Challenge 4

More data wrangling: mutate

Code

library(tidyverse)
library(lubridate)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in data

Code

set.seed(42)
# read in the data using readr
poul <- read_csv("_data/poultry_tidy.csv")
# sample a few data points
poul[sample(nrow(poul), 10), ]

Product	Year	Month	Price_Dollar
B/S Breast	2004	September	6.4250
B/S Breast	2008	September	6.4550
Bone-in Breast	2011	September	3.9050
B/S Breast	2012	February	7.0000
Whole Legs	2010	December	2.0350
Bone-in Breast	2011	February	3.9050
Thighs	2013	January	2.1625
Whole	2011	August	2.3500
Whole	2008	March	2.2050
B/S Breast	2013	December	7.0375

Code

# products
table(select(poul, Product))

Product
    B/S Breast Bone-in Breast         Thighs          Whole     Whole Legs 
           120            120            120            120            120 

Briefly describe the data

This dataset describes the Price <dbl> of poultry meat given the Product <chr>, Year <dbl>, and Month <dbl>. We can see that there are 5 types of meat.

Tidy Data (as needed)

There is no work to be done as the data is already very tidy.

Identify variables that need to be mutated

The Product category can be mutated into a <dbl> so that we can work with numbers instead of strings. The Month category should also be mutated into <dbl>, in case we wanted to do something like a one month ahead comparison (of course we would need to implement some sort of rollover function for the new year).

I will also create a Date column based on the Month and Year, as it will make plotting over time simpler.

Code

set.seed(42)

# product ids
poul_mut <- mutate(poul, Product_ID = recode(Product, "B/S Breast" = 1, "Bone-in Breast" = 2, "Thighs" = 3, "Whole" = 4, "Whole Legs" = 5))

# month number
poul_mut <- mutate(poul_mut, Month_num = recode(Month, "January" = 1, "February" = 2, "March" = 3, "April" = 4, "May" = 5, "June" = 6, "July" = 7, "August" = 8, "September" = 9, "October" = 10, "November" = 11, "December" = 12))

# date
poul_mut <- mutate(poul_mut, Date=make_date(Year, Month_num))

poul_mut[sample(nrow(poul_mut), 10), ]

Product	Year	Month	Price_Dollar	Product_ID	Month_num	Date
B/S Breast	2004	September	6.4250	1	9	2004-09-01
B/S Breast	2008	September	6.4550	1	9	2008-09-01
Bone-in Breast	2011	September	3.9050	2	9	2011-09-01
B/S Breast	2012	February	7.0000	1	2	2012-02-01
Whole Legs	2010	December	2.0350	5	12	2010-12-01
Bone-in Breast	2011	February	3.9050	2	2	2011-02-01
Thighs	2013	January	2.1625	3	1	2013-01-01
Whole	2011	August	2.3500	4	8	2011-08-01
Whole	2008	March	2.2050	4	3	2008-03-01
B/S Breast	2013	December	7.0375	1	12	2013-12-01

One comment to make is that since <date> requires a specific day (not just a month,year combo) it extrapolates that the date is the first of the month. This should be fine for most month-to-month visualizations, but should be noted as technically incorrect.