar`)%>% 
  
# first line groups the data by the price in the 
#Price_Dollar column so the data in the column is sorted into chunks of the same price. 
  
select(!(`Month`))%>% 
  
# This select function selects all columns except for the one called "Month" 
  
filter(Product=="B/S Breast") %>% 
  
# The line above selects only the Product in the 
#"Product" column called "B/S Breast, or boneless chicken breast" 
  
arrange(desc(`Price_Dollar`)) %>% 

# The above line sorts or arranges the column of 
# Price in dollars or "Price_Dollar" in descending order 
#starting above 7 dollars and going down closer to 6 dollars 
rename(Chicken_Bonless_Breast_Price=Price_Dollar)

# A tibble: 120 × 3
# Groups:   Chicken_Bonless_Breast_Price [8]
   Product     Year Chicken_Bonless_Breast_Price
   <chr>      <dbl>                        <dbl>
 1 B/S Breast  2013                         7.04
 2 B/S Breast  2013                         7.04
 3 B/S Breast  2013                         7.04
 4 B/S Breast  2013                         7.04
 5 B/S Breast  2013                         7.04
 6 B/S Breast  2013                         7.04
 7 B/S Breast  2013                         7.04
 8 B/S Breast  2013                         7.04
 9 B/S Breast  2013                         7.04
10 B/S Breast  2013                         7.04
# … with 110 more rows

# This above line renames the column 
#"Price_Dollar" into column "Chicken_Boneless_Breast_Price" 
# The line above takes the poultry data frame, it then finds the mean price in dollars and removes all N/A observations 

summarise(poultry, mean(`Price_Dollar`, na.rm = TRUE)) 

# A tibble: 1 × 1
  `mean(Price_Dollar, na.rm = TRUE)`
                               <dbl>
1                               3.39

The first line groups the data by the price in the Price_Dollar column so the data in the column is sorted into chunks of the same price.

The second line selects all columns except for the one called “Month”

The third line selects only the Product in the “Product” column called “B/S Breast, or boneless chicken breast”

The fourth line sorts or arranges the column of Price in dollars or “Price_Dollar” in descending order starting above 7 dollars and going down closer to 6 dollars This fifth line renames the column “Price_Dollar” into column “Chicken_Boneless_Breast_Price”

The final line of codes above takes the poultry data frame, it then finds the mean price in dollars and removes all N/A observations

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

Poultry Plot Post and Conclusion

By Noah Milstein

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