Homework 2

DACSS-601

library(readxl)
library(dplyr)
poultry_tidy<- read_excel(path="/Users/katpo/Documents/R/poultry_tidy.xlsx") 
  poultry_tidy

# A tibble: 600 x 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

colnames(poultry_tidy)

[1] "Product"      "Year"         "Month"        "Price_Dollar"

The column names refer to the type of poultry product (breast, thighs, whole bird, etc.), the year and month it was produced and the price. With all of these elements together, this data set tracks the price of poultry over a set period of time.

#Dimensions of the data set
dim(poultry_tidy)

[1] 600   4

I’m going to filter the data by Product (I chose whole legs)

poultry_legs<-filter(poultry_tidy, Product == "Whole Legs")

Next I’ll arrange the filtered data set by year

poultry_arranged<-arrange(poultry_legs,desc("Year"))
poultry_arranged

# A tibble: 120 x 4
   Product     Year Month     Price_Dollar
   <chr>      <dbl> <chr>            <dbl>
 1 Whole Legs  2013 January           2.04
 2 Whole Legs  2013 February          2.04
 3 Whole Legs  2013 March             2.04
 4 Whole Legs  2013 April             2.04
 5 Whole Legs  2013 May               2.04
 6 Whole Legs  2013 June              2.04
 7 Whole Legs  2013 July              2.04
 8 Whole Legs  2013 August            2.04
 9 Whole Legs  2013 September         2.04
10 Whole Legs  2013 October           2.04
# ... with 110 more rows

The filtered data still presents a broad range of information, so I’m going arrange the data by price (least to most expensive)

poultry_arranged %>%
  select(Year, Price_Dollar) %>%
  group_by(Year) %>%
  arrange(desc(Price_Dollar)) %>%
  slice(10)

# A tibble: 10 x 2
# Groups:   Year [10]
    Year Price_Dollar
   <dbl>        <dbl>
 1  2004         1.94
 2  2005         2.04
 3  2006         2.04
 4  2007         2.04
 5  2008         2.04
 6  2009         2.04
 7  2010         2.04
 8  2011         2.04
 9  2012         2.04
10  2013         2.04

The price of whole chicken legs remained, for the most part, stagnant (other than 2004-2005 when the price went from $1.94 to $2.04)