HW3

Homework 3

Read in the data set

# knitr::opts_chunk$set(echo = FALSE)
library(dplyr)
library(tidyverse)
library(readxl)

organic <- read_excel(
  path = "../Downloads/organiceggpoultry.xlsx",
  skip = 4
)

Tidy the data set

# name column to make it mutable
colnames(organic)[1] = "Date"

organic <- organic %>%
  # drop empty column
  select(-6) %>%
  # remove extra characters from Date
  mutate(Date = ifelse(grepl("/", Date), gsub(".{3}$", "", Date), Date)) %>%
  # separate Date into month and year variables
  separate(Date, sep = " ", into = c("Month", "Year")) %>%
  # fix abbreviated month name
  mutate(Month = str_replace(Month, "Jan", "January")) %>%
  # fill in missing year values
  fill(Year) %>%
  # convert mutated variables as numeric values (and replace strings with na)
  mutate(Year = as.numeric(Year)) %>%
  mutate(across(3:11, as.numeric))

head(organic)

# A tibble: 6 x 11
  Month     Year `Extra Large \nD~ `Extra Large 1/2 D~ `Large \nDozen`
  <chr>    <dbl>             <dbl>               <dbl>           <dbl>
1 January   2004              230                 132             230 
2 February  2004              230                 134.            226.
3 March     2004              230                 137             225 
4 April     2004              234.                137             225 
5 May       2004              236                 137             225 
6 June      2004              241                 137             231.
# ... with 6 more variables: Large 
1/2 Doz. <dbl>, Whole <dbl>,
#   B/S Breast <dbl>, Bone-in Breast <dbl>, Whole Legs <dbl>,
#   Thighs <dbl>

Explain the variables

cols(
  `Extra Large Dozen` = col_double(),
  `Extra Large 1/2 Doz.` = col_double(),
  `Large Dozen` = col_double(),
  `Large 1/2 Doz.` = col_double(),
  `Whole` = col_double(),
  `B/S Breast` = col_double(),
  `Bone-in Breast` = col_double(),
  `Whole Legs` = col_double(),
  `Thighs` = col_double(),
  `Month` = col_character(),
  `Year` = col_integer()
)

Identify research questions

How much did price fluctuate for each product between 2004 and 2013?

How did product prices rank or compare with each other over these years?